COMPOSITIONS AND METHODS FOR CANCER DETECTION

BACKGROUND

Early detection of cancer greatly increases the chance of successful treatment. However, most types of cancer are asymptotic at early stage, and thus more challenging to detect, making selection of a proper assay for early detection of a specific cancer more difficult. In addition, most cancers still lack effective screening recommendations or patient compliance with those recommendations. Typical challenges for cancer-screening tests include limited sensitivity and specificity. A high rate of false-positive results can be of particular concern, as it can create difficult management decisions for clinicians and patients who would not want to unnecessarily administer (or receive) anti-cancer therapy that may potentially have undesirable side effects. Conversely, a high rate of false-negative results fails to satisfy the purpose of the screening test, as patients who need therapy are missed, resulting in a treatment delay and consequently a reduced possibility of success.

SUMMARY

The present disclosure, among other things, provides insights and technologies for achieving effective pan-cancer screening from a biological sample. In some embodiments, such a biological sample is or comprises a bodily fluid-derived sample, e.g., in some embodiments a blood-derived sample. In some embodiments, the present disclosure, among other things, provides insights and technologies that are particularly useful for achieving effective screening of pan-solid tumor cancer (e.g., carcinoma, sarcoma, mixed types, etc.) from a biological sample (e.g., in some embodiments a bodily fluid-derived sample, such as, e.g., in some embodiments a blood-derived sample. In some embodiments, the present disclosure, among other things, provides insights and technologies that are useful for screening from a biological sample (e.g., in some embodiments a bodily fluid-derived sample, such as, e.g., in some embodiments blood-derived sample) at least 2 types of cancer, including, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or more types of cancer. Examples of different types of cancer that can be assayed using technologies described herein include but are not limited to bile duct cancer, bladder cancer, brain cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, eye cancer, head and neck cancer, gastrointestinal cancer, kidney cancer, liver cancer, lung cancer, mesothelioma, ovarian cancer, pancreatic cancer, prostate cancer, sarcomas, skin cancer, stomach cancer, testicular cancer, thymoma, and thyroid cancer. In some embodiments, provided technologies are effective for detection of early stage cancer (e.g., carcinoma, sarcoma, mixed types, etc.). In some embodiments, provided technologies are effective even when applied to populations comprising or consisting of asymptomatic individuals (e.g., due to sufficiently high sensitivity and/or specificity and/or low rates of false positive and/or false negative results). In some embodiments, provided technologies are effective when applied to populations comprising or consisting of individuals (e.g., asymptomatic individuals) without hereditary risk of developing cancer (e.g., carcinoma, sarcoma, mixed types, etc.). In some embodiments, provided technologies are effective when applied to populations comprising or consisting of symptomatic individuals (e.g., individuals suffering from one or more symptoms of cancer). In some embodiments, provided technologies are effective when applied to populations comprising or consisting of individuals at risk for cancer (e.g., individuals with hereditary and/or life-history associated risk factors for cancer). In some embodiments, provided technologies may be or include one or more compositions (e.g., molecular entities or complexes, systems, cells, collections, combinations, kits, etc.) and/or methods (e.g., of making, using, assessing, etc.), as will be clear to one skilled in the art reading the disclosure provided herein.

There are currently no pan-cancer screening assays that can provide effective cancer screening such that physicians and patients can decide on next steps based on the screening results (e.g., a follow-up test for specific cancer screening) that have been approved by a regulatory body or incorporated into medical practice guidelines. In some embodiments, the present disclosure identifies the source of a problem with certain prior technologies including, for example, certain conventional approaches to detection and diagnosis of cancer. For example, the present disclosure appreciates that many conventional diagnostic assays (e.g., imaging, scoping, and/or molecular tests based on cell-free nucleic acids, serum biomarkers, and/or bulk analysis of extracellular vesicles), can be time-consuming, costly, and/or lacking sensitivity and/or specificity sufficient to provide a reliable and comprehensive diagnostic assessment. In some embodiments, the present disclosure provides technologies (including systems, compositions, and methods) that solve such problems, among other things, by assaying a bodily fluid-derived sample (e.g., a blood-derived sample) from a subject in need of cancer screening for a plurality of (e.g., at least two or more) distinct biomarker combinations to determine in the bodily fluid-derived sample (e.g., a blood-derived sample) whether individual nanoparticles having a size range of interest that includes extracellular vesicles display co-localization of at least two biomarkers in a biomarker combination from that plurality. In some embodiments, each biomarker combination from a plurality to be detected in a bodily fluid-derived sample (e.g., a blood-derived sample) has been established to be able to detect at least one or more types of cancer (including, e.g., at least two or more, at least three or more, at least four or more types of cancer). In some embodiments, each biomarker combination from a plurality to be detected in a bodily fluid-derived sample (e.g., a blood-derived sample) has been established to be able to detect at least two or more types of cancer (including, e.g., at least three or more, at least four or more types of cancer). In some embodiments, a provided biomarker combination can comprise at least one extracellular vesicle-associated surface biomarker and at least one target biomarker such that the combination is useful for detection of at least two or more types of cancer, wherein such a target biomarker may be a surface biomarker, an internal biomarker and/or an RNA biomarker. In some embodiments, the present disclosure provides technologies (including systems, compositions, and methods) that solve such problems, among other things, by detecting at least two biomarker combinations using a target entity detection approach that was developed by Applicant and described in U.S. application Ser. No. 16/805,637 (published as US2020/0299780; issued as U.S. Pat. No. 11,085,089), and International Application PCT/US2020/020529 (published as WO2020180741), both filed Feb. 28, 2020 and entitled “Systems, Compositions, and Methods for Target Entity Detection,” which are based on interaction and/or co-localization of at least two or more target entities (e.g., a biomarker combination) in individual extracellular vesicles.

In some embodiments, extracellular vesicles for detection as described herein can be isolated from a bodily fluid of a subject by a size exclusion-based method. As will be understood by a skilled artisan, in some embodiments, a size exclusion-based method may provide a sample comprising nanoparticles having a size range of interest that includes extracellular vesicles. Accordingly, in some embodiments, provided technologies of the present disclosure encompass detection, in individual nanoparticles having a size range of interest (e.g., in some embodiments about 30 nm to about 1000 nm) that includes extracellular vesicles, of co-localization of at least two or more surface biomarkers (e.g., as described herein) that forms a target biomarker combination for a particular cancer. A skilled artisan reading the present disclosure will understand that various embodiments described herein in the context of “extracellular vesicle(s)” can be also applicable in the context of “nanoparticles” as described herein.

In some embodiments, the present disclosure, among other things, provides insights that screening of asymptotic individuals, e.g., regular screening prior to or otherwise in absence of developed symptom(s), can be beneficial, and even important for effective management (e.g., successful treatment) of cancer (e.g., carcinoma, sarcoma, mixed types, etc.). In some embodiments, the present disclosure provides cancer screening systems that can be implemented to detect cancer (e.g., carcinoma, sarcoma, mixed types, etc.), including early-stage cancer, in some embodiments in asymptomatic individuals. In some embodiments, provided technologies are implemented to achieve regular screening of asymptomatic individuals. The present disclosure provides, for example, compositions (e.g., reagents, kits, components, etc.), and methods of providing and/or using them, including strategies that involve regular testing of one or more individuals (e.g., symptomatic or asymptomatic individuals). The present disclosure defines usefulness of such systems, and provides compositions and methods for implementing them.

In some embodiments, provided technologies achieve detection (e.g., early detection, e.g., in asymptomatic individual(s) and/or population(s)) of one or more features (e.g., incidence, progression, responsiveness to therapy, recurrence, etc.) of cancer, with sensitivity and/or specificity (e.g., rate of false positive and/or false negative results) appropriate to permit useful application of provided technologies to single-time and/or regular (e.g., periodic) assessment. In some embodiments, provided technologies may achieve detection of cancer at early stage (e.g., Stage I and II) with a sensitivity of at least about 20%, including, e.g., at least about 30%, at least about 40%, at least about 50%, at least about 60%, at least about 70%, or higher). In some embodiments, provided technologies may achieve detection of cancer at early stage (e.g., Stage I and II) with a false negative rate of no more than 80%, including, e.g., no more than 70%, no more than 60%, no more than 50%, no more than 40%, or more. In some embodiments, provided technologies are useful in conjunction with regular medical examinations, such as but not limited to: physicals, general practitioner visits, cholesterol/lipid blood tests, diabetes screening, blood pressure screening, thyroid function tests, prostate cancer screening, mammograms, HPV/Pap smears, and/or vaccinations. In some embodiments, provided technologies are useful in conjunction with treatment regimen(s); in some embodiments, provided technologies may improve one or more characteristics (e.g., rate of success according to an accepted parameter) of such treatment regimen(s).

In some aspects, provided are technologies for use in classifying a subject (e.g., an asymptomatic subject) as having or being susceptible to cancer (e.g., carcinoma, sarcoma, mixed types, etc.) In some embodiments, the present disclosure provides methods or assays for classifying a subject (e.g., an asymptomatic subject) as having or being susceptible to cancer (e.g., carcinoma, sarcoma, mixed types, etc.). In some embodiments, a provided method or assay comprises assaying a sample (e.g., a blood-derived sample) from a subject for a plurality of distinct biomarker combinations to determine whether nanoparticles having the size range of interest that includes extracellular vesicles in the sample (e.g., blood-derived sample) display co-localization of at least two biomarkers in a biomarker combination from the plurality, wherein a first biomarker combination and a second biomarker combination each independently comprise at least two biomarkers, whose combined expression level has been determined to be associated with at least one type of cancer (including, e.g., at least two types of cancer).

In some embodiments, a provided method or assay comprises comparing sample information (determined from a subject's sample) indicative of co-localization level of biomarkers for each biomarker combination to reference information including a reference threshold level for each biomarker combination.

In some embodiments, a provide method or assay comprises classifying a subject from which a sample (e.g., a blood-derived sample) is obtained as having or being susceptible to cancer when the sample (e.g., a blood-derived sample) shows that a determined co-localization level of at least one biomarker combination is at or above a classification cutoff referencing a reference threshold level for the respective biomarker combination and optionally a reference threshold level for each other biomarker combination.

In some embodiments, a plurality of distinct biomarker combinations to be assayed in a sample (e.g., a blood-derived sample) includes at least 2 distinct biomarker combinations, including, e.g., at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, or more distinct biomarker combinations.

In some embodiments, at least a subset of (e.g., at least two or more) biomarker combinations within a selected plurality of biomarker combinations are complementary to each other. In some embodiments, all biomarker combinations within a selected plurality of biomarker combinations are complementary to each other such that each biomarker combination has been determined to be present in a different population of nanoparticles having a size range of interest that includes extracellular vesicles.

In some embodiments, at least one biomarker combination within a selected plurality of biomarker combinations is specific for a tissue or organ type. By way of example only, in some embodiments, at least one biomarker combination may be specific for lung tissue. In some embodiments, at least one biomarker combination may be specific for colorectal tissue. In some embodiments, at least one biomarker combination may be specific for prostate tissue. In some embodiments, at least one biomarker combination may be specific for pancreatic tissue. In some embodiments, at least one biomarker combination may be specific for liver tissue. In some embodiments, at least one biomarker combination may be specific for bile duct tissue. In some embodiments, at least one biomarker combination may be specific for breast tissue. In some embodiments, at least one biomarker combination may be specific for esophageal tissue.

In some embodiments, at least one biomarker combination within a selected plurality of biomarker combinations may be associated with at least one particular type of cancer, including, e.g., at least two types of cancer or more. For example, in some embodiments, at least one biomarker combination may be associated with lung cancer. In some embodiments, at least one biomarker combination may be associated with colorectal cancer. In some embodiments, at least one biomarker combination may be associated with prostate cancer. In some embodiments, at least one biomarker combination may be associated with pancreatic cancer. In some embodiments, at least one biomarker combination may be associated with liver cancer. In some embodiments, at least one biomarker combination may be associated with bile duct cancer. In some embodiments, at least one biomarker combination may be associated with breast cancer. In some embodiments, at least one biomarker combination may be associated with esophageal cancer.

In some embodiments, a plurality of biomarker combinations included in pan-cancer detection may comprise (i) at least one biomarker combination associated with breast cancer (e.g., as described herein); (ii) at least one biomarker combination associated with colorectal cancer (e.g., as described herein); (iii) at least one biomarker combination associated with lung cancer; (iv) at least one biomarker combination associated with ovarian cancer (e.g., as described herein); and (v) at least one biomarker combination associated with prostate cancer (e.g., as described herein).

In some embodiments, pan-cancer detection may be tailored to individual subjects or populations of subjects that are of a particular sex and/or gender (e.g., female subjects, male subjects, etc.). In some embodiments, a plurality of biomarker combinations included in pan-cancer detection for female subjects may comprise (i) at least one biomarker combination associated with breast cancer (e.g., as described herein); (ii) at least one biomarker combination associated with colorectal cancer (e.g., as described herein); (iii) at least one biomarker combination associated with lung cancer; and (iv) at least one biomarker combination associated with ovarian cancer (e.g., as described herein). In some embodiments, a plurality of biomarker combinations included in pan-cancer detection for male subjects may comprise (i) at least one biomarker combination associated with colorectal cancer (e.g., as described herein); (ii) at least one biomarker combination associated with lung cancer; and (iii) at least one biomarker combination associated with prostate cancer (e.g., as described herein).

In some embodiments, at least one biomarker combination within a selected plurality of biomarker combinations is specific for a cell origin. By way of example only, in some embodiments, at least one biomarker combination may be specific for epithelial cells. In some embodiments, at least one biomarker combination may be specific for mesodermal cells. In some embodiments, at least one biomarker combination may be specific for fibroblast cells. In some embodiments, at least one biomarker combination may be specific for squamous cells.

In some embodiments, at least one biomarker combination within a selected plurality of biomarker combinations is or comprise two or more surface biomarkers on cancer-associated nanoparticles having a size range of interest that includes extracellular vesicles. In some embodiments, exemplary surface biomarkers that can be selected for use in a provided biomarker combination include but are not limited to polypeptides encoded by human genes as follows: ALDH18A1, AP1M2, APOO, ARFGEF3, B3GNT3, BMPR1B, CADM4, CANT1, CD24, CDH1, CDH17, CDH2, CDH3, CEACAM5, CEACAM6, CLDN3, CLDN4, CLGN, CLN5, CYP2S1, DSG2, ELAPOR1, ENPP5, EPCAM, EPHB2, FAM241B, FERMT1, FOLR1, FZD2, GALNT14, GALNT6, GJB1, GNG4, GNPNAT1, GOLM1, GPR160, GPRIN1, GRHL2, HACD3, HS6ST2, IGSF3, ILDR1, KDELR3, KPNA2, KRTCAP3, LAMB3, LAMC2, LAPTM4B, LARGE2, LMNB1, LRRN1, LSR, MAL2, MARCKSL1, MARVELD2, MET, MUC1, MUC2, MUC4, MUC5AC, MUC13, NPTXR, NUP210, PARD6B, PMEPA1, PODXL2, PRAF2, PRSS8, RAB25, RAC3, RACGAP1, RAP2B, RCC2, RNF128, RNF43, RPN1, RPN2, SERINC2, SHISA2, SLC35A2, SLC39A6, SLC44A4, SLC4A4, SMIM22, SMPDL3B, SYAP1, SYT13, TMEM132A, TMEM238, TMEM9, TSPAN13, ULBP2, UNC13B, VTCN1, and combinations thereof.

In some embodiments, at least one biomarker combination within a selected plurality of biomarker combinations is or comprises two or more surface biomarkers, at least one of which is or comprises a polypeptide encoded by a human gene as follows: ABCA13, ADAM23, CYP4F11, HAS3, TMPRSS4, UGT1A6, PIGT, TOMM34, ACSL4, GPC3, ROBO1, SLC22A9, SLC38A3, TFR2, TM4SF4, TMPRSS6, ANXA13, CHST4, GAL3ST1, SNAP25, TMEM156, CLDN18, EPPK1, MUC1, MUC2, MUC4, MUC5AC, MUC13, OCLN, CFTR, GCNT3, ITGB6, ITGB6, LAD1, MSLN, TESC, LYPD6B, S100P, TMEM51, TNFRSF21, UPK1B, UPK2, ABCC4, FOLH1, RAB3B, STEAP2, TMPRSS2, TSPAN1, AP1S3, DSC2, DSG3, TMPRSS11D, KCNS1, LY6K, MUC4, SYNGR3, CELSR1, COX6C, ESR1, MUC1, ABCC11, ERBB2, SLC9A3R1, PROM1, PTK7, CDK4, DLK1, LMNB2, PCDH7, TMEM108, TYMS, SDC1, SLC34A2, BCAM, MUC16, and combinations thereof.

In some embodiments, at least one biomarker combination within a selected plurality of biomarker combinations is or comprises two or more surface biomarkers, at least one of which is or comprises a polypeptide encoded by a human gene as follows: ADAM17, ADAM28, ADAM8, ALCAM, AMHR2, AXL, BAG3, BSG, CCL2, CCL8, CCN1, CCN2, CCR5, CD274, CD38, CD44, CD47, CDH11, CETN1, CLDN1, CLEC2D, CLU, CSPG4, DKK1, DLL4, EGFR, ENPP3, EPHA10, ERBB3, FAP, FGF1, FGFR4, FLNA, FLNB, FLT4, FZD7, GFRA1, GM3, GPA33, GPC1, GPNMB, GUCY2C, HGF, ICAM1, IGF1R, IL1A, IL1RAP, IL6, ITGA6, ITGAV, KDR, KLK3, KLKB1, KRT8, LAG3, LGR5, LPR6, LY6E, MCAM, MDM2, MELTF, MERTK, MST1R, MUC1, MUC2, MUC4, MUC13, MUC17, MUC5AC, MUCL1, NOTCH2, NOTCH3, NRP1, NT5E, PI4K2A, P1, PLAUR, PLVAP, PPP1R3A, PRLR, PSCA, PVR, RET, S1PR1, SLC3A2, SLC7A11, SLC7A5, SPINK1, STAT3, STEAP1, TACSTD2, TF, TFRC, TGFBR2, TIGIT, TNC, TNFRSF10A, TNFRSF10B, TNFRSF12A, TNFRSF4, TNFSF11, TNFSF18, TPBG, VANGL2, VEGFA, VEGFC, and combinations thereof.

In some embodiments, at least one biomarker combination within a selected plurality of biomarker combinations is or comprises two or more surface biomarkers, at least one of which is or comprises a carbohydrate-dependent marker. Examples of carbohydrate-dependent or lipid-dependent markers that may be used in a biomarker combination include, but are not limited to Tn antigen, SialylTn (sTn) antigen, Thomsen-Friedenreich (T, TF) antigen, Lewis Y (also known as CD174) antigen, Lewis B antigen, Sialyl Lewis X (sLex) (also known as Sialyl SSEA-1 (SLX)) antigen, SSEA-1 (also known as Lewis X), beta1,6-branching, bisecting GlcNAc in a beta1,4-linkage, core fucosylation, Sialyl-T antigens (sT), Sialyl Lewis c antigen, Globo H, SSEA-3 (Gb5), SSEA-4 (sialy-Gb5), Gb3 (Globotriaose, CD77), Disialosyl-galactosylgloboside (DSGG), GalNAcDSLc4, Fucosyl GM1, GD1alpha, GD1a ganglioside, GD2 ganglioside, GD3 ganglioside, GM2 ganglioside, Lc3 ceramide, nLc4 ceramide, 9-O-Ac-GD2 ganglioside, 9-O-Ac-GD3 (CDw60) ganglioside, 9-O-Ac-GT3 ganglioside, Forssman antigen, Disialyl Lewis a antigen, Sialylparagloboside (SPG), Polysialic acid (PSA) linked to NCAM, Sialyl Lewis A antigen (also known as CA19-9), CanAg (glycoform of MUC1), Lewis Y/B antigen, Sialyltetraosyl carbohydrate, NeuGcGM3, GM3 (N-glycolylneuraminic acid (NeuGc, NGNA)-gangliosides GM3), phosphatidylserine, and combinations thereof.

In some embodiments, at least one biomarker combination within a selected plurality of biomarker combinations is or comprises two or more surface biomarkers, at least one of which is or comprises (i) one or more polypeptides encoded by human genes as follows: ABCC11, ABCC4, ACSL4, ACVR2B, ADGRF1, ALCAM, ALPL, ANO1, ANXA13, AP1M2, AP1S3, APOO, AQP5, ARFGEF3, ASPHD1, ATP1B1, B3GNT3, B3GNT5, BCAM, BSPRY, BST2, CANT1, CAP2, CARD11, CD133, CD24, CD274 (PD-L1), CD38, CD55, CD74, CDCP1, CDH1, CDH17, CDH2, CDH3, CDH6, CDHR5, CEACAM5, CEACAM6, CELSR1, CFB, CFTR, CHODL, CHST4, CIP2A, CKAP4, CLCA2, CLDN10, CLDN16, CLDN3, CLDN4, CLDN6, CLGN, CLN5, CLTRN, COX6C, CXCR4, CYP2S1, CYP4F11, DDR1, DEFB1, DLL4, DSC2, DSG2, DSG3, EDAR, EFNB1, EGFR, ENPP5, EPCAM, EPHB2, EPHB3, EPPK1, ERBB2, ERBB3, ESR1, FAM241B, FAP, FER1L6, FERMT1, FGFR4, FOLH1, FOLR1, FUT8, FXYD3, GAL3ST1, GALNT14, GALNT3, GALNT5, GALNT6, GALNT7, GBA, GCNT3, GFRA1, GJB1, GJB2, GLUL, GOLM1, GPC3, GPCR5A, GRB7, GRHL2, HACD3, HAS3, HKDC1, HS6ST2, HSD17B2, HTR3A, IG1FR, IGSF3, IHH, ILDR1, ITGAV, ITGB6, KCNQ1, KEL, KIF1A, KPNA2, KRTCAP3, LAD1, LAMB3, LAMC2, LAPTM4B, LARGE2, LEMD1, LMNB1, LRP2, LRRTM1, LSR, LY6E, LYPD6B, MAL2, MAP7, MARCKSL1, MARVELD2, MET, MIEN1, MSLN, MST1R, MUC1, MUC13, MUC16, MUC2, MUC4, MUC5AC, NAT8, NECTIN2, NOTCH3, NOX1, NRCAM, NUP155, NUP210, OCIAD2, OCLN, OXTR, PARD6B, PDZK1, PIGT, PIK3AP1, PLEKHF2, PLXNB1, PMEPA1, PODXL2, PPP3CA, PRLR, PROM1, PRR7, PRSS21, PSCA, PTGS1, PTK7, PTPRK, RAB25, RAB27B, RAB3B, RAB3D, RAC3, RDH11, RNF43, ROBO1, ROS1, S100P, SCGN, SDC1, SEPHS1, SFXN2, SHANK2, SHROOM3, SLC22A9, SLC2A1, SLC2A2, SLC34A2, SLC35B2, SLC38A3, SLC39A6, SLC44A3, SLC4A4, SLC7A11, SLC7A5, SLC9A3R1, SMIM22, SMPDL3B, SNAP25, SORD, SPINT2, ST14, STEAP1, STEAP2, SYT13, SYT7, TACSTD2, TESC, TFR2, TJP3, TM4SF4, TMEM132A, TMEM156, TMEM158, TMPRSS11D, TMPRSS2, TMPRSS4, TMPRSS6, TNFRSF10B, TNFRSF12A, TOMM20, TRPM4, TSPAN1, TSPAN8, UCHL1, UGT1A9, UGT2B7, UGT8, ULBP2, UNC13B, VEPH1, VTCN1, XBP1, or combinations thereof; and/or (ii) one or more carbohydrate-dependent markers as follows CA19-9 antigen, Lewis X antigen, Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, or combinations thereof.

In some embodiments, at least one biomarker combination within a selected plurality of biomarker combinations is or comprises two or more surface biomarkers, at least one of which is or comprises (i) one or more polypeptides encoded by human genes as follows: ABCC11, ABCC4, ACVR2B, ADGRF1, ALCAM, ALPL, AP1M2, APOO, AQP5, ARFGEF3, B3GNT3, B3GNT5, BCAM, BSPRY, BST2, CANT1, CD133, CD24, CD274 (PD-L1), CD38, CD55, CD74, CDCP1, CDH1, CDH17, CDH3, CDH6, CEACAM5, CEACAM6, CELSR1, CFB, CFTR, CHODL, CIP2A, CLDN16, CLDN3, CLDN4, CLDN6, CLGN, COX6C, CXCR4, CYP2S1, DDR1, DLL4, DSC2, DSG2, EDAR, EFNB1, EGFR, ENPP5, EPCAM, EPHB2, EPHB3, ERBB2, ERBB3, ESR1, FAM241B, FAP, FGFR4, FOLH1, FOLR1, FUT8, FXYD3, GALNT14, GALNT3, GALNT6, GALNT7, GFRA1, GJB1, GJB2, GOLM1, GPCR5A, GRB7, GRHL2, HACD3, HAS3, HTR3A, IG1FR, IHH, ILDR1, ITGAV, ITGB6, KCNQ1, KEL, KIF1A, KPNA2, LAMB3, LAMC2, LAPTM4B, LARGE2, LEMD1, LMNB1, LRP2, LRRTM1, LSR, LY6E, MAL2, MAP7, MARCKSL1, MET, MIEN1, MSLN, MST1R, MUC1, MUC13, MUC16, MUC2, MUC4, MUC5AC, NECTIN2, NOTCH3, NOX1, NRCAM, NUP155, NUP210, OCIAD2, OCLN, PARD6B, PIGT, PLEKHF2, PLXNB1, PMEPA1, PODXL2, PPP3CA, PRLR, PROM1, PRSS21, PSCA, PTGS1, PTK7, PTPRK, RAB25, RAB27B, RAB3B, RAB3D, RAC3, RDH11, RNF43, ROS1, SDC1, SEPHS1, SFXN2, SHROOM3, SLC2A1, SLC34A2, SLC35B2, SLC39A6, SLC4A4, SLC7A11, SLC9A3R1, SMIM22, SMPDL3B, SORD, SPINT2, ST14, STEAP1, STEAP2, SYT7, TACSTD2, TJP3, TMEM132A, TMPRSS2, TMPRSS4, TNFRSF10B, TNFRSF12A, TRPM4, TSPAN1, TSPAN8, UCHL1, UNC13B, XBP1, or combinations thereof; and/or (ii) one or more carbohydrate-dependent markers as follows: CA19-9, Lewis X antigen, Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, or combinations thereof.

In some embodiments, at least one biomarker combination within a selected plurality of biomarker combinations is or comprises a combination selected from the group consisting of: a SERINC2 polypeptide and a SMPDL3B polypeptide; or a RAB25 polypeptide and a SMPDL3B polypeptide; or a LMNB1 polypeptide and a SMIM22 polypeptide; or a CDH1 polypeptide and a SMPDL3B polypeptide; or a EPCAM polypeptide and a MARCKSL1 polypeptide; or a MARCKSL1 polypeptide and a PRSS8 polypeptide; or a ALDH18A1 polypeptide and a CLDN3 polypeptide; or a BMPR1B polypeptide and a LARGE2 polypeptide; or a AP1M2 polypeptide and a LSR polypeptide; or a BMPR1B polypeptide and a SMPDL3B polypeptide; or a MARVELD2 polypeptide and a SMPDL3B polypeptide; or a BMPR1B polypeptide and a MARCKSL1 polypeptide; or a GRHL2 polypeptide and a SMPDL3B polypeptide; or a EPCAM polypeptide and a SMPDL3B polypeptide; or a CLDN3 polypeptide and a SMPDL3B polypeptide; or a EPCAM polypeptide and a PODXL2 polypeptide; or a BMPR1B polypeptide and a RCC2 polypeptide; or a MARCKSL1 polypeptide and a MARVELD2 polypeptide; or a CLDN4 polypeptide and a PODXL2 polypeptide; or a CLDN3 polypeptide and a RPN1 polypeptide; or a BMPR1B polypeptide and a VTCN1 polypeptide; or a BMPR1B polypeptide and a RPN1 polypeptide; or a BMPR1B polypeptide and a KPNA2 polypeptide; or a CLGN polypeptide and a LMNB1 polypeptide; or a EPCAM polypeptide and a RPN1 polypeptide; or a BMPR1B polypeptide and a LMNB1 polypeptide; or a BMPR1B polypeptide and a RACGAP1 polypeptide; or a RACGAP1 polypeptide and a VTCN1 polypeptide; or a GOLM1 polypeptide and a RAB25 polypeptide; or a CLDN3 polypeptide and a RAB25 polypeptide; or a BMPR1B polypeptide and a CLDN3 polypeptide; or a CLDN3 polypeptide and a GOLM1 polypeptide; or a CDH1 polypeptide and a CLDN3 polypeptide; or a LMNB1 polypeptide and a VTCN1 polypeptide; or combinations thereof.

In some embodiments, at least one biomarker combination within a selected plurality of biomarker combinations is or comprises at least three biomarkers. In some embodiments, such a biomarker combination may be selected from the group consisting of: a BMPR1B polypeptide, a CLDN3 polypeptide, and a MARCKSL1 polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a HS6ST2 polypeptide; or a CDH2 polypeptide, a FERMT1 polypeptide, and a LRRN1 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a LSR polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a CLN5 polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a SMPDL3B polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a SMPDL3B polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a EPCAM polypeptide; or a BMPR1B polypeptide, a EPCAM polypeptide, and a MARCKSL1 polypeptide; or a CEACAM6 polypeptide, a HS6ST2 polypeptide, and a PODXL2 polypeptide; or a LAPTM4B polypeptide, a PODXL2 polypeptide, and a SMPDL3B polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a MARCKSL1 polypeptide; or a CLN5 polypeptide, a GALNT14 polypeptide, and a RNF128 polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a LAMC2 polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a SMPDL3B polypeptide; or a B3GNT3 polypeptide, a CDH3 polypeptide, and a GNG4 polypeptide; or a BMPR1B polypeptide, a EPCAM polypeptide, and a SLC39A6 polypeptide; or a CLGN polypeptide, a PODXL2 polypeptide, and a SLC39A6 polypeptide; or a B3GNT3 polypeptide, a LAMC2 polypeptide, and a MET polypeptide; or a BMPR1B polypeptide, a EPCAM polypeptide, and a PODXL2 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a PMEPA1 polypeptide; or a BMPR1B polypeptide, a LMNB1 polypeptide, and a VTCN1 polypeptide; or a CDH2 polypeptide, a CDH3 polypeptide, and a LAMB3 polypeptide; or a BMPR1B polypeptide, a KPNA2 polypeptide, and a VTCN1 polypeptide; or a CDH2 polypeptide, a CDH3 polypeptide, and a EPCAM polypeptide; or a CLGN polypeptide, a LMNB1 polypeptide, and a VTCN1 polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a MET polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a EPHB2 polypeptide; or a CDH1 polypeptide, a CDH2 polypeptide, and a CDH3 polypeptide; or combinations thereof.

In some embodiments, a biomarker combination within a selected plurality of biomarker combinations comprises a combination of biomarkers that has been determined to be associated with at least one cancer with predetermined specificity and sensitivity. In some embodiments, a biomarker combination has been determined to be associated with at least one cancer with a specificity within a range of 80%-100% and sensitivity within a range of 20%-100%. In some embodiments, a biomarker combination has been determined to be associated with at least one cancer with a specificity within a range of 85%-100% and sensitivity within a range of 30%-100%. In some embodiments, a biomarker combination has been determined to be associated with at least one cancer with a specificity within a range of 90%-100% and sensitivity within a range of 40%-100%. In some embodiments, a biomarker combination has been determined to be associated with at least one cancer with a specificity within a range of 95%-100% and sensitivity within a range of 50%-100%.

In some embodiments, a biomarker combination within a selected plurality of biomarker combinations comprises a combination of biomarkers that has been determined to be associated with at least two different cancers with predetermined specificity and sensitivity. In some embodiments, a biomarker combination has been determined to be associated with at least two different cancers with a specificity within a range of 50%-100% and sensitivity within a range of 10%-100%. In some embodiments, a biomarker combination has been determined to be associated with at least two different cancers with a specificity within a range of 60%-100% and sensitivity within a range of 20%-100%. In some embodiments, a biomarker combination has been determined to be associated with at least two different cancers with a specificity within a range of 70%-100% and sensitivity within a range of 30%-100%. In some embodiments, a biomarker combination has been determined to be associated with at least two different cancers with a specificity within a range of 80%-100% and sensitivity within a range of 40%-100%. In some embodiments, a biomarker combination has been determined to be associated with at least two different cancers with a specificity within a range of 90%-100% and sensitivity within a range of 50%-100%.

In some embodiments, a reference threshold level for each biomarker combination is determined by co-localization level observed in comparable samples from a population of non-cancer subjects. In some embodiments, a population of non-cancer subjects may comprise one or more of the following subject populations: healthy subjects, subjects diagnosed with benign tumors, and subjects with non-cancer-related diseases, disorders, and/or conditions.

A sample (e.g., a blood-derived sample) can be assayed for a plurality of distinct biomarker combinations using methods known in the art. In some embodiments, a sample (e.g., a blood-derived sample) has been subjected to size exclusion chromatography to isolate (e.g., directly from the sample) nanoparticles having a size range of interest that includes extracellular vesicles.

In some embodiments, a step of assaying a sample (e.g., a blood-derived sample) for a plurality of distinct biomarker combinations comprises a capture assay. In some embodiments, a capture assay may involve contacting a sample (e.g., a blood-derived sample) comprising extracellular vesicles with a capture agent comprising a target-capture moiety that binds to at least one extracellular vesicle-associated surface biomarker, which may be optionally conjugated to a solid substrate. Without limitations, an exemplary capture agent for an extracellular vesicle-associated surface biomarker may be or comprising a solid substrate (e.g., a magnetic bead) and an affinity agent (e.g., an antibody agent) that binds to an extracellular vesicle-associated surface biomarker.

In some embodiments, a biomarker combination within a selected plurality of biomarker combinations comprises an extracellular vesicle-associated surface biomarker or surface biomarker. In some embodiments, an extracellular vesicle-associated surface biomarker or surface biomarker for use in a biomarker combination described herein may be or comprise a tumor-specific biomarker and/or a tissue-specific biomarker (e.g., a cancerous tissue-specific biomarker). In some embodiments, such an extracellular vesicle-associated surface biomarker or surface biomarker may be or comprise a non-specific marker, e.g., it is present in one or more non-target tumors, and/or in one or more non-target tissues. In some embodiments, such a non-specific marker is considered multi-specific, (e.g., it is present in more than one target tumor, and/or in more than one target tissue). In some embodiments, an extracellular vesicle-associated surface biomarker or surface biomarker may be or comprise a polypeptide. For example, in some embodiments, an extracellular vesicle-associated surface biomarker or surface biomarker may be or comprise a polypeptide encoded by a human gene as follows: ALDH18A1, AP1M2, APOO, ARFGEF3, B3GNT3, BMPR1B, CADM4, CANT1, CD24, CDH1, CDH17, CDH2, CDH3, CEACAM5, CEACAM6, CLDN3, CLDN4, CLGN, CLN5, CYP2S1, DSG2, ELAPOR1, ENPP5, EPCAM, EPHB2, FAM241B, FERMT1, FOLR1, FZD2, GALNT14, GALNT6, GJB1, GNG4, GNPNAT1, GOLM1, GPR160, GPRIN1, GRHL2, HACD3, HS6ST2, IGSF3, ILDR1, KDELR3, KPNA2, KRTCAP3, LAMB3, LAMC2, LAPTM4B, LARGE2, LMNB1, LRRN1, LSR, MAL2, MARCKSL1, MARVELD2, MET, MUC1, MUC2, MUC4, MUC5AC, MUC13, NPTXR, NUP210, PARD6B, PMEPA1, PODXL2, PRAF2, PRSS8, RAB25, RAC3, RACGAP1, RAP2B, RCC2, RNF128, RNF43, RPN1, RPN2, SERINC2, SHISA2, SLC35A2, SLC39A6, SLC44A4, SLC4A4, SMIM22, SMPDL3B, SYAP1, SYT13, TMEM132A, TMEM238, TMEM9, TSPAN13, ULBP2, UNC13B, VTCN1, ABCA13, ADAM23, CYP4F11, HAS3, TMPRSS4, UGT1A6, PIGT, TOMM34, ACSL4, GPC3, ROBO1, SLC22A9, SLC38A3, TFR2, TM4SF4, TMPRSS6, ANXA13, CHST4, GAL3ST1, SNAP25, TMEM156, CLDN18, EPPK1, MUC13, OCLN, CFTR, GCNT3, ITGB6, ITGB6, LAD1, MSLN, TESC, LYPD6B, S100P, TMEM51, TNFRSF21, UPK1B, UPK2, ABCC4, FOLH1, RAB3B, STEAP2, TMPRSS2, TSPAN1, AP1S3, DSC2, DSG3, TMPRSS11D, KCNS1, LY6K, MUC4, SYNGR3, CELSR1, COX6C, ESR1, MUC1, ABCC11, ERBB2, SLC9A3R1, PROM1, PTK7, CDK4, DLK1, LMNB2, PCDH7, TMEM108, TYMS, SDC1, SLC34A2, BCAM, MUC16, ADAM17, ADAM28, ADAM8, ALCAM, AMHR2, AXL, BAG3, BSG, CCL2, CCL8, CCN1, CCN2, CCR5, CD274, CD38, CD44, CD47, CDH11, CETN1, CLDN1, CLEC2D, CLU, CSPG4, DKK1, DLL4, EGFR, ENPP3, EPHA10, ERBB3, FAP, FGF1, FGFR4, FLNA, FLNB, FLT4, FZD7, GFRA1, GM3, GPA33, GPC1, GPNMB, GUCY2C, HGF, ICAM1, IGF1R, IL1A, IL1RAP, IL6, ITGA6, ITGAV, KDR, KLK3, KLKB1, KRT8, LAG3, LGR5, LPR6, LY6E, MCAM, MDM2, MELTF, MERTK, MST1R, MUC17, MUC5AC, MUCL1, NOTCH2, NOTCH3, NRP1, NT5E, PI4K2A, P1, PLAUR, PLVAP, PPP1R3A, PRLR, PSCA, PVR, RET, S1PR1, SLC3A2, SLC7A11, SLC7A5, SPINK1, STAT3, STEAP1, TACSTD2, TF, TFRC, TGFBR2, TIGIT, TNC, TNFRSF10A, TNFRSF10B, TNFRSF12A, TNFRSF4, TNFSF11, TNFSF18, TPBG, VANGL2, VEGFA, VEGFC, and combinations thereof.

In some embodiments, an extracellular vesicle-associated surface biomarker or surface biomarker may be or comprise a carbohydrate-dependent or lipid-dependent marker. For example, in some embodiments, an extracellular vesicle-associated surface biomarker or surface biomarker may be or comprise a carbohydrate-dependent or lipid-dependent marker as follows: Tn antigen, SialylTn (sTn) antigen, Thomsen-Friedenreich (T, TF) antigen, Lewis Y antigen (also known as CD174), Sialyl Lewis X (sLex) (also known as Sialyl SSEA-1 (SLX)), SSEA-1/Lewis X antigen, beta1,6-branching, bisecting GlcNAc in a beta1,4-linkage, core fucosylation, Sialyl-T antigens (sT), Sialyl Lewis c antigen, Globo H, SSEA-3 (Gb5), SSEA-4 (sialy-Gb5), Gb3 (Globotriaose, CD77), Disialosyl-galactosylgloboside (DSGG), GalNAcDSLc4, Fucosyl GM1, GD1alpha ganglioside, GD1a ganglioside, GD2 ganglioside, GD3 ganglioside, GM2 ganglioside, Lc3 ceramide, nLc4 ceramide, 9-O-Ac-GD2 ganglioside, 9-O-Ac-GD3 (CDw60) ganglioside, 9-O-Ac-GT3 ganglioside, Forssman antigen, Disialyl Lewis a antigen, Sialylparagloboside (SPG), Polysialic acid (PSA) linked to NCAM, Sialyl Lewis A antigen (also known as CA19-9), CanAg (glycoform of MUC1), Lewis Y/B antigen, Lewis B antigen, Sialyltetraosyl carbohydrate, NeuGcGM3, GM3 (N-glycolylneuraminic acid (NeuGc, NGNA)-gangliosides GM3), phosphatidylserine, and combinations thereof.

In some embodiments, an extracellular vesicle-associated surface biomarker or surface biomarker may be or comprise (i) one or more polypeptides encoded by human genes as follows: ABCC11, ABCC4, ACSL4, ACVR2B, ADGRF1, ALCAM, ALPL, ANO1, ANXA13, AP1M2, AP1S3, APOO, AQP5, ARFGEF3, ASPHD1, ATP1B1, B3GNT3, B3GNT5, BCAM, BSPRY, BST2, CANT1, CAP2, CARD11, CD133, CD24, CD274 (PD-L1), CD38, CD55, CD74, CDCP1, CDH1, CDH17, CDH2, CDH3, CDH6, CDHR5, CEACAM5, CEACAM6, CELSR1, CFB, CFTR, CHODL, CHST4, CIP2A, CKAP4, CLCA2, CLDN10, CLDN16, CLDN3, CLDN4, CLDN6, CLGN, CLN5, CLTRN, COX6C, CXCR4, CYP2S1, CYP4F11, DDR1, DEFB1, DLL4, DSC2, DSG2, DSG3, EDAR, EFNB1, EGFR, ENPP5, EPCAM, EPHB2, EPHB3, EPPK1, ERBB2, ERBB3, ESR1, FAM241B, FAP, FER1L6, FERMT1, FGFR4, FOLH1, FOLR1, FUT8, FXYD3, GAL3ST1, GALNT14, GALNT3, GALNT5, GALNT6, GALNT7, GBA, GCNT3, GFRA1, GJB1, GJB2, GLUL, GOLM1, GPC3, GPCR5A, GRB7, GRHL2, HACD3, HAS3, HKDC1, HS6ST2, HSD17B2, HTR3A, IG1FR, IGSF3, IHH, ILDR1, ITGAV, ITGB6, KCNQ1, KEL, KIF1A, KPNA2, KRTCAP3, LAD1, LAMB3, LAMC2, LAPTM4B, LARGE2, LEMD1, LMNB1, LRP2, LRRTM1, LSR, LY6E, LYPD6B, MAL2, MAP7, MARCKSL1, MARVELD2, MET, MIEN1, MSLN, MST1R, MUC1, MUC13, MUC16, MUC2, MUC4, MUC5AC, NAT8, NECTIN2, NOTCH3, NOX1, NRCAM, NUP155, NUP210, OCIAD2, OCLN, OXTR, PARD6B, PDZK1, PIGT, PIK3AP1, PLEKHF2, PLXNB1, PMEPA1, PODXL2, PPP3CA, PRLR, PROM1, PRR7, PRSS21, PSCA, PTGS1, PTK7, PTPRK, RAB25, RAB27B, RAB3B, RAB3D, RAC3, RDH11, RNF43, ROBO1, ROS1, S100P, SCGN, SDC1, SEPHS1, SFXN2, SHANK2, SHROOM3, SLC22A9, SLC2A1, SLC2A2, SLC34A2, SLC35B2, SLC38A3, SLC39A6, SLC44A3, SLC4A4, SLC7A11, SLC7A5, SLC9A3R1, SMIM22, SMPDL3B, SNAP25, SORD, SPINT2, ST14, STEAP1, STEAP2, SYT13, SYT7, TACSTD2, TESC, TFR2, TJP3, TM4SF4, TMEM132A, TMEM156, TMEM158, TMPRSS11D, TMPRSS2, TMPRSS4, TMPRSS6, TNFRSF10B, TNFRSF12A, TOMM20, TRPM4, TSPAN1, TSPAN8, UCHL1, UGT1A9, UGT2B7, UGT8, ULBP2, UNC13B, VEPH1, VTCN1, XBP1, or combinations thereof; and/or (ii) one or more carbohydrate-dependent markers as follows CA19-9 antigen, Lewis X antigen, Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, or combinations thereof.

In some embodiments, a step of assaying a sample (e.g., a blood-derived sample) for a plurality of distinct biomarker combinations comprises a detection assay. In some embodiments, exemplary detection assays include but are not limited to immunoassays, which in some embodiments may be or comprise immuno-PCR, and/or proximity ligation assay.

In some embodiments, a detection assay can comprise a proximity ligation assay. In some embodiments, a proximity ligation assay may comprise contacting extracellular vesicles with at least one set of detection probes for each biomarker combination, each detection probe directed to a biomarker, which set comprises at least a first detection probe for a first biomarker and a second detection probe for a second biomarker, so that a combination comprising the extracellular vesicles and the set of detection probes is generated.

As will be understood by a skilled artisan, in some embodiments, a sample comprising extracellular vesicles may also comprise nanoparticles having a size range of interest that includes extracellular vesicles. Thus, in some embodiments, provided technologies of the present disclosure in the context of extracellular vesicles are also applicable to detection of nanoparticles having a size range interest that includes extracellular vesicles. Accordingly, in some embodiments, the present disclosure, among other things, provides technologies for detection, in individual nanoparticles having a size range of interest (e.g., in some embodiments about 30 nm to about 1000 nm) that includes extracellular vesicles, of co-localization of at least two or more surface biomarkers (e.g., as described herein) that forms a target biomarker signature of a particular cancer. For example, in some embodiments, a proximity ligation assay may comprise contacting such nanoparticles with at least one set of detection probes for each biomarker combination, each detection probe directed to a biomarker, which set comprises at least a first detection probe for a first biomarker and a second detection probe for a second biomarker, so that a combination comprising the nanoparticles and the set of detection probes is generated.

In some embodiments, at least one set of detection probes specifically binds to biomarkers on the surface of nanoparticles having a size range of interest that includes extracellular vesicles such that the biomarkers are detected in a sample with predetermined specificity and sensitivity. In some embodiments, at least one set of detection probes specifically binds to biomarkers on the surface of nanoparticles having a size range of interest that includes extracellular vesicles such that the biomarkers are detected in a sample with a specificity within a range of 80% to 100% and sensitivity within a range of 10% to 100%. In some embodiments, at least one set of detection probes specifically binds to biomarkers on the surface of nanoparticles having a size range of interest that includes extracellular vesicles such that the biomarkers are detected in a sample with a specificity within a range of 90% to 100% and sensitivity within a range of 30% to 100%. In some embodiments, at least one set of detection probes specifically binds to biomarkers on the surface of nanoparticles having a size range of interest that includes extracellular vesicles such that the biomarkers are detected in a sample with a specificity within a range of 95% to 100% and sensitivity within a range of 50% to 100%.

A set of detection probes comprises at least a first detection probe for a first biomarker and a second detection probe for a second biomarker. In some embodiments, a first detection probe comprises a first target-binding moiety and a first oligonucleotide domain coupled to the first target-binding moiety, the first oligonucleotide domain comprising a first double-stranded portion and a first single-stranded overhang extended from one end of the first oligonucleotide domain. In some embodiments, a second detection probe comprises a second target-binding moiety and a second oligonucleotide domain coupled to the second target-binding moiety, the second oligonucleotide domain comprising a second double-stranded portion and a second single-stranded overhang extended from one end of the second oligonucleotide domain, wherein the second single-stranded overhang comprises a nucleotide sequence complementary to at least a portion of the first single-stranded overhang and can thereby hybridize with the first single-stranded overhang. In some embodiments, a first oligonucleotide domain and a second oligonucleotide domain have a combined length such that, when the first and second biomarkers are simultaneously present on the nanoparticles having a size range of interest that includes extracellular vesicles and the probes of the set of detection probes are bound to their respective biomarkers on the nanoparticles, the first single-stranded overhang and the second single-stranded overhang can hybridize together, forming a double-stranded complex.

In some embodiments, a detection assay comprises contacting a double-stranded complex with a nucleic acid ligase to generate a ligated template comprising a strand of the first double-stranded portion and a strand of the second double-stranded portion.

In some embodiments, a detection assay comprises a step of amplifying a product that is associated with the co-localization, and detecting the presence of the amplified product.

In some embodiments, a sample (e.g., a blood-derived sample) for detection of a plurality of distinct biomarker combinations comprises: capturing nanoparticles having a size range of interest that includes extracellular vesicles from a sample with a capture agent that selectively interacts with a surface biomarker on the nanoparticles; and contacting the captured nanoparticles with at least one set of at least two detection probes that each selectively interacts with a surface biomarker on the nanoparticles; and detecting a product formed when the at least two detection probes of the set are in sufficiently close proximity, such detection indicating co-localization of the surface biomarkers. While such a proximity ligation assay may perform better, e.g., with higher specificity and/or sensitivity, than other existing proximity ligation assays, a person skilled in the art reading the present disclosure will appreciate that other forms of proximity ligation assays that are known in the art may be used instead.

The present disclosure, among other things, recognizes that detection of a plurality of cancer-associated biomarkers based on a bulk sample (e.g., a bulk sample of extracellular vesicles), rather than at a resolution of a single extracellular vesicle, typically does not provide sufficient specificity and/or sensitivity in determination of whether a subject from whom the sample is obtained is likely to be suffering from or susceptible to cancer. The present disclosure, among other things, provides technologies, including systems, compositions, and/or methods, that solve such problems, including for example by specifically requiring that individual extracellular vesicles for detection be characterized by presence of a biomarker combination comprising a combination of at least one or more extracellular vesicle-associated surface biomarkers and at least one or more target biomarkers. In particular embodiments, the present disclosure teaches technologies that require such individual extracellular vesicles be characterized by presence (e.g., by expression) of such a biomarker combination of cancer (e.g., in some embodiments characterized by carcinoma, sarcoma, mixed types, etc.), while extracellular vesicles that do not comprise the biomarker combination do not produce a detectable signal (e.g., a level that is above a reference level, e.g., by at least 10% or more, where in some embodiments, a reference level may be a level observed in a negative control sample, such as a sample in which individual extracellular vesicles comprising such a biomarker combination are absent).

In some embodiments, the present disclosure describes a method comprising steps of: (a) providing or obtaining a sample comprising nanoparticles having a size within the range of about 30 nm to about 1000 nm, which are isolated from a bodily fluid-derived sample (e.g., a blood-derived sample) of a subject; (b) detecting on surfaces of the nanoparticles co-localization of at least two surface biomarkers whose combined expression level has been determined to be associated with a given cancer; (c) comparing the detected co-localization level with the determined level; and (d) classifying the subject as having or being susceptible to cancer when the detected co-localization level is at or above the determined level. In some embodiments, a sample may be assayed for a plurality of (e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or more) biomarker combinations (e.g., as described herein) for detection of different cancers. In some embodiments, a subject is classified as having or being susceptible to cancer when at least one of the assayed biomarker combinations shows a co-localization level that is at or above the determined level.

Accordingly, in some embodiments, technologies provided herein can be useful for detection of incidence or recurrence of cancer in a subject and/or across a population of subjects. In some embodiments, technologies provided herein can be useful for detection of early stage (e.g., stage I and/or stage II) cancer, including, e.g., but not limited to carcinoma or sarcoma. In some embodiments, technologies provided herein can be useful for detection of late stage (e.g., stage III and/or stage IV) cancer, including, e.g., but not limited to carcinoma or sarcoma. In some embodiments, technologies provided herein can be used periodically (e.g., every year) to screen a human subject or across a population of human subjects for early-stage cancer or cancer recurrence.

In some embodiments, technologies provided herein are particularly useful for detection of a solid tumor cancer. Non-limiting examples of a solid tumor cancer include but are not limited to bile duct cancer, bladder cancer, brain cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, eye cancer, head and neck cancer, gastrointestinal cancer, kidney cancer, liver cancer, lung cancer, mesothelioma, ovarian cancer, pancreatic cancer, prostate cancer, sarcomas, skin cancer, stomach cancer, testicular cancer, thymoma, and thyroid cancer.

In some embodiments, a subject that is amenable to technologies provided herein for detection of incidence or recurrence of cancer may be an asymptomatic human subject and/or across an asymptomatic population. Such an asymptomatic subject may be a subject who has a family history of cancer, who has a life history which places them at increased risk for cancer, who has been previously treated for cancer, who is at risk of cancer recurrence after cancer treatment, and/or who is in remission after cancer treatment. In some embodiments, such an asymptomatic subject may be a subject who is determined to have a normal medical diagnosis result from, e.g., ultrasound, MRI, CT scanning, tissue biopsy, and/or molecular tests, for example, based on cell-free nucleic acids and/or serum metabolites/proteins. In some embodiments, such an asymptomatic subject may be a subject who is determined to have an abnormal medical diagnosis result from, e.g., ultrasound, MRI, CT scanning, tissue biopsy and/or molecular tests, for example, based on cell-free nucleic acids and/or serum metabolites/proteins, when compared to results as typically observed in non-cancer subjects and/or normal healthy subjects. Alternatively, in some embodiments, an asymptomatic subject may be a subject who has not been previously screened for cancer, who has not been diagnosed for cancer, and/or who has not previously received cancer therapy.

In some embodiments, a subject or population of subjects may be selected based on one or more characteristics such as age, race, geographic location, genetic history, personal and/or medical history (e.g., smoking, alcohol, drugs, carcinogenic agents, diet, obesity, diabetes, physical activity, sun exposure, radiation exposure, chronic inflammation (e.g., of the lung, colon, pancreas, etc.) and/or occupational hazard).

In some embodiments, technologies provided herein can be useful for selecting surgery or therapy for a subject who is suffering from or susceptible to cancer. In some embodiments, cancer surgery, therapy, and/or an adjunct therapy can be selected in light of findings based on technologies provided herein.

In some embodiments, technologies provided herein can be useful for monitoring and/or evaluating efficacy of therapy administered to a subject (e.g., cancer subject).

In some embodiments, the present disclosure provides technologies for managing patient care, e.g., for one or more individual subjects and/or across a population of subjects. To give but a few examples, in some embodiments, the present disclosure provides technologies that may be utilized in screening (e.g., temporally or incidentally motivated screening and/or non-temporally or incidentally motivated screening, e.g., periodic screening such as annual, semi-annual, bi-annual, or with some other frequency). For example, in some embodiments, provided technologies for use in temporally motivated screening can be useful for screening one or more individual subjects or across a population of subjects (e.g., asymptomatic subjects) who are older than a certain age (e.g., over 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, or older). In some embodiments, the age and/or age range for temporally motivated screening with provided technologies is tailored to be appropriate for certain populations of subjects (e.g., as determined by demographics, life-history, family history, etc.) In some embodiments, provided technologies for use in incidentally motivated screening can be useful for screening individual subjects who may have experienced an incident or event that motivates screening for cancer as described herein. For example, in some embodiments, an incidental motivation relating to determination of one or more indicators of cancer or susceptibility thereto may be or comprise, e.g., an incident based on their family history (e.g., a close relative such as blood-related relative was previously diagnosed for cancer), identification of one or more risk factors associated with cancer (e.g., life history risk factors including, but not limited to smoking, alcohol, diet, obesity, occupational hazard, etc.) and/or prior incidental findings from genetic tests (e.g., genome sequencing), and/or imaging diagnostic tests (e.g., ultrasound, computerized tomography (CT) and/or magnetic resonance imaging (MRI) scans), development of one or more signs or symptoms characteristic of cancer (e.g., abnormal medical results such as discovery of a breast mass, and/or symptoms potentially indicative of cancer etc.).

In some embodiments, provided technologies can inform decision making relating to whether health insurance providers reimburse (or not), e.g., for (1) screening itself (e.g., reimbursement available only for periodic/regular screening or available only for temporally and/or incidentally motivated screening); and/or for (2) initiating, maintaining, and/or altering therapy in light of findings by provided technologies. For example, in some embodiments, the present disclosure provides methods relating to (a) receiving results of a screening as described herein and also receiving a request for reimbursement of the screening and/or of a particular therapeutic regimen; (b) approving reimbursement of the screening if it was performed on a subject according to an appropriate schedule or response to a relevant incident and/or approving reimbursement of the therapeutic regimen if it represents appropriate treatment in light of the received screening results; and, optionally (c) implementing the reimbursement or providing notification that reimbursement is refused. In some embodiments, a therapeutic regimen is appropriate in light of received screening results if the received screening results detect a biomarker that represents an approved biomarker for the relevant therapeutic regimen (e.g., as may be noted in a prescribing information label and/or via an approved companion diagnostic). Alternatively or additionally, the present disclosure contemplates reporting systems (e.g., implemented via appropriate electronic device(s) and/or communications system(s)) that permit or facilitate reporting and/or processing of screening results, and/or of reimbursement decisions as described herein.

Some aspects provided herein relate to systems and kits for use in provided technologies. In some embodiments, a system or kit may comprise detection agents for a plurality of biomarker combinations described herein. In some embodiments, such a system or kit may comprise a plurality of sets of detection probes. In some embodiments, at least one set of detection probes is directed to each distinct biomarker combination, which set comprises at least two detection probes each directed to a biomarker, which in some embodiments may be or comprise surface biomarker(s) (e.g., ones described herein).

In some embodiments, a system and/or kit provided herein may include detection agents for performing a proximity ligation assay (e.g., ones as described herein). In some embodiments, such detection agents for performing a proximity ligation assay may comprise at least one set of detection probes, each directed to a biomarker. In some embodiments, detection probes each comprise: (i) a biomarker binding moiety that specifically binds to a biomarker (e.g., a surface biomarker) on nanoparticles having a size range of interest that includes extracellular vesicles from cancer cells; and (ii) an oligonucleotide domain coupled to the biomarker binding moiety, wherein the oligonucleotide domains of the probes within the set are arranged and constructed so that, when the probes are bound to their biomarkers, their oligonucleotide domains hybridize to one another to form a ligatable hybrid only when the biomarkers are in proximity to one another.

In some embodiments, a provided system and/or kit may comprise a plurality (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30 or more) of sets of detection probes, each set of which comprises two or more (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more) detection probes. In some embodiments, a system and/or kit comprises at least two sets of detection probes. In some embodiments, a system and/or kit comprises at least five sets of detection probes.

In some embodiments, each set of detection probes included in a system and/or kit is directed to one or more biomarkers of a distinct biomarker combination that has been determined to be associated with a particular cancer, at least one of which is or comprises (i) one or more polypeptides encoded by human genes as follows: ABCC11, ABCC4, ACVR2B, ADGRF1, ALCAM, ALPL, AP1M2, APOO, AQP5, ARFGEF3, B3GNT3, B3GNT5, BCAM, BSPRY, BST2, CANT1, CD133, CD24, CD274 (PD-L1), CD38, CD55, CD74, CDCP1, CDH1, CDH17, CDH3, CDH6, CEACAM5, CEACAM6, CELSR1, CFB, CFTR, CHODL, CIP2A, CLDN16, CLDN3, CLDN4, CLDN6, CLGN, COX6C, CXCR4, CYP2S1, DDR1, DLL4, DSC2, DSG2, EDAR, EFNB1, EGFR, ENPP5, EPCAM, EPHB2, EPHB3, ERBB2, ERBB3, ESR1, FAM241B, FAP, FGFR4, FOLH1, FOLR1, FUT8, FXYD3, GALNT14, GALNT3, GALNT6, GALNT7, GFRA1, GJB1, GJB2, GOLM1, GPCR5A, GRB7, GRHL2, HACD3, HAS3, HTR3A, IG1FR, IHH, ILDR1, ITGAV, ITGB6, KCNQ1, KEL, KIF1A, KPNA2, LAMB3, LAMC2, LAPTM4B, LARGE2, LEMD1, LMNB1, LRP2, LRRTM1, LSR, LY6E, MAL2, MAP7, MARCKSL1, MET, MIEN1, MSLN, MST1R, MUC1, MUC13, MUC16, MUC2, MUC4, MUC5AC, NECTIN2, NOTCH3, NOX1, NRCAM, NUP155, NUP210, OCIAD2, OCLN, PARD6B, PIGT, PLEKHF2, PLXNB1, PMEPA1, PODXL2, PPP3CA, PRLR, PROM1, PRSS21, PSCA, PTGS1, PTK7, PTPRK, RAB25, RAB27B, RAB3B, RAB3D, RAC3, RDH11, RNF43, ROS1, SDC1, SEPHS1, SFXN2, SHROOM3, SLC2A1, SLC34A2, SLC35B2, SLC39A6, SLC4A4, SLC7A11, SLC9A3R1, SMIM22, SMPDL3B, SORD, SPINT2, ST14, STEAP1, STEAP2, SYT7, TACSTD2, TJP3, TMEM132A, TMPRSS2, TMPRSS4, TNFRSF10B, TNFRSF12A, TRPM4, TSPAN1, TSPAN8, UCHL1, UNC13B, XBP1, or combinations thereof; and/or (ii) one or more carbohydrate-dependent markers as follows: CA19-9, Lewis X antigen, Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, or combinations thereof.

In some embodiments, at least one set of detection probes in a system and/or kit may be directed to detection of cancer. In some embodiments, at least two sets of detection probes in a system and/or kit may be directed to detection of at least two distinct cancers. In some embodiments, at least one set of detection probes in a system and/or kit may be directed to detection of a tissue marker. In some embodiments, at least one set of detection probes in a system and/or kit may be directed to detection of a non-specific marker, e.g., it is present in one or more different types of cancer, and/or in one or more different types of tissues. In some embodiments, such a non-specific marker is considered multi-specific, e.g., it is present in more than one type of cancer, and/or in more than one type of tissue.

In some embodiments, at least one set of detection probes provided in a system and/or kit detects a biomarker combination comprising at least two biomarkers. In some embodiments, at least one set of detection probes provided in a system and/or kit detects a biomarker combination comprising at least three biomarkers. In some embodiments, one or more biomarkers of a biomarker combination are or comprise surface biomarkers. In some embodiments, a system and/or kit includes a plurality of sets of detection probes that detect biomarker combinations as described herein.

In some embodiments, a system and/or kit includes a plurality of sets of detection probes, which sets are directed to distinct biomarker combinations comprising biomarkers that are associated with at least two types of cancer, which in some embodiments may be selected from the group consisting of bile duct cancer, bladder cancer, brain cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, eye cancer, head and neck cancer, gastrointestinal cancer, kidney cancer, liver cancer, lung cancer, mesothelioma, ovarian cancer, pancreatic cancer, prostate cancer, sarcomas, skin cancer, stomach cancer, testicular cancer, thymoma, and thyroid cancer.

In some embodiments, detection probes in a provided kit and/or system may be provided as a single mixture in a container. In some embodiments, multiple sets of detection probes may be provided as individual mixtures in separate containers. In some embodiments, each detection probe is provided individually in a separate container.

In some embodiments, a system and/or kit described herein may further comprise a capture agent. In some embodiments, a capture agent may comprise a target capture moiety directed to an extracellular vesicle-associated surface biomarker (e.g., ones described herein). In some embodiments, such a target capture moiety may be conjugated to a solid substrate. In some embodiments, such a solid substrate may be or comprise a magnetic bead. In some embodiments, an exemplary capture agent included in a provided system and/or kit may be or comprise a solid substrate (e.g., a magnetic bead) and an affinity agent (e.g., but not limited to an antibody agent) conjugated thereto, wherein the affinity agent comprises a target capture moiety directed to an extracellular vesicle-associated surface biomarker.

A skilled artisan reading the present disclosure will understand that a system or kit for detection of extracellular vesicles can also be employed to detect nanoparticles having a size range of interest that includes extracellular vesicles. Accordingly, in some embodiments, a system or kit for pan-cancer detection may comprise, for each cancer-associated biomarker combination (e.g., as described herein), (i) a capture agent for a first surface biomarker of the biomarker combination (e.g., as described herein) present on the surface of nanoparticles having a size range of interest that includes extracellular vesicles; and (ii) at least one or more detection agents directed to a second surface biomarker of the biomarker combination. In some embodiments, such nanoparticles have a size within the range of about 30 nm to about 1000 nm.

In some embodiments, the present disclosure describes a kit for pan-cancer detection comprising: for each cancer-associated biomarker combination (e.g., as described herein), (a) a capture agent comprising a target-capture moiety directed to a first surface biomarker of the biomarker combination; and (b) at least one set of detection probes, which set comprises at least two detection probes each directed to a second surface biomarker of the biomarker combination, wherein the detection probes each comprise: (i) a target binding moiety directed at the second surface biomarker; and (ii) an oligonucleotide domain coupled to the target binding moiety, the oligonucleotide domain comprising a double-stranded portion and a single-stranded overhang portion extended from one end of the oligonucleotide domain, wherein the single-stranded overhang portions of the at least two detection probes are characterized in that they can hybridize to each other when the at least two detection probes are bound to the same nanoparticle having a size within the range of about 30 nm to about 1000 nm.

In some embodiments, the first surface biomarker and the second surface biomarker(s) are each independently selected from (i) polypeptides encoded by human genes as follows: ABCC11, ABCC4, ACSL4, ACVR2B, ADGRF1, ALCAM, ALPL, ANO1, ANXA13, AP1M2, AP1S3, APOO, AQP5, ARFGEF3, ASPHD1, ATP1B1, B3GNT3, B3GNT5, BCAM, BSPRY, BST2, CANT1, CAP2, CARD11, CD133, CD24, CD274 (PD-L1), CD38, CD55, CD74, CDCP1, CDH1, CDH17, CDH2, CDH3, CDH6, CDHR5, CEACAM5, CEACAM6, CELSR1, CFB, CFTR, CHODL, CHST4, CIP2A, CKAP4, CLCA2, CLDN10, CLDN16, CLDN3, CLDN4, CLDN6, CLGN, CLN5, CLTRN, COX6C, CXCR4, CYP2S1, CYP4F11, DDR1, DEFB1, DLL4, DSC2, DSG2, DSG3, EDAR, EFNB1, EGFR, ENPP5, EPCAM, EPHB2, EPHB3, EPPK1, ERBB2, ERBB3, ESR1, FAM241B, FAP, FER1L6, FERMT1, FGFR4, FOLH1, FOLR1, FUT8, FXYD3, GAL3ST1, GALNT14, GALNT3, GALNT5, GALNT6, GALNT7, GBA, GCNT3, GFRA1, GJB1, GJB2, GLUL, GOLM1, GPC3, GPCR5A, GRB7, GRHL2, HACD3, HAS3, HKDC1, HS6ST2, HSD17B2, HTR3A, IG1FR, IGSF3, IHH, ILDR1, ITGAV, ITGB6, KCNQ1, KEL, KIF1A, KPNA2, KRTCAP3, LAD1, LAMB3, LAMC2, LAPTM4B, LARGE2, LEMD1, LMNB1, LRP2, LRRTM1, LSR, LY6E, LYPD6B, MAL2, MAP7, MARCKSL1, MARVELD2, MET, MIEN1, MSLN, MST1R, MUC1, MUC13, MUC16, MUC2, MUC4, MUC5AC, NAT8, NECTIN2, NOTCH3, NOX1, NRCAM, NUP155, NUP210, OCIAD2, OCLN, OXTR, PARD6B, PDZK1, PIGT, PIK3AP1, PLEKHF2, PLXNB1, PMEPA1, PODXL2, PPP3CA, PRLR, PROM1, PRR7, PRSS21, PSCA, PTGS1, PTK7, PTPRK, RAB25, RAB27B, RAB3B, RAB3D, RAC3, RDH11, RNF43, ROBO1, ROS1, S100P, SCGN, SDC1, SEPHS1, SFXN2, SHANK2, SHROOM3, SLC22A9, SLC2A1, SLC2A2, SLC34A2, SLC35B2, SLC38A3, SLC39A6, SLC44A3, SLC4A4, SLC7A11, SLC7A5, SLC9A3R1, SMIM22, SMPDL3B, SNAP25, SORD, SPINT2, ST14, STEAP1, STEAP2, SYT13, SYT7, TACSTD2, TESC, TFR2, TJP3, TM4SF4, TMEM132A, TMEM156, TMEM158, TMPRSS11D, TMPRSS2, TMPRSS4, TMPRSS6, TNFRSF10B, TNFRSF12A, TOMM20, TRPM4, TSPAN1, TSPAN8, UCHL1, UGT1A9, UGT2B7, UGT8, ULBP2, UNC13B, VEPH1, VTCN1, XBP1, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows CA19-9 antigen, Lewis X antigen, Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, and combinations thereof.

In some embodiments, the first surface biomarker and the second surface biomarker(s) are each independently selected from: (i) polypeptides encoded by human genes as follows: CEACAM5, MUC1, and combinations thereof; and/or (ii) carbohydrate-dependent markers: Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, and combinations thereof.

In some embodiments where a plurality of biomarker combinations comprises an intravesicular RNA (e.g., but not limited to mRNA and noncoding RNA such as, e.g., orphan noncoding RNA, long noncoding RNA, piwi-interacting RNA, microRNA, circular RNA, etc.) biomarker, such a system and/or kit may include detection agents for performing a nucleic acid detection assay. In some embodiments, such a system and/or kit may include detection agents for performing a quantitative reverse-transcription PCR, for example, which may comprise primers directed to intravesicular RNA (e.g., but not limited to mRNA and noncoding RNA such as, e.g., orphan noncoding RNA, long noncoding RNA, piwi-interacting RNA, microRNA, circular RNA, etc.) target(s)).

In some embodiments, a provided system and/or kit may comprise at least one additional reagent, e.g., to process a sample and/or nanoparticles (including, e.g., in some embodiments extracellular vesicles) therein. In some embodiments, a provided system and/or kit may comprise at least one chemical reagent to process nanoparticles (including, e.g., in some embodiments extracellular vesicles) in a sample, including, e.g., but not limited to a fixation agent, a permeabilization agent, and/or a blocking agent. In some embodiments, a provided system and/or kit may comprise a nucleic acid ligase and/or a nucleic acid polymerase. In some embodiments, a provided system and/or kit may comprise one or more primers and/or probes. In some embodiments, a provided system and/or kit may comprise one or more pairs of primers, for example for PCR, e.g., quantitative PCR (qPCR) reactions. In some embodiments, a provided system and/or kit may comprise one or more probes such as, for example, hydrolysis probes which may in some embodiments be designed to increase the specificity of qPCR (e.g., TaqMan probes). In some embodiments, a provided system and/or kit may comprise one or more multiplexing probes, for example as may be useful when simultaneous or parallel qPCR reactions are employed (e.g., to facilitate or improve readout).

In some embodiments, provided systems and/or kits can be used for screening (e.g., regular screening) and/or other assessment of individuals (e.g., asymptomatic or symptomatic subjects) for detection (e.g., early detection) of cancer. In some embodiments, provided systems and/or kits can be used for screening and/or other assessment of individuals susceptible to cancer (e.g., individuals with a known genetic, environmental, or experiential risk, etc.). In some embodiments, provided systems and/or kits can be used for monitoring recurrence of cancer in a subject who has been previously treated. In some embodiments, provided systems and/or kits can be used as a companion diagnostic in combination with a therapy for a subject who is suffering from cancer. In some embodiments, provided systems and/or kits can be used for monitoring or evaluating efficacy of a therapy administered to a subject who is suffering from cancer. In some embodiments, provided systems and/or kits can be used for selecting a therapy for a subject who is suffering from cancer. In some embodiments, provided systems and/or kits can be used for making a therapy decision and/or selecting a therapy for a subject with one or more symptoms (e.g., non-specific symptoms) associated with cancer.

In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a CLDN3 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise an EPCAM polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a MARCKSL1 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a VTCN1 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a PODXL2 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a LAPTM4B polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a CD24 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise an ENPP5 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a GRHL2 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a BMPR1B polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a CLGN polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a CDH2 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a CDH1 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a GNG4 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise an APOO polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a FAM241B polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a FOLR1 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a LAMC2 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a CDH3 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a CLDN4 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a TACSTD2 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a PMEPA1 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a RAB25 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a TNFRSF21 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a GJB1 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a RAP2B polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a FERMT1 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a RPN2 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise an ITGB6 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a RPN1 polypeptide.

In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise a CEACAM5 polypeptide. In some embodiments, an extracellular vesicle-associated surface biomarker and/or surface biomarker included in a biomarker combination may be or comprise one or more carbohydrate-dependent markers as follows: Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, or combinations thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram illustrating an exemplary workflow of profiling individual extracellular vesicles (EVs). The figure shows purification of EVs from plasma using size exclusion chromatography (SEC) and immunoaffinity capture of EVs displaying a specific EV-associated surface marker (Panel A); detection of co-localized target markers (e.g., intravesicular biomarkers or surface biomarkers) on captured EVs using a target entity detection assay according to some embodiments described herein (Panel B).

FIG. 2 is a schematic diagram illustrating a target entity detection assay according to some embodiments described herein. In some embodiments, a target entity detection assay uses a combination of detection probes, which combination is specific for detection of cancer. In some embodiments, a duplex system includes a first detection probe for a target biomarker 1 and a second detection probe for a target biomarker 2 are added to a sample comprising a biological entity (e.g., extracellular vesicle). In some embodiments, detection probes each comprise a target binding moiety (e.g., an affinity agent such as, e.g., an antibody agent against a target biomarker) coupled to an oligonucleotide domain, which comprises a double-stranded portion and a single-stranded overhang extended from one end of the oligonucleotide domain. A detection signal is generated when distinct target binding moieties (e.g., affinity agents such as, e.g., antibody agents against target biomarker 1 and target biomarker 2, respectively) of the first and second detection probes are localized to the same biological entity (e.g., an extracellular vesicle) in close proximity such that the corresponding single-stranded overhangs hybridize to each other, thus allowing ligation of their oligonucleotide domains to occur. For example, a control entity (e.g., a biological entity from a healthy subject sample) does not express one or both of target biomarker 1 and target biomarker 2, so no detection of signal can be generated. However, when a biological entity from a cancer sample (e.g., a cancer sample) expresses target biomarker 1 and target biomarker 2, and the target biomarkers are present within a short enough distance of each other in the same biological entity (e.g., extracellular vesicle), a detection signal is generated.

FIG. 3 is a schematic diagram illustrating a target entity detection assay according to some embodiments described herein. The figure shows an exemplary triplex target entity detection system, in which in some embodiments, three or more detection probes, each for a target biomarker, can be added to a sample comprising a biological entity (e.g., extracellular vesicle). In some embodiments, detection probes each comprise a target binding moiety (e.g., an affinity agent such as, e.g., an antibody agent against a target biomarker) coupled to an oligonucleotide domain, which comprises a double-stranded portion and a single-stranded overhang extended from one end of the oligonucleotide domain. A detection signal is generated when the corresponding single-stranded overhangs of all three or more detection probes hybridize to each other to form a linear double-stranded complex, and ligation of at least one strand of the double-stranded complex occurs, thus allowing a resulting ligated product to be detected.

FIG. 4 is a non-limiting example of a double-stranded complex comprising four detection probes connected to each other in a linear arrangement through hybridization of their respective single-stranded overhangs.

FIG. 5 is a schematic diagram illustrating a target entity detection assay of an exemplary embodiment described herein. In some embodiments, a plurality of detection probes, each for a distinct target, are added to a sample comprising a biological entity (e.g., extracellular vesicle). In some embodiments, detection probes each comprise a target binding moiety (e.g., an antibody agent) coupled to an oligonucleotide domain, which comprises a double-stranded portion and a single-stranded overhang extended from one end of the oligonucleotide domain. A detection signal is generated when all detection probes are localized to the same biological entity (e.g., an extracellular vesicle or analyte) in close proximity such that the corresponding single-stranded overhangs hybridize to form a linear double-stranded complex, and ligation of at least one strand of the resulting linear double-stranded complex occurs, thereby allowing a ligated product to be detected.

FIG. 6 is a schematic illustrating three data sets, wherein biomarker combination set three is complementary to biomarker combination set 1 and set 2.

FIG. 7 shows MIF RT-PCR signal (45-Ct) following EPCAM-targeted immunoaffinity capture for OVCAR-3 (positive cell line) and SK-MEL-1 (negative cell line) EVs. Multiple detergent (Tween-20) concentrations were evaluated, with 0% Tween showing greater delta values.

CERTAIN DEFINITIONS

Administering: As used herein, the term “administering” or “administration” typically refers to the administration of a composition to a subject to achieve delivery of an agent that is, or is included in, a composition to a target site or a site to be treated. Those of ordinary skill in the art will be aware of a variety of routes that may, in appropriate circumstances, be utilized for administration to a subject, for example a human. For example, in some embodiments, administration may be parenteral. In some embodiments, administration may be oral. In some embodiments, administration may involve only a single dose. In some embodiments, administration may involve application of a fixed number of doses. In some embodiments, administration may involve dosing that is intermittent (e.g., a plurality of doses separated in time) and/or periodic (e.g., individual doses separated by a common period of time) dosing. In some embodiments, administration may involve continuous dosing (e.g., perfusion) for at least a selected period of time.

Affinity Agent: The term “affinity agent” as used herein refers to an entity that is or comprises a target-binding moiety as described herein, and therefore binds to a target of interest (e.g., molecular target of interest such as a biomarker or an epitope). In many embodiments, an affinity agent in accordance with the present disclosure binds specifically with a biomarker as described herein. In many embodiments, an affinity agent in accordance with the present disclosure binds specifically with a surface biomarker as described herein. In some embodiments, an affinity agent in accordance with the present disclosure binds specifically with a carbohydrate-dependent marker as described herein. In some embodiments, an affinity agent may be or comprise an antibody agent (e.g., an antibody or other entity that is or includes an antigen-binding portion thereof). Alternatively or additionally, in some embodiments, an affinity agent may selected from the group consisting of affimers, aptamers, lectins, sialic acid-binding immunoglobulin-type lectins (siglecs), and combinations thereof, and/or another binding agent that may be considered a ligand. In some embodiments, a target (e.g., a biomarker target) of an affinity agent is or comprises one or more polypeptide, nucleic acid, carbohydrate, and/or lipid moieties and/or entities).

Agent: In general, the term “agent”, as used herein, is used to refer to an entity (e.g., for example, a lipid, metal, nucleic acid, polypeptide, polysaccharide, small molecule, etc, or complex, combination, mixture or system [e.g., cell, tissue, organism] thereof), or phenomenon (e.g., heat, electric current or field, magnetic force or field, etc). In appropriate circumstances, as will be clear from context to those skilled in the art, the term may be utilized to refer to an entity that is or comprises a cell or organism, or a fraction, extract, or component thereof. Alternatively or additionally, as context will make clear, the term may be used to refer to a natural product in that it is found in and/or is obtained from nature. In some instances, again as will be clear from context, the term may be used to refer to one or more entities that is man-made in that it is designed, engineered, and/or produced through action of the hand of man and/or is not found in nature. In some embodiments, an agent may be utilized in isolated or pure form; in some embodiments, an agent may be utilized in crude form. In some embodiments, potential agents may be provided as collections or libraries, for example that may be screened to identify or characterize active agents within them. In some cases, the term “agent” may refer to a compound or entity that is or comprises a polymer; in some cases, the term may refer to a compound or entity that comprises one or more polymeric moieties. In some embodiments, the term “agent” may refer to a compound or entity that is not a polymer and/or is substantially free of any polymer and/or of one or more particular polymeric moieties. In some embodiments, the term may refer to a compound or entity that lacks or is substantially free of any polymeric moiety.

Amplification: The terms “amplification” and “amplify” refers to a template-dependent process that results in an increase in the amount and/or levels of a nucleic acid molecule relative to its initial amount and/or level. A template-dependent process is generally a process that involves template-dependent extension of a primer molecule, wherein the sequence of the newly synthesized strand of nucleic acid is dictated by the well-known rules of complementary base pairing (see, for example, Watson, J. D. et al., In: Molecular Biology of the Gene, 4th Ed., W. A. Benjamin, Inc., Menlo Park, Calif. (1987); which is incorporated herein by reference for the purpose described herein).

Antibody agent: As used herein, the term “antibody agent” refers to an agent that specifically binds to a particular antigen. In some embodiments, an antibody agent refers to a polypeptide that includes canonical immunoglobulin sequence elements sufficient to confer specific binding to a particular target antigen. As is known in the art, intact antibodies as produced in nature are approximately 150 kD tetrameric agents comprised of two identical heavy chain polypeptides (about 50 kD each) and two identical light chain polypeptides (about 25 kD each) that associate with each other into what is commonly referred to as a “Y-shaped” structure. Each heavy chain is comprised of at least four domains (each about 110 amino acids long)—an amino-terminal variable (VH) domain (located at the tips of the Y structure), followed by three constant domains: CH1, CH2, and the carboxy-terminal CH3 (located at the base of the Y's stem). A short region, known as the “switch”, connects the heavy chain variable and constant regions. The “hinge” connects CH2 and CH3 domains to the rest of the antibody. Two disulfide bonds in this hinge region connect the two heavy chain polypeptides to one another in an intact antibody. Each light chain is comprised of two domains—an amino-terminal variable (VL) domain, followed by a carboxy-terminal constant (CL) domain, separated from one another by another “switch”. Intact antibody tetramers are comprised of two heavy chain-light chain dimers in which the heavy and light chains are linked to one another by a single disulfide bond; two other disulfide bonds connect the heavy chain hinge regions to one another, so that the dimers are connected to one another and the tetramer is formed. Naturally-produced antibodies are also glycosylated, typically on the CH2 domain. Each domain in a natural antibody has a structure characterized by an “immunoglobulin fold” formed from two beta sheets (e.g., 3-, 4-, or 5-stranded sheets) packed against each other in a compressed antiparallel beta barrel. Each variable domain contains three hypervariable loops known as “complement determining regions” (CDR1, CDR2, and CDR3) and four somewhat invariant “framework” regions (FR1, FR2, FR3, and FR4). When natural antibodies fold, the FR regions form the beta sheets that provide the structural framework for the domains, and the CDR loop regions from both the heavy and light chains are brought together in three-dimensional space so that they create a single hypervariable antigen binding site located at the tip of the Y structure. The Fc region of naturally-occurring antibodies binds to elements of the complement system, and also to receptors on effector cells, including for example effector cells that mediate cytotoxicity. As is known in the art, affinity and/or other binding attributes of Fc regions for Fc receptors can be modulated through glycosylation or other modification. In some embodiments, antibodies produced and/or utilized in accordance with the present invention include glycosylated Fc domains, including Fc domains with modified or engineered such glycosylation. For purposes of the present invention, in certain embodiments, any polypeptide or complex of polypeptides that includes sufficient immunoglobulin domain sequences as found in natural antibodies can be referred to and/or used as an “antibody”, whether such polypeptide is naturally produced (e.g., generated by an organism reacting to an antigen), or produced by recombinant engineering, chemical synthesis, or other artificial system or methodology. In some embodiments, an antibody is polyclonal; in some embodiments, an antibody is monoclonal. In some embodiments, an antibody has constant region sequences that are characteristic of rabbit, rodent (e.g., mouse, rat, hamster, etc.), camelid (e.g., llama, alpaca), sheep, goat, bovine, horse, chicken, donkey, shark, primate, human, or in vitro-derived (e.g., yeast, phage) antibodies. In some embodiments, antibody sequence elements are humanized, primatized, chimeric, etc., as is known in the art. Moreover, the term “antibody” as used herein, can refer in appropriate embodiments (unless otherwise stated or clear from context) to any of the art-known or developed constructs or formats for utilizing antibody structural and functional features in alternative presentation. For example, in some embodiments, an antibody utilized in accordance with the present invention is in a format selected from, but not limited to, IgA, IgG, IgE or IgM antibodies; bi- or multi-specific antibodies (e.g., Zybodies®, etc.); antibody fragments such as Fab fragments, Fab′ fragments, F(ab′)2 fragments, Fd fragments, and isolated CDRs or sets thereof; single chain Fvs; polypeptide-Fc fusions; single domain antibodies, alternative scaffolds or antibody mimetics (e.g., anticalins, FN3 monobodies, Affibodies, Affilins, Affimers, Affitins, Alphabodies, Avimers, Fynomers, Im7, VLR, VNAR, Trimab, CrossMab, Trident); nanobodies, binanobodies, di-sdFv, single domain antibodies, trifunctional antibodies, diabodies, and minibodies. etc. In some embodiments, relevant formats may be or include: Adnectins®; Affibodies®; Affilins®; Anticalins®; Avimers®; BiTE®s; cameloid antibodies; Centyrins®; ankyrin repeat proteins or DARPINs®; dual-affinity re-targeting (DART) agents; Fynomers®; shark single domain antibodies such as IgNAR; immune mobilizing monoclonal T cell receptors against cancer (ImmTACs); KALBITOR®s; MicroProteins; Nanobodies® minibodies; masked antibodies (e.g., Probodies®); Small Modular ImmunoPharmaceuticals (“SMIPs™”); single chain or Tandem diabodies (TandAb®); TCR-like antibodies; Trans-bodies®; TrimerX®; VHHs. In some embodiments, an antibody may lack a covalent modification (e.g., attachment of a glycan) that it would have if produced naturally. In some embodiments, an antibody may contain a covalent modification (e.g., attachment of a glycan, a payload [e.g., a detectable moiety, a therapeutic moiety, a catalytic moiety, etc], or other pendant group [e.g., poly-ethylene glycol, etc.]).

Antigen: As used herein, the term “antigen” refers to an entity (e.g., a molecule or a molecular structure such as, e.g., a peptide or protein, carbohydrate, lipoparticle, oligonucleotide, chemical molecule, or combinations thereof) that includes one or more epitopes and therefore is recognized and bound by an affinity agent (e.g., an antibody, affimer, or aptamer).

Approximately or about: As used herein, the term “approximately” or “about,” as applied to one or more values of interest, refers to a value that is similar to a stated reference value. In general, those skilled in the art, familiar within the context, will appreciate the relevant degree of variance encompassed by “about” or “approximately” in that context. For example, in some embodiments, the term “approximately” or “about” may encompass a range of values that are within 25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, or less of the referred value.

Aptamer: As used herein, the term “aptamer” typically refers to a nucleic acid molecule or a peptide molecule that binds to a specific target molecule (e.g., an epitope). In some embodiments, a nucleic acid aptamer may be described by a nucleotide sequence and is typically about 15-60 nucleotides in length. A nucleic acid aptamer may be or comprise a single stranded and/or double-stranded structure. In some embodiments, a nucleic acid aptamer may be or comprise DNA. In some embodiments, a nucleic acid aptamer may be or comprise RNA. Without wishing to be bound by any theory, it is contemplated that the chain of nucleotides in an aptamer form intramolecular interactions that fold the molecule into a complex three-dimensional shape, and this three-dimensional shape allows the aptamer to bind tightly to the surface of its target molecule. In some embodiments, a peptide aptamer may be described to have one or more peptide loops of variable sequence displayed by a protein scaffold. Peptide aptamers can be isolated from combinatorial libraries and often subsequently improved by directed mutation or rounds of variable region mutagenesis and selection. Given the extraordinary diversity of molecular shapes that exist within the universe of all possible nucleotide and/or peptide sequences, aptamers may be obtained for a wide array of molecular targets, including proteins and small molecules. In addition to high specificity, aptamers typically have very high affinities for their targets (e.g., affinities in the picomolar to low nanomolar range for proteins or polypeptides). Because aptamers are typically synthetic molecules, aptamers are amenable to a variety of modifications, which can optimize their function for particular applications.

Associated with: Two events or entities are “associated” with one another, as that term is used herein, if the presence, level and/or form of one is correlated with that of the other. For example, a particular biological phenomenon (e.g., expression of a specific biomarker) is considered to be associated with cancer (e.g., a specific type of cancer (e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types) and/or stage of cancer), if its presence correlates with incidence of and/or susceptibility of the cancer (e.g., across a relevant population).

Biological entity: In appropriate circumstances, as will be clear from context to those skilled in the art, the term “biological entity” may be utilized to refer to an entity or component that is present in a biological sample, e.g., in some embodiments derived or obtained from a subject, which, in some embodiments, may be or comprise a cell or an organism, such as an animal or human, or, in some embodiments, may be or comprise a biological tissue or fluid. In some embodiments, a biological entity is or comprises a cell or microorganism, or a fraction, extract, or component thereof (including, e.g., intracellular components and/or molecules secreted by a cell or microorganism). For example, in some embodiments, a biological entity is or comprises a cell. In some embodiments, a biological entity is or comprises a nanoparticle having a size within the range of about 30 nm to about 1000 nm, which in some embodiments are obtained from a bodily fluid sample (e.g., but not limited to a blood sample) of a subject. In some embodiments, such a nanoparticle may be or comprise a protein aggregate, including, e.g., in some embodiments comprising a glycan, and/or an extracellular vesicle. In some embodiments, such a nanoparticle may have a size within the range of about 30 nm to about 1000 nm, about 50 nm to about 500 nm, or about 75 nm to about 500 nm. In some embodiments, a biological entity is or comprises an extracellular vesicle. In some embodiments, a biological entity is or comprises a biological analyte (e.g., a metabolite, carbohydrate, protein or polypeptide, enzyme, lipid, organelle, cytokine, receptor, ligand, and any combinations thereof). In some embodiments, a biological entity present in a sample is in a native state (e.g., proteins or polypeptides remain in a naturally occurring conformational structure). In some embodiments, a biological entity is processed, e.g., by isolating from a sample or deriving from a naturally occurring biological entity. For example, a biological entity can be processed with one or more chemical agents such that it is more desirable for detection utilizing technologies provided herein. As an example only, a biological entity may be a cell or extracellular vesicle that is contacted with a fixative agent (e.g., but not limited to methanol and/or formaldehyde) to cause proteins and/or peptides present in the cell or extracellular vesicle to form crosslinks. In some embodiments, a biological entity is in an isolated or pure form (e.g., isolated from a bodily fluid sample such as, e.g., a blood, serum, plasma sample, etc.). In some embodiments, a biological entity may be present in a complex matrix (e.g., a bodily fluid sample such as, e.g., a blood, serum, or plasma sample, etc.). In some embodiments, a biological entity may be present in a complex matrix (e.g., a bodily fluid sample such as, e.g., a blood, serum, or plasma sample, etc.).

Biomarker: The term “biomarker” typically refers to an entity, event, or characteristic whose presence, level, degree, type, and/or form, correlates with a particular biological event or state of interest, so that it is considered to be a “marker” of that event or state. To give but a few examples, in some embodiments, a biomarker may be or comprise a marker for a particular disease state, or for likelihood that a particular disease, disorder or condition may develop, occur, or reoccur. In some embodiments, a biomarker may be or comprise a marker for a particular disease or therapeutic outcome, or likelihood thereof. In some embodiments, a biomarker may be or comprise a marker for a particular tissue (e.g., but not limited to brain, breast, colon, ovary and/or other tissues associated with a female reproductive system, pancreas, prostate and/or other tissues associated with a male reproductive system, liver, lung, and skin). Such a marker for a particular tissue, in some embodiments, may be specific for a healthy tissue, specific for a diseased tissue, or in some embodiments may be present in a normal healthy tissue and diseased tissue (e.g., a tumor); those skilled in the art, reading the present disclosure, will appreciate appropriate contexts for each such type of biomarker. In some embodiments, a biomarker may be or comprise a cancer-specific marker (e.g., a marker that is specific to a particular cancer). In some embodiments, a biomarker may be or comprise a non-specific cancer marker (e.g., a marker that is present in at least two or more cancers). A non-specific cancer marker may be or comprise, in some embodiments, a generic marker for cancers (e.g., a marker that is typically present in cancers, regardless of tissue types), or in some embodiments, a marker for cancers of a specific tissue (e.g., but not limited to brain, breast, colon, ovary and/or other tissues associated with a female reproductive system, pancreas, prostate and/or other tissues associated with a male reproductive system, liver, lung, and skin). Thus, in some embodiments, a biomarker is predictive; in some embodiments, a biomarker is prognostic; in some embodiments, a biomarker is diagnostic, of the relevant biological event or state of interest. A biomarker may be or comprise an entity of any chemical class, and may be or comprise a combination of entities. For example, in some embodiments, a biomarker may be or comprise a nucleic acid, a polypeptide, a lipid, a carbohydrate, a small molecule, an inorganic agent (e.g., a metal or ion), or a combination thereof. In some embodiments, a biomarker is or comprises a portion of a particular molecule, complex, or structure; e.g., in some embodiments, a biomarker may be or comprise an epitope. In some embodiments, a biomarker is a surface marker (e.g., a surface protein marker) of an extracellular vesicle associated with cancer (e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types). In some embodiments, a biomarker is intravesicular (e.g., a protein or RNA marker that is present within an extracellular vesicle). In some embodiments, a biomarker may be or comprise a genetic or epigenetic signature. In some embodiments, a biomarker may be or comprise a gene expression signature. In some embodiments, a “biomarker” appropriate for use in accordance with the present disclosure may refer to presence, level, and/or form of a molecular entity (e.g., epitope) present in a target marker. For example, in some embodiments, two or more “biomarkers” as molecular entities (e.g., epitopes) may be present on the same target marker (e.g., a marker protein such as a surface protein present in an extracellular vesicle).

Biomarker combination: The term “biomarker combination”, as used herein, refers to a combination of (e.g., at least 2 or more, including, e.g., at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 25, at least 30, or more) biomarkers, which combination correlates with a particular biological event or state of interest, so that one skilled in the art will appreciate that it may appropriately be considered to be a “signature” of that event or state. Thus, in some embodiments, a biomarker combination may constitute a target biomarker signature. To give but a few examples, in some embodiments, a biomarker combination may correlate with a particular disease or disease state, and/or with likelihood that a particular disease, disorder or condition may develop, occur, or reoccur. In some embodiments, a biomarker combination may correlate with a particular disease or therapeutic outcome, or likelihood thereof. In some embodiments, a biomarker combination may correlate with a specific cancer and/or stage thereof. In some embodiments, a biomarker combination may correlate with cancer and/or a stage and/or a subtype thereof (e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types). In some embodiments, a biomarker combination comprises a combination of (e.g., at least 2 or more, including, e.g., at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 25, at least 30, or more) biomarkers that together are specific for a cancer or a subtype and/or a disease stage thereof), though one or more biomarkers in such a combination may be directed to a target (e.g., a surface biomarker, an intravesicular biomarker, and/or an intravesicular RNA) that is not specific to the cancer. For example, in some embodiments, a biomarker combination may comprise at least one biomarker specific to a cancer or a stage and/or subtype thereof (i.e., a cancer-specific target), and may further comprise a biomarker that is not necessarily or completely specific for the cancer (e.g., that may also be found on some or all biological entities such as, e.g., cells, extracellular vesicles, etc., that are not cancerous, are not of the relevant cancer, and/or are not of the particular stage and/or subtype of interest). That is, as will be appreciated by those skilled in the art reading the present specification, so long as a combination of biomarkers utilized in a biomarker combination is or comprises a plurality of biomarkers that together are specific for the relevant target biological entities of interest (e.g., cancer cells of interest or extracellular vesicles secreted by cancer cells) (i.e., sufficiently distinguish the relevant target biological entities (e.g., cancer cells of interest or extracellular vesicles secreted by cancer cells) for detection from other biological entities not of interest for detection), such a combination of biomarkers is a useful biomarker combination in accordance with certain embodiments of the present disclosure.

Blood-derived sample: The term “blood-derived sample,” as used herein, refers to a sample derived from a blood sample (i.e., a whole blood sample) of a subject in need thereof. Examples of blood-derived samples include, but are not limited to, blood plasma (including, e.g., fresh frozen plasma), blood serum, blood fractions, plasma fractions, serum fractions, blood fractions comprising red blood cells (RBC), platelets, leukocytes, etc., and cell lysates including fractions thereof (for example, cells, such as red blood cells, white blood cells, etc., may be harvested and lysed to obtain a cell lysate). In some embodiments, a blood-derived sample that is used with methods, systems, and/or kits described herein is a plasma sample.

Cancer: The term “cancer” is used herein to generally refer to a disease or condition in which cells of a tissue of interest exhibit relatively abnormal, uncontrolled, and/or autonomous growth, so that they exhibit an aberrant growth phenotype characterized by a significant loss of control of cell proliferation. In some embodiments, cancer may comprise cells that are precancerous (e.g., benign), malignant, pre-metastatic, metastatic, and/or non-metastatic. The present disclosure provides technologies for detection of cancer (including, for example, in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types).

Capture assay: As used herein, the term “capture assay” refers to a process of isolating or separating a biological entity of interest from a sample (e.g., in some embodiments a bodily fluid-derived sample). In some embodiments, a biological entity of interest is isolated or separated from a sample (e.g., in some embodiments a bodily fluid-derived sample) using a capture probe described herein. In some embodiments, a biological entity of interest that binds to a capture probe described herein is subject to a detection assay described herein. In some embodiments, a biological entity of interest amenable to a capture assay described herein is or comprises nanoparticles having a size range of interest that includes extracellular vesicles. In some embodiments, such a nanoparticle may have a size within the range of about 30 nm to about 1000 nm, about 50 nm to about 500 nm, or about 75 nm to about 500 nm. In some embodiments, a biological entity of interest amenable to a capture assay described herein is or comprises extracellular vesicles (e.g., in some embodiments exosomes) of interest.

Capture probe: As used herein, the term “capture probe” refers to a capture agent for capturing a biological entity of interest from a sample (e.g., in some embodiments a blood-derived sample). In many embodiments described herein, a capture agent comprises at least one target-capture moiety that binds to a surface polypeptide of a biological entity of interest. In some embodiments, such a biological entity of interest is or comprises nanoparticles having a size range of interest that includes extracellular vesicles. In some embodiments, such nanoparticles may have a size within the range of about 30 nm to about 1000 nm, about 50 nm to about 500 nm, or about 75 nm to about 500 nm. In some embodiments, such a biological entity of interest comprises extracellular vesicles (e.g., in some embodiments exosomes). In some embodiments, a capture agent comprises at least one target moiety that binds to a surface biomarker (e.g., ones described herein) of nanoparticles having a size within the range of about 30 nm to about 1000 nm, including, e.g., extracellular vesicles (e.g., in some embodiments exosomes). In some embodiments, a target-capture moiety of a capture agent is or comprises an affinity agent described herein. In some embodiments, a target-capture moiety of a capture agent is or comprises an antibody agent. In some embodiments, a target-capture moiety of a capture agent is or comprises a lectin or a sialic acid-binding immunoglobulin-type lectin (siglec). In some embodiments, a capture agent may comprise a solid substrate such that its target-capture moiety is immobilized thereonto. In some embodiments, an exemplary solid substrate is a bead (e.g., a magnetic bead). In some embodiments, a capture probe is or comprises a population of magnetic beads comprising a target-capture moiety that specifically binds to a surface biomarker described herein.

Classification cutoff: As used herein, the term “classification cutoff” refers to a level, value, or score, or a set of values, or an indicator that is used to predict a subject's risk for a disease or condition (e.g., cancer), for example, by defining one or more dividing lines among two or more subsets of a population (e.g., normal healthy subjects and subjects with inflammatory conditions vs. cancer subjects). In some embodiments, a classification cutoff may be determined referencing at least one reference threshold level (e.g., reference cutoff) for a biomarker combination described herein, optionally in combination with other appropriate variables, e.g., age, life-history-associated risk factors, hereditary factors, physical and/or medical conditions of a subject. In some embodiments where a classification is based on a single biomarker combination (e.g., as described herein), a classification cutoff may be the same as a reference threshold (e.g., cutoff) pre-determined for the single biomarker combination. In some embodiments where a classification is based on two or more (e.g., 2, 3, 4, or more) biomarker combinations, a classification cutoff may reference two or more reference thresholds (e.g., cutoffs) each individually pre-determined for the corresponding biomarker combinations, and optionally incorporate one or more appropriate variables, e.g., age, life-history-associated risk factors, hereditary factors, physical and/or medical conditions of a subject. In some embodiments, a classification cutoff may be determined via a computer algorithm-mediated analysis that references at least one reference threshold level (e.g., reference cutoff) for a biomarker combination described herein, optionally in combination with other appropriate variables, e.g., age, life-history-associated risk factors, hereditary factors, physical and/or medical conditions of a subject.

Close proximity: The term “close proximity” as used herein, refers to a distance between two detection probes (e.g., two detection probes in a pair) that is sufficiently close enough such that an interaction between the detection probes (e.g., through respective oligonucleotide domains) is expected to likely occur. For example, in some embodiments, probability of two detection probes interacting with each other (e.g., through respective oligonucleotide domains) over a period of time when they are in sufficiently close proximity to each other under a specified condition (e.g., when detection probes are bound to respective targets in an extracellular vesicle is at least 50% or more, including, e.g., at least 60%, at least 70%, at least 80%, at least 90% or more. In some embodiments, a distance between two detection probes when they are in sufficiently close proximity to each other may range between approximately 0.1-1000 nm, or 0.5-500 nm, or 1-250 nm. In some embodiments, a distance between two detection probes when they are in sufficiently close proximity to each other may range between approximately 0.1-10 nm or between approximately 0.5-5 nm. In some embodiments, a distance between two detection probes when they are in sufficiently close proximity to each other may be less than 100 nm or shorter, including, e.g., less than 90 nm, less than 80 nm, less than 70 nm, less than 60 nm, less than 50 nm, less than 40 nm, less than 30 nm, less than 20 nm, less than 10 nm, less than 5 nm, less than 1 nm, or shorter. In some embodiments, a distance between two detection probes when they are in sufficiently close proximity to each other may range between approximately 40-1000 nm or 40 nm-500 nm.

Comparable: As used herein, the term “comparable” refers to two or more agents, entities, situations, sets of conditions, etc., that may not be identical to one another but that are sufficiently similar to permit comparison therebetween so that one skilled in the art will appreciate that conclusions may reasonably be drawn based on differences or similarities observed. In some embodiments, comparable sets of conditions, circumstances, individuals, or populations are characterized by a plurality of substantially identical features and one or a small number of varied features. Those of ordinary skill in the art will understand, in context, what degree of identity is required in any given circumstance for two or more such agents, entities, situations, sets of conditions, etc. to be considered comparable. For example, those of ordinary skill in the art will appreciate that sets of circumstances, individuals, or populations are comparable to one another when characterized by a sufficient number and type of substantially identical features to warrant a reasonable conclusion that differences in results obtained or phenomena observed under or with different sets of circumstances, individuals, or populations are caused by or indicative of the variation in those features that are varied.

Complementary: As used herein, the term “complementary” in the context of nucleic acid base-pairing refers to oligonucleotide hybridization related by base-pairing rules. For example, the sequence “C-A-G-T” is complementary to the sequence “G-T-C-A.” Complementarity can be partial or total. Thus, any degree of partial complementarity is intended to be included within the scope of the term “complementary” provided that the partial complementarity permits oligonucleotide hybridization. Partial complementarity is where one or more nucleic acid bases is not matched according to the base pairing rules. Total or complete complementarity between nucleic acids is where each and every nucleic acid base is matched with another base under the base pairing rules. In the context of identifying biomarker combinations for detection of a particular cancer, the term “complementary” is used herein in reference to sets of biomarkers having different information content (e.g., ability to detect cancer in distinct, substantially non-overlapping subgroups of subjects). For example, two sets of biomarkers—set 1 and set 2—are said to be “complementary” to each other if, for example, set 1 detects cancer in a group (e.g., group A) of subjects in a population, and set 2 detects cancer in a substantially separate and non-overlapping group of subjects in the same population (e.g., group B), but not in Group A. Similarly, set 1 does not detect cancer in a substantial number of subjects in Group B.

Detecting: The term “detecting” is used broadly herein to include appropriate means of determining the presence or absence of an extracellular vesicle expressing a biomarker combination of cancer (e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types) or any form of measurement indicative of such an extracellular vesicle. Thus, “detecting” may include determining, measuring, assessing, or assaying the presence or absence, level, amount, and/or location of an entity of interest (e.g., a surface biomarker, an intravesicular biomarker, or an intravesicular RNA biomarker) that corresponds to part of a biomarker combination in any way. In some embodiments, “detecting” may include determining, measuring, assessing, or quantifying a form of measurement indicative of an entity of interest (e.g., a ligated template indicative of a surface biomarker and/or an intravesicular biomarker, or a PCR amplification product indicative of an intravesicular mRNA). Quantitative and qualitative determinations, measurements or assessments are included, including semi-quantitative. Such determinations, measurements or assessments may be relative, for example when an entity of interest (e.g., a surface biomarker, an intravesicular biomarker, or an intravesicular RNA biomarker) or a form of measurement indicative thereof is being detected relative to a control reference, or absolute. As such, the term “quantifying” when used in the context of quantifying an entity of interest (e.g., a surface biomarker, an intravesicular biomarker, or an intravesicular RNA biomarker) or a form of measurement indicative thereof can refer to absolute or to relative quantification. Absolute quantification may be accomplished by correlating a detected level of an entity of interest (e.g., a surface biomarker, an intravesicular biomarker, or an intravesicular RNA biomarker) or a form of measurement indicative thereof to known control standards (e.g., through generation of a standard curve). Alternatively, relative quantification can be accomplished by comparison of detected levels or amounts between two or more different entities of interest (e.g., different surface biomarkers, intravesicular biomarkers, or intravesicular RNA biomarkers) to provide a relative quantification of each of the two or more different entities of interest, i.e., relative to each other.

Detection label: The term “detection label” as used herein refers to any element, molecule, functional group, compound, fragment or moiety that is detectable. In some embodiments, a detection label is provided or utilized alone. In some embodiments, a detection label is provided and/or utilized in association with (e.g., joined to) another agent. Examples of detection labels include, but are not limited to: various ligands, radionuclides (e.g., ³H, ¹⁴C, ¹⁸F, ¹⁹F, ³²P, ³⁵S, ¹³⁵I, ¹²⁵I, ¹²³I, ⁶⁴Cu, ¹⁸⁷Re, ¹¹¹In, ⁹⁰Y, ^99mTc, ¹⁷⁷Lu, ⁸⁹Zr, etc.), fluorescent dyes, chemiluminescent agents (such as, for example, acridinium esters, stabilized dioxetanes, and the like), bioluminescent agents, spectrally resolvable inorganic fluorescent semiconductors nanocrystals (i.e., quantum dots), metal nanoparticles (e.g., gold, silver, copper, platinum, etc.) nanoclusters, paramagnetic metal ions, enzymes, colorimetric labels (such as, for example, dyes, colloidal gold, and the like), biotin, digoxigenin, haptens, and proteins for which antisera or monoclonal antibodies are available.

Detection probe: The term “detection probe” typically refers to a probe directed to detection and/or quantification of a specific target. In some embodiments, a detection probe is a quantification probe, which provides an indicator representing level of a specific target. In accordance with the present disclosure, a detection probe refers to a composition comprising a target binding entity, directly or indirectly, coupled to an oligonucleotide domain, wherein the target binding entity specifically binds to a respective target (e.g., molecular target), and wherein at least a portion of the oligonucleotide domain is designed to permit hybridization with a portion of an oligonucleotide domain of another detection probe for a distinct target. In many embodiments, an oligonucleotide domain appropriate for use in the accordance with the present disclosure comprises a double-stranded portion and at least one single-stranded overhang. In some embodiments, an oligonucleotide domain may comprise a double-stranded portion and a single-stranded overhang at each end of the double-stranded portion. In some embodiments, a target binding entity of a detection probe is or comprises an affinity agent described herein. In some embodiments, a target binding entity of a detection probe is or comprises an antibody agent. In some embodiments, a target binding entity of a detection probe is or comprises a lectin or a sialic acid-binding immunoglobulin-type lectin (siglec).

Double-stranded: As used herein, the term “double-stranded” in the context of oligonucleotide domain is understood by those of skill in the art that a pair of oligonucleotides exist in a hydrogen-bonded, helical arrangement typically associated with, for example, nucleic acid such as DNA. In addition to the 100% complementary form of double-stranded oligonucleotides, the term “double-stranded” as used herein is also meant to refer to those forms which include mismatches (e.g., partial complementarity) and/or structural features as bulges, loops, or hairpins.

Double-stranded complex: As used herein, the term “double-stranded complex” typically refers to a complex comprising at least two or more (including, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more) detection probes (e.g., as provided and/or utilized herein), each directed to a target (which can be the same target or a distinct target), connected or coupled to one another in a linear arrangement through hybridization of complementary single-stranded overhangs of the detection probes. In some embodiments, such a double-stranded complex may comprise an extracellular vesicle, wherein respective target binding moieties of the detection probes are simultaneously bound to the extracellular vesicle.

Epitope: As used herein, the term “epitope” includes any moiety that is specifically recognized by an affinity agent (e.g., but not limited to an antibody, affimer, and/or aptamer). In some embodiments, an epitope is comprised of a plurality of chemical atoms or groups on an antigen. In some embodiments, such chemical atoms or groups are surface-exposed when the antigen adopts a relevant three-dimensional conformation. In some embodiments, such chemical atoms or groups are physically near to each other in space when the antigen adopts such a conformation. In some embodiments, at least some such chemical atoms are groups are physically separated from one another when the antigen adopts an alternative conformation (e.g., is linearized).

Extracellular vesicle: As used herein, the term “extracellular vesicle” typically refers to a vesicle outside of a cell, e.g., secreted by a cell. Examples of secreted vesicles include, but are not limited to exosomes, microvesicles, microparticles, ectosomes, oncosomes, and apoptotic bodies. Without wishing to be bound by theory, exosomes are nanometer-sized vesicles (e.g., between 40 nm and 120 nm) of endocytic origin that may form by inward budding of the limiting membrane of multivesicular endosomes (MVEs), while microvesicles typically bud from the cell surface and their size may vary between 50 nm and 1000 nm. In some embodiments, an extracellular vesicle is or comprises an exosome and/or a microvesicle. In some embodiments, a sample comprising an extracellular vesicle is substantially free of apoptotic bodies. In some embodiments, a sample comprising extracellular vesicles may comprise extracellular vesicles shed or derived from one or more tissues (e.g., cancerous tissues and/or non-cancerous or healthy tissues). In some embodiments, an extracellular vesicle in a sample may be shed or derived from a cancerous tumor. In some embodiments, an extracellular vesicle is shed or derived from a healthy tissue. In some embodiments, an extracellular vesicle is shed or derived from a benign tumor. In some embodiments, an extracellular vesicle is shed or derived from a tissue of a subject with symptoms (e.g., non-specific symptoms) associated with cancer.

Extracellular vesicle-associated membrane-bound polypeptide: As used herein, such a term refers to a polypeptide that is present in the membrane of an extracellular vesicle. In some embodiments, such a biomarker may be associated with the extracellular side of the membrane. In some embodiments, such a polypeptide may be tumor-specific. In some embodiments, such a polypeptide may be tissue-specific (e.g., breast tissue-specific, rectal tissue-specific, prostate tissue-specific, etc.). In some embodiments, such a polypeptide may be non-specific, e.g., it is present in one or more non-target tumors, and/or in one or more non-target tissues.

Hybridization: As used herein, the term “hybridizing”, “hybridize”, “hybridization”, “annealing”, or “anneal” are used interchangeably in reference to pairing of complementary nucleic acids using any process by which a strand of nucleic acid joins with a complementary strand through base pairing to form a hybridization complex. Hybridization and the strength of hybridization (e.g., strength of the association between the nucleic acids) is impacted by various factors including, e.g., the degree of complementarity between the nucleic acids, stringency of the conditions involved, the melting temperature (T) of the formed hybridization complex, and the G:C ratio within the nucleic acids.

Intravesicular protein biomarker: As used herein, the term “intravesicular protein biomarker” refers to a marker indicative of the state (e.g., presence, level, and/or activity) of a polypeptide that is present within a biological entity (e.g., a cell or an extracellular vesicle). In many embodiments, an intravesicular protein biomarker is associated with or present within an extracellular vesicle. In many embodiments, an intravesicular protein biomarker may be post-translationally modified in a reversible (e.g. phosphorylation) or irreversible (e.g. cleavage) manner. In some embodiments, an intravesicular protein biomarker may be or comprise a phosphorylated polypeptide. In some embodiments, an intravesicular protein biomarker may be or comprise a mutated polypeptide.

Intravesicular RNA biomarker: As used herein, the term “intravesicular RNA biomarker” refers to a marker indicative of the state (e.g., presence and/or level) of a RNA that is present within a biological entity (e.g., a cell or an extracellular vesicle). In many embodiments, an intravesicular RNA biomarker is associated with or present within an extracellular vesicle. In some embodiments, an intravesicular RNA biomarker is associated or specific to cancer. In some embodiments, an intravesicular RNA biomarker is or comprises an mRNA transcript. In some embodiments, an intravesicular RNA biomarker is or comprises a noncoding RNA. Exemplary noncoding RNAs may include, but are not limited to small nuclear RNA, microRNA (miRNA), small nucleolar RNA (snoRNA), circular RNA (circRNA), long noncoding RNA (lncRNA), small noncoding RNA, piwi-interacting RNA, etc.). Certain RNA biomarkers for cancer are described in the art, e.g., as described in Xi et al. “RNA Biomarkers: Frontier of Precision Medicine for Cancer” Noncoding RNA (2017) 3:9, the contents of which are incorporated herein by reference for purposes described herein. In some embodiments, an intravesicular RNA biomarker is or comprise an orphan noncoding RNA (oncRNA). Certain oncRNAs that are cancer-specific were identified and described in the art, e.g., as described in Teng et al. “Orphan noncoding RNAs: novel regulators and cancer biomarkers” Ann Transl Med (2019) 7:S21; Fish et al. “Cancer cells exploit an orphan RNA to drive metastatic progression” Nature Medicine (2018) 24: 1743-1751; International Patent Publication WO 2019/094780, each of which are incorporated herein by reference for purposes described herein. In some embodiments, an intravesicular RNA biomarker is or comprises a long non-coding RNA. Certain non-coding RNA biomarkers for cancer are described in the art, e.g., as described in Qian et al. “Long Non-coding RNAs in Cancer: Implications for Diagnosis, Prognosis, and Therapy” Front. Med. (2020) Volume 7, Article 612393, the contents of which are incorporated herein by reference for purposes described herein. In some embodiments, an intravesicular RNA biomarker is or comprises piwiRNA. In some embodiments, an intravesicular RNA biomarker is or comprises miRNA. In some embodiments, an intravesicular RNA biomarker is or comprises snoRNA. In some embodiments, an intravesicular RNA biomarker is or comprises circRNA.

Ligase: As used herein, the term “ligase” or “nucleic acid ligase” refers to an enzyme for use in ligating nucleic acids. In some embodiments, a ligase is enzyme for use in ligating a 3′-end of a polynucleotide to a 5′-end of a polynucleotide. In some embodiments, a ligase is an enzyme for use to perform a sticky-end ligation. In some embodiments, a ligase is an enzyme for use to perform a blunt-end ligation. In some embodiments, a ligase is or comprises a DNA ligase.

Life-history-associated risk factors: As used herein, the term “life-history risk factors” refers to individuals' actions, experiences, medical history, and/or exposures in their lives which may directly or indirectly increase such individuals' risk for a condition, e.g., cancer (e.g., breast cancer, colorectal cancer, prostate cancer, etc.) relative to individuals who do not have such actions, experiences, medical history, and/or exposures in their lives. In some embodiments, non-limiting examples of life-history-associated risk factors include smoking, alcohol, drugs, carcinogenic agents, diet, obesity, diabetes, physical activity, sun exposure, radiation exposure, bituminous smoke exposure, exposure to infectious agents such as viruses and bacteria, and/or occupational hazard (Reid et al., 2017; which is incorporated herein by reference for the purpose described herein). One skilled in the art recognizes that the above list of life-history-associated risk factors contributing to cancer (e.g., cancer) susceptibility is not exhaustive but constantly evolving.

Ligation: As used herein, the term “ligate”, “ligating or “ligation” refers to a method or composition known in the art for joining two oligonucleotides or polynucleotides. A ligation may be or comprise a sticky-end ligation or a blunt-end ligation. In some embodiments, ligation involved in provided technologies is or comprises a sticky-end ligation. In some embodiments, ligation refers to joining a 3′ end of a polynucleotide to a 5′ end of a polynucleotide. In some embodiments, ligation is facilitated by use of a nucleic acid ligase.

Nanoparticles: The term “nanoparticles” as used in the context of a sample for a detection assay (e.g., as described herein) refers to particles having a size range of about 30 nm to about 1000 nm. In some embodiments, nanoparticles have a size range of about 30 nm to about 750 nm. In some embodiments, nanoparticles have a size range of about 50 nm to about 750 nm. In some embodiments, nanoparticles have a size range of about 30 nm to about 500 nm. In some embodiments, nanoparticles have a size range of about 50 nm to about 500 nm. In some embodiments, nanoparticles are obtained from a bodily fluid sample of a subject, for example, in some embodiments by a size exclusion-based method (e.g., in some embodiments size exclusion chromatography). In some embodiments, nanoparticles are or comprise analyte aggregates, which in some embodiments may be or comprise protein or mucin aggregates. In some embodiments, nanoparticles are or comprise protein multimers. In some embodiments, nanoparticles are or comprise extracellular vesicles.

Non-cancer subjects: As used herein, the term “non-cancer subjects” generally refers to subjects who do not have any type of cancer, and more specifically carcinoma, sarcoma, melanoma, and mixed type. For example, in some embodiments, a non-cancer subject is a healthy subject. In some embodiments, a non-cancer subject is a healthy subject below age 55. In some embodiments, a non-cancer subject is a healthy subject of age 55 or above. In some embodiments, a non-cancer subject is a subject with non-tumor related health diseases, disorders, or conditions. In some embodiments, a non-cancer subject is a subject having a benign tumor.

Nucleic acid/Oligonucleotide: As used herein, the term “nucleic acid” refers to a polymer of at least 10 nucleotides or more. In some embodiments, a nucleic acid is or comprises DNA. In some embodiments, a nucleic acid is or comprises RNA. In some embodiments, a nucleic acid is or comprises peptide nucleic acid (PNA). In some embodiments, a nucleic acid is or comprises a single stranded nucleic acid. In some embodiments, a nucleic acid is or comprises a double-stranded nucleic acid. In some embodiments, a nucleic acid comprises both single and double-stranded portions. In some embodiments, a nucleic acid comprises a backbone that comprises one or more phosphodiester linkages. In some embodiments, a nucleic acid comprises a backbone that comprises both phosphodiester and non-phosphodiester linkages. For example, in some embodiments, a nucleic acid may comprise a backbone that comprises one or more phosphorothioate or 5′-N-phosphoramidite linkages and/or one or more peptide bonds, e.g., as in a “peptide nucleic acid”. In some embodiments, a nucleic acid comprises one or more, or all, natural residues (e.g., adenine, cytosine, deoxyadenosine, deoxycytidine, deoxyguanosine, deoxythymidine, guanine, thymine, uracil). In some embodiments, a nucleic acid comprises on or more, or all, non-natural residues. In some embodiments, a non-natural residue comprises a nucleoside analog (e.g., 2-aminoadenosine, 2-thiothymidine, inosine, pyrrolo-pyrimidine, 3-methyl adenosine, 5-methylcytidine, C-5 propynyl-cytidine, C-5 propynyl-uridine, 2-aminoadenosine, C5-bromouridine, C5-fluorouridine, C5-iodouridine, C5-propynyl-uridine, C5-propynyl-cytidine, C5-methylcytidine, 2-aminoadenosine, 7-deazaadenosine, 7-deazaguanosine, 8-oxoadenosine, 8-oxoguanosine, 6-O-methylguanine, 2-thiocytidine, methylated bases, intercalated bases, and combinations thereof). In some embodiments, a non-natural residue comprises one or more modified sugars (e.g., 2′-fluororibose, ribose, 2′-deoxyribose, arabinose, and hexose) as compared to those in natural residues. In some embodiments, a nucleic acid has a nucleotide sequence that encodes a functional gene product such as an RNA or polypeptide. In some embodiments, a nucleic acid has a nucleotide sequence that comprises one or more introns. In some embodiments, a nucleic acid may be prepared by isolation from a natural source, enzymatic synthesis (e.g., by polymerization based on a complementary template, e.g., in vivo or in vitro, reproduction in a recombinant cell or system, or chemical synthesis. In some embodiments, a nucleic acid is at least 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 7500, 8000, 8500, 9000, 9500, 10,000, 10,500, 11,000, 11,500, 12,000, 12,500, 13,000, 13,500, 14,000, 14,500, 15,000, 15,500, 16,000, 16,500, 17,000, 17,500, 18,000, 18,500, 19,000, 19,500, or 20,000 or more residues or nucleotides long.

Nucleotide: As used herein, the term “nucleotide” refers to its art-recognized meaning. When a number of nucleotides is used as an indication of size, e.g., of an oligonucleotide, a certain number of nucleotides refers to the number of nucleotides on a single strand, e.g., of an oligonucleotide.

Pan-cancer detection: As used herein, the term “pan-cancer detection” refers to technologies for assaying a sample from a subject to screen for a plurality of cancers (e.g., at least two or more cancers). In some embodiments, a pan-cancer detection assay comprises detecting a plurality of distinct biomarker combinations on the surface of and/or within extracellular vesicles in a sample from a subject, wherein detection results from the plurality of distinct biomarker combinations provide an indicator of whether the subject is having or at risk for having a particular cancer.

Patient: As used herein, the term “patient” refers to any organism who is suffering or at risk of a disease or disorder or condition. Typical patients include animals (e.g., mammals such as mice, rats, rabbits, non-human primates, and/or humans). In some embodiments, a patient is a human. In some embodiments, a patient is suffering from or susceptible to one or more diseases or disorders or conditions. In some embodiments, a patient displays one or more symptoms of a disease or disorder or condition. In some embodiments, a patient has been diagnosed with one or more diseases or disorders or conditions. In some embodiments, a disease or disorder or condition that is amenable to provided technologies is or includes cancer, or presence of one or more tumors. In some embodiments, a patient is receiving or has received certain therapy to diagnose and/or to treat a disease, disorder, or condition.

Plurality: The term “plurality”, as used herein refers to at least two or more. In some embodiments, a plurality refers to at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, or more.

Polypeptide: The term “polypeptide”, as used herein, typically has its art-recognized meaning of a polymer of at least three amino acids or more. Those of ordinary skill in the art will appreciate that the term “polypeptide” is intended to be sufficiently general as to encompass not only polypeptides having a complete sequence recited herein, but also to encompass polypeptides that represent functional, biologically active, or characteristic fragments, portions or domains (e.g., fragments, portions, or domains retaining at least one activity) of such complete polypeptides. In some embodiments, polypeptides may contain L-amino acids, D-amino acids, or both and/or may contain any of a variety of amino acid modifications or analogs known in the art. Useful modifications include, e.g., terminal acetylation, amidation, glycosylation, methylation, etc. In some embodiments, polypeptides may comprise natural amino acids, non-natural amino acids, synthetic amino acids, and combinations thereof (e.g., may be or comprise peptidomimetics).

Prevent or prevention: As used herein, “prevent” or “prevention,” when used in connection with the occurrence of a disease, disorder, and/or condition, refers to reducing the risk of developing the disease, disorder and/or condition and/or to delaying onset of one or more characteristics or symptoms of the disease, disorder or condition. Prevention may be considered complete when onset of a disease, disorder or condition has been delayed for a predefined period of time.

Primer: As used herein, the term “primer” refers to an oligonucleotide capable of acting as a point of initiation of synthesis when placed under conditions in which synthesis of a primer extension product which is complementary to a nucleic acid strand is induced (e.g., in the presence of nucleotides and an inducing agent such as DNA polymerase and at a suitable temperature and pH). A primer is preferably single stranded for maximum efficiency in amplification. A primer must be sufficiently long to prime the synthesis of extension products in the presence of the inducing agent. The exact lengths of a primer can depend on many factors, e.g., desired annealing temperature, etc.

Reference: As used herein, “reference” describes a standard or control relative to which a comparison is performed. For example, in some embodiments, an agent, animal, individual, population, sample, sequence or value of interest is compared with a reference or control agent, animal, individual, population, sample, sequence, or value. In some embodiments, a reference or control is tested and/or determined substantially simultaneously with the testing or determination of interest. In some embodiments, a reference or control is a historical reference or control, optionally embodied in a tangible medium. In some embodiments, a reference or control in the context of a reference level of a target refers to a level of a target in a normal healthy subject or a population of normal healthy subjects. In some embodiments, a reference or control in the context of a reference level of a target refers to a level of a target in a subject prior to a treatment. Typically, as would be understood by those skilled in the art, a reference or control is determined or characterized under comparable conditions or circumstances to those under assessment. In some embodiments, cell-line-derived extracellular vesicles are used as a reference or control. Those skilled in the art will appreciate when sufficient similarities are present to justify reliance on and/or comparison to a particular possible reference or control.

Risk: As will be understood from context, “risk” of a disease, disorder, and/or condition refers to a likelihood that a particular individual will develop the disease, disorder, and/or condition. In some embodiments, risk is expressed as a percentage. In some embodiments, risk is from 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90 up to 100%. In some embodiments risk is expressed as a risk relative to a risk associated with a reference sample or group of reference samples. In some embodiments, a reference sample or group of reference samples have a known risk of a disease, disorder, condition and/or event. In some embodiments a reference sample or group of reference samples are from individuals comparable to a particular individual. In some embodiments, relative risk is 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more.

Sample: As used herein, the term “sample” typically refers to an aliquot of material obtained or derived from a source of interest. In some embodiments, a sample is obtained or derived from a biological source (e.g., a tissue or organism or cell culture) of interest. In some embodiments, a source of interest may be or comprise a cell or an organism, such as an animal or human. In some embodiments, a source of interest is or comprises biological tissue or fluid. In some embodiments, a biological tissue or fluid may be or comprise amniotic fluid, aqueous humor, ascites, bile, bone marrow, blood, breast milk, cerebrospinal fluid, cerumen, chyle, chime, ejaculate, endolymph, exudate, feces, gastric acid, gastric juice, lymph, mucus, pericardial fluid, perilymph, peritoneal fluid, pleural fluid, pus, rheum, saliva, sebum, semen, serum, smegma, sputum, synovial fluid, sweat, tears, urine, vaginal secretions, vitreous humour, vomit, and/or combinations or component(s) thereof. In some embodiments, a biological fluid may be or comprise an intracellular fluid, an extracellular fluid, an intravesicular fluid (blood plasma), an interstitial fluid, a lymphatic fluid, and/or a transcellular fluid. In some embodiments, a biological tissue or sample may be obtained, for example, by aspirate, biopsy (e.g., fine needle or tissue biopsy), swab (e.g., oral, nasal, skin, or vaginal swab), scraping, surgery, washing or lavage (e.g., bronchoalveolar, ductal, nasal, ocular, oral, uterine, vaginal, or other washing or lavage). In some embodiments, a biological sample is or comprises a bodily fluid sample or a bodily fluid-derived sample. Examples of a bodily fluid include, but are not limited to an amniotic fluid, bile, blood, breast milk, bronchoalveolar lavage fluid (BAL), cerebrospinal fluid, dialysate, feces, saliva, semen, synovial fluid, tears, urine, etc. In some embodiments, a biological sample is or comprises a liquid biopsy. In some embodiments, a biological sample is or comprises cells obtained from an individual. In some embodiments, a sample is a “primary sample” obtained directly from a source of interest by any appropriate means. In some embodiments, as will be clear from context, the term “sample” refers to a preparation that is obtained by processing (e.g., by removing one or more components of and/or by adding one or more agents to) a primary sample. For example, a sample is a preparation that is processed by using a semi-permeable membrane or an affinity-based method such antibody-based method to separate a biological entity of interest from other non-target entities. Such a “processed sample” may comprise, for example, in some embodiments extracellular vesicles, while, in some embodiments, nucleic acids and/or proteins, etc., extracted from a sample. In some embodiments, a processed sample can be obtained by subjecting a primary sample to one or more techniques such as amplification or reverse transcription of nucleic acid, isolation and/or purification of certain components, etc.

Selective or specific: The term “selective” or “specific”, when used herein with reference to an agent having an activity, is understood by those skilled in the art to mean that the agent discriminates between potential target entities, states, or cells. For example, in some embodiments, an agent is said to bind “specifically” to its target if it binds preferentially with that target in the presence of one or more competing alternative targets. In many embodiments, specific interaction is dependent upon the presence of a particular structural feature of the target entity (e.g., an epitope, a cleft, a binding site). It is to be understood that specificity need not be absolute. In some embodiments, specificity may be evaluated relative to that of a target-binding moiety for one or more other potential target entities (e.g., competitors). In some embodiments, specificity is evaluated relative to that of a reference specific binding moiety. In some embodiments, specificity is evaluated relative to that of a reference non-specific binding moiety. In some embodiments, a target-binding moiety does not detectably bind to the competing alternative target under conditions of binding to its target entity. In some embodiments, a target-binding moiety binds with higher on-rate, lower off-rate, increased affinity, decreased dissociation, and/or increased stability to its target entity as compared with the competing alternative target(s).

Small molecule: As used herein, the term “small molecule” means a low molecular weight organic and/or inorganic compound. In general, a “small molecule” is a molecule that is less than about 5 kilodaltons (kD) in size. In some embodiments, a small molecule is less than about 4 kD, 3 kD, about 2 kD, or about 1 kD. In some embodiments, the small molecule is less than about 800 daltons (D), about 600 D, about 500 D, about 400 D, about 300 D, about 200 D, or about 100 D. In some embodiments, a small molecule is less than about 2000 g/mol, less than about 1500 g/mol, less than about 1000 g/mol, less than about 800 g/mol, or less than about 500 g/mol. In some embodiments, a small molecule is not a polymer. In some embodiments, a small molecule does not include a polymeric moiety. In some embodiments, a small molecule is not a protein or polypeptide (e.g., is not an oligopeptide or peptide). In some embodiments, a small molecule is not a polynucleotide (e.g., is not an oligonucleotide). In some embodiments, a small molecule is not a polysaccharide. In some embodiments, a small molecule does not comprise a polysaccharide (e.g., is not a glycoprotein, proteoglycan, glycolipid, etc.). In some embodiments, a small molecule is not a lipid. In some embodiments, a small molecule is biologically active. In some embodiments, suitable small molecules may be identified by methods such as screening large libraries of compounds (Beck-Sickinger & Weber (2001) Combinational Strategies in Biology and Chemistry (John Wiley & Sons, Chichester, Sussex); by structure-activity relationship by nuclear magnetic resonance (Shuker et al. (1996) “Discovering high-affinity ligands for proteins: SAR by NMR.” Science 274: 1531-1534); encoded self-assembling chemical libraries (Melkko et al. (2004) “Encoded self-assembling chemical libraries.” Nature Biotechnol. 22: 568-574); DNA-templated chemistry (Gartner et al. (2004) “DNA-templated organic synthesis and selection of a library of macrocycles.” Science 305: 1601-1605); dynamic combinatorial chemistry (Ramstrom & Lehn (2002) “Drug discovery by dynamic combinatorial libraries.” Nature Rev. Drug Discov. 1: 26-36); tethering (Arkin & Wells (2004) “Small-molecule inhibitors of protein-protein interactions: progressing towards the dream.” Nature Rev. Drug Discov. 3: 301-317); and speed screen (Muckenschnabel et al. (2004) “SpeedScreen: label-free liquid chromatography-mass spectrometry-based high-throughput screening for the discovery of orphan protein ligands.” Anal. Biochem. 324: 241-249). In some embodiments, a small molecule may have a dissociation constant for a target in the nanomolar range.

Specific binding: As used herein, the term “specific binding” refers to an ability to discriminate between possible binding partners in the environment in which binding is to occur. A target-binding moiety that interacts with one particular target when other potential different targets are present is said to “bind specifically” to the target with which it interacts. In some embodiments, specific binding is assessed by detecting or determining degree of association between a target-binding moiety and its partner; in some embodiments, specific binding is assessed by detecting or determining degree of dissociation of a target-binding moiety-partner complex; in some embodiments, specific binding is assessed by detecting or determining ability of a target-binding moiety to compete an alternative interaction between its partner and another entity. In some embodiments, specific binding is assessed by performing such detections or determinations across a range of concentrations.

Stage of cancer: As used herein, the term “stage of cancer” refers to a qualitative or quantitative assessment of the level of advancement of a cancer (e.g., breast cancer, colorectal cancer, prostate cancer, etc.). In some embodiments, criteria used to determine the stage of a cancer may include, but are not limited to, one or more of where the cancer is located in a body, tumor size, whether the cancer has spread to lymph nodes, whether the cancer has spread to one or more different parts of the body, etc. In some embodiments, cancer may be staged using the AJCC staging system. The AJCC staging system is a classification system, developed by the American Joint Committee on Cancer for describing the extent of disease progress in cancer patients, which utilizes in part the TNM scoring system: Tumor size, Lymph Nodes affected, Metastases. In some embodiments, cancer may be staged using a classification system that in part involves the TNM scoring system, according to which T refers to the size and extent of the main tumor, usually called the primary tumor; N refers to the number of nearby lymph nodes that have cancer; and M refers to whether the cancer has metastasized. In some embodiments, a cancer may be referred to as Stage 0 (abnormal cells are present but have not spread to nearby tissue, also called carcinoma in situ, or CIS; CIS is not cancer, but it may become cancer), Stage I-III (cancer is present; the higher the number, the larger the tumor and the more it has spread into nearby tissues), or Stage IV (the cancer has spread to distant parts of the body). In some embodiments, a cancer may be assigned to a stage selected from the group consisting of: in situ (abnormal cells are present but have not spread to nearby tissue); localized (cancer is limited to the place where it started, with no sign that it has spread); regional (cancer has spread to nearby lymph nodes, tissues, or organs): distant (cancer has spread to distant parts of the body); and unknown (there is not enough information to figure out the stage).

Subject: As used herein, the term “subject” refers to an organism from which a sample is obtained, e.g., for experimental, diagnostic, prophylactic, and/or therapeutic purposes. Typical subjects include animals (e.g., mammals such as mice, rats, rabbits, non-human primates, domestic pets, etc.) and humans. In some embodiments, a subject is a human subject, e.g., a human male or female subject. In some embodiments, a subject is suffering from cancer (e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types). In some embodiments, a subject is susceptible to cancer (e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types). In some embodiments, a subject displays one or more symptoms or characteristics of cancer. In some embodiments, a subject displays one or more non-specific symptoms of cancer. In some embodiments, a subject does not display any symptom or characteristic of cancer. In some embodiments, a subject is someone with one or more features characteristic of susceptibility to or risk of cancer. In some embodiments, a subject is a patient. In some embodiments, a subject is an individual to whom diagnosis and/or therapy is and/or has been administered. In some embodiments, a subject is an asymptotic subject. Such an asymptomatic subject may be a subject at average population risk or with hereditary risk. For example, such an asymptomatic subject may be a subject who has a family history of cancer, who has been previously treated for cancer, who is at risk of cancer recurrence after cancer treatment, who is in remission after cancer treatment, and/or who has been previously or periodically screened for the presence of at least one cancer biomarker. Alternatively, in some embodiments, an asymptomatic subject may be a subject who has not been previously screened for cancer, who has not been diagnosed for cancer, and/or who has not previously received cancer therapy. In some embodiments, a subject amenable to provided technologies is an individual selected based on one or more characteristics such as age, race, geographic location, genetic history, medical history, personal history (e.g., smoking, alcohol, drugs, carcinogenic agents, diet, obesity, physical activity, sun exposure, radiation exposure, exposure to infectious agents such as viruses, and/or occupational hazard).

Suffering from: An individual who is “suffering from” a disease, disorder, and/or condition has been diagnosed with and/or displays one or more symptoms of a disease, disorder, and/or condition.

Surface analyte: As used herein, a “surface analyte” refers to an analyte present on the surface of a biological entity (e.g., a cell or a nanoparticle from a biological sample). In some embodiments, a surface analyte is or comprises a surface polypeptide or surface protein. In some embodiments, a surface analyte is or comprises a glycan.

Surface biomarker: As used herein, a “surface biomarker” refers to a marker indicative of the state (e.g., presence, level, and/or activity) of a surface analyte (e.g., as described herein) of a biological entity (e.g., a cell or a nanoparticle including, e.g., in some embodiments an analyte aggregate (e.g., a protein or mucin aggregate) and/or an extracellular vesicle). In some embodiments, a surface biomarker is or comprises a surface protein biomarker. In some embodiments, a surface biomarker is or comprises a carbohydrate-dependent marker.

Surface polypeptide or surface protein: As used interchangeably herein, the terms “surface polypeptide” and “surface protein” refer to a polypeptide or protein present in and/or on the surface of a biological entity (e.g., a cell or a nanoparticle including, e.g., in some embodiments an analyte aggregate (e.g., a protein or mucin aggregate) and/or an extracellular vesicle, etc.) through direct or indirect interactions. As will be understood by a skilled artisan, a surface protein, in some embodiments, may comprise a post-translational modification, including, e.g., but not limited to glycosylation. In some embodiments, a surface polypeptide or protein may be or comprise a membrane-bound polypeptide. In some embodiments, a membrane-bound polypeptide refers to a polypeptide or protein with one or more domains or regions present in and/or on the surface of the membrane of a biological entity (e.g., a cell, an extracellular vesicle, etc.). In some embodiments, a membrane-bound polypeptide may comprise one or more domains or regions spanning and/or associated with the plasma membrane of a biological entity (e.g., a cell, an extracellular vesicle, etc.). In some embodiments, a membrane-bound polypeptide may comprise one or more domains or regions spanning and/or associated with the plasma membrane of a biological entity (e.g., a cell, an extracellular vesicle, etc.) and also protruding into the intracellular and/or intravesicular space. In some embodiments, a membrane-bound polypeptide may comprise one or more domains or regions associated with the plasma membrane of a biological entity (e.g., a cell, an extracellular vesicle, etc.), for example, via one or more non-peptidic linkages (e.g., through a glycosylphosphatidylinositol (GPI) anchor or lipidification or through non-covalent interaction). In some embodiments, a membrane-bound polypeptide may comprise one or more domains or regions that is/are anchored into either side of plasma membrane of a biological entity (e.g., a cell, an extracellular vesicle, etc.). In some embodiments, a surface protein is associated with or present on the surface of a nanoparticle (e.g., as described herein). In some embodiments, a surface protein is associated with or present within an extracellular vesicle. In some embodiments, a surface protein may be associated with or present within a cancer associated-extracellular vesicle (e.g., an extracellular vesicle obtained or derived from a bodily fluid-derived sample (e.g., but not limited to a blood-derived sample) of a subject suffering from or susceptible to cancer. As will be understood by a skilled artisan, detection of the presence of at least a portion of a surface polypeptide or surface protein on/within extracellular vesicles can facilitate separation and/or isolation of cancer-associated extracellular vesicles from a biological sample (e.g., a blood or blood-derived sample) from a subject. In some embodiments, detection of the presence of a surface polypeptide or surface protein may be or comprise detection of an intravesicular portion (e.g., an intravesicular epitope) of such a surface polypeptide or surface protein. In some embodiments, detection of the presence of a surface polypeptide or surface protein may be or comprise detection of a membrane-spanning portion of such a surface polypeptide or surface protein. In some embodiments, detection of the presence of a surface polypeptide or surface protein may be or comprise detection of an extravesicular portion of such a surface polypeptide or surface protein.

Surface protein biomarker: As used herein, the term “surface protein biomarker” refers to a marker indicative of the state (e.g., presence, level, and/or activity) of a surface protein (e.g., as described herein) of a biological entity (e.g., a cell or a nanoparticle including, e.g., in some embodiments an analyte aggregate (e.g., a protein or mucin aggregate) and/or an extracellular vesicle). In some embodiments, a surface protein refers to a polypeptide or protein with one or more domains or regions located in or on the surface of the membrane of a biological entity (e.g., a cell or an extracellular vesicle). In some embodiments, a surface protein biomarker may be or comprise an epitope that is present on the interior side (intravesicular) or the exterior side (extravesicular) of the membrane. In some embodiments, a surface protein biomarker is associated with or present in an extracellular vesicle. In some embodiments, a surface protein biomarker may be or comprise a mutated polypeptide. In some embodiments, a surface protein biomarker may be post-translationally modified (e.g., but not limited to glycosylated, phosphorylated, etc.). In some embodiments, a surface protein biomarker may be post-translationally processed and present in the form of a truncated polypeptide, for example, as a result of proteolytic cleavage. In some embodiments, a surface protein biomarker may be or comprise an epitope that is present on the exterior surface of a nanoparticle.

Susceptible to: An individual who is “susceptible to” a disease, disorder, and/or condition is one who has a higher risk of developing the disease, disorder, and/or condition than does a member of the general public. In some embodiments, an individual who is susceptible to a disease, disorder, and/or condition may not have been diagnosed with the disease, disorder, and/or condition. In some embodiments, an individual who is susceptible to a disease, disorder, and/or condition may exhibit symptoms of the disease, disorder, and/or condition. In some embodiments, an individual who is susceptible to a disease, disorder, and/or condition may not exhibit symptoms of the disease, disorder, and/or condition. In some embodiments, an individual who is susceptible to a disease, disorder, and/or condition will develop the disease, disorder, and/or condition. In some embodiments, an individual who is susceptible to a disease, disorder, and/or condition will not develop the disease, disorder, and/or condition.

Target-binding moiety: In general, the terms “target-binding moiety” and “binding moiety” are used interchangeably herein to refer to any entity or moiety that binds to a target of interest (e.g., molecular target of interest such as a biomarker or an epitope). In many embodiments, a target-binding moiety of interest is one that binds specifically with its target (e.g., a target biomarker) in that it discriminates its target from other potential binding partners in a particular interaction context. In general, a target-binding moiety may be or comprise an entity or moiety of any chemical class (e.g., polymer, non-polymer, small molecule, polypeptide, carbohydrate, lipid, nucleic acid, etc.). In some embodiments, a target-binding moiety is a single chemical entity. In some embodiments, a target-binding moiety is a complex of two or more discrete chemical entities associated with one another under relevant conditions by non-covalent interactions. For example, those skilled in the art will appreciate that in some embodiments, a target-binding moiety may comprise a “generic” binding moiety (e.g., one of biotin/avidin/streptavidin and/or a class-specific antibody) and a “specific” binding moiety (e.g., an antibody or aptamers with a particular molecular target) that is linked to the partner of the generic biding moiety. In some embodiments, such an approach can permit modular assembly of multiple target binding moieties through linkage of different specific binding moieties with a generic binding moiety partner.

Therapeutic agent: As used interchangeably herein, the phrase “therapeutic agent” or “therapy” refers to an agent or intervention that, when administered to a subject or a patient, has a therapeutic effect and/or elicits a desired biological and/or pharmacological effect. In some embodiments, a therapeutic agent or therapy is any substance that can be used to alleviate, ameliorate, relieve, inhibit, prevent, delay onset of, reduce severity of, and/or reduce incidence of one or more symptoms or features of a disease, disorder, and/or condition. In some embodiments, a therapeutic agent or therapy is a medical intervention (e.g., surgery, radiation, phototherapy) that can be performed to alleviate, relieve, inhibit, present, delay onset of, reduce severity of, and/or reduce incidence of one or more symptoms or features of a disease, disorder, and/or condition.

Threshold level (e.g., cutoff): As used herein, the term “threshold level” refers to a level that are used as a reference to attain information on and/or classify the results of a measurement, for example, the results of a measurement attained in an assay. For example, in some embodiments, a threshold level (e.g., a cutoff) means a value measured in an assay that defines the dividing line between two subsets of a population (e.g., normal, diseased controls and/or benign tumors vs. cancer). Thus, a value that is equal to or higher than the threshold level defines one subset of the population, and a value that is lower than the threshold level defines the other subset of the population. A threshold level can be determined based on one or more control samples or across a population of control samples. A threshold level can be determined prior to, concurrently with, or after the measurement of interest is taken. In some embodiments, a threshold level can be a range of values.

Treat: As used herein, the term “treat,” “treatment,” or “treating” refers to any method used to partially or completely alleviate, ameliorate, relieve, inhibit, prevent, delay onset of, reduce severity of, and/or reduce incidence of one or more symptoms or features of a disease, disorder, and/or condition. Treatment may be administered to a subject who does not exhibit signs of a disease, disorder, and/or condition. In some embodiments, treatment may be administered to a subject who exhibits only early signs of the disease, disorder, and/or condition, for example for the purpose of decreasing the risk of developing pathology associated with the disease, disorder, and/or condition. In some embodiments, treatment may be administered to a subject at a later-stage of disease, disorder, and/or condition.

Standard techniques may be used for recombinant DNA, oligonucleotide synthesis, and tissue culture and transformation (e.g., electroporation, lipofection). Enzymatic reactions and purification techniques may be performed according to manufacturer's specifications or as commonly accomplished in the art or as described herein. The foregoing techniques and procedures may be generally performed according to conventional methods well known in the art and as described in various general and more specific references that are cited and discussed throughout the present specification. See e.g., Sambrook et al., Molecular Cloning: A Laboratory Manual (2d ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y. (1989)), which is incorporated herein by reference for the purpose described herein.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

Cancer is a major public health issue affecting millions of people across the world each year. In 2020, an estimated 1,806,590 new cases of cancer were diagnosed in the United States alone, and over 600,000 Americans died from the disease. The most common cancers (listed in descending order according to estimated new cases in 2020) are breast cancer, lung and bronchus cancer, prostate cancer, colon and rectum cancer, melanoma of the skin, bladder cancer, non-Hodgkin lymphoma, kidney and renal pelvis cancer, endometrial cancer, leukemia, pancreatic cancer, thyroid cancer, and liver cancer. Cancer affects people of all ages, sexes, races, geographic locations, and nationalities.

Cancer is a complex disease with many different types and subtypes. Cancer can be generally categorized into 5 types based on the cell-type or tissue of origin: (1) carcinomas, which begin in the skin and tissues that line the internal organs; (2) sarcomas, which develop in the bone, cartilage, fat, muscle, and other connective tissues; (3) leukemia, which begins in the blood and bone marrow; (4) lymphoma, which begins in the immune system; and (5) central nervous system cancers, which develop in the brain, spinal cord, and peripheral nervous system. Examples of cancer include but are not limited to breast cancer, brain cancer (e.g., glioblastoma (GBM), neuroblastoma, medulloblastoma, malignant meningioma, neurofibrosarcoma, etc.), skin cancer, gastrointestinal cancers (e.g., stomach cancer, esophageal cancer, pancreatic cancer, colorectal cancer, liver cancer, etc.), cancers of the reproductive organs (e.g. ovarian cancer, uterine cancer, cervical cancer, prostate cancer, testicular cancer, etc.), cancers of the connective tissue (e.g., fibrous tissue cancer, fat cancer, cartilage cancer, bone cancer, etc.), cancers of the endothelium and/or mesothelium (e.g., blood vessel cancer, lymph vessel cancer, mesothelioma, etc.), cancers of the blood and lymphoid cells (e.g., leukemia, plasmacytoma, multiple myeloma, Hodgkin lymphoma, Non-Hodgkin lymphoma, etc.), cancers of the muscle (e.g., smooth muscle cancer, striated muscle cancer, etc.), cancers of epithelial tissues (e.g., squamous cell carcinomas, epidermoid carcinomas, adenocarcinomas, hepatoma, hepatocellular carcinoma, transitional cell carcinoma, choriocarcinoma, seminoma, etc.), amine precursor uptake and decarboxylation system cancers (e.g., pituitary cancer, parathyroid cancer, thyroid cancer, bronchial cancer, pancreatic cancer, etc.), schwannomas, etc.) Cancer can develop in virtually any cell-comprising tissue.

Common types of screenings for cancer may include physical examinations (e.g., colonoscopy for colorectal cancer, digital rectal examination (DRE) for prostate cancer, and visual inspection for skin cancer), imaging methods (e.g., ultrasound, MRI, CT scan), biopsies, and/or molecular assays to detect cancer-associated molecular abnormalities in a sample taken from a patient. However, there is currently no inexpensive or widely available screening method for the detection of cancer from a variety of organs in blood samples, especially not for asymptomatic individuals.

The present disclosure, among other things, identifies the source of a problem with certain prior technologies including, for example, certain conventional approaches to detection and diagnosis of cancer. For example, the present disclosure appreciates that many conventional diagnostic assays, e.g., ultrasound, tissue biopsy, scoping, and/or CT scanning, can be time-consuming, costly, and/or lacking sensitivity and/or specificity sufficient to provide a reliable and comprehensive diagnostic assessment. In some embodiments, the present disclosure provides technologies (including, e.g., systems, compositions, and methods) that solve such problems, among other things, by identification of biomarker combinations that are predicted to exhibit high sensitivity and specificity for cancer based on bioinformatics analysis. In some embodiments, the present disclosure provides technologies (including, e.g., systems, compositions, and methods) that solve such problems, by detecting co-localization of a biomarker combination that is associated with cancer (e.g., identified by bioinformatics analysis) in individual extracellular vesicles, which comprises at least one extracellular vesicle-associated surface biomarker and at least one biomarker selected from the group consisting of surface biomarkers, internal protein biomarkers, and RNA biomarkers present in extracellular vesicles associated with cancer. In some embodiments, the present disclosure provides technologies (including, e.g., systems, compositions, and methods) that solve such problems, among other things, by detecting such biomarker combination of cancer using a target entity detection approach that was developed by Applicant and described in U.S. application Ser. No. 16/805,637 (published as US2020/0299780; issued as U.S. Pat. No. 11,085,089), and International Application PCT/US2020/020529 (published as WO2020180741), both filed Feb. 28, 2020 and entitled “Systems, Compositions, and Methods for Target Entity Detection,” which are based on interaction and/or co-localization of a biomarker combination in individual extracellular vesicles. The contents of each of the aforementioned disclosures is incorporated herein by reference in their entirety.

In some embodiments, extracellular vesicles for detection as described herein can be isolated from a bodily fluid of a subject by a size exclusion-based method. As will be understood by a skilled artisan, in some embodiments, a size exclusion-based method may provide a sample comprising nanoparticles having a size range of interest that includes extracellular vesicles. Accordingly, in some embodiments, provided technologies of the present disclosure encompass detection, in individual nanoparticles having a size range of interest (e.g., in some embodiments about 30 nm to about 1000 nm) that includes extracellular vesicles, of co-localization of at least two or more surface biomarkers (e.g., as described herein) that forms a target biomarker signature of a given cancer. A skilled artisan reading the present disclosure will understand that various embodiments described herein in the context of “extracellular vesicle(s)” (e.g., assays for detecting individual extracellular vesicles and/or provided “extracellular vesicle-associated surface biomarkers”) can be also applicable in the context of “nanoparticles” as described herein.

The present disclosure, among other things, provides insights and technologies for achieving effective cancer screening, e.g., for early detection of cancer (e.g., in some embodiments characterized by carcinoma, sarcoma, mixed types, etc.). In some embodiments, the present disclosure provides technologies for early detection of cancer in subjects who may be experiencing one more symptoms associated with cancer. In some embodiments, the present disclosure provides technologies for early detection of cancer in subjects who are at hereditary risks for cancer. In some embodiments, the present disclosure provides technologies for early detection of cancer in subjects who may be at hereditary risk and/or experiencing one or more symptoms associated with cancer. In some embodiments, the present disclosure provides technologies for early detection of cancer in subjects who may have life-history risk factors. In some embodiments, the present disclosure provides technologies for screening individuals, e.g., individuals with certain risks (e.g., hereditary risk, life history associated risk, or average risk) for early stage cancer (e.g., in some embodiments characterized by carcinoma, sarcoma, mixed types, etc.)). In some embodiments, provided technologies are effective for detection of early stage cancer (e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types). In some embodiments, provided technologies are effective when applied to populations comprising or consisting of individuals having one or more symptoms that may be associated with cancer. In some embodiments, provided technologies are effective even when applied to populations comprising or consisting of asymptomatic or symptomatic individuals (e.g., due to sufficiently high sensitivity and/or low rates of false positive and/or false negative results). In some embodiments, provided technologies are effective when applied to populations comprising or consisting of individuals (e.g., asymptomatic or symptomatic individuals) without hereditary risk, and/or life-history related risk of developing cancer. In some embodiments, provided technologies are effective when applied to populations comprising or consisting of individuals (e.g., asymptomatic or symptomatic individuals) with hereditary risk for developing cancer. In some embodiments, provided technologies are effective when applied to populations comprising or consisting of individuals susceptible to cancer (e.g., individuals with a known genetic, environmental, or experiential risk, etc.). In some embodiments, provided technologies may be or include one or more compositions (e.g., molecular complexes, systems, collections, combinations, kits, etc.) and/or methods (e.g., of making, using, assessing, etc.), as will be clear to one skilled in the art reading the disclosure provided herein.

In some embodiments, provided technologies achieve detection (e.g., early detection, e.g., in asymptomatic individual(s) and/or population(s)) of one or more features (e.g., incidence, progression, responsiveness to therapy, recurrence, etc.) of cancer, with sensitivity and/or specificity (e.g., rate of false positive and/or false negative results) appropriate to permit useful application of provided technologies to single-time and/or regular (e.g., periodic) assessment. In some embodiments, provided technologies are useful in conjunction with an individual's regular medical examinations, such as but not limited to: physicals, general practitioner visits, cholesterol/lipid blood tests, diabetes screening (e.g., diabetes (type 2) screening), colonoscopies, blood pressure screening, thyroid function tests, prostate cancer screening, mammograms, HPV/Pap smears, and/or vaccinations. In some embodiments, provided technologies are useful in conjunction with treatment regimen(s); in some embodiments, provided technologies may improve one or more characteristics (e.g., rate of success according to an accepted parameter) of such treatment regimen(s).

In some embodiments, the present disclosure, among other things, provides insights that screening of asymptotic individuals, e.g., regular screening prior to or otherwise in absence of developed symptom(s), can be beneficial, and even important for effective management (e.g., successful treatment) of cancer. In some embodiments, the present disclosure provides cancer screening systems that can be implemented to detect cancer, including early-stage cancer, in some embodiments in asymptomatic individuals (e.g., without hereditary, and/or life-history associated risks in cancer). In some embodiments, provided technologies are implemented to achieve regular screening of asymptomatic individuals (e.g., with or without hereditary risk(s) in cancer). In some embodiments, provided technologies are implemented to achieve regular screening of symptomatic individuals (e.g., with or without hereditary and/or life-history associated risk(s) in cancer). The present disclosure provides, for example, compositions (e.g., reagents, kits, components, etc.), and methods of providing and/or using them, including strategies that involve regular testing of one or more individuals (e.g., asymptomatic individuals). The present disclosure defines usefulness of such systems, and provides compositions and methods for implementing them.

I. General Cancer Detection

Cancer places a significant burden on the healthcare system in the United States and in many countries across the world. In the United States, the rate of new cases of cancer (cancer incidence) is 442.4 per 100,000 men and women per year (based on 2013-2017 cases). The cancer death rate (cancer mortality) is 158.3 per 100,000 men and women per year (based on 2013-2017 deaths). The cancer mortality rate is higher among men than women (189.5 per 100,000 men and 135.7 per 100,000 women). When comparing groups based on race/ethnicity and sex, cancer mortality is highest in African American men (227.3 per 100,000) and lowest in Asian/Pacific Islander women (85.6 per 100,000).

As of January 2019, there were an estimated 16.9 million cancer survivors in the United States. The number of cancer survivors is projected to increase to 22.2 million by 2030. Approximately 39.5% of men and women will be diagnosed with cancer at some point during their lifetimes (based on 2015-2017 data). In 2020, an estimated 16,850 children and adolescents ages 0 to 19 will be diagnosed with cancer and 1,730 will die of the disease.

Estimated national expenditures for cancer care in the United States in 2018 were $150.8 billion. In future years, costs are likely to increase as the population ages and more people develop cancer. Costs are also likely to increase as new, and often more expensive, treatments are adopted as standards of care.

In general, consuming tobacco and tobacco smoke increase rates of all cancer types. The International Agency for Research on Cancer (IARC) has identified at least 50 known carcinogens in tobacco smoke. Examples of such carcinogens include but are not limited to tobacco-specific N-nitrosamines (TSNAs) formed by nitrosation of nicotine during tobacco processing and during smoking. The chemical 4-(methylnitrosamino)-1(3-pyridyl)-1-butanone (NNK) is known to induce cancer experimental animals. NNK is known to bind to DNA and create DNA adducts, leading to DNA damage. Failure to repair this damage can lead to permanent mutations. NNK is associated with DNA mutations resulting in the activation of K-ras oncogenes, which is detected in humans.

Current methodologies for detecting cancer are often costly, invasive, and/or not available widely enough to promote earlier detection of cancer (when treatment is more successful) that will improve patient outcomes and decrease the burden on the healthcare system.

In some embodiments, the present disclosure provides technologies for effective screening of cancer in individuals at hereditary risk, or in individuals with life-history associated-risks. In some embodiments, the present disclosure provides technologies for effective screening of cancer in average-risk individuals. In some embodiments, the present disclosure provides technologies for effective screening of cancer in individuals with one or more symptoms associated with cancer. In some embodiments, the present disclosure provides technologies for effective screening of cancer in asymptomatic individuals. Despite being relatively common in both men and women, there is currently no recommended cancer screening tool that is non-invasive based on a subject's blood sample and intended for screening asymptomatic and/or average-risk individuals (e.g., individuals under the age of 55 years, or individuals over the age of 55 years. This is due, in part, to the cost, limited availability, potential side effects, and/or poor performance (e.g., high false positive rate, or ineffectualness) of existing cancer and cancer screening technologies. Given the incidence of cancer in average-risk individuals, inadequate test specificities, which can vary with different cancers, can result in false positive results that outnumber true positives by more than an order of magnitude. This places a significant burden on the healthcare system and on the individuals being screened as false positive results lead to additional tests, unnecessary surgeries, and emotional/physical distress (Wu et al., 2016).

Several different biomarker classes have been studied for a cancer liquid biopsy assay including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), bulk proteins, and extracellular vesicles (EVs). EVs are particularly promising due to their abundance and stability in the bloodstream relative to ctDNA and CTCs, indicating improved sensitivity for early stage cancers. Moreover, EVs contain cargo (i.e., proteins, RNA, metabolites, carbohydrates, and other molecules) that originated from the same cell, providing superior specificity over bulk protein measurements. While the diagnostic utility EVs has been studied, much of this work has pertained to bulk EV measurements or low-throughput single-EV analyses.

In some embodiments, the present disclosure provides an insight that a particularly useful cancer screening test may be characterized by: (1) ultrahigh specificity (e.g., >98%) to minimize the number of false positives, and (2) high sensitivity (e.g., >40%) for stage I and II cancer (i.e., when prognosis is most favorable). For example, in some embodiments, a particularly useful cancer screening test may be characterized by a specificity of >98% and a sensitivity of >50%, for example, for stage I and II cancer. In some embodiments, a particularly useful cancer screening test may be characterized by a specificity of >98% and a sensitivity of >60%, for example, for stage I and II cancer. In some embodiments, a particularly useful cancer screening test may be characterized by a specificity of >98% and a sensitivity of >70%, for example, for stage I and II cancer. In some embodiments, a particularly useful cancer screening test may be characterized by a specificity of >99.5% and a sensitivity of >65%, for example, for stage I and II cancer. In some embodiments, a particularly useful cancer screening test may be characterized by a specificity of >99.5% and a sensitivity of >60%, for example, for stage I and II cancer. In some embodiments, a particularly useful cancer screening test may be characterized by a specificity of 98% or higher and a sensitivity of >10% or higher (including, e.g., >15%, >20%, >25%). In some embodiments, a particularly useful cancer screening test may be characterized by a specificity of 99% or higher and a sensitivity of 50% or higher. In some embodiments, a particularly useful cancer screening test may be characterized by a specificity of 90% or higher and a sensitivity of 50% or higher.

In some embodiments, the present disclosure provides an insight that a cancer screening test involving more than one set of biomarker combinations (e.g., at least two orthogonal biomarker combinations as described herein) can increase specificity and/or sensitivity of such an assay, as compared to that is achieved by one set of biomarker combination. For example, in some embodiments, a cancer screening test involving at least two orthogonal biomarker combinations can achieve a specificity of at least 98% and a sensitivity of at least 50%. In some embodiments, a cancer screening test involving at least two orthogonal biomarker combinations can achieve a specificity of at least 98% and a sensitivity of at least 60%. In some embodiments, a cancer screening test involving at least two orthogonal biomarker combinations can achieve a specificity of 99% and a sensitivity of 50% or higher.

In some embodiments, the present disclosure provides an insight that a particularly useful cancer screening test may be characterized by an acceptable positive predictive value (PPV) at an economically justifiable cost. PPV is the likelihood a patient has the disease following a positive test, and is influenced by sensitivity, specificity, and/or disease prevalence. In some embodiments, assays described herein can be useful for early cancer detection that achieves a PPV of greater than 10% or higher, including, e.g., greater than 15%, greater than 20%, or greater than 25% or higher, with a specificity cutoff of at least 70% or higher, including, e.g., at least 75%, at least 80%, at least 85%, or higher. In some embodiments, assays described herein are particularly useful for early cancer detection that achieves a PPV of greater than 10% or higher, including, e.g., greater than 15%, greater than 20%, or greater than 25% or higher, with a specificity cutoff of at least 85% or higher, including, e.g., at least 90%, at least 95%, or higher (e.g., a specificity cutoff of at least 98% for subjects at hereditary risk for cancer, or a specificity cutoff of at least 99.5% for subjects experiencing one or more symptoms associated with cancer).

In some embodiments, assays described herein are particularly useful as a first screening test for early cancer detection. In some embodiments, subjects who have received a positive test result from assays described herein are recommended to receive a follow-up test (e.g., colonoscopy, mammogram, biopsy, etc.). In some such embodiments, assays described herein can be useful for early cancer detection that achieves a PPV of greater than 2% or higher, including, e.g., greater than 3%, greater than 4%, greater than 5%, greater than 6% greater than 7%, greater than 8%, greater than 9%, greater than 10%, greater than 15%, greater than 20%, or greater than 25% or higher. In some such embodiments, assays described herein can achieve a specificity cutoff of at least 70% or higher, including, e.g., at least 75%, at least 80%, at least 85%, or higher. In some such embodiments, assays described herein can achieve a specificity cutoff of at least 85% or higher, including, e.g., at least 90%, at least 95% or higher (e.g., a specificity cutoff of at least 98% for subjects at hereditary risk for cancer, or with a specificity cutoff of at least 99.5% for subjects experiencing one or more symptoms associated with cancer).

Several different biomarker classes have been studied for a cancer liquid biopsy assay including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), bulk proteins, and extracellular vesicles (EVs). EVs are particularly promising due to their abundance and stability in the bloodstream relative to ctDNA and CTCs, suggesting improved sensitivity for early stage cancers. Moreover, EVs contain cargo (i.e., proteins, RNA, metabolites) that originated from the same cell, providing superior specificity over bulk protein measurements. While the diagnostic utility of EVs has been studied, much of this work has pertained to bulk EV measurements or low-throughput single-EV analyses.

Certain Cancers Amenable to Technologies Described Herein

In some embodiments, technologies provided herein are useful for screening individuals who would otherwise not be screened for cancer (e.g., due to limitations of certain current technologies), thereby enriching a population of individuals (including, e.g., asymptomatic individuals) for subjects who may indeed require further diagnostic assessments and/or treatment. In some embodiments, technologies provided herein are useful for screening individuals who would otherwise not be screened for a certain cancer type and/or subtype (e.g., due to limitations of certain current technologies), thereby enriching a population of individuals (including, e.g., asymptomatic individuals) for subjects who may indeed require further diagnostic assessments and/or treatment.

In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Adrenocortical carcinoma (ACC). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Bladder Urothelial Carcinoma (BLCA). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Brain Lower Grade Glioma (LGG). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Breast invasive carcinoma (BRCA). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Cholangiocarcinoma (CHOL). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Colon adenocarcinoma (COAD). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Esophageal carcinoma (ESCA). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Glioblastoma multiforme (GBM). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Head and Neck squamous cell carcinoma (HNSC). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Kidney Chromophobe (KICH). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Kidney renal clear cell carcinoma (KIRC). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Kidney renal papillary cell carcinoma (KIRP). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Liver hepatocellular carcinoma (LIHC). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Lung adenocarcinoma (LUAD). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Lung squamous cell carcinoma (LUSC). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Mesothelioma (MESO). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Ovarian serous cystadenocarcinoma (OV). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Pancreatic adenocarcinoma (PAAD). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Pheochromocytoma and Paraganglioma (PCPG). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Prostate adenocarcinoma (PRAD). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Rectum adenocarcinoma (READ). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Sarcoma (SARC). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Skin Cutaneous Melanoma (SKCM). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Stomach adenocarcinoma (STAD). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Testicular Germ Cell Tumors (TGCT). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Thymoma (THYM). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Thyroid carcinoma (THCA). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Uterine Carcinosarcoma (UCS). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Uterine Corpus Endometrial Carcinoma (UCEC). In some embodiments, technologies described herein comprise use of biomarker combinations that can enrich a population for subjects who may be suffering from or be susceptible to Uveal Melanoma (UVM).

In some embodiments, technologies provided herein may be useful for screening subjects at: increased risk of developing cancer, for example, due to inherited risk factors and/or lifestyle risk factors, or at average risk of developing cancer. In some embodiments, technologies provided herein may be useful for: triaging subjects with abnormal masses, monitoring disease progression, monitoring treatment efficacy, monitoring disease recurrence, and/or as a companion diagnostic for prediction of therapeutic response. In some embodiments, technologies provided herein may be utilized as part of a compound screening protocol, e.g., in conjunction with one or more other diagnostic and/or screening assays. In some embodiments, technologies provided herein may be utilized in place of current screening assays.

Bile Duct Cancer

In some embodiments, technologies provided herein may be particularly suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to bile duct cancer. In general, bile duct cancer is defined based on where it starts, for example, inside the liver (intrahepatic) makes up 5-10% of cases, while outside the liver (extrahepatic) occurs more often and is more treatable. Extrahepatic cancer can form in one of two areas: 1) the hilum region, where the left and right bile ducts come together to form the common hepatic duct (perihilar cancer), or 2) the distal region, where the common bile duct passes through the pancreas (distal cancer).

There is currently very little evidence for any genetic risk factors for bile duct cancer. However, bile duct cancer life-history associated risk factors include: age (e.g., older than 65-years of age), long-term inflammation, sclerosing cholangitis, bile duct stones, choledochal cysts, liver fluke infection, reflux from the pancreas, liver cirrhosis, inflammatory bowel diseases (e.g., Crohn's disease, ulcerative colitis, etc.), obesity, diabetes, viral hepatitis, excessive alcohol consumption, or combinations thereof. The three types of cholangiocarcinoma do not usually cause any symptoms in their early stages, as such, this cancer is usually not diagnosed until it has already spread beyond the bile ducts to other tissues. Later-stage bile duct cancer symptoms are often resultant from bile duct blockage by the associated tumor, these symptoms can include: jaundice, extreme tiredness (fatigue), itching, dark-colored urine, loss of appetite, unintentional weight loss, abdominal pain, and light-colored and greasy stools. These symptoms are often described as “nonspecific” because they can be features of many different diseases.

Current screening and/or diagnostic assays for bile duct cancer include: serum blood tests (e.g., bilirubin, and/or CA 19-9 (a CA 19-9 level >100 U/mL (normal <40 U/mL) has 75% sensitivity and 80% specificity in identifying patients who have cholangiocarcinoma), abdominal ultrasound, CT scan, Endoscopy/Cholangioscopy, Endoscopic retrograde cholangiopancreatography (ERCP) with X-ray, Magnetic resonance cholangiopancreatography (MRCP), or Percutaneous transhepatic cholangiography (PTC) with X-ray.

In some embodiments, technologies described herein can be utilized in place of or in conjunction with: serum blood tests (e.g., bilirubin and/or CA 19-9), abdominal ultrasound, CT scan, endoscopy/cholangioscopy, ERCP, MRCP, and/or PTC.

Bladder Cancer

In April of 2019, the USPTF recommended against screening asymptomatic subjects at average risk for the presence of bladder cancer, concluding that current evidence is insufficient to assess the balance of benefits and harms of screening for bladder cancer in asymptomatic adults using current technologies. Known risk factors for development of bladder cancer include: smoking, workplace chemical exposures (e.g., production of rubber, leather, textiles, paint), diabetes treatment with pioglitazone (Actos®), presence of arsenic in drinking water, being a white male, being over the age of 55, having a history of chronic bladder irritation from infections, kidney/bladder stones, history of having catheters, presence of bladder birth defects (e.g., urachus, exstrophy, etc.), genetics/family history (e.g., Lynch syndrome, Cowden disease (PTEN mutations), retinoblastoma (RB1 mutations), etc.), chemotherapy with cytoxan or radiation therapy, or combinations thereof.

Currently, detection and/or reporting of hematuria is a key diagnostic marker for early bladder cancer detection. Current diagnostic methods include cytology and cystoscopy analysis, and prognosis is generally determined by histopathology and chromosome analysis (fluorescence in situ hybridization (FISH)). There is no currently approved method for predicting a subject's response to therapy, and current assays used for monitoring of recurrence include cytology analysis, cystoscopy, detection of urine proteins (NMP22 and/or BTA), and FISH. A promising bladder cancer marker may be Survivin mRNA (encoded by gene BIRC5), this mRNA creates a protein 16.5 kDa in size that is a member of the inhibitor of apoptosis protein family.

In some embodiments, technologies described herein can be utilized in place of or in conjunction with: serum blood tests (e.g., for Survivin), cytology, histopathology, FISH, and/or cystoscopy analysis.

Brain Cancer

Primary brain tumors are not the same as metastatic tumors that originate in other organs, such as the lung or breast, and then spread to the brain. In adults, metastatic tumors to the brain are more common than primary brain tumors and these tumors are often not treated using the same therapeutic regimes. Brain cancers can include: Gliomas (e.g., Astrocytomas, Oligodendrogliomas, Ependymomas, etc.), Meningiomas, Schwannomas (neurilemmomas), Medulloblastomas, Gangliogliomas, and/or Craniopharyngiomas. Currently, there are no recommended tests to screen for brain and/or spinal cord tumors in asymptomatic people. In general, brain tumors are found when a person goes to a doctor due to unusual signs and/or symptoms. The most common screening methods for detection of brain cancer include Magnetic Resonance Imaging (MRI) and computed tomography (CT) scans.

There are known risk factors (both genetic and lifestyle) associated with development of brain cancer, which include for example: inherited conditions (e.g., such as neurofibromatosis or tuberous sclerosis), age, gender, exposure to certain chemicals (e.g., potentially: solvents, pesticides, oil products, rubber, and/or vinyl chloride), exposure to biological agents (e.g., exposure to infections (e.g., viral, fungal, bacterial, etc.)), ethnicity, exposure to ionizing radiation, serious head injuries, a history of seizures, or combinations thereof.

In some embodiments, technologies described herein can be utilized in place of or in conjunction with MRI scans and/or CT scans.

Breast Cancer

According to the CDC, controllable life-history associated risk factors for female breast cancer include not being physically active, being obese or overweight after menopause, taking estrogen or hormone replacements, reproductive history (e.g., having children past the age of 30), being a smoker, and alcohol consumption. Uncontrollable life-history risk factors for female breast cancer include age, genetic mutations, having dense breasts, reproductive history (e.g., starting menopause after age 55), family history, or combinations thereof.

Current methods for screening and/or diagnosis of breast cancer include MRI scanning, CT scanning, ultrasound, mammogram, and/or blood biomarker tests. A number of these conventional methods for detecting breast cancer suffer from a low positive predictive value (PPV). For example, mammogram screening has a low PPV for early stage breast cancers (4-28%). Additionally, there are many different subtypes of breast cancer, which respond to different types of therapy. For example, a breast cancer tumor cells may have higher than normal levels of hormone receptors such as Estrogen Receptor (ER, as in ER+ breast cancer), Human Epidermal Growth Factor Receptor 2 (HER2, as in HER2+ breast cancer), and/or Progesterone Receptor (PR, as in PR+ breast cancer). Breast cancer that is not positive for ER, PR, or HER2 is referred to as triple negative breast cancer (TNBC). The hormone receptor status of breast cancer has traditionally been determined by tissue biopsy. Determination of such hormone receptor status is important for selecting breast cancer treatment options, as cancers of different hormone receptor statuses respond differently to therapy. In some embodiments, technologies provided herein allow for the determination of breast cancer subtype through a less costly and more reliable method for detection of early stage breast cancer than those traditionally used to diagnose breast cancer.

In some embodiments, technologies described herein can be utilized in place of or in conjunction with: MRI scanning, CT scanning, ultrasound, mammogram, and/or blood biomarker test results (e.g., CA-125, CEA, CA19-9, PRL, HGF, OPN, MPO, or TIMP-1).

Cervical Cancer

In some embodiments, technologies provided herein may be particularly suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to cervical cancer. Cervical cancer makes up approximately 3.2% of total worldwide cancers, and has a worldwide yearly incidence rate of approximately 570,000 cases, and 310,000 deaths. There are approximately 13,800 new cases of cervical cancer each year in the USA, and approximately 4,300 deaths. There are an estimated 290,000 individuals living with cervical cancer in the USA, and there is a 65.8% five-year survival rate (see e.g., Bray, et al., 2018. CA: a cancer journal for clinicians, 68(6), pp. 394-424, and ACS US Cancer Facts and Figures 2020, and the SEER database (US incidence, prevalence, and survival data); each of which is incorporated herein in their entirety for the purposes described herein). Risk factors associated with cervical cancer include previous infection with the Human papillomavirus (HPV). The current standard of care for regular screening for cervical cancer are HPV testing and Pap tests. Pre-cancerous changes can be detected by the Pap test and treated to prevent cancer from developing. The HPV test looks for infection by high-risk types of HPV that are more likely to cause pre-cancers and cancers of the cervix. There are two primary types of cervical cancers, squamous cell carcinoma (˜90% of cases), and adenocarcinoma (the majority of the remaining cases). Less common cervical cancers include adenosquamous carcinomas or mixed carcinomas.

In some embodiments, technologies described herein can be utilized in place of or in conjunction with: HPV testing and/or Pap testing.

Colorectal Cancer

There are known risk factors (both genetic and life-history) associated with development of colorectal cancer, which include, for example: age, family history of colorectal cancer (e.g., in a first-degree relative), personal history of colorectal adenomas, personal history of colorectal cancer or ovarian cancer, personal history of long-standing chronic ulcerative colitis or Crohn colitis, excessive alcohol use, tobacco use, ethnicity/race, obesity, or combinations thereof. Colorectal cancer can occur as a result of various genetic mutations and/or syndromes, for example: Polyposis syndromes such as Familial adenomatous polyposis (FAP) and attenuated FAP (AFAP) which are associated with APC mutations, MUTYH-associated polyposis, Oligopolypopsis associated with POLE and/or POLD1 mutations, Colorectal polyps associated with NTHL1 mutations, Juvenile polyposis syndrome associated with BMPR1A and/or SMAD4 mutations; Hereditary nonpolyposis colorectal cancer (HNPCC)/Lynch Syndrome associated with mutations in DNA mismatch repair genes MLH1, MSH2, MSH6, and/or PMS2, and EPCAM, Cowden syndrome associated with PTEN mutations, Peutz-Jeghers syndrome associated with STK11 mutations, or combinations thereof. Current diagnostic and/or screening assays for colorectal cancer include but are not limited to: colonoscopy, high sensitivity guaiac-based fecal occult blood or immunochemical based fecal occult blood fecal immunochemical test (FIT), sigmoidoscopy with or without FIT, stool DNA assessment (e.g., Cologuard testing), CT colonography, flexible sigmoidoscopy, serology tests (e.g., SEPT9 DNA test), or combinations thereof.

In some embodiments, technologies provided herein can be utilized in place of or in conjunction with: colonoscopy, high sensitivity guaiac-based fecal occult blood or immunochemical based fecal occult blood fecal immunochemical test (FIT), sigmoidoscopy with or without FIT, stool DNA assessment (e.g., Cologuard testing), CT colonography, flexible sigmoidoscopy, serology tests (e.g., SEPT9 DNA test), or combinations thereof.

Esophageal Cancer

Esophageal cancer is primarily of two types, squamous cell carcinoma (ESSC), and adenocarcinoma (ESAD). Squamous cell carcinoma (˜31.4% of cases) forms from thin, flat cells that line the inside of the esophagus, and is generally found in the upper and middle part of the esophagus. Adenocarcinoma (˜64.1% of cases) begins in glandular cells that produce and secrete fluids such as mucus and line the esophagus, this type of cancer usually forms near the stomach, at the lower part of the esophagus.

There are known risk factors (both genetic and life-history) associated with development of esophageal cancer, which include, for example: tobacco use, excessive alcohol consumption, being malnourished, being infected with human papillomavirus, having tylosis, having achalasia, having swallowed lye, drinking very hot liquids on a regular basis, having gastroesophageal reflux disease (GERD), having Barrett's esophagus, being overweight, having a history of using drugs that relax the lower esophageal sphincter, or combinations of the same. Esophageal cancer can occur as a result of various genetic mutations and/or syndromes, for example, tylosis with esophageal cancer which is caused by inherited changes in the RHBDF2 gene, Bloom syndrome which is caused by changes in the BLM gene, Fanconi anemia which is caused by mutations in FANC genes, and Familial Barrett's Esophagus for which causative genetic associations are still being elucidated. Esophageal cancer screening for the general asymptomatic population is not recommended, and is not considered to outweigh the potential harms and serious side effects associated with current screening methodologies. Current screening and/or diagnostic assays for detecting esophageal cancer include: esophagoscopy, biopsy, brush cytology, balloon cytology, chromoendoscopy, and/or fluorescence spectroscopy.

In some embodiments, technologies provided herein can be utilized in place of or in conjunction with: esophagoscopy, biopsy, brush cytology, balloon cytology, chromoendoscopy, and/or fluorescence spectroscopy.

Kidney Cancer

There are known risk factors (both genetic and life-history) associated with development of kidney cancer, and these include for example: sex (e.g., kidney cancer is twice as common in men (e.g., lifetime risk factor of about 2%) than in women), ethnicity (e.g., kidney cancer is more common in African Americans and American Indian/Alaska Natives), smoking, obesity, high blood pressure, a family history of kidney cancer, certain chemical exposures, advanced kidney disease, acetaminophen use, or combinations thereof. Kidney cancer can occur as a result of various genetic mutations and/or syndromes. For example, several genetic syndromes that lead to hereditary risk for development of kidney cancer include: Von Hippel-Lindau Disease (VHL gene mutations), Hereditary papillary renal cell carcinoma (MET gene mutations), Hereditary leiomyoma-renal cell carcinoma (FH gene mutations), Birt-Hogg-Dube syndrome (FLCN gene mutations), Familial renal cancer (SDHB and SDHD gene mutations), Cowden syndrome (PTEN gene mutations), Tuberous sclerosis (TSC1 and TSC2 gene mutations). Pediatric kidney cancer is commonly known as Wilm's tumor, and arises from immature kidney cells, this disease is often associated with syndromes such as WAGR syndrome, Denys-Drash syndrome, and/or Beckwith-Wiedemann syndrome. An attending physician will often recommend that individuals with genetic risk factors associated with kidney cancer get regular imaging tests such as CT, MRI, or ultrasound scans at younger ages, to look for kidney tumors.

There are no recommended screening tests for kidney cancer in people who are not at an increased risk; this is likely to be due to the fact that no currently available test has been shown to lower the overall risk of dying from kidney cancer. In general, existing screening tests do not differentiate benign and cancerous conditions. For example, a routine urine test (urinalysis), which is sometimes part of a complete medical checkup, may find small amounts of blood in the urine of some people with early kidney cancer, however, many conditions other than kidney cancer may cause blood in the urine (e.g., including: urinary tract infections, bladder infections, bladder cancer, and benign (non-cancerous) kidney conditions such as kidney stones). In addition, sometimes people with kidney cancer do not have blood in their urine until the cancer is quite large and might have spread to other parts of the body. Diagnostic imaging tests such as computed tomography (CT) scans and magnetic resonance imaging (MRI) scans may find small kidney cancers, however, these tests are expensive and are not routinely available. As an alternative, ultrasound may be used to detect some early kidney cancers, but this test often cannot differentiate between benign tumors and small renal cell carcinomas. Many kidney cancers are found relatively early during their development, often while they are still limited to the kidney, however, an appreciable number are discovered at a more advanced stage. Kidney cancers can occasionally grow relatively large without causing any pain or other appreciable symptoms. As kidneys are deep inside the body, small kidney tumors often cannot be seen or felt during a physical exam.

In some embodiments, technologies provided herein can be utilized in place of or in conjunction with: urinalysis, CT scan, MRI scan, and/or ultrasound.

Liver Cancer

There are known risk factors (both genetic and life-history) associated with development of liver cancer, and these include for example: gender, race/ethnicity (in the United States, Asian Americans and Pacific Islanders have the highest rates of liver cancer, followed by Hispanics/Latinos, American Indians/Alaska Natives, African Americans, and whites), liver cirrhosis, non-alcoholic fatty liver disease (e.g., non-alcoholic steatohepatitis), alcoholic fatty liver disease, primary biliary cirrhosis, diabetes (e.g., type II diabetes), excessive alcohol use, chronic viral hepatitis B and/or hepatitis C infection, exposure to aflatoxins, exposure to vinyl chloride and/or thorium dioxide, tobacco use, long-term anabolic steroid use, obesity, or combinations thereof. Liver cancer can occur as a result of various genetic mutations and/or syndromes. For example, several genetic syndromes that lead to hereditary risk for development of liver cancer include: Hereditary hemochromatosis (HFE mutations), Tyrosinemia (mutations in the FAH, TAT, and HPD genes cause tyrosinemia types I, II, and III, respectively), Alpha1-antitrypsin deficiency (SERPINA1 mutations), Porphyria cutanea tarda (UROD mutations), Glycogen storage diseases (mutations in G6PC or SLC37A4 cause glycogen storage disease type Ia and Ib, respectively), and/or Wilson disease (ATP7B mutations). There is no CDC approved screening assay recommended for testing asymptomatic members of the general population. Current screening and/or diagnostic methods include but are not limited to: serum blood tests (e.g., measurement of alpha fetoprotein), multiphase CT and MRI exams, ultrasound, ultrasonography (US), computed tomography (CT) (e.g., triple-phase CT scan), magnetic resonance imaging (MRI), biopsy and histological analysis (e.g., staining for several biomarkers, e.g., staining for glypican-3 (GPC3), heat shock protein 70 (HSP70), and glutamine synthetase), contrast-enhanced ultrasound (CEUS), or combinations thereof.

In some embodiments, technologies provided herein can be used in place of or in conjunction with: serum blood tests (e.g., measurement of alpha fetoprotein), multiphase CT and MRI exams, ultrasound, ultrasonography (US), computed tomography (CT) (e.g., triple-phase CT scan), magnetic resonance imaging (MRI), biopsy and histological analysis (e.g., staining for several biomarkers, e.g., staining for glypican-3 (GPC3), heat shock protein 70 (HSP70), and glutamine synthetase), contrast-enhanced ultrasound (CEUS), or combinations thereof.

Lung Cancer

Different types of lung cancer are described histologically by the types of cells the pathologist sees under the microscope. An estimated ˜85% of lung cancers are non-small cell lung cancer (NSCLC), and ˜15% of lung cancers are small cell lung cancer (SCLC). There are three major types of non-small cell lung cancer: ˜40% of NSCLCs are lung adenocarcinoma ˜30% of NSCLCs are squamous cell lung cancer (also called epidermoid carcinoma), and ˜10% of NSCLCs are large cell lung cancer. There are a number of known lung cancer driver mutations (e.g., TP53, EGFR, LRP1B, etc.), and a number of driver mutations currently have FDA-approved targeted therapy drugs available, e.g., targeting proteins encoded by the genes EGFR, ALK, ROS1, NTRK, BRAF, MET, and RET. There are a number of FDA-approved biomarker-driven targeted therapies for lung adenocarcinoma, and currently one approved immunotherapy drug that is prescribed based on PD-L1 biomarker status. In addition, there are multiple immunotherapy drugs that can be prescribed regardless of a patient's PD-L1 status.

There are certain risk factors associated with lung cancer, these include e.g., life history risk factors including but not limited to: smoking, alcohol use, drug use, exposure to carcinogenic agents, poor diet, obesity, diabetes, chronic obstructive pulmonary disease (COPD), certain physical activity, sun exposure, radiation exposure, bituminous smoke exposure, exposure to infectious agents such as viruses and bacteria, and/or occupational hazard. In December 2013, the USPSTF recommended that high-risk individuals undergo screening tests, particularly annual screening using low-dose computed tomography (LDCT) in adults aged 55 to 80 years who have a 30 pack-year smoking history and currently smoke or have quit within the past five-years. The USPSTF has recommended screening using current technologies should be discontinued once a person has not smoked for five-years or develops a health problem that substantially limits life expectancy or the ability or willingness to have curative lung surgery.

In some embodiments, technologies provided herein can be utilized in place of or in conjunction with: LDCT, CT scan, MRI scan, sputum testing, and/or ultrasound.

Ovarian Cancer

The strongest risk factor for ovarian cancer is a family history of breast or ovarian cancer. Risk of developing invasive epithelial ovarian cancer is increased by ˜50% among women with a first-degree relative with a history of ovarian cancer, and by 10% with a first-degree relative with breast cancer. It is estimated that ˜18% of epithelial ovarian cancer cases, particularly high-grade serous carcinomas, are likely due to inherited mutations that confer elevated risk. Mutations in BRCA1 and/or BRCA2 are considered likely causative for almost 40% of ovarian cancer cases in women with a family history of the disease. Among women with BRCA1 or BRCA2 mutations, the risk of developing ovarian cancer by age 80 is 44% and 17%, respectively (Torre et al., 2018, which is incorporated herein by reference in its entirety for the purposes described herein). As germline genetic screening for women with breast cancer becomes more common, it will help to identify additional risk-mutation carriers whose daughters are also at hereditary risk for breast and/or ovarian cancer. In addition, women with inherited colon cancer risk (e.g., Lynch syndrome) related to germline mutations in DNA mismatch repair (MMR) genes (e.g., MLH1, MSH2, MSH6, EPCAM, and/or PMS2) have approximately an 8% risk of developing ovarian cancer (commonly non-serous epithelial tumors) by age 70 compared to 0.7% risk in the general population. The current NCCN ovarian cancer practice guidelines recommend that asymptomatic women with hereditary risk be tested twice a year with a combination of serum CA-125 level measurements, and transvaginal ultrasound (TVUS). The USPSTF has recommended against screening asymptomatic women for ovarian cancer using CA125, and there is currently no FDA approved test for ovarian cancer screening in average risk women.

In some embodiments, technologies provided herein can be utilized in place of or in conjunction with: TVUS, CA-125 level measurements, CT scan, MRI scan, and/or ultrasound.

Pancreatic Cancer

In some embodiments, technologies provided herein may be particularly suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to pancreatic cancer. Pancreatic cancer makes up approximately 2.5% of total worldwide cancers, and has a worldwide yearly incidence rate of approximately 460,000 cases, and 432,000 deaths. There are approximately 58,000 new cases of pancreatic cancer each year in the USA, and approximately 47,000 deaths. There are an estimated 73,000 individuals living with pancreatic cancer in the USA, and there is a dismal 9.3% five-year survival rate (see e.g., Bray, et al., 2018. CA: a cancer journal for clinicians, 68(6), pp. 394-424, and ACS US Cancer Facts and Figures 2020, and the SEER database (US incidence, prevalence, and survival data); each of which is incorporated herein in their entirety for the purposes described herein). The United States Preventive Services Task Force (USPSTF) currently recommends against screening for pancreatic cancer in the general population, as they have concluded that the potential benefits of screening for pancreatic cancer in asymptomatic adults using current technologies does not outweigh the harms. The USPSTF has stated that there is no evidence supporting the accuracy of CT scan, MRI, or endoscopic ultrasonography for detecting pancreatic cancer in the general population. However, the USPSTF has recommended individuals with strong family histories or known genetic risks are encouraged to participate in surveillance programs at experienced cancer centers, where screening generally comprises pancreatic CT scans and/or endoscopic ultrasound (EUS). To aid diagnosis, serum CA19-9 tests may also be appropriate, while serum CA19-9 tests have a low PPV, 0.5-0.9% (asymptomatic individuals) or ˜1.8% (symptomatic individuals), this test can facilitate assessment of cancer stage and prediction of surgical respectability, as well as disease prognosis and/or monitoring of response to treatment.

There are known risk factors (both genetic and life-history) associated with development of pancreatic cancer. Approximately 10% of pancreatic cancer patients have a positive family history or inherited genetic mutations that increase cancer risk. Risk factors for pancreatic cancer include: BRCA mutations, mutations in BRCA2 convey an approximately 3 to 10 fold increased risk of developing pancreatic cancer, culminating in a 10% lifetime risk of developing pancreatic cancer; CFTR mutations, mutations in CFTR are often causative for development of cystic fibrosis a disease that can cause pancreatic insufficiency and chronic pancreatitis, the risk of developing pancreatic cancer is 5 to 6 fold greater in people who have cystic fibrosis when compared to the general population; Familial Adenomatous Polyposis (FAP), FAP is a rare hereditary form of autosomal dominant colon cancer caused by mutations in the FAP gene, individuals with FAP have a 100- to 200-fold increased risk of developing periampullary carcinoma when compared to the general population and the incidence of ampullary tumors is increased 200- to 300-fold; Familial Atypical Multiple Mole Melanoma (FAMMM), FAMMM is characterized by melanoma diagnosis in younger individuals and many skin moles and multiple primary melanomas, individuals with FAMMM have a 13 to 22 fold increased risk of developing pancreatic cancer; Hereditary Nonpolyposis Colorectal Cancer (HNPCC) or Lynch Syndrome, HNPCC is an inherited condition associated with ˜5% of colon cancer cases, individuals with HNPCC have approximately a 9 fold increased risk of developing pancreatic cancer; Hereditary Pancreatitis, hereditary pancreatitis is a rare inherited condition that usually starts before age 20, characterized by recurrent episodes of severe inflammation of the pancreas, it can lead to chronic pancreatitis and approximately a 40-55% lifetime risk of developing pancreatic cancer, individuals with hereditary pancreatitis who also smoke may develop earlier onset pancreatic cancer; PALB2 mutations, approximately 1-3% of patients with familial pancreatic cancer have inherited mutations in the PALB2 gene; Peutz-Jeghers Syndrome (SKT11), is characterized by polyps in the small intestine and pigmented spots on the lips and nose, individuals with this syndrome have a 11-36% lifetime risk of developing pancreatic cancer; New onset type 2 diabetes, hyperglycemia and diabetes often precedes pancreatic cancer diagnosis by 30-36 months, and ˜22% of patients diagnosed with pancreatic cancer have new onset diabetes (see e.g., Sharma et al., Gastroenterology 2018 August; 155(2):490-500; which is incorporated herein by reference for the purposes described herein). The National Comprehensive Cancer Network (NCCN) guidelines for pancreatic cancer have recently been updated to include a recommendation to test all patients for germline mutations in ATM, BRCA1/2, CDKN2A, MLH1, MSH2, MSH6, EPCAM, PALB2, STK11 and TP53 (NCCN Practice Guideline Version 1.2020, 2019).

In some embodiments, technologies provided herein can be utilized in place of or in conjunction with: endoscopic ultrasound, CA19-9 serum level analysis, CT scan, MRI scan, and/or ultrasound.

Prostate Cancer

Almost all prostate cancers are adenocarcinomas, which develop from the gland cells (the cells that make the prostate fluid that is added to the semen). Some prostate cancers grow and spread quickly, but most grow slowly. Autopsy studies show that many older men (and even some younger men) who died of other causes also had prostate cancer that never affected them during their lives. In many cases, neither they nor their doctors even knew they had it.

Most prostate cancers are asymptomatic and can be found early through screening. More advanced prostate cancers can sometimes cause symptoms, such as: problems urinating (e.g., including a slow or weak urinary stream or the need to urinate more often), blood in the urine or semen, difficulty getting an erection (erectile dysfunction or ED), pain in the hips, pain in the back (spine), pain in the chest (ribs), pain in other areas due to cancer dissemination, weakness or numbness in the legs or feet, and/or loss of bladder or bowel control from tumor induced pressure on the spinal cord.

There are known risk factors (both genetic and life-history) associated with development of prostate cancer, these include: age (e.g., the diseases is rare in men younger than 40 but risk rises rapidly after age 50), race/ethnicity (e.g., the disease develops more often in men of African-American ancestry, and is less common in men of Asian or Hispanic ancestry), geography (e.g., the disease is more common in North America, Europe and Australia, and is less common in Asia, Africa, and Central/South America), family history/genetics (e.g., having a father or brother with prostate cancer more than doubles a man's risk of developing the disease), certain germline mutations (e.g., mutations in BRCA1, BRCA2, CHEK2, ATM, PALB2, RAD51D, DNA mismatch repair genes (e.g., MSH2, MSH6, MLH1, and PMS2), RNASEL (formerly HPC1), and/or HOXB13), diet (e.g., consumption of large amounts of red meat and/or high-fat foods may increases risk), obesity (e.g., being overweight may increase the risk of having an aggressive form of the disease), chemical exposures (e.g., there is some evidence for increased risks to firefighters and/or people previously exposed to Agent Orange), or combinations thereof.

In general, prostate cancers are first identified as a result of screening with a serum prostate-specific antigen (PSA) test or a digital rectal exam (DRE). For PSA tests, most men without prostate cancer have PSA levels under 4 ng/mL of blood, however, a level below 4 ng/mL is not a guarantee that a man doesn't have cancer. Men with a PSA level between 4 and 10 ng/mL (e.g., often referred to as the “borderline range”) have about a 25% chance of having prostate cancer. Men with a PSA level of more than 10 ng/mL have a chance of having prostate cancer that is over 50%. For DRE tests, a physician inserts a gloved, lubricated finger into the rectum to feel for any bumps or hard areas on the prostate that might be cancer.

In May of 2018, the USPSTF a recommended screening test strategy for men aged 55 to 69 years that comprises annual serum PSA measurements. The USPSTF recommends against PSA-based screening for prostate cancer in men >70 years. The USPSTF recommends the decision for periodic prostate-specific antigen (PSA)-based screening for prostate cancer be taken on an individual specific basis, e.g., where men discuss the potential benefits and harms of screening with their clinician and to incorporate their values and preferences in the decision. Certain benefits of current screening methods for prostate cancer includes a small potential benefit of reducing the chance of death from prostate cancer in some men. While certain harms of current screening methods include: an overabundance of false-positive results that require additional testing and possible prostate biopsy; overdiagnosis and overtreatment; undue anxiety and psychological distress; and treatment complications, such as incontinence and erectile dysfunction.

Current diagnostic methods are predominated by needle biopsy procedures. These are done either through the wall of the rectum (a transrectal biopsy) or through the skin between the scrotum and anus (a transperineal biopsy). When the needle is removed from the subject, a small cylinder (core) of prostate tissue is sampled. Generally, a physician will obtain approximately 12 core samples from different parts of the prostate. These core samples are then biopsied, and rated as negative (no cancer cells), suspicious (something abnormal, but not necessarily cancer), or positive (cancer cells were seen in the biopsy samples). If prostate cancer is found on a biopsy, it will be assigned a grade, this grade is based on how abnormal the cancer looks under the microscope. Higher grade cancers look more abnormal, and are more likely to grow and spread quickly. There are two main ways to describe the grade of a prostate cancer, 1) via a Gleeson Score and 2) the extent of the cancer (e.g. bilateral vs. unilateral, number of cores positive for cancer, percent of cancerous cells in each core).

In some embodiments, technologies provided herein can be utilized in place of or in conjunction with: PSA serum measurements, needle biopsy, and/or digital rectal exam (DRE).

Stomach Cancer

There are known risk factors (both genetic and life-history) associated with development of stomach cancer, these include: Helicobacter pylori infection, being older than 45-years of age, being male, a history of smoking, alcohol consumption, obesity, vegetable consumption, fruit consumption, high salt intake, intestinal metaplasia, genetics/family history e.g., Hereditary diffuse gastric cancer (mutations in CGH1), Lynch Syndrome (mutations in MLH1, MSH2, MSH6, PMS2, or EPCAM), Hereditary breast/ovarian cancer (mutations in BRCA1 and/or BRCA2), Li-Fraumeni Syndrome (mutations in TP53), Familial adenomatous polyposis (mutations in APC), Juvenile polyposis syndrome (mutations in SMAD4 and/or BMPR1A), and Preutz-Jeghers syndrome (mutations in STK11). Universal screening for stomach cancer in the USA using current technologies is not recommended by the USPSTF, likely due to lack of cost effectiveness. Standard screening/diagnostic methodologies include: Esophagogastroduodenonoscopy (EGD), and Esophagogastroduodenonoscopy with endoscopic ultrasound (EUS).

In some embodiments, technologies provided herein can be utilized in place of or in conjunction with: EGD, and/or EUS.

II. Provided Biomarkers and/or Biomarker Combinations for Pan-Cancer Detection

In some aspects, provided are technologies for use in classifying a subject (e.g., an asymptomatic subject) as having or being susceptible to cancer (e.g., carcinoma, sarcoma, mixed types, etc.). In some embodiments, the present disclosure provides methods or assays for classifying a subject (e.g., an asymptomatic subject) as having or being susceptible to cancer (e.g., carcinoma, sarcoma, mixed types, etc.). In some embodiments, a provided method or assay comprises assaying a sample (e.g., a blood-derived sample) from a subject for a plurality of distinct biomarker combinations to determine in the sample (e.g., blood-derived sample) whether nanoparticles having a size range of interest that includes extracellular vesicles display at least a biomarker combination from the plurality (e.g., co-localization of at least two biomarkers), wherein the plurality of biomarker combinations each independently comprise at least two biomarkers, whose combined expression level has been determined to be associated with at least one type of cancer (including, e.g., at least two types of cancer).

In some embodiments, at least one biomarker combination within a selected plurality of biomarker combinations is or comprises two or more surface biomarkers, at least one of which is or comprises a polypeptide encoded by a human gene as follows: ABCA13, ADAM23, CYP4F11, HAS3, TMPRSS4, UGT1A6, PIGT, TOMM34, ACSL4, GPC3, ROBO1, SLC22A9, SLC38A3, TFR2, TM4SF4, TMPRSS6, ANXA13, CHST4, GAL3ST1, SNAP25, TMEM156, CLDN18, EPPK1, MUC13, OCLN, CFTR, GCNT3, ITGB6, ITGB6, LAD1, MSLN, TESC, LYPD6B, S100P, TMEM51, TNFRSF21, UPK1B, UPK2, ABCC4, FOLH1, RAB3B, STEAP2, TMPRSS2, TSPAN1, AP1S3, DSC2, DSG3, TMPRSS11D, KCNS1, LY6K, MUC4, SYNGR3, CELSR1, COX6C, ESR1, MUC1, ABCC11, ERBB2, SLC9A3R1, PROM1, PTK7, CDK4, DLK1, LMNB2, PCDH7, TMEM108, TYMS, SDC1, SLC34A2, BCAM, MUC16, and combinations thereof.

In some embodiments, at least one biomarker combination within a selected plurality of biomarker combinations is or comprises two or more surface biomarkers, at least one of which is or comprises a polypeptide encoded by a human gene as follows: ADAM17, ADAM28, ADAM8, ALCAM, AMHR2, AXL, BAG3, BSG, CCL2, CCL8, CCN1, CCN2, CCR5, CD274, CD38, CD44, CD47, CDH11, CETN1, CLDN1, CLEC2D, CLU, CSPG4, DKK1, DLL4, EGFR, ENPP3, EPHA10, ERBB3, FAP, FGF1, FGFR4, FLNA, FLNB, FLT4, FZD7, GFRA1, GM3, GPA33, GPC1, GPNMB, GUCY2C, HGF, ICAM1, IGF1R, IL1A, IL1RAP, IL6, ITGA6, ITGAV, KDR, KLK3, KLKB1, KRT8, LAG3, LGR5, LPR6, LY6E, MCAM, MDM2, MELTF, MERTK, MST1R, MUC1, MUC2, MUC4, MUC13, MUC17, MUC5AC, MUCL1, NOTCH2, NOTCH3, NRP1, NT5E, PI4K2A, P1, PLAUR, PLVAP, PPP1R3A, PRLR, PSCA, PVR, RET, S1PR1, SLC3A2, SLC7A11, SLC7A5, SPINK1, STAT3, STEAP1, TACSTD2, TF, TFRC, TGFBR2, TIGIT, TNC, TNFRSF10A, TNFRSF10B, TNFRSF12A, TNFRSF4, TNFSF11, TNFSF18, TPBG, VANGL2, VEGFA, VEGFC, and combinations thereof.

In some embodiments, at least one biomarker combination within a selected plurality of biomarker combinations is or comprises two or more surface biomarkers, at least one of which is or comprises a carbohydrate-dependent marker. Examples of carbohydrate-dependent or lipid-dependent markers that may be used in a biomarker combination include, but are not limited to Tn antigen, SialylTn (sTn) antigen, Thomsen-Friedenreich (T, TF) antigen, Lewis Y (also known as CD174) antigen, Lewis B antigen, Sialyl Lewis X (sLex) (also known as Sialyl SSEA-1 (SLX)) antigen, SSEA-1 (also known as Lewis X), beta1,6-branching, bisecting GlcNAc in a beta1,4-linkage, core fucosylation, Sialyl-T antigens (sT), Sialyl Lewis c antigen, Globo H, SSEA-3 (Gb5), SSEA-4 (sialy-Gb5), Gb3 (Globotriaose, CD77), Disialosyl-galactosylgloboside (DSGG), GalNAcDSLc4, Fucosyl GM1, GD1alpha, GD1a ganglioside, GD2 ganglioside, GD3 ganglioside, GM2 ganglioside, Lc3 ceramide, nLc4 ceramide, 9-O-Ac-GD2 ganglioside, 9-O-Ac-GD3 (CDw60) ganglioside, 9-O-Ac-GT3 ganglioside, Forssman antigen, Disialyl Lewis a antigen, Sialylparagloboside (SPG), Polysialic acid (PSA) linked to NCAM, Sialyl Lewis A antigen (also known as CA19-9), CanAg (glycoform of MUC1), Lewis Y/B antigen, Sialyltetraosyl carbohydrate, NeuGcGM3, GM3 (N-glycolylneuraminic acid (NeuGc, NGNA)-gangliosides GM3), phosphatidylserine, and combinations thereof.

In some embodiments, at least one biomarker combination within a selected plurality of biomarker combinations is or comprises two or more surface biomarkers, which combination is determined to be associated with at least two (including, e.g., at least three, at least four, or more) cancers, wherein one of the surface biomarkers is or comprises a MUC1 polypeptide, a CEACAM5 polypeptide, a Lewis Y antigen (also known as CD174), SialyTn (sTn), antigen, a Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, or combinations thereof, and at least another surface biomarker is or comprise (i) one or more polypeptides encoded by a human gene as described herein, e.g., in some embodiments as described in this section “Provided Biomarkers and/or Biomarker Combinations for Pan-Cancer Detection”, and/or (ii) one or more carbohydrate-dependent and/or lipid-dependent biomarkers as described herein, e.g., in some embodiments as described in this section “Provided Biomarkers and/or Biomarker Combinations for Pan-Cancer Detection.”

The present disclosure, among other things, provides various biomarkers or combinations thereof (e.g., biomarker combinations) and sets of biomarker combinations (e.g., sets of complementary biomarker combinations) for detection of cancer. Such biomarker combinations that are predicted to exhibit ability to detect multiple cancers, for example, at least two or more cancers, were discovered by a multi-pronged bioinformatics analysis and biological approach, which for example, in some embodiments involve computational analysis of a diverse set of data, e.g., in some embodiments comprising one or more of sequencing data, expression data, mass spectrometry, histology, post-translational modification data, and/or in vitro and/or in vivo experimental data through machine learning and/or computational modeling.

In some embodiments, a biomarker combination of cancer comprises at least one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, or more) surface biomarker (e.g., in some embodiments surface polypeptide present in extracellular vesicles associated with cancer and/or a specific tissue of interest; “extracellular vesicle-associated surface biomarker”) and at least one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, or more) target biomarkers selected from the group consisting of surface biomarker(s), intravesicular biomarker(s), and intravesicular RNA biomarker(s), such that the combination of such surface biomarker(s) and such target biomarker(s) present a biomarker combination of cancer that provides (a) high specificity (e.g., greater than 98% or higher such as greater than 99%, or greater than 99.5%) to minimize the number of false positives, and (b) high sensitivity (e.g., greater than 40%, greater than 50%, greater than 60%, greater than 70%, greater than 80%) for stage I and II cancer when prognosis is most favorable.

In some embodiments, the present disclosure recognizes that in certain embodiments, sensitivity and specificity rates for subjects with different cancer risk levels may vary depending upon the risk tolerance of the attending physician and/or the guidelines set forth by interested medical consortia. In some embodiments, lower specificity and/or sensitivity may be used for screening patients at higher risk of cancer (e.g., patients with life-history-associated risk factors, symptomatic patients, or patients with a family history of cancer, etc.) as compared to that for patients with lower risk for cancer. For example, in some embodiments, biomarker combinations described herein that are useful for detection of cancer may provide a specificity of at least 70% including, e.g., at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98%, at least 99.5%, or higher. Additionally or alternatively, in some embodiments, biomarker combinations described herein that are useful for detection of cancer may provide a sensitivity of at least 50% including, e.g., at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98%, at least 99.5%, or higher.

In certain embodiments, subjects at risk of cancer may be served with an 85% specificity rate or higher (including, e.g., at least 90%, at least 95% or higher specificity rate) with 50% sensitivity or higher (including, e.g., at least 60%, at least 70%, at least 80%, or higher sensitivity). In certain embodiments, at risk subjects with life-history-associated risk factors may be served with an 85% specificity rate or higher (including, e.g., at least 90%, at least 95% or higher specificity rate) with 50% sensitivity or higher (including, e.g., at least 60%, at least 70%, at least 80%, or higher sensitivity). In certain embodiments, symptomatic subjects may be served with an 85% specificity rate or higher (including, e.g., at least 90%, at least 95% or higher specificity rate) with 50% sensitivity or higher (including, e.g., at least 60%, at least 70%, at least 80%, or higher sensitivity). In certain embodiments, non-symptomatic subjects may be served with an 85% specificity rate or higher (including, e.g., at least 90%, at least 95% or higher specificity rate) with 50% sensitivity or higher (including, e.g., at least 60%, at least 70%, at least 80%, or higher sensitivity). In certain embodiments, subjects at risk of cancer may be served with a 99.5% specificity rate with 70% sensitivity or a 98% specificity rate with 80% sensitivity. In certain embodiments, at risk subjects with life-history-associated risk factors may be served with a 99.5% specificity rate with 70% sensitivity or a 98% specificity rate with 80% sensitivity. In some embodiments, an assay described herein for detection of cancer in at-risk subjects (e.g., with life-history-associated risk factors) may have a set sensitivity rate that is lower than 80% sensitivity, including e.g., less than 70%, less than 60%, less than 50% or lower sensitivity rate. In certain embodiments, non-symptomatic subjects may be served with a 99.5% specificity rate with 70% sensitivity or a 98% specificity rate with 80% sensitivity. In some embodiments, an assay described herein for detection of cancer in non-symptomatic subjects may have a set sensitivity rate that is lower than 80% sensitivity, including e.g., less than 70%, less than 60%, less than 50% or lower sensitivity rate. In some embodiments, technologies and/or assays described herein for detection of cancer in a symptomatic subject may have a lower sensitivity and/or specificity requirement than those for detection of cancer in an asymptomatic subject. In some embodiments, an assay described herein for detection of cancer in a symptomatic subject may have a set specificity rate that is lower than 99.5% specificity, including e.g., less than 99% sensitivity, less than 95%, less than 90%, or less than 85% specificity rate. In some embodiments, an assay described herein for detection of cancer in a symptomatic subject may have a set sensitivity rate that is lower than 80% sensitivity, including e.g., less than 70%, or less than 60% sensitivity rate.

In some embodiments, the present disclosure, among other things, appreciates that a biomarker combination of cancer that provides a positive predictive value (PPV) of 2% or higher can be useful for screening individuals at risk for cancer. In some embodiments, a biomarker combination of cancer comprises at least one surface biomarker (e.g., in some embodiments surface biomarker present on the surfaces of extracellular vesicles associated with cancer) and at least one target biomarker selected from the group consisting of surface biomarker(s), intravesicular biomarker(s), and intravesicular RNA biomarker(s), such that the combination of such surface biomarker(s) and such target biomarker(s) present a biomarker combination of cancer that provides a positive predictive value (PPV) of at least 2% or higher, including, e.g., at least 3%, at least 4%, at least 5%, at least 6%, at least 7%, at least 8%, at least 9%, at least 10% or higher, at least 15% or higher, at least 20% or higher, at least 25% or higher, and/or at least 30% or higher, in high-risk population.

In general, gene identifiers used herein refer to the Gene Identification catalogued by the UniProt Consortium (UniProt.org); one skilled in the art will understand that certain genes can be known by multiple names and will also readily recognize such multiple names.

In general, carbohydrate identifiers used herein refer to Kegg Cancer-associated Carbohydrates database (genome.jp/kegg/disease/br08441.html); one skilled in the art will understand that certain carbohydrates can be known by multiple names and will also readily recognize such multiple names.

In some embodiments, a target biomarker included in a biomarker combination of cancer is or comprises a surface biomarker selected from the group consisting of: Delta-1-pyrroline-5-carboxylate synthase (ALDH18A1) polypeptide, AP-1 complex subunit mu-2 (AP1M2) polypeptide, MICOS complex subunit MIC26 (APOO) polypeptide, Brefeldin A-inhibited guanine nucleotide-exchange protein 3 (ARFGEF3) polypeptide, N-acetyllactosaminide beta-1,3-N-acetylglucosaminyltransferase 3 (B3GNT3) polypeptide, Bone morphogenetic protein receptor type-1B (BMPR1B) polypeptide, Cell adhesion molecule 4 (CADM4) polypeptide, Soluble calcium-activated nucleotidase 1 (CANT1) polypeptide, Signal transducer CD24 (CD24) polypeptide, Cadherin-1 (CDH1) polypeptide, Cadherin-17 (CDH17) polypeptide, Cadherin-2 (CDH2) polypeptide, Cadherin-3 (CDH3) polypeptide, Carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) polypeptide, Carcinoembryonic antigen-related cell adhesion molecule 6 (CEACAM6) polypeptide, Claudin-3 (CLDN3) polypeptide, Claudin-4 (CLDN4) polypeptide, Calmegin (CLGN) polypeptide, Ceroid-lipofuscinosis neuronal protein 5 (CLN5) polypeptide, Cytochrome P450 2S1 (CYP2S1) polypeptide, Desmoglein-2 (DSG2) polypeptide, Endosome/lysosome-associated apoptosis and autophagy regulator 1 (ELAPOR1) polypeptide, Ectonucleotide pyrophosphatase/phosphodiesterase family member 5 (ENPP5) polypeptide, Epithelial cell adhesion molecule (EPCAM) polypeptide, Ephrin type-B receptor 2 (EPHB2) polypeptide, Protein FAM241B (FAM241B) polypeptide, Fermitin family homolog 1 (FERMT1) polypeptide, Folate receptor alpha (FOLR1) polypeptide, Frizzled-2 (FZD2) polypeptide, Polypeptide N-acetylgalactosaminyltransferase 14 (GALNT14) polypeptide, Polypeptide N-acetylgalactosaminyltransferase 6 (GALNT6) polypeptide, Gap junction beta-1 protein (GJB1) polypeptide, Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-4 (GNG4) polypeptide, Glucosamine 6-phosphate N-acetyltransferase (GNPNAT1) polypeptide, Golgi membrane protein 1 (GOLM1) polypeptide, Probable G-protein coupled receptor 160 (GPR160) polypeptide, G protein-regulated inducer of neurite outgrowth 1 (GPRIN1) polypeptide, Grainyhead-like protein 2 homolog (GRHL2) polypeptide, Very-long-chain (3R)-3-hydroxyacyl-CoA dehydratase 3 (HACD3) polypeptide, Heparan-sulfate 6-O-sulfotransferase 2 (HS6ST2) polypeptide, Immunoglobulin superfamily member 3 (IGSF3) polypeptide, Immunoglobulin-like domain-containing receptor 1 (ILDR1) polypeptide, ER lumen protein-retaining receptor 3 (KDELR3) polypeptide, Importin subunit alpha-1 (KPNA2) polypeptide, Keratinocyte-associated protein 3 (KRTCAP3) polypeptide, Laminin subunit beta-3 (LAMB3) polypeptide, Laminin subunit gamma-2 (LAMC2) polypeptide, Lysosomal-associated transmembrane protein 4B (LAPTM4B) polypeptide, LARGE xylosyl- and glucuronyltransferase 2 (LARGE2) polypeptide, Lamin-B1 (LMNB1) polypeptide, Leucine-rich repeat neuronal protein 1 (LRRN1) polypeptide, Lipolysis-stimulated lipoprotein receptor (LSR) polypeptide, Protein MAL2 (MAL2) polypeptide, MARCKS-related protein (MARCKSL1) polypeptide, MARVEL domain-containing protein 2 (MARVELD2) polypeptide, Hepatocyte growth factor receptor (MET) polypeptide, Neuronal pentraxin receptor (NPTXR) polypeptide, Nuclear pore membrane glycoprotein 210 (NUP210) polypeptide, Partitioning defective 6 homolog beta (PARD6B) polypeptide, Protein TMEPAI (PMEPA1) polypeptide, Podocalyxin-like protein 2 (PODXL2) polypeptide, PRA1 family protein 2 (PRAF2) polypeptide, Prostasin (PRSS8) polypeptide, Ras-related protein Rab-25 (RAB25) polypeptide, Ras-related C3 botulinum toxin substrate 3 (RAC3) polypeptide, Rac GTPase-activating protein 1 (RACGAP1) polypeptide, Ras-related protein Rap-2b (RAP2B) polypeptide, Protein RCC2 (RCC2) polypeptide, E3 ubiquitin-protein ligase RNF128 (RNF128) polypeptide, E3 ubiquitin-protein ligase RNF43 (RNF43) polypeptide, Dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit 1 (RPN1) polypeptide, Dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit 2 (RPN2) polypeptide, Serine incorporator 2 (SERINC2) polypeptide, Protein shisa-2 homolog (SHISA2) polypeptide, UDP-galactose translocator (SLC35A2) polypeptide, Zinc transporter ZIP6 (SLC39A6) polypeptide, Choline transporter-like protein 4 (SLC44A4) polypeptide, Electrogenic sodium bicarbonate cotransporter 1 (SLC4A4) polypeptide, Small integral membrane protein 22 (SMIM22) polypeptide, Acid sphingomyelinase-like phosphodiesterase 3b (SMPDL3B) polypeptide, Synapse-associated protein 1 (SYAP1) polypeptide, Synaptotagmin-13 (SYT13) polypeptide, Transmembrane protein 132A (TMEM132A) polypeptide, Transmembrane protein 238 (TMEM238) polypeptide, Proton-transporting V-type ATPase complex assembly regulator TMEM9 (TMEM9) polypeptide, Tetraspanin-13 (TSPAN13) polypeptide, UL16-binding protein 2 (ULBP2) polypeptide, Protein unc-13 homolog B (UNC13B) polypeptide, V-set domain-containing T-cell activation inhibitor 1 (VTCN1) polypeptide, ATP-binding cassette sub-family A member 13 (ABCA13) polypeptide, Disintegrin and metalloproteinase domain-containing protein 23 (ADAM23) polypeptide, Cytochrome P450 4F11 (CYP4F11) polypeptide, Hyaluronan synthase 3 (HAS3) polypeptide, Transmembrane protease serine 4 (TMPRSS4) polypeptide, UDP-glucuronosyltransferase 1-6 (UGT1A6) polypeptide, GPI transamidase component PIG-T (PIGT) polypeptide, Mitochondrial import receptor subunit TOM34 (TOMM34) polypeptide, Long-chain-fatty-acid-CoA ligase 4 (ACSL4) polypeptide, Glypican-3 (GPC3) polypeptide, Roundabout homolog 1 (ROBO1) polypeptide, Solute carrier family 22 member 9 (SLC22A9) polypeptide, Sodium-coupled neutral amino acid transporter 3 (SLC38A3) polypeptide, Transferrin receptor protein 2 (TFR2) polypeptide, Transmembrane 4 L6 family member 4 (TM4SF4) polypeptide, Transmembrane protease serine 6 (TMPRSS6) polypeptide, Annexin A13 (ANXA13) polypeptide, Carbohydrate sulfotransferase 4 (CHST4) polypeptide, Galactosylceramide sulfotransferase (GAL3ST1) polypeptide, Synaptosomal-associated protein 25 (SNAP25) polypeptide, Transmembrane protein 156 (TMEM156) polypeptide, Claudin-18 (CLDN18) polypeptide, Epiplakin (EPPK1) polypeptide, Mucin-13 (MUC13) polypeptide, Occludin (OCLN) polypeptide, Cystic fibrosis transmembrane conductance regulator (CFTR) polypeptide, Beta-1,3-galactosyl-O-glycosyl-glycoprotein beta-1,6-N-acetylglucosaminyltransferase 3 (GCNT3) polypeptide, Integrin beta-6 (ITGB6) polypeptide, Ladinin-1 (LAD1) polypeptide, Mesothelin (MSLN) polypeptide, Calcineurin B homologous protein 3 (TESC) polypeptide, Calcineurin B homologous protein 3 (TESC) polypeptide, Ly6/PLAUR domain-containing protein 6B (LYPD6B) polypeptide, Protein S100-P (S100P) polypeptide, Transmembrane protein 51 (TMEM51) polypeptide, Tumor necrosis factor receptor superfamily member 21 (TNFRSF21) polypeptide, Uroplakin-1b (UPK1B) polypeptide, Uroplakin-2 (UPK2) polypeptide, ATP-binding cassette sub-family C member 4 (ABCC4) polypeptide, Glutamate carboxypeptidase 2 (FOLH1) polypeptide, Ras-related protein Rab-3B (RAB3B) polypeptide, Metalloreductase STEAP2 (STEAP2) polypeptide, Transmembrane protease serine 2 (TMPRSS2) polypeptide, Tetraspanin-1 (TSPAN1) polypeptide, AP-1 complex subunit sigma-3 (AP1S3) polypeptide, Desmocollin-2 (DSC2) polypeptide, Desmoglein-3 (DSG3) polypeptide, Transmembrane protease serine 11D (TMPRSS11D) polypeptide, Potassium voltage-gated channel subfamily S member 1 (KCNS1) polypeptide, Lymphocyte antigen 6K (LY6K) polypeptide, Mucin-4 (MUC4) polypeptide, Synaptogyrin-3 (SYNGR3) polypeptide, Cadherin EGF LAG seven-pass G-type receptor 1 (CELSR1) polypeptide, Cytochrome c oxidase subunit 6C (COX6C) polypeptide, Estrogen receptor (ESR1) polypeptide, Mucin-1 (MUC1) polypeptide, ATP-binding cassette sub-family C member 11 (ABCC11) polypeptide, Receptor tyrosine-protein kinase erbB-2 (ERBB2) polypeptide, Na(+)/H(+) exchange regulatory cofactor NHE-RF1 (SLC9A3R1) polypeptide, Prominin-1 (PROM1) polypeptide, Inactive tyrosine-protein kinase 7 (PTK7) polypeptide, Cyclin-dependent kinase 4 (CDK4) polypeptide, Protein delta homolog 1 (DLK1) polypeptide, Lamin-B2 (LMNB2) polypeptide, Protocadherin-7 (PCDH7) polypeptide, Transmembrane protein 108 (TMEM108) polypeptide, Thymidylate synthase (TYMS) polypeptide, Syndecan-1 (SDC1) polypeptide, Sodium-dependent phosphate transport protein 2B (SLC34A2) polypeptide, Basal cell adhesion molecule (BCAM) polypeptide, Mucin-16 (MUC16) polypeptide, Disintegrin and metalloproteinase domain-containing protein 17 (ADAM17) polypeptide, Disintegrin and metalloproteinase domain-containing protein 28 (ADAM28) polypeptide, Disintegrin and metalloproteinase domain-containing protein 8 (ADAM8) polypeptide, CD166 antigen (ALCAM) polypeptide, Anti-Muellerian hormone type-2 receptor (AMHR2) polypeptide, Tyrosine-protein kinase receptor UFO (AXL) polypeptide, BAG family molecular chaperone regulator 3 (BAG3) polypeptide, Basigin (BSG) polypeptide, Glycoform of MUC1 (CanAg) polypeptide, C—C motif chemokine 2 (CCL2) polypeptide, C—C motif chemokine 8 (CCL8) polypeptide, CCN family member 1 (CCN1) polypeptide, CCN family member 2 (CCN2) polypeptide, C—C chemokine receptor type 5 (CCR5) polypeptide, Programmed cell death 1 ligand 1 (CD274) polypeptide, ADP-ribosyl cyclase/cyclic ADP-ribose hydrolase 1 (CD38) polypeptide, CD44 antigen (CD44) polypeptide, Leukocyte surface antigen CD47 (CD47) polypeptide, Cadherin-11 (CDH11) polypeptide, Centrin-1 (CETN1) polypeptide, Claudin-1 (CLDN1) polypeptide, C-type lectin domain family 2 member D (CLEC2D) polypeptide, Clusterin (CLU) polypeptide, Chondroitin sulfate proteoglycan 4 (CSPG4) polypeptide, Dickkopf-related protein 1 (DKK1) polypeptide, Delta-like protein 4 (DLL4) polypeptide, Epidermal growth factor receptor (EGFR) polypeptide, Ectonucleotide pyrophosphatase/phosphodiesterase family member 3 (ENPP3) polypeptide, Ephrin type-A receptor 10 (EPHA10) polypeptide, Receptor tyrosine-protein kinase erbB-3 (ERBB3) polypeptide, Prolyl endopeptidase FAP (FAP) polypeptide, Fibroblast growth factor 1 (FGF1) polypeptide, Fibroblast growth factor receptor 4 (FGFR4) polypeptide, Filamin-A (FLNA) polypeptide, Filamin-B (FLNB) polypeptide, Vascular endothelial growth factor receptor 3 (FLT4) polypeptide, Frizzled-7 (FZD7) polypeptide, GDNF family receptor alpha-1 (GFRA1) polypeptide, glycosphingolipid N-glycolylneuraminic acid (NeuGc, NGNA)-gangliosides GM3 (GM3) polypeptide, Cell surface A33 antigen (GPA33) polypeptide, Glypican-1 (GPC1) polypeptide, Transmembrane glycoprotein NMB (GPNMB) polypeptide, Heat-stable enterotoxin receptor (GUCY2C) polypeptide, Hepatocyte growth factor (HGF) polypeptide, Intercellular adhesion molecule 1 (ICAM1) polypeptide, Insulin-like growth factor 1 receptor (IGF1R) polypeptide, Interleukin-1 alpha (IL1A) polypeptide, Interleukin 1 Receptor Accessory Protein (IL1RAP) polypeptide, Interleukin-6 (IL6) polypeptide, Integrin alpha-6 (ITGA6) polypeptide, Integrin alpha-V (ITGAV) polypeptide, Vascular endothelial growth factor receptor 2 (KDR) polypeptide, Prostate-specific antigen (KLK3) polypeptide, Plasma kallikrein (KLKB1) polypeptide, Keratin, type II cytoskeletal 8 (KRT8) polypeptide, Lymphocyte activation gene 3 protein (LAG3) polypeptide, Leucine-rich repeat-containing G-protein coupled receptor 5 (LGR5) polypeptide, LDL Receptor Related Protein 6 (LPR6) polypeptide, Lymphocyte antigen 6E (LY6E) polypeptide, Cell surface glycoprotein MUC18 (MCAM) polypeptide, E3 ubiquitin-protein ligase Mdm2 (MDM2) polypeptide, Melanotransferrin (MELTF) polypeptide, Tyrosine-protein kinase Mer (MERTK) polypeptide, Macrophage-stimulating protein receptor (MST1R) polypeptide, Mucin-17 (MUC17) polypeptide, Mucin-5AC (MUC5AC) polypeptide, Mucin-like protein 1 (MUCL1) polypeptide, Neurogenic locus notch homolog protein 2 (NOTCH2) polypeptide, Neurogenic locus notch homolog protein 3 (NOTCH3) polypeptide, Neuropilin-1 (NRP1) polypeptide, 5′-nucleotidase (NT5E) polypeptide, Phosphatidylinositol 4-kinase type 2-alpha (PI4K2A) polypeptide, Placenta-specific protein 1 (PLAC1) polypeptide, Urokinase plasminogen activator surface receptor (PLAUR) polypeptide, Plasmalemma vesicle-associated protein (PLVAP) polypeptide, Protein phosphatase 1 regulatory subunit 3A (PPP1R3A) polypeptide, Prolactin receptor (PRLR) polypeptide, Prostate stem cell antigen (PSCA) polypeptide, Poliovirus receptor (PVR) polypeptide, Proto-oncogene tyrosine-protein kinase receptor Ret (RET) polypeptide, Sphingosine 1-phosphate receptor 1 (S1PR1) polypeptide, 4F2 cell-surface antigen heavy chain (SLC3A2) polypeptide, Cystine/glutamate transporter (SLC7A11) polypeptide, Large neutral amino acids transporter small subunit 1 (SLC7A5) polypeptide, Serine protease inhibitor Kazal-type 1 (SPINK1) polypeptide, Signal transducer and activator of transcription 3 (STAT3) polypeptide, Metalloreductase STEAP1 (STEAP1) polypeptide, Tumor-associated calcium signal transducer 2 (TACSTD2) polypeptide, Serotransferrin (TF) polypeptide, Transferrin receptor protein 1 (TFRC) polypeptide, TGF-beta receptor type-2 (TGFBR2) polypeptide, T-cell immunoreceptor with Ig and ITIM domains (TIGIT) polypeptide, Tenascin (TNC) polypeptide, Tumor necrosis factor receptor superfamily member 10A (TNFRSF10A) polypeptide, Tumor necrosis factor receptor superfamily member 10B (TNFRSF10B) polypeptide, Tumor necrosis factor receptor superfamily member 12A (TNFRSF12A) polypeptide, Tumor necrosis factor receptor superfamily member 4 (TNFRSF4) polypeptide, Tumor necrosis factor ligand superfamily member 11 (TNFSF11) polypeptide, Tumor necrosis factor ligand superfamily member 18 (TNFSF18) polypeptide, Trophoblast glycoprotein (TPBG) polypeptide, Vang-like protein 2 (VANGL2) polypeptide, Vascular endothelial growth factor A (VEGFA) polypeptide, Vascular endothelial growth factor C (VEGFC) polypeptide, Sialyltetraosyl carbohydrate, Phosphatidylserine, Carbohydrate antigen 19-9 (also known as Sialyl Lewis A (CA19-9)), Lewis Y/B antigen, Truncated O-glycan Tn (Tn), Truncated O-glycans SialylTn (SialylTn (sTn)), Truncated O-glycans Thomsen-Friedenreich (Thomsen-Friedenreich (T, TF)), Lewis Y antigen (also known as CD174), Lewis B antigen, Sialyl Lewis X (sLex) (also known as Sialyl SSEA-1 (SLX)) antigen, SSEA-1/Lewis X (SSEA-1/Lewis X) antigen, Glycosphingolipid NeuGcGM3 (NeuGcGM3), N-glycans beta1,6-branching (beta1,6-branching), N-glycans bisecting GlcNAc in a beta1,4-linkage (bisecting GlcNAc in a beta1,4-linkage), N-glycans core fucosylation (core fucosylation), Truncated O-glycans Sialyl-T antigens (Sialyl-T antigens (sT)), Sialyl Lewis c (Sialyl Lewis c) antigen, Glycosphingolipid Globo H (Globo H), Glycosphingolipid SSEA-3 (SSEA-3 (Gb5)), Glycosphingolipid SSEA-4 (SSEA-4 (sialy-Gb5)), Glycosphingolipid Gb3 (Gb3 (Globotriaose, CD77)), Glycosphingolipid Disialosyl-galactosylgloboside (Disialosyl-galactosylgloboside (DSGG)), Glycosphingolipid GalNAcDSLc4 (GalNAcDSLc4), Glycosphingolipid Fucosyl GM1 (Fucosyl GM1), Glycosphingolipid GD1alpha (GD1alpha ganglioside), Glycosphingolipid GD1a (GD1a ganglioside), Glycosphingolipid GD2 (GD2 ganglioside), Glycosphingolipid GD3 (GD3 ganglioside), Glycosphingolipid GM2 (GM2 ganglioside), Glycosphingolipid Lc3 (Lc3 ceramide), Glycosphingolipid nLc4 (nLc4 ceramide), Glycosphingolipid 9-O-Ac-GD2 (9-O-Ac-GD2 ganglioside), Glycosphingolipid 9-O-Ac-GD3 (CDw60) (9-O-Ac-GD3 (CDw60) ganglioside), Glycosphingolipid 9-O-Ac-GT3 (9-O-Ac-GT3 ganglioside), Glycosphingolipid Forssman antigen (Forssman antigen), Glycosphingolipid Disialyl Lewis a antigen (Disialyl Lewis a antigen), Glycosphingolipid Sialylparagloboside (SPG) (Sialylparagloboside (SPG)), Glycosphingolipid Polysialic acid (PSA) linked to NCAM (Polysialic acid (PSA) linked to NCAM), and combinations thereof.

In some embodiments, a biomarker combination comprises one or more extracellular vesicle-associated surface biomarkers and/or one or more surface biomarkers each independently selected from a list consisting of: a ALDH18A1 polypeptide, a AP1M2 polypeptide, a APOO polypeptide, a ARFGEF3 polypeptide, a B3GNT3 polypeptide, a BMPR1B polypeptide, a CADM4 polypeptide, a CANT1 polypeptide, a CD24 polypeptide, a CDH1 polypeptide, a CDH17 polypeptide, a CDH2 polypeptide, a CDH3 polypeptide, a CEACAM5 polypeptide, a CEACAM6 polypeptide, a CLDN3 polypeptide, a CLDN4 polypeptide, a CLGN polypeptide, a CLN5 polypeptide, a CYP2S1 polypeptide, a DSG2 polypeptide, a ELAPOR1 polypeptide, a ENPP5 polypeptide, a EPCAM polypeptide, a EPHB2 polypeptide, a FAM241B polypeptide, a FERMT1 polypeptide, a FOLR1 polypeptide, a FZD2 polypeptide, a GALNT14 polypeptide, a GALNT6 polypeptide, a GJB1 polypeptide, a GNG4 polypeptide, a GNPNAT1 polypeptide, a GOLM1 polypeptide, a GPR160 polypeptide, a GPRIN1 polypeptide, a GRHL2 polypeptide, a HACD3 polypeptide, a HS6ST2 polypeptide, a IGSF3 polypeptide, a ILDR1 polypeptide, a KDELR3 polypeptide, a KPNA2 polypeptide, a KRTCAP3 polypeptide, a LAMB3 polypeptide, a LAMC2 polypeptide, a LAPTM4B polypeptide, a LARGE2 polypeptide, a LMNB1 polypeptide, a LRRN1 polypeptide, a LSR polypeptide, a MAL2 polypeptide, a MARCKSL1 polypeptide, a MARVELD2 polypeptide, a MET polypeptide, a NPTXR polypeptide, a NUP210 polypeptide, a PARD6B polypeptide, a PMEPA1 polypeptide, a PODXL2 polypeptide, a PRAF2 polypeptide, a PRSS8 polypeptide, a RAB25 polypeptide, a RAC3 polypeptide, a RACGAP1 polypeptide, a RAP2B polypeptide, a RCC2 polypeptide, a RNF128 polypeptide, a RNF43 polypeptide, a RPN1 polypeptide, a RPN2 polypeptide, a SERINC2 polypeptide, a SHISA2 polypeptide, a SLC35A2 polypeptide, a SLC39A6 polypeptide, a SLC44A4 polypeptide, a SLC4A4 polypeptide, a SMIM22 polypeptide, a SMPDL3B polypeptide, a SYAP1 polypeptide, a SYT13 polypeptide, a TMEM132A polypeptide, a TMEM238 polypeptide, a TMEM9 polypeptide, a TSPAN13 polypeptide, a ULBP2 polypeptide, a UNC13B polypeptide, a VTCN1 polypeptide, a ABCA13 polypeptide, a ADAM23 polypeptide, a CYP4F11 polypeptide, a HAS3 polypeptide, a TMPRSS4 polypeptide, a UGT1A6 polypeptide, a PIGT polypeptide, a TOMM34 polypeptide, a ACSL4 polypeptide, a GPC3 polypeptide, a ROBO1 polypeptide, a SLC22A9 polypeptide, a SLC38A3 polypeptide, a TFR2 polypeptide, a TM4SF4 polypeptide, a TMPRSS6 polypeptide, a ANXA13 polypeptide, a CHST4 polypeptide, a GAL3ST1 polypeptide, a SNAP25 polypeptide, a TMEM156 polypeptide, a CLDN18 polypeptide, a EPPK1 polypeptide, a MUC13 polypeptide, a OCLN polypeptide, a CFTR polypeptide, a GCNT3 polypeptide, a ITGB6 polypeptide, a LAD1 polypeptide, a MSLN polypeptide, a TESC polypeptide, a TESC polypeptide, a LYPD6B polypeptide, a S100P polypeptide, a TMEM51 polypeptide, a TNFRSF21 polypeptide, a UPK1B polypeptide, a UPK2 polypeptide, a ABCC4 polypeptide, a FOLH1 polypeptide, a RAB3B polypeptide, a STEAP2 polypeptide, a TMPRSS2 polypeptide, a TSPAN1 polypeptide, a AP1S3 polypeptide, a DSC2 polypeptide, a DSG3 polypeptide, a TMPRSS11D polypeptide, a KCNS1 polypeptide, a LY6K polypeptide, a MUC4 polypeptide, a SYNGR3 polypeptide, a CELSR1 polypeptide, a COX6C polypeptide, a ESR1 polypeptide, a MUC1 polypeptide, a ABCC11 polypeptide, a ERBB2 polypeptide, a SLC9A3R1 polypeptide, a PROM1 polypeptide, a PTK7 polypeptide, a CDK4 polypeptide, a DLK1 polypeptide, a LMNB2 polypeptide, a PCDH7 polypeptide, a TMEM108 polypeptide, a TYMS polypeptide, a SDC1 polypeptide, a SLC34A2 polypeptide, a BCAM polypeptide, a MUC16 polypeptide, a ADAM17 polypeptide, a ADAM28 polypeptide, a ADAM8 polypeptide, a ALCAM polypeptide, a AMHR2 polypeptide, a AXL polypeptide, a BAG3 polypeptide, a BSG polypeptide, CanAg (a glycoform of MUC1), a CCL2 polypeptide, a CCL8 polypeptide, a CCN1 polypeptide, a CCN2 polypeptide, a CCR5 polypeptide, a CD274 polypeptide, a CD38 polypeptide, a CD44 polypeptide, a CD47 polypeptide, a CDH11 polypeptide, a CETN1 polypeptide, a CLDN1 polypeptide, a CLEC2D polypeptide, a CLU polypeptide, a CSPG4 polypeptide, a DKK1 polypeptide, a DLL4 polypeptide, a EGFR polypeptide, a ENPP3 polypeptide, a EPHA10 polypeptide, a ERBB3 polypeptide, a FAP polypeptide, a FGF1 polypeptide, a FGFR4 polypeptide, a FLNA polypeptide, a FLNB polypeptide, a FLT4 polypeptide, a FZD7 polypeptide, a GFRA1 polypeptide, a GM3 polypeptide, a GPA33 polypeptide, a GPC1 polypeptide, a GPNMB polypeptide, a GUCY2C polypeptide, a HGF polypeptide, a ICAM1 polypeptide, a IGF1R polypeptide, a IL1A polypeptide, a IL1RAP polypeptide, a IL6 polypeptide, a ITGA6 polypeptide, a ITGAV polypeptide, a KDR polypeptide, a KLK3 polypeptide, a KLKB1 polypeptide, a KRT8 polypeptide, a LAG3 polypeptide, a LGR5 polypeptide, a LPR6 polypeptide, a LY6E polypeptide, a MCAM polypeptide, a MDM2 polypeptide, a MELTF polypeptide, a MERTK polypeptide, a MST1R polypeptide, a MUC17 polypeptide, a MUC5AC polypeptide, a MUCL1 polypeptide, a NOTCH2 polypeptide, a NOTCH3 polypeptide, a NRP1 polypeptide, a NT5E polypeptide, a PI4K2A polypeptide, a PLAC1 polypeptide, a PLAUR polypeptide, a PLVAP polypeptide, a PPP1R3A polypeptide, a PRLR polypeptide, a PSCA polypeptide, a PVR polypeptide, a RET polypeptide, a S1PR1 polypeptide, a SLC3A2 polypeptide, a SLC7A11 polypeptide, a SLC7A5 polypeptide, a SPINK1 polypeptide, a STAT3 polypeptide, a STEAP1 polypeptide, a TACSTD2 polypeptide, a TF polypeptide, a TFRC polypeptide, a TGFBR2 polypeptide, a TIGIT polypeptide, a TNC polypeptide, a TNFRSF10A polypeptide, a TNFRSF10B polypeptide, a TNFRSF12A polypeptide, a TNFRSF4 polypeptide, a TNFSF11 polypeptide, a TNFSF18 polypeptide, a TPBG polypeptide, a VANGL2 polypeptide, a VEGFA polypeptide, a VEGFC polypeptide, CanAg, Sialyltetraosyl carbohydrate, Phosphatidylserine, Sialyl Lewis A/CA19-9, Lewis Y/B antigen, Lewis B antigen, Tn antigen, SialylTn (sTn) antigen, Thomsen-Friedenreich (T, TF) antigen, Lewis Y antigen (also known as CD174), Sialyl Lewis X (sLex) (also known as Sialyl SSEA-1 (SLX)), SSEA-1/Lewis X antigen, NeuGcGM3, beta1,6-branching, bisecting GlcNAc in a beta1,4-linkage, core fucosylation antigen, Sialyl-T antigens (sT), Sialyl Lewis c antigen, Globo H antigen, SSEA-3 (Gb5), SSEA-4 (sialy-Gb5), Gb3 (Globotriaose, CD77), Disialosyl-galactosylgloboside (DSGG), GalNAcDSLc4, Fucosyl GM1, GD1alpha, GD1a ganglioside, GD2 ganglioside, GD3 ganglioside, GM2 ganglioside, Lc3 ceramide, nLc4 ceramide, 9-O-Ac-GD2 ganglioside, 9-O-Ac-GD3 (CDw60) ganglioside, 9-O-Ac-GT3 ganglioside, Forssman antigen, Disialyl Lewis a antigen, Sialylparagloboside (SPG), Polysialic acid (PSA) linked to NCAM, and combinations thereof.

In some embodiments, a biomarker combination comprises one or more extracellular vesicle-associated surface biomarkers and/or one or more surface biomarkers, which are determined to be shared by certain cancers. In some embodiments, such surface biomarkers are each independently selected from a list consisting of: CLDN3 polypeptide, EPCAM polypeptide, MARCKSL1 polypeptide, VTCN1 polypeptide, PODXL2 polypeptide, LAPTM4B polypeptide, CD24 polypeptide, ENPP5 polypeptide, GRHL2 polypeptide, BMPR1B polypeptide, CLGN polypeptide, CDH2 polypeptide, CDH1 polypeptide, GNG4 polypeptide, APOO polypeptide, FAM241B polypeptide, FOLR1 polypeptide, LAMC2 polypeptide, CDH3 polypeptide, CLDN4 polypeptide, TACSTD2 polypeptide, PMEPA1 polypeptide, RAB25 polypeptide, TNFRSF21 polypeptide, GJB1 polypeptide, RAP2B polypeptide, FERMT1 polypeptide, RPN2 polypeptide, ITGB6 polypeptide, RPN1 polypeptide, and combinations thereof.

In some embodiments, a target biomarker in a biomarker combination of cancer is or comprises an intravesicular biomarker, which is determined to be specific for certain cancers. In some embodiments, an intravesicular biomarker described herein may comprise at least one post-translational modification.

In some embodiments, a biomarker combination comprises one or more intravesicular RNA (e.g., but not limited to mRNA and noncoding RNA such as, e.g., orphan noncoding RNA, long noncoding RNA, piwi-interacting RNA, microRNA, circular RNA, etc.) biomarkers that have been determined to be associated with certain cancers.

In some embodiments, at least one extracellular vesicle-associated surface biomarker and at least one surface biomarker(s) of a biomarker combination for cancer are distinct. For example, in some embodiments, a biomarker combination for cancer comprises at least one extracellular vesicle-associated surface biomarker and at least one surface biomarker.

In some embodiments, a biomarker combination for cancer comprises at least one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, or more) surface biomarker (e.g., ones described herein) present on the surface of nanoparticles having a size range of interest that includes extracellular vesicles, e.g., in some embodiments, nanoparticles having a size within the range of about 30 nm to about 1000 nm) and at least one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, or more) intravesicular biomarkers (e.g., ones described herein). In some such embodiments, the surface biomarker(s) and the intravesicular biomarker(s) can be encoded by the same gene, while the former is present on the surface of the nanoparticles and the latter is contained within the extracellular vesicle (e.g. cargo). In some such embodiments, the surface biomarker(s) and the intravesicular biomarker(s) can be encoded by different genes.

In some embodiments, a biomarker combination for cancer comprises at least one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, or more) extracellular vesicle-associated surface biomarkers (e.g., ones described herein) and at least one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, or more) intravesicular biomarkers (e.g., ones described herein). In some such embodiments, the extracellular vesicle-associated surface biomarker(s) and the intravesicular biomarker(s) can be encoded by the same gene, while the former is expressed in the membrane of the extracellular vesicle and the latter is contained within the extracellular vesicle (e.g., cargo). In some such embodiments, the extracellular vesicle-associated surface biomarker(s) and the intravesicular biomarker(s) can be encoded by different genes.

In some embodiments, a biomarker combination for cancer comprises at least one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, or more) surface biomarkers (e.g., ones described herein) and at least one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, or more) intravesicular RNA (e.g., mRNA) biomarkers (e.g., ones described herein). In some such embodiments, the surface biomarker(s) and the intravesicular RNA (e.g., but not limited to mRNA and noncoding RNA such as, e.g., orphan noncoding RNA, long noncoding RNA, piwi-interacting RNA, microRNA, circular RNA, etc.) biomarker(s) can be encoded by the same gene. In some such embodiments, the surface biomarker(s) and the intravesicular RNA (e.g., but not limited to mRNA and noncoding RNA such as, e.g., orphan noncoding RNA, long noncoding RNA, piwi-interacting RNA, microRNA, circular RNA, etc.) biomarker(s) can be encoded by different genes.

In some embodiments, a biomarker combination for cancer comprises at least one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, or more) extracellular vesicle-associated surface biomarkers (e.g., ones described herein) and at least one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, or more) intravesicular RNA (e.g., but not limited to mRNA and noncoding RNA such as, e.g., orphan noncoding RNA, long noncoding RNA, piwi-interacting RNA, microRNA, circular RNA, etc.) biomarkers (e.g., ones described herein). In some such embodiments, the extracellular vesicle-associated surface biomarker(s) and the intravesicular RNA (e.g., but not limited to mRNA and noncoding RNA such as, e.g., orphan noncoding RNA, long noncoding RNA, piwi-interacting RNA, microRNA, circular RNA, etc.) biomarker(s) can be encoded by the same gene. In some such embodiments, the extracellular vesicle-associated surface biomarker(s) and the intravesicular RNA (e.g., but not limited to mRNA and noncoding RNA such as, e.g., orphan noncoding RNA, long noncoding RNA, piwi-interacting RNA, microRNA, circular RNA, etc.) biomarker(s) can be encoded by different genes.

In some embodiments, any one of provided biomarkers can be detected and/or measured by protein and/or RNA (e.g., mRNA) expression levels in wild-type form.

In some embodiments, any one of provided biomarkers can be detected and/or measured by protein and/or RNA (e.g., mRNA) expression levels in mutant form. Thus, in some embodiments, mutant-specific detection of provided biomarkers (e.g., proteins and/or RNA such as, e.g., mRNAs) can be included.

As noted herein, in some embodiments, a biomarker is or comprises a particular form of one or more polypeptides or proteins (e.g., a pro-form, a truncated form, a modified form such as a glycosylated, phosphorylated, acetylated, methylated, ubiquitylated, lipidated form, etc). In some embodiments, detection of such form detects a plurality (and, in some embodiments, substantially all) polypeptides present in that form (e.g., containing a particular modification such as, for example, a particular glycosylation, e.g., sialyl-Tn (sTn) glycosylation, e.g., a truncated O-glycan containing a sialic acid α-2,6 linked to GalNAc α-O-Ser/Thr.

Accordingly, in some embodiments, a surface biomarker can be or comprise a glycosylation moiety (e.g., an sTn antigen moiety, a Tn antigen moiety, or a T antigen moiety). Thompsen-nouvelle (Tn) antigen is an O-linked glycan that is thought to be associated with a broad array of tumors. Tn is a single alpha-linked GalNAc added to Ser or Thr as the first step of a major O-linked glycosylation pathway. A skilled artisan will understand that in certain embodiments, T antigen typically refers to an O-linked glycan with the structure Galβ1-3GalNAc—.

In some embodiments, a surface protein biomarker can be or comprise a tumor-associated post-translational modification. In some embodiments, such a post-translational modification can be or comprise tumor-specific glycosylation patterns such as mucins with glycans aberrantly truncated at the initial GalNAc (e.g., Tn), or combinations thereof. In some embodiments, a surface protein biomarker can be or comprise a tumor-specific proteoform of mucin resulting from altered splicing and/or translation (isoforms) or proteolysis (cancer specific protease activity resulting in aberrant cleavage products).

In some embodiments, a biomarker combination is useful for detecting a subtype of cancer, for example, based on cell types. In some embodiments, a biomarker combination may be useful for detecting carcinoma. In some embodiments, a biomarker combination may be useful for detecting sarcoma.

In some embodiments, a biomarker combination is useful in detecting a subtype of cancer, for example, based on hormone status.

III. Exemplary Cancer-Specific Biomarkers and/or Biomarker Combinations that can be Included in Pan-Cancer Detection

In some embodiments, at least one or more biomarker combinations that are able to detect a particular cancer can be included in pan-cancer detection. In some embodiments, at least one or more biomarker combinations that are able to detect multiple cancer types can be included in pan-cancer detection. In some embodiments, cancer-specific biomarkers and/or biomarker combinations described herein can be used in combination with pan-cancer biomarker combinations described in the section “Provided Biomarkers and/or Biomarker Combinations for Pan-Cancer Detection” above.

In some embodiments, pan-cancer detection may encompass a plurality of (e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or more) biomarker combinations (e.g., as described herein) that are useful for detection of at least 5 different cancers. For example, in some embodiments, pan-cancer detection may encompass a plurality of (e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or more) biomarker combinations (e.g., as described herein) that are useful for detection of breast cancer, colorectal cancer, lung cancer, ovarian cancer, and prostate cancer.

In some embodiments, at least one biomarker combination within a selected plurality of biomarker combinations is or comprises two or more surface biomarkers, which combination is determined to be associated with a particular cancer, wherein one of the surface biomarkers is or comprises a MUC1 polypeptide, a CEACAM5 polypeptide, a Lewis Y antigen (also known as CD174), SialyTn(sTn), antigen, a Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, or combinations thereof, and at least another surface biomarker is or comprise (i) one or more polypeptides encoded by a human gene as described herein, e.g., in some embodiments as described in this section “Exemplary cancer-specific biomarkers and/or biomarker combinations that can be include in pan-cancer detection”, and/or (ii) one or more carbohydrate-dependent and/or lipid-dependent biomarkers as described herein, e.g., in some embodiments as described in this section “Exemplary cancer-specific biomarkers and/or biomarker combinations that can be include in pan-cancer detection.” In some such embodiments, a plurality of (e.g., at least two, at least three, at least four, at least five, or more) biomarker combinations, each for a particular cancer, can be included for pan-cancer detection.

In some embodiments, at least five biomarker combinations, each for a particular cancer, can be included for pan-cancer detection. In some embodiments, each biomarker combination may comprise at least two surface biomarkers, each of which can be independently selected from: (i) polypeptides encoded by human genes as follows: ABCC11, ABCC4, ACVR2B, ADGRF1, ALCAM, ALPL, AP1M2, APOO, AQP5, ARFGEF3, B3GNT3, B3GNT5, BCAM, BSPRY, BST2, CANT1, CD133, CD24, CD274 (PD-L1), CD38, CD55, CD74, CDCP1, CDH1, CDH17, CDH3, CDH6, CEACAM5, CEACAM6, CELSR1, CFB, CFTR, CHODL, CIP2A, CLDN16, CLDN3, CLDN4, CLDN6, CLGN, COX6C, CXCR4, CYP2S1, DDR1, DLL4, DSC2, DSG2, EDAR, EFNB1, EGFR, ENPP5, EPCAM, EPHB2, EPHB3, ERBB2, ERBB3, ESR1, FAM241B, FAP, FGFR4, FOLH1, FOLR1, FUT8, FXYD3, GALNT14, GALNT3, GALNT6, GALNT7, GFRA1, GJB1, GJB2, GOLM1, GPCR5A, GRB7, GRHL2, HACD3, HAS3, HTR3A, IG1FR, IHH, ILDR1, ITGAV, ITGB6, KCNQ1, KEL, KIF1A, KPNA2, LAMB3, LAMC2, LAPTM4B, LARGE2, LEMD1, LMNB1, LRP2, LRRTM1, LSR, LY6E, MAL2, MAP7, MARCKSL1, MET, MIEN1, MSLN, MST1R, MUC1, MUC13, MUC16, MUC2, MUC4, MUC5AC, NECTIN2, NOTCH3, NOX1, NRCAM, NUP155, NUP210, OCIAD2, OCLN, PARD6B, PIGT, PLEKHF2, PLXNB1, PMEPA1, PODXL2, PPP3CA, PRLR, PROM1, PRSS21, PSCA, PTGS1, PTK7, PTPRK, RAB25, RAB27B, RAB3B, RAB3D, RAC3, RDH11, RNF43, ROS1, SDC1, SEPHS1, SFXN2, SHROOM3, SLC2A1, SLC34A2, SLC35B2, SLC39A6, SLC4A4, SLC7A11, SLC9A3R1, SMIM22, SMPDL3B, SORD, SPINT2, ST14, STEAP1, STEAP2, SYT7, TACSTD2, TJP3, TMEM132A, TMPRSS2, TMPRSS4, TNFRSF10B, TNFRSF12A, TRPM4, TSPAN1, TSPAN8, UCHL1, UNC13B, XBP1, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: CA19-9, Lewis X antigen, Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, and combinations thereof.

In some embodiments, pan-cancer detection can comprise (i) at least one biomarker combination described herein for detection of breast cancer; (ii) at least one biomarker combination described herein for detection of colorectal cancer; (iii) at least one biomarker combination described herein for detection of lung cancer; (iv) at least one biomarker combination described herein for detection of ovarian cancer; and (v) at least one biomarker combination described herein for detection of prostate cancer.

In some embodiments, pan-cancer detection may be tailored to individual subjects or populations of subjects that are of a particular sex and/or gender (e.g., female subjects, male subjects, etc.). In some embodiments, pan-cancer detection for female subjects can comprise (i) at least one biomarker combination described herein for detection of breast cancer; (ii) at least one biomarker combination described herein for detection of colorectal cancer; (iii) at least one biomarker combination described herein for detection of lung cancer; and (iv) at least one biomarker combination described herein for detection of ovarian cancer. In some embodiments, pan-cancer detection for male subjects can comprise (i) at least one biomarker combination described herein for detection of colorectal cancer; (ii) at least one biomarker combination described herein for detection of lung cancer; and (iii) at least one biomarker combination described herein for detection of prostate cancer.

In some embodiments, a biomarker combination comprises at least two biomarkers, selected from the group consisting of: a CLDN3 and a MARCKSL1 polypeptide; or a EPCAM and a MARCKSL1 polypeptide; or a AP1M2 and a MARCKSL1 polypeptide; or a AP1M2 and a SMPDL3B polypeptide; or a BMPR1B and a EPCAM polypeptide; or a ILDR1 and a MARCKSL1 polypeptide; or a EPCAM and a PODXL2 polypeptide; or a AP1M2 and a BMPR1B polypeptide; or a BMPR1B and a MARCKSL1 polypeptide; or a ILDR1 and a SMPDL3B polypeptide; or a CLDN3 and a SMPDL3B polypeptide; or a CLDN4 and a SMPDL3B polypeptide; or a BMPR1B and a CLDN3 polypeptide; or a BMPR1B and a ILDR1 polypeptide; or a BMPR1B and a CLDN4 polypeptide; or a BMPR1B and a SMPDL3B polypeptide; or a BMPR1B and a SERINC2 polypeptide; or a SERINC2 and a SMPDL3B polypeptide; or a RAB25 and a SMPDL3B polypeptide; or a BMPR1B and a RAB25 polypeptide; or a CLDN4 and a MARCKSL1 polypeptide; or a BMPR1B and a PODXL2 polypeptide; or a MARCKSL1 and a RAB25 polypeptide; or a AP1M2 and a PODXL2 polypeptide; or a EPCAM and a SLC39A6 polypeptide; or a APOO and a CLDN3 polypeptide; or a MARCKSL1 and a SMIM22 polypeptide; or a LMNB1 and a SMIM22 polypeptide; or a SMIM22 and a VTCN1 polypeptide; or a LMNB1 and a VTCN1 polypeptide; or a CDH1 and a SMPDL3B polypeptide; or a ILDR1 and a SLC39A6 polypeptide; or a PODXL2 and a SMPDL3B polypeptide; or a CDH3 and a EPCAM polypeptide; or a MARCKSL1 and a SLC39A6 polypeptide; or a EPCAM and a SMPDL3B polypeptide; or a SLC39A6 and a SMIM22 polypeptide; or a MARCKSL1 and a PRSS8 polypeptide; or a ALDH18A1 and a CLDN3 polypeptide; or a BMPR1B and a SLC39A6 polypeptide; or a APOO and a BMPR1B polypeptide; or a BMPR1B and a CDH1 polypeptide; or a CDH3 and a SMPDL3B polypeptide; or a CLDN3 and a RPN1 polypeptide; or a BMPR1B and a VTCN1 polypeptide; or a BMPR1B and a RPN1 polypeptide; or a BMPR1B and a KPNA2 polypeptide; or a CLGN and a LMNB1 polypeptide; or a EPCAM and a RPN1 polypeptide; or a BMPR1B and a LMNB1 polypeptide; or a BMPR1B and a RACGAP1 polypeptide; or a RACGAP1 and a VTCN1 polypeptide; or a GOLM1 and a RAB25 polypeptide; or a CLDN3 and a RAB25 polypeptide; or a CLDN3 and a GOLM1 polypeptide; or a CDH1 and a CLDN3 polypeptide; or a CLGN and a VTCN1 polypeptide; or a CEACAM5 and a PMEPA1 polypeptide; or a CDH3 and a PMEPA1 polypeptide; or a EPCAM and a LMNB1 polypeptide; or a EPCAM and a VTCN1 polypeptide; or a LMNB1 and a RPN1 polypeptide; or a RPN1 and a VTCN1 polypeptide; or a BMPR1B and a CLGN polypeptide; or a CLGN and a EPCAM polypeptide; or a CLDN3 and a LMNB1 polypeptide; or a BMPR1B and a GOLM1 polypeptide; or a EPCAM and a KPNA2 polypeptide; or a KPNA2 and a LMNB1 polypeptide; or a CEACAM6 and a EPHB2 polypeptide; or a CDH1 and a GOLM1 polypeptide; or a DSG2 and a RAB25 polypeptide; or a KPNA2 and a VTCN1 polypeptide; or a CLDN4 and a GOLM1 polypeptide; or a CLDN3 and a KPNA2 polypeptide; or a CLDN3 and a VTCN1 polypeptide; or a EPCAM and a GOLM1 polypeptide; or a CLGN and a RAP2B polypeptide; or a RAP2B and a VTCN1 polypeptide; or a CEACAM5 and a MET polypeptide; or a CDH3 and a MET polypeptide; or a CEACAM6 and a FERMT1 polypeptide; or a EPCAM and a RAB25 polypeptide; or a ENPP5 and a EPCAM polypeptide; or a CLDN3 and a PRSS8 polypeptide; or a ENPP5 and a RAB25 polypeptide; or a CDH3 and a CEACAM5 polypeptide; or a CDH3 and a EPHB2 polypeptide; or a CDH3 and a CEACAM6 polypeptide; or a CDH2 and a LAMB3 polypeptide; or a EPHB2 and a FOLR1 polypeptide; or a CLDN3 and a FOLR1 polypeptide; or a FOLR1 and a LMNB1 polypeptide; or a CDH2 and a EPCAM polypeptide; or a FOLR1 and a VTCN1 polypeptide; or a CDH3 and a FERMT1 polypeptide; or a FOLR1 and a KPNA2 polypeptide; or a CDH2 and a CDH3 polypeptide; or a CDH3 and a LAMB3 polypeptide; or a CLDN4 and a ENPP5 polypeptide; or a CDH2 and a ENPP5 polypeptide; or a CEACAM5 and a FERMT1 polypeptide; or a CEACAM5 and a LAMB3 polypeptide; or a CEACAM6 and a GJB1 polypeptide; or a CDH2 and a CLDN4 polypeptide; or a CDH1 and a CDH2 polypeptide; or a CDH1 and a CDH3 polypeptide; or a CD24 and a MET polypeptide; or a CDH2 and a MET polypeptide; or a CLDN4 and a MET polypeptide; or a CD24 and a CDH2 polypeptide; or a CDH2 and a RAP2B polypeptide; or a CD24 and a RAP2B polypeptide; or a CDH3 and a KPNA2 polypeptide; or a CADM4 and a CDH2 polypeptide; or a CADM4 and a FERMT1 polypeptide; or a GJB1 and a RPN1 polypeptide; or a GJB1 and a KPNA2 polypeptide; or combinations thereof.

In some embodiments, a biomarker combination comprises at least three biomarkers, selected from the group consisting of: a BMPR1B polypeptide, a CLDN3 polypeptide, and a MARCKSL1 polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a HS6ST2 polypeptide; or a CDH2 polypeptide, a FERMT1 polypeptide, and a LRRN1 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a LSR polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a CLN5 polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a SMPDL3B polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a SMPDL3B polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a EPCAM polypeptide; or a BMPR1B polypeptide, a EPCAM polypeptide, and a MARCKSL1 polypeptide; or a CEACAM6 polypeptide, a HS6ST2 polypeptide, and a PODXL2 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a ILDR1 polypeptide; or a LMNB1 polypeptide, a SMIM22 polypeptide, and a VTCN1 polypeptide; or a LAPTM4B polypeptide, a PODXL2 polypeptide, and a SMPDL3B polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a MARCKSL1 polypeptide; or a CLN5 polypeptide, a GALNT14 polypeptide, and a RNF128 polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a LAMC2 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a PRAF2 polypeptide; or a MARCKSL1 polypeptide, a SLC39A6 polypeptide, and a SMIM22 polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a SMPDL3B polypeptide; or a B3GNT3 polypeptide, a CDH3 polypeptide, and a GNG4 polypeptide; or a CEACAM5 polypeptide, a HS6ST2 polypeptide, and a SHISA2 polypeptide; or a BMPR1B polypeptide, a EPCAM polypeptide, and a SLC39A6 polypeptide; or a CLGN polypeptide, a PODXL2 polypeptide, and a SLC39A6 polypeptide; or a B3GNT3 polypeptide, a LAMC2 polypeptide, and a MET polypeptide; or a BMPR1B polypeptide, a EPCAM polypeptide, and a PODXL2 polypeptide; or a AP1M2 polypeptide, a BMPR1B polypeptide, and a MARCKSL1 polypeptide; or a HS6ST2 polypeptide, a LAMB3 polypeptide, and a LAMC2 polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a CYP2S1 polypeptide; or a BMPR1B polypeptide, a DSG2 polypeptide, and a ILDR1 polypeptide; or a EPCAM polypeptide, a HS6ST2 polypeptide, and a ULBP2 polypeptide; or a EPCAM polypeptide, a HS6ST2 polypeptide, and a LRRN1 polypeptide; or a AP1M2 polypeptide, a BMPR1B polypeptide, and a SMPDL3B polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a CYP2S1 polypeptide; or a GJB1 polypeptide, a KDELR3 polypeptide, and a SHISA2 polypeptide; or a CDH1 polypeptide, a HS6ST2 polypeptide, and a ULBP2 polypeptide; or a PODXL2 polypeptide, a SLC39A6 polypeptide, and a SMIM22 polypeptide; or a BMPR1B polypeptide, a CLDN3 polypeptide, and a SMPDL3B polypeptide; or a CDH3 polypeptide, a GJB1 polypeptide, and a LAPTM4B polypeptide; or a ILDR1 polypeptide, a LAMC2 polypeptide, and a RAP2B polypeptide; or a BMPR1B polypeptide, a ELAPOR1 polypeptide, and a GPRIN1 polypeptide; or a CANT1 polypeptide, a CDH3 polypeptide, and a GJB1 polypeptide; or a CDH2 polypeptide, a IGSF3 polypeptide, and a SHISA2 polypeptide; or a B3GNT3 polypeptide, a LAMC2 polypeptide, and a SHISA2 polypeptide; or a BMPR1B polypeptide, a ILDR1 polypeptide, and a SMPDL3B polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a PMEPA1 polypeptide; or a PARD6B polypeptide, a SLC39A6 polypeptide, and a SYT13 polypeptide; or a CDH2 polypeptide, a MAL2 polypeptide, and a SHISA2 polypeptide; or a CYP2S1 polypeptide, a FERMT1 polypeptide, and a ULBP2 polypeptide; or a AP1M2 polypeptide, a CDH2 polypeptide, and a MARCKSL1 polypeptide; or a CDH1 polypeptide, a CDH2 polypeptide, and a HS6ST2 polypeptide; or a CDH2 polypeptide, a LSR polypeptide, and a SHISA2 polypeptide; or a CDH1 polypeptide, a CDH2 polypeptide, and a SHISA2 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a SYT13 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a RAP2B polypeptide; or a BMPR1B polypeptide, a ILDR1 polypeptide, and a MARCKSL1 polypeptide; or a CLDN3 polypeptide, a ENPP5 polypeptide, and a SLC39A6 polypeptide; or a BMPR1B polypeptide, a LMNB1 polypeptide, and a VTCN1 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a ILDR1 polypeptide; or a BMPR1B polypeptide, a CD24 polypeptide, and a GPRIN1 polypeptide; or a BMPR1B polypeptide, a CLGN polypeptide, and a MARCKSL1 polypeptide; or a FZD2 polypeptide, a PODXL2 polypeptide, and a SMIM22 polypeptide; or a BMPR1B polypeptide, a MARCKSL1 polypeptide, and a SMIM22 polypeptide; or a CDH2 polypeptide, a CDH3 polypeptide, and a LAMB3 polypeptide; or a CLGN polypeptide, a SLC39A6 polypeptide, and a SMPDL3B polypeptide; or a FZD2 polypeptide, a SMPDL3B polypeptide, and a VTCN1 polypeptide; or a EPCAM polypeptide, a HS6ST2 polypeptide, and a SHISA2 polypeptide; or a HS6ST2 polypeptide, a IGSF3 polypeptide, and a LMNB1 polypeptide; or a ILDR1 polypeptide, a MARCKSL1 polypeptide, and a PODXL2 polypeptide; or a B3GNT3 polypeptide, a HS6ST2 polypeptide, and a SHISA2 polypeptide; or a BMPR1B polypeptide, a SERINC2 polypeptide, and a SMPDL3B polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a SLC39A6 polypeptide; or a GJB1 polypeptide, a IGSF3 polypeptide, and a SLC39A6 polypeptide; or a AP1M2 polypeptide, a CLGN polypeptide, and a MARCKSL1 polypeptide; or a BMPR1B polypeptide, a ILDR1 polypeptide, and a SLC39A6 polypeptide; or a CDH2 polypeptide, a CDH3 polypeptide, and a SHISA2 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a GNG4 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a GJB1 polypeptide; or a BMPR1B polypeptide, a CLDN3 polypeptide, and a TMEM238 polypeptide; or a CLDN3 polypeptide, a CLN5 polypeptide, and a HS6ST2 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a KPNA2 polypeptide; or a BMPR1B polypeptide, a CDH1 polypeptide, and a MARCKSL1 polypeptide; or a BMPR1B polypeptide, a CLDN4 polypeptide, and a SMPDL3B polypeptide; or a CDH2 polypeptide, a EPCAM polypeptide, and a RCC2 polypeptide; or a GALNT14 polypeptide, a LAPTM4B polypeptide, and a PODXL2 polypeptide; or a ILDR1 polypeptide, a PODXL2 polypeptide, and a ULBP2 polypeptide; or a BMPR1B polypeptide, a RAB25 polypeptide, and a SMPDL3B polypeptide; or a CDH3 polypeptide, a GJB1 polypeptide, and a SMPDL3B polypeptide; or a FOLR1 polypeptide, a MARCKSL1 polypeptide, and a SMPDL3B polypeptide; or a GALNT6 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a LAMB3 polypeptide, and a ULBP2 polypeptide; or a LAMB3 polypeptide, a LAMC2 polypeptide, and a RAP2B polypeptide; or a PMEPA1 polypeptide, a SLC39A6 polypeptide, and a SMIM22 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a RAC3 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a TMEM132A polypeptide; or a CYP2S1 polypeptide, a LAMC2 polypeptide, and a ULBP2 polypeptide; or a GJB1 polypeptide, a IGSF3 polypeptide, and a RAP2B polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a LSR polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a CLN5 polypeptide; or a CDH3 polypeptide, a GJB1 polypeptide, and a SLC35A2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a SMPDL3B polypeptide; or combinations thereof.

In some embodiments, a biomarker combination detects at least five different cancers at an overall sensitivity of 30%, and such biomarker combinations comprises at least three biomarkers, selected from the group consisting of: a CDH3 polypeptide, an EPCAM polypeptide, and a SMPDL3B polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a ILDR1 polypeptide; or combinations thereof.

In some embodiments, a biomarker combination detects at least five different cancers at an overall sensitivity of 20%, and such biomarker combinations comprises at least three biomarkers, selected from the group consisting of: a CDH3 polypeptide, a EPCAM polypeptide, and a HS6ST2 polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a SMPDL3B polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a ILDR1 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a LSR polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a LAMC2 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a EPCAM polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a CYP2S1 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a RCC2 polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a SMPDL3B polypeptide; or a FERMT1 polypeptide, a HS6ST2 polypeptide, and a KPNA2 polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a LAMC2 polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a MARCKSL1 polypeptide; or a CYP2S1 polypeptide, a ILDR1 polypeptide, and a LAMC2 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a LAMB3 polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a HS6ST2 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a SYT13 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a LMNB1 polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a RCC2 polypeptide; or a CDH2 polypeptide, a EPCAM polypeptide, and a RCC2 polypeptide; or a B3GNT3 polypeptide, a LAMC2 polypeptide, and a MET polypeptide; or a AP1M2 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a CDH3 polypeptide, a SMPDL3B polypeptide, and a SYT13 polypeptide; or a CDH2 polypeptide, a LAMC2 polypeptide, and a SMPDL3B polypeptide; or a EPCAM polypeptide, a HS6ST2 polypeptide, and a ULBP2 polypeptide; or a CYP2S1 polypeptide, a SYT13 polypeptide, and a VTCN1 polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a SLC39A6 polypeptide; or a CYP2S1 polypeptide, a GNG4 polypeptide, and a LAMC2 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a KPNA2 polypeptide; or a CDH1 polypeptide, a HS6ST2 polypeptide, and a ULBP2 polypeptide; or a CDH2 polypeptide, a LAPTM4B polypeptide, and a SMPDL3B polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a PMEPA1 polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a HS6ST2 polypeptide; or a CEACAM5 polypeptide, a HS6ST2 polypeptide, and a SHISA2 polypeptide; or a CYP2S1 polypeptide, a FERMT1 polypeptide, and a ULBP2 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a LSR polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a MARVELD2 polypeptide; or a HS6ST2 polypeptide, a LAMB3 polypeptide, and a LMNB1 polypeptide; or a AP1M2 polypeptide, a CDH2 polypeptide, and a MARCKSL1 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a MARCKSL1 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a RAP2B polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a RACGAP1 polypeptide; or a CYP2S1 polypeptide, a ILDR1 polypeptide, and a PMEPA1 polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a LAPTM4B polypeptide; or a CDH2 polypeptide, a LAPTM4B polypeptide, and a PODXL2 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a SHISA2 polypeptide; or a CDH3 polypeptide, a FZD2 polypeptide, and a SYT13 polypeptide; or a EPCAM polypeptide, a HS6ST2 polypeptide, and a LRRN1 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a LAPTM4B polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a MAL2 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a RCC2 polypeptide; or a EPHB2 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a AP1M2 polypeptide, a HS6ST2 polypeptide, and a ULBP2 polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a EPHB2 polypeptide; or a GALNT14 polypeptide, a LAMB3 polypeptide, and a LAMC2 polypeptide; or a GNG4 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a KRTCAP3 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a MARVELD2 polypeptide; or a CDH1 polypeptide, a CYP2S1 polypeptide, and a HS6ST2 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a SYT13 polypeptide; or a CDH3 polypeptide, a GJB1 polypeptide, and a SMPDL3B polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a ILDR1 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a KRTCAP3 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a PODXL2 polypeptide; or a GALNT6 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a ILDR1 polypeptide, a LAMB3 polypeptide, and a LAMC2 polypeptide; or a CDH2 polypeptide, a CDH3 polypeptide, and a EPCAM polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a CLN5 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a MET polypeptide; or a FERMT1 polypeptide, a HS6ST2 polypeptide, and a RAP2B polypeptide; or a HS6ST2 polypeptide, a KPNA2 polypeptide, and a LAMB3 polypeptide; or a HS6ST2 polypeptide, a LAMB3 polypeptide, and a LAMC2 polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a MARCKSL1 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a ILDR1 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a LAMC2 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a TMEM238 polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a MARCKSL1 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a NUP210 polypeptide; or a FERMT1 polypeptide, a GNG4 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a LAMB3 polypeptide, and a ULBP2 polypeptide; or a ILDR1 polypeptide, a LAMC2 polypeptide, and a RAP2B polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a FZD2 polypeptide; or a CLN5 polypeptide, a PARD6B polypeptide, and a SYT13 polypeptide; or a CYP2S1 polypeptide, a FERMT1 polypeptide, and a HS6ST2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a RAP2B polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a PODXL2 polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a SMPDL3B polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a SMPDL3B polypeptide; or a CLDN3 polypeptide, a CLN5 polypeptide, and a HS6ST2 polypeptide; or a LAPTM4B polypeptide, a PODXL2 polypeptide, and a SMPDL3B polypeptide; or a B3GNT3 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a B3GNT3 polypeptide, a HS6ST2 polypeptide, and a SHISA2 polypeptide; or a AP1M2 polypeptide, a HS6ST2 polypeptide, and a PODXL2 polypeptide; or a HS6ST2 polypeptide, a KPNA2 polypeptide, and a MARVELD2 polypeptide; or a HS6ST2 polypeptide, a LSR polypeptide, and a MAL2 polypeptide; or a CLN5 polypeptide, a ILDR1 polypeptide, and a LAMC2 polypeptide; or a B3GNT3 polypeptide, a LAMB3 polypeptide, and a ULBP2 polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a LRRN1 polypeptide; or a CDH3 polypeptide, a EPHB2 polypeptide, and a LAMC2 polypeptide; or a DSG2 polypeptide, a LAMB3 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a LSR polypeptide, and a PODXL2 polypeptide; or a HS6ST2 polypeptide, a MARCKSL1 polypeptide, and a MARVELD2 polypeptide; or a B3GNT3 polypeptide, a CDH3 polypeptide, and a GNG4 polypeptide; or a B3GNT3 polypeptide, a LAMC2 polypeptide, and a SHISA2 polypeptide; or a B3GNT3 polypeptide, a LAMC2 polypeptide, and a ULBP2 polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a PRAF2 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a HS6ST2 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a SMIM22 polypeptide; or a GALNT14 polypeptide, a LAMC2 polypeptide, and a RAP2B polypeptide; or a GALNT6 polypeptide, a GNG4 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a ILDR1 polypeptide, and a LAMC2 polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a SLC39A6 polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a FERMT1 polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a CYP2S1 polypeptide; or a CLDN3 polypeptide, a HS6ST2 polypeptide, and a PODXL2 polypeptide; or a CYP2S1 polypeptide, a FERMT1 polypeptide, and a MET polypeptide; or a CYP2S1 polypeptide, a ILDR1 polypeptide, and a ULBP2 polypeptide; or a EPCAM polypeptide, a LMNB1 polypeptide, and a VTCN1 polypeptide; or a FERMT1 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a FERMT1 polypeptide, a HS6ST2 polypeptide, and a ULBP2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a RACGAP1 polypeptide; or a LAMB3 polypeptide, a LAMC2 polypeptide, and a RAP2B polypeptide; or a LAPTM4B polypeptide, a LMNB1 polypeptide, and a VTCN1 polypeptide; or a MARCKSL1 polypeptide, a PMEPA1 polypeptide, and a SMIM22 polypeptide; or a MARCKSL1 polypeptide, a PODXL2 polypeptide, and a TMEM238 polypeptide; or a CD24 polypeptide, a CLN5 polypeptide, and a SYT13 polypeptide; or a CDH17 polypeptide, a FOLR1 polypeptide, and a MET polypeptide; or a CDH2 polypeptide, a FAM241B polypeptide, and a ILDR1 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a KRTCAP3 polypeptide; or a CEACAM5 polypeptide, a PMEPA1 polypeptide, and a SHISA2 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a RNF43 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a LMNB1 polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a PRAF2 polypeptide; or a EPCAM polypeptide, a FZD2 polypeptide, and a VTCN1 polypeptide; or a BMPR1B polypeptide, a EPCAM polypeptide, and a MARCKSL1 polypeptide; or a CDH2 polypeptide, a LMNB1 polypeptide, and a LRRN1 polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a EPCAM polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a KPNA2 polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a LSR polypeptide; or a CEACAM5 polypeptide, a CLN5 polypeptide, and a SHISA2 polypeptide; or a CEACAM6 polypeptide, a HS6ST2 polypeptide, and a PODXL2 polypeptide; or a CYP2S1 polypeptide, a LAMC2 polypeptide, and a ULBP2 polypeptide; or a FAM241B polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a GALNT14 polypeptide, a PMEPA1 polypeptide, and a SYT13 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a MAL2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a ULBP2 polypeptide; or a LMNB1 polypeptide, a SMIM22 polypeptide, and a VTCN1 polypeptide; or a BMPR1B polypeptide, a CLDN3 polypeptide, and a MARCKSL1 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a MAL2 polypeptide; or a CLDN3 polypeptide, a HS6ST2 polypeptide, and a MET polypeptide; or a B3GNT3 polypeptide, a FZD2 polypeptide, and a LAMC2 polypeptide; or a B3GNT3 polypeptide, a CDH3 polypeptide, and a FZD2 polypeptide; or a CDH1 polypeptide, a CDH2 polypeptide, and a HS6ST2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a SMPDL3B polypeptide; or a AP1M2 polypeptide, a HS6ST2 polypeptide, and a LMNB1 polypeptide; or a CYP2S1 polypeptide, a FAM241B polypeptide, and a HS6ST2 polypeptide; or a HS6ST2 polypeptide, a KPNA2 polypeptide, and a MAL2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a PMEPA1 polypeptide; or a ILDR1 polypeptide, a LAMC2 polypeptide, and a MET polypeptide; or a LAMB3 polypeptide, a LAMC2 polypeptide, and a MET polypeptide; or a B3GNT3 polypeptide, a LAMC2 polypeptide, and a PMEPA1 polypeptide; or a CDH2 polypeptide, a CDH3 polypeptide, and a LAMB3 polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a FZD2 polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a GNG4 polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a LSR polypeptide; or a AP1M2 polypeptide, a CDH2 polypeptide, and a CDH3 polypeptide; or a CDH2 polypeptide, a IGSF3 polypeptide, and a SHISA2 polypeptide; or a CDH3 polypeptide, a DSG2 polypeptide, and a LAMC2 polypeptide; or a EPCAM polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a PODXL2 polypeptide; or a LAMB3 polypeptide, a LAMC2 polypeptide, and a ULBP2 polypeptide; or a LMNB1 polypeptide, a PARD6B polypeptide, and a VTCN1 polypeptide; or a AP1M2 polypeptide, a CYP2S1 polypeptide, and a HS6ST2 polypeptide; or a APOO polypeptide, a CYP2S1 polypeptide, and a HS6ST2 polypeptide; or a CDH1 polypeptide, a CDH2 polypeptide, and a LAMC2 polypeptide; or a CDH1 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a CDH2 polypeptide, a LSR polypeptide, and a SHISA2 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a CYP2S1 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a LSR polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a MAL2 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a LAMB3 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a DSG2 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a TMEM132A polypeptide; or a LMNB1 polypeptide, a RPN1 polypeptide, and a VTCN1 polypeptide; or a PARD6B polypeptide, a PMEPA1 polypeptide, and a SYT13 polypeptide; or a GPRIN1 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or combinations thereof.

In some embodiments, a biomarker combination detects at least 8 different cancers at an overall sensitivity of 10%, and such biomarker combinations comprises at least three biomarkers, selected from the group consisting of: a CDH3 polypeptide, a EPCAM polypeptide, and a HS6ST2 polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a SMPDL3B polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a ILDR1 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a LSR polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a LAMC2 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a EPCAM polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a CYP2S1 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a RCC2 polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a SMPDL3B polypeptide; or a FERMT1 polypeptide, a HS6ST2 polypeptide, and a KPNA2 polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a LAMC2 polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a MARCKSL1 polypeptide; or a CYP2S1 polypeptide, a ILDR1 polypeptide, and a LAMC2 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a LAMB3 polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a HS6ST2 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a SYT13 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a LMNB1 polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a RCC2 polypeptide; or a CDH2 polypeptide, a EPCAM polypeptide, and a RCC2 polypeptide; or a B3GNT3 polypeptide, a LAMC2 polypeptide, and a MET polypeptide; or a AP1M2 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a CDH3 polypeptide, a SMPDL3B polypeptide, and a SYT13 polypeptide; or a CDH2 polypeptide, a LAMC2 polypeptide, and a SMPDL3B polypeptide; or a EPCAM polypeptide, a HS6ST2 polypeptide, and a ULBP2 polypeptide; or a CYP2S1 polypeptide, a SYT13 polypeptide, and a VTCN1 polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a SLC39A6 polypeptide; or a CYP2S1 polypeptide, a GNG4 polypeptide, and a LAMC2 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a KPNA2 polypeptide; or a CDH1 polypeptide, a HS6ST2 polypeptide, and a ULBP2 polypeptide; or a CDH2 polypeptide, a LAPTM4B polypeptide, and a SMPDL3B polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a PMEPA1 polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a HS6ST2 polypeptide; or a CEACAM5 polypeptide, a HS6ST2 polypeptide, and a SHISA2 polypeptide; or a CYP2S1 polypeptide, a FERMT1 polypeptide, and a ULBP2 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a LSR polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a MARVELD2 polypeptide; or a HS6ST2 polypeptide, a LAMB3 polypeptide, and a LMNB1 polypeptide; or a AP1M2 polypeptide, a CDH2 polypeptide, and a MARCKSL1 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a MARCKSL1 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a RAP2B polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a RACGAP1 polypeptide; or a CYP2S1 polypeptide, a ILDR1 polypeptide, and a PMEPA1 polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a LAPTM4B polypeptide; or a CDH2 polypeptide, a LAPTM4B polypeptide, and a PODXL2 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a SHISA2 polypeptide; or a CDH3 polypeptide, a FZD2 polypeptide, and a SYT13 polypeptide; or a EPCAM polypeptide, a HS6ST2 polypeptide, and a LRRN1 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a LAPTM4B polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a MAL2 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a RCC2 polypeptide; or a EPHB2 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a AP1M2 polypeptide, a HS6ST2 polypeptide, and a ULBP2 polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a EPHB2 polypeptide; or a GALNT14 polypeptide, a LAMB3 polypeptide, and a LAMC2 polypeptide; or a GNG4 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a KRTCAP3 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a MARVELD2 polypeptide; or a CDH1 polypeptide, a CYP2S1 polypeptide, and a HS6ST2 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a SYT13 polypeptide; or a CDH3 polypeptide, a GJB1 polypeptide, and a SMPDL3B polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a ILDR1 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a KRTCAP3 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a PODXL2 polypeptide; or a GALNT6 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a ILDR1 polypeptide, a LAMB3 polypeptide, and a LAMC2 polypeptide; or a CDH2 polypeptide, a CDH3 polypeptide, and a EPCAM polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a CLN5 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a MET polypeptide; or a FERMT1 polypeptide, a HS6ST2 polypeptide, and a RAP2B polypeptide; or a HS6ST2 polypeptide, a KPNA2 polypeptide, and a LAMB3 polypeptide; or a HS6ST2 polypeptide, a LAMB3 polypeptide, and a LAMC2 polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a MARCKSL1 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a ILDR1 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a LAMC2 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a TMEM238 polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a MARCKSL1 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a NUP210 polypeptide; or a FERMT1 polypeptide, a GNG4 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a LAMB3 polypeptide, and a ULBP2 polypeptide; or a ILDR1 polypeptide, a LAMC2 polypeptide, and a RAP2B polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a FZD2 polypeptide; or a CLN5 polypeptide, a PARD6B polypeptide, and a SYT13 polypeptide; or a CYP2S1 polypeptide, a FERMT1 polypeptide, and a HS6ST2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a RAP2B polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a PODXL2 polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a SMPDL3B polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a SMPDL3B polypeptide; or a CLDN3 polypeptide, a CLN5 polypeptide, and a HS6ST2 polypeptide; or a LAPTM4B polypeptide, a PODXL2 polypeptide, and a SMPDL3B polypeptide; or a B3GNT3 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a B3GNT3 polypeptide, a HS6ST2 polypeptide, and a SHISA2 polypeptide; or a AP1M2 polypeptide, a HS6ST2 polypeptide, and a PODXL2 polypeptide; or a HS6ST2 polypeptide, a KPNA2 polypeptide, and a MARVELD2 polypeptide; or a HS6ST2 polypeptide, a LSR polypeptide, and a MAL2 polypeptide; or a CLN5 polypeptide, a ILDR1 polypeptide, and a LAMC2 polypeptide; or a B3GNT3 polypeptide, a LAMB3 polypeptide, and a ULBP2 polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a LRRN1 polypeptide; or a CDH3 polypeptide, a EPHB2 polypeptide, and a LAMC2 polypeptide; or a DSG2 polypeptide, a LAMB3 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a LSR polypeptide, and a PODXL2 polypeptide; or a HS6ST2 polypeptide, a MARCKSL1 polypeptide, and a MARVELD2 polypeptide; or a B3GNT3 polypeptide, a CDH3 polypeptide, and a GNG4 polypeptide; or a B3GNT3 polypeptide, a LAMC2 polypeptide, and a SHISA2 polypeptide; or a B3GNT3 polypeptide, a LAMC2 polypeptide, and a ULBP2 polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a PRAF2 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a HS6ST2 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a SMIM22 polypeptide; or a GALNT14 polypeptide, a LAMC2 polypeptide, and a RAP2B polypeptide; or a GALNT6 polypeptide, a GNG4 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a ILDR1 polypeptide, and a LAMC2 polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a SLC39A6 polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a FERMT1 polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a CYP2S1 polypeptide; or a CLDN3 polypeptide, a HS6ST2 polypeptide, and a PODXL2 polypeptide; or a CYP2S1 polypeptide, a FERMT1 polypeptide, and a MET polypeptide; or a CYP2S1 polypeptide, a ILDR1 polypeptide, and a ULBP2 polypeptide; or a EPCAM polypeptide, a LMNB1 polypeptide, and a VTCN1 polypeptide; or a FERMT1 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a FERMT1 polypeptide, a HS6ST2 polypeptide, and a ULBP2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a RACGAP1 polypeptide; or a LAMB3 polypeptide, a LAMC2 polypeptide, and a RAP2B polypeptide; or a LAPTM4B polypeptide, a LMNB1 polypeptide, and a VTCN1 polypeptide; or a MARCKSL1 polypeptide, a PMEPA1 polypeptide, and a SMIM22 polypeptide; or a MARCKSL1 polypeptide, a PODXL2 polypeptide, and a TMEM238 polypeptide; or a CD24 polypeptide, a CLN5 polypeptide, and a SYT13 polypeptide; or a CDH17 polypeptide, a FOLR1 polypeptide, and a MET polypeptide; or a CDH2 polypeptide, a FAM241B polypeptide, and a ILDR1 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a KRTCAP3 polypeptide; or a CEACAM5 polypeptide, a PMEPA1 polypeptide, and a SHISA2 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a RNF43 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a LMNB1 polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a PRAF2 polypeptide; or a EPCAM polypeptide, a FZD2 polypeptide, and a VTCN1 polypeptide; or a BMPR1B polypeptide, a EPCAM polypeptide, and a MARCKSL1 polypeptide; or a CDH2 polypeptide, a LMNB1 polypeptide, and a LRRN1 polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a EPCAM polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a KPNA2 polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a LSR polypeptide; or a CEACAM5 polypeptide, a CLN5 polypeptide, and a SHISA2 polypeptide; or a CEACAM6 polypeptide, a HS6ST2 polypeptide, and a PODXL2 polypeptide; or a CYP2S1 polypeptide, a LAMC2 polypeptide, and a ULBP2 polypeptide; or a FAM241B polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a GALNT14 polypeptide, a PMEPA1 polypeptide, and a SYT13 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a MAL2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a ULBP2 polypeptide; or a LMNB1 polypeptide, a SMIM22 polypeptide, and a VTCN1 polypeptide; or a BMPR1B polypeptide, a CLDN3 polypeptide, and a MARCKSL1 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a MAL2 polypeptide; or a CLDN3 polypeptide, a HS6ST2 polypeptide, and a MET polypeptide; or a B3GNT3 polypeptide, a FZD2 polypeptide, and a LAMC2 polypeptide; or a B3GNT3 polypeptide, a CDH3 polypeptide, and a FZD2 polypeptide; or a CDH1 polypeptide, a CDH2 polypeptide, and a HS6ST2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a SMPDL3B polypeptide; or combinations thereof.

One of skill in the art will understand that individual sensitivity for each cancer within pan-cancer detection (e.g., at least two or more, including, e.g., at least three, at least four, at least five, at least six, at least seven, at least eight, or more cancers) that has an overall sensitivity can vary. For example, for pan-cancer detection that has an overall sensitivity of about 30%, certain cancer(s) within the pan-cancer detection can have an individual sensitivity of greater than 30%, while certain other cancer(s) within the pan-cancer detection can have an individual sensitivity of lower than 30%.

Adrenocortical Carcinoma (ACC)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Adrenocortical carcinoma (ACC) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to ACC.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to ACC comprises at least three biomarkers, selected from the group consisting of: a CDH2 polypeptide, a ENPP5 polypeptide, and a RNF128 polypeptide; or a CDH2 polypeptide, a LAPTM4B polypeptide, and a PODXL2 polypeptide; or a APOO polypeptide, a GJB1 polypeptide, and a IGSF3 polypeptide; or a CDH2 polypeptide, a FERMT1 polypeptide, and a NPTXR polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a PRAF2 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of ACC can be used as a 2-biomarker combination for detection of ACC.

Bladder Urothelial Carcinoma (BLCA)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Bladder Urothelial Carcinoma (BLCA) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to BLCA.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to BLCA comprises at least three biomarkers, selected from the group consisting of: a AP1M2 polypeptide, a HS6ST2 polypeptide, and a ULBP2 polypeptide; or a B3GNT3 polypeptide, a LAMB3 polypeptide, and a ULBP2 polypeptide; or a B3GNT3 polypeptide, a CDH3 polypeptide, and a FZD2 polypeptide; or a CDH1 polypeptide, a HS6ST2 polypeptide, and a ULBP2 polypeptide; or a HS6ST2 polypeptide, a LSR polypeptide, and a PODXL2 polypeptide; or a FERMT1 polypeptide, a HS6ST2 polypeptide, and a ULBP2 polypeptide; or a ILDR1 polypeptide, a PODXL2 polypeptide, and a ULBP2 polypeptide; or a B3GNT3 polypeptide, a LAMC2 polypeptide, and a ULBP2 polypeptide; or a AP1M2 polypeptide, a HS6ST2 polypeptide, and a PODXL2 polypeptide; or a AP1M2 polypeptide, a HS6ST2 polypeptide, and a KPNA2 polypeptide; or a CDH3 polypeptide, a EPHB2 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a LAMB3 polypeptide, and a ULBP2 polypeptide; or a FZD2 polypeptide, a PODXL2 polypeptide, and a SMIM22 polypeptide; or a LAMC2 polypeptide, a LSR polypeptide, and a ULBP2 polypeptide; or a B3GNT3 polypeptide, a FZD2 polypeptide, and a LAMC2 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of BLCA can be used as a 2-biomarker combination for detection of BLCA.

Brain Lower Grade Glioma (LGG)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Brain Lower Grade Glioma (LGG) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to LGG.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to LGG comprises at least three biomarkers, selected from the group consisting of: a CDH2 polypeptide, a FERMT1 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a SLC4A4 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a PRAF2 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a PODXL2 polypeptide; or a CDH2 polypeptide, a GPRIN1 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a PODXL2 polypeptide, and a SLC4A4 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a NPTXR polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a TMEM132A polypeptide; or a CDH2 polypeptide, a GOLM1 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a FERMT1 polypeptide, and a NPTXR polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a RAC3 polypeptide; or a CADM4 polypeptide, a CDH2 polypeptide, and a FERMT1 polypeptide; or a CADM4 polypeptide, a GPRIN1 polypeptide, and a LRRN1 polypeptide; or a CADM4 polypeptide, a CDH2 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a GNG4 polypeptide, and a LRRN1 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of LGG can be used as a 2-biomarker combination for detection of LGG.

Breast Cancer Including Breast Invasive Carcinoma (BRCA)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of breast cancer can be included in pan-cancer detection. In some embodiments, provided biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to breast cancer.

In some embodiments, biomarkers or biomarker combinations for breast cancer detection that are useful to be included in pan-cancer detection are described in U.S. Provisional Application No. 63/224,374, (the “'374 application”) and the International PCT application that claims priority to the '374 application and was filed on Jul. 21, 2022 the entire content of each of which is incorporated herein by reference.

In some embodiments, one or more biomarkers that are suitable for detection of breast cancer and are useful to be included in pan-cancer detection can be selected from: (i) polypeptides encoded by human genes as follows: ABCC11, ABCD3, ACSL3, ALCAM, ALDH18A1, AP1M2, AP2B1, APOO, APP, ARFGEF3, ATP6AP2, BROX, BSPRY, CA12, CALU, CANT1, CANX, CDH1, CDH3, CELSR1, CELSR2, CIP2A, CLGN, CLN5, CLSTN2, CLTC, CNNM4, COPA, COX6C, DAG1, DNAJC1, DSC2, DSG2, DSG3, EFHD1, EGFR, ENPP1, EPCAM, EPHB3, EPPK1, ERBB2, ERBB3, ERBB4, ERMP1, ESR1, FAM120A, FGFR4, FUT8, GALNT3, GALNT6, GALNT7, GBP5, GDAP1, GDI2, GFRA1, GNPNAT1, GOLM1, GOLPH3L, GPRIN1, GRB7, GRHL2, HACD3, HID1, IGF1R, ITGA11, ITGB6, ITPR2, KCTD3, KIF16B, KIF1A, KPNA2, LAMC2, LAMP2, LAMTOR2, LANCL2, LMNB1, LRBA, LRP2, LRRC59, LSR, MAGI3, MAP7, MAPT, MARCKSL1, MEAK7, MELK, MIEN1, MTCH2, MUC1, MYO6, NCAM2, NECTIN2, NECTIN4, NUCB2, NUP155, NUP210, OCLN, PARD6B, PDIA6, PIGT, PLEKHF2, PLGRKT, PLOD1, PREX1, PROM1, PTK7, PTPRF, PTPRK, QSOX1, RAB25, RAB27B, RAB30, RABEP1, RAC3, RACGAP1, RAP2B, RCC2, REEP6, RPN1, SCUBE2, SEC23B, SEPHS1, SFXN2, SHROOM3, SIPA1L3, SLC1A4, SLC35B2, SLC9A3R1, SPTLC2, SSR1, ST14, STARD10, STX6, SUMO1, SYAP1, SYT7, SYTL2, TACSTD2, TJP3, TMED2, TMED3, TMEM132A, TMEM87B, TMPO, TOM1L1, TOMM34, TRAF4, YES1, ZMPSTE24, ADAM8, CCL8, CCN1, CCR5, CD274, CD44, CDH11, CSPG4, DLL4, EPHA10, FGF1, FLNA, FZD7, GPNMB, IL1RAP, ITGA6, LY6E, MCAM, MELTF, MERTK, MUC16, NRP1, NT5E, PRLR, RET, S1PR1, SLC39A6, SLC3A2, SLC7A11, SLC7A5, STAT3, SUSD3, TF, TMPRSS1, TNC, TNFRSF12A, VANGL2, VEGFA, VTCN1, XBP1, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: CA15-3 antigen, CA27-29 antigen, Phosphatidylserine, Tn antigen, SialylTn (sTn) antigen, Thomsen-Friedenreich (T, TF) antigen, Lewis Y antigen (also known as CD174), Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), Sialyl Lewis A antigen (also known as CA19-9), SSEA-1 (also known as Lewis X antigen), NeuGcGM3 (N-glycolyl GM3 ganglioside), and combinations thereof.

In some embodiments, one or more biomarkers that are suitable for detection of breast cancer and are useful to be included in pan-cancer detection can be selected from: (i) polypeptides encoded by human genes as follows: ABCC11, AP1M2, APOO, ARFGEF3, BSPRY, CANT1, CDH1, CDH3, CELSR1, CIP2A, CLGN, COX6C, DSC2, DSG2, EGFR, EPCAM, EPHB3, ERBB2, ERBB3, ESR1, FGFR4, FUT8, GALNT3, GALNT6, GALNT7, GFRA1, GOLM1, GRB7, GRHL2, HACD3, ITGB6, KIF1A, KPNA2, LAMC2, LMNB1, LRP2, LSR, MARCKSL1, MIEN1, MUC1, NECTIN2, NUP155, NUP210, OCLN, PARD6B, PLEKHF2, PRLR, PROM1, PTK7, PTPRK, RAB25, RAB27B, RAC3, SEPHS1, SFXN2, SHROOM3, SLC35B2, SLC9A3R1, ST14, SYT7, TJP3, TMEM132A, XBP1, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: Lewis Y antigen (also known as CD174), Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, and combinations thereof.

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Breast invasive carcinoma (BRCA) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to BRCA.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to BRCA comprises at least three biomarkers, selected from the group consisting of: a GALNT6 polypeptide, a SLC39A6 polypeptide, and a SMIM22 polypeptide; or a PARD6B polypeptide, a SLC39A6 polypeptide, and a SMIM22 polypeptide; or a MARCKSL1 polypeptide, a SLC39A6 polypeptide, and a SMIM22 polypeptide; or a APOO polypeptide, a SLC39A6 polypeptide, and a SMIM22 polypeptide; or a SLC39A6 polypeptide, a SMIM22 polypeptide, and a TSPAN13 polypeptide; or a SLC39A6 polypeptide, a SMIM22 polypeptide, and a SYAP1 polypeptide; or a CANT1 polypeptide, a SLC39A6 polypeptide, and a SMIM22 polypeptide; or a FAM241B polypeptide, a SLC39A6 polypeptide, and a SMIM22 polypeptide; or a ELAPOR1 polypeptide, a MARCKSL1 polypeptide, and a SLC39A6 polypeptide; or a ARFGEF3 polypeptide, a SLC39A6 polypeptide, and a SMIM22 polypeptide; or a AP1M2 polypeptide, a GPR160 polypeptide, and a SLC39A6 polypeptide; or a SLC39A6 polypeptide, a SMIM22 polypeptide, and a TMEM9 polypeptide; or a ILDR1 polypeptide, a MARCKSL1 polypeptide, and a SLC39A6 polypeptide; or a PODXL2 polypeptide, a SLC39A6 polypeptide, and a SMIM22 polypeptide; or a SHISA2 polypeptide, a SLC39A6 polypeptide, and a SLC44A4 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of breast cancer can be used as a 2-biomarker combination for detection of breast cancer.

Endocervical Adenocarcinoma (CESC)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Endocervical Adenocarcinoma (CESC) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to CESC.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to CESC comprises at least three biomarkers, selected from the group consisting of: a ILDR1 polypeptide, a PODXL2 polypeptide, and a ULBP2 polypeptide; or a HS6ST2 polypeptide, a LAMB3 polypeptide, and a LMNB1 polypeptide; or a HS6ST2 polypeptide, a LAMB3 polypeptide, and a ULBP2 polypeptide; or a AP1M2 polypeptide, a HS6ST2 polypeptide, and a ULBP2 polypeptide; or a CDH1 polypeptide, a HS6ST2 polypeptide, and a ULBP2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a ULBP2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a LMNB1 polypeptide; or a ILDR1 polypeptide, a LAMC2 polypeptide, and a RAP2B polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a RAP2B polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a RACGAP1 polypeptide; or a HS6ST2 polypeptide, a LAMB3 polypeptide, and a RACGAP1 polypeptide; or a HS6ST2 polypeptide, a LAMB3 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a LMNB1 polypeptide, and a LSR polypeptide; or a LAMC2 polypeptide, a LSR polypeptide, and a ULBP2 polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a ULBP2 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of CESC can be used as a 2-biomarker combination for detection of CESC.

Cholangiocarcinoma (CHOL)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Cholangiocarcinoma (CHOL) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to CHOL.

In some embodiments, biomarkers or biomarker combinations for CHOL detection that are useful to be included in pan-cancer detection are described in U.S. Provisional Application No. 63/224,382, (the “'382 application”) and the International PCT Application that claims priority to the '382 application and was filed on Jul. 21, 2022 the entire content of each of which is incorporated herein by reference.

In some embodiments, one or more biomarkers that are suitable for detection of CHOL and are useful to be included in pan-cancer detection can be selected from: (i) polypeptides encoded by human genes as follows: ANXA13, AQP1, ASPHD1, ATP1B1, B3GNT3, CDC42EP1, CDH1, CDH2, CFTR, CHST4, CLDN1, CLDN10, CLDN9, CLTRN, CPNE7, CRB3, DEFB1, EFNA4, EPCAM, FAM171A1, FAM241B, FGFR2, FGFR4, FRAS1, GAL3ST1, GGT1, GJB1, GRID1, HKDC1, HPN, IGSF3, KRTCAP3, LAD1, LAMC2, LPAR2, LSR, LYPD1, LYPD6B, MAL2, MARVELD2, MMP15, MPC2, MPP6, MUC1, MUC2, MUC4, MUC5AC, NCEH1, NRSN2, OCLN, OXTR, PARD6B, PDGFC, PIGT, PIK3AP1, PMEPA1, RAB25, RHOV, SHANK2, SLC39A6, SLC44A3, SLC4A4, SLC52A3, SMIM22, SNAP25, SYT13, TESC, TGFA, TM4SF4, TMCO1, TMEM132A, TMEM156, TMPRSS13, TNFRSF12A, TNFRSF21, TOMM20, UGT2A3, VEPH1, VTCN1, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: Lewis Y antigen (also known as CD174), Tn antigen, Thomsen-Friedenreich (T, TF) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), and combinations thereof.

In some embodiments, one or more biomarkers that are suitable for detection of CHOL and are useful to be included in pan-cancer detection can be selected from: (i) polypeptides encoded by human genes as follows: ANXA13, ASPHD1, B3GNT3, CDH1, CDH2, CHST4, CLDN10, CLTRN, DEFB1, FGFR4, GAL3ST1, GJB1, HKDC1, IGSF3, KRTCAP3, LAD1, LAMC2, LSR, LYPD6B, MUC1, MUC2, MUC4, MUC5AC, OXTR, PIK3AP1, SHANK2, SLC44A3, SNAP25, SYT13, TESC, TM4SF4, TMEM156, VEPH1, VTCN1, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: Lewis Y antigen (also known as CD174), Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, and combinations thereof.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to CHOL comprises at least three biomarkers, selected from the group consisting of: a PARD6B polypeptide, a PMEPA1 polypeptide, and a SYT13 polypeptide; or a B3GNT3 polypeptide, a LAMC2 polypeptide, and a PMEPA1 polypeptide; or a CDH1 polypeptide, a CDH2 polypeptide, and a LAMC2 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a LAMC2 polypeptide; or a CYP2S1 polypeptide, a SYT13 polypeptide, and a VTCN1 polypeptide; or a CDH1 polypeptide, a CDH2 polypeptide, and a ILDR1 polypeptide; or a PARD6B polypeptide, a SLC39A6 polypeptide, and a SYT13 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a SYT13 polypeptide; or a CDH2 polypeptide, a FAM241B polypeptide, and a ILDR1 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a MAL2 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a EPCAM polypeptide; or a APOO polypeptide, a GJB1 polypeptide, and a IGSF3 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a RAP2B polypeptide; or a CDH2 polypeptide, a EPCAM polypeptide, and a RCC2 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a RCC2 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of CHOL can be used as a 2-biomarker combination for detection of CHOL.

Colon Adenocarcinoma (COAD)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Colon Adenocarcinoma (COAD) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to COAD.

In some embodiments, biomarkers or biomarker combinations for COAD detection that are useful to be included in pan-cancer detection are described in U.S. Provisional Application No. 63/224,378, (the “'378 application”) and the International PCT Application that claims priority to the '378 application and was filed on Jul. 21, 2022 the entire content of each of which is incorporated herein by reference.

In some embodiments, one or more biomarkers that are suitable for detection of COAD and are useful to be included in pan-cancer detection can be selected from: (i) polypeptides encoded by human genes as follows: ACSL5, ACVR2B, ALDH18A1, ALG5, AP1M2, ATP1B1, B3GNT3, BCAP31, CASK, CD133, CDH1, CDH17, CDH3, CEACAM5, CEACAM6, CFB, CFTR, CHDH, CHMP4B, CISD2, CLDN3, CLDN4, CLIC1, COPG2, CYP2S1, DPEP1, DSG2, EDAR, EPCAM, EPHB2, EPHB3, ERMP1, FAM241B, FERMT1, GALNT3, GNPNAT1, GOLIM4, GPA33, GPCR5A, HACD3, HEPH, HKDC1, HS6ST2, IHH, ILDR1, ITGA2, KCNQ1, KEL, KPNA2, LAD1, LAMC2, LBR, LMNB1, LMNB2, LSR, MAP7, MARCKSL1, MARVELD2, MGAT5, MLEC, MUC1, MUC13, NCEH1, NDUFS6, NLN, NOX1, NUP210, OCIAD2, PGAM5, PIGR, PIGT, PLEK2, PMEPA1, PTK7, RAB25, RAP2A, RAP2B, RCC2, RNF43, RPN1, RPN2, RPS3, RUVBL2, S100P, SLC12A2, SLC25A6, SLC2A1, SMIM22, SNTB1, SORD, SSR4, ST14, STOML2, STT3B, SYAP1, TESC, TM9SF2, TMED2, TMPO, TOMM22, TOMM34, AMHR2, CLDN1, DLL4, EGFR, ERBB2, FAP, FGFR4, FOLR1, GUCY2C, IGF1R, IL1A, ITGAV, KRT8, LGR5, LPR6, MET, MST1R, MUC5AC, TNFRSF10B, VEGFA, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: CanAg (glycoform of MUC1), Lewis Y/B antigen, Lewis B Antigen, Sialyltetraosyl carbohydrate, Tn antigen, SialylTn (sTn) antigen, Thomsen-Friedenreich (T, TF) antigen, Lewis Y antigen (also known as CD174), Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), Sialyl Lewis A antigen (also known as CA19-9), SSEA-1 (also known as Lewis X antigen), NeuGcGM3, and combinations thereof.

In some embodiments, one or more biomarkers that are suitable for detection of COAD and are useful to be included in pan-cancer detection can be selected from: (i) polypeptides encoded by human genes as follows: ACVR2B, B3GNT3, CD133, CDH17, CDH3, CEACAM5, CEACAM6, CFB, CFTR, CYP2S1, DLL4, EDAR, EPCAM, EPHB2, EPHB3, ERBB2, FAP, GPCR5A, IHH, ILDR1, ITGAV, KCNQ1, KEL, MARCKSL1, MST1R, MUC1, MUC5AC, NOX1, OCIAD2, RNF43, SMIM22, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, and combinations thereof.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to COAD comprises at least three biomarkers, selected from the group consisting of: a CDH17 polypeptide, a CDH3 polypeptide, and a FERMT1 polypeptide; or a CDH17 polypeptide, a CDH3 polypeptide, and a GALNT6 polypeptide; or a CDH17 polypeptide, a CDH3 polypeptide, and a CYP2S1 polypeptide; or a CDH17 polypeptide, a CDH3 polypeptide, and a PMEPA1 polypeptide; or a CDH17 polypeptide, a CDH3 polypeptide, and a MARCKSL1 polypeptide; or a CDH17 polypeptide, a CDH3 polypeptide, and a RNF128 polypeptide; or a CDH17 polypeptide, a CDH3 polypeptide, and a PODXL2 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a GJB1 polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a CYP2S1 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a EPCAM polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a FERMT1 polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a EPHB2 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a CYP2S1 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a CLN5 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a PODXL2 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of COAD can be used as a 2-biomarker combination for detection of COAD.

Esophageal Carcinoma (ESCA)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Esophageal Carcinoma (ESCA) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to ESCA.

In some embodiments, biomarkers or biomarker combinations for ESCA detection that are useful to be included in pan-cancer detection are described in U.S. Provisional Application No. 63/224,390, (the “'390 application”) and the International PCT Application that claims priority to the '390 application and was filed on Jul. 21, 2022 the entire content of each of which is incorporated herein by reference.

In some embodiments, one or more biomarkers that are suitable for detection of ESCA and are useful to be included in pan-cancer detection can be selected from: (i) polypeptides encoded by human genes as follows: ABCA12, ABCC1, ANO1, AP1S3, B3GNT3, CD24, CDCP1, CDH1, CDH17, CDH3, CEACAM5, CEACAM6, CELSR1, CLCA2, CREB3L, CYP2S1, CYP4F11, DSC2, DSG2, DSG3, EPCAM, EPHB2, EPPK1, FAT1, FAT2, FERMT1, FUT2, GALNT3, GALNT5, GCNT3, GJB2, HAS3, HS6ST2, ITGA2, ITGB6, JUP, KDELR3, KPNA2, LAD1, LAMB3, LAMC2, LAMP3, LAPTM4B, LSR, MAL2, MARVELD2, MET, MGAM2, MUC1, MUC13, MUC4, NCEH1, NECTIN1, PANX2, PHLDA2, PIGT, PMEPA1, PRR7, PRSS21, PTPRH, RNF128, SLC7A11, SLC7A5, TACSTD2, TENM2, TGFA, TMC5, TMEM132A, TMEM158, TMPRSS11D, TMPRSS4, TNFRSF21, TOR4A, TTYH3, UGT8, ULBP2, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: Lewis Y antigen (also known as CD174), Tn antigen, SialylTn (sTn) antigen, Thomsen-Friedenreich (T, TF) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), and combinations thereof.

In some embodiments, one or more biomarkers that are suitable for detection of ESCA and are useful to be included in pan-cancer detection can be selected from: (i) polypeptides encoded by human genes as follows: ANO1, AP1S3, B3GNT3, CDCP1, CEACAM5, CEACAM6, CELSR1, CLCA2, CYP2S1, CYP4F11, DSC2, DSG2, DSG3, EPCAM, EPPK1, GALNT3, HS6ST2, ITGB6, LAMB3, LAMC2, LSR, MAL2, MARVELD2, MUC1, MUC13, PRR7, SLC7A5, TMEM158, TMPRSS11D, UGT8, ULBP2, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, and combinations thereof.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to ESCA comprises at least three biomarkers, selected from the group consisting of: a CYP2S1 polypeptide, a FERMT1 polypeptide, and a ULBP2 polypeptide; or a CYP2S1 polypeptide, a ILDR1 polypeptide, and a ULBP2 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a ILDR1 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a EPCAM polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a TMEM238 polypeptide; or a CYP2S1 polypeptide, a LAMC2 polypeptide, and a ULBP2 polypeptide; or a CYP2S1 polypeptide, a LAMB3 polypeptide, and a ULBP2 polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a CYP2S1 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a LAMC2 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a LAMC2 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a CYP2S1 polypeptide; or a CYP2S1 polypeptide, a ILDR1 polypeptide, and a LAMC2 polypeptide; or a CYP2S1 polypeptide, a FERMT1 polypeptide, and a MET polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a RACGAP1 polypeptide; or a HS6ST2 polypeptide, a LAMB3 polypeptide, and a LAMC2 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of ESCA can be used as a 2-biomarker combination for detection of ESCA.

Glioblastoma Multiforme (GBM)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Glioblastoma multiforme (GBM) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to GBM.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to GBM comprises at least three biomarkers, selected from the group consisting of: a CDH2 polypeptide, a LRRN1 polypeptide, and a PRAF2 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a SLC4A4 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a TMEM132A polypeptide; or a CDH2 polypeptide, a LMNB1 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a RAC3 polypeptide; or a CDH2 polypeptide, a GPRIN1 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a TMEM9 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a GNG4 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a RACGAP1 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a PODXL2 polypeptide; or a CDH2 polypeptide, a IGSF3 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a HACD3 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a EPHB2 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a GOLM1 polypeptide, and a LRRN1 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of GBM can be used as a 2-biomarker combination for detection of GBM. Head and Neck squamous cell carcinoma (HNSC)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Head and Neck squamous cell carcinoma (HNSC) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to HNSC.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to HNSC comprises at least three biomarkers, selected from the group consisting of: a CYP2S1 polypeptide, a LAMC2 polypeptide, and a ULBP2 polypeptide; or a LAMB3 polypeptide, a LAMC2 polypeptide, and a ULBP2 polypeptide; or a LAMB3 polypeptide, a LAMC2 polypeptide, and a RAP2B polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a LAMC2 polypeptide; or a LAMC2 polypeptide, a LSR polypeptide, and a ULBP2 polypeptide; or a GALNT14 polypeptide, a LAMC2 polypeptide, and a ULBP2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a ULBP2 polypeptide; or a GALNT14 polypeptide, a LAMB3 polypeptide, and a LAMC2 polypeptide; or a CYP2S1 polypeptide, a LAMB3 polypeptide, and a ULBP2 polypeptide; or a HS6ST2 polypeptide, a LAMB3 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a RAP2B polypeptide; or a CDH3 polypeptide, a EPHB2 polypeptide, and a LAMC2 polypeptide; or a CDH3 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a CDH3 polypeptide, a DSG2 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a LAMB3 polypeptide, and a ULBP2 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of HNSC can be used as a 2-biomarker combination for detection of HNSC.

Kidney Chromophobe (KICH)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Kidney Chromophobe (KICH) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to KICH.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to KICH comprises at least three biomarkers, selected from the group consisting of: a BMPR1B polypeptide, a DSG2 polypeptide, and a ILDR1 polypeptide; or a APOO polypeptide, a BMPR1B polypeptide, and a ILDR1 polypeptide; or a BMPR1B polypeptide, a HACD3 polypeptide, and a ILDR1 polypeptide; or a BMPR1B polypeptide, a CLN5 polypeptide, and a ILDR1 polypeptide; or a BMPR1B polypeptide, a GOLM1 polypeptide, and a ILDR1 polypeptide; or a AP1M2 polypeptide, a BMPR1B polypeptide, and a DSG2 polypeptide; or a CLN5 polypeptide, a PARD6B polypeptide, and a SYT13 polypeptide; or a BMPR1B polypeptide, a CDH1 polypeptide, and a ILDR1 polypeptide; or a BMPR1B polypeptide, a GPR160 polypeptide, and a ILDR1 polypeptide; or a APOO polypeptide, a BMPR1B polypeptide, and a CDH1 polypeptide; or a ALDH18A1 polypeptide, a BMPR1B polypeptide, and a SYT13 polypeptide; or a BMPR1B polypeptide, a CADM4 polypeptide, and a ILDR1 polypeptide; or a BMPR1B polypeptide, a GNPNAT1 polypeptide, and a ILDR1 polypeptide; or a AP1M2 polypeptide, a APOO polypeptide, and a BMPR1B polypeptide; or a BMPR1B polypeptide, a CDH1 polypeptide, and a GOLM1 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of KICH can be used as a 2-biomarker combination for detection of KICH.

Kidney Renal Clear Cell Carcinoma (KIRC).

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Kidney renal clear cell carcinoma (KIRC) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to KIRC.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to KIRC comprises at least three biomarkers, selected from the group consisting of: a CLN5 polypeptide, a GALNT14 polypeptide, and a RNF128 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a GALNT14 polypeptide; or a GALNT14 polypeptide, a MET polypeptide, and a RNF128 polypeptide; or a GALNT14 polypeptide, a PMEPA1 polypeptide, and a RNF128 polypeptide; or a GALNT14 polypeptide, a RAP2B polypeptide, and a RNF128 polypeptide; or a CDH2 polypeptide, a GALNT14 polypeptide, and a PMEPA1 polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a GALNT14 polypeptide; or a CDH2 polypeptide, a DSG2 polypeptide, and a GALNT14 polypeptide; or a CDH2 polypeptide, a GALNT14 polypeptide, and a UNC13B polypeptide; or a CDH2 polypeptide, a FOLR1 polypeptide, and a GALNT14 polypeptide; or a GALNT14 polypeptide, a PMEPA1 polypeptide, and a SYT13 polypeptide; or a GALNT14 polypeptide, a RAP2B polypeptide, and a SYT13 polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a CLN5 polypeptide; or a CLN5 polypeptide, a GALNT14 polypeptide, and a SYT13 polypeptide; or a ARFGEF3 polypeptide, a CDH2 polypeptide, and a GALNT14 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of KIRC can be used as a 2-biomarker combination for detection of KIRC.

Kidney renal papillary cell carcinoma (KIRP).

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Kidney renal papillary cell carcinoma (KIRP) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to KIRP.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to KIRP comprises at least three biomarkers, selected from the group consisting of: a CDH2 polypeptide, a CLDN4 polypeptide, and a MET polypeptide; or a GALNT14 polypeptide, a MET polypeptide, and a RNF128 polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a GALNT14 polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a MET polypeptide; or a CLN5 polypeptide, a GALNT14 polypeptide, and a RNF128 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a GALNT14 polypeptide; or a GALNT14 polypeptide, a RAP2B polypeptide, and a SYT13 polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a CLN5 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a RAP2B polypeptide; or a CDH2 polypeptide, a GALNT14 polypeptide, and a UNC13B polypeptide; or a CDH2 polypeptide, a DSG2 polypeptide, and a GALNT14 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a ILDR1 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a SMPDL3B polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a UNC13B polypeptide; or a CDH2 polypeptide, a CLDN4 polypeptide, and a CLN5 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of KIRP can be used as a 2-biomarker combination for detection of KIRP.

Liver Hepatocellular Carcinoma (LIHC)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Liver hepatocellular carcinoma (LIHC) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to LIHC.

In some embodiments, biomarkers or biomarker combinations for LIHC detection that are useful to be included in pan-cancer detection are described in U.S. Provisional Application No. 63/224,381, (the “'381 application”) and the International PCT Application that claims priority to the '381 application and was filed on Jul. 21, 2022 the entire content of each of which is incorporated herein by reference.

In some embodiments, one or more biomarkers that are suitable for detection of LIHC and are useful to be included in pan-cancer detection can be selected from: (i) polypeptides encoded by human genes as follows: ACBD3, ACSL4, ACY3, ANXA13, AP1M2, APOO, ATP1B1, ATP2B2, ATRN, CADM1, CAP2, CD63, CDH2, CDHR5, CKAP4, CLGN, COX6C, CXADR, CYP4F11, EPCAM, EPHX1, FGFR4, G6PD, GBA, GJB1, GLUL, GPC3, HKDC1, HPN, HSD17B2, IGSF8, KDELR1, LAD1, LAMC1, LAMTOR2, LBR, LSR, MARCKS, MARVELD2, MET, MPC2, MUC13, NAT8, NDUFA2, OCLN, PDZK1, PIGT, QPCTL, RAC3, RALBP1, ROBO1, ROMO1, S100P, SCAMP3, SCGN, SDC2, SLC22A9, SLC29A1, SLC2A2, SLC35B2, SLC38A3, TFR2, TM4SF4, TMCO1, TMEM209, TMPRSS6, TOMM20, TOMM22, TOR1AIP2, UGT1A6, UGT1A9, UGT2B7, UNC13B, VAT1, VPS28, DKK1, DLK1, ENPP3, MUC1, PI4K2A, PLVAP, SPINK1, TNFRSF10A, TNFSF18, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: Lewis Y antigen (also known as CD174), Tn antigen, Thomsen-Friedenreich (T, TF) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), and combinations thereof.

In some embodiments, one or more biomarkers that are suitable for detection of LIHC and are useful to be included in pan-cancer detection can be selected from: (i) polypeptides encoded by human genes as follows: ACSL4, ANXA13, AP1M2, ATP1B1, CAP2, CDH2, CDHR5, CKAP4, EPCAM, GBA, GJB1, GLUL, GPC3, MARVELD2, MET, MUC13, NAT8, PDZK1, ROBO1, SCGN, SLC22A9, SLC2A2, SLC35B2, SLC38A3, TFR2, TM4SF4, TMPRSS6, TOMM20, UGT1A9, UGT2B7, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: Lewis Y antigen (also known as CD174), Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, and combinations thereof.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to LIHC comprises at least three biomarkers, selected from the group consisting of: a APOO polypeptide, a GJB1 polypeptide, and a IGSF3 polypeptide; or a CDH1 polypeptide, a CDH2 polypeptide, and a HS6ST2 polypeptide; or a CDH1 polypeptide, a CDH2 polypeptide, and a ILDR1 polypeptide; or a GJB1 polypeptide, a IGSF3 polypeptide, and a KPNA2 polypeptide; or a GJB1 polypeptide, a IGSF3 polypeptide, and a RAP2B polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a RAP2B polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a MAL2 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a ILDR1 polypeptide; or a GJB1 polypeptide, a IGSF3 polypeptide, and a LAPTM4B polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a SYT13 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a EPCAM polypeptide; or a CDH1 polypeptide, a CDH2 polypeptide, and a MARCKSL1 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a UNC13B polypeptide; or a CDH2 polypeptide, a FAM241B polypeptide, and a ILDR1 polypeptide; or a GJB1 polypeptide, a IGSF3 polypeptide, and a RCC2 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of LIHC can be used as a 2-biomarker combination for detection of LIHC.

Lung Cancer

In some embodiments, at least one or more biomarker combinations that are suitable for detection of lung cancer can be included in pan-cancer detection. In some embodiments, provided biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to lung cancer.

In some embodiments, biomarkers or biomarker combinations for lung cancer detection that are useful to be included in pan-cancer detection are described in International Application No. PCT/US21/40971 (published as WO2022011197), the entire content of which is incorporated herein by reference.

In some embodiments, one or more biomarkers that are suitable for detection of lung cancer and are useful to be included in pan-cancer detection can be selected from: (i) polypeptides encoded by human genes as follows: ADGRF1, ABCC3, ALCAM, ARSL, B3GNT3, B3GNT5, CDCP1, CDH1, CDH3, CD55, CD274 (PD-L1), CEACAM5, CEACAM6, CELSR1, CLDN18, CLDN3, CLDN4, CLDN7, CLIC6, DMBT1, DSG2, EGFR, EPCAM, EPHX3, EVA1A, FAM241B, FOLR1, FXYD3, GALNT14, GJB1, GJB2, GPC4, HAS3, HS6ST2, IG1FR, KDELR3, KRTCAP3, LAMB3, LAPTM4B, LARGE2, LFNG, LSR, MAL2, MANEAL, MET, MSLN, MUC1, MUC21, NRCAM, PIGT, PODXL2, PRRG4, PRSS21, ROS1, SDC1, SERINC2, SEZ6L2, SLC34A2, SLC44A4, SLC6A14, SLC7A7, SLC7A11, SMIM22, SMPDL3B, ST14, TACSTD2, TMC4, TMC5, TMEM45B, TMPRSS2, TMPRSS4, TNFRSF10B, TSPAN1, TSPAN8, UCHL1, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: Lewis X antigen, Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, and combinations thereof.

In some embodiments, one or more biomarkers that are suitable for detection of lung cancer and are useful to be included in pan-cancer detection can be selected from: (i) polypeptides encoded by human genes as follows: ADGRF1, ALCAM, B3GNT3, B3GNT5, CDCP1, CDH1, CDH3, CD55, CD274 (PD-L1), CEACAM5, CEACAM6, CLDN3, CLDN4, DSG2, EGFR, EPCAM, FAM241B, FOLR1, FXYD3, GALNT14, GJB1, GJB2, HAS3, IG1FR, LAMB3, LAPTM4B, LARGE2, MAL2, MET, MSLN, MUC1, NRCAM, PIGT, PODXL2, PRSS21, ROS1, SDC1, SLC34A2, SLC7A11, SMIM22, SMPDL3B, ST14, UCHL1, TACSTD2, TMPRSS4, TSPAN8, TNFRSF10B, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: Lewis X antigen, Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, and combinations thereof.

Lung Adenocarcinoma (LUAD)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Lung adenocarcinoma (LUAD) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to LUAD.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to LUAD comprises at least three biomarkers, selected from the group consisting of: a CEACAM6 polypeptide, a HS6ST2 polypeptide, and a PODXL2 polypeptide; or a CEACAM6 polypeptide, a HS6ST2 polypeptide, and a LARGE2 polypeptide; or a CEACAM6 polypeptide, a HS6ST2 polypeptide, and a MARCKSL1 polypeptide; or a HS6ST2 polypeptide, a MAL2 polypeptide, and a SMPDL3B polypeptide; or a CEACAM6 polypeptide, a HS6ST2 polypeptide, and a LSR polypeptide; or a CEACAM6 polypeptide, a HS6ST2 polypeptide, and a RAP2B polypeptide; or a AP1M2 polypeptide, a CEACAM6 polypeptide, and a HS6ST2 polypeptide; or a APOO polypeptide, a CEACAM6 polypeptide, and a HS6ST2 polypeptide; or a ALDH18A1 polypeptide, a CEACAM6 polypeptide, and a HS6ST2 polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a HS6ST2 polypeptide; or a CEACAM6 polypeptide, a HS6ST2 polypeptide, and a NUP210 polypeptide; or a CEACAM6 polypeptide, a GPR160 polypeptide, and a HS6ST2 polypeptide; or a CEACAM6 polypeptide, a HS6ST2 polypeptide, and a RCC2 polypeptide; or a CEACAM6 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a SMPDL3B polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of LUAD can be used as a 2-biomarker combination for detection of LUAD.

Lung Squamous Cell Carcinoma (LUSC)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Lung squamous cell carcinoma (LUSC) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to LUSC.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to LUSC comprises at least three biomarkers, selected from the group consisting of: a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a LAMC2 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a KPNA2 polypeptide; or a APOO polypeptide, a CYP2S1 polypeptide, and a HS6ST2 polypeptide; or a CYP2S1 polypeptide, a FAM241B polypeptide, and a HS6ST2 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a LSR polypeptide; or a CYP2S1 polypeptide, a ILDR1 polypeptide, and a ULBP2 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a ILDR1 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a RCC2 polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a RAP2B polypeptide; or a CYP2S1 polypeptide, a LAMC2 polypeptide, and a ULBP2 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a LAMB3 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a RACGAP1 polypeptide; or a CYP2S1 polypeptide, a LAMB3 polypeptide, and a ULBP2 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a LAPTM4B polypeptide; or a HS6ST2 polypeptide, a LAMC2 polypeptide, and a LSR polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of LUSC can be used as a 2-biomarker combination for detection of LUSC.

Mesothelioma (MESO)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Mesothelioma (MESO) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to MESO.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to MESO comprises at least three biomarkers, selected from the group consisting of: a CDH2 polypeptide, a EPHB2 polypeptide, and a LRRN1 polypeptide; or a CDH1 polypeptide, a CDH2 polypeptide, and a CDH3 polypeptide; or a CDH2 polypeptide, a LAMC2 polypeptide, and a SMPDL3B polypeptide; or a CDH2 polypeptide, a CDH3 polypeptide, and a LAMB3 polypeptide; or a CDH1 polypeptide, a CDH2 polypeptide, and a LAMC2 polypeptide; or a CDH2 polypeptide, a LAPTM4B polypeptide, and a SMPDL3B polypeptide; or a CDH2 polypeptide, a LMNB1 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a TMEM132A polypeptide; or a AP1M2 polypeptide, a CDH2 polypeptide, and a CDH3 polypeptide; or a CDH3 polypeptide, a EPHB2 polypeptide, and a LAMC2 polypeptide; or a CDH2 polypeptide, a CDH3 polypeptide, and a SHISA2 polypeptide; or a AP1M2 polypeptide, a CDH2 polypeptide, and a SLC39A6 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a RCC2 polypeptide; or a AP1M2 polypeptide, a CDH2 polypeptide, and a CLN5 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a RACGAP1 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of MESO can be used as a 2-biomarker combination for detection of MESO.

Ovarian Serous Cystadenocarcinoma (OV)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Ovarian serous cystadenocarcinoma (OV) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to OV.

In some embodiments, biomarkers or biomarker combinations for OV detection that are useful to be included in pan-cancer detection are described in International Application No. PCT/US21/13776 (published as WO2021146659), the entire content of which is incorporated herein by reference.

In some embodiments, one or more biomarkers that are suitable for detection of OV and are useful to be included in pan-cancer detection can be selected from: (i) polypeptides encoded by human genes as follows: ALPL, AQP5, BCAM, BST2, CD24, CD74, CDH6, CHODL, CLDN16, CLDN3, CLDN6, CXCR4, DDR1, EFNB1, EPCAM, FOLR1, HTR3A, LEMD1, LRRTM1, LY6E, MSLN, MUC1, MUC16, NOTCH3, PLXNB1, PTGS1, SLC2A1, SLC34A2, SPINT2, ST14, TACSTD2, TNFRSF12A, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis A antigen (also known as CA19-9), Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, and combinations thereof.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to OV comprises at least three biomarkers, selected from the group consisting of: a CLDN3 polypeptide, a EPHB2 polypeptide, and a FOLR1 polypeptide; or a CLDN3 polypeptide, a FOLR1 polypeptide, and a LAPTM4B polypeptide; or a CLDN3 polypeptide, a FOLR1 polypeptide, and a MARCKSL1 polypeptide; or a CLDN3 polypeptide, a FZD2 polypeptide, and a LAPTM4B polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a LAPTM4B polypeptide; or a FOLR1 polypeptide, a KPNA2 polypeptide, and a VTCN1 polypeptide; or a BMPR1B polypeptide, a CLDN3 polypeptide, and a TMEM238 polypeptide; or a FOLR1 polypeptide, a MARCKSL1 polypeptide, and a SMPDL3B polypeptide; or a FOLR1 polypeptide, a LMNB1 polypeptide, and a VTCN1 polypeptide; or a CLDN3 polypeptide, a FZD2 polypeptide, and a VTCN1 polypeptide; or a CDH2 polypeptide, a FOLR1 polypeptide, and a SMPDL3B polypeptide; or a BMPR1B polypeptide, a CLDN3 polypeptide, and a LAPTM4B polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a FZD2 polypeptide; or a APOO polypeptide, a CLDN3 polypeptide, and a FZD2 polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a RCC2 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of OV can be used as a 2-biomarker combination for detection of OV.

Pancreatic Adenocarcinoma (PAAD)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Pancreatic adenocarcinoma (PAAD) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to PAAD.

In some embodiments, biomarkers or biomarker combinations for PAAD detection that are useful to be included in pan-cancer detection are described in U.S. Provisional Application No. 63/224,379, (the “'379 application”) and the International PCT Application that claims priority to the '379 application and was filed on Jul. 21, 2022 the entire content of each of which is incorporated herein by reference.

In some embodiments, one or more biomarkers that are suitable for detection of PAAD and are useful to be included in pan-cancer detection can be selected from: (i) polypeptides encoded by human genes as follows: ADGRG1, ANO1, AP1M2, ATP1B1, CARD11, CDCP1, CDH1, CDH11, CDH17, CEACAM5, CEACAM6, CFTR, CLIC3, CLN5, CNTN1, CYP2S1, DSG2, EPCAM, EPHA2, FER1L6, FERMT1, GALNT3, GALNT5, GATM, GCNT3, GOLM1, GP2, GPRC5A, GPX8, HACD3, HKDC1, HSD17B2, ITGA11, ITGA2, ITGB4, ITGB6, LAD1, LAMA3, LAMB3, LAMC2, LOXL2, LSR, MARCKSL1, MET, MMP14, MOXD1, MSLN, MUC1, MUC13, PCDH1, PIGT, PIK3AP1, PROM1, QSOX1, RAB25, RAB27B, RAP2B, S100A6, S100P, SCGN, SDR16C5, SHROOM3, SLC4A4, SMPDL3B, SPARC, SRC, ST14, TACSTD2, TESC, THY1, TJP3, TSPAN8, VASP, VNN1, VWA1, ADAM17, BAG3, CCN2, CETN1, EGFR, ERBB3, GUCY2C, ICAM1, IGF1R, IL1A, MDM2, MUC17, MUC5AC, MUCL1, NOTCH2, NOTCH3, PLAUR, SLC44A4, TF, TFRC, TNFRSF10B, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: Lewis Y antigen (also known as CD174), Sialyl Lewis A antigen (also known as CA19-9), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, and combinations thereof.

In some embodiments, one or more biomarkers that are suitable for detection of PAAD and are useful to be included in pan-cancer detection can be selected from: (i) polypeptides encoded by human genes as follows: AP1M2, CARD11, CDH1, CEACAM5, CEACAM6, CFTR, CLN5, CYP2S1, EPCAM, FER1L6, FERMT1, GALNT3, GALNT5, GCNT3, HSD17B2, ITGB6, LAD1, LAMB3, LAMC2, LSR, MARCKSL1, MSLN, MUC1, MUC13, RAB25, S100P, SCGN, SLC4A4, TACSTD2, TESC, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, and combinations thereof.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to PAAD comprises at least three biomarkers, selected from the group consisting of: a B3GNT3 polypeptide, a LAMC2 polypeptide, and a PMEPA1 polypeptide; or a B3GNT3 polypeptide, a LAMC2 polypeptide, and a SHISA2 polypeptide; or a B3GNT3 polypeptide, a FZD2 polypeptide, and a LAMC2 polypeptide; or a CYP2S1 polypeptide, a SYT13 polypeptide, and a VTCN1 polypeptide; or a B3GNT3 polypeptide, a LAMC2 polypeptide, and a MET polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a SYT13 polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a GJB1 polypeptide; or a CDH3 polypeptide, a FZD2 polypeptide, and a SYT13 polypeptide; or a ILDR1 polypeptide, a LAMB3 polypeptide, and a LAMC2 polypeptide; or a CEACAM5 polypeptide, a PMEPA1 polypeptide, and a SHISA2 polypeptide; or a PARD6B polypeptide, a PMEPA1 polypeptide, and a SYT13 polypeptide; or a CYP2S1 polypeptide, a ILDR1 polypeptide, and a PMEPA1 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a PMEPA1 polypeptide; or a CDH3 polypeptide, a SMPDL3B polypeptide, and a SYT13 polypeptide; or a GALNT14 polypeptide, a PMEPA1 polypeptide, and a SYT13 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of PAAD can be used as a 2-biomarker combination for detection of PAAD.

Pheochromocytoma and Paraganglioma (PCPG)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Pheochromocytoma and Paraganglioma (PCPG) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to PCPG.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to PCPG comprises at least three biomarkers, selected from the group consisting of: a CDH2 polypeptide, a CLN5 polypeptide, and a GNG4 polypeptide; or a BMPR1B polypeptide, a CDH2 polypeptide, and a GNG4 polypeptide; or a BMPR1B polypeptide, a CDH2 polypeptide, and a HS6ST2 polypeptide; or a BMPR1B polypeptide, a CLGN polypeptide, and a PODXL2 polypeptide; or a ARFGEF3 polypeptide, a CDH2 polypeptide, and a GALNT14 polypeptide; or a BMPR1B polypeptide, a CD24 polypeptide, and a GPRIN1 polypeptide; or a CLGN polypeptide, a PODXL2 polypeptide, and a SLC39A6 polypeptide; or a CDH2 polypeptide, a GALNT14 polypeptide, and a PODXL2 polypeptide; or a BMPR1B polypeptide, a CDH2 polypeptide, and a PODXL2 polypeptide; or a APOO polypeptide, a CDH2 polypeptide, and a PODXL2 polypeptide; or a CDH2 polypeptide, a PODXL2 polypeptide, and a UNC13B polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a PODXL2 polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a PRAF2 polypeptide; or a CD24 polypeptide, a CDH2 polypeptide, and a SLC39A6 polypeptide; or a BMPR1B polypeptide, a ELAPOR1 polypeptide, and a GPRIN1 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of PCPG can be used as a 2-biomarker combination for detection of PCPG.

Prostate Adenocarcinoma (PRAD)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Prostate adenocarcinoma (PRAD) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to PRAD.

In some embodiments, biomarkers or biomarker combinations for PRAD detection that are useful to be included in pan-cancer detection are described in U.S. Provisional Application No. 63/224,380, (the “'380 application”) and the International PCT Application that claims priority to the '380 application and was filed on Jul. 21, 2022 the entire content of each of which is incorporated herein by reference.

In some embodiments, one or more biomarkers that are suitable for detection of PRAD and are useful to be included in pan-cancer detection can be selected from: (i) polypeptides encoded by human genes as follows: ABCC4, ABHD17C, AD11, AGTRAP, AP1M2, APOO, ARFGEF3, ATP2C1, BCAM, CADM4, CANT1, CDH1, CHMP4C, CLDN3, CLDN4, CLGN, CLN5, CYB561, DNAJC30, ENPP5, EPCAM, ERGIC1, FAAH, FOLH1, GALNT3, GNG4, GNPNAT1, GOLM1, GRHL2, HID1, HOMER2, HPN, LCP1, LRIG1, MAP7, MARCKSL1, MARVELD2, MBOAT2, MIA3, MUC1, NAAA, NDUFA2, PMEPA1, PODXL2, PPP3CA, PRSS8, RAB3B, RAB3D, RAP1GAP, RDH11, SCARB2, SERINC5, SFXN2, SHROOM2, SHROOM3, SLC35F2, SLC39A6, SLC39A7, SLC4A4, SMPDL3B, SORD, STEAP1, STEAP2, SYNGR2, SYT7, TMC5, TMED3, TMEM141, TMEM192, TMEM9, TMPRSS2, TRPM4, TSPAN1, UNC13B, VWA1, Y1PF1, ADAM17, CCL2, CD274, CD38, CLEC2D, ERBB2, FLNA, FLNB, GPC1, IL6, ITGAV, KLK3, KLKB1, P1, PPP1R3A, PSCA, PVR, SLC44A4, TGFBR2, TNFRSF4, TNFSF11, VEGFC, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: Tn antigen, SialylTn (sTn) antigen, Thomsen-Friedenreich (T, TF) antigen, Lewis Y antigen (also known as CD174), Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), and combinations thereof.

In some embodiments, one or more biomarkers that are suitable for detection of PRAD and are useful to be included in pan-cancer detection can be selected from: (i) polypeptides encoded by human genes as follows: ABCC4, AP1M2, ARFGEF3, CANT1, CD38, CDH1, CLDN3, CLDN4, CLGN, ENPP5, FOLH1, GOLM1, GRHL2, MAP7, MARCKSL1, MUC1, PMEPA1, PODXL2, PPP3CA, PSCA, RAB3B, RAB3D, RDH11, SLC39A6, SLC4A4, SMPDL3B, SORD, STEAP1, STEAP2, SYT7, TMPRSS2, TRPM4, TSPAN1, UNC13B, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows: Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, and combinations thereof.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to PRAD comprises at least three biomarkers, selected from the group consisting of: a BMPR1B polypeptide, a CLDN3 polypeptide, and a MARCKSL1 polypeptide; or a BMPR1B polypeptide, a CLDN3 polypeptide, and a GOLM1 polypeptide; or a BMPR1B polypeptide, a CLDN3 polypeptide, and a PODXL2 polypeptide; or a PODXL2 polypeptide, a SLC39A6 polypeptide, and a SLC44A4 polypeptide; or a AP1M2 polypeptide, a BMPR1B polypeptide, and a PODXL2 polypeptide; or a BMPR1B polypeptide, a ILDR1 polypeptide, and a PODXL2 polypeptide; or a BMPR1B polypeptide, a CLDN4 polypeptide, and a PODXL2 polypeptide; or a BMPR1B polypeptide, a ILDR1 polypeptide, and a MARCKSL1 polypeptide; or a BMPR1B polypeptide, a EPCAM polypeptide, and a PODXL2 polypeptide; or a AP1M2 polypeptide, a BMPR1B polypeptide, and a GOLM1 polypeptide; or a BMPR1B polypeptide, a ILDR1 polypeptide, and a SLC39A6 polypeptide; or a BMPR1B polypeptide, a CANT1 polypeptide, and a ILDR1 polypeptide; or a AP1M2 polypeptide, a BMPR1B polypeptide, and a MARCKSL1 polypeptide; or a BMPR1B polypeptide, a MARCKSL1 polypeptide, and a SLC44A4 polypeptide; or a MARCKSL1 polypeptide, a SLC39A6 polypeptide, and a SLC44A4 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of PRAD can be used as a 2-biomarker combination for detection of PRAD.

Rectum Adenocarcinoma (READ)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Rectum adenocarcinoma (READ) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to READ.

In some embodiments, biomarkers or biomarker combinations for READ detection that are useful to be included in pan-cancer detection are described in U.S. Provisional Application No. 63/224,378, (the “'378 application”) and the International PCT Application that claims priority to the '378 application and was filed on Jul. 21, 2022 the entire content of each of which is incorporated herein by reference.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to READ comprises at least three biomarkers, selected from the group consisting of: a CDH17 polypeptide, a CDH3 polypeptide, and a FERMT1 polypeptide; or a CDH17 polypeptide, a CDH3 polypeptide, and a GALNT6 polypeptide; or a CDH17 polypeptide, a CDH3 polypeptide, and a PMEPA1 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a GJB1 polypeptide; or a CDH17 polypeptide, a CDH3 polypeptide, and a RNF128 polypeptide; or a CDH17 polypeptide, a CDH3 polypeptide, and a CYP2S1 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a FERMT1 polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a CYP2S1 polypeptide; or a CDH17 polypeptide, a CDH3 polypeptide, and a MARCKSL1 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a EPCAM polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a CLN5 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a CYP2S1 polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a EPHB2 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a PMEPA1 polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a MARCKSL1 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of READ can be used as a 2-biomarker combination for detection of READ.

Sarcoma (SARC)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Sarcoma (SARC) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to SARC.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to SARC comprises at least three biomarkers, selected from the group consisting of: a CDH2 polypeptide, a LMNB1 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a EPHB2 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a RACGAP1 polypeptide; or a GNG4 polypeptide, a LMNB1 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a RAC3 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a PRAF2 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a SHISA2 polypeptide; or a CDH2 polypeptide, a IGSF3 polypeptide, and a SHISA2 polypeptide; or a GNG4 polypeptide, a LRRN1 polypeptide, and a RACGAP1 polypeptide; or a GPRIN1 polypeptide, a LRRN1 polypeptide, and a PRAF2 polypeptide; or a CDH2 polypeptide, a IGSF3 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a RCC2 polypeptide; or a CDH2 polypeptide, a GPRIN1 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a LRRN1 polypeptide; or a GOLM1 polypeptide, a GPRIN1 polypeptide, and a LRRN1 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of SARC can be used as a 2-biomarker combination for detection of SARC.

Skin Cutaneous Melanoma (SKCM)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Skin Cutaneous Melanoma (SKCM) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to SKCM.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to SKCM comprises at least three biomarkers, selected from the group consisting of: a GJB1 polypeptide, a IGSF3 polypeptide, and a RAP2B polypeptide; or a GJB1 polypeptide, a IGSF3 polypeptide, and a RCC2 polypeptide; or a GJB1 polypeptide, a IGSF3 polypeptide, and a KPNA2 polypeptide; or a APOO polypeptide, a GJB1 polypeptide, and a IGSF3 polypeptide; or a GJB1 polypeptide, a IGSF3 polypeptide, and a LAPTM4B polypeptide; or a GJB1 polypeptide, a IGSF3 polypeptide, and a SLC39A6 polypeptide; or a GJB1 polypeptide, a KDELR3 polypeptide, and a SHISA2 polypeptide; or a APOO polypeptide, a CDH3 polypeptide, and a GJB1 polypeptide; or a CDH3 polypeptide, a GJB1 polypeptide, and a KPNA2 polypeptide; or a CDH3 polypeptide, a GJB1 polypeptide, and a LAPTM4B polypeptide; or a CDH3 polypeptide, a GJB1 polypeptide, and a RPN2 polypeptide; or a CDH3 polypeptide, a GJB1 polypeptide, and a RPN1 polypeptide; or a CDH3 polypeptide, a GJB1 polypeptide, and a SLC35A2 polypeptide; or a ALDH18A1 polypeptide, a CDH3 polypeptide, and a GJB1 polypeptide; or a CDH2 polypeptide, a IGSF3 polypeptide, and a SHISA2 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of SKCM can be used as a 2-biomarker combination for detection of SKCM.

Stomach Adenocarcinoma (STAD)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Stomach adenocarcinoma (STAD) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to STAD.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to STAD comprises at least three biomarkers, selected from the group consisting of: a CDH3 polypeptide, a CYP2S1 polypeptide, and a SYT13 polypeptide; or a CYP2S1 polypeptide, a FERMT1 polypeptide, and a PMEPA1 polypeptide; or a CYP2S1 polypeptide, a ILDR1 polypeptide, and a PMEPA1 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a GJB1 polypeptide; or a CDH17 polypeptide, a CDH3 polypeptide, and a CYP2S1 polypeptide; or a CYP2S1 polypeptide, a FERMT1 polypeptide, and a MET polypeptide; or a CDH3 polypeptide, a CEACAM5 polypeptide, and a TMEM238 polypeptide; or a MARCKSL1 polypeptide, a PODXL2 polypeptide, and a TMEM238 polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a CYP2S1 polypeptide; or a CDH17 polypeptide, a CDH3 polypeptide, and a SHISA2 polypeptide; or a CDH17 polypeptide, a CDH3 polypeptide, and a GALNT6 polypeptide; or a CDH17 polypeptide, a CDH3 polypeptide, and a PMEPA1 polypeptide; or a CDH3 polypeptide, a CEACAM6 polypeptide, and a CYP2S1 polypeptide; or a CDH17 polypeptide, a CDH3 polypeptide, and a FERMT1 polypeptide; or a CDH17 polypeptide, a FOLR1 polypeptide, and a MET polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of STAD can be used as a 2-biomarker combination for detection of STAD.

Testicular Germ Cell Tumors (TGCT)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Testicular Germ Cell Tumors (TGCT) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to TGCT.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to TGCT comprises at least three biomarkers, selected from the group consisting of: a CDH2 polypeptide, a EPCAM polypeptide, and a RCC2 polypeptide; or a CDH2 polypeptide, a CDH3 polypeptide, and a EPCAM polypeptide; or a CDH2 polypeptide, a LAPTM4B polypeptide, and a PODXL2 polypeptide; or a AP1M2 polypeptide, a CDH2 polypeptide, and a MARCKSL1 polypeptide; or a AP1M2 polypeptide, a CDH2 polypeptide, and a CDH3 polypeptide; or a CDH3 polypeptide, a CYP2S1 polypeptide, and a EPCAM polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a LMNB1 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a LAPTM4B polypeptide; or a CDH2 polypeptide, a LMNB1 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a LAPTM4B polypeptide, and a SMPDL3B polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a KPNA2 polypeptide; or a CYP2S1 polypeptide, a HS6ST2 polypeptide, and a RCC2 polypeptide; or a GNG4 polypeptide, a LMNB1 polypeptide, and a LRRN1 polypeptide; or a CDH2 polypeptide, a LRRN1 polypeptide, and a RCC2 polypeptide; or a LAPTM4B polypeptide, a LARGE2 polypeptide, and a SMPDL3B polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of TGCT can be used as a 2-biomarker combination for detection of TGCT.

Thymoma (THYM)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Thymoma (THYM) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to THYM.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to THYM comprises at least three biomarkers, selected from the group consisting of: a CDH2 polypeptide, a CDH3 polypeptide, and a SHISA2 polypeptide; or a CDH1 polypeptide, a CDH2 polypeptide, and a HS6ST2 polypeptide; or a CDH1 polypeptide, a CDH2 polypeptide, and a SHISA2 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a SHISA2 polypeptide; or a CDH2 polypeptide, a IGSF3 polypeptide, and a SHISA2 polypeptide; or a HS6ST2 polypeptide, a IGSF3 polypeptide, and a LMNB1 polypeptide; or a CDH2 polypeptide, a MARVELD2 polypeptide, and a SHISA2 polypeptide; or a CDH2 polypeptide, a LSR polypeptide, and a SHISA2 polypeptide; or a CDH1 polypeptide, a CDH2 polypeptide, and a CDH3 polypeptide; or a CDH2 polypeptide, a FERMT1 polypeptide, and a HS6ST2 polypeptide; or a HS6ST2 polypeptide, a ILDR1 polypeptide, and a SHISA2 polypeptide; or a BMPR1B polypeptide, a CDH2 polypeptide, and a HS6ST2 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a SHISA2 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a RCC2 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a ILDR1 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of THYM can be used as a 2-biomarker combination for detection of THYM.

Thyroid Carcinoma (THCA)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Thyroid carcinoma (THCA) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to THCA.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to THCA comprises at least three biomarkers, selected from the group consisting of: a ILDR1 polypeptide, a MET polypeptide, and a SHISA2 polypeptide; or a CDH2 polypeptide, a SHISA2 polypeptide, and a SMPDL3B polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a SHISA2 polypeptide; or a CDH1 polypeptide, a CDH2 polypeptide, and a SHISA2 polypeptide; or a ILDR1 polypeptide, a SHISA2 polypeptide, and a SMPDL3B polypeptide; or a CDH2 polypeptide, a MAL2 polypeptide, and a SHISA2 polypeptide; or a CDH2 polypeptide, a EPCAM polypeptide, and a SHISA2 polypeptide; or a ILDR1 polypeptide, a MET polypeptide, and a SMPDL3B polypeptide; or a CDH2 polypeptide, a MARVELD2 polypeptide, and a SHISA2 polypeptide; or a CLN5 polypeptide, a ILDR1 polypeptide, and a SHISA2 polypeptide; or a CDH2 polypeptide, a LSR polypeptide, and a SHISA2 polypeptide; or a CDH2 polypeptide, a ILDR1 polypeptide, and a SHISA2 polypeptide; or a AP1M2 polypeptide, a CDH2 polypeptide, and a CLN5 polypeptide; or a ILDR1 polypeptide, a MET polypeptide, and a RNF128 polypeptide; or a CDH2 polypeptide, a CLN5 polypeptide, and a EPCAM polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of THCA can be used as a 2-biomarker combination for detection of THCA Uterine Carcinosarcoma (UCS)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Uterine Carcinosarcoma (UCS) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to UCS.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to UCS comprises at least three biomarkers, selected from the group consisting of: a CDH3 polypeptide, a FZD2 polypeptide, and a SYT13 polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a FZD2 polypeptide; or a CLDN3 polypeptide, a FZD2 polypeptide, and a LAPTM4B polypeptide; or a LAPTM4B polypeptide, a PODXL2 polypeptide, and a SMPDL3B polypeptide; or a CDH2 polypeptide, a LAPTM4B polypeptide, and a SMPDL3B polypeptide; or a FZD2 polypeptide, a LMNB1 polypeptide, and a VTCN1 polypeptide; or a CDH3 polypeptide, a EPCAM polypeptide, and a FZD2 polypeptide; or a CDH2 polypeptide, a LSR polypeptide, and a SHISA2 polypeptide; or a FZD2 polypeptide, a KPNA2 polypeptide, and a VTCN1 polypeptide; or a CDH2 polypeptide, a LAPTM4B polypeptide, and a PODXL2 polypeptide; or a CDH2 polypeptide, a CLDN3 polypeptide, and a LAPTM4B polypeptide; or a EPCAM polypeptide, a HS6ST2 polypeptide, and a LRRN1 polypeptide; or a FZD2 polypeptide, a SMPDL3B polypeptide, and a VTCN1 polypeptide; or a CDH3 polypeptide, a CLDN3 polypeptide, and a FZD2 polypeptide; or a APOO polypeptide, a CLDN3 polypeptide, and a FZD2 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of UCS can be used as a 2-biomarker combination for detection of UCS.

Uterine Corpus Endometrial Carcinoma (UCEC)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Uterine Corpus Endometrial Carcinoma (UCEC) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to UCEC.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to UCEC comprises at least three biomarkers, selected from the group consisting of: a FZD2 polypeptide, a SMPDL3B polypeptide, and a VTCN1 polypeptide; or a CLDN3 polypeptide, a LAPTM4B polypeptide, and a TMEM132A polypeptide; or a EPCAM polypeptide, a FZD2 polypeptide, and a VTCN1 polypeptide; or a BMPR1B polypeptide, a CLDN3 polypeptide, and a MARCKSL1 polypeptide; or a CLDN3 polypeptide, a FZD2 polypeptide, and a LAPTM4B polypeptide; or a BMPR1B polypeptide, a EPCAM polypeptide, and a MARCKSL1 polypeptide; or a APOO polypeptide, a CLDN3 polypeptide, and a FZD2 polypeptide; or a CLDN3 polypeptide, a FZD2 polypeptide, and a VTCN1 polypeptide; or a LAPTM4B polypeptide, a PODXL2 polypeptide, and a SMPDL3B polypeptide; or a LAPTM4B polypeptide, a SMPDL3B polypeptide, and a VTCN1 polypeptide; or a AP1M2 polypeptide, a BMPR1B polypeptide, and a SMPDL3B polypeptide; or a BMPR1B polypeptide, a CLDN3 polypeptide, and a SMPDL3B polypeptide; or a CLDN3 polypeptide, a LMNB1 polypeptide, and a VTCN1 polypeptide; or a BMPR1B polypeptide, a SERINC2 polypeptide, and a SMPDL3B polypeptide; or a FZD2 polypeptide, a PODXL2 polypeptide, and a SMIM22 polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of UCEC can be used as a 2-biomarker combination for detection of UCEC.

Uveal Melanoma (UVM)

In some embodiments, at least one or more biomarker combinations that are suitable for detection of Uveal Melanoma (UVM) can be included in pan-cancer detection. In some embodiments, biomarker combinations can enrich a population for subjects who may likely be suffering from or be susceptible to UVM.

In some embodiments, a biomarker combination suitable for enriching a population for subjects who may be likely suffering from or be likely susceptible to UVM comprises at least three biomarkers, selected from the group consisting of: a GALNT14 polypeptide, a LAPTM4B polypeptide, and a PODXL2 polypeptide; or a LAPTM4B polypeptide, a PODXL2 polypeptide, and a SMPDL3B polypeptide; or a CDH2 polypeptide, a LAPTM4B polypeptide, and a PODXL2 polypeptide; or a CDH2 polypeptide, a GALNT14 polypeptide, and a PODXL2 polypeptide; or a LRRN1 polypeptide, a PODXL2 polypeptide, and a SLC39A6 polypeptide; or a BMPR1B polypeptide, a CDH1 polypeptide, and a PODXL2 polypeptide; or a CDH1 polypeptide, a HS6ST2 polypeptide, and a PODXL2 polypeptide; or a GJB1 polypeptide, a IGSF3 polypeptide, and a RAP2B polypeptide; or a CDH1 polypeptide, a HS6ST2 polypeptide, and a MET polypeptide; or combinations thereof. In some embodiments, any two biomarkers of the 3-biomaker combinations as described herein for detection of UVM can be used as a 2-biomarker combination for detection of UVM.

IV. Exemplary Methods of Detecting Provided Markers and/or Biomarker Combinations Described Herein

In general, the present disclosure provides technologies according to which a biomarker combination is analyzed and/or assessed in a bodily fluid-derived sample (e.g., but not limited to a blood-derived sample) comprising extracellular vesicles from a subject in need thereof; in some embodiments, a diagnosis or therapeutic decision is made based on such analysis and/or assessment.

In some embodiments, methods of detecting a biomarker combination include methods for detecting one or more provided markers of a biomarker combination as proteins, glycans, or proteoglycans (including, e.g., but not limited to a protein with a carbohydrate or glycan moiety). Exemplary protein-based methods of detecting one or more provided markers include, but are not limited to, proximity ligation assay, mass spectrometry (MS) and immunoassays, such as immunoprecipitation; western blot; ELISA; immunohistochemistry; immunocytochemistry; flow cytometry; and immuno-PCR. In some embodiments, an immunoassay can be a chemiluminescent immunoassay. In some embodiments, an immunoassay can be a high-throughput and/or automated immunoassay platform.

In some embodiments, methods of detecting one or more provided markers as proteins, glycans, or proteoglycans (including, e.g., but not limited to a protein with a carbohydrate or glycan moiety) in a sample comprise contacting a sample with one or more antibody agents directed to the provided markers of interest. In some embodiments, such methods also comprise contacting the sample with one or more detection labels. In some embodiments, antibody agents are labeled with one or more detection labels.

In some embodiments, detecting binding between a biomarker of interest and an antibody agent for the biomarker of interest includes determining absorbance values or emission values for one or more detection agents. For example, the absorbance values or emission values are indicative of amount and/or concentration of biomarker of interest expressed by extracellular vesicles (e.g., higher absorbance is indicative of higher level of biomarker of interest expressed by extracellular vesicles). In some embodiments, absorbance values or emission values for detection agents are above a threshold value. In some embodiments, absorbance values or emission values for detection agents is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, at least 1.9, at least 2.0, at least 2.5, at least 3.0, at least 3.5 fold or greater than a threshold value. In some embodiments, the threshold value is determined across a population of a control or reference group (e.g., non-cancer subjects).

In some embodiments, methods of detecting one or more provided markers include methods for detecting one or more provided markers as nucleic acids. Exemplary nucleic acid-based methods of detecting one or more provided markers include, but are not limited to, performing nucleic acid amplification methods, such as polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), transcription-mediated amplification (TMA), ligase chain reaction (LCR), strand displacement amplification (SDA), and nucleic acid sequence based amplification (NASBA). In some embodiments, a nucleic acid-based method of detecting one or more provided markers includes detecting hybridization between one or more nucleic acid probes and one or more nucleotide sequences that encode a biomarker of interest. In some embodiments, the nucleic acid probes are each complementary to at least a portion of one of the one or more nucleotide sequences that encode the biomarker of interest. In some embodiments, the nucleotide sequences s that encode the biomarker of interest include DNA (e.g., cDNA). In some embodiments, the nucleotide sequences that encode the biomarker of interest include RNA. In some embodiments, the nucleotide sequences that encode the biomarker of interest may be or comprise mRNA. In some embodiments, the nucleotide sequences that encode the biomarker of interest may be or comprise microRNA. In some embodiments, the nucleotide sequences that encode the biomarker of interest may be or comprise noncoding RNA, which in some embodiments may be or comprise orphan noncoding RNA (oncRNA). In some embodiments, the nucleotide sequences that encode the biomarker of interest may be or comprise long noncoding RNA (lncRNA). In some embodiments, the nucleotide sequences that encode the biomarker of interest may be or comprise piwi-interacting RNA (piwiRNA). In some embodiments, the nucleotide sequences that encode the biomarker of interest may be or comprise circular RNA (circRNA). In some embodiments, the nucleotide sequences that encode the biomarker of interest may be or comprise small nucleolar RNA (snoRNA).

In some embodiments, methods of detecting one or more provided markers involve proximity-ligation-immuno quantitative polymerase chain reaction (pliq-PCR). Pliq-PCR can have certain advantages over other technologies to profile EVs. For example, pliq-PCR can have a sensitivity three orders of magnitude greater than other standard immunoassays, such as ELISAs (Darmanis et al., 2010; which is incorporated herein by reference for the purpose described herein). In some embodiments, a pliq-PCR reaction can be designed to have an ultra-low LOD, which enables to detect trace levels of tumor-derived EVs, for example, down to a thousand EVs per mL.

In some embodiments, methods for detecting one or more provided markers may involve other technologies for detecting EVs, including, e.g., Nanoplasmic Exosome (nPLEX) Sensor (Im et al., 2014; which is incorporated herein by reference for the purpose described herein) and the Integrated Magnetic-Electrochemical Exosome (iMEX) Sensor (Jeong et al., 2016; which is incorporated herein by reference for the purpose described herein), which have reported LODs of ˜10³and ˜10⁴EVs, respectively (Shao et al., 2018; which is incorporated herein by reference for the purpose described herein).

In some embodiments, methods for detecting one or more provided biomarkers in extracellular vesicles can be based on bulk EV sample analysis.

In some embodiments, methods for detecting one or more provided biomarkers in extracellular vesicles can be based on profiling individual EVs (e.g., single-EV profiling assays), which is further discussed in the section entitled “Exemplary Methods for Profiling Nanoparticles Having a Size Range of Interest that Includes Individual Extracellular Vesicles (EVs)” below.

A skilled artisan reading the present disclosure will understand that the assays described herein for detecting or profiling individual EVs can be also used to detect biomarker combinations on the surface of nanoparticles having a size range of interest (e.g., as described herein) that includes extracellular vesicles (e.g., as described herein).

In some embodiments, nanoparticles having a size range of interest that includes extracellular vesicles in a sample may be captured or immobilized on a solid substrate prior to detecting one or more provided biomarkers in accordance with the present disclosure. In some embodiments, nanoparticles having a size range of interest that includes extracellular vesicles may be captured on a solid substrate surface by non-specific interaction, including, e.g., adsorption. In some embodiments, nanoparticles having a size range of interest that includes extracellular vesicles may be selectively captured on a solid substrate surface. For example, in some embodiments, a solid substrate surface may be coated with an agent that specifically binds to nanoparticles having a size range of interest that includes extracellular vesicles (e.g., an antibody agent specifically targeting such nanoparticles, e.g., associated with cancer). In some embodiments, a solid substrate surface may be coated with a member of an affinity binding pair and an entity of interest (e.g., extracellular vesicles) to be captured may be conjugated to a complementary member of the affinity binding pair. In some embodiments, an exemplary affinity binding pair includes, e.g., but is not limited to biotin and avidin-like molecules such as streptavidin. As will be understood by those of skilled in the art, other appropriate affinity binding pairs can also be used to facilitate capture of an entity of interest to a solid substrate surface. In some embodiments, an entity of interest may be captured on a solid substrate surface by application of a current, e.g., as described in Ibsen et al. ACS Nano., 11: 6641-6651 (2017) and Lewis et al. ACS Nano., 12: 3311-3320 (2018), both of which are incorporated herein by reference for the purpose described herein, and both of which describe use of an alternating current electrokinetic microarray chip device to isolate extracellular vesicles from an undiluted human blood or plasma sample.

A solid substrate may be provided in a form that is suitable for capturing nanoparticles having a size range of interest that includes extracellular vesicles and does not interfere with downstream handling, processing, and/or detection. For example, in some embodiments, a solid substrate may be or comprise a bead (e.g., a magnetic bead). In some embodiments, a solid substrate may be or comprise a surface. For example, in some embodiments, such a surface may be a capture surface of an assay chamber (including, e.g., a tube, a well, a microwell, a plate, a filter, a membrane, a matrix, etc.). Accordingly, in some embodiments, a method described herein comprises, prior to detecting provided biomarkers in a sample, capturing or immobilizing nanoparticles having a size range of interest that includes extracellular vesicles on a solid substrate.

In some embodiments, a sample may be processed, e.g., to remove undesirable entities such as cell debris or cells, prior to capturing nanoparticles having a size range of interest that includes extracellular vesicles on a solid substrate surface. For example, in some embodiments, such a sample may be subjected to centrifugation, e.g., to remove cell debris, cells, and/or other particulates. Additionally or alternatively, in some embodiments, such a sample may be subjected to size-exclusion-based purification or filtration. Various size-exclusion-based purification or filtration are known in the art and those skilled in the art will appreciate that in some cases, a sample may be subjected to a spin column purification based on specific molecular weight or particle size cutoff. Those skilled in the art will also appreciate that appropriate molecular weight or particle size cutoff for purification purposes can be selected, e.g., based on the size of extracellular vesicles. For example, in some embodiments, size-exclusion separation methods may be applied to samples comprising extracellular vesicles to isolate a fraction of nanoparticles that include extracellular vesicles of a certain size (e.g., greater than 30 nm and no more than 1000 nm, or greater than 70 nm and no more than 200 nm). Typically, extracellular vesicles may range from 30 nm to several micrometers in diameter. See, e.g., Chuo et al., “Imaging extracellular vesicles: current and emerging methods” Journal of Biomedical Sciences 25: 91 (2018) which is incorporated herein by reference for the purpose described herein, which provides information of sizes for different extracellular vesicle (EV) subtypes: migrasomes (0.5-3 μm), microvesicles (0.1-1 μm), oncosomes (1-10 μm), exomeres (<50 nm), small exosomes (60-80 nm), and large exosomes (90-120 nm). In some embodiments, nanoparticles having a size range of about 30 nm to 1000 nm may be isolated, for example, in some embodiments by one or more size-exclusion separation methods, for detection assay. In some embodiments, specific EV subtype(s) may be isolated, for example, in some embodiments by one or more size-exclusion separation methods, for detection assay.

In some embodiments, nanoparticles having a size range of interest that includes extracellular vesicles in a sample may be processed prior to detecting one or more provided biomarkers of a biomarker combination for cancer. Different sample processing and/or preparation can be performed, e.g., to stabilize targets (e.g., target biomarkers) in nanoparticles having a size range of interest that includes extracellular vesicles to be detected, and/or to facilitate exposure of targets (e.g., intravesicular proteins and/or RNA such as mRNA) to a detection assay (e.g., as described herein), and/or to reduce non-specific binding. Examples of such sample processing and/or preparation are known in the art and include, but are not limited to, crosslinking molecular targets (e.g., fixation), permeabilization of biological entities (e.g., cells or nanoparticles having a size range of interest that includes extracellular vesicles), and/or blocking non-specific binding sites.

In one aspect, the present disclosure provides a method for detecting whether a biomarker combination of cancer is present or absent in a biological sample from a subject in need thereof, which may be in some embodiments a biological sample (e.g., but not limited to a blood-derived sample) comprising nanoparticles having a size range of interest that includes extracellular vesicles. In some embodiments, such a method comprises (a) detecting, in a biological sample such as a bodily fluid sample (e.g., in some embodiments a blood-derived sample such as, e.g., a plasma sample) from a subject, biological entities of interest (including, e.g., nanoparticles having a size range of interest that includes extracellular vesicles) having a biomarker combination of cancer; and (b) comparing sample information indicative of the level of the biomarker combination-expressing biological entities of interest (e.g., nanoparticles having a size range of interest that includes extracellular vesicles) in the biological sample (e.g., a bodily fluid sample such as, e.g., in some embodiments a blood-derived sample) to reference information including a reference threshold level. In some embodiments, a reference threshold level corresponds to a level of biological entities of interest (e.g., nanoparticles having a size range of interest that includes extracellular vesicles) that express such a biomarker combination in comparable samples from a population of reference subjects, e.g., non-cancer subjects. In some embodiments, exemplary non-cancer subjects include healthy subjects (e.g., healthy subjects of specified age ranges, such as e.g., below age 55 or above age 55), subjects with non-cancer-related health diseases, disorders, or conditions (including, e.g., subjects having benign tumors, inflammatory disorders, etc.), and combinations thereof.

In some embodiments, a sample is pre-screened for certain characteristics prior to utilization in an assay as described herein. In some embodiments, a sample meeting certain pre-screening criteria is more suitable for diagnostic applications than a sample failing pre-screening criteria. For example, in some embodiments samples are visually inspected for appearance using known standards, e.g., is the sample normal, hemolyzed (red), icteric (yellow), and/or lipemic (whitish/turbid). In some embodiments, samples can then be rated on a known standard scale (e.g., 1, 2, 3, 4, 5) and the results are recorded. In some embodiments, samples are visually inspected for hemolysis (e.g., heme) and rated on a scale from 1-5, where the visual inspection correlates with a known concentration, e.g., where 1 denotes approximately 0 mg/dL, 2 denotes approximately 50 mg/dL, 3 denotes approximately 150 mg/dL, 4 denotes approximately 250 mg/dL, and 5 denotes approximately 525 mg/dL. In some embodiments, samples are visually inspected icteric levels (e.g., bilirubin) and rated on a scale from 1-5, where the visual inspection correlates with a known concentration, e.g., where 1 denotes approximately 0 mg/dL, 2 denotes approximately 1.7 mg/dL, 3 denotes approximately 6.6 mg/dL, 4 denotes approximately 16 mg/dL, and 5 denotes approximately 30 mg/dL. In some embodiments, samples are visually inspected for turbidity (e.g. lipids) and rated on a scale from 1-5, where the visual inspection correlates with a known concentration, e.g., where 1 denotes approximately 0 mg/dL, 2 denotes approximately 125 mg/dL, 3 denotes approximately 250 mg/dL, 4 denotes approximately 500 mg/dL, and 5 denotes approximately 1000 mg/dL.

In some embodiments, samples scoring lower than a certain level on one or more metrics, e.g., equal to or lower than a score of 4, may be utilized in an assay as described herein. In some embodiments, samples scoring lower than a certain level on one or more metrics, e.g., equal to or lower than a score of 3, may be utilized in an assay as described herein. In some embodiments, samples scoring lower than a certain level on one or more metrics, e.g., equal to or lower than a score of 2, may be utilized in an assay as described herein. In some embodiments, samples scoring lower than a certain level on all three metrics (e.g., hemolyzed, icteric, and lipemic) e.g., equal to or lower than a score of 2, may be utilized in an assay as described herein. In some embodiments, low visual inspection scores on pre-screening criteria such as hemolysis, bilirubin, and/or lipemia (e.g., equal to or lower than a score of 2) may have no appreciable effect (e.g., not be correlated with) on diagnostic properties (e.g., Ct values) produced in an assay as described herein.

In some embodiments, a sample is determined to be positive for the presence of a biomarker combination (e.g., ones described herein) when it shows an elevated level of nanoparticles (having a size range of interest that includes extracellular vesicles) that present the biomarker combination on their surface, relative to a reference threshold level (e.g., ones described herein). In some embodiments, a sample is determined to be positive for the presence of a biomarker combination (e.g., as reflected by the level of biomarker combination-expressing extracellular vesicles) if its level is at least 30% or higher, including, e.g., at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95% or higher, as compared to a reference threshold level. In some embodiments, a sample is determined to be positive for the presence of a biomarker combination (e.g., as reflected by the level of biomarker combination-expressing extracellular vesicles) if its level is at least 2-fold or higher, including, e.g., at least 3-fold, at least 4-fold, at least 5-fold, at least 6-fold, at least 7-fold, at least 8-fold, at least 9-fold, at least 10-fold, at least 50-fold, at least 100-fold, at least 250-fold, at least 500-fold, at least 750-fold, at least 1000-fold, at least 2500-fold, at least 5000-fold, or higher, as compared to a reference threshold level.

In some embodiments, a binary classification system may be used to determine whether a sample is positive for the presence of a biomarker combination (e.g., ones described herein). For example, in some embodiments, a sample is determined to be positive for the presence of a target biomarker signature (e.g., as reflected by the level of biomarker combination-expressing extracellular vesicles) if its level is at or above a reference threshold level, e.g., a cutoff value. In some embodiments, such a reference threshold level (e.g., a cutoff value) may be determined by selecting a certain number of standard deviations away from an average value obtained from control subjects such that a desired sensitivity and/or specificity of a cancer detection assay (e.g., ones described herein) can be achieved. In some embodiments, such a reference threshold level (e.g., a cutoff value) may be determined by selecting a certain number of standard deviations away from a maximum assay signal obtained from control subjects such that a desired sensitivity and/or specificity of a cancer detection assay (e.g., ones described herein) can be achieved. In some embodiments, such a reference threshold level (e.g., a cutoff value) may be determined by selecting the less restrictive of either (i) a certain number of standard deviations away from an average value obtained from control subjects, or (ii) a certain number of standard deviations away from a maximum assay signal obtained from control subjects, such that a desired sensitivity and/or specificity of a cancer detection assay (e.g., ones described herein) can be achieved. In some embodiments, control subjects for determination of a reference threshold level (e.g., a cutoff value) may include, but are not limited to healthy subjects, subjects with inflammatory conditions (e.g., Crohn's disease, ulcerative colitis, endometriosis, etc.), subjects with benign tumors, and combinations thereof. In some embodiments, healthy subjects and subjects with inflammatory conditions that are associated with tissues of interest but that are not cancerous (including, e.g., atherosclerosis, heart disease, chronic kidney disease, diabetes, inflammatory bowel disease, fatty liver disease, chronic obstructive pulmonary disease, endometriosis, rheumatoid arthritis, obesity, pancreatitis etc.) are included in determination of a reference threshold level (e.g., a cutoff value). In some embodiments, subjects with benign tumors are not included in determination of a reference threshold level (e.g., a cutoff value). In some embodiments, a reference threshold level (e.g., a cutoff value) may be determined by selecting at least 1.5 standard deviations (SDs) or higher (including, e.g., at least 1.6, at least 1.7, at least 1.8, at least 1.9, at least 2, at least 2.1, at least 2.2, at least 2.3, at least 2.4, at least 2.5, at least 2.6, at least 2.7, at least 2.8, at least 2.9, at least 3, at least 3.1, at least 3.2, at least 3.3, at least 3.4, at least 3.5, at least 3.6 or higher SDs) away from (i) an average value obtained from control subjects, or (ii) a maximum assay signal obtained from control subjects, such that a desired specificity (e.g., at least 95% or higher specificity [including, e.g., at least 96%, at least 97%, at least 98%, at least 99%, or higher specificity] such as in some embodiments at least 99.8% specificity) of a cancer detection assay (e.g., ones described herein) can be achieved. In some embodiments, a reference threshold level (e.g., a cutoff value) may be determined by selecting at least 2.9 SDs (e.g., at least 2.93 SDs) away from (i) an average value obtained from control subjects, or (ii) a maximum assay signal obtained from control subjects, such that a desired specificity (e.g., at least 99%, or higher specificity) of a cancer detection assay (e.g., ones described herein) can be achieved. In some embodiments, a reference threshold level (e.g., a cutoff value) may be determined by selecting at least 2.9 SDs (e.g., at least 2.93 SDs) away from the less restrictive of (i) an average value obtained from control subjects, or (ii) a maximum assay signal obtained from control subjects, such that a desired specificity (e.g., at least 99%, or higher specificity) of a cancer detection assay (e.g., ones described herein) can be achieved. In some embodiments, such a reference threshold level (e.g., a cutoff value) may be determined based on expression level (e.g., transcript level) of a target biomarker in normal healthy tissues vs. in cancer samples such that the specificity and/or sensitivity of interest (e.g., as described herein) can be achieved. In some embodiments, a reference threshold level (e.g., a cutoff value) may vary dependent on, for example, cancer stages and/or subtypes and/or patient characteristics, for example, patient age, risks factors for cancer (e.g., hereditary risk vs. average risk, life-history-associated risk factors), symptomatic/asymptomatic status, and combinations thereof.

In some embodiments, a reference threshold level (e.g., a cutoff value) may be determined based on a log-normal distribution around healthy subjects (e.g., of specified age ranges), and optionally subjects with inflammatory conditions that are associated with tissues of interest but that are not cancerous (including, e.g., atherosclerosis, heart disease, chronic kidney disease, diabetes, inflammatory bowel disease, fatty liver disease, chronic obstructive pulmonary disease, endometriosis, rheumatoid arthritis, obesity, pancreatitis etc.), and selection of a level that is necessary to achieve the specificity of interest, e.g., based on prevalence of cancer or a subtype thereof (e.g., including but not limited to in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types). In some embodiments, specificity of interest may be at least 70%, including, e.g., at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98%, at least 99%, at least 99.5% or higher.

The present disclosure, among other things, also provides technologies for determining whether a subject as having or being susceptible to cancer, for example, from a sample comprising nanoparticles with a size range of interest that includes extracellular vesicles. For example, in some embodiments, when a biological sample (e.g., a bodily fluid sample such as, e.g., but not limited to a blood-derived sample) from a subject in need thereof shows a level of biomarker combination-expressing extracellular vesicles that is at or above a reference threshold level, e.g., cutoff value (e.g., as determined in accordance with the present disclosure), then the subject is classified as having or being susceptible to cancer. In some such embodiments, a reference threshold level (e.g., cutoff value) may be determined based on a log-normal distribution around healthy subjects (e.g., of specified age ranges), and optionally subjects with inflammatory conditions that are associated with tissues of interest but that are not cancerous (including, e.g., atherosclerosis, heart disease, chronic kidney disease, diabetes, inflammatory bowel disease, fatty liver disease, chronic obstructive pulmonary disease, endometriosis, rheumatoid arthritis, obesity, pancreatitis etc.) and selection of a level that is necessary to achieve the specificity of interest, e.g., based on prevalence of cancer or a subtype thereof (e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types). In some embodiments, specificity of interest may be at least 70%, including, e.g., at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98%, at least 99%, at least 99.5% or higher.

In some embodiments, a reference threshold level (e.g., a cutoff value) may be determined based on expression level (e.g., transcript level) of individual target biomarker(s) of a biomarker combination in normal healthy tissues vs. in cancer samples such that the specificity and/or sensitivity of interest (e.g., as described herein) can be achieved. In some embodiments, a reference threshold level (e.g., a cutoff value) may vary dependent on, for example, cancer stages and/or subtypes and/or patient characteristics, for example, patient age, risks factors for cancer (e.g., hereditary risk vs. average risk, life-history-associated risk factors), symptomatic/asymptomatic status, and combinations thereof.

In some embodiments, when a biological sample from a subject in need thereof shows a level of biomarker combination that satisfies a reference threshold level, then the subject is classified as having or being susceptible to cancer. For example, in some embodiments, when a biological sample (e.g., a bodily fluid sample such as, e.g., but not limited to a blood-derived sample) from a subject in need thereof shows an elevated level of biomarker combination-expressing extracellular vesicles relative to a reference threshold level, then the subject is classified as having or being susceptible to cancer. In some embodiments, a subject in need thereof is classified as having or being susceptible to cancer when the subject's biological sample (e.g., a bodily fluid sample such as, e.g., but not limited to a blood-derived sample) shows a level of biomarker combination-expressing extracellular vesicles that is at least 30% or higher, including, e.g., at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95% or higher, as compared to a reference threshold level. In some embodiments, a subject in need thereof is classified as having or being susceptible to cancer when the subject's biological sample (e.g., a bodily fluid sample such as, e.g., but not limited to a blood-derived sample) shows a level of biomarker combination-expressing extracellular vesicles that is at least 2-fold or higher, including, e.g., at least 3-fold, at least 4-fold, at least 5-fold, at least 6-fold, at least 7-fold, at least 8-fold, at least 9-fold, at least 10-fold, at least 20-fold, at least 30-fold, at least 40-fold, at least 50-fold, at least 60-fold, at least 70-fold, at least 80-fold, at least 90-fold, at least 100-fold, at least 250-fold, at least 500-fold, at least 750-fold, at least 1000-fold, or higher, as compared to a reference threshold level.

When a biological sample (e.g., a bodily fluid sample such as, e.g., but not limited to a blood-derived sample) from a subject in need thereof shows a comparable level to a reference threshold level, then the subject is classified as not likely to have or as not likely to be susceptible to cancer. In some such embodiments, a reference threshold level corresponds to a level of extracellular vesicles that express a biomarker combination in comparable samples from a population of reference subjects, e.g., non-cancer subjects. In some embodiments, exemplary non-cancer subjects include healthy subjects (e.g., healthy subjects of specified age ranges, such as e.g., below age 55 or above age 55), subjects with non-tumor related health diseases, disorders, or conditions (including, e.g., subjects having symptoms of cancerous diseases or disorders but not cancer), subjects having benign tumors, and combinations thereof.

V. Exemplary Methods for Profiling Individual Nanoparticles Having a Size Range of Interest that Includes Extracellular Vesicles (EVs)

In some embodiments, assays for profiling individual extracellular vesicles (e.g., single EV profiling assays) can be used to detect one or more provided biomarkers of one or more biomarker combinations for cancer (e.g., ones described herein). For example, in some embodiments, such an assay may involve (i) a capture assay through targeting one or more provided markers of a biomarker combination for cancer and (ii) a detection assay for at least one or more additional provided markers of such a biomarker combination for cancer, wherein such a capture assay is performed prior to such a detection assay.

A skilled artisan reading the present disclosure will understand that assays described herein for detecting or profiling individual extracellular vesicles can also detect surface biomarkers present on the surfaces of nanoparticles having a size of interest (e.g., in some embodiments a size within the range of about 30 nm to about 1000 nm) that includes extracellular vesicles.

In some embodiments, a capture assay is performed to selectively capture tumor-associated nanoparticles having a size range of interest that includes extracellular vesicles (e.g., cancer-associated extracellular vesicles) from a biological sample (e.g., a bodily fluid sample such as, e.g., but not limited to a blood-derived sample) of a subject in need thereof. In some embodiments, a capture assay is performed to selectively capture nanoparticles of a certain size range that includes extracellular vesicles, and/or certain characteristic(s), for example, extracellular vesicles associated with cancer. In some such embodiments, prior to a capture assay, a biological sample (e.g., a bodily fluid sample such as, e.g., but not limited to a blood-derived sample) may be pre-processed to remove contaminants, including, e.g., but not limited to soluble proteins and interfering entities such as, e.g., cell debris. For example, in some embodiments, nanoparticles having a size range of interest that includes extracellular vesicles are purified from a biological sample (e.g., a bodily fluid sample such as, e.g., but not limited to a blood-derived sample) of a subject using size exclusion chromatography. In some such embodiments, nanoparticles having a size range of interest that includes extracellular vesicles can be directly purified from a biological sample (e.g., a bodily fluid sample such as, e.g., but not limited to a blood-derived sample) using size exclusion chromatography, which in some embodiments may remove at least 90% or higher (including, e.g., at least 93%, 95%, 97%, 99% or higher) of soluble proteins and other interfering agents such as, e.g., cell debris.

In some embodiments, a capture assay comprises a step of contacting biological sample (e.g., a bodily fluid sample such as, e.g., but not limited to a blood-derived sample) with at least one capture agent comprising a target-capture moiety that binds to at least one or more provided biomarkers of a biomarker combination for cancer. In some embodiments, a capture assay may be multiplexed, which comprises a step of contacting a biological sample (e.g., a bodily fluid sample such as, e.g., but not limited to a blood-derived sample) with a set of capture agents, each capture agent comprising a target-capture moiety that binds to a distinct provided biomarker of a biomarker combination for cancer. In some embodiments, a target-capture moiety is directed to an extracellular vesicle-associated surface biomarker and/or surface biomarker (e.g., ones as described and/or utilized herein).

In some embodiments, such a target-capture moiety may be immobilized on a solid substrate. Accordingly, in some embodiments, a capture agent employed in a capture assay is or comprises a solid substrate comprising at least one or more (e.g., 1, 2, 3, 4, 5, or more) target-capture moiety conjugated thereto, each target-capture moiety directed to an extracellular vesicle-associated surface biomarker and/or surface biomarker (e.g., ones as described and/or utilized herein). A solid substrate may be provided in a form that is suitable for capturing nanoparticles having a size range of interest that includes extracellular vesicles and does not interfere with downstream handling, processing, and/or detection. For example, in some embodiments, a solid substrate may be or comprise a bead (e.g., a magnetic bead). In some embodiments, a solid substrate may be or comprise a surface. For example, in some embodiments, such a surface may be a capture surface of an assay chamber (including, e.g., a tube, a well, a microwell, a plate, a filter, a membrane, a matrix, etc.). In some embodiments, a capture agent is or comprises a magnetic bead comprising a target-capture moiety conjugated thereto.

In some embodiments, a detection assay is performed to detect one or more provided biomarkers of a biomarker combination for cancer (e.g., ones that are different from ones targeted in a capture assay) in nanoparticles having a size range of interest that includes extracellular vesicles that are captured by a capture assay (e.g., as described above). In some embodiments, a detection assay may comprise immuno-PCR. In some embodiments, an immuno-PCR may involve at least one probe targeting a single provided biomarker (e.g., ones described herein) of a biomarker combination for cancer. In some embodiments, an immuno-PCR may involve a plurality of (e.g., at least two, at least three, at least four, or more) probes directed to different epitopes of the same biomarker (e.g., ones described herein) of a biomarker combination. In some embodiments, an immuno-PCR may involve a plurality of (e.g., at least two, at least three, at least four, or more) probes, each directed to a different provided biomarker described herein.

In some embodiments, a detection assay may comprise reverse transcription polymerase chain reaction (RT-PCR). In some embodiments, an RT-PCR may involve at least one primer/probe set targeting a single provided biomarker described herein. In some embodiments, an RT-PCR may involve a plurality of (e.g., at least two, at least three, at least four, or more) primer/probe sets, each set directed to a different provided biomarker described herein.

In some embodiments, a detection assay may comprise a proximity-ligation-immuno quantitative polymerase chain reaction (pliq-PCR), for example, to determine co-localization of one or more provided biomarkers of a biomarker combination for cancer within nanoparticles having a size range of interest that includes extracellular vesicles (e.g., captured extracellular vesicles that express at least one extracellular vesicle-associated surface biomarker).

In some embodiments, a detection assay employs a target entity detection system that was developed by Applicant and described in U.S. application Ser. No. 16/805,637 (published as US2020/0299780; issued as U.S. Pat. No. 11,085,089), and International Application PCT/US2020/020529 (published as WO2020180741), both filed Feb. 28, 2020 and entitled “Systems, Compositions, and Methods for Target Entity Detection” (the “'089 patent” and the “'529 application”; both of which are incorporated herein by reference in their entirety) which are, in part, based on interaction and/or co-localization of a biomarker combination in individual extracellular vesicles. For example, such a target entity detection system (as described in the '089 patent and '529 application and also further described below in the section entitled “Provided Target Entity Detection Systems and Methods Involving the Same”) can detect in a sample (e.g., in a biological, environmental, or other sample), in some embodiments at a single entity level, entities of interest (e.g., biological or chemical entities of interest, such as extracellular vesicles or analytes) comprising at least one or more (e.g., at least two or more) targets (e.g., molecular targets). Those skilled in the art, reading the present disclosure, will recognize that provided target entity detection systems are useful for a wide variety of applications and/or purposes, including, e.g., for detection of cancer. For example, in some embodiments, provided target entity detection systems may be useful for medical applications and/or purposes. In some embodiments, provided target entity detection systems may be useful to screen (e.g., regularly screen) individuals (e.g., in some embodiments which may be asymptomatic individuals, or in some embodiments which may be individuals experiencing one or more symptoms associated with cancer, or in some embodiments which may be individuals at risk for cancer such as, e.g., individuals with a hereditary risk for cancer and/or life-history-associated risk factor, including individuals who smoke and/or are obese) for a disease or condition (e.g., cancer). In some embodiments, provided target entity detection systems may be useful to screen (e.g., regularly screen) individuals (e.g., in some embodiments which may be asymptomatic individuals, or in some embodiments which may be individuals experiencing one or more symptoms associated with cancer, or in some embodiments which may be individuals at risk for cancer such as, e.g., individuals with a hereditary risk for cancer and/or life-history-associated risk factor, including individuals who smoke and/or are obese) for different types of cancer. In some embodiments, provided target entity detection systems are effective even when applied to populations comprising or consisting of asymptomatic individuals (e.g., due to sufficiently high sensitivity and/or low rates of false positive and/or false negative results). In some embodiments, provided target entity detection systems may be useful as a companion diagnostic in conjunction with a disease treatment (e.g., treatment of cancer).

In some embodiments, a plurality of (e.g., at least two or more) detection assays may be performed to detect a plurality of biomarkers (e.g., at least two or more) of one or more biomarker combinations for cancer (e.g., ones that are different from ones targeted in a capture assay) in nanoparticles having a size range of interest that includes extracellular vesicles, e.g., ones that are captured by a capture assay (e.g., as described above). For example, in some embodiments, a plurality of detection assays may comprise (i) a provided target entity detection system or a system described in the '089 patent and '529 application and/or described herein; and (ii) immuno-PCR. In some embodiments, a plurality of detection assays may comprise (i) a provided target entity detection system or a system described in the '089 patent and '529 application and/or described herein; and (ii) RT-PCR.

For example, in some embodiments, a subject's sample comprising extracellular vesicles may be first subjected to detection of surface biomarkers (e.g., as described herein) using a target entity detection system or a system described in the '089 patent and '529 application and/or described herein and then subjected to a lysis buffer to release intravesicular analytes, followed by a nucleic acid assay (e.g., in some embodiments RT-qPCR) for detection of one or more intravesicular RNA biomarkers. In some embodiments, one or more intravesicular RNA biomarkers may be or comprise an mRNA transcript encoded by a biomarker gene described herein. In some embodiments, one or more intravesicular RNA biomarkers may be or comprise a microRNA. In some embodiments, one or more intravesicular RNA biomarkers may be or comprise an orphan noncoding RNA. In some embodiments, one or more intravesicular RNA biomarkers may be or comprise a long noncoding RNA. In some embodiments, one or more intravesicular RNA biomarkers may be or comprise a piwi-interacting RNA. In some embodiments, one or more intravesicular RNA biomarkers may be or comprise a circular RNA. In some embodiments, one or more intravesicular RNA biomarkers may be or comprise a small nucleolar RNA.

VI. Provided Target Entity Detection Systems and Methods Involving the Same

In some embodiments, a target entity detection system that can be useful in a detection assay for one or more provided biomarkers of one or more biomarker combinations for cancer (e.g., ones described herein) includes a plurality of detection probes each for a specific target (e.g., a provided biomarker of a biomarker combination). In some embodiments, such a system may comprise at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 40, at least 50, or more detection probes each for a specific target (e.g., a provided biomarker of a biomarker combination). In some embodiments, such a system may comprise 2-50 detection probes each for a specific target (e.g., a provided biomarker of a biomarker combination). In some embodiments, such a system may comprise 2-30 detection probes each for a specific target (e.g., a provided biomarker of a biomarker combination). In some embodiments, such a system may comprise 2-25 detection probes each for a specific target (e.g., a provided biomarker of a biomarker combination). In some embodiments, such a system may comprise 5-30 detection probes each for a specific target (e.g., a provided biomarker of a biomarker combination). In some embodiments, such a system may comprise 5-25 detection probes each for a specific target (e.g., a provided biomarker of a biomarker combination). In some embodiments, at least two of such detection probes in a set may be directed to the same biomarker of a biomarker combination. In some embodiments, at least two of such detection probes in a set may be directed to the same epitope of the same biomarker of a biomarker combination. In some embodiments, at least two of such detection probes in a set may be directed to different epitopes of the same biomarker of a biomarker combination.

In some embodiments, a target entity detection system that can be useful in a detection assay for one or more provided biomarkers of one or more biomarker combinations for cancer (e.g., ones described herein) includes a plurality of complementary biomarker combinations each for a specific target (e.g., a series of complementary tissue-specific biomarker combinations). In some embodiments, such a system may comprise at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 40, at least 50, or more complementary biomarker combinations each for a specific target (e.g., a tissue-specific biomarker combination). In some embodiments, such a system may comprise 2-50 biomarker combinations each for a specific target (e.g., a tissue-specific biomarker combination). In some embodiments, such a system may comprise 2-30 biomarker combinations each for a specific target (e.g., a tissue-specific biomarker combination). In some embodiments, such a system may comprise 2-25 biomarker combinations each for a specific target (e.g., a tissue-specific biomarker combination). In some embodiments, such a system may comprise 5-30 biomarker combinations each for a specific target (e.g., a tissue-specific biomarker combination). In some embodiments, such a system may comprise 5-25 biomarker combinations each for a specific target (e.g., a tissue-specific biomarker combination).

In some embodiments, a target entity detection system that can be useful in a detection assay for one or more provided biomarkers of one or more biomarker combinations for cancer (e.g., ones described herein) includes a plurality of biomarker combinations probes each specific to one or more cancers of origin (e.g., a series of complementary tissue- and/or multi-tissue specific biomarker combinations). In some embodiments, such a system may comprise at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 40, at least 50, or more complementary biomarker combinations each specific to one or more cancers of origin (e.g., a tissue- and/or multi-tissue specific biomarker combination). In some embodiments, such a system may comprise 2-50 complementary biomarker combinations each specific to one or more cancers of origin (e.g., a tissue- and/or multi-tissue specific biomarker combination). In some embodiments, such a system may comprise 2-30 complementary biomarker combinations each specific to one or more cancers of origin (e.g., a tissue- and/or multi-tissue specific biomarker combination). In some embodiments, such a system may comprise 2-25 complementary biomarker combinations each specific to one or more cancers of origin (e.g., a tissue- and/or multi-tissue specific biomarker combination). In some embodiments, such a system may comprise 5-30 complementary biomarker combinations each specific to one or more cancers of origin (e.g., a tissue- and/or multi-tissue specific biomarker combination). In some embodiments, such a system may comprise 5-25 complementary biomarker combinations each specific to one or more cancers of origin (e.g., a tissue- and/or multi-tissue specific biomarker combination).

In some embodiments, detection probes appropriate for use in a target entity detection system provided herein may be used for detection of a disease or condition, e.g., cancer. In some embodiments, detection probes appropriate for use in a target entity detection system provided herein may permit detection of at least two or more diseases or conditions, e.g., one of which is cancer. In some embodiments, detection probes appropriate for use in a target entity detection system provided herein may permit detection of cancer of certain subtypes including but not limited to, e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types, and other specified types of cancer as known in the art (SEER Cancer Statistics Review 1975-2017). In some embodiments, detection probes appropriate for use in a target entity detection system provided herein may permit detection of cancer of certain stages, including, e.g., stage I, stage II, stage III, and/or stage IV. Accordingly, in some embodiments, detection probes appropriate for use in a target entity detection system provided herein may comprise a plurality (e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, or more) of sets of biomarker combinations (e.g., as described herein), wherein each set is at least in part complementary to the other sets, and directed to detection of a different disease or a different type of disease or condition. For example, in some embodiments, detection probes appropriate for use in a target entity detection system provided herein may comprise a plurality (e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, or more) of sets of biomarker combinations (e.g., as described herein), wherein in some embodiments, each set is directed to detection of a one or more different types of cancer, at least one of which is non-overlapping with another biomarker combination, or in some embodiments, each set is directed to detection of cancer of various subtypes (e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types) and/or stages.

Detection Probes

In some embodiments, a detection probe as provided and/or utilized herein comprises a target-binding moiety and an oligonucleotide domain coupled to the target-binding moiety. In some embodiments, an oligonucleotide domain coupled to a target-binding moiety may comprise a double-stranded portion and a single-stranded overhang extended from at least one end of the oligonucleotide domain. In some embodiments, an oligonucleotide domain coupled to a target-binding moiety may comprise a double-stranded portion and a single-stranded overhang extended from each end of the oligonucleotide domain. In some embodiments, detection probes may be suitable for proximity-ligation-immuno quantitative polymerase chain reaction (pliq-PCR) and be referred to as pliq-PCR detection probes.

A. Target-Binding Moieties

A target-binding moiety that is coupled to an oligonucleotide domain is an entity or an agent that specifically binds to a target (e.g., a provided biomarker of a biomarker combination; those skilled in the art will appreciate that, where the target biomarker is a particular form or moiety/component, the target-binding moiety specifically binds to that form or moiety/component). In some embodiments, a target-binding moiety may have a binding affinity (e.g., as measured by a dissociation constant) for a target (e.g., molecular target) of at least about 10⁻⁴M, at least about 10⁻⁵M, at least about 10⁻⁶M, at least about 10⁻⁷M, at least about 10⁻⁸M, at least about 10⁻⁹M, or lower. Those skilled in the art will appreciate that, in some cases, binding affinity (e.g., as measured by a dissociation constant) may be influenced by non-covalent intermolecular interactions such as hydrogen bonding, electrostatic interactions, hydrophobic and Van der Waals forces between the two molecules. Alternatively or additionally, binding affinity between a ligand and its target molecule may be affected by the presence of other molecules. Those skilled in the art will be familiar with a variety of technologies for measuring binding affinity and/or dissociation constants in accordance with the present disclosure, including, e.g., but not limited to ELISAs, surface plasmon resonance (SPR) assays, Luminex Single Antigen (LSA) assays, bio-layer interferometry (BLI) (e.g., Octet) assays, grating-coupled interferometry, and spectroscopic assays.

In some embodiments, a target-binding moiety is assessed for off-target effect. In some embodiments, a target-binding moiety is assessed using immunocapture followed by mass spectrometry (e.g., to reveal off target binding events in a complex sample). In some embodiments, a target-binding moiety is assessed using protein or glycan arrays, e.g., where many thousands of human proteins or glycans are arrayed on a chip and an antibody's binding is profiled across all available targets (e.g., a specific antibody will only bind to a target of interest). In some embodiments, a target-binding moiety is assessed using traditional immunoassays such as western blot. In some embodiments, a target-binding moiety is assessed for generic off-target non-specific binding (e.g., binding to other antibodies, DNA, lipids, etc.). In some embodiments, such generic off-target non-specific binding may be measured and identified using a negative control to identify a false positive signal (e.g., suggesting that one or more antibodies bind non-specifically, and not to a target).

In some embodiments, a target-binding moiety may be or comprise an agent of any chemical class such as, for example, a carbohydrate, a nucleic acid, a lipid, a metal, a polypeptide, a small molecule, etc., and/or a combination thereof. In some embodiments, a target-binding moiety may be or comprise an affinity agent such as an antibody, affimer, aptamer, lectin, siglec, etc. In some embodiments, a target-binding moiety is or comprises an antibody agent, e.g., an antibody agent that specifically binds to a target or an epitope thereof, e.g., a provided biomarker of a biomarker combination for cancer or an epitope thereof. In some embodiments, a target-binding moiety is or comprises a lectin or siglec that specifically binds to a carbohydrate-dependent marker as provided herein. In some embodiments, a target-binding moiety for a provided biomarker may be a commercially available. In some embodiments, a target-binding moiety for a provided biomarker may be designed and created for the purpose of use in assays as described herein. In some embodiments, a target-binding moiety is or comprises an aptamer, e.g., an aptamer that specifically binds to a target or an epitope thereof, e.g., a provided biomarker of a biomarker combination for cancer or an epitope thereof. In some embodiments, a target-binding moiety is or comprises an affimer molecule that specifically binds to a target or an epitope thereof, e.g., a provided biomarker of a biomarker combination for cancer or an epitope thereof. In some embodiments, such an affimer molecule can be or comprise a peptide or polypeptide that binds to a target or an epitope thereof (e.g., as described herein) with similar specificity and affinity to that of a corresponding antibody. In some embodiments, a target may be or comprise a target that is associated with cancer. For example, in some such embodiments, a cancer-associated target can be or comprise a target that is associated with more than one cancer (i.e., at least two or more cancers). In some embodiments, a cancer-associated target can be or comprise a target that is typically associated with cancers. In some embodiments, a cancer-associated target can be or comprise a target that is associated with cancers of a specific tissue, e.g., cancer. In some embodiments, a cancer-associated target can be or comprise a target that is specific to a particular cancer, e.g., a particular cancer and more specifically in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types.

In some embodiments, a target-binding moiety recognizes and specifically binds to a target present in a biological entity (including, e.g., but not limited to cells and/or extracellular vesicles). For example, in some embodiments, a target-binding moiety may recognize and specifically bind to a tumor-associated antigen or epitope thereof. In some embodiments, a tumor-associated antigen may be or comprise an antigen that is associated with a cancer such as, for example, skin cancer, brain cancer (including, e.g., glioblastoma), breast cancer, liver cancer, lung cancer, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, etc. In some embodiments, a target-binding moiety may recognize a tumor antigen associated with cancer (e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types). In some embodiments, a target-binding moiety may recognize a tumor antigen associated with in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types.

In some embodiments, a target-binding moiety may specifically bind to an intravesicular target, e.g., a provided intravesicular protein or RNA (e.g., mRNA). In some embodiments, a target-binding moiety may specifically bind to a surface target that is present on/within nanoparticles having a size range of interest that includes extracellular vesicles, e.g., a membrane-bound polypeptide present on cancer-associated extracellular vesicles.

In some embodiments, a target-binding moiety is directed to a biomarker for a specific condition or disease (e.g., cancer), which biomarker is or has been determined, for example, by analyzing a population or library (e.g., tens, hundreds, thousands, tens of thousands, hundreds of thousands, or more) of patient biopsies and/or patient data to identify such a biomarker (e.g., a predictive biomarker).

In some embodiments, a relevant biomarker may be one identified and/or characterized, for example, via data analysis. In some embodiments, for example, a diverse set of data (e.g., in some embodiments comprising one or more of bulk RNA sequencing, single-cell RNA (scRNA) sequencing, mass spectrometry, histology, post-translational modification data, in vitro and/or in vivo experimental data) can be analyzed through machine learning and/or computational modeling to identify biomarkers (e.g., predictive markers) that are highly specific to a disease or condition (e.g., cancer).

In some embodiments, a target-binding moiety is directed to a tissue-specific target, for example, a target that is associated with a specific tissue such as, for example, brain, breast, colon, ovary and/or other tissues associated with a female reproductive system, pancreas, prostate and/or other tissues associated with a male reproductive system, liver, lung, and skin. In some embodiments, such a tissue-specific target may be associated with a normal healthy tissue and/or a diseased tissue, such as a tumor. In some embodiments, a target-binding moiety is directed to a target that is specifically associated with a normal healthy condition of a subject. In some embodiments, a target-binding moiety may recognize a tissue specific antigen.

In some embodiments, individual target binding entities utilized in a plurality of detection probes (e.g., as described and/or utilized herein) are directed to different targets. In some embodiments, such different targets may represent different marker proteins or polypeptides. In some embodiments, such different targets may represent different epitopes of the same marker proteins or polypeptides. In some embodiments, two or more individual target binding entities utilized in a plurality of detection probes (e.g., as described and/or utilized herein) may be directed to the same target.

In some embodiments, individual target binding entities utilized in a plurality of detection probes for detection of cancer may be directed to different target biomarkers of a biomarker combination for cancer (e.g., ones as described in the section entitled “Provided Biomarkers and/or Biomarker combinations for Detection of Cancer” above).

In some embodiments, individual target binding entities utilized in a plurality of detection probes for detection of cancer may be directed to the same target biomarker of a biomarker combination for cancer (e.g., ones as described in the section entitled “Provided Biomarkers and/or Biomarker combinations for Detection of Cancer” above). In some embodiments, such target binding entities may be directed to the same or different epitopes of the same target biomarker of such a biomarker combination for cancer.

B. Oligonucleotide Domains

In some embodiments, an oligonucleotide domain for use in accordance with the present disclosure (e.g., that may be coupled to a target-binding moiety) may comprise a double-stranded portion and a single-stranded overhang extended from one or both ends of the oligonucleotide domain. In some embodiments where an oligonucleotide domain comprises a single-stranded overhang extended from each end, a single-stranded overhang is extended from a different strand of a double-stranded portion. In some embodiments where an oligonucleotide domain comprises a single-stranded overhang extended from one end of the oligonucleotide domain, the other end of the oligonucleotide domain may be a blunt end.

In some embodiments, an oligonucleotide domain may comprise ribonucleotides, deoxyribonucleotides, synthetic nucleotide residues that are capable of participating in Watson-Crick type or analogous base pair interactions, and any combinations thereof. In some embodiments, an oligonucleotide domain is or comprises DNA. In some embodiments, an oligonucleotide domain is or comprises peptide nucleic acid (PNA).

In some embodiments, an oligonucleotide may have a length that is determined, at least in part, for example, by, e.g., the physical characteristics of an entity of interest (e.g., biological entity such as extracellular vesicles) to be detected, and/or selection and localization of molecular targets in an entity of interest (e.g., biological entity such as extracellular vesicles) to be detected. In some embodiments, an oligonucleotide domain of a detection probe is configured to have a length such that when a first detection probe and a second detection probe bind to an entity of interest (e.g., biological entity such as extracellular vesicles), the first single-stranded overhang and the second single-stranded overhang are in sufficiently close proximity to permit interaction (e.g., hybridization) between the single-stranded overhangs. For example, when an entity of interest (e.g., biological entity) is an extracellular vesicle (e.g., an exosome), oligonucleotide domains of detection probes can each independently have a length such that their respective single-stranded overhangs are in sufficiently close proximity to anneal or interact with each other when the corresponding detection probes are bound to the same extracellular vesicle. For example, in some embodiments, oligonucleotide domains of detection probes for use in detecting extracellular vesicles (e.g., an exosome) may each independently have a length of about 20 nm to about 200 nm, about 40 nm to about 500 nm, about 40 nm to about 300 nm, or about 50 nm to about 150 nm. In some embodiments, oligonucleotide domains of detection probes for use in detecting extracellular vesicles (e.g., an exosome) may each independently have a length of about 20 nm to about 200 nm. In some embodiments, lengths of oligonucleotide domains of detection probes in a set can each independently vary to increase and/or maximize the probability of them finding each other when they simultaneously bind to the same entity of interest. Such oligonucleotide domains designed for use in detection probes for detecting extracellular vesicles can also be used in detection probes for detecting nanoparticles having a size range of interest that includes extracellular vesicles.

Accordingly, in some embodiments, an oligonucleotide domain for use in technologies provided herein may have a length in the range of about 20 up to about 1000 nucleotides. In some embodiments, an oligonucleotide domain may have a length in the range of about 30 up to about 1000 nucleotides, In some embodiments, an oligonucleotide domain may have a length in the range of about 30 to about 500 nucleotides, from about 30 to about 250 nucleotides, from about 30 to about 200 nucleotides, from about 30 to about 150 nucleotides, from about 40 to about 150 nucleotides, from about 40 to about 125 nucleotides, from about 40 to about 100 nucleotides, from about 40 to about 60 nucleotides, from about 50 to about 90 nucleotides, from about 50 to about 80 nucleotides. In some embodiments, an oligonucleotide domain may have a length of at least 20 or more nucleotides, including, e.g., at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 250, at least 500, at least 750, at least 1000 nucleotides or more. In some embodiments, an oligonucleotide domain may have a length of no more than 1000 nucleotides or lower, including, e.g., no more than 900, no more than 800, no more than 700, no more than 600, no more than 500, no more than 400, no more than 300, no more than 200, no more than 100, no more than 90, no more than 80, no more than 70, no more than 60, no more than 50, no more than 40 nucleotides, no more than 30 nucleotides, no more than 20 nucleotides or lower.

In some embodiments, an oligonucleotide domain may have a length of about 20 nm to about 500 nm. In some embodiments, an oligonucleotide domain may have a length of about 20 nm to about 400 nm, about 30 nm to about 200 nm, about 50 nm to about 100 nm, about 30 nm to about 70 nm, or about 40 nm to about 60 nm. In some embodiments, an oligonucleotide domain may have a length of at least about 20 nm or more, including, e.g., at least about 30 nm, at least about 40 nm, at least about 50 nm, at least about 60 nm, at least about 70 nm, at least about 80 nm, at least about 90 nm, at least about 100 nm, at least about 200 nm, at least about 300 nm, at least about 400 nm or more. In some embodiments, an oligonucleotide domain may have a length of no more than 1000 nm or lower, including, e.g., no more than 900 nm, no more than 800 nm, no more than 700 nm, no more than 600 nm, no more than 500 nm, no more than 400 nm, no more than 300 nm, no more than 200 nm, no more than 100 nm or lower.

In some embodiments, a double-stranded portion of an oligonucleotide domain for use in technologies provided herein may have a length in the range of about 30 up to about 1000 nucleotides. In some embodiments, a double-stranded portion of an oligonucleotide domain may have a length in the range of about 30 to about 500 nucleotides, from about 30 to about 250 nucleotides, from about 30 to about 200 nucleotides, from about 30 to about 150 nucleotides, from about 40 to about 150 nucleotides, from about 40 to about 125 nucleotides, from about 40 to about 100 nucleotides, from about 50 to about 90 nucleotides, from about 50 to about 80 nucleotides. In some embodiments, a double-stranded portion of an oligonucleotide domain may have a length of at least 30 or more nucleotides, including, e.g., at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 250, at least 500, at least 750, at least 1000 nucleotides or more. In some embodiments, a double-stranded portion of an oligonucleotide domain may have a length of no more than 1000 nucleotides or lower, including, e.g., no more than 900, no more than 800, no more than 700, no more than 600, no more than 500, no more than 400, no more than 300, no more than 200, no more than 100, no more than 90, no more than 80, no more than 70, no more than 60, no more than 50, no more than 40 nucleotides or lower.

In some embodiments, a double-stranded portion of an oligonucleotide domain may have a length of about 20 nm to about 500 nm. In some embodiments, a double-stranded portion of an oligonucleotide domain may have a length of about 20 nm to about 400 nm, about 30 nm to about 200 nm, about 50 nm to about 100 nm, about 30 nm to about 70 nm, or about 40 nm to about 60 nm. In some embodiments, a double-stranded portion of an oligonucleotide domain may have a length of at least about 20 nm or more, including, e.g., at least about 30 nm, at least about 40 nm, at least about 50 nm, at least about 60 nm, at least about 70 nm, at least about 80 nm, at least about 90 nm, at least about 100 nm, at least about 200 nm, at least about 300 nm, at least about 400 nm or more. In some embodiments, a double-stranded portion of an oligonucleotide domain may have a length of no more than 1000 nm or lower, including, e.g., no more than 900 nm, no more than 800 nm, no more than 700 nm, no more than 600 nm, no more than 500 nm, no more than 400 nm, no more than 300 nm, no more than 200 nm, no more than 100 nm or lower.

In some embodiments, a double-stranded portion of an oligonucleotide domain is characterized in that when detection probes are connected to each other through hybridization of respective complementary single-stranded overhangs (e.g., as described and/or utilized herein), the combined length of the respective oligonucleotide domains (including, if any, a linker that links a target-binding moiety to an oligonucleotide domain) is long enough to allow respective target binding entities to substantially span the full characteristic length (e.g., diameter) of an entity of interest (e.g., an extracellular vesicle). For example, in some embodiments where extracellular vesicles are entities of interest, a combined length of oligonucleotide domains (including, if any, a linker that links a target-binding moiety to an oligonucleotide domain) of detection probes may be approximately 50 to 200 nm, when the detection probes are fully connected to each other.

In some embodiments, a double-stranded portion of an oligonucleotide domain may comprise a binding site for a primer. In some embodiments, such a binding site for a primer may comprise a nucleotide sequence that is designed to reduce or minimize the likelihood for miss-priming or primer dimers. Such a feature, in some embodiments, can decrease the lower limit of detection and thus increase the sensitivity of systems provided herein. In some embodiments, a binding site for a primer may comprise a nucleotide sequence that is designed to have a similar annealing temperature as another primer binding site.

In some embodiments, a double-stranded portion of an oligonucleotide domain may comprise a nucleotide sequence designed to reduce or minimize overlap with nucleic acid sequences (e.g., DNA and/or RNA sequences) typically associated with genome and/or gene transcripts (e.g., genomic DNA and/or RNA, such as mRNA of genes) of a subject (e.g., a human subject). Such a feature, in some embodiments, may reduce or minimize interference of any genomic DNA and/or mRNA transcripts of a subject that may be present (e.g., as contaminants) in a sample during detection.

In some embodiments, a double-stranded portion of an oligonucleotide domain may have a nucleotide sequence designed to reduce or minimize formation of self-dimers, homo-dimers, or hetero-dimers.

In some embodiments, a single-stranded overhang of an oligonucleotide domain for use in technologies provided herein may have a length of about 2 to about 20 nucleotides. In some embodiments, a single-stranded overhang of an oligonucleotide domain may have a length of about 2 to about 15 nucleotides, from about 2 to about 10 nucleotides, from about 3 to about 20 nucleotides, from about 3 to about 15 nucleotides, from about 3 to about 10 nucleotides. In some embodiments, a single-stranded overhang can have at least 1 to 5 nucleotides in length. In some embodiments, a single-stranded overhang of an oligonucleotide domain may have a length of at least 2 or more nucleotides, including, e.g., at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 20 nucleotides, or more. In some embodiments, a single-stranded overhang of an oligonucleotide domain may have a length of no more than 20 nucleotides or lower, including, e.g., no more than 15, no more than 14, no more than 13, no more than 12, no more than 11, no more than 10, no more than 9, no more than 8, no more than 7, no more than 6, no more than 5, no more than 4 nucleotides or lower.

In some embodiments, a single-stranded overhang of an oligonucleotide domain may have a length of about 1 nm to about 10 nm. In some embodiments, a single-stranded overhang of an oligonucleotide domain may have a length of about 1 nm to about 5 nm. In some embodiments, a single-stranded overhang of an oligonucleotide domain may have a length of at least about 0.5 nm or more, including, e.g., at least about 1 nm, at least about 1.5 nm, at least about 2 nm, at least about 3 nm, at least about 4 nm, at least about 5 nm, at least about 6 nm, at least about 7 nm, at least about 8 nm, at least about 9 nm, at least about 10 nm or more. In some embodiments, a single-stranded overhang of an oligonucleotide domain may have a length of no more than 10 nm or lower, including, e.g., no more than 9 nm, no more than 8 nm, no more than 7 nm, no more than 6 nm, no more than 5 nm, no more than 4 nm, no more than 3 nm, no more than 2 nm, no more than 1 nm or lower.

A single-stranded overhang of an oligonucleotide domain is designed to comprise a nucleotide sequence that is complementary to at least a portion of a single-stranded overhang of a second detection probe such that a double-stranded complex comprising a first detection probe and a second detection probe can be formed through hybridization of the complementary single-stranded overhangs. In some embodiments, nucleotide sequences of complementary single-stranded overhangs are selected for optimal ligation efficiency in the presence of an appropriate nucleic acid ligase. In some embodiments, a single-stranded overhang has a nucleotide sequence preferentially selected for efficient ligation by a specific nucleic acid ligase of interest (e.g., a DNA ligase such as a T4 or T7 ligase). For example, such a single-stranded overhang may have a nucleotide sequence of GAGT, e.g., as described in Song et al., “Enzyme-guided DNA sewing architecture” Scientific Reports 5: 17722 (2015), which is incorporated herein by reference for the purpose described herein.

When two detection probes couple together through hybridization of respective complementary single-stranded overhangs, their respective oligonucleotide domains comprising the hybridized single-stranded overhangs can, in some embodiments, have a combined length of about 90%-110% or about 95%-105% of a characteristic length (e.g., diameter) of an entity of interest (e.g., a biological entity). For example, in some embodiments when a biological entity is an exosome, the combined length can be about 50 nm to about 200 nm, or about 75 nm to about 150 nm, or about 80 nm to about 120 nm.

C. Coupling Between a Target-Binding Moiety and an Oligonucleotide Domain

An oligonucleotide domain and a target-binding moiety can be coupled together in a detection probe by a covalent linkage, and/or by a non-covalent association (such as, e.g., a protein-protein interaction such as streptavidin-biotin interaction and/or an ionic interaction). In some embodiments, a detection probe appropriate for use in accordance with the present disclosure is a conjugate molecule comprising a target-binding moiety and an oligonucleotide domain, where the two components are typically covalently coupled to each other, e.g., directly through a bond, or indirectly through one or more linkers. In some embodiments, a target-binding moiety is coupled to one of two strands of an oligonucleotide domain by a covalent linkage (e.g., directly through a bond or indirectly through one or more linkers) and/or by a non-covalent association (such as, e.g., a protein-protein interaction such as streptavidin-biotin interaction and/or ionic interaction).

Where linkers are employed, in some embodiments, linkers are chosen to provide for covalent attachment of a target-binding moiety to one or both strands of an oligonucleotide domain through selected linkers. In some embodiments, linkers are chosen such that the resulting covalent attachment of a target-binding moiety to one or both strands of an oligonucleotide domain maintains the desired binding affinity of the target-binding moiety for its target. In some embodiments, linkers are chosen to enhance binding specificity of a target-binding moiety for its target. Linkers and/or conjugation methods of interest may vary widely depending on a target-binding moiety, e.g., its size and/or charges. In some embodiments, linkers are biologically inert.

A variety of linkers and/or methods for coupling a target-binding moiety to an oligonucleotide is known to one of ordinary skill in the art and can be used in accordance with the present disclosure. In some embodiments, a linker can comprise a spacer group at either end with a reactive functional group at either end capable of covalent attachment to a target-binding moiety. Examples of spacer groups that can be used in linkers include, but are not limited to, aliphatic and unsaturated hydrocarbon chains (including, e.g., C4, C5, C6, C7, C8, C9, C10, C11, C12, C13, C14, C15, C16, C17, C18, C19, C20, or longer), spacers containing heteroatoms such as oxygen (e.g., ethers such as polyethylene glycol) or nitrogen (polyamines), peptides, carbohydrates, cyclic or acyclic systems that may contain heteroatoms. Non-limiting examples of a reactive functional group to facilitate covalent attachment include nucleophilic functional groups (e.g., amines, alcohols, thiols, and/or hydrazides), electrophilic functional groups (e.g., aldehydes, esters, vinyl ketones, epoxides, isocyanates, and/or maleimides), functional groups capable of cycloaddition reactions, forming disulfide bonds, or binding to metals. In some embodiments, exemplary reactive functional groups, but are not limited to, primary and secondary amines, hydroxamic acids, N-hydroxysuccinimidyl (NHS) esters, dibenzocyclooctyne (DBCO)-NHS esters, azido-NHS esters, azidoacetic acid NHS ester, propargyl-NHS ester, trans-cyclooctene-NHS esters, N-hydroxysuccinimidyl carbonates, oxycarbonylimidazoles, nitrophenylesters, trifluoroethyl esters, glycidyl ethers, vinylsulfones, maleimides, azidobenzoyl hydrazide, N-[4-(p-azidosalicylamino)butyl]-3′-[2′-pyridyldithio]propionamid), bis-sulfosuccinimidyl suberate, dimethyladipimidate, disuccinimidyltartrate, N-maleimidobutyryloxysuccinimide ester, N-hydroxy sulfosuccinimidyl-4-azidobenzoate, N-succinimidyl [4-azidophenyl]-1,3′-dithiopropionate, N-succinimidyl [4-iodoacetyl]aminobenzoate, glutaraldehyde, and succinimidyl 4-[N-maleimidomethyl]cyclohexane-1-carboxylate, 3-(2-pyridyldithio)propionic acid N-hydroxysuccinimide ester (SPDP), 4-(N-maleimidomethyl)-cyclohexane-1-carboxylic acid N-hydroxysuccinimide ester (SMCC), and any combinations thereof.

In some embodiments, a target-binding moiety (e.g., a target binding antibody agent) is coupled or conjugated to one or both strands of an oligonucleotide domain using N-hydrosysuccinimide (NHS) ester chemistry. NHS esters react with free primary amines and result in stable covalent attachment. In some embodiments, a primary amino group can be positioned at a terminal end with a spacer group, e.g., but not limited to an aliphatic and unsaturated hydrocarbon chain (e.g., a C6 or C12 spacer group).

In some embodiments, a target-binding moiety (e.g., a target-binding affinity agent) can be coupled or conjugated to one or both strands of an oligonucleotide domain using a site-specific conjugation method known in the art, e.g., to enhance the binding specificity of conjugated target-binding moiety (e.g., conjugated target-binding affinity agent). Examples of a site-specific conjugation method include, but are not limited to coupling or conjugation through a disulfide bond, C-terminus, carbohydrate residue or glycan, and/or unnatural amino acid labeling. In some embodiments where a target-binding moiety is or comprises an affinity agent, an oligonucleotide can be coupled or conjugated to the target-binding moiety via at least one or more free amine groups present in the target-binding moiety. In some embodiments, an oligonucleotide can be coupled or conjugated to a target-binding moiety that is or comprises an affinity agent via at least one or more reactive thiol groups present in the target-binding moiety. In some embodiments, an oligonucleotide can be coupled or conjugated to a target-binding moiety that is or comprises an antibody agent or a peptide aptamer via at least one or more carbohydrate residues present in the target-binding moiety.

In some embodiments, a plurality of oligonucleotides (e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least ten, or more) can be coupled or conjugated to a target-binding moiety (e.g., a target binding antibody agent).

Exemplary Duplex Target Entity Detection System

In some embodiments, a target entity detection system as provided by the present disclosure (and useful, for example, for detecting, e.g., at a single entity level, extracellular vesicles associated with cancer) may comprise a first population of first detection probes (e.g., as described and/or utilized herein) for a provided target biomarker (e.g., ones described herein) and a second population of second detection probes (e.g., as described and/or utilized herein) for a provided target biomarker (e.g., ones described herein). In some embodiments, the first detection probes and the second detection probes are directed to the same provided target biomarker. In some embodiments, the first detection probes and the second detection probes are directed to different provided target biomarkers.

FIG. 2 illustrates an exemplary duplex target entity detection system for detecting, at a single entity level, an entity of interest (e.g., biological entity such as an extracellular vesicle) comprising (i) at least one target (e.g., a provided biomarker of a biomarker combination for cancer) which expression level is high enough such that two molecules of the same target (e.g., a provided biomarker of a biomarker combination for cancer) are found in close proximity, or (ii) at least two or more distinct targets (e.g., provided biomarkers of a biomarker combination for cancer). A first detection probe comprises a first target-binding moiety (e.g., directed to a target cancer marker 1) and a first oligonucleotide domain coupled to the first target-binding moiety, the first oligonucleotide domain comprising a first double-stranded portion and a first single-stranded overhang extended from one end of the first oligonucleotide domain. As shown in FIG. 2, a first oligonucleotide domain may be resulted from hybridization of a longer strand (strand 3) and a shorter strand (strand 1), thereby forming a double-stranded portion and a single-stranded overhang at one end. In some embodiments, a first target-binding moiety (e.g., directed to target cancer marker 1) is coupled (e.g., covalently coupled) to a 5′ end or 3′ end of a strand of a first oligonucleotide domain (e.g., strand 1). In some embodiments, a 5′ end or 3′ end of a strand that is coupled to a first target-binding moiety may be modified with a linker (e.g., as described and/or utilized herein with or without a spacer group). In some embodiments, a 5′ end of another strand of a first oligonucleotide domain (e.g., strand 3) has a free phosphate group.

In the embodiment depicted in FIG. 2, a second detection probe comprises a second target-binding moiety (e.g., directed to a target cancer marker 2) and a second oligonucleotide domain coupled to the second target-binding moiety, the second oligonucleotide domain comprising a second double-stranded portion and a second single-stranded overhang extended from one end of the second oligonucleotide domain. As shown in FIG. 2, a second oligonucleotide domain may be resulted from hybridization of a longer strand (strand 4) and a shorter strand (strand 2), thereby forming a double-stranded portion and a single-stranded overhang at one end. In some embodiments, a second target-binding moiety (e.g., directed to a target cancer marker 2) is coupled (e.g., covalently coupled) to a 5′ end of a strand of a second oligonucleotide domain (e.g., strand 2). In some embodiments, a 5′ end of a strand that is coupled to a second target-binding moiety may be modified with a linker (e.g., as described and/or utilized herein with or without a spacer group). In some embodiments, a 5′ end of another strand of a second oligonucleotide domain (e.g., strand 4) has a free phosphate group.

At least portions of a first single-stranded overhang and a second single-stranded overhang are complementary to each other such that they can hybridize to form a double-stranded complex when they are in sufficiently close proximity, e.g., when a first detection probe and a second detection probe simultaneously bind to the same entity of interest (e.g., biological entity such as extracellular vesicle). In some embodiments, a first single-stranded overhang and a second single-stranded overhang have equal lengths such that when they hybridize to form a double-stranded complex, there is no gap (other than a nick to be ligated) between their respective oligonucleotide domains and each respective target-binding moiety is located at an opposing end of the double-stranded complex. For example, in some embodiments, a double-stranded complex forms before ligation occurs, wherein the double-stranded complex comprises a first detection probe and a second detection probe coupled to each other through direct hybridization of their respective single-stranded overhangs (e.g., having 4 nucleotides in length), wherein each respective target-binding moiety (e.g., directed to a target cancer marker 1 and a target cancer marker 2, respectively) is present at opposing ends of the double-stranded complex. In such embodiments, both strands of the double-stranded complex (comprising a nick between respective oligonucleotide domains) are ligatable, e.g., for amplification and detection. In some embodiments, a double-stranded complex (e.g., before ligation occurs) can comprise an entity of interest (e.g., a biological entity such as an extracellular vesicle), wherein a first target-binding moiety (e.g., directed to a target cancer marker 1) and a second target-binding moiety (e.g., directed to a target cancer marker 2) are simultaneously bound to the entity of interest.

In some embodiments of a duplex target entity detection system for detection of cancer (e.g., breast cancer, colorectal cancer, prostate cancer, etc.), a first target-binding moiety of a first detection probe may be directed to a first target surface biomarker (e.g., ones provided in the section entitled “Provided Biomarkers and/or Biomarker Combinations for Detection of Cancer”), while a second target-binding moiety of a second detection probe may be directed to a second target surface biomarker (e.g., ones provided in the section entitled “Provided Biomarkers and/or Biomarker Combinations for Detection of Cancer”). In some embodiments, a first target-binding moiety of a first detection probe may be directed to a first target intravesicular biomarker (e.g., ones provided in the section entitled “Provided Biomarkers and/or Biomarker Combinations for Detection of Cancer”), while a second target-binding moiety of a second detection probe may be directed to a second target intravesicular biomarker (e.g., ones provided in the section entitled “Provided Biomarkers and/or Biomarker Combinations for Detection of Cancer”). In some embodiments, the first target-binding moiety and the second target-binding moiety may be directed to the same or different epitopes of the same target surface biomarker or of the same target intravesicular biomarker. In some embodiments, the first target-binding moiety and the second target-binding moiety may be directed to the different target surface biomarkers or different target intravesicular biomarkers. In some embodiments, the double stranded portion of a first oligonucleotide domain and a second oligonucleotide domain may be the same. In some embodiments, the double-stranded portion of a first oligonucleotide domain and a second oligonucleotide domain may be different.

In some embodiments, a duplex target entity detection system for detection of cancer (e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types) may comprise at least two distinct sets of detection probes. For example, in some embodiments, each set may be directed to a distinct biomarker combination comprising one or more target biomarkers (e.g., ones described herein).

In some embodiments, a duplex target entity detection system comprising at least two distinct sets of detection probes may also comprise a capture assay comprising a capture agent directed to an extracellular vesicle-associated surface biomarker.

In some embodiments, any combination of biomarker probes (e.g., a biomarker combination as described herein) including capture probes or detection probes as described herein may be utilized in combination with any other set of biomarker probes (e.g., a biomarker combination) including capture probes or detection probes as described herein.

Exemplary Triplex or Multiplex (n≥3) Target Entity Detection System

In some embodiments, a target entity detection system as provided by the present disclosure (and useful, for example, for detecting, e.g., at a single entity level, extracellular vesicles associated with cancer) may comprise n populations of distinct detection probes (e.g., as described and/or utilized herein), wherein n≥3. For example, in some embodiments when n=3, a target entity detection system may comprise a first detection probe (e.g., as described and/or utilized herein) for a first target, a population of a second detection probe (e.g., as described and/or utilized herein) for a second target, and a population of a third detection probe (e.g., as described and/or utilized herein) for a third target.

FIG. 3 illustrates an exemplary triplex target entity detection system for detecting, at a single entity level, an entity of interest (e.g., a biological entity such as an extracellular vesicle) comprising three distinct molecular targets. A first detection probe comprises a first target-binding moiety (e.g., anti-cancer marker 1 antibody agent) and a first oligonucleotide domain coupled to the first target-binding moiety, the first oligonucleotide domain comprising a first double-stranded portion and a first single-stranded overhang extended from one end of the first oligonucleotide domain. As shown in FIG. 3, a first oligonucleotide domain may be resulted from hybridization of a longer strand (strand 8) and a shorter strand (strand 1), thereby forming a double-stranded portion and a single-stranded overhang at one end. In some embodiments, a first target-binding moiety (e.g., anti-cancer marker 1 antibody agent) is coupled (e.g., covalently coupled) to a 5′ end of a strand of a first oligonucleotide domain (e.g., strand 1). In some embodiments, a 5′ end of a strand that is coupled to a first target-binding moiety may be modified with a linker (e.g., as described and/or utilized herein with or without a spacer group). In some embodiments, a 5′ end of another strand of a first oligonucleotide domain (e.g., strand 8) has a free phosphate group.

In the embodiment depicted in FIG. 3, a second detection probe comprises a second target-binding moiety (e.g., anti-cancer marker 3 antibody agent) and a second oligonucleotide domain coupled to the second target-binding moiety, the second oligonucleotide domain comprising a second double-stranded portion and a second single-stranded overhang extended from one end of the second oligonucleotide domain. As shown in FIG. 3, a second oligonucleotide domain may be resulted from hybridization of a longer strand (strand 4) and a shorter strand (strand 2), thereby forming a double-stranded portion and a single-stranded overhang at one end. In some embodiments, a second target-binding moiety (e.g., anti-cancer marker 3 antibody agent) is coupled (e.g., covalently coupled) to a 5′ end of a strand of a second oligonucleotide domain (e.g., strand 2). In some embodiments, a 5′ end of a strand that is coupled to a second target-binding moiety may be modified with a linker (e.g., as described and/or utilized herein with or without a spacer group). In some embodiments, a 5′ end of another strand of a second oligonucleotide domain (e.g., strand 4) has no free phosphate group.

A third detection probe comprises a third target-binding moiety (e.g., anti-cancer marker 2 antibody agent) and a third oligonucleotide domain coupled to the third target-binding moiety, the third oligonucleotide domain comprising a third double-stranded portion and a single-stranded overhang extended from each end of the third oligonucleotide domain. For example, a single-stranded overhang is extended from one end of a strand of a third oligonucleotide domain while another single-stranded overhang is extended from an opposing end of a different strand of the third oligonucleotide domain. As shown in FIG. 3, a third oligonucleotide domain may be resulted from hybridization of portions of two strands (e.g., strands 9 and 10), thereby forming a double-stranded portion and a single-stranded overhang at each end. For example, a single-stranded overhang (3A) is formed at a 5′ end of strand 9 of a third detection probe, wherein the 5′ end of strand 9 has a free phosphate group. Additionally, a single-stranded overhang (3B) is formed at a 5′ end of strand 10 of the same third detection probe and a third target-binding moiety (e.g., anti-target 2 antibody agent) is also coupled (e.g., covalently coupled) to the 5′ end of strand 10. In some embodiments, a 5′ end of a strand (e.g., strand 10) that is coupled to a third target-binding moiety may be modified with a linker (e.g., as described and/or utilized herein with or without a spacer group).

When all three detection probes are in sufficiently close proximity, e.g., when all three detection probes simultaneously bind to the same entity of interest (e.g., biological entity), (i) at least a portion of a single-stranded overhang (e.g., 3A) of a third detection probe is hybridized to a corresponding complementary portion of a single-stranded overhang of a second detection probe, and (ii) at least a portion of another single-stranded overhang (e.g., 3B) of the third detection probe is hybridized to a corresponding complementary portion of a single-stranded overhang of a first detection probe. As a result, a double-stranded complex comprising all three detection probes coupled to each other in a linear arrangement is formed by direct hybridization of corresponding single-stranded overhangs. See, e.g., FIG. 3.

In some embodiments involving use of at least three or more (n>3) detection probes in provided technologies, when single-stranded overhangs of detection probes anneal to each respective partner(s) to form a double-stranded complex, at least (n-2) target-binding moiety/moieties is/are present at internal position(s) of the double-stranded complex. In such embodiments, it is desirable to have internal target binding moieties present in a single strand of the double-stranded complex such that another strand of the double-stranded complex is free of any internal target binding moieties and is thus ligatable to form a ligated template. e.g., for amplification and detection. See, e.g., FIG. 3 (using three detection probes), FIG. 4 (using four detection probes), and FIG. 5 (using n detection probes).

In some embodiments where a strand of a double-stranded complex comprises at least one or more internal target binding moieties, the strand comprises a gap between an end of an oligonucleotide strand of a detection probe to which the internal target-binding moiety is coupled and an end of an oligonucleotide strand of another detection probe. The size of the gap is large enough that the strand becomes non-ligatable in the presence of a nucleic acid ligase. In some embodiments, the gap may be 2-8 nucleotides in size or 2-6 nucleotides in size. In some embodiments, the gap is 6 nucleotides in size. In some embodiments, the overlap (hybridization region between single-stranded overhangs) can be 2-15 nucleotides in length or 4-10 nucleotides in length. In some embodiments, the overlap (hybridization region between single-stranded overhangs) is 8 nucleotides in length. The size of the gap and/or hybridization region are selected to provide an optimum signal separation from a ligated template (comprising no internal target binding moieties) and non-ligated template (comprising at least one internal target-binding moiety). It should be noted that while FIGS. 3-5 do not show binding of detection probes to an entity of interest (e.g., a biological entity), a double-stranded complex (e.g., before ligation occurs) can comprise an entity of interest (e.g., a biological entity such as extracellular vesicles), wherein at least three or more target binding moieties are simultaneously bound to the entity of interest.

In some embodiments, selection of a combination (e.g., a set) of detection probes (e.g., number of detection probes and/or specific biomarkers) for use in a target entity detection system provided herein (e.g., a duplex, triplex or multiplex target entity detection system described herein) is based on, for example, a desired specificity and/or a desired sensitivity that is deemed to be optimal for a particular application. For example, in some embodiments, a combination of detection probes is selected for detection of cancer (e.g., for stage I, II, III, or IV) such that it provides a specificity of at least 95% or higher, including, e.g., at least 96%, at least 97%, at least 98%, at least 99%, at least 99.5%, at least 99.7%, at least 99.8% or higher. In some embodiments, a combination of detection probes is selected for detection of cancer (e.g., for stage I, II, III, or IV) such that it provides a sensitivity of at least 30% or higher, including, e.g., at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95% or higher. In some embodiments, a combination of detection probes is selected for detection of cancer (e.g., for stage I, II, III, or IV) such that it provides a positive predictive value of at least 8% or higher, including, e.g., at least 9%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 40%, at least 50%, or higher. In some embodiments, a combination of detection probes is selected for detection of cancer (e.g., breast cancer, colorectal cancer, prostate cancer, etc.) (e.g., for stage I, II, III, or IV) such that it provides a positive predictive value of at least 2% or higher, including, e.g., at least 3%, at least 4%, at least 5%, at least 6%, at least 7%, at least 8%, at least 9%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 40%, at least 50%, or higher. In some embodiments, a combination of detection probes is selected for detection of cancer (e.g., for stage I, II, III, or IV) such that it provides a limit of detection (LOD) below 1×107 EV/mL sample or lower, including, e.g., below 7×10⁶EV/mL sample, below 6×10⁶EV/mL sample, below 5×10⁶EV/mL sample, below 4×10⁶EV/mL sample, below 3×10⁶EV/mL sample, below 2×10⁶EV/mL sample, below 1×10⁶EV/mL sample, or lower. In some embodiments, such cancer detection assay may be used to detect different subtypes of cancer including, e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types and other specified types of cancer as known in the art (SEER Cancer Statistics Review 1975-2017). In some embodiments, such cancer detection assay may be used to detect cancer of an epithelial origin. In some embodiments, such cancer detection assay may be used to detect carcinoma, sarcoma, melanoma, and mixed types. In some embodiments, such cancer detection assay may be used to detect cancer characterized by hormone status (e.g., for treatment purposes, e.g., detection of a cancer subtype, e.g., ER+, HER2+, and/or triple negative breast cancer, etc.).

In some embodiments, a combination (e.g., a set) of detection probes, rather than individual detection probes, confers specificity to detection of a disease, disorder, or condition (e.g., a particular cancer (e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types) and/or a stage of cancer as described herein), for example, one or more individual probes may be directed to a target that itself is not specific to cancer. For example, in some embodiments, a useful combination of detection probes in a target entity detection system provided herein (e.g., a duplex, triplex or multiplex target entity detection system described herein) may comprise at least one detection probe directed to a target specific for the relevant disease, disorder, or condition (i.e., a target that is specific to the relevant disease, disorder, or condition), and may further comprise at least one detection probe directed to a target that is not necessarily or completely specific for the relevant disease, disorder, or condition (e.g., that may also be found on some or all cells that are healthy, are not of the particular disease, disorder, or condition, and/or are not of the particular disease stage of interest). That is, as will be appreciated by those skilled in the art reading the present specification, so long as the set of detection probes utilized in accordance with the present invention is or comprises a plurality of individual detection probes that together are specific for detection of the relevant disease, disorder, or condition (i.e., sufficiently distinguish biological entities for detection that are associated with the relevant disease, disorder, or condition from other biological entities not of interest for detection), the set is useful in accordance with certain embodiments of the present disclosure.

In some embodiments, a target entity detection system provided herein (e.g., a duplex, triplex or multiplex target entity detection system described herein) can comprise at least one or more (e.g., at least 2 or more) control probes (in addition to target-specific detection probes, e.g., as described and/or utilized herein, for example, in some embodiments to recognize disease-specific biomarkers such as cancer-specific biomarkers and/or tissue-specific biomarkers). In some embodiments, a control probe is designed such that its binding to an entity of interest (e.g., a biological entity) inhibits (completely or partially) generation of a detection signal.

In some embodiments, a control probe comprises a control binding moiety and an oligonucleotide domain (e.g., as described and/or utilized herein) coupled to the control binding moiety, the oligonucleotide domain comprising a double-stranded portion and a single-stranded overhang extended from one end of the oligonucleotide domain. A control binding moiety is an entity or moiety that bind to a control reference. In some embodiments, a control reference can be or comprise a biomarker that is preferentially associated with a normal healthy cell. In some embodiments, a control reference can be or comprise a biomarker preferentially associated from a non-target tissue. In some embodiments, inclusion of a control probe can selectively remove or minimize detectable signals generated from false positives (e.g., entities of interest comprising a control reference, optionally in combination with one or more targets to be detected). Other control probes described in U.S. application Ser. No. 16/805,637 (published as US2020/0299780; issued as U.S. Pat. No. 11,085,089), and International Application PCT/US2020/020529 (published as WO2020180741), both filed Feb. 28, 2020 and entitled “Systems, Compositions, and Methods for Target Entity Detection,” the entire contents of each application are incorporated herein by reference in their entirety, can be useful in provided target entity detections systems.

In some embodiments, the present disclosure provides insights, among other things, that detection probes as described or utilized herein may non-specifically bind to a solid substrate surface and some of them may remain in an assay sample even after multiple washes to remove any excess or unbound detection probes; and that such non-specifically bound detection probes may come off from the solid substrate surface and become free-floating in a ligation reaction, thus allowing them to interact with one another to generate a non-specific ligated template that produces an undesirable background signal. Accordingly, in some embodiments, a target entity detection system provided herein (e.g., a duplex, triplex, or multiplex target entity detection described herein) can comprise at least one or more (e.g., at least 2 or more) inhibitor oligonucleotides that are designed to capture residual detection probes that are not bound to an entity of interest but remain as free agents in a ligation reaction, thereby preventing such free-floating detection probes from interacting with other free-floating complementary detection probes to produce an undesirable background signal. In some embodiments, an inhibitor oligonucleotide may be or comprise a single-stranded or double-stranded oligonucleotide comprising a binding domain for a single-stranded overhang of a detection probe (e.g., as described or utilized herein), wherein the inhibitor oligonucleotide does not comprise a primer binding site. The absence of such a primer binding site in an inhibitor oligonucleotide prevents a primer from binding to a non-specific ligated template resulting from ligation of a detectable probe to an inhibitor oligonucleotide, thereby reducing or inhibiting the non-specific ligated template from amplification and/or detection, e.g., by polymerase chain reaction.

In some embodiments, an inhibitor oligonucleotide comprises a binding domain for a single-stranded overhang of a detection probe (e.g., as described or utilized herein), wherein the binding domain is or comprises a nucleotide sequence that is substantially complementary to the single-stranded overhang of the detection probe such that a free, unbound detection probe having a complementary single-stranded overhang can bind to the binding domain of the inhibitor oligonucleotide. In some embodiments, an inhibitor oligonucleotide may have a hairpin at one end. In some embodiments, an inhibitor oligonucleotide may be a single-stranded oligonucleotide comprising at one end a binding domain for a single-stranded overhang of a detection probe, wherein a portion of the single-stranded oligonucleotide can self-hybridize to form a hairpin at another end.

In some embodiments, a target entity detection system provided herein (e.g., a duplex, triplex or multiplex target entity detection system described herein) does not comprise a connector oligonucleotide that associates an oligonucleotide domain of a detection probe with an oligonucleotide domain of another detection probe. In some embodiments, a connector oligonucleotide is designed to bridge oligonucleotide domains of any two detection probes that would not otherwise interact with each other when they bind to an entity of interest. In some embodiments, a connector oligonucleotide is designed to hybridize with at least a portion of an oligonucleotide domain of a detection probe and at least a portion of an oligonucleotide domain of another detection probe. A connector oligonucleotide can be single-stranded, double-stranded, or a combination thereof. A connector oligonucleotide is free of any target-binding moiety (e.g., as described and/or utilized herein) or control binding moiety. In at least some embodiments, no connector oligonucleotides are necessary to indirectly connect oligonucleotide domains of detection probes; in some embodiments, such connector oligonucleotides are not utilized, in part because detection probes as provided and/or utilized herein are designed such that their respective oligonucleotide domains have a sufficient length to reach and interact with each other when they are in sufficiently close proximity, e.g., when the detection probes simultaneously bind to an entity of interest (e.g., a biological entity such as an extracellular vesicle).

Methods of Using Provided Target Entity Detection Systems

Provided target entity detection systems are useful in detecting an entity of interest (e.g., a biological entity such as extracellular vesicles) in a sample (e.g., in a biological, environmental, or other sample) for various applications and/or purposes associated with detection of cancer. Accordingly, some aspects provided herein relate to methods of using a plurality of (e.g., at least 2, at least 3, or more) detection probes appropriate for use in accordance with the present disclosure. In some embodiments, a method comprises contacting an entity of interest (e.g., a biological entity such as extracellular vesicles) in a sample (e.g., a blood or blood-derived sample from a human subject) with a set of detection probes comprising at least 2 or more (including, e.g., at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20 or more) detection probes as described and/or utilized herein. In some embodiments, a method comprises subjecting a sample comprising an entity of interest (e.g., a biological entity such as extracellular vesicles) to a target entity detection system (e.g., as provided herein). A plurality of detection probes (e.g., at least two or more) can be added to a sample comprising an entity of interest (e.g., a biological entity such as extracellular vesicles) at the same time or at different times (e.g., sequentially). In some embodiments, a method may comprise, prior to contacting with a plurality of detection probes, contacting a sample comprising an entity of interest with at least one capture agent directed to an extracellular vesicle-associated surface biomarker.

In certain embodiments, a provided target entity detection system for use in a method described herein may comprise a plurality of (e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20 or more) distinct sets (e.g., combinations) of detection probes (e.g., as described herein). In some embodiments, a method comprises contacting an entity of interest (e.g., a biological entity such as extracellular vesicles) in a sample (e.g., a blood or blood-derived sample from a human subject) with a plurality of sets of detection probes, wherein each set may comprise at least 2 or more (including, e.g., at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20 or more) detection probes as described and/or utilized herein. In some embodiments, a method comprises subjecting a sample comprising an entity of interest (e.g., a biological entity such as extracellular vesicles) to a target entity detection system (e.g., as provided herein). A plurality of detection probes and/or detection probe combinations (e.g., at least two or more) can be added to a sample comprising an entity of interest (e.g., a biological entity such as extracellular vesicles) at the same time or at different times (e.g., sequentially). In some embodiments, a method may comprise, prior to contacting with a plurality of detection probes, contacting a sample comprising an entity of interest with at least one capture agent directed to an extracellular vesicle-associated surface biomarker.

In some embodiments, the relationship between results (e.g., Ct values and/or relative number of ligated nucleic acid templates (e.g., ligated DNA templates)) from profiling one or more biomarker combinations in a sample can be combined with clinical information (including, e.g., but not limited to patient age, past medical history, etc.) and/or other information to better classify patients with or at risk for cancer. Various classification algorithms can be used to interpret the relationship between multiple variables to increase an assay's sensitivity and/or specificity. In some embodiments, such algorithms include, but are not limited to, logistic regression models, support vector machines, gradient boosting machines, random forest algorithms, Naive Bayes algorithms, K-nearest neighborhood algorithms, and combinations thereof. In some embodiments, performance (e.g., accuracy) of assays described herein can be improved, e.g., by selection of biomarker combinations (e.g., as described herein), selection of other factors or variables (e.g., clinical information and/or lifestyle information) to include an algorithm, and/or selection of the type of algorithm itself.

In certain embodiments, technologies described herein utilize a predictive algorithm that is trained and validated using data sets as described herein. In certain embodiments, technologies described herein are utilized to generate a risk score using an algorithm created from training samples which is designed to take into account results from at least two, e.g., at least two, at least 3, at least 4, at least 5, or more than 5 separate assays comprising biomarker combinations (e.g., as described herein). In certain embodiments, an algorithm-generated risk score can be generated at least in part using diagnostic data (e.g., raw and/or normalized Ct values) from at least one individual assay (e.g., individual biomarker combination). In certain embodiments, a reference threshold can be included within a risk score. In certain embodiments, multiple threshold levels denoting multiple different degrees of cancer risk may be included in a risk score. In some embodiments, separate biomarker combination assays may be performed as individual assays in a series of assays, and individual assays may be weighted equally or differently in a predictive algorithm. In some embodiments, for example, weighting of individual assays combined in an algorithm (e.g., a cohort of biomarker assays) may be determined by a number of factors including but not limited to the sensitivity of an individual assay, the specificity of an individual assay, the reproducibility of an individual assay, the variability of an individual assay, the positive predictive value of an individual assay, and/or the lowest limit of detection of a specific assay. In some embodiments, a cohort of biomarker assays may be ranked according to a characteristic (e.g., sensitivity, specificity, lowest limit of detection etc.) and the biomarker assays may then be weighted based upon their relative rank.

In some embodiments, a risk score generated by an algorithm (as described herein) can be presented in a suitable manner, e.g., on a nominal scale, e.g., on a scale of 0-100 reflecting a number of likelihoods, e.g., including but not limited to the likelihood a subject has cancer, the likelihood a subject will develop cancer, and/or the likely stage of cancer. In some embodiments, a higher risk score can demonstrate that there is an increasing likelihood of disease pathology, e.g., lower to higher values may reflect healthy controls, benign controls, stage I, stage II, stage III, and stage IV cancers. In some embodiments, a risk score can be utilized to reduce the potential of cross reactivity of technologies as described herein when compared with other cancer types.

In some embodiments, a risk score may be generated from a combination of data derived from assays as described herein coupled with other applicable diagnostic data such as age, life history, MRI results, CT scanning, ultrasound, mammogram, blood biomarker test results, or any combination thereof. In some embodiments, a risk score provides predictive value above and beyond that of conventional standard of care diagnostic assay predictive values, e.g., higher than predictive values provided by mammogram, ultrasound, or other cancer screening assays utilized in isolation or in combination with another diagnostic assay. In some embodiments, a risk score may be generated that has high specificity for cancer (e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types) and has low sensitivity for other cancers.

In some embodiments, a risk score may have an associated clinical cutoff for detection of cancer. For example, in some embodiments, a risk score's clinical cutoff for detection may require an assay that yields at least 40%, e.g., at least 50%, at least 60%, or greater sensitivity for detection of both early and late stage cancer and has a minimum of 90% specificity, e.g., at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99% or greater specificity in a generally healthy population of subjects (e.g., aged 40 to 85 years of age) or in a population of subjects with hereditary risk. In some embodiments, sensitivity and specificity targets are the approximate lower bounds of the two-sided 95% confidence interval for the targeted 77% sensitivity and 99.5% specificity.

In some embodiments, a training study is performed to provide the necessary data required to program a risk score algorithm. In some embodiments, such a training study may comprise a cohort of samples from a range of suppliers, including at least commercial suppliers, biobanks, purpose driven studies, and/or physicians. In some embodiments, a training study may comprise positive samples from cancer patients (e.g., stage I, stage II, stage III, and/or stage IV), positive control samples from cancer cell lines, negative samples from benign tumor patients, negative samples from inflammatory condition patients (e.g., Crohn's disease, endometriosis, diabetes type II, lupus, pancreatitis, rheumatoid arthritis, ulcerative colitis, etc.), negative samples from healthy patients, or any combination thereof. In some embodiments, a training study may comprise samples from patients of any appropriate age range, e.g., <31 years old, 31-40 years old, 41-50 years old, 51-60 years old, 61-70 years old, 71-80 years old, or >80 years old. In some embodiments, a training study may comprise samples from patients of any race/ethnicity/descent, (e.g., Caucasians, Africans, Asians, etc.).

In some embodiments, a validation study is performed to provide the necessary data required to confirm a risk score algorithm's utility. In some embodiments, such a validation study may comprise a cohort of samples from a range of suppliers, including at least commercial suppliers, biobanks, purpose driven studies, and/or physicians. In some embodiments, a validation study may comprise positive samples from cancer patients (e.g., stage I, stage II, stage III, and/or stage IV), positive control samples from cancer cell lines, negative samples from benign tumor patients, negative samples from inflammatory condition patients (e.g., Crohn's disease, endometriosis, diabetes type II, lupus, pancreatitis, rheumatoid arthritis, ulcerative colitis, etc.), negative samples from healthy patients, or any combination thereof. In some embodiments, a validation study may comprise samples from patients of any appropriate age range, e.g., <31 years old, 31-40 years old, 41-50 years old, 51-60 years old, 61-70 years old, 71-80 years old, or >80 years old. In some embodiments, a validation study may comprise samples from patients of any race/ethnicity/descent, (e.g., Caucasians, Africans, Asians etc.).

In certain embodiments, at least one biomarker combination comprising at least one surface biomarker (e.g., extracellular vesicle-associated surface biomarker) and at least one (including, e.g., at least two, or more) target biomarker (which may be selected from any of surface biomarkers described herein, intravesicular biomarkers described herein, and/or intravesicular RNA biomarkers described herein) may be embodied in a cancer detection assay. In some such embodiments, at least one capture agent is directed to the surface biomarker, and at least one set of detection probes is directed to one or more of such target biomarkers described herein.

In certain embodiments, at least two (including, e.g., at least three or more) distinct biomarker combinations each comprising at least one surface biomarker (e.g., extracellular vesicle-associated surface biomarker) and at least one (including, e.g., at least two, or more) target biomarker (which may be selected from any of surface biomarkers described herein, intravesicular biomarkers described herein, and/or intravesicular RNA biomarkers described herein) may be embodied in a cancer detection assay.

In some embodiments, each distinct biomarker combination may have a different pre-determined cutoff value for individually determining whether a sample is positive for cancer. In some embodiments, a sample is determined to be positive for cancer if assay readout is above at least one of cutoff values for a plurality of (e.g., at least 2 or more) biomarker combinations. In some embodiments, a diagnostic value or a risk score cutoff can be determined based on a plurality of (e.g., at least 2, at least 3 or more) biomarker combinations.

Accordingly, in some embodiments, a sample can be divided into aliquots such that a different capture agent and/or a different set of detection probes (e.g., each directed to detection of a distinct disease or condition) can be added to a different aliquot. In such embodiments, provided technologies can be implemented with one aliquot at a time or multiple aliquots at a time (e.g., for parallel assays to increase throughput).

In some embodiments, amount of detection probes that is added to a sample provides a sufficiently low concentration of detection probes in a mixture to ensure that the detection probes will not randomly come into close proximity with one another in the absence of binding to an entity of interest (e.g., biological entity), at least not to any great or substantial degree. As such, in many embodiments, when detection probes simultaneously bind to the same entity of interest (e.g., biological entity) through the binding interaction between respective targeting binding moieties of the detection probes and the binding sites of an entity of interest (e.g., a biological entity), the detection probes come into sufficiently close proximity to one another to form double-stranded complex (e.g., as described herein). In some embodiments, the concentration of detection probes in a mixture following combination with a sample may range from about 1 fM to 1 μM, such as from about 1 pM to about 1 nM, including from about 1 pM to about 100 nM.

In some embodiments, the concentration of an entity of interest (e.g., a biological entity) in a sample is sufficiently low such that a detection probe binding to one entity of interest (e.g., a biological entity) will not randomly come into close proximity with another detection probe binding to another entity of interest (e.g., biological entity) in the absence of respective detection probes binding to the same entity of interest (e.g., biological entity), at least not to any great or substantial degree. By way of example only, the concentration of an entity of interest (e.g., biological entity) in a sample is sufficiently low such that a first target detection probe binding to a non-target entity of interest (e.g., a non-cancerous biological entity such as an extracellular vesicle comprising a first target) will not randomly come into close proximity with another different target detection probe that is bound to another non-target entity of interest (e.g., a non-cancerous biological entity such as an extracellular vesicle), at least not to any great or substantial degree, to generate a false positive detectable signal.

Following contacting an entity of interest (e.g., biological entity) in a sample with a set of detection probes, such a mixture may be incubated for a period of time sufficient for the detection probes to bind corresponding targets (e.g., molecular targets), if present, in the entity of interest to form a double-stranded complex (e.g., as described herein). In some embodiments, such a mixture is incubated for a period of time ranging from about 5 min to about 5 hours, including from about 30 min to about 2 hours, at a temperature ranging from about 10 to about 50° C., including from about 20° C. to about 37° C.

A double-stranded complex (resulted from contacting an entity of interest such as a biological entity with detection probes) can then be subsequently contacted with a nucleic acid ligase to perform nucleic acid ligation of a free 3′ end hydroxyl and 5′ end phosphate end of oligonucleotide strands of detection probes, thereby generating a ligated template comprising oligonucleotide strands of at least two or more detection probes. In some embodiments, prior to contacting an assay sample comprising a double-stranded complex with a nucleic acid ligase, at least one or more inhibitor oligonucleotide (e.g., as described herein) can be added to the assay sample such that the inhibitor oligonucleotide can capture any residual free-floating detection probes that may otherwise interact with each other during a ligation reaction.

As is known in the art, ligases catalyze the formation of a phosphodiester bond between juxtaposed 3′-hydroxyl and 5′-phosphate termini of two immediately adjacent nucleic acids when they are annealed or hybridized to a third nucleic acid sequence to which they are complementary. Any known nucleic acid ligase (e.g., DNA ligases) may be employed, including but not limited to temperature sensitive and/or thermostable ligases. Non-limiting examples of temperature sensitive ligases include bacteriophage T4 DNA ligase, bacteriophage T7 ligase, and E. coli ligase. Non-limiting examples of thermostable ligases include Taq ligase, Tth ligase, and Pfu ligase. Thermostable ligase may be obtained from thermophilic or hyperthermophilic organisms, including but not limited to, prokaryotic, eukaryotic, or archaeal organisms. In some embodiments, a nucleic acid ligase is a DNA ligase. In some embodiments, a nucleic acid ligase can be a RNA ligase.

In some embodiments, in a ligation step, a suitable nucleic acid ligase (e.g., a DNA ligase) and any reagents that are necessary and/or desirable are combined with the reaction mixture and maintained under conditions sufficient for ligation of the hybridized ligation oligonucleotides to occur. Ligation reaction conditions are well known to those of skill in the art. During ligation, a reaction mixture, in some embodiments, may be maintained at a temperature ranging from about 20° C. to about 45° C., such as from about 25° C. to about 37° C. for a period of time ranging from about 5 minutes to about 16 hours, such as from about 1 hour to about 4 hours. In yet other embodiments, a reaction mixture may be maintained at a temperature ranging from about 35° C. to about 45° C., such as from about 37° C. to about 42° C., e.g., at or about 38° C., 390 C, 40° C. or 41° C., for a period of time ranging from about 5 minutes to about 16 hours, such as from about 1 hour to about 10 hours, including from about 2 to about 8 hours.

Detection of such a ligated template can provide information as to whether an entity of interest (e.g., a biological entity) in a sample is positive or negative for targets to which detection probes are directed. For example, a detectable level of such a ligated template is indicative of a tested entity of interest (e.g., a biological entity) comprising targets (e.g., molecular targets) of interest. In some embodiments, a detectable level is a level that is above a reference level, e.g., by at least 10% or more, including, e.g., at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or more. In some embodiments, a reference level may be a level observed in a negative control sample, such as a sample in which an entity of interest comprising such targets is absent. Conversely, a non-detectable level (e.g., a level that is below the threshold of a detectable level) of such a ligated template indicates that at least one of targets (e.g., molecular targets) of interest is absent from a tested entity of interest (e.g., a biological entity). Those of skill in the art will appreciate that a threshold that separates a detectable level from a non-detectable level may be determined based on, for example, a desired sensitivity level, and/or a desired specificity level that is deemed to be optimal for each application and/or purpose. For example, in some embodiments, a specificity of 99.7% may be achieved using a system provided herein, for example by setting a threshold that is three standard deviations above a reference level (e.g., a level observed in a negative control sample, such as, e.g., a sample derived from one or more normal healthy individuals). Additionally or alternatively, those of skill in the art will appreciate that a threshold of a detectable level (e.g., as reflected by a detection signal intensity) may be 1 to 100-fold above a reference level.

In some embodiments, a method provided herein comprises, following ligation, detecting a ligated template, e.g., as a measure of the presence and/or amount of an entity of interest in a sample. In various embodiments, detection of a ligated template may be qualitative or quantitative. As such, in some embodiments where detection is qualitative, a method provides a reading or evaluation, e.g., assessment, of whether or not an entity of interest (e.g., a biological entity) comprising at least two or more targets (e.g., molecular targets) is present in a sample being assayed. In other embodiments, a method provides a quantitative detection of whether an entity of interest (e.g., a biological entity) comprising at least two or more targets (e.g., molecular targets) is present in a sample being assayed, e.g., an evaluation or assessment of the actual amount of an entity of interest (e.g., a biological entity) comprising at least two or more targets (e.g., molecular targets) in a sample being assayed. In some embodiments, such quantitative detection may be absolute or relative.

A ligated template formed by using technologies provided herein may be detected by an appropriate method known in the art. Those of skill in the art will appreciate that appropriate detection methods may be selected based on, for example, a desired sensitivity level and/or an application in which a method is being practiced. In some embodiments, a ligated template can be directly detected without any amplification, while in other embodiments, ligated template may be amplified such that the copy number of the ligated template is increased, e.g., to enhance sensitivity of a particular assay. Where detection without amplification is practicable, a ligated template may be detected in a number of different ways. For example, oligonucleotide domains of detection probes (e.g., as described and/or utilized herein) may have been directly labeled, e.g., fluorescently or radioisotopically labeled, such that a ligated template is directly labeled. For example, in some embodiments, an oligonucleotide domain of a detection probe (e.g., as provided and/or utilized herein) can comprise a detectable label. A detectable label may be a composition detectable by spectroscopic, photochemical, biochemical, immunochemical, electrical, optical or chemical means. Such labels include biotin for staining with labeled Streptavidin conjugate, magnetic beads (e.g., Dynabeads®), fluorescent dyes (e.g., fluorescein, Texas red, rhodamine, green fluorescent protein, and the like), radiolabels (e.g., ³H, ¹²⁵I, ³⁴S, ¹⁴C, or ³²P), enzymes (e.g., horse radish peroxidase, alkaline phosphatase and others commonly used in an ELISA), and calorimetric labels such as colloidal gold or colored glass or plastic (e.g., polystyrene, polypropylene, latex, etc.) beads. In some embodiments, a directly labeled ligated template may be size separated from the remainder of the reaction mixture, including unligated directly labeled ligation oligonucleotides, in order to detect the ligated template.

In some embodiments, detection of a ligated template can include an amplification step, where the copy number of ligated nucleic acids is increased, e.g., in order to enhance sensitivity of the assay. The amplification may be linear or exponential, as desired, where amplification can include, but is not limited to polymerase chain reaction (PCR); quantitative PCR, isothermal amplification, NASBA, digital droplet PCR, etc.

Various technologies for achieving PCR amplification are known in the art; those skilled in the art will be well familiar with a variety of embodiments of PCR technologies, and will readily be able to select those suitable to amplify a ligated template generated using technologies provided herein. For example, in some embodiments, a reaction mixture that includes a ligated template is combined with one or more primers that are employed in the primer extension reaction, e.g., PCR primers (such as forward and reverse primers employed in geometric (or exponential) amplification or a single primer employed in a linear amplification). Oligonucleotide primers with which one or more ligated templates are contacted should be of sufficient length to provide for hybridization to complementary template DNA under appropriate annealing conditions. Primers are typically at least 10 bp in length, including, e.g., at least 15 bp in length, at least 20 bp in length, at least 25 bp in length, at least 30 bp in length or longer. In some embodiments, the length of primers can typically range from about 15 to 50 bp in length, from about 18 to 30 bp, or about 20 to 35 bp in length. Ligated templates may be contacted with a single primer or a set of two primers (forward and reverse primers), depending on whether primer extension, linear, or exponential amplification of the template DNA is desired.

In addition to the above components, a reaction mixture comprising a ligated template typically includes a polymerase and deoxyribonucleoside triphosphates (dNTPs). The desired polymerase activity may be provided by one or more distinct polymerase enzymes. In preparing a reaction mixture, e.g., for amplification of a ligated template, various constituent components may be combined in any convenient order. For example, an appropriate buffer may be combined with one or more primers, one or more polymerases and a ligated template to be detected, or all of the various constituent components may be combined at the same time to produce the reaction mixture.

VII. Uses

In some embodiments, one or more provided biomarkers of one or more biomarker combinations for cancer can be detected in a sample comprising biological entities (including, e.g., cells, circulating tumor cells, cell-free DNA, extracellular vesicles, etc.), for example, using methods of detecting and/or assays as described herein. In some embodiments, one or more provided biomarkers of one or more biomarker combinations for cancer can be detected in a sample comprising nanoparticles having a size range of interest that includes extracellular vesicles, for example, using methods of detecting and/or assays as described herein.

In some embodiments, a sample may be or comprise a biological sample. In some embodiments, a biological sample is a bodily fluid sample of a subject (e.g., a human subject). In some embodiments, a biological sample can be derived from a blood or blood-derived sample of a subject (e.g., a human subject) in need of such an assay. In some embodiments, a biological sample can be or comprise a primary sample (e.g., a tissue or tumor sample) from a subject (e.g., a human subject) in need of such an assay. In some embodiments, a biological sample can be processed to separate one or more entities of interest (e.g., biological entity) from non-target entities of interest, and/or to enrich one or more entities of interest (e.g., biological entity). In some embodiments, an entity of interest present in a sample may be or comprise a biological entity, e.g., a cell or a nanoparticle having a size range of interest that includes extracellular vesicles (e.g., an exosome). In some embodiments, such a biological entity (e.g., extracellular vesicle) may be processed or contacted with a chemical reagent, e.g., to stabilize and/or crosslink targets (e.g., provided target biomarkers) to be assayed in the biological entity and/or to reduce non-specific binding with detection probes. In some embodiments, a biological entity is or comprises a cell, which may be optionally processed, e.g., with a chemical reagent for stabilizing and/or crosslinking targets (e.g., molecular targets) and/or for reducing non-specific binding. In some embodiments, a biological entity is or comprises an extracellular vesicle (e.g., an exosome), which may be optionally processed, e.g., with a chemical reagent for stabilizing and/or crosslinking targets (e.g., molecular targets) and/or for reducing non-specific binding.

In some embodiments, technologies provided herein can be useful for managing patient care, e.g., for one or more individual subjects and/or across a population of subjects. By way of example only, in some embodiments, provided technologies may be utilized in screening, which for example, may be performed periodically, such as annually, semi-annually, bi-annually, or with some other frequency as deemed to be appropriate by those skilled in the art. In some embodiments, such a screening may be temporally motivated or incidentally motivated. For example, in some embodiments, provided technologies may be utilized in temporally motivated screening for one or more individual subjects or across a population of subjects (e.g., asymptomatic subjects) who are older than a certain age (e.g., over 40, 45, 50, 55, 60, 65, 70, 75, 80, or older). As will be appreciated by those skilled in the art, in some embodiments, the screening age and/or frequency may be determined based on, for example, but not limited to prevalence of a disease, disorder, or condition (e.g., cancer such as cancer). In some embodiments, provided technologies may be utilized in incidentally-motivated screening for individual subjects who may have experienced an incident or event that motivates screening for a particular disease, disorder, or condition (e.g., cancer such as cancer). For example, in some embodiments, an incidental motivation relating to determination of one or more indicators of a disease, disorder, or condition (e.g., cancer such as cancer) or susceptibility thereto may be or comprise, e.g., an incident based on their family history (e.g., a close relative such as blood-related relative was previously diagnosed for such a disease, disorder, or condition such as cancer), identification of one or more life-history associated risk factors for a disease, disorder, or condition (e.g., cancer) and/or prior incidental findings from genetic tests (e.g., genome sequencing), and/or imaging diagnostic tests (e.g., mammogram, ultrasound, computerized tomography (CT) and/or magnetic resonance imaging (MRI) scans), development of one or more signs or symptoms characteristic of a particular disease, disorder, or condition associated with cancer, subjects having benign tumors, and combinations thereof, and/or other incidents or events as will be appreciated by those skilled in the art.

In some embodiments, provided technologies for managing patient care can inform treatment and/or payment (e.g., reimbursement for treatment) decisions and/or actions. For example, in some embodiments, provided technologies can provide determination of whether individual subjects have one or more indicators of risk, incidence, or recurrence of a disease disorder, or condition (e.g., cancer such as cancer), thereby informing physicians and/or patients when to provide/receive therapeutic or prophylactic recommendations and/or to initiate such therapy in light of such findings. In some embodiments, such individual subjects may be asymptomatic subjects, who may be temporally-motivated or incidentally-motivated to be screened at a regular frequency (e.g., annually, semi-annually, bi-annually, or other frequency as deemed to be appropriate by those skilled in the art). In some embodiments, such individual subjects may be experiencing one or more symptoms that may be associated with cancer, who may be temporally-motivated or incidentally-motivated to be screened at a regular frequency (e.g., annually, semi-annually, bi-annually, or other frequency as deemed to be appropriate by those skilled in the art). In some embodiments, such individual subjects may be subjects having a benign breast tumor and/or a chronic inflammatory condition, who may be temporally-motivated or incidentally-motivated screened at a regular frequency (e.g., annually, semi-annually, bi-annually, or other frequency as deemed to be appropriate by those skilled in the art). In some embodiments, such individual subjects may be subjects at hereditary risk for cancer, who may be temporally-motivated or incidentally-motivated to be screened at a regular frequency (e.g., annually, semi-annually, bi-annually, or other frequency as deemed to be appropriate by those skilled in the art). In some embodiments, such individual subjects may be subjects with life-history associated risk, who may be temporally-motivated or incidentally-motivated screened at a regular frequency (e.g., annually, semi-annually, bi-annually, or other frequency as deemed to be appropriate by those skilled in the art). In some embodiments, such individual subjects may be obese and/or smoking subjects (e.g., a BMI over 30 and/or heavy smokers), who may be temporally-motivated or incidentally-motivated screened at a regular frequency (e.g., annually, semi-annually, bi-annually, or other frequency as deemed to be appropriate by those skilled in the art). In some embodiments, such obese and/or smoking subjects may be experiencing abdominal pain.

Additionally or alternatively, in some embodiments, provided technologies can inform physicians and/or patients of treatment selection, e.g., based on findings of specific responsiveness biomarkers (e.g., cancer responsiveness biomarkers). In some embodiments, provided technologies can provide determination of whether individual subjects are responsive to current treatment, e.g., based on findings of changes in one or more levels of molecular targets associated with a disease, thereby informing physicians and/or patients of efficacy of such therapy and/or decisions to maintain or alter therapy in light of such findings. In some embodiments, provided technologies can provide determination of whether individual subjects are likely to be responsive to a recommended treatment, e.g., based on findings of molecular targets (e.g., provided biomarkers of one or more biomarker combinations for cancer) that predict therapeutic effects of a recommended treatment on individual subjects, thereby informing physicians and/or patients of potential efficacy of such therapy and/or decisions to administer or alter therapy in light of such findings.

In some embodiments, provided technologies can inform decision making relating to whether health insurance providers reimburse (or not), e.g., for (1) screening itself (e.g., reimbursement available only for periodic/regular screening or available only for temporally- and/or incidentally-motivated screening); and/or for (2) initiating, maintaining, and/or altering therapy in light of findings by provided technologies. For example, in some embodiments, the present disclosure provides methods relating to (a) receiving results of a screening that employs provided technologies and also receiving a request for reimbursement of the screening and/or of a particular therapeutic regimen; (b) approving reimbursement of the screening if it was performed on a subject according to an appropriate schedule (based on, e.g., screening age such as older than a certain age, e.g., over 40, 45, 50, 55, 60, 65, 70, 75, 80, or older, and/or screening frequency such as, e.g., every 3 months, every 6 months, every year, every 2 years, every 3 years or at some other frequencies) or in response to a relevant incident and/or approving reimbursement of the therapeutic regimen if it represents appropriate treatment in light of the received screening results; and, optionally (c) implementing the reimbursement or providing notification that reimbursement is refused. In some embodiments, a therapeutic regimen is appropriate in light of received screening results if the received screening results detect a biomarker that represents an approved biomarker for the relevant therapeutic regimen (e.g., as may be noted in a prescribing information label and/or via an approved companion diagnostic).

Alternatively or additionally, the present disclosure contemplates reporting systems (e.g., implemented via appropriate electronic device(s) and/or communications system(s)) that permit or facilitate reporting and/or processing of screening results (e.g., as generated in accordance with the present disclosure), and/or of reimbursement decisions as described herein. Various reporting systems are known in the art; those skilled in the art will be well familiar with a variety of such embodiments, and will readily be able to select those suitable for implementation.

EXEMPLARY USES
A. Detection of Cancer Incidence or Recurrence

The present disclosure, among other things, recognizes that detection of a single cancer-associated biomarker in a biological entity (e.g., extracellular vesicle) or a plurality of cancer-associated biomarkers based on a bulk sample, rather than at a resolution of a single biological entity (e.g., individual extracellular vesicles), typically does not provide sufficient specificity and/or sensitivity in determination of whether a subject from whom the biological entity is obtained is likely to be suffering from or susceptible to cancer (e.g., a solid tumor cancer). The present disclosure, among other things, provides technologies, including compositions and/or methods, that solve such problems, including for example by specifically requiring that an entity (e.g., a nanoparticle having a size range of interest that includes an extracellular vesicle) for detection be characterized by presence of a combination of at least two or more targets (e.g., at least two or more provided biomarkers of a biomarker combination for cancer). In particular embodiments, the present disclosure teaches technologies that require such an entity (e.g., a nanoparticle having a size range of interest that includes an extracellular vesicle) be characterized by presence (e.g., by expression) of a combination of molecular targets that is specific to cancer (i.e., “biomarker combination” of a relevant cancer, e.g., cancer), while biological entities (e.g., nanoparticles having a size range of interest that includes extracellular vesicles) that do not comprise the targeted combination (e.g., biomarker combination) do not produce a detectable signal. Accordingly, in some embodiments, technologies provided herein can be useful for detection of risk, incidence, and/or recurrence of cancer in a subject. In some such embodiments, technologies provided herein are useful for detection of risk, incidence, and/or recurrence of cancer in a subject. For example, in some embodiments, a combination of two or more provided biomarkers are selected for detection of a specific cancer (e.g., cancer) or various cancers (one of which includes cancer). In some embodiments, a specific combination of provided biomarkers for detection of cancer can be determined by analyzing a population or library (e.g., tens, hundreds, thousands, tens of thousands, hundreds of thousands, or more) of cancer patient biopsies and/or patient data to identify such a predictive combination. In some embodiments, a relevant combination of biomarkers may be one identified and/or characterized, for example, via data analysis. For example, in some embodiments, data analysis may comprise a bioinformatic analysis, for example, as described in Examples 6-8. In some embodiments, for example, a diverse set of cancer-associated data (e.g., in some embodiments comprising one or more of bulk RNA sequencing, single-cell RNA (scRNA) sequencing, mass spectrometry, histology, post-translational modification data, in vitro and/or in vivo experimental data) can be analyzed through machine learning and/or computational modeling to identify a combination of predictive markers that is highly specific to cancer. In some embodiments, a combination of predictive markers to distinguish stages of cancer (e.g., cancer) can be determined in silico based on comparing and analyzing diverse data (e.g., in some embodiments comprising bulk RNA sequencing, scRNA sequencing, mass spectrometry, histology, post-translational modification data, in vitro and/or in vivo experimental data) relating to different stages of cancer (e.g., cancer). For example, in some embodiments, technologies provided herein can be used to distinguish cancer subjects from non-cancer subjects, including, e.g., healthy subjects, subjects diagnosed with benign tumors or abdominal masses, and subjects with non-cancer-related diseases, disorders, and/or conditions (e.g., subjects with non-cancer, or subjects with inflammatory conditions that are associated with tissues of interest but that are not cancerous, including, e.g., atherosclerosis, heart disease, chronic kidney disease, diabetes, inflammatory bowel disease, fatty liver disease, chronic obstructive pulmonary disease, endometriosis, rheumatoid arthritis, obesity, pancreatitis, etc.). In some embodiments, technologies provided herein can be useful for early detection of cancer, e.g., detection of cancer of stage I or stage II. In some embodiments, technologies provided herein can be useful for detection of one or more cancer subtypes, including, e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types and other specified types of cancer as known in the art (SEER Cancer Statistics Review 1975-2017). In some embodiments, technologies provided herein can be useful for screening individuals at hereditary risk, life-history associated risk, or average risk for early stage cancer (e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types).

In some embodiments, technologies provided herein can be useful for screening a subject for risk, incidence, or recurrence of a specific cancer in a single assay. For example, in some embodiments, technologies provided herein is useful for screening a subject for risk, incidence, or recurrence of cancer. In some embodiments, technologies provided herein can be used to screen a subject for risk or incidence of a specific cancer or a plurality of (e.g., at least 2, at least 3, or more) cancers in a single assay. For example, in some embodiments, technologies provided herein can be used to screen a subject for a plurality of cancers in a single assay, one of which includes cancer and other cancers to be screened can be selected from the group comprising brain cancer (including, e.g., glioblastoma), cancer, ovarian cancer, pancreatic cancer, prostate cancer, liver cancer, lung cancer, and skin cancer.

In some embodiments, provided technologies can be used periodically (e.g., every year, every two years, every three years, etc.) to screen a human subject for cancer (e.g., early-stage cancer) or cancer recurrence. In some embodiments, a human subject amenable to such screening may be an adult or an elderly. In some embodiments, a human subject amenable to such screening may be older than a specified age, e.g., age 20 and above, age 25 and above, age 30 and above, age 35 and above, age 40 and above, age 45 and above, age 55 and above, age 65 and above, age 70 and above, at least age 75 above, or age 80 and above. In some embodiments, a human subject amenable to such screening may have an age of about 50 or above. In some embodiments, a human subject amenable to such screening may have an age of 50 or less. In some embodiments, a human subject amenable to such screening may have an age over 20. In some embodiments, a human subject who is determined to have a genetic predisposition to cancer may be screened at a younger age than a human subject who has no family history risk.

In some embodiments, a subject that is amenable to provided technologies for detection of incidence or recurrence of cancer may be a human subject with a smoking or obesity history (e.g., a heavy smoker and/or a BMI over 30), who in some embodiments may be experiencing one or more symptoms associated with cancer or a subset thereof (e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types). In some embodiments, a subject that is amenable to provided technologies for detection of incidence or recurrence of cancer may be a human subject who is at least 40 years old and is determined to have a benign tumor and/or one or more chronic inflammatory conditions. In some embodiments, a subject that is amenable to provided technologies for detection of incidence or recurrence of cancer may be a subject who has a family history of cancer (e.g., subjects having one or more first-degree relatives with a history of cancer), who has been previously treated for cancer (e.g., cancer), who is at risk of cancer recurrence after cancer treatment, who is in remission after cancer treatment, and/or who has been previously or periodically screened for cancer, e.g., by screening for the presence of at least one cancer biomarker (e.g., as described herein).

In some embodiments, the present disclosure, among other things, provides insights that technologies described and/or utilized herein may be particularly useful for screening certain populations of subjects, e.g., subjects who are at higher susceptibility to developing cancer. In some embodiments, the present disclosure, among other things, recognizes that the resulting PPVs of technologies described and/or utilized herein for cancer detection may be higher in cancer prone or susceptible populations. In some embodiments, the present disclosure, among other things, provides insights that screening of smoking or obese individuals, e.g., regular screening prior to or otherwise in absence of developed symptom(s), can be beneficial, and even important for effective management (e.g., successful treatment) of cancer. In some embodiments, the present disclosure provides cancer screening systems that can be implemented to detect cancer, including early-stage cancer, in some embodiments in obese and/or smoking individuals (e.g., with or without hereditary and/or life-history risks in cancer and/or with or without symptoms associated with cancer). In some embodiments, provided technologies can be implemented to achieve regular screening of obese and/or smoking individuals (e.g., with or without hereditary and/or life-history risks in cancer and/or with or without symptoms associated with cancer). In some embodiments, provided technologies achieve detection (e.g., early detection, e.g., in symptomatic or asymptomatic individual(s) and/or population(s)) of one or more features (e.g., incidence, progression, responsiveness to therapy, recurrence, etc.) of cancer, with sensitivity and/or specificity (e.g., rate of false positive and/or false negative results) appropriate to permit useful application of provided technologies to single-time and/or regular (e.g., periodic) assessment. In some embodiments, provided technologies are useful in conjunction with a subject's periodic physical examination (e.g., every year, every other year, or at an interval approved by the attending physician). In some embodiments, provided technologies are useful in conjunction with treatment regimen(s); in some embodiments, provided technologies may improve one or more characteristics (e.g., rate of success according to an accepted parameter) of such treatment regimen(s).

In some embodiments, a subject that is amenable to provided technologies for detection of incidence or recurrence of cancer may be an asymptomatic human subject and/or across an asymptomatic population of subjects. Such an asymptomatic subject and/or across an asymptomatic population of subjects may be subject(s) who has/have a family history of cancers such as lung cancer, liver cancer, breast cancer, ovarian cancer, prostate cancer, etc. (e.g., individuals having one or more first-degree relatives with a history of cancers known to be associated with genetic risk factors), who has been previously treated for cancer (e.g., cancer), who is at risk of cancer recurrence after cancer treatment, who is in remission after cancer treatment, and/or who has been previously or periodically screened for cancer, e.g., by screening for the presence of at least one cancer biomarker, for example, via mammogram or other means (e.g., ultrasound, X-ray imaging, low-dose CT scanning, MRI, and/or molecular tests based on cell-free nucleic acids, serum biomarkers (e.g., AFP, Angiopoietin-2, AXL, CA-125, CA 15-3, CA19-9, CD44, CEA, CYFRA 21-1, DKK1, Endoglin, FGF2, Follistatin, Galectin-3, G-CSF, GDF15, HE4, HGF, IL-6, IL-8, Kallikrein-6, Leptin, Mesothelin, Midkine, Myeloperoxidase, NSE, OPG, OPN, PAR, Prolactin, sEGFR, sFas, SHBG, sHER2/sEGFR2/sErbB2, sPECAM-1, TGFa, Thrombospondin-2, TIMP-1, TIMP-2, and/or other serum biomarkers described in Cohen et al. Science (2018) 359: 926-930, the contents of which are incorporated herein for the purposes described herein). Alternatively, in some embodiments, an asymptomatic subject may be a subject who has not been previously screened for cancer, who has not been diagnosed for cancer, and/or who has not previously received cancer therapy. In some embodiments, an asymptomatic subject may be a subject with a benign tumor. In some embodiments, an asymptomatic subject may be a subject who is susceptible to cancer (e.g., at an average population risk, at an elevated life-history associated risk, or with hereditary risk for cancer).

In some embodiments, a subject or population of subjects that are amenable to provided technologies for detection of cancer may be selected based on one or more characteristics such as age, race, geographic location, genetic history, medical history, personal history (e.g., smoking, alcohol, drugs, carcinogenic agents, diet, obesity, physical activity, sun exposure, radiation exposure, and/or occupational hazard). For example, in some embodiments, a subject or population of subjects that are amenable to provided technologies for detection of cancer may be a subject or a population of subjects determined to currently be or have been a smoker (e.g., cigarettes, cigars, pipe, and/or hookah) or obese.

In some embodiments, a subject or population of subjects that are amenable to provided technologies for detection of cancer may be a subject or a population of subjects determined to have one or more germline mutations in genes associated with hereditary risk for cancer (e.g., BRCA1, BRCA2, ATM, TP53, CHEK2, PTEN, CDH1, STK11, PALB2, MSH2, MLH1, MSH6, PMS2, NF1, NF2, RB1, RET, APC, VHL, MUTYH, FANC, etc.), and combinations thereof.

In some embodiments, a subject or population of subjects that are amenable to provided technologies for detection of cancer may be a subject or a population of subjects diagnosed with an imaging-confirmed mass.

In some embodiments, a subject or population of subjects that are amenable to provided technologies for detection of cancer may be a subject or a population of subjects at hereditary risk or life-history associated risk before undergoing a biopsy and/or a surgical procedure.

In some embodiments, a subject or population of subjects that are amenable to provided technologies for detection of cancer may be a subject or population of subjects determined to have a mass. In some embodiments, a subject or population of subjects that are amenable to provided technologies for detection of cancer may be a subject or population of subjects using birth control or post-menopausal hormone therapy. In some embodiments, a subject or population of subjects that are amenable to provided technologies for detection of cancer may be a subject or population of subjects who have been previously breast-feeding. In some embodiments, a subject or population that are amenable to provided technologies for detection of cancer may be subject or population of subjects who are overweight or obese. In some embodiments, a subject or population of subjects that are amenable to provided technologies for detection of cancer may be a subject or population of subjects determined to have hereditary mutations in genes associated with hereditary risk for cancer (e.g., BRCA1, BRCA2, ATM, TP53, CHEK2, PTEN, CDH1, STK11, PALB2, etc.). In some embodiments, a subject or population of subjects that are amenable to provided technologies for detection of cancer may be a subject or population of subjects exposed to radiation therapy and/or chemotherapy.

In some embodiments, a subject or population of subjects that are amenable to provided technologies for detection of cancer may be a subject or a population of subjects with one or more non-specific symptoms of cancer.

In some embodiments, a subject or population of subjects that are amenable to provided technologies for detection of cancer may be a subject or a population of subjects of diverse descendants such as Asians, African Americans, Caucasians, Native Hawaiians or other Pacific Islanders, Hispanics or Latinos, American Indians or Alaska natives, non-Hispanic blacks, or non-Hispanic whites. In some embodiments, a subject or population of subjects that are amenable to provided technologies for detection of cancer may be a subject or a population of subjects of diverse descendants such as Asian Pacific Islanders, Hispanics, American Indian/Alaska natives, non-Hispanic black, or non-Hispanic white. In some embodiments, a subject or population of subjects that are amenable to provided technologies for detection of cancer may be a subject or a population of subjects of any race and/or any ethnicity.

In some embodiments, a subject or population of subjects that are amenable to provided technologies for detection of cancer may have been previously subjected to mammogram, ultrasound, low-dose CT scanning, MRI, and/or molecular tests based on cell-free nucleic acids and/or serum metabolites/proteins. In some embodiments, such subjects may have received a negative indication of cancer (e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types) from such diagnostic tests. In some embodiments, such subjects may have received a positive indication of cancer from such diagnostic tests.

In some embodiments, technologies provided herein can be used in combination with other diagnostics assays including, e.g., but not limited to (i) physicals, general practitioner visits, cholesterol/lipid blood tests, diabetes (type 2) screening, colonoscopies, blood pressure screening, thyroid function tests, prostate cancer screening, mammograms, HPV/Pap smears, and/or vaccinations; (ii) mammogram, ultrasound, and/or molecular tests based on cell-free nucleic acids from blood, and/or serum biomarkers (e.g., AFP, Angiopoietin-2, AXL, CA-125, CA 15-3, CA19-9, CD44, CEA, CYFRA 21-1, DKK1, Endoglin, FGF2, Follistatin, Galectin-3, G-CSF, GDF15, HE4, HGF, IL-6, IL-8, Kallikrein-6, Leptin, Mesothelin, Midkine, Myeloperoxidase, NSE, OPG, OPN, PAR, Prolactin, sEGFR, sFas, SHBG, sHER2/sEGFR2/sErbB2, sPECAM-1, TGFa, Thrombospondin-2, TIMP-1, TIMP-2, and/or other serum biomarkers described in Cohen et al. Science (2018) 359: 926-930, the contents of which are incorporated herein for the purposes described herein); (iii) a genetic assay to screen blood plasma for genetic mutations in circulating tumor DNA and/or protein biomarkers linked to cancer; (iv) an assay involving immunofluorescence staining to identify cell phenotype and marker expression, followed by amplification and analysis by next-generation sequencing; and (v) germline and somatic mutation assays, or assays involving cell-free tumor DNA, liquid biopsy, serum biomarker, cell-free DNA, and/or circulating tumor cells.

B. Selection of Cancer Therapy (e.g., Cancer Therapy)

In some embodiments, provided technologies can be used for selecting an appropriate treatment for a cancer patient (e.g., a patient suffering from or susceptible to cancer). For example, some embodiments provided herein relate to a companion diagnostic assay for classification of patients for cancer therapy (e.g., cancer and/or adjunct treatment) which comprises assessment in a patient sample (e.g., a blood or blood-derived sample from a cancer patient) of a selected combination of provided biomarkers using technologies provided herein. Based on such an assay outcome, patients who are determined to be more likely to respond to a cancer therapy and/or an adjunct therapy can be administered such a therapy, or patients who are determined to be non-responsive to a specific such therapy can be administered a different therapy. Non-limiting examples of a cancer therapy and/or an adjunct therapy include 5-Fluorouracil, 6-Mercaptopurine (6-MP), 6-Thioguanine, Aldesleukin, Interleukin-2 (IL-2), Alemtuzumab, Alpha Interferon, Anastrozole, Arabinosylcytosine (ARA-C), Cytarabine, Asparaginase, Bevacizumab, Bexarotene, Bicalutamide, Bleomycin, Bortezomib, Busulfan, Capecitabine, Carboplatin, Carmustine, Chlorambucil, Cisplatin, Cyclophosphamide, Dacarbazine, Daunorubicin, Denileukin diftitox, Docetaxel, Doxorubicin, Epirubicin, Etoposide, Exemestane, Fludarabine, Flutamide, Fulvestrant, Gefitinib, Gemcitabine, Gemtuzumab, Hydroxyurea, Ibritumomab, Idarubicin, Ifosfamide, Imatinib Mesylate, Imiquimod, Irinotecan, Lapatinib, Lenalidomide, Letrozole, Lomustine, Mechlorethamine, Megestrol, Melphalan, Methotrexate, Mitomycin C, Mitoxantrone, Oxaliplatin, Paclitaxel, Pembrolizumab, Raloxifene, Rituximab, Sorafenib, Streptozocin, Sunitinib, Tamoxifen, Temozolomide, Topotecan, Toremifene, Tositumomab, Trastuzumab, Vinblastine, Vincristine, Vindesine, Vinorelbine, etc.; and FDA-approved antibody-based therapeutics for cancer including, e.g., [fam-]trastuzumab deruxtecan, Abciximab, Adalimumab, Ado-trastuzumab emtansine, Aducanumab, aducanumab-avwa, Alemtuzumab, Alirocumab, Amivantamab, amivantamab-vmjw, Anifrolumab, Ansuvimab-zykl, Atezolizumab, Atoltivimab, Avelumab, Balstilimab, Basiliximab, belantamab, Belantamab mafodotin, Belimumab, Benralizumab, Bevacizumab, Bezlotoxumab, Bimekizumab, Blinatumomab, Brentuximab vedotin, Brodalumab, Brolucizumab, brolucizumab-dbll, Burosumab, burosumab-twza, Canakinumab, Caplacizumab, caplacizumab-yhdp, Catumaxomab, Cemiplimab, cemiplimab-rwlc, Certolizumab pegol, Cetuximab, Crizanlizumab, crizanlizumab-tmca, Daclizumab, Daratumumab, Denosumab, deruxtecan-nxki, Dinutuximab, Dostarlimab, dostarlimab-gxly, Dupilumab, Durvalumab, Eculizumab, Edrecolomab, Efalizumab, Elotuzumab, Emapalumab, emapalumab-lzsg, Emicizumab, enfortumab, Enfortumab vedotin, Eptinezumab, eptinezumab-jjmr, Erenumab, erenumab-aooe, Evinacumab, Evolocumab, fam-trastuzumab, Faricimab, Fremanezumab, fremanezumab-vfrm, Galcanezumab, galcanezumab-gnlm, Gemtuzumab ozogamicin, Golimumab, govitecan-hziy, Guselkumab, Ibalizumab, ibalizumab-uiyk, Ibritumomab tiuxetan, Idarucizumab, Inebilizumab, inebilizumab-cdon, Infliximab, Inolimomb, Inotuzumab ozogamicin, Ipilimumab, Isatuximab, isatuximab-irfc, Ixekizumab, Lanadelumab, lanadelumab-flyo, loncastuximab, Loncastuximab tesirine, mafodotin-blmf, maftivimab, Margetuximab-cmkb, Mepolizumab, Mogamulizumab, mogamulizumab-kpkc, moxetumomab, Moxetumomab pasudotox, Muromonab-CD3, Narsoplimab, Natalizumab, Naxitamab-gqgk, Nebacumab, Necitumumab, Nivolumab, Obiltoxaximab, Obinutuzumab, Ocrelizumab, odesivimab-ebgn, Ofatumumab, Olaratumab, Omalizumab, Omburtamab, Oportuzumab monatox, Palivizumab, Panitumumab, pasudotox-tdfk, Pembrolizumab, Penpulimab, Pertuzumab, polatuzumab, Polatuzumab vedotin, Ramucirumab, Ranibizumab, Ravulizumab, ravulizumab-cwvz, Raxibacumab, Reslizumab, Retifanlimab, Risankizumab, risankizumab-rzaa, Rituximab, Romosozumab, romosozumab-aqqg, sacituzumab, Sacituzumab govitecan, Sarilumab, Satralizumab, satralizumab-mwge, Secukinumab, Siltuximab, Sintilimab, Sutimlimab (BIVV009), Tafasitamab, tafasitamab-cxix, Tanezumab, Teplizumab, Teprotumumab, teprotumumab-trbw, tesirine-lpyl, Tezepelumab, Tildrakizumab, tildrakizumab-asmn, Tisotumab vedotin, Tocilizumab, Toripalimab, Tositumomab-I131, Tralokinumab, Trastuzumab, Ublituximab, Ustekinumab, Vedolizumab, vedotin-ejfv, vedotin-piiq, and other antibodies (e.g., as listed online at antibodysociety.org/resources/approved-antibodies/, the contents of which are incorporated herein for the purposes described herein), and/or combinations thereof.

C. Evaluation of Treatment Efficacy (e.g., Cancer Treatment Efficacy)

In some embodiments, technologies provided herein can be used for monitoring and/or evaluating efficacy of an anti-cancer therapy administered to a cancer patient (e.g., cancer patient). For example, a biological sample (e.g., a bodily fluid sample such as, e.g., but not limited to a blood sample) can be collected from a cancer patient prior to or receiving an anti-cancer therapy (e.g., 5-Fluorouracil, 6-Mercaptopurine (6-MP), 6-Thioguanine, Aldesleukin, Interleukin-2 (IL-2), Alemtuzumab, Alpha Interferon, Anastrozole, Arabinosylcytosine (ARA-C), Cytarabine, Asparaginase, Bevacizumab, Bexarotene, Bicalutamide, Bleomycin, Bortezomib, Busulfan, Capecitabine, Carboplatin, Carmustine, Chlorambucil, Cisplatin, Cyclophosphamide, Dacarbazine, Daunorubicin, Denileukin diftitox, Docetaxel, Doxorubicin, Epirubicin, Etoposide, Exemestane, Fludarabine, Flutamide, Fulvestrant, Gefitinib, Gemcitabine, Gemtuzumab, Hydroxyurea, Ibritumomab, Idarubicin, Ifosfamide, Imatinib Mesylate, Imiquimod, Irinotecan, Lapatinib, Lenalidomide, Letrozole, Lomustine, Mechlorethamine, Megestrol, Melphalan, Methotrexate, Mitomycin C, Mitoxantrone, Oxaliplatin, Paclitaxel, Raloxifene, Rituximab, Sorafenib, Streptozocin, Sunitinib, Tamoxifen, Temozolomide, Topotecan, Toremifene, Tositumomab, Trastuzumab, Vinblastine, Vincristine, Vindesine, Vinorelbine) at a first time point to detect or measure tumor burdens, e.g., by detecting presence or amount of nanoparticles having a size range of interest that includes extracellular vesicles comprising a selected combination of biomarkers that is specific to detection of cancer. After a period of treatment, a second biological sample (e.g., a bodily fluid sample such as, e.g., but not limited to a blood sample) can be collected from the same cancer patient to detect changes in tumor burdens, e.g., by detecting absence or reduction in amount of nanoparticles having a size range of interest that includes extracellular vesicles comprising a selected combination of biomarkers that is specific to detection of cancer. By monitoring levels and/or changes in tumor burdens over the course of treatment, appropriate course of action, e.g., increasing or decreasing the dose of a therapeutic agent, and/or administering a different therapeutic agent, can be taken.

VIII. Kits

Also provided are kits that find use in practicing technologies as described above. In some embodiments, a kit comprises a plurality of detection probes (e.g., as described and/or utilized herein). In some embodiments, a provided kit may comprise two or more (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more) detection probes. In some embodiments, individual detection probes may be directed at different targets. In some embodiments, two or more individual detection probes may be directed to the same target. In some embodiments, a provided kit comprises two or more different detection probes directed at different targets, and optionally may include at least one additional detection probe also directed at a target to which another detection probe is directed. In some embodiments, a provided kit comprises a plurality of subsets of detection probes, each of which comprises two or more detection probes directed at the same target. In some embodiments, a plurality of detection probes may be provided as a mixture in a container. In some embodiments, multiple subsets of detection probes may be provided as individual mixtures in separate containers. In some embodiments, each detection probe is provided individually in a separate container.

In some embodiments, a kit for detection of cancer comprises: (a) a capture agent comprising a target-capture moiety directed to an extracellular vesicle-associated surface biomarker; and (b) a set of detection probes, which set comprises at least two detection probes each directed to a target biomarker of a biomarker combination for cancer, wherein the detection probes each comprise: (i) a target binding moiety directed the target biomarker of the biomarker combination for cancer; and (ii) an oligonucleotide domain coupled to the target binding moiety, the oligonucleotide domain comprising a double-stranded portion and a single-stranded overhang portion extended from one end of the oligonucleotide domain, wherein the single-stranded overhang portions of the at least two detection probes are characterized in that they can hybridize to each other when the at least two detection probes are bound to the same extracellular vesicle.

In some embodiments, the present disclosure describes a kit for detection of cancer comprising: (a) a capture agent comprising a target-capture moiety directed to a first surface biomarker; and (b) at least one set of detection probes, which set comprises at least two detection probes each directed to a second surface biomarker, wherein the detection probes each comprise: (i) a target binding moiety directed at the second surface biomarker; and (ii) an oligonucleotide domain coupled to the target binding moiety, the oligonucleotide domain comprising a double-stranded portion and a single-stranded overhang portion extended from one end of the oligonucleotide domain, wherein the single-stranded overhang portions of the at least two detection probes are characterized in that they can hybridize to each other when the at least two detection probes are bound to the same nanoparticle having the size within the range of about 30 nm to about 1000 nm; wherein at least the first surface biomarker and the second surface biomarker form a target biomarker signature determined to be associated with cancer, and wherein the first and second surface biomarkers are each independently selected from: (i) polypeptides encoded by human genes as follows: ALDH18A1, AP1M2, APOO, ARFGEF3, B3GNT3, BMPR1B, CADM4, CANT1, CD24, CDH1, CDH17, CDH2, CDH3, CEACAM5, CEACAM6, CLDN3, CLDN4, CLGN, CLN5, CYP2S1, DSG2, ELAPOR1, ENPP5, EPCAM, EPHB2, FAM241B, FERMT1, FOLR1, FZD2, GALNT14, GALNT6, GJB1, GNG4, GNPNAT1, GOLM1, GPR160, GPRIN1, GRHL2, HACD3, HS6ST2, IGSF3, ILDR1, KDELR3, KPNA2, KRTCAP3, LAMB3, LAMC2, LAPTM4B, LARGE2, LMNB1, LRRN1, LSR, MAL2, MARCKSL1, MARVELD2, MET, NPTXR, NUP210, PARD6B, PMEPA1, PODXL2, PRAF2, PRSS8, RAB25, RAC3, RACGAP1, RAP2B, RCC2, RNF128, RNF43, RPN1, RPN2, SERINC2, SHISA2, SLC35A2, SLC39A6, SLC44A4, SLC4A4, SMIM22, SMPDL3B, SYAP1, SYT13, TMEM132A, TMEM238, TMEM9, TSPAN13, ULBP2, UNC13B, VTCN1, ABCA13, ADAM23, CYP4F11, HAS3, TMPRSS4, UGT1A6, PIGT, TOMM34, ACSL4, GPC3, ROBO1, SLC22A9, SLC38A3, TFR2, TM4SF4, TMPRSS6, ANXA13, CHST4, GAL3ST1, SNAP25, TMEM156, CLDN18, EPPK1, MUC13, OCLN, CFTR, GCNT3, ITGB6, LAD1, MSLN, TESC, LYPD6B, S100P, TMEM51, TNFRSF21, UPK1B, UPK2, ABCC4, FOLH1, RAB3B, STEAP2, TMPRSS2, TSPAN1, AP1S3, DSC2, DSG3, TMPRSS11D, KCNS1, LY6K, MUC2, MUC4, SYNGR3, CELSR1, COX6C, ESR1, MUC1, ABCC11, ERBB2, SLC9A3R1, PROM1, PTK7, CDK4, DLK1, LMNB2, PCDH7, TMEM108, TYMS, SDC1, SLC34A2, BCAM, MUC16, ADAM17, ADAM28, ADAM8, ALCAM, AMHR2, AXL, BAG3, BSG, CCL2, CCL8, CCN1, CCN2, CCR5, CD274, CD38, CD44, CD47, CDH11, CETN1, CLDN1, CLEC2D, CLU, CSPG4, DKK1, DLL4, EGFR, ENPP3, EPHA10, ERBB3, FAP, FGF1, FGFR4, FLNA, FLNB, FLT4, FZD7, GFRA1, GM3, GPA33, GPC1, GPNMB, GUCY2C, HGF, ICAM1, IGF1R, IL1A, IL1RAP, IL6, ITGA6, ITGAV, KDR, KLK3, KLKB1, KRT8, LAG3, LGR5, LPR6, LY6E, MCAM, MDM2, MELTF, MERTK, MST1R, MUC17, MUC5AC, MUCL1, NOTCH2, NOTCH3, NRP1, NT5E, PI4K2A, P1, PLAUR, PLVAP, PPP1R3A, PRLR, PSCA, PVR, RET, S1PR1, SLC3A2, SLC7A11, SLC7A5, SPINK1, STAT3, STEAP1, TACSTD2, TF, TFRC, TGFBR2, TIGIT, TNC, TNFRSF10A, TNFRSF10B, TNFRSF12A, TNFRSF4, TNFSF11, TNFSF18, TPBG, VANGL2, VEGFA, VEGFC, and combinations thereof; and/or (ii) at least one carbohydrate-dependent and/or lipid-dependent marker as follows: CanAg, Sialyltetraosyl carbohydrate, Phosphatidylserine, Sialyl Lewis A/CA19-9, Lewis Y/B antigen, Tn antigen, SialylTn (sTn) antigen, Thomsen-Friedenreich (T, TF) antigen, Lewis Y antigen (also known as CD174), Lewis B antigen, Sialyl Lewis X (sLex) (also known as Sialyl SSEA-1 (SLX)), SSEA-1/Lewis X antigen, NeuGcGM3, beta1,6-branching, bisecting GlcNAc in a beta1,4-linkage, core fucosylation antigen, Sialyl-T antigens (sT), Sialyl Lewis c antigen, Globo H antigen, SSEA-3 (Gb5), SSEA-4 (sialy-Gb5), Gb3 (Globotriaose, CD77), Disialosyl-galactosylgloboside (DSGG), GalNAcDSLc4, Fucosyl GM1, GD1alpha ganglioside, GD1a ganglioside, GD2 ganglioside, GD3 ganglioside, GM2 ganglioside, Lc3 ceramide, nLc4 ceramide, 9-O-Ac-GD2 ganglioside, 9-O-Ac-GD3 (CDw60) ganglioside, 9-O-Ac-GT3 ganglioside, Forssman antigen, Disialyl Lewis a antigen, Sialylparagloboside (SPG), Polysialic acid (PSA) linked to NCAM, and combinations thereof. In some embodiments, the first and the second surface biomarkers are different. In some embodiments, the first and the second surface biomarkers are the same (with the same or different epitopes).

In many embodiments described herein, a biomarker combination for cancer comprises:

at least one extracellular vesicle-associated surface biomarker biomarker and at least one target biomarker selected from the group consisting of: surface biomarkers, intravesicular biomarkers, and intravesicular RNA biomarkers, wherein:

- the surface biomarkers are selected from (i) polypeptides encoded by human genes as follows: ALDH18A1, AP1M2, APOO, ARFGEF3, B3GNT3, BMPR1B, CADM4, CANT1, CD24, CDH1, CDH17, CDH2, CDH3, CEACAM5, CEACAM6, CLDN3, CLDN4, CLGN, CLN5, CYP2S1, DSG2, ELAPOR1, ENPP5, EPCAM, EPHB2, FAM241B, FERMT1, FOLR1, FZD2, GALNT14, GALNT6, GJB1, GNG4, GNPNAT1, GOLM1, GPR160, GPRIN1, GRHL2, HACD3, HS6ST2, IGSF3, ILDR1, KDELR3, KPNA2, KRTCAP3, LAMB3, LAMC2, LAPTM4B, LARGE2, LMNB1, LRRN1, LSR, MAL2, MARCKSL1, MARVELD2, MET, NPTXR, NUP210, PARD6B, PMEPA1, PODXL2, PRAF2, PRSS8, RAB25, RAC3, RACGAP1, RAP2B, RCC2, RNF128, RNF43, RPN1, RPN2, SERINC2, SHISA2, SLC35A2, SLC39A6, SLC44A4, SLC4A4, SMIM22, SMPDL3B, SYAP1, SYT13, TMEM132A, TMEM238, TMEM9, TSPAN13, ULBP2, UNC13B, VTCN1, ABCA13, ADAM23, CYP4F11, HAS3, TMPRSS4, UGT1A6, PIGT, TOMM34, ACSL4, GPC3, ROBO1, SLC22A9, SLC38A3, TFR2, TM4SF4, TMPRSS6, ANXA13, CHST4, GAL3ST1, SNAP25, TMEM156, CLDN18, EPPK1, MUC13, OCLN, CFTR, GCNT3, ITGB6, LAD1, MSLN, TESC, LYPD6B, S100P, TMEM51, TNFRSF21, UPK1B, UPK2, ABCC4, FOLH1, RAB3B, STEAP2, TMPRSS2, TSPAN1, AP1S3, DSC2, DSG3, TMPRSS11D, KCNS1, LY6K, MUC2, MUC4, SYNGR3, CELSR1, COX6C, ESR1, MUC1, ABCC11, ERBB2, SLC9A3R1, PROM1, PTK7, CDK4, DLK1, LMNB2, PCDH7, TMEM108, TYMS, SDC1, SLC34A2, BCAM, MUC16, ADAM17, ADAM28, ADAM8, ALCAM, AMHR2, AXL, BAG3, BSG, CCL2, CCL8, CCN1, CCN2, CCR5, CD274, CD38, CD44, CD47, CDH11, CETN1, CLDN1, CLEC2D, CLU, CSPG4, DKK1, DLL4, EGFR, ENPP3, EPHA10, ERBB3, FAP, FGF1, FGFR4, FLNA, FLNB, FLT4, FZD7, GFRA1, GM3, GPA33, GPC1, GPNMB, GUCY2C, HGF, ICAM1, IGF1R, IL1A, IL1RAP, IL6, ITGA6, ITGAV, KDR, KLK3, KLKB1, KRT8, LAG3, LGR5, LPR6, LY6E, MCAM, MDM2, MELTF, MERTK, MST1R, MUC17, MUC5AC, MUCL1, NOTCH2, NOTCH3, NRP1, NT5E, PI4K2A, P1, PLAUR, PLVAP, PPP1R3A, PRLR, PSCA, PVR, RET, S1PR1, SLC3A2, SLC7A11, SLC7A5, SPINK1, STAT3, STEAP1, TACSTD2, TF, TFRC, TGFBR2, TIGIT, TNC, TNFRSF10A, TNFRSF10B, TNFRSF12A, TNFRSF4, TNFSF11, TNFSF18, TPBG, VANGL2, VEGFA, VEGFC, or combinations thereof; and/or (ii) at least one carbohydrate-dependent and/or lipid-dependent marker as follows: CanAg, Sialyltetraosyl carbohydrate, Phosphatidylserine, Sialyl Lewis A/CA19-9, Lewis Y/B antigen, Tn antigen, SialylTn (sTn) antigen, Thomsen-Friedenreich (T, TF) antigen, Lewis Y antigen (also known as CD174), Lewis B antigen, Sialyl Lewis X (sLex) (also known as Sialyl SSEA-1 (SLX)), SSEA-1/Lewis X antigen, NeuGcGM3, beta1,6-branching, bisecting GlcNAc in a beta1,4-linkage, core fucosylation antigen, Sialyl-T antigens (sT), Sialyl Lewis c antigen, Globo H antigen, SSEA-3 (Gb5), SSEA-4 (sialy-Gb5), Gb3 (Globotriaose, CD77), Disialosyl-galactosylgloboside (DSGG), GalNAcDSLc4, Fucosyl GM1, GD1alpha ganglioside, GD1a ganglioside, GD2 ganglioside, GD3 ganglioside, GM2 ganglioside, Lc3 ceramide, nLc4 ceramide, 9-O-Ac-GD2 ganglioside, 9-O-Ac-GD3 (CDw60) ganglioside, 9-O-Ac-GT3 ganglioside, Forssman antigen, Disialyl Lewis a antigen, Sialylparagloboside (SPG), Polysialic acid (PSA) linked to NCAM, or combinations thereof.

In some embodiments, at least one of the surface biomarkers utilized in a provided kit is selected from: (i) a polypeptide encoded by human gene as follows: MUC1 and CEACAM5; and/or (ii) carbohydrate-dependent markers as follows: Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, and combinations thereof.

In some embodiments, a kit for detection of cancer comprises: (a) a capture agent comprising a target-capture moiety directed to a surface biomarker (e.g., a surface biomarker present on the surface of a nanoparticle having a size range of interest that includes an extracellular vesicle); and (b) a set of detection probes, which set comprises at least two detection probes each directed to a target biomarker of a biomarker combination for cancer, wherein the detection probes each comprise: (i) a target binding moiety directed the target biomarker of the biomarker combination for cancer; and (ii) an oligonucleotide domain coupled to the target binding moiety, the oligonucleotide domain comprising a double-stranded portion and a single-stranded overhang portion extended from one end of the oligonucleotide domain, wherein the single-stranded overhang portions of the at least two detection probes are characterized in that they can hybridize to each other when the at least two detection probes are bound to the same nanoparticle. In these embodiments, such a biomarker combination for cancer comprises at least one surface biomarker as described herein (e.g., a surface biomarker present on the surface of a nanoparticle having a size range of interest that includes an extracellular vesicle) and at least one target biomarker selected from the group consisting of: surface biomarkers (e.g., as described herein), intravesicular biomarkers, and intravesicular RNA biomarkers. In some embodiments, one or more surface biomarkers utilized in a provided kit are selected from: (i) polypeptides encoded by human genes as follows: ALDH18A1, AP1M2, APOO, ARFGEF3, B3GNT3, BMPR1B, CADM4, CANT1, CD24, CDH1, CDH17, CDH2, CDH3, CEACAM5, CEACAM6, CLDN3, CLDN4, CLGN, CLN5, CYP2S1, DSG2, ELAPOR1, ENPP5, EPCAM, EPHB2, FAM241B, FERMT1, FOLR1, FZD2, GALNT14, GALNT6, GJB1, GNG4, GNPNAT1, GOLM1, GPR160, GPRIN1, GRHL2, HACD3, HS6ST2, IGSF3, ILDR1, KDELR3, KPNA2, KRTCAP3, LAMB3, LAMC2, LAPTM4B, LARGE2, LMNB1, LRRN1, LSR, MAL2, MARCKSL1, MARVELD2, MET, NPTXR, NUP210, PARD6B, PMEPA1, PODXL2, PRAF2, PRSS8, RAB25, RAC3, RACGAP1, RAP2B, RCC2, RNF128, RNF43, RPN1, RPN2, SERINC2, SHISA2, SLC35A2, SLC39A6, SLC44A4, SLC4A4, SMIM22, SMPDL3B, SYAP1, SYT13, TMEM132A, TMEM238, TMEM9, TSPAN13, ULBP2, UNC13B, VTCN1, ABCA13, ADAM23, CYP4F11, HAS3, TMPRSS4, UGT1A6, PIGT, TOMM34, ACSL4, GPC3, ROBO1, SLC22A9, SLC38A3, TFR2, TM4SF4, TMPRSS6, ANXA13, CHST4, GAL3ST1, SNAP25, TMEM156, CLDN18, EPPK1, MUC13, OCLN, CFTR, GCNT3, ITGB6, LAD1, MSLN, TESC, LYPD6B, S100P, TMEM51, TNFRSF21, UPK1B, UPK2, ABCC4, FOLH1, RAB3B, STEAP2, TMPRSS2, TSPAN1, AP1S3, DSC2, DSG3, TMPRSS11D, KCNS1, LY6K, MUC2, MUC4, SYNGR3, CELSR1, COX6C, ESR1, MUC1, ABCC11, ERBB2, SLC9A3R1, PROM1, PTK7, CDK4, DLK1, LMNB2, PCDH7, TMEM108, TYMS, SDC1, SLC34A2, BCAM, MUC16, ADAM17, ADAM28, ADAM8, ALCAM, AMHR2, AXL, BAG3, BSG, CCL2, CCL8, CCN1, CCN2, CCR5, CD274, CD38, CD44, CD47, CDH11, CETN1, CLDN1, CLEC2D, CLU, CSPG4, DKK1, DLL4, EGFR, ENPP3, EPHA10, ERBB3, FAP, FGF1, FGFR4, FLNA, FLNB, FLT4, FZD7, GFRA1, GM3, GPA33, GPC1, GPNMB, GUCY2C, HGF, ICAM1, IGF1R, IL1A, IL1RAP, IL6, ITGA6, ITGAV, KDR, KLK3, KLKB1, KRT8, LAG3, LGR5, LPR6, LY6E, MCAM, MDM2, MELTF, MERTK, MST1R, MUC17, MUC5AC, MUCL1, NOTCH2, NOTCH3, NRP1, NT5E, PI4K2A, P1, PLAUR, PLVAP, PPP1R3A, PRLR, PSCA, PVR, RET, S1PR1, SLC3A2, SLC7A11, SLC7A5, SPINK1, STAT3, STEAP1, TACSTD2, TF, TFRC, TGFBR2, TIGIT, TNC, TNFRSF10A, TNFRSF10B, TNFRSF12A, TNFRSF4, TNFSF11, TNFSF18, TPBG, VANGL2, VEGFA, VEGFC, or combinations thereof; and/or (ii) at least one carbohydrate-dependent and/or lipid-dependent marker as follows: CanAg, Sialyltetraosyl carbohydrate, Phosphatidylserine, Sialyl Lewis A/CA19-9, Lewis Y/B antigen, Tn antigen, SialylTn (sTn) antigen, Thomsen-Friedenreich (T, TF) antigen, Lewis Y antigen (also known as CD174), Lewis B antigen, Sialyl Lewis X (sLex) (also known as Sialyl SSEA-1 (SLX)), SSEA-1/Lewis X antigen, NeuGcGM3, beta1,6-branching, bisecting GlcNAc in a beta1,4-linkage, core fucosylation antigen, Sialyl-T antigens (sT), Sialyl Lewis c antigen, Globo H antigen, SSEA-3 (Gb5), SSEA-4 (sialy-Gb5), Gb3 (Globotriaose, CD77), Disialosyl-galactosylgloboside (DSGG), GalNAcDSLc4, Fucosyl GM1, GD1alpha ganglioside, GD1a ganglioside, GD2 ganglioside, GD3 ganglioside, GM2 ganglioside, Lc3 ceramide, nLc4 ceramide, 9-O-Ac-GD2 ganglioside, 9-O-Ac-GD3 (CDw60) ganglioside, 9-O-Ac-GT3 ganglioside, Forssman antigen, Disialyl Lewis a antigen, Sialylparagloboside (SPG), Polysialic acid (PSA) linked to NCAM, or combinations thereof. In some embodiments, one or more intravesicular biomarkers utilized in a provided kit are selected from polypeptides encoded by human genes as follows: AARD, AGR2, AGR3, AIM1, ALDH3B2, ANKRD30A, ANXA9, AP1M2, AR, BARX2, BCL2, BIRC5, BSPRY, C15orf48, C1orf116, C1orf64, C9orf152, CALML5, CAMSAP3, CAPN13, CAPN8, CBLC, CCNO, CENPF, CLIC6, CPA3, CRABP2, CYP4X1, DNAJC12, DTL, EHF, ELF3, EPN3, ESR1, ESRP1, ESRP2, FAM111B, FAM83D, FAM83H, FOXA1, FS1P1, GATA3, GRHL2, HMGCS2, HOOK1, HOXC10, IRF6, IRX2, IRX3, IRX5, KIF12, KIF4A, KRT14, KRT15, KRT17, KRT18, KRT19, KRT23, KRT6B, KRT7, KRT8, LMX1B, MAP7, MEX3A, MISP, MYB, MYBL2, NAT1, NEK2, OVOL2, PARD6B, PKIB, PKP3, PLEKHS1, PRR15, PRR15L, RASEF, RORC, S100A1, S100A14, SBK1, SPDEF, SPINT1, TFAP2A, TFAP2B, TFAP2C, THRSP, TRPS1, UBE2C, VAV3, WWC1, ZC3H11A, ZNF552, and combinations thereof. In some embodiments, an intravesicular biomarker described herein may comprise at least one post-translational modification. In some embodiments, one or more intravesicular RNA biomarkers utilized in a provided kit are selected from: RNA transcripts (e.g., mRNA transcripts) encoded by human genes as follows: AARD, ADAM12, AGR2, AGR3, AIM1, ALDH3B2, ANKRD30A, ANO1, ANXA9, AP1M2, AR, BARX2, BCL2, BIK, BIRC5, BMPR1B, BNIPL, BSPRY, C15orf48, C1orf116, C1orf210, C1orf64, C9orf152, CA12, CACNG4, CALML5, CAMSAP3, CAPN13, CAPN8, CBLC, CCNO, CD24, CDH1, CDS, CEACAM6, CELSR1, CENPF, CLDN3, CLDN4, CLDN7, CLIC6, COL17A1, CPA3, CRABP2, CRB3, CXADR, CYP4X1, CYP4Z, DEGS2, DNAJC12, DSP, DTL, EHF, ELF3, EPCAM, EPN3, ERBB3, ESR1, ESRP1, ESRP2, F2RL2, FAM111B, FAM83D, FAM83H, FOXA1, FSIP1, FXYD3, GABRP, GALNT6, GATA3, GGT6, GRHL2, HCAR1, HMGCS2, HOOK1, HOXC10, HPN, IGSF9, IRF6, IRX2, IRX3, IRX5, ITGB6, KIAA1324, KIF12, KIF4A, KRT14, KRT15, KRT17, KRT18, KRT19, KRT23, KRT6B, KRT7, KRT8, LAMPS, LMX1B, LRRC15, MAL2, MAP7, MARVELD2, MEX3A, MISP, MUC1, MYB, MYBL2, NAT1, NEK2, NKAIN1, OLR1, OVOL2, PARD6B, PDZKI1P1, PKIB, PKP3, PLEKHS1, PRLR, PROM1, PROM2, PRR15, PRR15L, PRSS8, RAB25, RAB27B, RASEF, RHOV, RORC, S100A1, S00A14, SBK1, SDC1, SERINC2, SHISA2, SLC39A6, SLC44A4, SMIM22, SPDEF, SPINT1, SUSD3, SUSD4, TACSTD2, TFAP2A, TFAP2B, TFAP2C, THRSP, TJP3, TMC5, TMEM125, TMPRSS3, TNS4, TREM2, TRPS1, TSPAN1, TTC39A, UBE2C, VAV3, VTCN1, WNK4, WWC1, ZC3H11A, ZNF552, and combinations thereof.

In some embodiments where a biomarker combination comprises at least two surface biomarkers, the surface biomarkers are different. In some embodiments where a biomarker combination comprises at least two surface biomarkers, the surface biomarkers are the same (with the same or different epitopes).

In some embodiments, a capture agent provided in a kit comprises a target-capture moiety directed to an extracellular vesicle-associated surface biomarker or surface biomarker, which is or comprises (i) a polypeptide encoded by a human gene as follows: ALDH18A1, AP1M2, APOO, ARFGEF3, B3GNT3, BMPR1B, CADM4, CANT1, CD24, CDH1, CDH17, CDH2, CDH3, CEACAM5, CEACAM6, CLDN3, CLDN4, CLGN, CLN5, CYP2S1, DSG2, ELAPOR1, ENPP5, EPCAM, EPHB2, FAM241B, FERMT1, FOLR1, FZD2, GALNT14, GALNT6, GJB1, GNG4, GNPNAT1, GOLM1, GPR160, GPRIN1, GRHL2, HACD3, HS6ST2, IGSF3, ILDR1, KDELR3, KPNA2, KRTCAP3, LAMB3, LAMC2, LAPTM4B, LARGE2, LMNB1, LRRN1, LSR, MAL2, MARCKSL1, MARVELD2, MET, NPTXR, NUP210, PARD6B, PMEPA1, PODXL2, PRAF2, PRSS8, RAB25, RAC3, RACGAP1, RAP2B, RCC2, RNF128, RNF43, RPN1, RPN2, SERINC2, SHISA2, SLC35A2, SLC39A6, SLC44A4, SLC4A4, SMIM22, SMPDL3B, SYAP1, SYT13, TMEM132A, TMEM238, TMEM9, TSPAN13, ULBP2, UNC13B, VTCN1, ABCA13, ADAM23, CYP4F11, HAS3, TMPRSS4, UGT1A6, PIGT, TOMM34, ACSL4, GPC3, ROBO1, SLC22A9, SLC38A3, TFR2, TM4SF4, TMPRSS6, ANXA13, CHST4, GAL3ST1, SNAP25, TMEM156, CLDN18, EPPK1, MUC13, OCLN, CFTR, GCNT3, ITGB6, LAD1, MSLN, TESC, LYPD6B, S100P, TMEM51, TNFRSF21, UPK1B, UPK2, ABCC4, FOLH1, RAB3B, STEAP2, TMPRSS2, TSPAN1, AP1S3, DSC2, DSG3, TMPRSS11D, KCNS1, LY6K, MUC2, MUC4, SYNGR3, CELSR1, COX6C, ESR1, MUC1, ABCC11, ERBB2, SLC9A3R1, PROM1, PTK7, CDK4, DLK1, LMNB2, PCDH7, TMEM108, TYMS, SDC1, SLC34A2, BCAM, MUC16, ADAM17, ADAM28, ADAM8, ALCAM, AMHR2, AXL, BAG3, BSG, CCL2, CCL8, CCN1, CCN2, CCR5, CD274, CD38, CD44, CD47, CDH11, CETN1, CLDN1, CLEC2D, CLU, CSPG4, DKK1, DLL4, EGFR, ENPP3, EPHA10, ERBB3, FAP, FGF1, FGFR4, FLNA, FLNB, FLT4, FZD7, GFRA1, GM3, GPA33, GPC1, GPNMB, GUCY2C, HGF, ICAM1, IGF1R, IL1A, IL1RAP, IL6, ITGA6, ITGAV, KDR, KLK3, KLKB1, KRT8, LAG3, LGR5, LPR6, LY6E, MCAM, MDM2, MELTF, MERTK, MST1R, MUC17, MUC5AC, MUCL1, NOTCH2, NOTCH3, NRP1, NT5E, PI4K2A, P1, PLAUR, PLVAP, PPP1R3A, PRLR, PSCA, PVR, RET, S1PR1, SLC3A2, SLC7A11, SLC7A5, SPINK1, STAT3, STEAP1, TACSTD2, TF, TFRC, TGFBR2, TIGIT, TNC, TNFRSF10A, TNFRSF10B, TNFRSF12A, TNFRSF4, TNFSF11, TNFSF18, TPBG, VANGL2, VEGFA, VEGFC, or combinations thereof; and/or (ii) at least one carbohydrate-dependent and/or lipid-dependent marker as follows: CanAg, Sialyltetraosyl carbohydrate, Phosphatidylserine, Sialyl Lewis A/CA19-9, Lewis Y/B antigen, Tn antigen, SialylTn (sTn) antigen, Thomsen-Friedenreich (T, TF) antigen, Lewis Y antigen (also known as CD174), Lewis B antigen, Sialyl Lewis X (sLex) (also known as Sialyl SSEA-1 (SLX)), SSEA-1/Lewis X antigen, NeuGcGM3, beta1,6-branching, bisecting GlcNAc in a beta1,4-linkage, core fucosylation antigen, Sialyl-T antigens (sT), Sialyl Lewis c antigen, Globo H antigen, SSEA-3 (Gb5), SSEA-4 (sialy-Gb5), Gb3 (Globotriaose, CD77), Disialosyl-galactosylgloboside (DSGG), GalNAcDSLc4, Fucosyl GM1, GD1alpha ganglioside, GD1a ganglioside, GD2 ganglioside, GD3 ganglioside, GM2 ganglioside, Lc3 ceramide, nLc4 ceramide, 9-O-Ac-GD2 ganglioside, 9-O-Ac-GD3 (CDw60) ganglioside, 9-O-Ac-GT3 ganglioside, Forssman antigen, Disialyl Lewis a antigen, Sialylparagloboside (SPG), Polysialic acid (PSA) linked to NCAM, or combinations thereof.

In some embodiments, a target binding moiety of at least two detection probes provided in a kit is each directed to the same target biomarker of a biomarker combination. In some such embodiments, an oligonucleotide domain of such at least two detection probes are different

In some embodiments, a target binding moiety of at least two detection probes provided in a kit is each directed to a distinct target biomarker of a biomarker combination.

In some embodiments, a target binding moiety of a detection probe may be or comprise an affinity agent, which in some embodiments may be or comprise an antibody (e.g., a monoclonal antibody). In some embodiments, a target binding moiety of a detection probe may be or comprise an affinity agent, which in some embodiments may be or comprise a lectin or siglec.

In some embodiments, a kit may comprise at least one chemical reagent such as a fixation agent, a permeabilization agent, and/or a blocking agent.

In some embodiments, a kit may comprise one or more nucleic acid ligation reagents (e.g., a nucleic acid ligase such as a DNA ligase and/or a buffer solution).

In some embodiments, a kit may comprise at least one or more amplification reagents such as PCR amplification reagents. In some embodiments, a kit may comprise one or more nucleic acid polymerases (e.g., DNA polymerases), one or more pairs of primers, nucleotides, and/or a buffered solution.

In some embodiments, a kit may comprise a solid substrate for capturing an entity (e.g., biological entity) of interest. For example, such a solid substrate may be or comprise a bead (e.g., a magnetic bead). In some embodiments, such a solid substrate may be or comprise a surface. In some embodiments, a surface may be or comprise a capture surface (e.g., an entity capture surface) of an assay chamber, such as, e.g., a filter, a matrix, a membrane, a plate, a tube, a well (e.g., but not limited to a microwell), etc. In some embodiments, a surface (e.g., a capture surface) of a solid substrate can be coated with a capture agent (e.g., affinity agent) for an entity (e.g., biological entity) of interest.

In some embodiments, a set of detection probes provided in a kit may be selected for diagnosis of cancer.

In some embodiments, a set of detection probes provided in a kit may be selected for diagnosis of carcinoma or sarcoma.

In some embodiments, a set of detection probes provided in a kit may be selected for diagnosis of cancer characterized by a hormone status. For example, in some embodiments where breast cancer detection is desired, such hormone status may include but is not limited to ER+, HER2+, and/or triple negative.

In some embodiments, a kit may comprise a plurality of sets of detection probes, wherein each set of detection probes is directed for detection of a specific cancer and comprises at least 2 or more detection probes. For example, such a kit can be used to screen a subject for various cancers (e.g., in some embodiments characterized by carcinoma, sarcoma, melanoma, and mixed types) including but not limited to: skin cancer, lung cancer, breast cancer, ovarian cancer, pancreatic cancer, prostate cancer, brain cancer, and/or liver cancer in a single assay.

In some embodiments, kits provided herein may include instructions for practicing methods described herein. These instructions may be present in kits in a variety of forms, one or more of which may be present in the kits. One form in which these instructions may be present is as printed information on a suitable medium or substrate, e.g., a piece or pieces of paper on which the information is printed, in the packaging of kits, in a package insert, etc. Yet another means may be a computer readable medium, e.g., diskette, CD, USB drive, etc., on which instructional information has been recorded. Yet another means that may be present is a website address which may be used via the internet to access instructional information. Any convenient means may be present in the kits.

In some embodiments where kits are for use as companion diagnostics, such kits can include instructions for identifying patients that are likely to respond to a therapeutic agent (e.g., identification of biomarkers that are indicative of patient responsiveness to the therapeutic agent). In some embodiments, such kits can comprise a therapeutic agent for use in tandem with the companion diagnostic test.

Other features of the invention will become apparent in the course of the following description of exemplary embodiments, which are given for illustration of the invention and are not intended to be limiting thereof.

EXEMPLIFICATION
Example 1: Detection of an Exemplary Biomarker Combination in Individual Extracellular Vesicles Associated with Cancer

The present Example describes synthesis of detection probes for targets (e.g., target biomarker(s)) each comprising a target-binding moiety and an oligonucleotide domain (comprising a double-stranded portion and a single stranded overhang) coupled to the target-binding moiety. The present Example further demonstrates that use of such detection probes to detect the presence or absence of biological entities (e.g., extracellular vesicles) comprising two or more distinct targets.

In some embodiments, a detection probe can comprise a double-stranded oligonucleotide with an antibody agent specific to a target cancer biomarker at one end and a single stranded overhang at another end. When two or more detection probes are bound to the same biological entity (e.g., an extracellular vesicle), the single-stranded overhangs of the detection probes are in close proximity such that they can hybridize to each other to form a double-stranded complex, which can be subsequently ligated and amplified for detection.

This study employed at least two detection probes in a set. In some embodiments, such at least two detection probes are directed to the same target biomarker. In some embodiments, such at least two detection probes directed to the same target, which may be directed to different epitopes of the same target or to the same epitope of the same target. In some embodiments, such at least two detection probes are directed to distinct targets. A skilled artisan reading the present disclosure will understand that two detection probes can be directed to different target biomarkers, or that three or more detection probes, each directed towards a distinct target protein, may be used. Further, compositions and methods described in this Example can be extended to applications in different biological samples (e.g., comprising extracellular vesicles).

Overview of an Exemplary Assay

In some embodiments, a target entity detection system described herein is a duplex system. In some embodiments, such a duplex system, e.g., as illustrated in FIG. 2, utilizes two antibodies that each recognize a different epitope. Paired double-stranded template DNAs are also utilized in qPCR, each of which has specific four-base 5′ overhangs complementary to the 5′ overhang on its partner. Each antibody may be conjugated with one of the two double-stranded DNA templates. When the antibodies bind their target epitopes, the sticky ends of the respective templates can hybridize. These sticky ends may then be ligated together by T7 ligase, prior to PCR amplification. For hybridization between the two DNA templates to occur, the two antibodies need to be bound close enough to each other (within 50 to 60 nm, the length of the DNA linker and antibody). Any templates that bind but remain unligated will not produce PCR product, as shown in FIG. 2.

Exemplary Methods
Oligonucleotides

In some embodiments, oligonucleotides can have the following sequence structure and modifications. It is noted that the strand numbers below correspond to the numerical values associated with strands shown in FIG. 2.

Strand 1 v1

/5AzideN/CAGTCTGACACAGCAGTCGTTAATCGTCGCTGCTACCCTTGACATCCGTG

ACTGGCTAGACAGAGGTGT, where /5AzideN/ refers to an azide group linked to the 5′

oligonucleotide terminus via a NHS ester linker,

or

/5AmMC12/CAGTCTGACACAGCAGTCGTTAATCGTCGCTGCTACCCTTGACATCCGT

GACTGGCTAGACAGAGGTGT, where /5AmMC12/ refers to an amine group (e.g., a

primary amino group) linked to the 5′ oligonucleotide terminus via a 12-carbon spacer,

or

/5ThiolMC6/CAGTCTGACACAGCAGTCGTTAATCGTCGCTGCTACCCTTGACATCCGT

GACTGGCTAGACAGAGGTGT, where /5ThiolMC6/ refers to a thiol linked to the 5′

oligonucleotide terminus via a 6-carbon spacer.

Strand 2 v1:

/5AzideN/GACCTGACCTACAGTGACCATAGCCTTGCCTGATTAGCCACTGTCCAGTT

TGGCTCCTGGTCTCACTAG, where /5AzideN/ refers to an azide group linked to the 5′

oligonucleotide terminus via a NHS ester linker,

or

/5AmMC12/GACCTGACCTACAGTGACCATAGCCTTGCCTGATTAGCCACTGTCCAGT

TTGGCTCCTGGTCTCACTAG, where /5AmMC1/ refers to an amine group (e.g., a primary

amino group) linked to the 5′ oligonucleotide terminus via a 12-carbon spacer,

or

/5ThiolMC6/GACCTGACCTACAGTGACCATAGCCTTGCCTGATTAGCCACTGTCCAGT

TTGGCTCCTGGTCTCACTAG, where /5ThiolMC6/ refers to a thiol linked to the 5′

oligonucleotide terminus via a 6-carbon spacer

Strand 3 v1:

/5Phos/GAGTACACCTCTGTCTAGCCAGTCACGGATGTCAAGGGTAGCAGCGACGAT

TAACGACTGCTGTGTCAGACTG, wherein /5Phos/ refers to a phosphate group linked to the

5′ oligonucleotide terminus

Strand 4 v1:

/5Phos/ACTCCTAGTGAGACCAGGAGCCAAACTGGACAGTGGCTAATCAGGCAAGGC

TATGGTCACTGTAGGTCAGGTC, wherein /5Phos/ refers to a phosphate group linked to the

5′ oligonucleotide terminus

Strand 5 v1:

CAGTCTGACACAGCAGTCGT

Strand 6 v1:

GACCTGACCTACAGTGACCA

Strand 7 (Probe) v1:

/56-FAM/TGGCTAGAC/ZEN/AGAGGTGTACTCCTAGTGAGA/3IABKFQ/, wherein /56-

FAM/ refers to a fluorescein (e.g., 6-FAM) at the 5′ oligonucleotide terminus;

and

/3IABKFQ/ refers to a fluorescein quencher at the 3′ oligonucleotide terminus

Strand 1 v2:

/5AzideN/CAGTCTGACTCACCACTCGTTAATCGTCGCTGCTACCCTTGACATCCGTGA

CTGGCTAGACAGAGGTGT, where /5AzideN/ refers to an azide group linked to the 5′

oligonucleotide terminus via a NHS ester linker,

or

/5AmMC12/CAGTCTGACTCACCACTCGTTAATCGTCGCTGCTACCCTTGACATCCGT

GACTGGCTAGACAGAGGTGT, where /5AmMC12/ refers to an amine group (e.g., a

primary amino group) linked to the 5′ oligonucleotide terminus via a 12-carbon spacer,

or

/5ThiolMC6/CAGTCTGACTCACCACTCGTTAATCGTCGCTGCTACCCTTGACATCCGT

GACTGGCTAGACAGAGGTGT, where /5ThiolMC6/ refers to a thiol linked to the 5′

oligonucleotide terminus via a 6-carbon spacer

Strand 2 v2:

/5AzideN/CACCAGACCTACGAAGTCCATAGCCTTGCCTGATTAGCCACTGTCCAGTT

TGGCTCCTGGTCTCACTAG, where /5AzideN/ refers to an azide group linked to the 5′

oligonucleotide terminus via a NHS ester linker,

or

/5AmMC12/CACCAGACCTACGAAGTCCATAGCCTTGCCTGATTAGCCACTGTCCAGT

TTGGCTCCTGGTCTCACTAG, where /5AmMC1/ refers to an amine group (e.g., a primary

amino group) linked to the 5′ oligonucleotide terminus via a 12-carbon spacer,

or

/5ThiolMC6/CACCAGACCTACGAAGTCCATAGCCTTGCCTGATTAGCCACTGTCCAG

TTTGGCTCCTGGTCTCACTAG, where /5ThiolMC6/ refers to a thiol linked to the 5′

oligonucleotide terminus via a 6-carbon spacer

Strand 3 v2:

/5Phos/GAGTACACCTCTGTCTAGCCAGTCACGGATGTCAAGGGTAGCAGCGACGAT

TAACGAGTGGTGAGTCAGACTG, wherein /5Phos/ refers to a phosphate group linked to

the 5′ oligonucleotide terminus

Strand 4 v2:

/5Phos/ACTCCTAGTGAGACCAGGAGCCAAACTGGACAGTGGCTAATCAGGCAAGGC

TATGGACTTCGTAGGTCTGGTG, wherein /5Phos/ refers to a phosphate group linked to the

5′ oligonucleotide terminus

Strand 5 v2:

CAGTCTGACTCACCACTCGT

Strand 6 v2:

CACCAGACCTACGAAGTCCA

Strand 7 (Probe) v2:

/56-FAM/TGGCTAGAC/ZEN/AGAGGTGTACTCCTAGTGAGA/3IABKFQ/, wherein /56-

FAM/ refers to a fluorescein (e.g., 6-FAM) at the 5′ oligonucleotide terminus;

and

/3IABKPQ/ refers to a fluorescein quencher at the 3′ oligonucleotide terminus.

Strand 1 v1-med:

/5AzideN/CAGTCTGACACAGCAGTCGTGACTGGCTAGACAGAGGTGT, where

/5AzideN/ refers to an azide group linked to the 5′ oligonucleotide terminus via a NHS ester

linker,

or

/5AmMC12/CAGTCTGACACAGCAGTCGTGACTGGCTAGACAGAGGTGT, where

/5AmMC12/ refers to an amine group (e.g., a primary amino group) linked to the 5′

oligonucleotide terminus via a 12-carbon spacer,

or

/5ThiolMC6/CAGTCTGACACAGCAGTCGTGACTGGCTAGACAGAGGTGT, where

/5ThiolMC6/ refers to a thiol linked to the 5′ oligonucleotide terminus via a 6-carbon

spacer.

Strand 2 v1-med:

/5AzideN/GACCTGACCTACAGTGACCATTGGCTCCTGGTCTCACTAG, where /5AzideN/

refers to an azide group linked to the 5′ oligonucleotide terminus via a NHS ester linker,

or

/5AmMC12/GACCTGACCTACAGTGACCATTGGCTCCTGGTCTCACTAG, where

/5AmMC1/ refers to an amine group (e.g., a primary amino group) linked to the 5′

oligonucleotide terminus via a 12-carbon spacer,

or

/5ThiolMC6/GACCTGACCTACAGTGACCATTGGCTCCTGGTCTCACTAG, where

/5ThiolMC6/ refers to a thiol linked to the 5′ oligonucleotide terminus via a 6-carbon spacer

Strand 3 v1-med:

/5Phos/GAGTACACCTCTGTCTAGCCAGTCACGACTGCTGTGTCAGACTG, wherein

/5Phos/ refers to a phosphate group linked to the 5′ oligonucleotide terminus

Strand 4 v1-med:

/5Phos/ACTCCTAGTGAGACCAGGAGCCAATGGTCACTGTAGGTCAGGTC, wherein

/5Phos/ refers to a phosphate group linked to the 5′ oligonucleotide terminus

Strand 5 v1:

CAGTCTGACACAGCAGTCGT

Strand 6 v1:

GACCTGACCTACAGTGACCA

Strand 7 (Probe) v1:

/56-FAM/TGGCTAGAC/ZEN/AGAGGTGTACTCCTAGTGAGA/3IABKFQ/, wherein /56-

FAM/ refers to a fluorescein (e.g., 6-FAM) at the 5′ oligonucleotide terminus;

and

/3IABKFQ/ refers to a fluorescein quencher at the 3′ oligonucleotide terminus.

Antibody-Oligonucleotide (e.g., Antibody-DNA) Conjugation:

Antibody aliquots ranging from 25-100 μg may be conjugated with oligonucleotide strands. For example, 60 μg aliquots of antibodies may be conjugated with hybridized strands 1+3 and 2+4, for example, using copper-free click chemistry. The first step may be to prepare DBCO-functionalized antibodies to participate in the conjugation reaction with azide-modified oligonucleotide domain (e.g., DNA domain). This may begin with reacting the antibodies with the DBCO-PEG5-NHS heterobifunctional cross linker. The reaction between the NHS ester and available lysine groups may be allowed to take place at room temperature for 2 hours, after which unreacted crosslinker may be removed using centrifugal ultrafiltration. To complete the conjugation, azide-modified oligonucleotide domains (e.g., DNA domain) and the DBCO-functionalized antibodies may be allowed to react overnight at room temperature. The concentration of conjugated antibody may be measured, for example, using the Qubit protein assay.

Cell Culture

Negative control cells (e.g., non-cancer cells such as healthy cells) may be grown in Eagle's Minimum Essential Medium (EMEM) with 10% exosome-free FBS and 50 units of penicillin/streptomycin per mL. Cancer cells may be grown in Roswell Park Memorial Institute (RPMI 1640) with 10% exosome-free FBS and 50 units of penicillin/streptomycin per mL. There are currently dozens, if not more, exemplary cancer cell lines that may be useful to develop an assay for detection of cancer. Cell lines may be grown in complete media supplemented with exosome-depleted fetal bovine serum per the recommendation of the cell line supplier or inventor.

Purification of Extracellular Vesicles from Cell Culture Medium

In some embodiments, cancer cells and negative control cells may be grown in their respective media until they reach ˜80% confluence. The cell culture medium may be collected and spun at 300 RCF for 5 minutes at room temperature (RT) to remove cells and debris. The supernatant may then be collected and used in assays as described herein or frozen at −80° C.

Thawing

If prior to use, samples were stored at −80° C., they are thawed. In brief, 50 mL tubes containing frozen conditioned media placed in plastic racks, the racks are placed in an empty ice bucket. Room temperature (RT) water is added, and samples are allowed to thaw, with periodic inversion/shaking to facilitate thawing. Tubes are consolidated such that all the tubes for each cell line are the same volume. A typical purification volume is approximately 200 mLs of spent medium per cell line. If larger batches are desired, this volume can be increased.

Clarification

In some embodiments, samples are clarified prior to use. Clarification of media serves to remove cells and debris. In brief, 1) spin at 1300 RCF for 10 mins; transfer supernatant to a new 50 mL conical tube using a pipette, leaving ˜1 cm of medium (to avoid disturbing the pellet), the remaining media is not decanted; 2) spin at 2000 RCF for 30 mins; transfer supernatant to a new 50 mL conical tube using a pipette, leaving ˜1 cm of medium (to avoid disturbing the pellet), the remaining media is not decanted.

Concentrate Media

In some embodiments, samples are concentrated. In brief: 1) a single 15 mL 10 kDa MWCO filter is used for approximately 100 mLs of medium (for example, for a 200 mL batch, two 10 kDa MWCO ultrafiltration tubes will be needed). In some embodiments, the same ultrafiltration column can be sequentially added to and re-spun to enable the concentration of large volumes of medium. In general, columns were utilized according to the manufacturer's protocol. Columns are spun for 10-12 minutes each time, at maximum speed (2500 to 4,300 RCF). 2) When each of the two tubes containing the same spent medium reaches ˜1500 uL, the two tubes are combined into one, the now empty Amicon tube may be utilized as a balance. 3) When removing the concentrated medium, the sides of the concentration chamber may be flushed to release as many entrapped EVs as possible, while avoiding frothing, the consolidated media may be concentrated until there is 1 mL left. 4) The media is transferred to a 1.5 mL protein LoBind tube, with the 1 mL line marked, if necessary, volume is corrected to 1 mL with 20 nm filtered 1×PBS.

Final Clarification Spin

To remove any remaining debris, the concentrated media can be centrifuged at 10,000 RCF for 10 minutes at 21° C. in a tabletop Eppendorf centrifuge.

Run Concentrated Media Through Prepared IZON Columns

Izon columns are washed as described by the manufacturer, 20 nm filtered 1×PBS can be used to both wash the columns and recover the samples. 1 mL of concentrated spent medium can be run through the column and fractions can be collected (e.g., fractions 7, 8, and 9) in 5-mL Eppendorf flip-cap tubes, following the manufacturer's protocol.

Particle Counts:

Particle counts may be obtained, e.g., using a SpectraDyne particle counting instrument using the TS400 chips, to measure nanoparticle range between 65 and 1000 nm. In some embodiments, a particle size that is smaller than 65 nm or larger than 1000 nm may be desirable.

Generation of Patient Plasma Pools:

In some embodiments, pooled patient plasma pools may be utilized. In brief, 1 mL aliquots of patient plasma may be thawed at room temperature for at least 30 minutes. The tubes may be vortexed briefly and spun down to consolidate plasma to the bottom of each tube. Plasma samples from a given patient cohort may be combined in an appropriately sized container and mixed thoroughly by end-over-end mixing. Each plasma pool may be split into 1 mL aliquots in Protein Lo-bind 1.5 mL Eppendorf tubes and refrozen at −80° C.

Whole-Plasma Clarification (Optional):

In some embodiments, prior to EVs purification, samples may be blinded by personnel who would not participate in sample-handling. The patient-identification information may only be revealed after the experiment is completed to enable data analysis. 1 mL aliquots of whole plasma may be removed from storage at −80° C. and subjected to three clarification spins to remove cells, platelets, and debris.

Size-Exclusion Chromatography Purification of EVs from Clarified Plasma:

Each clarified plasma sample (individual samples or pooled samples) may be run through a single-use, size-exclusion purification column to isolate the EVs. Nanoparticles having a size range of about 65 nm to about 1000 nm may be collected for each sample. In some embodiments, particle size that is smaller than 65 nm or larger than 1000 nm may be desirable.

Capture-Antibody Conjugation to Magnetic-Capture Beads:

Antibodies may be conjugated to magnetic beads (e.g., epoxy-functionalized Dynabeads™). Briefly, beads may be weighed in a sterile environment and resuspended in buffer. Antibodies may be, at approximately 8 μg of Ab per mg of bead, mixed with the functionalized beads and the conjugation reaction may take place overnight at 37° C. with end-over-end mixing. The beads may be washed several times using the wash buffer provided by the conjugation kit and may be stored at 4° C. in the provided storage buffer, or at −20° C. in a glycerol-based storage buffer.

Direct Capture of Purified Plasma EVs Using Antibody-Conjugated Magnetic Beads:

For biomarker capture, a diluted sample of purified plasma EVs may be incubated with magnetic beads conjugated with respective antibodies for an appropriate time period at an appropriate temperature, e.g., at room temperature.

Binding of Antibody-Oligonucleotide Conjugates to EVs Bound on Magnetic Capture Beads:

Antibody-oligonucleotide conjugates may be diluted in an appropriate buffer at their optimal concentrations. Antibody probes may be allowed to interact with a sample comprising EVs bound on magnetic capture beads.

Post-Binding Washes:

In some embodiments, samples may be washed, e.g., multiple times, in an appropriate buffer.

Ligation:

After the wash to remove unbound antibody-oligonucleotide conjugates, the beads with bound extracellular vesicles and bound antibody-oligonucleotide conjugates may be contacted with a ligation mix. The mixtures may then be incubated for 20 minutes at RT.

PCR:

Following ligation, the beads with bound extracellular vesicles and bound antibody-oligonucleotide conjugates may be contacted with a PCR mix. PCR may be performed in a 96-well plate, e.g., on the Quant Studio 3, with the following exemplary PCR protocol: hold at 95° C. for 1 minute, perform 50 cycles of 95° C. for 5 seconds and 62° C. for 15 seconds. The rate of temperature change may be chosen to be standard (e.g., 2° C. per second). A single qPCR reaction may be performed for each experimental replicate and ROX may be used as the passive reference to normalize the qPCR signals. Data may then be downloaded from the Quant Studio 3 machine and analyzed and plotted in Python 3.7.

Data Analysis:

In some embodiments, a binary classification system can be used for data analysis. In some embodiments, signals from a detection assay may be normalized based on a reference signal. For example, in some embodiments, normalized signals for a single antibody duplex may be calculated by choosing a reference sample. In some embodiments, the equations used to calculate the normalized signal for an arbitrary sample i are given below, where Signal_maxis the signal from the highest concentration cell-line EVs standard.

$Δ {Ct}_{i} = {Ct}_{ref} - {Ct}_{i}$

${Signal}_{i} = 2^{Δ {Ct}_{i}}$

$Norm {Signal}_{i} = \frac{{Signal}_{i}}{{Signal}_{\max}}$

Discussion

The present Example describes the use of biomarker combinations in the assay described in FIGS. 1 and 2 (e.g. the biomarkers used in combination with a duplex assay). The assay may be capable of detecting cancer with >99% specificity. In some embodiments, a biomarker combination includes capture and detection probes. In some embodiments, use of two or more biomarker combinations in an assay may increase the specificity of the assay.

In some embodiments, a dendron, which can add up to 16 strands of oligonucleotide domain (e.g., DNA) per antibody, can be used instead of one or two strands of DNA per antibody, for example, to enhance signal-to-noise.

Example 2: Assessment of Extracellular Vesicle (EV) Surface Biomarkers as Cancer Biomarkers

In some embodiments, cancer detection includes detection of at least EV surface biomarker(s) following immunoaffinity capture of extracellular vesicles.

In some embodiments, one or more surface biomarkers or extracellular membrane biomarkers that are present on extracellular vesicles (“capture biomarkers”) can be used for immunoaffinity capture of cancer-associated extracellular vesicles. Examples of such capture biomarkers may include, but are not limited to (i) polypeptides encoded by human genes as follows: ALDH18A1, AP1M2, APOO, ARFGEF3, B3GNT3, BMPR1B, CADM4, CANT1, CD24, CDH1, CDH17, CDH2, CDH3, CEACAM5, CEACAM6, CLDN3, CLDN4, CLGN, CLN5, CYP2S1, DSG2, ELAPOR1, ENPP5, EPCAM, EPHB2, FAM241B, FERMT1, FOLR1, FZD2, GALNT14, GALNT6, GJB1, GNG4, GNPNAT1, GOLM1, GPR160, GPRIN1, GRHL2, HACD3, HS6ST2, IGSF3, ILDR1, KDELR3, KPNA2, KRTCAP3, LAMB3, LAMC2, LAPTM4B, LARGE2, LMNB1, LRRN1, LSR, MAL2, MARCKSL1, MARVELD2, MET, NPTXR, NUP210, PARD6B, PMEPA1, PODXL2, PRAF2, PRSS8, RAB25, RAC3, RACGAP1, RAP2B, RCC2, RNF128, RNF43, RPN1, RPN2, SERINC2, SHISA2, SLC35A2, SLC39A6, SLC44A4, SLC4A4, SMIM22, SMPDL3B, SYAP1, SYT13, TMEM132A, TMEM238, TMEM9, TSPAN13, ULBP2, UNC13B, VTCN1, ABCA13, ADAM23, CYP4F11, HAS3, TMPRSS4, UGT1A6, PIGT, TOMM34, ACSL4, GPC3, ROBO1, SLC22A9, SLC38A3, TFR2, TM4SF4, TMPRSS6, ANXA13, CHST4, GAL3ST1, SNAP25, TMEM156, CLDN18, EPPK1, MUC13, OCLN, CFTR, GCNT3, ITGB6, LAD1, MSLN, TESC, LYPD6B, S100P, TMEM51, TNFRSF21, UPK1B, UPK2, ABCC4, FOLH1, RAB3B, STEAP2, TMPRSS2, TSPAN1, AP1S3, DSC2, DSG3, TMPRSS11D, KCNS1, LY6K, MUC2, MUC4, SYNGR3, CELSR1, COX6C, ESR1, MUC1, ABCC11, ERBB2, SLC9A3R1, PROM1, PTK7, CDK4, DLK1, LMNB2, PCDH7, TMEM108, TYMS, SDC1, SLC34A2, BCAM, MUC16, ADAM17, ADAM28, ADAM8, ALCAM, AMHR2, AXL, BAG3, BSG, CCL2, CCL8, CCN1, CCN2, CCR5, CD274, CD38, CD44, CD47, CDH11, CETN1, CLDN1, CLEC2D, CLU, CSPG4, DKK1, DLL4, EGFR, ENPP3, EPHA10, ERBB3, FAP, FGF1, FGFR4, FLNA, FLNB, FLT4, FZD7, GFRA1, GM3, GPA33, GPC1, GPNMB, GUCY2C, HGF, ICAM1, IGF1R, IL1A, IL1RAP, IL6, ITGA6, ITGAV, KDR, KLK3, KLKB1, KRT8, LAG3, LGR5, LPR6, LY6E, MCAM, MDM2, MELTF, MERTK, MST1R, MUC17, MUC5AC, MUCL1, NOTCH2, NOTCH3, NRP1, NT5E, PI4K2A, P1, PLAUR, PLVAP, PPP1R3A, PRLR, PSCA, PVR, RET, S1PR1, SLC3A2, SLC7A11, SLC7A5, SPINK1, STAT3, STEAP1, TACSTD2, TF, TFRC, TGFBR2, TIGIT, TNC, TNFRSF10A, TNFRSF10B, TNFRSF12A, TNFRSF4, TNFSF11, TNFSF18, TPBG, VANGL2, VEGFA, VEGFC, or combinations thereof; and/or (ii) at least one carbohydrate-dependent and/or lipid-dependent marker as follows: CanAg, Sialyltetraosyl carbohydrate, Phosphatidylserine, Sialyl Lewis A/CA19-9, Lewis Y/B antigen, Tn antigen, SialylTn (sTn) antigen, Thomsen-Friedenreich (T, TF) antigen, Lewis Y antigen (also known as CD174), Lewis B antigen, Sialyl Lewis X (sLex) (also known as Sialyl SSEA-1 (SLX)), SSEA-1/Lewis X antigen, NeuGcGM3, beta1,6-branching, bisecting GlcNAc in a beta1,4-linkage, core fucosylation antigen, Sialyl-T antigens (sT), Sialyl Lewis c antigen, Globo H antigen, SSEA-3 (Gb5), SSEA-4 (sialy-Gb5), Gb3 (Globotriaose, CD77), Disialosyl-galactosylgloboside (DSGG), GalNAcDSLc4, Fucosyl GM1, GD1alpha ganglioside, GD1a ganglioside, GD2 ganglioside, GD3 ganglioside, GM2 ganglioside, Lc3 ceramide, nLc4 ceramide, 9-O-Ac-GD2 ganglioside, 9-O-Ac-GD3 (CDw60) ganglioside, 9-O-Ac-GT3 ganglioside, Forssman antigen, Disialyl Lewis a antigen, Sialylparagloboside (SPG), Polysialic acid (PSA) linked to NCAM, or combinations thereof.

In some embodiments, one or more surface biomarkers or extracellular membrane biomarkers that are present on extracellular vesicles (“capture biomarkers”) can be used for immunoaffinity capture of cancer-associated extracellular vesicles. Examples of such capture biomarkers may include, but are not limited to (i) polypeptides encoded by human genes as follows: ABCC11, ABCC4, ACSL4, ACVR2B, ADGRF1, ALCAM, ALPL, ANO1, ANXA13, AP1M2, AP1S3, APOO, AQP5, ARFGEF3, ASPHD1, ATP1B1, B3GNT3, B3GNT5, BCAM, BSPRY, BST2, CANT1, CAP2, CARD11, CD133, CD24, CD274 (PD-L1), CD38, CD55, CD74, CDCP1, CDH1, CDH17, CDH2, CDH3, CDH6, CDHR5, CEACAM5, CEACAM6, CELSR1, CFB, CFTR, CHODL, CHST4, CIP2A, CKAP4, CLCA2, CLDN10, CLDN16, CLDN3, CLDN4, CLDN6, CLGN, CLN5, CLTRN, COX6C, CXCR4, CYP2S1, CYP4F11, DDR1, DEFB1, DLL4, DSC2, DSG2, DSG3, EDAR, EFNB1, EGFR, ENPP5, EPCAM, EPHB2, EPHB3, EPPK1, ERBB2, ERBB3, ESR1, FAM241B, FAP, FER1L6, FERMT1, FGFR4, FOLH1, FOLR1, FUT8, FXYD3, GAL3ST1, GALNT14, GALNT3, GALNT5, GALNT6, GALNT7, GBA, GCNT3, GFRA1, GJB1, GJB2, GLUL, GOLM1, GPC3, GPCR5A, GRB7, GRHL2, HACD3, HAS3, HKDC1, HS6ST2, HSD17B2, HTR3A, IG1FR, IGSF3, IHH, ILDR1, ITGAV, ITGB6, KCNQ1, KEL, KIF1A, KPNA2, KRTCAP3, LAD1, LAMB3, LAMC2, LAPTM4B, LARGE2, LEMD1, LMNB1, LRP2, LRRTM1, LSR, LY6E, LYPD6B, MAL2, MAP7, MARCKSL1, MARVELD2, MET, MIEN1, MSLN, MST1R, MUC1, MUC13, MUC16, MUC2, MUC4, MUC5AC, NAT8, NECTIN2, NOTCH3, NOX1, NRCAM, NUP155, NUP210, OCIAD2, OCLN, OXTR, PARD6B, PDZK1, PIGT, PIK3AP1, PLEKHF2, PLXNB1, PMEPA1, PODXL2, PPP3CA, PRLR, PROM1, PRR7, PRSS21, PSCA, PTGS1, PTK7, PTPRK, RAB25, RAB27B, RAB3B, RAB3D, RAC3, RDH11, RNF43, ROBO1, ROS1, S100P, SCGN, SDC1, SEPHS1, SFXN2, SHANK2, SHROOM3, SLC22A9, SLC2A1, SLC2A2, SLC34A2, SLC35B2, SLC38A3, SLC39A6, SLC44A3, SLC4A4, SLC7A11, SLC7A5, SLC9A3R1, SMIM22, SMPDL3B, SNAP25, SORD, SPINT2, ST14, STEAP1, STEAP2, SYT13, SYT7, TACSTD2, TESC, TFR2, TJP3, TM4SF4, TMEM132A, TMEM156, TMEM158, TMPRSS11D, TMPRSS2, TMPRSS4, TMPRSS6, TNFRSF10B, TNFRSF12A, TOMM20, TRPM4, TSPAN1, TSPAN8, UCHL1, UGT1A9, UGT2B7, UGT8, ULBP2, UNC13B, VEPH1, VTCN1, XBP1, and combinations thereof; and/or (ii) carbohydrate-dependent markers as follows CA19-9 antigen, Lewis X antigen, Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, and combinations thereof.

In some embodiments, captured EVs can be read out using at least one (e.g., 1, 2, 3, or more) surface biomarker, which is or comprises (i) at least one polypeptide encoded by a human gene as follows: ALDH18A1, AP1M2, APOO, ARFGEF3, B3GNT3, BMPR1B, CADM4, CANT1, CD24, CDH1, CDH17, CDH2, CDH3, CEACAM5, CEACAM6, CLDN3, CLDN4, CLGN, CLN5, CYP2S1, DSG2, ELAPOR1, ENPP5, EPCAM, EPHB2, FAM241B, FERMT1, FOLR1, FZD2, GALNT14, GALNT6, GJB1, GNG4, GNPNAT1, GOLM1, GPR160, GPRIN1, GRHL2, HACD3, HS6ST2, IGSF3, ILDR1, KDELR3, KPNA2, KRTCAP3, LAMB3, LAMC2, LAPTM4B, LARGE2, LMNB1, LRRN1, LSR, MAL2, MARCKSL1, MARVELD2, MET, NPTXR, NUP210, PARD6B, PMEPA1, PODXL2, PRAF2, PRSS8, RAB25, RAC3, RACGAP1, RAP2B, RCC2, RNF128, RNF43, RPN1, RPN2, SERINC2, SHISA2, SLC35A2, SLC39A6, SLC44A4, SLC4A4, SMIM22, SMPDL3B, SYAP1, SYT13, TMEM132A, TMEM238, TMEM9, TSPAN13, ULBP2, UNC13B, VTCN1, ABCA13, ADAM23, CYP4F11, HAS3, TMPRSS4, UGT1A6, PIGT, TOMM34, ACSL4, GPC3, ROBO1, SLC22A9, SLC38A3, TFR2, TM4SF4, TMPRSS6, ANXA13, CHST4, GAL3ST1, SNAP25, TMEM156, CLDN18, EPPK1, MUC13, OCLN, CFTR, GCNT3, ITGB6, LAD1, MSLN, TESC, LYPD6B, S100P, TMEM51, TNFRSF21, UPK1B, UPK2, ABCC4, FOLH1, RAB3B, STEAP2, TMPRSS2, TSPAN1, AP1S3, DSC2, DSG3, TMPRSS11D, KCNS1, LY6K, MUC2, MUC4, SYNGR3, CELSR1, COX6C, ESR1, MUC1, ABCC11, ERBB2, SLC9A3R1, PROM1, PTK7, CDK4, DLK1, LMNB2, PCDH7, TMEM108, TYMS, SDC1, SLC34A2, BCAM, MUC16, ADAM17, ADAM28, ADAM8, ALCAM, AMHR2, AXL, BAG3, BSG, CCL2, CCL8, CCN1, CCN2, CCR5, CD274, CD38, CD44, CD47, CDH11, CETN1, CLDN1, CLEC2D, CLU, CSPG4, DKK1, DLL4, EGFR, ENPP3, EPHA10, ERBB3, FAP, FGF1, FGFR4, FLNA, FLNB, FLT4, FZD7, GFRA1, GM3, GPA33, GPC1, GPNMB, GUCY2C, HGF, ICAM1, IGF1R, IL1A, IL1RAP, IL6, ITGA6, ITGAV, KDR, KLK3, KLKB1, KRT8, LAG3, LGR5, LPR6, LY6E, MCAM, MDM2, MELTF, MERTK, MST1R, MUC17, MUC5AC, MUCL1, NOTCH2, NOTCH3, NRP1, NT5E, PI4K2A, P1, PLAUR, PLVAP, PPP1R3A, PRLR, PSCA, PVR, RET, S1PR1, SLC3A2, SLC7A11, SLC7A5, SPINK1, STAT3, STEAP1, TACSTD2, TF, TFRC, TGFBR2, TIGIT, TNC, TNFRSF10A, TNFRSF10B, TNFRSF12A, TNFRSF4, TNFSF11, TNFSF18, TPBG, VANGL2, VEGFA, VEGFC, or combinations thereof; and/or (ii) at least one carbohydrate-dependent and/or lipid-dependent marker as follows: CanAg, Sialyltetraosyl carbohydrate, Phosphatidylserine, Sialyl Lewis A/CA19-9, Lewis Y/B antigen, Tn antigen, SialylTn (sTn) antigen, Thomsen-Friedenreich (T, TF) antigen, Lewis Y antigen (also known as CD174) antigen, Lewis B antigen, Sialyl Lewis X (sLex) (also known as Sialyl SSEA-1 (SLX)), SSEA-1/Lewis X antigen, NeuGcGM3, beta1,6-branching, bisecting GlcNAc in a beta1,4-linkage, core fucosylation antigen, Sialyl-T antigens (sT), Sialyl Lewis c antigen, Globo H antigen, SSEA-3 (Gb5), SSEA-4 (sialy-Gb5), Gb3 (Globotriaose, CD77), Disialosyl-galactosylgloboside (DSGG), GalNAcDSLc4, Fucosyl GM1, GD1alpha ganglioside, GD1a ganglioside, GD2 ganglioside, GD3 ganglioside, GM2 ganglioside, Lc3 ceramide, nLc4 ceramide, 9-O-Ac-GD2 ganglioside, 9-O-Ac-GD3 (CDw60) ganglioside, 9-O-Ac-GT3 ganglioside, Forssman antigen, Disialyl Lewis a antigen, Sialylparagloboside (SPG), Polysialic acid (PSA) linked to NCAM, or combinations thereof. In some embodiments, captured EVs can be read out using a set of detection probes (e.g., as utilized and/or described herein), at least two of which are directed to one or more (e.g., 1, 2, 3, or more) surface biomarkers, which are or comprise (i) polypeptides encoded by human genes as follows: ALDH18A1, AP1M2, APOO, ARFGEF3, B3GNT3, BMPR1B, CADM4, CANT1, CD24, CDH1, CDH17, CDH2, CDH3, CEACAM5, CEACAM6, CLDN3, CLDN4, CLGN, CLN5, CYP2S1, DSG2, ELAPOR1, ENPP5, EPCAM, EPHB2, FAM241B, FERMT1, FOLR1, FZD2, GALNT14, GALNT6, GJB1, GNG4, GNPNAT1, GOLM1, GPR160, GPRIN1, GRHL2, HACD3, HS6ST2, IGSF3, ILDR1, KDELR3, KPNA2, KRTCAP3, LAMB3, LAMC2, LAPTM4B, LARGE2, LMNB1, LRRN1, LSR, MAL2, MARCKSL1, MARVELD2, MET, NPTXR, NUP210, PARD6B, PMEPA1, PODXL2, PRAF2, PRSS8, RAB25, RAC3, RACGAP1, RAP2B, RCC2, RNF128, RNF43, RPN1, RPN2, SERINC2, SHISA2, SLC35A2, SLC39A6, SLC44A4, SLC4A4, SMIM22, SMPDL3B, SYAP1, SYT13, TMEM132A, TMEM238, TMEM9, TSPAN13, ULBP2, UNC13B, VTCN1, ABCA13, ADAM23, CYP4F11, HAS3, TMPRSS4, UGT1A6, PIGT, TOMM34, ACSL4, GPC3, ROBO1, SLC22A9, SLC38A3, TFR2, TM4SF4, TMPRSS6, ANXA13, CHST4, GAL3ST1, SNAP25, TMEM156, CLDN18, EPPK1, MUC13, OCLN, CFTR, GCNT3, ITGB6, LAD1, MSLN, TESC, LYPD6B, S100P, TMEM51, TNFRSF21, UPK1B, UPK2, ABCC4, FOLH1, RAB3B, STEAP2, TMPRSS2, TSPAN1, AP1S3, DSC2, DSG3, TMPRSS11D, KCNS1, LY6K, MUC4, SYNGR3, CELSR1, COX6C, ESR1, MUC1, ABCC11, ERBB2, SLC9A3R1, PROM1, PTK7, CDK4, DLK1, LMNB2, PCDH7, TMEM108, TYMS, SDC1, SLC34A2, BCAM, MUC16, ADAM17, ADAM28, ADAM8, ALCAM, AMHR2, AXL, BAG3, BSG, CCL2, CCL8, CCN1, CCN2, CCR5, CD274, CD38, CD44, CD47, CDH11, CETN1, CLDN1, CLEC2D, CLU, CSPG4, DKK1, DLL4, EGFR, ENPP3, EPHA10, ERBB3, FAP, FGF1, FGFR4, FLNA, FLNB, FLT4, FZD7, GFRA1, GM3, GPA33, GPC1, GPNMB, GUCY2C, HGF, ICAM1, IGF1R, IL1A, IL1RAP, IL6, ITGA6, ITGAV, KDR, KLK3, KLKB1, KRT8, LAG3, LGR5, LPR6, LY6E, MCAM, MDM2, MELTF, MERTK, MST1R, MUC17, MUC5AC, MUCL1, NOTCH2, NOTCH3, NRP1, NT5E, PI4K2A, P1, PLAUR, PLVAP, PPP1R3A, PRLR, PSCA, PVR, RET, S1PR1, SLC3A2, SLC7A11, SLC7A5, SPINK1, STAT3, STEAP1, TACSTD2, TF, TFRC, TGFBR2, TIGIT, TNC, TNFRSF10A, TNFRSF10B, TNFRSF12A, TNFRSF4, TNFSF11, TNFSF18, TPBG, VANGL2, VEGFA, VEGFC, or combinations thereof; and/or (ii) at least one carbohydrate-dependent and/or lipid-dependent marker as follows: CanAg, Sialyltetraosyl carbohydrate, Phosphatidylserine, Sialyl Lewis A/CA19-9, Lewis Y/B antigen, Tn antigen, SialylTn (sTn) antigen, Thomsen-Friedenreich (T, TF) antigen, Lewis Y antigen (also known as CD174), Lewis B antigen, Sialyl Lewis X (sLex) (also known as Sialyl SSEA-1 (SLX)), SSEA-1/Lewis X antigen, NeuGcGM3, beta1,6-branching, bisecting GlcNAc in a beta1,4-linkage, core fucosylation antigen, Sialyl-T antigens (sT), Sialyl Lewis c antigen, Globo H antigen, SSEA-3 (Gb5), SSEA-4 (sialy-Gb5), Gb3 (Globotriaose, CD77), Disialosyl-galactosylgloboside (DSGG), GalNAcDSLc4, Fucosyl GM1, GD1alpha ganglioside, GD1a ganglioside, GD2 ganglioside, GD3 ganglioside, GM2 ganglioside, Lc3 ceramide, nLc4 ceramide, 9-O-Ac-GD2 ganglioside, 9-O-Ac-GD3 (CDw60) ganglioside, 9-O-Ac-GT3 ganglioside, Forssman antigen, Disialyl Lewis a antigen, Sialylparagloboside (SPG), Polysialic acid (PSA) linked to NCAM, or combinations thereof. In some embodiments, a set of detection probes comprises two detection probes each directed to the same surface biomarker. In some embodiments, a set of detection probes comprises two detection probes each directed to a distinct surface biomarker.

In some embodiments, captured EVs can be read out using at least one (e.g., 1, 2, 3, or more) surface biomarker, which is or comprises at least one of (i) a polypeptide encoded by human genes as follows: ABCC11, ABCC4, ACSL4, ACVR2B, ADGRF1, ALCAM, ALPL, ANO1, ANXA13, AP1M2, AP1S3, APOO, AQP5, ARFGEF3, ASPHD1, ATP1B1, B3GNT3, B3GNT5, BCAM, BSPRY, BST2, CANT1, CAP2, CARD11, CD133, CD24, CD274 (PD-L1), CD38, CD55, CD74, CDCP1, CDH1, CDH17, CDH2, CDH3, CDH6, CDHR5, CEACAM5, CEACAM6, CELSR1, CFB, CFTR, CHODL, CHST4, CIP2A, CKAP4, CLCA2, CLDN10, CLDN16, CLDN3, CLDN4, CLDN6, CLGN, CLN5, CLTRN, COX6C, CXCR4, CYP2S1, CYP4F11, DDR1, DEFB1, DLL4, DSC2, DSG2, DSG3, EDAR, EFNB1, EGFR, ENPP5, EPCAM, EPHB2, EPHB3, EPPK1, ERBB2, ERBB3, ESR1, FAM241B, FAP, FER1L6, FERMT1, FGFR4, FOLH1, FOLR1, FUT8, FXYD3, GAL3ST1, GALNT14, GALNT3, GALNT5, GALNT6, GALNT7, GBA, GCNT3, GFRA1, GJB1, GJB2, GLUL, GOLM1, GPC3, GPCR5A, GRB7, GRHL2, HACD3, HAS3, HKDC1, HS6ST2, HSD17B2, HTR3A, IG1FR, IGSF3, IHH, ILDR1, ITGAV, ITGB6, KCNQ1, KEL, KIF1A, KPNA2, KRTCAP3, LAD1, LAMB3, LAMC2, LAPTM4B, LARGE2, LEMD1, LMNB1, LRP2, LRRTM1, LSR, LY6E, LYPD6B, MAL2, MAP7, MARCKSL1, MARVELD2, MET, MIEN1, MSLN, MST1R, MUC1, MUC13, MUC16, MUC2, MUC4, MUC5AC, NAT8, NECTIN2, NOTCH3, NOX1, NRCAM, NUP155, NUP210, OCIAD2, OCLN, OXTR, PARD6B, PDZK1, PIGT, PIK3AP1, PLEKHF2, PLXNB1, PMEPA1, PODXL2, PPP3CA, PRLR, PROM1, PRR7, PRSS21, PSCA, PTGS1, PTK7, PTPRK, RAB25, RAB27B, RAB3B, RAB3D, RAC3, RDH11, RNF43, ROBO1, ROS1, S100P, SCGN, SDC1, SEPHS1, SFXN2, SHANK2, SHROOM3, SLC22A9, SLC2A1, SLC2A2, SLC34A2, SLC35B2, SLC38A3, SLC39A6, SLC44A3, SLC4A4, SLC7A11, SLC7A5, SLC9A3R1, SMIM22, SMPDL3B, SNAP25, SORD, SPINT2, ST14, STEAP1, STEAP2, SYT13, SYT7, TACSTD2, TESC, TFR2, TJP3, TM4SF4, TMEM132A, TMEM156, TMEM158, TMPRSS11D, TMPRSS2, TMPRSS4, TMPRSS6, TNFRSF10B, TNFRSF12A, TOMM20, TRPM4, TSPAN1, TSPAN8, UCHL1, UGT1A9, UGT2B7, UGT8, ULBP2, UNC13B, VEPH1, VTCN1, XBP1, or combinations thereof; and/or at least one of (ii) a carbohydrate-dependent marker as follows: CA19-9 antigen, Lewis X antigen, Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, or combinations thereof.

In some embodiments, captured EVs can be read out using a set of detection probes (e.g., as utilized and/or described herein), at least two of which are directed to one or more (e.g., 1, 2, 3, or more) surface biomarkers, which are or comprise (i) one or more polypeptides encoded by human genes as follows: ABCC11, ABCC4, ACSL4, ACVR2B, ADGRF1, ALCAM, ALPL, ANO1, ANXA13, AP1M2, AP1S3, APOO, AQP5, ARFGEF3, ASPHD1, ATP1B1, B3GNT3, B3GNT5, BCAM, BSPRY, BST2, CANT1, CAP2, CARD11, CD133, CD24, CD274 (PD-L1), CD38, CD55, CD74, CDCP1, CDH1, CDH17, CDH2, CDH3, CDH6, CDHR5, CEACAM5, CEACAM6, CELSR1, CFB, CFTR, CHODL, CHST4, CIP2A, CKAP4, CLCA2, CLDN10, CLDN16, CLDN3, CLDN4, CLDN6, CLGN, CLN5, CLTRN, COX6C, CXCR4, CYP2S1, CYP4F11, DDR1, DEFB1, DLL4, DSC2, DSG2, DSG3, EDAR, EFNB1, EGFR, ENPP5, EPCAM, EPHB2, EPHB3, EPPK1, ERBB2, ERBB3, ESR1, FAM241B, FAP, FER1L6, FERMT1, FGFR4, FOLH1, FOLR1, FUT8, FXYD3, GAL3ST1, GALNT14, GALNT3, GALNT5, GALNT6, GALNT7, GBA, GCNT3, GFRA1, GJB1, GJB2, GLUL, GOLM1, GPC3, GPCR5A, GRB7, GRHL2, HACD3, HAS3, HKDC1, HS6ST2, HSD17B2, HTR3A, IG1FR, IGSF3, IHH, ILDR1, ITGAV, ITGB6, KCNQ1, KEL, KIF1A, KPNA2, KRTCAP3, LAD1, LAMB3, LAMC2, LAPTM4B, LARGE2, LEMD1, LMNB1, LRP2, LRRTM1, LSR, LY6E, LYPD6B, MAL2, MAP7, MARCKSL1, MARVELD2, MET, MIEN1, MSLN, MST1R, MUC1, MUC13, MUC16, MUC2, MUC4, MUC5AC, NAT8, NECTIN2, NOTCH3, NOX1, NRCAM, NUP155, NUP210, OCIAD2, OCLN, OXTR, PARD6B, PDZK1, PIGT, PIK3AP1, PLEKHF2, PLXNB1, PMEPA1, PODXL2, PPP3CA, PRLR, PROM1, PRR7, PRSS21, PSCA, PTGS1, PTK7, PTPRK, RAB25, RAB27B, RAB3B, RAB3D, RAC3, RDH11, RNF43, ROBO1, ROS1, S100P, SCGN, SDC1, SEPHS1, SFXN2, SHANK2, SHROOM3, SLC22A9, SLC2A1, SLC2A2, SLC34A2, SLC35B2, SLC38A3, SLC39A6, SLC44A3, SLC4A4, SLC7A11, SLC7A5, SLC9A3R1, SMIM22, SMPDL3B, SNAP25, SORD, SPINT2, ST14, STEAP1, STEAP2, SYT13, SYT7, TACSTD2, TESC, TFR2, TJP3, TM4SF4, TMEM132A, TMEM156, TMEM158, TMPRSS11D, TMPRSS2, TMPRSS4, TMPRSS6, TNFRSF10B, TNFRSF12A, TOMM20, TRPM4, TSPAN1, TSPAN8, UCHL1, UGT1A9, UGT2B7, UGT8, ULBP2, UNC13B, VEPH1, VTCN1, XBP1, or combinations thereof; and/or (ii) one or more carbohydrate-dependent markers as follows: CA19-9 antigen, Lewis X antigen, Lewis Y antigen (also known as CD174), SialylTn (sTn) antigen, Sialyl Lewis X (sLex) antigen (also known as Sialyl SSEA-1 (SLX)), T antigen, Tn antigen, or combinations thereof.

In some embodiments, a set of detection probes comprises two detection probes each directed to the same surface biomarker. In some embodiments, a set of detection probes comprises two detection probes each directed to a distinct surface biomarker.

Example 3: Assessment of mRNA in Extracellular Vesicles (Intravesicular mRNA) as Cancer Biomarkers

In some embodiments, cancer detection includes detection of at least intravesicular mRNA(s) following immunoaffinity capture of extracellular vesicles.

In some embodiments, one or more surface proteins or extracellular membrane proteins that are present on extracellular vesicles (“capture proteins”) can be used for immunoaffinity capture of cancer-associated extracellular vesicles. Examples of such capture protein biomarkers may include, but are not limited to polypeptides encoded by human genes as described in Example 2 and carbohydrate-dependent and/or lipid-dependent markers as described in Example 2.

In some embodiments, EV nucleic acid detection assay (e.g., reverse transcription PCR using primer-probe sets) and biomarker-validation process (e.g., ones described herein such as in Example 1) can be used to assess mRNA biomarker candidates for cancer. In some embodiments, an antibody directed to a capture biomarker (e.g., a surface biomarker present in cancer-associated EVs) is conjugated to magnetic beads and evaluated, optionally first on cell-line EVs then on patient samples, for its ability to bind the specific target biomarker. The antibody-coated bead is assessed for its ability to capture cancer-associated EVs and the captured EVs by the antibody-coated bead is profiled for their mRNA contents, for example, using one-step quantitative reverse transcription PCR (RT-qPCR) master mix.

In some embodiments, captured EVs can be read out by detection of at least one (e.g., 1, 2, 3, or more) intravesicular RNA biomarkers (e.g., mRNA biomarkers described above); and at least one (e.g., 1, 2, 3, or more) surface biomarkers (e.g., as described in Example 2). For example, in some embodiments, an intravesicular RNA biomarker may be or comprise an mRNA transcript encoded by a human gene described herein. In some embodiments, an intravesicular RNA biomarker may be or comprise a microRNA. In some embodiments, an intravesicular RNA biomarker may be or comprise long noncoding RNA. In some embodiments, an intravesicular RNA biomarker may be or comprise piwi-interacting RNA. In some embodiments, an intravesicular RNA biomarker may be or comprise circular RNA. In some embodiments, an intravesicular RNA biomarker may be or comprise small nucleolar RNA. In some embodiments, an intravesicular RNA biomarker may be or comprise an orphan noncoding RNA.

In some embodiments, captured EVs can be read out (i) by detection of one or more (e.g., 1, 2, 3, or more) intravesicular RNA biomarkers described herein using RT-qPCR (“intravesicular biomarker detection); and (ii) by using a set of detection probes (e.g., as utilized and/or described herein), at least one of which are directed to one or more (e.g., 1, 2, 3, or more) of EV surface biomarkers described in Example 2 (“surface biomarker detection”). In some embodiments, intravesicular biomarker detection is performed after surface biomarker detection. For example, in some embodiments, captured EVs after intravesicular biomarker detection can be contacted with a lysing agent to release intravascular analytes (including, e.g., intravesicular RNA biomarkers) for detection and analysis.

In some embodiments for surface biomarker detection, a set of detection probes comprises at least one detection probe directed to an EV surface biomarker. In some such embodiments, a set of detection probes comprises at least two detection probes directed to the same EV surface biomarker (with the same or different epitopes). In some such embodiments, a set of detection probes comprises at least two detection probes directed to distinct EV surface biomarkers.

In some embodiments, a set of detection probes comprises at least one detection probe directed to an EV surface biomarker. In some such embodiments, a set of detection probes comprises at least two detection probes directed to the same EV surface biomarker (with the same or different epitopes). In some such embodiments, a set of detection probes comprises at least two detection probes directed to distinct EV surface biomarkers. In some embodiments, a sample comprising an EV surface biomarker and intravesicular mRNA can be contacted with an anti-EV surface biomarker affinity agent (e.g., an antibody directed to EV surface biomarker as described in Example 2) conjugated to a single-stranded oligonucleotide (e.g., DNA) that serves as one of two primers in a pair for an intravesicular mRNA biomarker (e.g., described in Example 3) such that the anti-EV surface biomarker affinity agent is bound to the EV surface biomarker while the conjugated single-stranded oligonucleotide is hybridized with the intravesicular mRNA biomarker present in the same sample. A second primer of the pair and an RT-qPCR probe are then added to perform an RT-qPCR for detection of the presence of an intravesicular mRNA and an EV surface biomarker in a single sample.

In some embodiments, captured EVs can be read out by detection of at least one (e.g., 1, 2, 3, or more) mRNA biomarker described above; and at least one (e.g., 1, 2, 3, or more) EV intravesicular biomarkers described in Example 4. In some such embodiments, captured EVs can be read out (i) by detection of one or more (e.g., 1, 2, 3, or more) mRNAs; and (ii) by using a set of detection probes (e.g., as utilized and/or described herein), at least one of which are directed to one or more (e.g., 1, 2, 3, or more) intravesicular biomarkers described in Example 4. In some embodiments, a set of detection probes comprises at least one detection probe directed to an intravesicular biomarker (e.g., as described herein). In some embodiments, a set of detection probes comprises at least two detection probes each directed to the same intravesicular biomarker (e.g., with the same epitope or different epitopes). In some embodiments, a set of detection probes comprises at least two detection probes each directed to a distinct intravesicular biomarker (e.g., as described herein). In some embodiments, a sample comprising EV intravesicular biomarker and intravesicular mRNA can be contacted with an anti-EV intravesicular biomarker affinity agent (e.g., an antibody directed to EV intravesicular biomarker as described in Example 5) conjugated to a single-stranded oligonucleotide (e.g., DNA) that serves as one of two primers in a pair for an intravesicular mRNA biomarker (e.g., described in Example 4) such that the anti-EV intravesicular biomarker affinity agent is bound to the EV intravesicular biomarker while the conjugated single-stranded oligonucleotide is hybridized with the intravesicular mRNA biomarker present in the same sample. A second primer of the pair and an RT-qPCR probe are then added to perform an RT-qPCR for detection of the presence of an intravesicular mRNA and an intravesicular biomarker in a single sample.

The present Example further demonstrates exemplary methods for detection of at least one (e.g., 1, 2, 3, or more) intravesicular RNA biomarker in extracellular vesicles derived from cancer cell lines. In some embodiments, such a method comprises immunoaffinity capture of extracellular vesicles as described herein (e.g., via a surface-bound protein such as a surface biomarker described herein), followed by detection of intravesicular RNA, for example, by reverse-transcription qPCR (RT-qPCR). In some embodiments, extracellular vesicles are captured by a cancer-associated surface biomarker, e.g., in some embodiments using antibody-functionalized solid substrate (e.g., magnetic beads). In some embodiments, captured extracellular vesicles are lysed to release their nucleic acid cargo prior to detection of intravesicular RNA. In some embodiments, intravesicular RNA is or comprises mRNA.

In some embodiments, cell lines were selected that originate from or are associated with cancer (e.g., a particular cancer type). In some embodiments, such cell lines were selected that originate from or are associated with colon/colorectal cancer, leukemia, melanoma, ovarian cancer, or sarcoma (e.g., rhabdoid tumor). In some embodiments, G-401, K562, NIH:OVCAR-3, SK-MEL-1, or T84 cell lines were selected.

In some embodiments, extracellular vesicles were purified from conditioned cell culture medium, counted, immunoaffinity captured, and washed via methods as described herein (e.g., as described in Example 1).

Each RT-qPCR reaction mixture included a PCR reaction mixture (e.g., 50% (volume) Luna One-Step reaction mix, 5% (volume) Luna WarmStart RT enzyme mix, 5% (volume) primer-TaqMan probe mixture), and a variable combination of water, captured extracellular vesicles, and lysing agent. RT-qPCR was performed, for example, on the Quant Studio 3, with a suitable PCR protocol, e.g., hold at 55° C. for 10 minutes, hold at 95° C. for 1 minute, perform 50 cycles of 95° C. for 5 seconds and 62° C. for 15 seconds, and standard melt curve. The rate of temperature change was chosen to be standard (2° C. per second). All qPCRs were performed in doublets or triplets and ROX was used as the passive reference to normalize the qPCR signals. Data was then downloaded from the Quant Studio 3 machine and analyzed and plotted in Python 3.7. Primers and TaqMan probes for each gene were purchased from Integrated DNA Technologies (IDT) as a 20× concentrate.

As an initial experiment, MIF mRNA was found to be detected in 5e7 bulk extracellular vesicles that were lysed with 1% IGEPAL. Table 1 shows MIF expression in transcript per million (TPM) from different cell lines.

TABLE 1

Summary of MIF mRNA expression for cancer cell lines.

MIF RT-PCR signal (45-Ct) for a panel of bulk cell line

EVs with varying gene expression. Samples were tested

in singlicate and 5e7 EVs were used per reaction.

Cell Line
MIF Expression (TPM)

SK-MEL-1
1302.5

NIH:OVCAR-3
1175.3

T84
196.8

G-401
0.8

A similar experiment was performed to further demonstrate this approach across different intravesicular RNA biomarkers. Table 2 shows mRNA transcript expression levels in 5e7 bulk extracellular vesicles from different cell lines and shows that mRNA is detectable in cell-line EVs at levels that are dependent on cell gene expression.

TABLE 2

Summary of expression of four different

mRNA transcripts for cancer cell lines

mRNA
Expression
RT-PCR signal of 5e7 lysed EVs

transcript
Cell Line
(TPM)
Replicate 1
Replicate 2

CLDN6
OVCAR-3
203.4
34.0
33.9

SK-MEL-1
0.1
40.5
Undetected

No template
—
Undetected
Undetected

control

FAM83A
K-562
220.1
29.9
29.7

G-401
0.1
Undetected
Undetected

No template
—
Undetected
Undetected

control

HMGB3
NCI-H1781
759.5
27.4
27.3

OVCAR-3
293.6
37.4
37.5

No template
—
Undetected
Undetected

control

B3GNT3
NCI-H1781
134.9
33.6
33.9

G-401
0
Undetected
Undetected

No template
—
Undetected
Undetected

control

Additionally, an experiment was performed to detect the colocalization of at least one intravesicular RNA biomarkers with at least one surface biomarker (e.g., a surface marker that is associated with extracellular vesicles). In some embodiments, extracellular vesicles are captured using antibody-functionalized beads directed to a surface biomarker that is present on the surface of the extracellular vesicles. For example, in the present Example, EPCAM-targeted beads were used to capture extracellular vesicles. Bound extracellular vesicles were lysed and MIF mRNA content was quantified via RT-qPCR. Results are shown in FIG. 7. In some embodiments, a positive control cell line is selected that expresses a surface biomarker for capture and/or an intravesicular RNA biomarker for detection (e.g., EPCAM+, MIF+), while a negative control cell line is selected that does not express a target biomarker for capture and/or detection (e.g., EPCAM−, MIF+). NIH:OVCAR-3 was selected as a positive control cell line and SK-MEL-1 was selected as a negative control cell line. Multiple detergent conditions were also assessed in this experiment to assess the effect of detergent (e.g., Tween-20) concentration on assay performance. These data indicate that reducing detergent (e.g., Tween-20) concentration can improve assay performance. Without wishing to be bound by a particular theory, this effect may likely be due to preservation of membrane integrity during extracellular vesicle capture, as Tween-20 may permeabilize membranes.

The present Example demonstrates that intravesicular RNA can be detected via RT-qPCR. In particular, the present Example demonstrates that colocalization of surface biomarkers and intravesicular RNA in extracellular vesicles can be detected by immunoaffinity capture via a surface biomarker followed by RT-qPCR analysis of intravascular RNA.

Example 4: Assessment of Intravesicular Biomarkers as Cancer Biomarkers

In some embodiments, cancer detection includes detection of at least intravesicular protein(s) following immunoaffinity capture of extracellular vesicles.

In some embodiments, one or more surface proteins or extracellular membrane biomarkers that are present on extracellular vesicles (“capture biomarkers”) can be used for immunoaffinity capture of cancer-associated extracellular vesicles. Examples of such capture biomarkers may include, but are not limited to polypeptides encoded by human genes as described in Example 2 and carbohydrate-dependent and/or lipid-dependent biomarkers as described in Example 2.

In some embodiments, EV immunoassay methodology (e.g., ones described herein such as in Example 1) and biomarker-validation process (e.g., ones described herein such as in Example 1) can be used to assess intravesicular proteins as biomarkers for cancer. In some embodiments, an antibody directed to a capture biomarker (e.g., a surface protein present in cancer-associated EVs) is conjugated to magnetic beads and evaluated, first on cell-line EVs then on patient samples, for its ability to bind the specific target protein biomarker. The antibody-coated bead is assessed for its ability to capture cancer-associated EVs and the captured EVs by the antibody-coated beads are fixed and/or permeabilized prior to being profiled for their intravesicular proteins using a target entity detection system (e.g., a duplex system as described herein involving a set of two detection probes, each directed to a target marker that is distinct from the capture protein). In some embodiments, an intravesicular biomarker described herein may comprise at least one post-translational modification. In some embodiments, captured EVs after fixation and/or permeabilization can be read out using a set of detection probes (e.g., as utilized and/or described herein), at least two of which are directed to one or more (e.g., 1, 2, 3, or more) intravesicular biomarkers described above. In some embodiments, a set of detection probes comprises two detection probes each directed to the same intravesicular biomarker. In some embodiments, a set of detection probes comprises two detection probes each directed to a distinct intravesicular biomarker.

In some embodiments, captured EVs after fixation and/or permeabilization can be read out using (i) at least one (e.g., 1, 2, 3, or more) intravesicular marker described above; and (ii) at least one (e.g., 1, 2, 3, or more) EV surface biomarkers described in Example 2. In some embodiments, captured EVs after fixation and/or permeabilization can be read out using a set of detection probes (e.g., as utilized and/or described herein), which comprises (i) a first detection probe directed to one or more (e.g., 1, 2, 3, or more) intravesicular markers described above; and (ii) a second detection probe directed to one or more (e.g., 1, 2, 3, or more) of EV surface biomarkers described in Example 2. In some embodiments, captured EVs after fixation and/or permeabilization can be read out by detecting an EV intravesicular marker and an EV intravesicular mRNA together in a single sample as described in Example 3 above.

Example 5: Development of a Cancer Liquid Biopsy Assay

The present Example describes development of a cancer liquid biopsy assay, for example, for screening hereditary- and average-risk individuals. Despite the success of some cancer specific assays for diagnosis of certain cancer, it may be desirable to develop a non-invasive cancer screening test from blood that may exhibit two features to provide clinical utility: (1) ultrahigh specificity (>99.5%) to minimize the number of false positives, and (2) high sensitivity (>40%) for stage I and II cancer when prognosis is most favorable. The development of such a test has the potential to save hundreds of thousands of lives each year.

Several different biomarker classes have been studied for a cancer liquid biopsy assay including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), bulk proteins, and extracellular vesicles (EVs). EVs are particularly promising due to their abundance and stability in the bloodstream relative to ctDNA and CTCs, suggesting improved sensitivity for early-stage cancers. Moreover, EVs contain cargo (e.g., proteins, RNA, metabolites) that originated from the same cell, providing superior specificity over bulk protein measurements. While the diagnostic utility EVs has been studied, much of this work has pertained to bulk EV measurements or low-throughput single-EV analyses.

This present Example describes one aspect of an exemplary approach for early stage cancer detection through the profiling of individual extracellular vesicles (EVs) in human plasma. EVs, including exosomes and microvesicles, contain co-localized proteins, RNAs, metabolites, and other compounds representative of their cell of origin (Kosaka et al., 2019; which is incorporated herein by reference for the purpose described herein). The detection of strategically chosen co-localized markers within a single EV can enable the identification of cell type with ultrahigh specificity, including the ability to distinguish cancer cells from normal tissues. As opposed to other cancer diagnostic approaches that rely on cell death for biomarkers to enter the blood (i.e., cfDNA), EVs are released at a high rate by functioning cells. Single cells have been shown to release as many as 10,000 EVs per day in vitro (Balaj et al., 2011; which is incorporated herein by reference for the purpose described herein). In addition, it is widely accepted that cancer cells release EVs at a higher rate than healthy cells (Bebelman et al. 2018; which is incorporated herein by reference for the purpose described herein).

In one aspect, the present disclosure provides insights and technologies involving identification of genes that are upregulated in cancer versus healthy tissues using Applicant's proprietary bioinformatic biomarker discovery process. From a list of upregulated biomarkers, biomarker combinations that are predicted to exhibit high sensitivity and specificity for cancer are designed. Using an exemplary individual EV assay (see, e.g., illustrated in FIG. 1 or 2 and/or described herein), co-localization of such biomarkers on an individual vesicle may be detected, indicating that the grouping of biomarkers originated from the same cell. This provides superior specificity to bulk biomarker measurements, including bulk EV assays, given that many upregulated cancer biomarkers are expressed by one or more healthy tissues. Through the careful design of biomarker combinations, signals from competing tissues can be reduced or eliminated, including those closely related to cancer. In some embodiments, the present disclosure provides technologies with ultrahigh specificity that is particularly helpful as a cancer screening test for which the prevalence of disease is low and a high positive-predictive value (>10%) is required (Seltzer et al., 1995; which is incorporated herein by reference for the purpose described herein).

Biomarker Discovery

In some embodiments, a biomarker discovery process leverages bioinformatic analysis of large databases and an understanding of the biology of cancer and extracellular vesicles.

Individual Extracellular Vesicle Analysis

The detection of tumor-derived EVs in the blood requires an assay that has sufficient selectivity and sensitivity to detect relatively few tumor-derived EVs per milliliter of plasma in a background of 10 billion EVs from a diverse range of healthy tissues. The present disclosure, among other things, provides technologies that address this challenge. For example, in some embodiments, an assay for individual extracellular vesicle analysis is illustrated in FIG. 1, which is performed in three key steps as outlined below:

- 1. EVs are purified from patient plasma using size-exclusion chromatography (SEC), which removes greater than 99% of soluble proteins and other interfering compounds.
- 2. Tumor-specific EVs are captured using antibody-functionalized magnetic beads specific to a membrane-associated surface biomarker.
- 3. A modified version of proximity-ligation-immuno quantitative polymerase chain reaction (pliq-PCR) is performed to determine the co-localization of additional biomarkers contained on or within the captured EVs.

In many embodiments of a modified version of a pliq-PCR assay, two or more different antibody-oligonucleotide conjugates are added to the EVs captured by the antibody-functionalized magnetic bead and the antibodies subsequently bind to their biomarker targets. The oligonucleotides are composed of dsDNA with single-stranded overhangs that are complementary, and thus, capable of hybridizing when in close proximity (i.e., when the corresponding biomarker targets are located on the same EV). After washing away unbound antibody-oligonucleotide species, adjacently bound antibody-oligonucleotide species are ligated using a standard DNA ligase reaction. Subsequent qPCR of the ligated template strands enables the detection and relative quantification of co-localized biomarker species. In some embodiments, two to twenty distinct antibody-oligonucleotide probes can be incorporated into such an assay, e.g., as described in U.S. application Ser. No. 16/805,637 (published as US2020/0299780; issued as U.S. Pat. No. 11,085,089), and International Application PCT/US2020/020529 (published as WO2020180741), both filed Feb. 28, 2020 and entitled “Systems, Compositions, and Methods for Target Entity Detection”; which are both incorporated herein by reference in their entirety for any purpose.

pliq-PCR has numerous advantages over other technologies to profile EVs. For example, pliq-PCR has a sensitivity three orders of magnitude greater than other standard immunoassays, such as ELISAs (Darmanis et al., 2010; which is incorporated herein by reference for the purpose described herein). The ultra-low LOD of a well-optimized pliq-PCR reaction enables detection of trace levels of tumor-derived EVs, down to a thousand EVs per mL. This compares favorably with other emerging EV analysis technologies, including the Nanoplasmic Exosome (nPLEX) Sensor (Im et al., 2014; which is incorporated herein by reference for the purpose described herein) and the Integrated Magnetic-Electrochemical Exosome (iMEX) Sensor (Jeong et al., 2016; which is incorporated herein by reference for the purpose described herein), which have reported LODs of ˜10³and ˜10⁴EVs, respectively (Shao et al., 2018; which is incorporated herein by reference for the purpose described herein). Moreover, in some embodiments, a modified version of pliq-PCR approach does not require complicated equipment and can uniquely detect the co-localization of multiple biomarkers on individual EVs.

In some embodiments, to further improve the sensitivity and specificity of an individual EV profiling assay, other classes of EV biomarkers include mRNA and intravesicular proteins (in addition to EV surface biomarker) can be identified and included in an assay.

Preliminary Work

Through preliminary studies, a workflow was developed in which biomarker candidates are validated to be present in EVs and capable of being detected by commercially available antibodies or mRNA primer-probe sets. For a given biomarker of interest, one or more cell lines expressing (positive control) and not expressing the biomarker of interest (negative control) can be cultured to harvest their EVs through concentrating their cell culture media and performing purification to isolate nanoparticles having a size range of interest (e.g., using SEC). Typically, extracellular vesicles may range from 30 nm to several micrometers in diameter. See, e.g., Chuo et al., “Imaging extracellular vesicles: current and emerging methods” Journal of Biomedical Sciences 25: 91 (2018) which is incorporated herein by reference for the purpose described herein, which provides information of sizes for different extracellular vesicle (EV) subtypes: migrasomes (0.5-3 μm), microvesicles (0.1-1 μm), oncosomes (1-10 μm), exomeres (<50 nm), small exosomes (60-80 nm), and large exosomes (90-120 nm). In some embodiments, nanoparticles having a size range of about 30 nm to 1000 nm may be isolated for detection assay. In some embodiments, specific EV subtype(s) may be isolated for detection assay.

To further improve the performance of an exemplary single EV profile assay (e.g., ones described herein) for detection of cancer, in some embodiments, additional biomarker candidates including membrane-bound proteins and intravesicular mRNAs/proteins can be identified.

In some embodiments, it was previously demonstrated by Applicant the feasibility of EV-mRNA detection using purified cell-line EVs in bulk. Through immunoaffinity capture of a membrane bound protein marker, this approach enables the detection of two co-localized biomarkers. Moreover, EV-mRNA detection requires a simpler protocol because RT-qPCR can be performed directly after immunoaffinity capture. In some embodiments, mRNA detection using EVs can be performed by capturing EVs using capture probes (e.g., as described herein) and detecting a particular cancer mRNA biomarker. EVs that express both capture probe marker and cancer mRNA biomarker are selectively detected.

Example 6: Bioinformatically-Identified Biomarkers and Biomarker Combinations

The present Example illustrates an exemplary bioinformatically driven approach for identification of certain biomarkers and biomarker combinations that can be useful for cancer diagnosis.

Bioinformatic Filtering

There are more than 55,000 transcripts captured in the Genotype-Tissue Expression (GTEx) database (e.g., a primary data resource for normal tissue gene expression) and the Cancer Genome Atlas (TCGA) database (e.g., a primary data resource for cancer tissue gene expression). To identify biomarkers that are useful for detection of cancer, two filtering steps were applied to the data.

In some embodiments, UniProt filter was used. Biomarkers that have a valid UniProt entry, which includes evidence that a biomarker protein was found to be associated with a membrane, were considered in the analysis (e.g., proteins with no evidence of being membrane associated were optionally filtered out). Such a filtering step may optionally distinguish between different membranes of interest or level of confidence of the provided evidence.

In some embodiments, Vesiclepedia filter was used. Vesiclepedia (a repository of extracellular vesicle publications) was used to filter the results. Vesiclepedia lists the number of EV related references published for each gene (e.g., Entrez). These references were used as a proxy for presence of a given biomarker in or on EVs. If no EV-related publications exist for a given biomarker, it is less likely to be an actual EV biomarker, and was thus filtered from the list of biomarkers for further consideration.

Minimum Expression and Differentiation Filtering

In some embodiments, a minimum expression level of a biomarker is considered. Low biomarker expression may produce stochastic noise and make robust signal detection difficult and unreliable. To overcome this challenge, one or more (including all of) of the following expression filters were applied. In particular embodiments, four expression filters were applied.

Minimal Expression in Cancers of Interest

In some embodiments, a minimum number of samples were used to show expression levels that were detectable in the various cancers of interest, while leaving room for discovery of subtypes that potentially have differential gene expression profiles. To achieve this filter, in some embodiments, the 80th percentile of gene expression in the TCGA cancer of interest (e.g., cancer) was calculated, and in some embodiments, biomarkers that have a transcript per million (TPM) value of >15 at the 80^thpercentile were considered.

Minimal Expression in Cancer Cell-Lines

In some embodiments, positive control cell-lines were utilized for testing of antibodies directed towards bioinformatically-predicted biomarkers. In some embodiments, the Cancer Cell Line Encyclopedia (CCLE) gene expression set, which contains >1000 cell-line profiles, was utilized to reduce biomarker lists to those for which cell-lines expressing a biomarker of interest exist. In some embodiments, the 90^thpercentile of expression for each biomarker across cancer-specific cell-lines was calculated, and in some embodiments, biomarkers with a TPM >15 at the 90^thpercentile were considered.

Minimum Expression in the Cancer of Interest on a Protein Level

One skilled in the art will understand that not all genes that are expressed are ultimately translated into proteins. Accordingly, in some embodiments, mass spectrometry data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) were utilized to filter for protein-expressing genes. In some embodiments, biomarkers with a spectral count greater than 10 were considered to be expressed.

Minimum Differentiation Against Normal Tissue

In some embodiments, assays described herein achieved superior specificity by requiring co-expression of at least two biomarkers, and in some embodiments, at least three biomarkers, on the same extracellular vesicle. Simple differential gene expression of normal tissues yielded too many false negative values. Instead, in some embodiments, a biomarker combination comprises a combination of biomarkers that may include biomarkers that were highly expressed in multiple tissue types, but only when they were paired with other biomarkers that provided additional discriminatory power (e.g., highly tissue specific and/or highly cancer specific). However, such an analysis could capture housekeeping genes, such as GAPDH, which were ubiquitously expressed, and accordingly were not necessarily useful as discriminatory biomarkers. To remove such markers, in some embodiments, a z-score comparing cancerous tissue (e.g., cancer) and every tissue type in GTEx for a given biomarker was calculated. In some embodiments, a biomarker with a z-score of 5 at the 80^thpercentile, in at least one normal tissue type was selected (e.g., at least one normal tissue was clearly excluded by a biomarker candidate).

Simulation and Stochastic Sampling

Discriminatory power of a biomarker combination candidate or biomarker combination candidate comprising at least two or more (including, e.g., at least three or more) biomarkers can be determined by simulating and comparing expression of such a biomarker combination candidate in normal subjects (e.g., subjects who were determined not to have cancer) to that in cancer subjects. Combinations of at least 2 and at least 3 biomarkers were generated based on filtered biomarker sets. An EV score, which estimated the number of EVs generated by a profiled tissue, was calculated for a given combination by multiplying TPM values of all markers in a given combination.

To simulate a population of normal subjects, a cohort of 5000 plasma samples from 5000 “healthy individuals” was created. Individual samples were created by randomly selecting tissue samples from each of the 54 tissues in the GTEx database and multiplying the TPM values of expressed genes with the estimated weight in grams of each organ based on a healthy individual. EV scores were then summed for an individual across tissues to simulate an individual. EV scores were then summed across tissues for a simulated individual. In addition to a healthy cohort, 5000 samples from various “cancer individuals” were created by repeating the “healthy” pool generation technique, but with an added step of adding EV scores of randomly selected cancers (e.g., ACC, BLCA, LGG, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSCC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, MESO, OV, PAAD, PCPG, PRAD, READ, SARC, SKCM, STAD, TGCT, THYM, THCA, UCS, UCEC, and/or UVM)) samples from TCGA, multiplying the sample by 1, 10, or 100, corresponding to a 1 g, a 10 g, or a 100 g tumor. Using these two sample pools of “healthy” and “cancer” individuals, sensitivity for each biomarker combination candidate at 99% specificity was calculated. This metric was then used to rank biomarker combination candidates.

For initial biomarker combination selection, in some embodiments, 1 million combinations of three biomarkers were randomly sampled, and in some embodiments simulations were conducted using a 100 g tumor, and 1000 individuals in each of the cancer and the healthy pool. In some embodiments, biomarker combinations were then ranked based on their sensitivity value at 99% specificity. In some embodiments, single biomarkers were then ranked based on the top 0.5 percentile of their rank in the combination list.

In some embodiments, biomarker combinations were selected based on complementarity (for example, see FIG. 6). Complementary biomarker combinations comprise differing information content, which facilitates detecting different, at least partially non-overlapping subgroups of patients (e.g., cancer derived extracellular vesicles), and thus potentially different cancer types. For example, as shown in FIG. 6, Set 1 and Set 2 of biomarker combinations detect a highly overlapping group of patients and are therefore not considered complementary. Conversely, biomarker combination Set 3 can detect a separate group of patients, and is therefore complementary to biomarker combination Set 1 and/or Set 2.

Example 7: Correlation of Bioinformatically-Identified Biomarkers and Biomarker Combinations with Pathways Known in the Art

The present Example describes a gene set enrichment analysis for determination of overlap between certain bioinformatically-predicted biomarkers and published gene pathways. One skilled in the art will recognize that in certain cases, lists of single genes can be challenging to appropriately interpret. Fortunately, there are resources that provide functional lists of genes, such as, for example, lists of genes that encode proteins that are components of the same biochemical pathway or phenomenon. Comparing a bioinformatically-identified list of biomarkers to known gene sets and biochemical pathways can impose structure on a list of biomarkers.

Table 3 shows an enrichment analysis of certain bioinformatically-identified biomarkers when compared to all gene sets (e.g., from the Molecular Signature Database Category 2—Canonical pathways (v.7.4.) from the Broad Institute). This database includes, among other resources, KEGG, Biocarta, and Reactome data. Each p-value is a result of a Chi-square test, comparing a particular gene set with a list of certain bioinformatically-identified biomarkers against the background of all genes in MSigDB C2-CP database. Biomarkers were ranked with the highest overlap first, and in some embodiments, overlaps with a nominal p-value of 0.05 were considered.

Table 3 shows certain molecular pathways that are enriched in a list of bioinformatically identified biomarkers, following correction for multiple testing, several molecular pathways exhibited a false discovery rate (FDR) of less than 0.05. Such molecular pathways provide a biological theme for certain bioinformatically identified biomarkers.

TABLE 3

Enrichment analysis of certain bioinformatically identified Cancer biomarkers)

Gene Ontology Pathway, Source and
Raw P

Pathway
Exemplary Biomarkers

Description
value
FDR
Gene #
Included

REACTOME_DEFECTIVE_GALNT3_CAUSES_FAMILIAL_—
0.00E+00
0.00E+00
16
MUC1, MUC13, MUC16,

HYPERPHOSPHATEMIC_TUMORAL_CALCINOSIS_HFTC

MUC17, MUC4,

MUC5AC, MUCL1

REACTOME_DEFECTIVE_C1GALT1C1_CAUSES_TN_—
0.00E+00
0.00E+00
17
MUC1, MUC13, MUC16,

POLYAGGLUTINATION_SYNDROME_TNPS

MUC17, MUC4,

MUC5AC, MUCL1

PID_VEGF_VEGFR_PATHWAY
0.00E+00
0.00E+00
10
FLT4, KDR, NRP1,

VEGFA, VEGFC

PID_INTEGRIN3_PATHWAY
0.00E+00
0.00E+00
43
CCN1, CD47, ITGAV,

KDR, PLAUR, PVR,

SDC1, TGFBR2, TNC,

VEGFA

REACTOME_CELL_JUNCTION_ORGANIZATION
0.00E+00
0.00E+00
92
CDH1, CDH11, CDH17,

CDH2, CDH3, CLDN1,

CLDN18, CLDN3,

CLDN4, FLNA, ITGA6,

LAMB3, LAMC2,

PARD6B, PVR

REACTOME_O_LINKED_GLYCOSYLATION_OF_MUCINS
0.00E+00
0.00E+00
62
B3GNT3, CHST4,

GALNT14, GALNT6,

GCNT3, MUC1, MUC13,

MUC16, MUC17, MUC4,

MUC5AC, MUCL1

PID_A6B1_A6B4_INTEGRIN_PATHWAY
0.00E+00
0.00E+00
46
CDH1, EGFR, ERBB2,

ERBB3, IL1A, ITGA6,

LAMB3, LAMC2, MET,

MST1R

REACTOME_TERMINATION_OF_O_GLYCAN_BIOSYNTHESIS
0.00E+00
0.00E+00
23
MUC1, MUC13, MUC16,

MUC17, MUC4,

MUC5AC, MUCL1

REACTOME_VEGF_LIGAND_RECEPTOR_INTERACTIONS
0.00E+00
0.00E+00
8
FLT4, KDR, VEGFA,

VEGFC

REACTOME_DECTIN_2_FAMILY
0.00E+00
0.00E+00
26
MUC1, MUC13, MUC16,

MUC17, MUC4,

MUC5AC, MUCL1

WP_HIPPOMERLIN_SIGNALING_DYSREGULATION
0.00E+00
0.00E+00
123
CCN2, CD44, CDH1,

CDH11, CDH17, CDH2,

CDH3, EGFR, FGFR4,

FLT4, IGF1R, ITGA6,

ITGAV, ITGB6, KDR,

MET

REACTOME_CELL_CELL_JUNCTION_ORGANIZATION
0.00E+00
0.00E+00
65
CDH1, CDH11, CDH17,

CDH2, CDH3, CLDN1,

CLDN18, CLDN3,

CLDN4, PARD6B, PVR

REACTOME_CELL_CELL_COMMUNICATION
0.00E+00
0.00E+00
130
CD47, CDH1, CDH11,

CDH17, CDH2, CDH3,

CLDN1, CLDN18,

CLDN3, CLDN4, FLNA,

ITGA6, LAMB3, LAMC2,

PARD6B, PVR

BIOCARTA_HER2_PATHWAY
0.00E+00
0.00E+00
23
EGFR, ERBB2, ERBB3,

ESR1, IL6, STAT3

KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION
0.00E+00
0.00E+00
265
AMHR2, BMPR1B,

CCL2, CCL8, CCR5,

EGFR, FLT4, HGF, IL1A,

IL6, KDR, MET, PRLR,

TGFBR2, TNFRSF10A,

TNFRSF10B,

TNFRSF12A, TNFRSF21,

TNFRSF4, TNFSF11,

TNFSF18, VEGFA,

VEGFC

KEGG_CELL_ADHESION_MOLECULES_CAMS
0.00E+00
0.00E+00
133
ALCAM, CD274, CDH1,

CDH2, CDH3, CLDN1,

CLDN18, CLDN3,

CLDN4, ICAM1, ITGA6,

ITGAV, OCLN, PVR,

SDC1

PID_INTEGRIN5_PATHWAY
0.00E+00
0.00E+00
17
CCN1, ITGAV, ITGB6,

PLAUR, SDC1

REACTOME_APOPTOTIC_CLEAVAGE_OF_CELL_ADHESION_—
0.00E+00
1.00E−07
11
CDH1, DSG2, DSG3,

PROTEINS

OCLN

REACTOME_O_LINKED_GLYCOSYLATION
0.00E+00
3.00E−07
111
B3GNT3, CHST4,

GALNT14, GALNT6,

GCNT3, LARGE2, MUC1,

MUC13, MUC16, MUC17,

MUC4, MUC5AC,

MUCL1

WP_EGFR_TYROSINE_KINASE_INHIBITOR_RESISTANCE
0.00E+00
5.00E−07
84
AXL, EGFR, ERBB2,

ERBB3, HGF, IGF1R, IL6,

KDR, MET, STAT3,

VEGFA

KEGG_FOCAL_ADHESION
0.00E+00
9.00E−07
199
EGFR, ERBB2, FLNA,

FLNB, FLT4, HGF,

IGF1R, ITGA6, ITGAV,

ITGB6, KDR, LAMB3,

LAMC2, MET, RAC3,

TNC, VEGFA, VEGFC

WP_DIFFERENTIATION_PATHWAY
0.00E+00
1.00E−06
48
DKK1, FGF1, HGF, IL6,

NT5E, TF, TNFSF11,

VEGFA

PID_AJDISS_2PATHWAY
0.00E+00
1.00E−06
48
CDH1, CDH2, EGFR,

GFRA1, IGF1R, MET,

RET, ROBO1

BIOCARTA_EEA1_PATHWAY
0.00E+00
6.20E−06
7
EGFR, TF, TFRC

WP_EXTRACELLULAR_VESICLEMEDIATED_SIGNALING_—
0.00E+00
7.10E−06
30
EGFR, ERBB2, HGF,

IN_RECIPIENT_CELLS

MET, PROM1, TGFBR2

KEGG_BLADDER_CANCER
0.00E+00
1.85E−05
42
CDH1, CDK4, EGFR,

ERBB2, MDM2, VEGFA,

VEGFC

PID_TCPTP_PATHWAY
0.00E+00
1.85E−05
42
EGFR, HGF, KDR,

KPNA2, MET, STAT3,

VEGFA

KEGG_PATHWAYS_IN_CANCER
0.00E+00
2.20E−05
325
CDH1, CDK4, EGFR,

ERBB2, FGF1, FZD2,

FZD7, HGF, IGF1R, IL6,

ITGA6, ITGAV, KLK3,

LAMB3, LAMC2, MDM2,

MET, RAC3, RET,

STAT3, TGFBR2,

VEGFA, VEGFC

WP_EPITHELIAL_TO_MESENCHYMAL_TRANSITION_—
0.00E+00
2.59E−05
164
CDH1, CDH2, CLDN1,

IN_COLORECTAL_CANCER

CLDN18, CLDN3,

CLDN4, DLK1, DLL4,

FZD2, FZD7, NOTCH2,

NOTCH3, OCLN,

TGFBR2, TMPRSS4

WP_FOCAL_ADHESION
0.00E+00
2.76E−05
202
EGFR, ERBB2, FLNA,

FLNB, HGF, IGFIR,

ITGA6, ITGAV, ITGB6,

KDR, LAMB3, LAMC2,

MET, RAC3, TNC,

VEGFA, VEGFC

REACTOME_DISEASES_ASSOCIATED_WITH_O_GLYCOSYLATION_—
0.00E+00
2.86E−05
68
MUC1, MUC13, MUC16,

OF_PROTEINS

MUC17, MUC4,

MUC5AC, MUCL1,

NOTCH2, NOTCH3

WP_PATHWAYS_REGULATING_HIPPO_SIGNALING
0.00E+00
4.72E−05
99
CDH1, CDH11, CDH17,

CDH2, CDH3, EGFR,

FGFR4, FLT4, IGF1R,

KDR, MET

WP_MAMMARY_GLAND_DEVELOPMENT_PATHWAY_PREGNANCY_—
0.00E+00
5.91E−05
33
EGFR, ERBB2, ERBB3,

AND_LACTATION_STAGE_3_OF_4

ESR1, PRLR, TNFSF11

REACTOME_ADHERENS_JUNCTIONS_INTERACTIONS
0.00E+00
5.91E−05
33
CDH1, CDH11, CDH17,

CDH2, CDH3, PVR

REACTOME_INTEGRIN_CELL_SURFACE_INTERACTIONS
0.00E+00
6.70E−05
85
BSG, CD44, CD47, CDH1,

ICAM1, ITGA6, ITGAV,

ITGB6, KDR, TNC

REACTOME_TFAP2_AP_2_FAMILY_REGULATES_TRANSCRIPTION_—
0.00E+00
9.55E−05
15
EGFR, ERBB2, ESR1,

OF_GROWTH_FACTORS_AND_THEIR_RECEPTORS

VEGFA

REACTOME_CELL_SURFACE_INTERACTIONS_—
0.00E+00
1.43E−04
194
BSG, CD44, CD47,

AT_THE_VASCULAR_WALL

CEACAM5, CEACAM6,

EPCAM, GPC1, ITGA6,

ITGAV, MERTK, SDC1,

SLC3A2, SLC7A11,

SLC7A5, TNFRSF10A,

TNFRSF10B

REACTOME_BASIGIN_INTERACTIONS
1.00E−07
2.82E−04
25
BSG, ITGA6, SLC3A2,

SLC7A11, SLC7A5

REACTOME_DISEASES_OF_GLYCOSYLATION
1.00E−07
3.67E−04
143
CSPG4, GPC1, GPC3,

MUC1, MUC13, MUC16,

MUC17, MUC4,

MUC5AC, MUCL1,

NOTCH2, NOTCH3,

SDC1

REACTOME_POST_TRANSLATIONAL_MODIFICATION_—
2.00E−07
6.64E−04
94
CEACAM5, LY6E, LY6K,

SYNTHESIS_OF_GPI_ANCHORED_PROTEINS

LYPD6B, MELTF, MSLN,

PIGT, PLAUR, PSCA,

ULBP2

PID_INTEGRIN1_PATHWAY
5.00E−07
1.47E−03
66
CSPG4, ITGA6, ITGAV,

LAMB3, LAMC2,

PLAUR, TNC, VEGFA

WP_BREAST_CANCER_PATHWAY
9.00E−07
2.73E−03
156
CDK4, DLL4, EGFR,

ERBB2, ESR1, FGF1,

FLT4, FZD2, FZD7,

IGF1R, NOTCH2,

NOTCH3, TNFSF11

KEGG_ECM_RECEPTOR_INTERACTION
1.00E−06
2.82E−03
84
CD44, CD47, ITGA6,

ITGAV, ITGB6, LAMB3,

LAMC2, SDC1, TNC

WP_SARSCOV2_AND_COVID19_PATHWAY
1.10E−06
3.28E−03
10
NRP1, TMPRSS2,

TMPRSS4

WP_BLADDER_CANCER
1.30E−06
3.67E−03
41
CDH1, CDK4, EGFR,

ERBB2, MDM2, VEGFA

PID_FGF_PATHWAY
1.50E−06
4.32E−03
55
CDH1, CDH2, FGF1,

FGFR4, HGF, MET,

PLAUR

KEGG_PANCREATIC_CANCER
1.60E−06
4.46E−03
70
CDK4, EGFR, ERBB2,

RAC3, STAT3, TGFBR2,

VEGFA, VEGFC

KEGG_MELANOMA
2.00E−06
5.77E−03
71
CDH1, CDK4, EGFR,

FGF1, HGF, IGF1R,

MDM2, MET

REACTOME_TIGHT_JUNCTION_INTERACTIONS
2.30E−06
6.49E−03
30
CLDN1, CLDN18,

CLDN3, CLDN4,

PARD6B

REACTOME_REGULATION_OF_INSULIN_LIKE_GROWTH_FACTOR_—
2.50E−06
7.15E−03
124
CCN1, CDH2, GOLM1,

IGF_TRANSPORT_AND_UPTAKE_BY_INSULIN_—

GPC3, IL6, KLK3,

LIKE_GROWTH_FACTOR_BINDING_PROTEINS_IGFBPS

MELTF, MSLN, TF,

TMEM132A, TNC

WP_PI3KAKT_SIGNALING_PATHWAY
2.50E−06
7.15E−03
345
CDK4, EGFR, FGF1,

FGFR4, FLT4, GNG4,

HGF, IGF1R, IL6, ITGA6,

ITGAV, ITGB6, KDR,

LAMB3, LAMC2, MDM2,

MET, PRLR, TNC,

VEGFA, VEGFC

REACTOME_DEFECTIVE_B4GALT7_CAUSES_EDS_PROGEROID_TYPE
3.90E−06
1.12E−02
20
CSPG4, GPC1, GPC3,

SDC1

PID_INTEGRIN4_PATHWAY
4.30E−06
1.25E−02
11
ITGA6, LAMB3, LAMC2

REACTOME_TYPE_I_HEMIDESMOSOME_ASSEMBLY
4.30E−06
1.25E−02
11
ITGA6, LAMB3, LAMC2

NABA_ECM_AFFILIATED
5.80E−06
1.65E−02
170
ANXA13, CLEC2D,

CSPG4, GPC1, GPC3,

MUC1, MUC13, MUC16,

MUC17, MUC4,

MUC5AC, MUCL1, SDC1

WP_FOCAL_ADHESIONPI3KAKTMTORSIGNALING_PATHWAY
6.70E−06
1.91E−02
309
EGFR, FGF1, FGFR4,

FLT4, GNG4, HGF,

IGF1R, ITGA6, ITGAV,

ITGB6, KDR, LAMB3,

LAMC2, MDM2, MET,

PRLR, TNC, VEGFA,

VEGFC

PID_S1P_S1P1_PATHWAY
7.80E−06
2.22E−02
21
ITGAV, KDR, S1PR1,

VEGFA

REACTOME_INTERLEUKIN_10_SIGNALING
8.10E−06
2.33E−02
46
CCL2, CCR5, ICAM1,

IL1A, IL6, STAT3

REACTOME_CONSTITUTIVE_SIGNALING_BY_ABERRANT_—
1.01E−05
2.89E−02
78
EGFR, ERBB2, ERBB3,

PI3K_IN_CANCER

ESR1, FGF1, FGFR4,

HGF, MET

WP_IRON_METABOLISM_IN_PLACENTA
1.33E−05
3.81E−02
12
TF, TFR2, TFRC

WP_IL10_ANTIINFLAMMATORY_SIGNALING_PATHWAY
1.33E−05
3.81E−02
12
IL1A, IL6, STAT3

REACTOME_EXTRACELLULAR_MATRIX_ORGANIZATION
2.24E−05
6.42E−02
301
ADAM17, ADAM8, BSG,

CD44, CD47, CDH1,

CEACAM6, ICAM1,

ITGA6, ITGAV, ITGB6,

KDR, KLKB1, LAMB3,

LAMC2, SDC1,

TMPRSS6, TNC

BIOCARTA_ERBB3_PATHWAY
2.72E−05
7.77E−02
5
EGFR, ERBB3

WP_TCA_CYCLE_NUTRIENT_UTILIZATION_AND_—
2.72E−05
7.77E−02
5
EGFR, STAT3

INVASIVENESS_OF_OVARIAN_CANCER

REACTOME_GRB7_EVENTS_IN_ERBB2_SIGNALING
2.72E−05
7.77E−02
5
ERBB2, ERBB3

REACTOME_DEFECTIVE_LFNG_CAUSES_SCDO3
2.72E−05
7.77E−02
5
NOTCH2, NOTCH3

REACTOME_MET_ACTIVATES_PTPN11
2.72E−05
7.77E−02
5
HGF, MET

REACTOME_MET_INTERACTS_WITH_TNS_PROTEINS
2.72E−05
7.77E−02
5
HGF, MET

BIOCARTA_IL10_PATHWAY
3.44E−05
9.81E−02
13
IL1A, IL6, STAT3

REACTOME_ERBB2_ACTIVATES_PTK6_SIGNALING
3.44E−05
9.81E−02
13
EGFR, ERBB2, ERBB3

WP_NEOVASCULARISATION_PROCESSES
4.41E−05
1.26E−01
37
DLL4, EPHB2, FLT4,

KDR, NOTCH3

REACTOME_PI3K_AKT_SIGNALING_IN_CANCER
4.68E−05
1.33E−01
105
EGFR, ERBB2, ERBB3,

ESR1, FGF1, FGFR4,

HGF, MDM2, MET

REACTOME_APOPTOTIC_CLEAVAGE_OF_CELLULAR_PROTEINS
6.17E−05
1.76E−01
38
CDH1, DSG2, DSG3,

LMNB1, OCLN

PID_INTEGRIN_A9B1_PATHWAY
6.96E−05
1.98E−01
25
ADAM8, TNC, VEGFA,

VEGFC

WP_HAIR_FOLLICLE_DEVELOPMENT_—
7.55E−05
2.15E−01
89
ADAM17, CCN2, DKK1,

CYTODIFFERENTIATION_PART_3_OF_3

DSC2, EGFR, LGR5,

NOTCH2, PROM1

REACTOME_SIGNALING_BY_PTK6
7.68E−05
2.19E−01
54
CDK4, EGFR, ERBB2,

ERBB3, GPNMB, STAT3

REACTOME_DEFECTIVE_EXT2_CAUSES_EXOSTOSES_2
7.77E−05
2.21E−01
14
GPC1, GPC3, SDC1

WP_EBOLA_VIRUS_PATHWAY_ON_HOST
9.76E−05
2.78E−01
131
ADAM17, AXL, EGFR,

FLNA, FLNB, FOLR1,

IGF1R, ITGA6, ITGAV,

MERTK

REACTOME_INTERLEUKIN_4_AND_INTERLEUKIN_—
1.07E−04
3.04E−01
111
CCL2, HGF, ICAM1,

13_SIGNALING

IL1A, IL6, MUC1, S1PR1,

STAT3, VEGFA

REACTOME_A_TETRASACCHARIDE_LINKER_SEQUENCE_—
1.08E−04
3.08E−01
26
CSPG4, GPC1, GPC3,

IS_REQUIRED_FOR_GAG_SYNTHESIS

SDC1

KEGG_ADHERENS_JUNCTION
1.14E−04
3.24E−01
73
CDH1, EGFR, ERBB2,

IGF1R, MET, RAC3,

TGFBR2

WP_FERROPTOSIS
1.15E−04
3.26E−01
40
ACSL4, SLC3A2,

SLC7A11, TF, TFRC

PID_AMB2_NEUTROPHILS_PATHWAY
1.53E−04
4.34E−01
41
CCN2, ICAM1, IL6,

MST1R, PLAUR

PID_ECADHERIN_STABILIZATION_PATHWAY
1.53E−04
4.34E−01
41
CDH1, EGFR, HGF,

IGF1R, MET

WP_COVID19_ADVERSE_OUTCOME_PATHWAY
1.58E−04
4.48E−01
15
CCL2, IL6, TMPRSS2

PID_ERBB_NETWORK_PATHWAY
1.58E−04
4.48E−01
15
EGFR, ERBB2, ERBB3

REACTOME_ERBB2_REGULATES_CELL_MOTILITY
1.58E−04
4.48E−01
15
EGFR, ERBB2, ERBB3

WP_CANONICAL_AND_NONCANONICAL_NOTCH_SIGNALING
1.64E−04
4.64E−01
27
DLK1, DLL4, NOTCH2,

NOTCH3

REACTOME_SYNDECAN_INTERACTIONS
1.64E−04
4.64E−01
27
ITGA6, ITGAV, SDC1,

TNC

BIOCARTA_LDL_PATHWAY
1.64E−04
4.65E−01
6
CCL2, IL6

BIOCARTA_MSP_PATHWAY
1.64E−04
4.65E−01
6
CCL2, MST1R

WP_ROBO4_AND_VEGF_SIGNALING_PATHWAYS_CROSSTALK
1.64E−04
4.65E−01
6
KDR, VEGFA

REACTOME_PRE_NOTCH_PROCESSING_IN_THE_ENDOPLASMIC_—
1.64E−04
4.65E−01
6
NOTCH2, NOTCH3

RETICULUM

REACTOME_MET_RECEPTOR_ACTIVATION
1.64E−04
4.65E−01
6
HGF, MET

REACTOME_MET_ACTIVATES_PI3K_AKT_SIGNALING
1.64E−04
4.65E−01
6
HGF, MET

REACTOME_PTK6_PROMOTES_HIF1A_STABILIZATION
1.64E−04
4.65E−01
6
EGFR, GPNMB

WP_HEAD_AND_NECK_SQUAMOUS_CELL_CARCINOMA
1.91E−04
5.41E−01
76
CDK4, EGFR, ERBB2,

IGF1R, NOTCH2,

TGFBR2, VEGFA

REACTOME_NON_INTEGRIN_MEMBRANE_ECM_INTERACTIONS
2.29E−04
6.47E−01
59
ITGA6, ITGAV, LAMB3,

LAMC2, SDC1, TNC

WP_EXTRACELLULAR_VESICLES_IN_THE_CROSSTALK_—
2.40E−04
6.77E−01
28
EGFR, IL6, KDR, STAT3

OF_CARDIAC_CELLS

REACTOME_METABOLISM_OF_RNA
2.89E−04
8.16E−01
672
NUP210

REACTOME_PI3K_EVENTS_IN_ERBB2_SIGNALING
2.94E−04
8.29E−01
16
EGFR, ERBB2, ERBB3

PID_NECTIN_PATHWAY
4.77E−04
1.00E+00
30
CDH1, CLDN1, ITGAV,

PVR

REACTOME_LAMININ_INTERACTIONS
4.77E−04
1.00E+00
30
ITGA6, ITGAV, LAMB3,

LAMC2

REACTOME_MET_ACTIVATES_PTK2_SIGNALING
4.77E−04
1.00E+00
30
HGF, LAMB3, LAMC2,

MET

BIOCARTA_LAIR_PATHWAY
5.09E−04
1.00E+00
17
ICAM1, IL1A, IL6

WP_CELLS_AND_MOLECULES_INVOLVED_IN_LOCAL_ACUTE_—
5.09E−04
1.00E+00
17
ICAM1, IL1A, IL6

INFLAMMATORY_RESPONSE

REACTOME_DISEASES_OF_METABOLISM
5.10E−04
1.00E+00
246
CSPG4, GPC1, GPC3,

MUC1, MUC13, MUC16,

MUC17, MUC4,

MUC5AC, MUCL1,

NOTCH2, NOTCH3,

SDC1, SLC34A2

WP_SIGNALING_PATHWAYS_IN_GLIOBLASTOMA
5.53E−04
1.00E+00
83
CDK4, EGFR, ERBB2,

ERBB3, IGF1R, MDM2,

MET

REACTOME_ATTACHMENT_OF_GPI_ANCHOR_TO_UPAR
5.99E−04
1.00E+00
7
PIGT, PLAUR

REACTOME_SIGNALING_BY_NOTCH1_T_7_9_NOTCH1_—
5.99E−04
1.00E+00
7
ADAM17, DLL4

M1580_K2555_TRANSLOCATION_MUTANT

REACTOME_HS_GAG_BIOSYNTHESIS
6.50E−04
1.00E+00
31
GPC1, GPC3, HS6ST2,

SDC1

REACTOME_TRANSFERRIN_ENDOCYTOSIS_AND_RECYCLING
6.50E−04
1.00E+00
31
STEAP2, TF, TFR2, TFRC

WP_DEVELOPMENT_OF_URETERIC_COLLECTION_SYSTEM
6.63E−04
1.00E+00
47
CELSR1, GFRA1, RET,

ROBO1, VANGL2

WP_MAMMARY_GLAND_DEVELOPMENT_PATHWAY_—
8.30E−04
1.00E+00
18
CDH1, CLDN4, ITGA6

EMBRYONIC_DEVELOPMENT_STAGE_1_OF_4

WP_TGFB_SIGNALING_IN_THYROID_CELLS_FOR_—
8.30E−04
1.00E+00
18
CDH1, CDH2, TNC

EPITHELIALMESENCHYMAL_TRANSITION

REACTOME_HEMOSTASIS
1.03E−03
1.00E+00
678
ABCC4, BSG, CD44,

CD47, CEACAM5,

CEACAM6, CLU,

EPCAM, FLNA, GNG4,

GPC1, HGF, ITGA6,

ITGAV, KLKB1, MERTK,

PCDH7, PLAUR,

RACGAP1, SDC1,

SLC3A2, SLC7A11,

SLC7A5, TF,

TNFRSF10A,

TNFRSF10B, VEGFA,

VEGFC

KEGG_ENDOCYTOSIS
1.11E−03
1.00E+00
181
CCR5, EGFR, ERBB3,

FGFR4, IGF1R, KDR,

MDM2, MET, PARD6B,

RET, TFRC

REACTOME_AMINO_ACID_TRANSPORT_—
1.15E−03
1.00E+00
33
SLC38A3, SLC3A2,

ACROSS_THE_PLASMA_MEMBRANE

SLC7A11, SLC7A5

WP_PANCREATIC_ADENOCARCINOMA_PATHWAY
1.20E−03
1.00E+00
89
CDK4, EGFR, ERBB2,

RAC3, STAT3, TGFBR2,

VEGFA

REACTOME_NEGATIVE_REGULATION_—
1.51E−03
1.00E+00
113
EGFR, ERBB2, ERBB3,

OF_THE_PI3K_AKT_NETWORK

ESR1, FGF1, FGFR4,

HGF, MET

BIOCARTA_NEUTROPHIL_PATHWAY
1.59E−03
1.00E+00
8
CD44, ICAM1

WP_LDLRAD4_AND_WHAT_WE_KNOW_ABOUT_IT
1.59E−03
1.00E+00
8
PMEPA1, TGFBR2

REACTOME_TRAIL_SIGNALING
1.59E−03
1.00E+00
8
TNFRSF10A,

TNFRSF10B

WP_NETRINUNC5B_SIGNALING_PATHWAY
1.74E−03
1.00E+00
52
CCL2, CCN2, ICAM1,

IL1A, KDR

REACTOME_APOPTOTIC_EXECUTION_PHASE
1.74E−03
1.00E+00
52
CDH1, DSG2, DSG3,

LMNB1, OCLN

WP_PHOTODYNAMIC_THERAPYINDUCED_NFKB_SURVIVAL_—
1.89E−03
1.00E+00
35
ICAM1, IL1A, IL6,

SIGNALING

VEGFA

WP_HYPERTROPHY_MODEL
1.91E−03
1.00E+00
20
CCN1, IL1A, VEGFA

REACTOME_DEGRADATION_OF_THE_EXTRACELLULAR_—
2.07E−03
1.00E+00
140
ADAM17, ADAM8, BSG,

MATRIX

CD44, CDH1, KLKB1,

LAMB3, LAMC2,

TMPRSS6

WP_ESC_PLURIPOTENCY_PATHWAYS
2.40E−03
1.00E+00
118
BMPR1B, EGFR, FGF1,

FGFR4, FZD2, FZD7,

MDM2, STAT3

KEGG_ARRHYTHMOGENIC_RIGHT_VENTRICULAR_—
2.47E−03
1.00E+00
74
CDH2, DSC2, DSG2,

CARDIOMYOPATHY_ARVC

ITGA6, ITGAV, ITGB6

WP_PRIMARY_FOCAL_SEGMENTAL_—
2.47E−03
1.00E+00
74
CDH2, CLDN1, DKK1,

GLOMERULOSCLEROSIS_FSGS

ITGAV, KRT8, PLAUR

PID_AVB3_INTEGRIN_PATHWAY
2.47E−03
1.00E+00
74
IGF1R, ITGAV, KDR,

SDC1, TGFBR2, VEGFA

WP_SPINAL_CORD_INJURY
2.62E−03
1.00E+00
119
CCL2, CD47, CDK4,

CSPG4, EGFR, ICAM1,

IL1A, IL6

WP_OSTEOBLAST_DIFFERENTIATION
2.62E−03
1.00E+00
119
BMPR1B, DLL4, FGF1,

FGFR4, FZD2, FZD7,

NOTCH2, NOTCH3

REACTOME_SIGNAL_TRANSDUCTION_BY_L1
2.74E−03
1.00E+00
21
EGFR, ITGAV, NRP1

REACTOME_HEPARAN_SULFATE_HEPARIN_HS_GAG_METABOLISM
2.84E−03
1.00E+00
55
CSPG4, GPC1, GPC3,

HS6ST2, SDC1

WP_ARRHYTHMOGENIC_RIGHT_VENTRICULAR_CARDIOMYOPATHY
3.15E−03
1.00E+00
76
CDH2, DSC2, DSG2,

ITGA6, ITGAV, ITGB6

BIOCARTA_LYMPHOCYTE_PATHWAY
3.43E−03
1.00E+00
9
CD44, ICAM1

WP_MICRORNA_FOR_TARGETING_CANCER_—
3.43E−03
1.00E+00
9
VEGFA, VEGFC

GROWTH_AND_VASCULARIZATION_IN_GLIOBLASTOMA

WP_ROLE_OF_ALTERED_GLYCOLYSATION_—
3.43E−03
1.00E+00
9
IL6, MUC1

OF_MUC1_IN_TUMOUR_MICROENVIRONMENT

REACTOME_CD163_MEDIATING_AN_ANTI_—
3.43E−03
1.00E+00
9
ADAM17, IL6

INFLAMMATORY_RESPONSE

PID_ERBB4_PATHWAY
3.63E−03
1.00E+00
38
ADAM17, ERBB2,

MDM2, PRLR

REACTOME_TRANSCRIPTIONAL_REGULATION_—
3.63E−03
1.00E+00
38
EGFR, ERBB2, ESR1,

BY_THE_AP_2_TFAP2_FAMILY_OF_TRANSCRIPTION_—

VEGFA

FACTORS

REACTOME_OLFACTORY_SIGNALING_PATHWAY
3.71E−03
1.00E+00
400

REACTOME_SHC1_EVENTS_IN_ERBB2_SIGNALING
3.80E−03
1.00E+00
22
EGFR, ERBB2, ERBB3

REACTOME_HS_GAG_DEGRADATION
3.80E−03
1.00E+00
22
GPC1, GPC3, SDC1

PID_SHP2_PATHWAY
3.83E−03
1.00E+00
57
EGFR, IGF1R, IL6, KDR,

VEGFA

REACTOME_GLYCOSAMINOGLYCAN_METABOLISM
3.98E−03
1.00E+00
124
B3GNT3, CD44, CSPG4,

GPC1, GPC3, HAS3,

HS6ST2, SDC1

KEGG_OLFACTORY_TRANSDUCTION
4.29E−03
1.00E+00
389

REACTOME_SIGNALING_BY_RECEPTOR_TYROSINE_KINASES
4.69E−03
1.00E+00
504
ADAM17, AXL, EGFR,

ERBB2, ERBB3, ESR1,

FGF1, FGFR4, FLT4,

HGF, IGF1R, ITGAV,

KDR, LAMB3, LAMC2,

MET, MST1R, NRP1,

STAT3, VEGFA, VEGFC

WP_INTERACTIONS_BETWEEN_IMMUNE_—
5.32E−03
1.00E+00
40
CCL2, CD274, STAT3,

CELLS_AND_MICRORNAS_IN_TUMOR_MICROENVIRONMENT

TGFBR2

REACTOME_RNA_POLYMERASE_II_TRANSCRIPTION
6.01E−03
1.00E+00
1374
CCN2, CDK4, COX6C,

EGFR, ERBB2, ESR1,

GPRIN1, IL6, MDM2,

MET, NOTCH2,

NOTCH3, OCLN,

TNFRSF10A,

TNFRSF10B, VEGFA

REACTOME_DISEASES_ASSOCIATED_WITH_—
6.35E−03
1.00E+00
41
CSPG4, GPC1, GPC3,

GLYCOSAMINOGLYCAN_METABOLISM

SDC1

REACTOME_MET_PROMOTES_CELL_MOTILITY
6.35E−03
1.00E+00
41
HGF, LAMB3, LAMC2,

MET

WP_MAMMARY_GLAND_DEVELOPMENT_PATHWAY_INVOLUTION_—
6.37E−03
1.00E+00
10
CDH1, STAT3

STAGE_4_OF_4

REACTOME_NEF_MEDIATED_DOWNREGULATION_—
6.37E−03
1.00E+00
10
AP1M2, AP1S3

OF_MHC_CLASS_I_COMPLEX_CELL_SURFACE_—

EXPRESSION

REACTOME_MET_RECEPTOR_RECYCLING
6.37E−03
1.00E+00
10
HGF, MET

REACTOME_SIGNALING_BY_VEGF
6.54E−03
1.00E+00
106
AXL, FLT4, ITGAV,

KDR, NRP1, VEGFA,

VEGFC

NABA_MATRISOME_ASSOCIATED
6.70E−03
1.00E+00
751
ADAM17, ADAM23,

ADAM28, ADAM8,

ANXA13, CCL2, CCL8,

CLEC2D, CSPG4, FGF1,

GPC1, GPC3, HGF, IL1A,

IL6, MUC1, MUC13,

MUC16, MUC17, MUC4,

MUC5AC, MUCL1,

S100P, SDC1, TNFSF11,

TNFSF18, VEGFA,

VEGFC

KEGG_NICOTINATE_AND_NICOTINAMIDE_METABOLISM
6.73E−03
1.00E+00
24
CD38, ENPP3, NT5E

KEGG_DORSO_VENTRAL_AXIS_FORMATION
6.73E−03
1.00E+00
24
EGFR, NOTCH2,

NOTCH3

REACTOME_GROWTH_HORMONE_RECEPTOR_SIGNALING
6.73E−03
1.00E+00
24
ADAM17, PRLR, STAT3

KEGG_TIGHT_JUNCTION
7.23E−03
1.00E+00
132
CDK4, CLDN1, CLDN18,

CLDN3, CLDN4, OCLN,

PARD6B, RAB3B

REACTOME_RESPONSE_TO_ELEVATED_PLATELET_CYTOSOLIC_CA2
7.23E−03
1.00E+00
132
ABCC4, CLU, FLNA,

HGF, PCDH7, TF,

VEGFA, VEGFC

PID_UPA_UPAR_PATHWAY
7.51E−03
1.00E+00
42
EGFR, HGF, ITGAV,

PLAUR

WP_LUNG_FIBROSIS
8.38E−03
1.00E+00
63
CCL2, CCN2, FGF1, HGF,

IL6

WP_NOTCH_SIGNALING_PATHWAY_NETPATH
8.38E−03
1.00E+00
63
ADAM17, DLL4,

NOTCH2, NOTCH3,

STAT3

WP_HYPOTHESIZED_PATHWAYS_IN_PATHOGENESIS_OF_—
8.67E−03
1.00E+00
25
CCN2, FLNA, TGFBR2

CARDIOVASCULAR_DISEASE

REACTOME_NOTCH3_ACTIVATION_AND_TRANSMISSION_—
8.67E−03
1.00E+00
25
DLL4, EGFR, NOTCH3

OF_SIGNAL_TO_THE_NUCLEUS

WP_GENES_CONTROLLING_NEPHROGENESIS
8.82E−03
1.00E+00
43
KDR, NOTCH2, RET,

VEGFA

KEGG_HEMATOPOIETIC_CELL_LINEAGE
9.83E−03
1.00E+00
87
CD38, CD44, IL1A, IL6,

ITGA6, TFRC

REACTOME_METABOLISM_OF_LIPIDS
9.91E−03
1.00E+00
741
ACSL4, CYP4F11,

HACD3, PI4K2A,

SLC44A4, TNFRSF21

KEGG_ABC_TRANSPORTERS
1.03E−02
1.00E+00
44
ABCA13, ABCC11,

ABCC4, CFTR

WP_IL18_SIGNALING_PATHWAY
1.06E−02
1.00E+00
279
BSG, CCL2, CLDN1,

CLDN3, CLDN4, ICAM1,

IL6, LMNB2, PIGT, TF,

TNFSF11, ULBP2,

VEGFA

BIOCARTA_MONOCYTE_PATHWAY
1.06E−02
1.00E+00
11
CD44, ICAM1

BIOCARTA_NPC_PATHWAY
1.06E−02
1.00E+00
11
KPNA2, NUP210

BIOCARTA_CFTR_PATHWAY
1.06E−02
1.00E+00
11
CFTR, SLC9A3R1

REACTOME_INTERLEUKIN_6_SIGNALING
1.06E−02
1.00E+00
11
IL6, STAT3

REACTOME_REGULATION_BY_C_FLIP
1.06E−02
1.00E+00
11
TNFRSF10A,

TNFRSF10B

REACTOME_MET_ACTIVATES_RASSIGNALING
1.06E−02
1.00E+00
11
HGF, MET

REACTOME_MET_ACTIVATES_RAP1_AND_RAC1
1.06E−02
1.00E+00
11
HGF, MET

PID_INTEGRIN_CS_PATHWAY
1.10E−02
1.00E+00
26
ITGA6, ITGAV, ITGB6

REACTOME_SIGNALING_BY_ERBB2_IN_CANCER
1.10E−02
1.00E+00
26
EGFR, ERBB2, ERBB3

WP_VEGFAVEGFR2_SIGNALING_PATHWAY
1.16E−02
1.00E+00
438
BSG, CCL2, CCN1,

CCN2, DKK1, DLL4,

EPHB2, FLNB, GPC1,

ICAM1, ITGAV, KDR,

MDM2, OCLN, PLAUR,

S1PR1, STAT3, VEGFA

REACTOME_ANTIGEN_PROCESSING_UBIQUITINATION_—
1.27E−02
1.00E+00
308

PROTEASOME_DEGRADATION

BIOCARTA_ERK_PATHWAY
1.36E−02
1.00E+00
27
EGFR, IGF1R, STAT3

BIOCARTA_VEGF_PATHWAY
1.36E−02
1.00E+00
27
FLT4, KDR, VEGFA

PID_GLYPICAN_1PATHWAY
1.36E−02
1.00E+00
27
GPC1, TGFBR2, VEGFA

PID_SYNDECAN_1_PATHWAY
1.37E−02
1.00E+00
46
BSG, HGF, MET, SDC1

REACTOME_CLASS_I_MHC_MEDIATED_ANTIGEN_—
1.42E−02
1.00E+00
377
ITGAV

PROCESSING_PRESENTATION

WP_NOTCH_SIGNALING
1.57E−02
1.00E+00
47
ADAM17, DLL4,

NOTCH2, NOTCH3

KEGG_NOTCH_SIGNALING_PATHWAY
1.57E−02
1.00E+00
47
ADAM17, DLL4,

NOTCH2, NOTCH3

REACTOME_SENSORY_PERCEPTION
1.58E−02
1.00E+00
575
GPC1, GPC3, SDC1,

SNAP25

BIOCARTA_CBL_PATHWAY
1.63E−02
1.00E+00
12
EGFR, MET

REACTOME_MISCELLANEOUS_SUBSTRATES
1.63E−02
1.00E+00
12
CYP2S1, CYP4F11

REACTOME_TP53_REGULATES_TRANSCRIPTION_—
1.63E−02
1.00E+00
12
TNFRSF10A,

OF_DEATH_RECEPTORS_AND_LIGANDS

TNFRSF10B

BIOCARTA_NO1_PATHWAY
1.66E−02
1.00E+00
28
FLT4, KDR, VEGFA

PID_WNT_SIGNALING_PATHWAY
1.66E−02
1.00E+00
28
DKK1, FZD2, FZD7

REACTOME_CELL_CYCLE
1.74E−02
1.00E+00
693
CDK4, LMNB1, MDM2,

NUP210, RCC2, TYMS

WP_ARYL_HYDROCARBON_RECEPTOR_NETPATH
1.78E−02
1.00E+00
48
EGFR, ESR1, RET,

VEGFA

PID_INTEGRIN2_PATHWAY
2.01E−02
1.00E+00
29
CCN1, ICAM1, PLAUR

PID_S1P_S1P3_PATHWAY
2.01E−02
1.00E+00
29
ITGAV, SIPR1, VEGFA

REACTOME_TNFS_BIND_THEIR_PHYSIOLOGICAL_RECEPTORS
2.01E−02
1.00E+00
29
TNFRSF4, TNFSF11,

TNFSF18

REACTOME_DOWNREGULATION_OF_ERBB2_SIGNALING
2.01E−02
1.00E+00
29
EGFR, ERBB2, ERBB3

REACTOME_DISEASES_OF_SIGNAL_TRANSDUCTION_—
2.10E−02
1.00E+00
394
ADAM17, DKK1, DLL4,

BY_GROWTH_FACTOR_RECEPTORS_AND_SECOND_—

EGFR, ERBB2, ERBB3,

MESSENGERS

ESR1, FGF1, FGFR4,

HGF, KDR, MDM2, MET,

RNF43, STAT3, TGFBR2

WP_HEPATITIS_C_AND_HEPATOCELLULAR_CARCINOMA
2.27E−02
1.00E+00
50
CD44, IL6, STAT3,

VEGFA

REACTOME_CHONDROITIN_SULFATE_DERMATAN_SULFATE_—
2.27E−02
1.00E+00
50
CSPG4, GPC1, GPC3,

METABOLISM

SDC1

WP_ESTROGEN_RECEPTOR_PATHWAY
2.34E−02
1.00E+00
13
ESR1, STAT3

WP_MAMMARY_GLAND_DEVELOPMENT_PATHWAY_—
2.34E−02
1.00E+00
13
ERBB2, ESR1

PUBERTY_STAGE_2_OF_4

WP_DISORDERS_OF_FOLATE_METABOLISM_AND_TRANSPORT
2.34E−02
1.00E+00
13
FOLR1, TYMS

REACTOME_DOWNREGULATION_OF_ERBB2_ERBB3_SIGNALING
2.34E−02
1.00E+00
13
ERBB2, ERBB3

KEGG_O_GLYCAN_BIOSYNTHESIS
2.39E−02
1.00E+00
30
GALNT14, GALNT6,

GCNT3

REACTOME_SLC_TRANSPORTER_DISORDERS
2.52E−02
1.00E+00
99
BSG, NUP210, SLC34A2,

SLC35A2, SLC3A2,

SLC4A4

REACTOME_DNA_REPAIR
2.58E−02
1.00E+00
332
KPNA2

REACTOME_ACTIVATED_NOTCH1_—
2.82E−02
1.00E+00
31
ADAM17, DLK1, DLL4

TRANSMITS_SIGNAL_TO_THE_NUCLEUS

0

PID_PTP1B_PATHWAY
2.83E−02
1.00E+00
52
CDH2, EGFR, PRLR,

STAT3

WP_NEURAL_CREST_DIFFERENTIATION
3.08E−02
1.00E+00
102
CDH1, CDH2, DLL4,

GJB1, NOTCH2,

NOTCH3

REACTOME_POST_TRANSLATIONAL_PROTEIN_MODIFICATION
3.19E−02
1.00E+00
1435
B3GNT3, CCN1, CDH2,

CEACAM5, CFTR,

CHST4, ESR1, FOLR1,

GALNT14, GALNT6,

GCNT3, GNPNAT1,

GOLM1, GPC3, IL6,

KDELR3, LARGE2,

LY6E, LY6K, LYPD6B,

MDM2, MELTF, MSLN,

MUC1, MUC13, MUC16,

MUC17, MUC4,

MUC5AC, MUCL1,

NUP210, PIGT, PLAUR,

PSCA, RAB25, RAB3B,

RNF128, RPN1, RPN2,

TF, TGFBR2,

TMEM132A, TNC,

ULBP2

BIOCARTA_LYM_PATHWAY
3.20E−02
1.00E+00
14
ICAM1, IL1A

WP_OSTEOBLAST_SIGNALING
3.20E−02
1.00E+00
14
ITGAV, TNFSF11

REACTOME_TRYPTOPHAN_CATABOLISM
3.20E−02
1.00E+00
14
SLC3A2, SLC7A5

WP_OVARIAN_INFERTILITY_GENES
3.29E−02
1.00E+00
32
BMPR1B, CDK4, PRLR

PID_WNT_NONCANONICAL_PATHWAY
3.29E−02
1.00E+00
32
FLNA, FZD2, FZD7

REACTOME_NEDDYLATION
3.46E−02
1.00E+00
234

WP_COPPER_HOMEOSTASIS
3.48E−02
1.00E+00
54
ADAM17, MDM2,

STEAP1, STEAP2

NABA_MATRISOME
3.65E−02
1.00E+00
1026
ADAM17, ADAM23,

ADAM28, ADAM8,

ANXA13, CCL2, CCL8,

CCN1, CCN2, CLEC2D,

CSPG4, FGF1, GPC1,

GPC3, HGF, IL1A, IL6,

LAMB3, LAMC2, MUC1,

MUC13, MUC16, MUC17,

MUC4, MUC5AC,

MUCL1, S100P, SDC1,

TNC, TNFSF11,

TNFSF18, VEGFA,

VEGFC

WP_3Q29_COPY_NUMBER_VARIATION_SYNDROME
3.73E−02
1.00E+00
79
ERBB2, MELTF, MUC4,

TF, TFRC

REACTOME_SIGNALING_BY_MET
3.73E−02
1.00E+00
79
HGF, LAMB3, LAMC2,

MET, STAT3

BIOCARTA_MET_PATHWAY
3.81E−02
1.00E+00
33
HGF, MET, STAT3

REACTOME_TRANSPORT_OF_INORGANIC_CATIONS_—
3.94E−02
1.00E+00
106
SLC34A2, SLC38A3,

ANIONS_AND_AMINO_ACIDS_OLIGOPEPTIDES

SLC3A2, SLC4A4,

SLC7A11, SLC7A5

REACTOME_CELL_CYCLE_MITOTIC
3.95E−02
1.00E+00
561
CDK4, LMNB1, NUP210,

RCC2, TYMS

REACTOME_ORGANELLE_BIOGENESIS_AND_MAINTENANCE
4.16E−02
1.00E+00
296
APOO

BIOCARTA_GRANULOCYTES_PATHWAY
4.21E−02
1.00E+00
15
ICAM1, IL1A

WP_PHYTOCHEMICAL_ACTIVITY_ON_NRF2_TRANSCRIPTIONAL_—
4.21E−02
1.00E+00
15
EPHB2, SLC7A11

ACTIVATION

BIOCARTA_ERYTH_PATHWAY
4.21E−02
1.00E+00
15
IL1A, IL6

SA_G1_AND_S_PHASES
4.21E−02
1.00E+00
15
CDK4, MDM2

REACTOME_ANCHORING_FIBRIL_FORMATION
4.21E−02
1.00E+00
15
LAMB3, LAMC2

REACTOME_SIGNALING_BY_NOTCH1_HD_DOMAIN_—
4.21E−02
1.00E+00
15
ADAM17, DLL4

MUTANTS_IN_CANCER

REACTOME_PHOSPHOLIPASE_C_MEDIATED_CASCADE_FGFR4
4.21E−02
1.00E+00
15
FGF1, FGFR4

WP_NUCLEAR_RECEPTORS_METAPATHWAY
4.26E−02
1.00E+00
32
ABCC4, CCL2, CDK4,

EGFR, ESR1, HGF,

S100P, SLC39A6,

SLC7A11, SLC7A5,

STAT3, TGFBR2,

UGT1A6

WP_SIGNALING_OF_HEPATOCYTE_GROWTH_FACTOR_RECEPTOR
4.36E−02
1.00E+00
34
HGF, MET, STAT3

PID_P53_DOWNSTREAM_PATHWAY
4.64E−02
1.00E+00
137
DKK1, EGFR, HGF,

MDM2, MET,

TNFRSF10A,

TNFRSF10B

REACTOME_M_PHASE
4.96E−02
1.00E+00
417
LMNB1, NUP210, RCC2

WP_ALPHA_6_BETA_4_SIGNALING_PATHWAY
4.96E−02
1.00E+00
35
ITGA6, LAMB3, LAMC2

PID_ARF6_PATHWAY
4.96E−02
1.00E+00
35
EGFR, HGF, MET

Example 8: Correlation of Bioinformatically-Identified Biomarkers and Biomarker Combinations with Clinical Covariates and Known Somatic Mutational Drivers

The present Example illustrates potential associations between known cancer clinical covariates and certain bioinformatically-predicted biomarkers; and potential associations between known cancer mutational drivers and certain bioinformatically-predicted biomarkers.

In some embodiments, one or more clinical covariates were considered in addition to gene expression of certain bioinformatically-identified biomarkers. Such analysis can be useful to provide an indication on potential subgroups, including staging, lymph node involvement, microsatellite instability (MSI), and others.

In some embodiments, clinical covariates included nodal involvement (e.g., n0, n1, nib, n2, n3), cancer stage, histological type, menopause, and hormone receptor status. In some embodiments, cancer stage included stage I, stage II, stage III, or stage IV cancers.

Clinical covariate analysis may not identify any strong enrichments within the TCGA sample, demonstrating that certain bioinformatically-identified biomarker combinations can be particularly useful to identify cancer samples irrespective of a particular clinical covariate.

In some embodiments, one or more somatic mutational drivers (including, e.g., mutation and copy number of alteration profiles) are considered in addition to gene expression of certain bioinformatically-identified biomarkers. For example, certain major known mutational drivers of cancer include, but are not limited to mutations in VHL, MLL2, MLL3, ARID1A, PBRM1, TP53, EGFR, FLT3, CDKN2A, RB1, CDKN1A, KRAS, PIK3CA, PTEN, PIK3R1, APC, CTNNB1, KEAP1, DNMT3A, NFE2L2, NAV3, NOTCH1, MALAT1, NPM1, NRAS, BRAF, IDH1, PIK3CA, or combinations thereof. For each of these drivers, cancer-associated mutations may include copy number alterations (CNAs; including, e.g., but not limited to amplification and/or deletion) and/or mutations (including, e.g., but not limited to in frame mutation, missense mutation, splice, and/or truncating mutation). A clustering analysis is performed to identify associations between bioinformatically-predicted biomarkers, biomarker combinations, and certain major mutational drivers of cancer.

This mutational driver analysis may not identify any strong enrichments within the TCGA sample, demonstrating that certain bioinformatically-predicted biomarkers and/or biomarker combinations can be particularly useful to identify cancer samples irrespective of a particular mutational driver.

Example 9: Assessment of Certain Surface Biomarkers and Combinations Thereof as Targets for Capture Probes and/or Detection Probes for Target Entity Detection Systems Described Herein

The present Example describes exemplary characterization of surface biomarkers for use in assays as described herein (e.g., for detection of various cancers). In some embodiments, a surface biomarker was assessed as a target for a capture probe of assays described herein. In some embodiments, a surface biomarker was assessed as a target for a detection probe of assays described herein.

In this Example where a surface biomarker was assessed as target for a capture probe of assays described herein, a target-capture moiety (e.g., in some embodiments an antibody agent) that binds to a particular surface biomarker of interest was immobilized on a solid substrate to form a capture probe. The capture probe was then added to conditioned media from a selected cell line to capture nanoparticles (i) having a size range of interest (e.g., about 30 nm to about 1000 nm) that included extracellular vesicles, and (ii) having on their surfaces the particular surface biomarker of interest. Captured nanoparticles that included extracellular vesicles were then read out by a set of detection probes (as described herein) each directed to a canonical exosome marker. For example, CD63, CD81, and CD9 are canonical exosome markers that are highly expressed in multiple tissues and cell lines (see, for example, Bobrie et al., Journal of extracellular vesicles 1.1, 2012, incorporated herein by reference). Unconditioned media (e.g., buffer or media which does not contain nanoparticles having a size range of interest (e.g., about 30 nm to about 1000 nm) that included extracellular vesicles) was used as a negative control.

In this Example where a surface biomarker was assessed as target for a detection probe of assays described herein, a target-capture moiety (e.g., in some embodiments an antibody agent) that binds to a canonical exosome marker (e.g., in some embodiments CD63 or CD81) was immobilized on a solid substrate to form a capture probe. The capture probe was then added to conditioned media from a selected cell line to capture nanoparticles (i) having a size range of interest (e.g., about 30 nm to about 1000 nm) that included extracellular vesicles, and (ii) having on their surfaces the particular biomarker of interest. Captured nanoparticles that included extracellular vesicles were then read out by a set of detection probes (as described herein) each directed to a particular surface biomarker of interest. Unconditioned media (e.g., buffer or media which does not contain nanoparticles having a size range of interest (e.g., about 30 nm to about 1000 nm) that included extracellular vesicles) was used as a negative control.

In some embodiments, a positive cell line is selected that expresses a target biomarker of interest, while a negative cell line is selected that does not express a target biomarker of interest. In some embodiments, such positive and negative cell lines are selected that originate from or are associated with a particular cancer type. In some embodiments, such cell lines were selected that originate from or are associated with breast cancer, colon/colorectal cancer, lung cancer, lymphoma, ovarian cancer, pancreatic cancer, prostate cancer, sarcoma (e.g., rhabdoid tumor), or skin cancer. In some embodiments, A549, AsPC-1, AU565, BT-20, BxPC-3, Caov-3, COLO 201, COR-L95, COV362, COV413A, COV644, Farage, G-401, HCC1419, HCC1500, HCC4006, HCT 116, HT-1080, HT-29, LS1034, MCF7, MeWo, NCI-H146, NCI-H1781, NCI-H1819, NCI-H441, NCI-H520, NIH:OVCAR-3, OVISE, OVKATE, OVSAHO, PC-3, Ramos, SK-MEL-1, SK-MES-1, SK-OV-3, SU-DHL-1, SUP-M2, SW 900, or T84 cell lines were selected.

Table 4 shows absolute and delta Ct values for certain surface biomarkers assayed individually as targets for a capture probe. Ct values were read from qPCR where the numeric value corresponds to the number of PCR cycles (i.e., higher values indicate less signal). CD63, CD81, or CD9 were used as a target for a detection probe. As shown in Table 4, certain surface biomarkers may be particularly useful as a target for a capture probe in assays as described herein. For example, surface biomarkers with high delta Ct values (e.g., delta Ct values greater than 2, including, e.g., greater than 3, greater than 4, greater than 5, or higher) may be particularly useful as targets for capture probes. Likewise, such characterization may also be helpful in identifying target-capture moieties that are particularly useful as capture probes. In some embodiments, surface biomarkers MUC1 and other mucins (e.g., MUC4 and MUC16) are particularly useful targets for capture probes. In some embodiments, surface biomarkers that comprise glycosylation, e.g., sTn antigen, sLex antigen, are particularly useful targets for capture probes.

TABLE 4

Characterization of certain surface biomarkers individually as

targets for a capture probe.

CD63
CD81
CD9

(+)
(−)
No

(+)
(−)
No

(+)
(−)
No

Target

abs
abs
EV

abs
abs
EV

abs
abs
EV

Biomarker
dCt
Ct
Ct
abs Ct
dCt
Ct
Ct
abs Ct
dCt
Ct
Ct
abs Ct

ABCC4
—
—
—
—
3.89
26.19
30.09
31.91
—
—
—
—

ALCAM
14.18
25.95
40.12
42.6
8.68
28.51
37.19
45
—
—
—
—

CD24
4.6
23.35
27.96
37.81
6.06
21.45
27.51
40.46
—
—
—
—

CD47
5.19
27.51
32.7
33.48
8.44
25.43
33.87
36.15
—
—
—
—

CEACAM5
7.24
26.15
33.39
33.36
8.26
27.94
36.2
37.32
—
—
—
—

CEACAM6
10.12
24.01
34.13
34.77
14.6
19.69
34.29
39.72
—
—
—
—

CD38
—
—
—
—
11.5
20.14
31.63
30.39
—
—
—
—

CFTR
−4.64
33.75
29.1
38.05
1.52
24.17
25.69
40
—
—
—
—

CDH11
−0.13
31.18
31.05
38.32
−1.68
32.01
30.34
33.45
2.44
28.18
30.62
32.48

CDH3
0.52
32.8
33.33
44.3
6.92
27.52
34.45
40.53
—
—
—
—

DLL4
—
—
—
—
0.7
29.91
30.61
30.44
13.12
23.03
36.15
38.16

DSC2
1.11
31.65
32.76
32.58
1.64
33.38
35.02
37.1
—
—
—
—

EPCAM
8.31
20.25
28.56
28.85
6.45
22.07
28.52
28.79
—
—
—
—

EGFR
7.21
26.67
33.88
34.37
11.71
22.88
34.58
35.34
—
—
—
—

EPHB2
—
—
—
—
7.29
31.94
39.23
40
—
—
—
—

ERBB2
1.81
31.74
33.55
37.46
9.79
20.82
30.62
30.83
—
—
—
—

ERBB3
—
—
—
—
6.88
29.83
36.71
35.22
—
—
—
—

FOLH1
—
—
—
—
2.86
26.61
29.47
30.48
—
—
—
—

FOLR1
9.26
22.32
31.58
34.16
10.78
20.31
31.09
34.11
—
—
—
—

FAP
4.12
31.83
35.95
36.32
−0.41
29.89
29.48
31.35
3.98
28.5
32.47
35.19

HACD3
0.73
35.77
36.51
40.16
3.96
32.76
36.72
39.84
—
—
—
—

IGF1R
6.94
24.24
31.18
30.68
9.71
21.98
31.69
36.96
—
—
—
—

ITGAV
—
—
—
—
−2.67
24.98
22.31
40
7.99
18.16
26.15
32.31

LAMB3
6.39
29.11
35.51
38.06
8.06
27.15
35.21
42.79
—
—
—
—

MUC1
6.33
22.21
28.54
38.51
8.35
19.69
28.04
42.59
—
—
—
—

MET
7.01
25.02
32.03
34.12
4.57
27.83
32.4
36.48
—
—
—
—

MARCKSL1
1.78
30.72
32.49
31.86
3.94
29.59
33.53
34
—
—
—
—

MSLN
3.73
28.75
32.48
42.71
5.13
26.2
31.34
45
—
—
—
—

MUC4
8.16
24.4
32.56
35.46
9.15
25.88
35.03
36.79
—
—
—
—

NT5E
13.82
26.65
40.47
41.46
9.14
28.08
37.22
45
—
—
—
—

CD274
6.59
26.69
33.28
34.05
5.77
28.64
34.42
40.52
—
—
—
—

PRLR
—
—
—
—
7.68
20.59
28.28
31.76
—
—
—
—

PSCA
—
—
—
—
1.59
28.86
30.45
31.93
—
—
—
—

PTK7
2.98
30.88
33.86
39.6
3.73
32.68
36.41
38.08
—
—
—
—

SLC7A5
—
—
—
—
7.57
24.9
32.48
32.7
—
—
—
—

SDC1
0.82
29.07
29.88
31.5
4.12
25.64
29.76
32.19
—
—
—
—

sTn antigen
2.08
23.71
25.78
41.51
4.25
21.49
25.73
42.34
—
—
—
—

Tn antigen
7.67
22.52
30.19
39.2
10.03
20.29
30.32
35.58
—
—
—
—

STEAP1
3.56
28.41
31.97
30.44
−0.49
26.75
26.26
26.93
1.34
27.44
28.78
29.33

T antigen
6.34
23.46
29.8
36.7
8.17
21.89
30.06
40.57
—
—
—
—

TACSTD2
5.88
23.96
29.85
38.67
7.14
22.12
29.25
40.39
—
—
—
—

TFRC
6.71
22.34
29.05
32.24
6.77
21.06
27.83
36.58
—
—
—
—

TMPRSS2
0.24
22.87
23.11
22.29
0.81
20.99
21.8
20.98
3.85
22.59
26.44
26.63

TNFRSF10B
1.36
28.7
30.07
36.53
2.18
30.07
32.25
37.06
—
—
—
—

TSPAN1
—
—
—
—
10.19
22.08
32.27
35.48
—
—
—
—

dCt: delta Ct between positive and negative cell-line;

abs Ct: absolute Ct;

(+): positive cell line;

(−): negative cell line.

Table 5 shows absolute and delta Ct values for certain surface biomarkers assayed individually as targets for a detection probe. Ct values were read from qPCR where the numeric value corresponds to the number of PCR cycles (i.e., higher values indicate less signal). CD63 or CD81 were used as a target for a capture probe. As shown in Table 5, certain surface biomarkers may be particularly useful as a target for a detection probe in assays as described herein. For example, surface biomarkers with high delta Ct values (e.g., delta Ct values greater than 2, including, e.g., greater than 3, greater than 4, greater than 5, or higher) may be particularly useful as targets for detection probes. Likewise, such characterization may also be helpful in identifying target-capture moieties that are particularly useful as detection probes. In some embodiments, surface biomarkers shown in Table 5 can be used as targets for detection probes.

TABLE 5

Screening for target biomarkers individually as targets for a

detection probe.

CD63
CD81

Target

(−)
No

(−)
No

Biomarker
dCt
(+) abs Ct
abs Ct
EV abs Ct
dCt
(+) abs Ct
abs Ct
EV abs Ct

ALCAM
10.46
23.75
34.21
35.21
11
22.92
33.93
35.16

BCAM
1.67
29.55
31.22
30.67
7.69
27.9
35.6
34.06

CD47
20.83
21.97
42.8
41.54
18.5
23.73
42.24
40.02

CEACAM5
0.53
28.58
29.12
29.05
5.54
29.89
35.43
35.93

CEACAM6
7.59
24.81
32.39
32.39
17.22
21.3
38.51
38.79

CD38
—
—
—
—
17.25
21.95
39.19
39.54

CDH1
1.24
28.24
29.48
29.78
8.85
27.19
36.04
36.23

CDH2
6.91
33.36
40.27
40.94
3.77
33.1
36.87
40.54

CDH11
—
—
—
—
1.18
34.64
35.82
36.29

CDH3
5.88
31.31
37.19
42.4
3.9
32.72
36.62
45

CLDN1
11.09
27.8
38.89
42.12
15.13
26.92
42.05
42.39

CLDN3
4.46
28.09
32.55
33.18
8.75
27.35
36.1
37.58

DLL4
—
—
—
—
11.1
28.9
40
40

DSC2
0
29.74
29.74
29.73
4.77
33
37.76
39.38

EPCAM
14.94
20.69
35.62
36.47
14.59
22.06
36.65
38.6

EGFR
2.75
26.08
28.83
28.74
12.78
23.28
36.06
37.01

ERBB2
—
—
—
—
16.91
23.09
40
40

FOLR1
6.79
22.88
29.67
29.74
15.09
20.65
35.74
35.57

GOLM1
10.61
32.2
42.81
45
7.76
31.39
39.15
37.92

HACD3
4.72
28.31
33.03
37.5
4.06
28.48
32.54
37.82

IGF1R
7.05
23
30.05
29.57
13.21
21.89
35.1
37.47

LAPTM4B
5.49
32.86
38.35
39.1
4.87
34.08
38.95
45

MUC1
13.59
21.07
34.66
35.81
16.64
18.07
34.71
38.56

MET
4.85
25.6
30.45
30.63
10.33
26.05
36.38
37.21

MARCKSL1
0.31
33.57
33.88
37.49
10.83
28.02
38.84
42.15

MSLN
10.88
28.23
39.11
42.48
11.24
27.44
38.68
45

MUC4
7.63
29.99
37.62
37.82
3.34
29.98
33.32
35.39

NT5E
14.55
27.63
42.18
40.53
11.34
27.99
39.33
45

PRLR
—
—
—
—
12.54
26.78
39.32
39.3

PSCA
—
—
—
—
10.95
29.05
40
39.46

SLC7A5
—
—
—
—
11.77
22.53
34.3
34.75

SDC1
−0.03
28.69
28.66
29.04
3.36
32.65
36.01
36.26

sTn antigen
9.86
29.29
39.15
37.48
10.35
26.89
37.24
41.43

Tn antigen
3.88
32.68
36.56
37.76
6.04
30.46
36.51
37.87

T antigen
7.54
31.78
39.33
42.6
8.78
29.12
37.91
38.5

TACSTD2
14.24
22.41
36.65
35.94
14.6
21.21
35.8
36

TFRC
5.24
26.82
32.06
36.7
5.45
23.81
29.27
43.28

TSPAN1
—
—
—
—
8.71
29.07
37.78
38.06

VTCN1
8.4
34.32
42.72
43.95
7.49
32.51
40
40.78

dCt: delta Ct between positive and negative cell-line;

abs Ct: absolute Ct;

(+): positive cell line;

(−): negative cell line.

Multiple canonical exosome markers were used for characterization of each surface biomarker as indicated herein because each canonical exosome marker can vary in expression level across exosomes (e.g., exosomes derived from a specific sample). For example, certain exosomes may express a high level of CD63, but not CD81 or CD9, or vice versa. Therefore, as shown in Tables 4 and 5, Ct values may vary between canonical exosome markers for a given surface biomarker.

In some embodiments, certain surface biomarkers were characterized in combination as a target for a capture probe (e.g., as described herein) and as a target for a detection probe (as described herein), of assays described herein. For example, a biomarker combination comprising surface biomarkers of ALCAM and CD274 encompasses combinations where ALCAM is the target for a capture probe and CD274 is the target for a detection probe; and also combinations where CD274 is the target for a capture probe and ALCAM is the target for a detection probe. Such 2-biomarker combinations can be useful for the detection of at least a particular cancer.

In the present Example, certain biomarker combinations as shown in Table 6 were assessed in indication-specific cell lines. In some embodiments, such cell lines were selected that originate from or are associated with breast cancer, colon/colorectal cancer, leukemia, lung cancer, lymphoma, ovarian cancer, pancreatic cancer, prostate cancer, or skin cancer. Such biomarker combinations include at least two surface biomarkers as shown in Table 6. In some embodiments, a cancer-specific cell line utilized for biomarker combination characterization was originated from or associated with a particular cancer.

TABLE 6

Characterization of certain exemplary biomarker combinations.

Biomarker

(+) -
No EV -

Combination
dCt
absolute Ct
absolute Ct
Indication

ALCAM, ALCAM
19.26
25.74
45.00
Lung Cancer

ALCAM, CD274
8.84
36.16
45.00
Lung Cancer

ALCAM, CEACAM5
9.65
32.25
41.90
Prostate Cancer

ALCAM, CEACAM6
10.59
31.71
42.30
Prostate Cancer

ALCAM, EGFR
7.17
37.83
45.00
Lung Cancer

ALCAM, EPCAM
20.70
21.57
42.27
Lung Cancer

ALCAM, FOLR1
2.93
42.07
45.00
Ovarian Cancer

ALCAM, LAMB3
3.60
37.62
41.22
Ovarian Cancer

ALCAM, LeX
15.50
26.25
41.76
Lung Cancer

ALCAM, LEY
20.84
21.25
42.09
Lung Cancer

ALCAM, MARCKSL1
11.61
33.39
45.00
Lung Cancer

ALCAM, MET
9.65
32.47
42.12
Lung Cancer

ALCAM, MUC1
15.35
26.78
42.13
Lung Cancer

ALCAM, MUC4
13.13
28.00
41.14
Lung Cancer

ALCAM, NT5E
14.05
30.95
45.00
Lung Cancer

ALCAM, PS
16.68
21.48
38.16
Lung Cancer

ALCAM, PTK7
2.65
42.35
45.00
Ovarian Cancer

ALCAM, SDC1
8.63
36.37
45.00
Lung Cancer

ALCAM, SLC34A2
5.63
39.37
45.00
Prostate Cancer

ALCAM, sLeX
19.34
25.66
45.00
Lung Cancer

ALCAM, sTn
16.60
28.41
45.00
Lung Cancer

ALCAM, T
15.07
29.94
45.00
Ovarian Cancer

ALCAM, TACSTD2
15.02
29.98
45.00
Lung Cancer

ALCAM, TFRC
7.54
37.46
45.00
Lung Cancer

ALCAM, TNFRSF10B
5.79
35.34
41.12
Lung Cancer

BCAM, CA19-9
2.81
29.94
32.75
Ovarian Cancer

BCAM, CD24
2.88
31.12
33.99
Ovarian Cancer

BCAM, CD47
2.79
26.64
29.43
Ovarian Cancer

BCAM, EPCAM
5.40
29.84
35.23
Ovarian Cancer

BCAM, FOLR1
11.17
23.51
34.69
Ovarian Cancer

BCAM, MSLN
2.62
33.10
35.72
Ovarian Cancer

BCAM, MUC1
12.84
25.41
38.25
Ovarian Cancer

BCAM, MUC16
6.11
30.89
37.00
Ovarian Cancer

BCAM, sTn
2.76
32.21
34.98
Colon/Colorectal Cancer

BCAM, sTn
9.24
25.74
34.98
Ovarian Cancer

BCAM, T
3.38
32.98
36.35
Ovarian Cancer

BCAM, TACSTD2
7.16
30.60
37.76
Ovarian Cancer

BCAM, TACSTD2
2.86
34.90
37.76
Pancreatic Cancer

BCAM, Tn
6.50
28.07
34.56
Ovarian Cancer

CA19-9, CD24
7.99
25.10
33.09
Ovarian Cancer

CA19-9, CD47
15.47
19.52
34.98
Ovarian Cancer

CA19-9, CDH1
2.62
32.94
35.56
Ovarian Cancer

CA19-9, CDH1
7.01
28.54
35.56
Pancreatic Cancer

CA19-9, CDH2
10.45
24.30
34.74
Ovarian Cancer

CA19-9, CDH3
7.73
30.22
37.96
Lung Cancer

CA19-9, CDH3
6.76
31.20
37.96
Pancreatic Cancer

CA19-9, CLDN1
10.74
24.21
34.95
Ovarian Cancer

CA19-9, CLDN1
4.92
30.04
34.95
Pancreatic Cancer

CA19-9, CLDN3
10.56
25.54
36.10
Ovarian Cancer

CA19-9, EPCAM
15.42
22.73
38.15
Ovarian Cancer

CA19-9, FOLR1
16.24
18.38
34.61
Ovarian Cancer

CA19-9, LAPTM4B
5.58
32.71
38.29
Ovarian Cancer

CA19-9, MSLN
13.78
23.57
37.35
Ovarian Cancer

CA19-9, MUC1
17.66
16.41
34.07
Ovarian Cancer

CA19-9, MUC1
13.84
20.23
34.07
Pancreatic Cancer

CA19-9, MUC16
19.99
16.41
36.40
Ovarian Cancer

CA19-9, SLC34A2
6.45
28.33
34.78
Ovarian Cancer

CA19-9, sTn
8.67
24.35
33.02
Ovarian Cancer

CA19-9, T
10.41
25.29
35.70
Ovarian Cancer

CA19-9, TACSTD2
17.04
19.68
36.73
Lung Cancer

CA19-9, TACSTD2
16.80
19.92
36.73
Pancreatic Cancer

CA19-9, VTCN1
3.96
27.83
31.79
Ovarian Cancer

CD24, CD47
16.27
20.46
36.74
Ovarian Cancer

CD24, CDH1
4.65
31.50
36.15
Breast Cancer

CD24, CDH3
3.37
33.92
37.29
Breast Cancer

CD24, CLDN1
9.70
24.91
34.61
Ovarian Cancer

CD24, CLDN3
8.84
27.60
36.44
Colon/Colorectal Cancer

CD24, CLDN3
4.42
32.03
36.44
Lung Cancer

CD24, EPCAM
3.70
31.62
35.31
Lung Cancer

CD24, EPCAM
16.04
19.27
35.31
Ovarian Cancer

CD24, FOLR1
16.54
17.05
33.58
Ovarian Cancer

CD24, LAPTM4B
3.18
33.38
36.56
Ovarian Cancer

CD24, MSLN
7.24
29.29
36.54
Ovarian Cancer

CD24, MUC1
4.51
28.52
33.04
Lung Cancer

CD24, MUC1
15.39
17.65
33.04
Ovarian Cancer

CD24, MUC16
19.14
18.76
37.90
Ovarian Cancer

CD24, sTn
2.83
33.08
35.91
Colon/Colorectal Cancer

CD24, sTn
11.25
24.65
35.91
Ovarian Cancer

CD24, T
7.67
28.19
35.86
Ovarian Cancer

CD24, TACSTD2
2.86
29.69
32.55
Lung Cancer

CD24, TACSTD2
12.23
20.32
32.55
Ovarian Cancer

CD24, Tn
3.53
30.32
33.85
Leukemia

CD24, Tn
10.36
23.48
33.85
Lung Cancer

CD274, CD274
12.71
32.29
45.00
Lymphoma

CD274, CEACAM6
19.70
25.30
45.00
Lung Cancer

CD274, EGFR
11.78
33.22
45.00
Lung Cancer

CD274, EPCAM
14.81
27.59
42.39
Lung Cancer

CD274, FOLR1
12.09
32.91
45.00
Lung Cancer

CD274, LeX
16.93
28.07
45.00
Lung Cancer

CD274, LEY
10.50
28.63
39.13
Lung Cancer

CD274, MARCKSL1
5.17
31.97
37.15
Lung Cancer

CD274, MET
8.51
36.49
45.00
Lung Cancer

CD274, MUC1
11.35
33.65
45.00
Lung Cancer

CD274, MUC4
6.29
29.44
35.73
Lung Cancer

CD274, NT5E
16.70
28.30
45.00
Lung Cancer

CD274, PS
4.08
32.77
36.86
Lymphoma

CD274, SLC34A2
10.43
31.02
41.45
Lung Cancer

CD274, sLeX
17.46
27.54
45.00
Lung Cancer

CD274, sTn
8.29
30.52
38.81
Lung Cancer

CD274, T
3.84
34.86
38.71
Lung Cancer

CD274, TACSTD2
10.29
31.65
41.95
Lung Cancer

CD274, TFRC
17.35
27.65
45.00
Lymphoma

CD274, TNFRSF10B
10.07
31.17
41.25
Lymphoma

CD47, CDH2
5.08
29.21
34.29
Lung Cancer

CD47, CDH2
5.72
28.57
34.29
Ovarian Cancer

CD47, CDH3
10.78
31.09
41.87
Lung Cancer

CD47, CLDN1
10.46
24.41
34.87
Ovarian Cancer

CD47, EPCAM
16.21
17.49
33.70
Lung Cancer

CD47, EPCAM
9.48
24.23
33.70
Prostate Cancer

CD47, FOLR1
18.53
17.50
36.03
Ovarian Cancer

CD47, LAPTM4B
3.71
30.97
34.68
Lung Cancer

CD47, MSLN
7.89
34.26
42.16
Lung Cancer

CD47, MSLN
13.77
28.39
42.16
Ovarian Cancer

CD47, MUC1
3.10
32.68
35.78
Lung Cancer

CD47, MUC1
16.57
19.21
35.78
Ovarian Cancer

CD47, MUC16
22.07
19.60
41.66
Ovarian Cancer

CD47, SLC34A2
6.41
31.18
37.59
Lung Cancer

CD47, SLC34A2
13.76
23.84
37.59
Ovarian Cancer

CD47, sTn
17.48
23.91
41.40
Colon/Colorectal Cancer

CD47, sTn
24.31
17.08
41.40
Ovarian Cancer

CD47, T
16.31
18.75
35.06
Ovarian Cancer

CD47, TACSTD2
5.50
29.18
34.68
Lung Cancer

CD47, TACSTD2
15.15
19.53
34.68
Ovarian Cancer

CD47, Tn
20.56
19.72
40.28
Lung Cancer

CD47, Tn
13.70
26.58
40.28
Prostate Cancer

CD47, VTCN1
2.33
30.27
32.59
Ovarian Cancer

CDH1, CDH3
5.11
30.32
35.44
Lung Cancer

CDH1, EPCAM
8.80
27.92
36.72
Breast Cancer

CDH1, EPCAM
8.88
27.84
36.72
Lung Cancer

CDH1, FOLR1
3.12
33.12
36.24
Ovarian Cancer

CDH1, MUC1
6.91
28.62
35.53
Lung Cancer

CDH1, MUC16
12.22
32.78
45.00
Ovarian Cancer

CDH1, SLC34A2
2.08
36.47
38.55
Lung Cancer

CDH1, sTn
7.51
30.04
37.55
Colon/Colorectal Cancer

CDH1, sTn
7.34
30.22
37.55
Lung Cancer

CDH1, T
3.21
35.39
38.60
Ovarian Cancer

CDH1, TACSTD2
6.99
31.13
38.12
Lung Cancer

CDH2, CDH3
2.40
32.58
34.97
Lung Cancer

CDH2, EPCAM
9.86
25.97
35.83
Ovarian Cancer

CDH2, FOLR1
4.66
30.53
35.19
Ovarian Cancer

CDH2, MUC1
3.73
32.04
35.77
Ovarian Cancer

CDH2, MUC16
12.49
32.51
45.00
Ovarian Cancer

CDH2, SLC34A2
4.55
37.81
42.36
Ovarian Cancer

CDH2, sTn
4.61
32.67
37.28
Lung Cancer

CDH2, sTn
2.92
34.36
37.28
Prostate Cancer

CDH2, T
14.22
30.78
45.00
Lung Cancer

CDH2, T
10.75
34.25
45.00
Prostate Cancer

CDH2, Tn
5.27
32.51
37.78
Lung Cancer

CDH2, Tn
4.26
33.51
37.78
Prostate Cancer

CDH3, CLDN3
3.51
31.43
34.94
Colon/Colorectal Cancer

CDH3, EPCAM
4.27
33.05
37.32
Breast Cancer

CDH3, EPCAM
9.38
27.94
37.32
Lung Cancer

CDH3, MSLN
2.61
36.70
39.31
Ovarian Cancer

CDH3, MUC1
6.44
28.25
34.70
Lung Cancer

CDH3, MUC16
3.25
32.46
35.71
Ovarian Cancer

CDH3, sTn
4.93
34.29
39.22
Colon/Colorectal Cancer

CDH3, sTn
5.87
33.35
39.22
Lung Cancer

CDH3, T
5.14
37.89
43.03
Lung Cancer

CDH3, T
9.96
33.06
43.03
Prostate Cancer

CDH3, TACSTD2
13.14
31.86
45.00
Lung Cancer

CDH3, TACSTD2
8.82
36.18
45.00
Pancreatic Cancer

CDH3, Tn
7.14
31.39
38.53
Prostate Cancer

CEACAM5, CEACAM5
5.40
30.07
35.46
Lung Cancer

CEACAM5, CEACAM6
7.97
28.45
36.42
Pancreatic Cancer

CEACAM5, DSC2
2.39
36.19
38.58
Colon/Colorectal Cancer

CEACAM5, EGFR
4.46
33.42
37.88
Pancreatic Cancer

CEACAM5, EPCAM
2.30
33.59
35.89
Pancreatic Cancer

CEACAM5, LeX
14.15
21.10
35.26
Colon/Colorectal Cancer

CEACAM5, LEY
9.94
28.34
38.28
Colon/Colorectal Cancer

CEACAM5, MARCKSL1
3.00
34.99
37.99
Prostate Cancer

CEACAM5, MUC1
8.34
29.87
38.22
Prostate Cancer

CEACAM5, MUC4
11.85
24.19
36.04
Pancreatic Cancer

CEACAM5, NT5E
7.53
34.28
41.81
Pancreatic Cancer

CEACAM5, PS
7.89
29.49
37.39
Colon/Colorectal Cancer

CEACAM5, SDC1
2.09
36.12
38.21
Pancreatic Cancer

CEACAM5, SLC34A2
4.31
40.70
45.00
Prostate Cancer

CEACAM5, sLeX
12.25
24.10
36.34
Colon/Colorectal Cancer

CEACAM5, sTn
14.67
23.96
38.63
Colon/Colorectal Cancer

CEACAM5, T
8.19
36.81
45.00
Colon/Colorectal Cancer

CEACAM5, TACSTD2
16.31
28.69
45.00
Pancreatic Cancer

CEACAM5, TFRC
10.36
31.93
42.29
Lung Cancer

CEACAM5, Tn
3.64
33.28
36.91
Prostate Cancer

CEACAM6, CEACAM6
21.98
19.64
41.62
Lung Cancer

CEACAM6, DSC2
6.72
38.28
45.00
Colon/Colorectal Cancer

CEACAM6, EGFR
20.42
24.58
45.00
Pancreatic Cancer

CEACAM6, EPCAM
11.10
23.48
34.58
Pancreatic Cancer

CEACAM6, FOLR1
12.19
24.23
36.41
Lung Cancer

CEACAM6, LAMB3
4.04
31.21
35.25
Pancreatic Cancer

CEACAM6, LeX
23.42
21.58
45.00
Colon/Colorectal Cancer

CEACAM6, LEY
15.40
29.60
45.00
Colon/Colorectal Cancer

CEACAM6, MARCKSL1
9.86
28.30
38.16
Prostate Cancer

CEACAM6, MET
4.13
31.55
35.67
Pancreatic Cancer

CEACAM6, MSLN
10.12
31.85
41.97
Pancreatic Cancer

CEACAM6, MUC1
11.89
25.93
37.82
Lung Cancer

CEACAM6, NT5E
17.95
22.66
40.61
Pancreatic Cancer

CEACAM6, PS
7.03
27.08
34.11
Colon/Colorectal Cancer

CEACAM6, SDC1
6.72
38.28
45.00
Lung Cancer

CEACAM6, SLC34A2
9.95
29.48
39.44
Lung Cancer

CEACAM6, sLeX
9.25
26.13
35.38
Colon/Colorectal Cancer

CEACAM6, sTn
9.65
31.70
41.35
Colon/Colorectal Cancer

CEACAM6, T
21.18
21.30
42.49
Lung Cancer

CEACAM6, TACSTD2
16.22
20.72
36.94
Pancreatic Cancer

CEACAM6, TFRC
5.81
30.41
36.22
Lung Cancer

CEACAM6, Tn
2.47
34.36
36.83
Prostate Cancer

CEACAM6, TNFRSF10B
3.15
26.82
29.97
Pancreatic Cancer

CLDN1, EPCAM
9.43
23.38
32.81
Ovarian Cancer

CLDN1, FOLR1
9.26
22.95
32.21
Ovarian Cancer

CLDN1, MSLN
4.93
30.13
35.06
Ovarian Cancer

CLDN1, MUC1
13.23
20.22
33.45
Ovarian Cancer

CLDN1, MUC16
13.42
22.30
35.72
Ovarian Cancer

CLDN1, SLC34A2
3.92
29.76
33.68
Ovarian Cancer

CLDN1, sTn
15.75
20.61
36.36
Ovarian Cancer

CLDN1, sTn
3.27
33.09
36.36
Pancreatic Cancer

CLDN1, TACSTD2
12.65
22.16
34.81
Ovarian Cancer

CLDN1, Tn
2.25
20.59
22.83
Ovarian Cancer

CLDN3, EPCAM
11.23
18.63
29.86
Colon/Colorectal Cancer

CLDN3, FOLR1
6.56
21.82
28.39
Ovarian Cancer

CLDN3, MSLN
3.00
35.75
38.74
Lung Cancer

CLDN3, MSLN
7.98
30.77
38.74
Ovarian Cancer

CLDN3, MUC1
8.44
24.50
32.94
Lung Cancer

CLDN3, MUC1
10.44
22.50
32.94
Ovarian Cancer

CLDN3, MUC16
12.20
23.13
35.33
Ovarian Cancer

CLDN3, STn
14.27
22.01
36.27
Colon/Colorectal Cancer

CLDN3, T
7.79
25.69
33.48
Ovarian Cancer

CLDN3, TACSTD2
5.91
28.26
34.18
Lung Cancer

CLDN3, TACSTD2
10.88
23.29
34.18
Ovarian Cancer

DSC2, EGFR
4.17
40.83
45.00
Lung Cancer

DSC2, EPCAM
6.20
38.80
45.00
Prostate Cancer

DSC2, FOLR1
8.30
36.70
45.00
Breast Cancer

DSC2, IGSF3
4.33
37.31
41.64
Breast Cancer

DSC2, LeX
5.46
36.12
41.58
Lung Cancer

DSC2, LEY
4.33
34.67
39.00
Colon/Colorectal Cancer

DSC2, MARCKSL1
4.37
37.88
42.25
Prostate Cancer

DSC2, MUC1
9.21
35.79
45.00
Breast Cancer

DSC2, NT5E
3.93
38.20
42.13
Colon/Colorectal Cancer

DSC2, sLeX
8.93
36.07
45.00
Lung Cancer

DSC2, TACSTD2
6.78
35.13
41.91
Breast Cancer

DSC2, TFRC
4.75
36.46
41.20
Colon/Colorectal Cancer

DSC2, TNFRSF10B
7.82
37.18
45.00
Prostate Cancer

EGFR, EGFR
7.81
33.98
41.79
Breast Cancer

EGFR, EPCAM
10.82
31.15
41.98
Breast Cancer

EGFR, FOLR1
14.60
30.40
45.00
Ovarian Cancer

EGFR, IGSF3
8.92
33.21
42.13
Ovarian Cancer

EGFR, LeX
6.12
30.82
36.95
Lung Cancer

EGFR, LEY
16.18
26.08
42.26
Ovarian Cancer

EGFR, MARCKSL1
10.87
30.44
41.31
Pancreatic Cancer

EGFR, MET
13.00
32.00
45.00
Pancreatic Cancer

EGFR, MSLN
5.17
34.84
40.01
Pancreatic Cancer

EGFR, MUC1
5.24
33.14
38.39
Breast Cancer

EGFR, MUC4
12.48
25.31
37.79
Pancreatic Cancer

EGFR, NT5E
19.39
25.61
45.00
Pancreatic Cancer

EGFR, PS
5.00
30.50
35.50
Ovarian Cancer

EGFR, SDC1
2.84
42.16
45.00
Breast Cancer

EGFR, SLC34A2
6.60
34.75
41.36
Lung Cancer

EGFR, sLeX
12.55
32.45
45.00
Pancreatic Cancer

EGFR, sTn
3.90
28.17
32.07
Pancreatic Cancer

EGFR, T
17.43
27.57
45.00
Lung Cancer

EGFR, TACSTD2
4.38
40.62
45.00
Breast Cancer

EGFR, TNFRSF10B
14.09
27.88
41.97
Pancreatic Cancer

EPCAM, EPCAM
3.50
18.44
21.93
Ovarian Cancer

EPCAM, FOLR1
18.35
20.48
38.83
Ovarian Cancer

EPCAM, LAPTM4B
5.24
28.25
33.49
Lung Cancer

EPCAM, LeX
27.03
17.97
45.00
Colon/Colorectal Cancer

EPCAM, LEY
16.95
21.98
38.93
Breast Cancer

EPCAM, MARCKSL1
7.32
28.73
36.05
Lung Cancer

EPCAM, MET
8.95
30.64
39.59
Lung Cancer

EPCAM, MSLN
5.76
28.70
34.46
Ovarian Cancer

EPCAM, MUC1
15.38
20.61
35.99
Lung Cancer

EPCAM, MUC16
19.32
18.41
37.73
Ovarian Cancer

EPCAM, MUC4
7.68
22.69
30.37
Lung Cancer

EPCAM, NT5E
15.74
26.21
41.95
Lung Cancer

EPCAM, PS
8.17
18.90
27.07
Lung Cancer

EPCAM, SLC34A2
6.15
30.20
36.35
Lung Cancer

EPCAM, sLeX
15.16
19.62
34.79
Colon/Colorectal Cancer

EPCAM, sTn
16.09
19.18
35.27
Colon/Colorectal Cancer

EPCAM, sTn
15.54
19.73
35.27
Ovarian Cancer

EPCAM, T
13.85
22.86
36.71
Ovarian Cancer

EPCAM, T
15.78
20.93
36.71
Prostate Cancer

EPCAM, TACSTD2
22.96
19.33
42.29
Lung Cancer

EPCAM, TACSTD2
22.25
20.03
42.29
Ovarian Cancer

EPCAM, TFRC
7.00
28.39
35.39
Colon/Colorectal Cancer

EPCAM, Tn
15.06
21.19
36.25
Ovarian Cancer

EPCAM, VTCN1
3.06
33.69
36.76
Breast Cancer

EPCAM, VTCN1
10.34
26.42
36.76
Ovarian Cancer

FOLR1, FOLR1
24.00
18.45
42.45
Ovarian Cancer

FOLR1, LAPTM4B
2.39
32.03
34.42
Ovarian Cancer

FOLR1, LeX
25.17
15.67
40.84
Ovarian Cancer

FOLR1, LEY
12.90
23.05
35.95
Ovarian Cancer

FOLR1, MARCKSL1
5.54
30.80
36.34
Ovarian Cancer

FOLR1, MET
3.04
35.20
38.24
Lung Cancer

FOLR1, MSLN
12.73
24.74
37.47
Ovarian Cancer

FOLR1, MUC1
18.35
17.18
35.53
Ovarian Cancer

FOLR1, MUC16
19.73
18.16
37.89
Ovarian Cancer

FOLR1, MUC4
10.23
26.24
36.48
Lung Cancer

FOLR1, NT5E
15.16
29.84
45.00
Lung Cancer

FOLR1, PS
9.41
24.93
34.34
Ovarian Cancer

FOLR1, SLC34A2
15.89
22.01
37.90
Ovarian Cancer

FOLR1, sLeX
17.90
23.65
41.55
Lung Cancer

FOLR1, sTn
21.09
15.77
36.86
Ovarian Cancer

FOLR1, T
15.93
29.07
45.00
Ovarian Cancer

FOLR1, TACSTD2
24.14
20.86
45.00
Ovarian Cancer

FOLR1, TFRC
11.37
27.19
38.56
Ovarian Cancer

FOLR1, Tn
18.45
16.56
35.01
Ovarian Cancer

FOLR1, TNFRSF10B
3.89
32.82
36.71
Lung Cancer

FOLR1, VTCN1
4.42
30.52
34.94
Ovarian Cancer

IGF1R, LeX
3.21
26.26
29.48
Breast Cancer

IGF1R, LEY
4.94
26.08
31.03
Breast Cancer

IGF1R, MUC1
6.73
23.38
30.12
Breast Cancer

IGF1R, NT5E
8.63
36.37
45.00
Ovarian Cancer

IGF1R, PS
4.35
28.05
32.40
Breast Cancer

IGF1R, TFRC
6.59
27.12
33.70
Breast Cancer

IGSF3, LeX
5.44
29.50
34.94
Ovarian Cancer

IGSF3, LEY
4.55
31.12
35.67
Ovarian Cancer

IGSF3, NT5E
7.63
37.37
45.00
Lung Cancer

IGSF3, SDC1
7.33
37.67
45.00
Breast Cancer

IGSF3, TACSTD2
2.21
33.11
35.32
Breast Cancer

IGSF3, TFRC
2.20
32.81
35.02
Skin Cancer

LAMB3, LEY
3.07
33.49
36.56
Colon/Colorectal Cancer

LAMB3, MUC1
2.07
33.58
35.66
Pancreatic Cancer

LAMB3, NT5E
5.25
35.72
40.97
Pancreatic Cancer

LAMB3, T
3.88
29.02
32.90
Lung Cancer

LAMB3, Tn
6.91
27.38
34.29
Prostate Cancer

LAPTM4B, MUC1
5.16
30.70
35.86
Ovarian Cancer

LAPTM4B, MUC16
4.92
31.10
36.01
Ovarian Cancer

LAPTM4B, SLC34A2
2.10
34.53
36.63
Lung Cancer

LAPTM4B, sTn
4.75
31.49
36.24
Ovarian Cancer

LAPTM4B, TACSTD2
5.37
30.94
36.31
Ovarian Cancer

LAPTM4B, Tn
6.53
29.45
35.99
Ovarian Cancer

LeX, LeX
6.14
21.38
27.52
Colon/Colorectal Cancer

LeX, LEY
14.07
21.44
35.50
Breast Cancer

LeX, MARCKSL1
15.68
29.32
45.00
Ovarian Cancer

LeX, MET
17.70
27.30
45.00
Lung Cancer

LeX, MSLN
16.38
25.91
42.29
Ovarian Cancer

LeX, MUC1
21.84
17.50
39.33
Ovarian Cancer

LeX, MUC4
6.96
22.69
29.65
Lung Cancer

LeX, NT5E
23.57
21.43
45.00
Lung Cancer

LeX, PS
15.62
20.69
36.32
Breast Cancer

LeX, SDC1
14.03
30.97
45.00
Lung Cancer

LeX, SLC34A2
22.45
19.97
42.41
Ovarian Cancer

LeX, sLeX
7.86
19.57
27.43
Colon/Colorectal Cancer

LeX, sTn
11.96
18.90
30.86
Ovarian Cancer

LeX, T
7.56
28.64
36.20
Ovarian Cancer

LeX, TACSTD2
20.25
19.21
39.46
Ovarian Cancer

LeX, TFRC
17.34
24.32
41.66
Ovarian Cancer

LeX, TNFRSF10B
3.80
30.89
34.69
Lung Cancer

LEY, LEY
9.98
22.71
32.68
Breast Cancer

LEY, MARCKSL1
10.57
27.75
38.32
Lung Cancer

LEY, MET
2.35
34.81
37.16
Colon/Colorectal Cancer

LEY, MSLN
9.14
28.46
37.61
Ovarian Cancer

LEY, MUC1
16.20
21.71
37.91
Ovarian Cancer

LEY, MUC4
7.55
25.79
33.34
Lung Cancer

LEY, NT5E
23.77
21.23
45.00
Ovarian Cancer

LEY, PS
12.98
21.09
34.07
Breast Cancer

LEY, SDC1
6.05
35.57
41.61
Colon/Colorectal Cancer

LEY, SLC34A2
9.40
32.93
42.33
Ovarian Cancer

LEY, sLeX
5.96
27.44
33.40
Colon/Colorectal Cancer

LEY, sTn
4.36
28.06
32.42
Colon/Colorectal Cancer

LEY, T
5.59
31.44
37.03
Ovarian Cancer

LEY, TACSTD2
22.44
19.25
41.69
Breast Cancer

LEY, TFRC
19.25
23.01
42.26
Breast Cancer

LEY, Tn
4.74
32.79
37.53
Lung Cancer

LEY, TNFRSF10B
6.31
29.63
35.94
Colon/Colorectal Cancer

MARCKSL1, MET
6.14
35.88
42.03
Pancreatic Cancer

MARCKSL1, MUC1
7.83
33.72
41.55
Ovarian Cancer

MARCKSL1, NT5E
13.85
31.15
45.00
Pancreatic Cancer

MARCKSL1, PS
5.78
28.41
34.19
Lung Cancer

MARCKSL1, SLC34A2
5.37
39.63
45.00
Lung Cancer

MARCKSL1, sLeX
2.84
29.52
32.36
Lung Cancer

MARCKSL1, STn
12.70
28.81
41.50
Ovarian Cancer

MARCKSL1, T
6.86
34.37
41.23
Prostate Cancer

MARCKSL1, TACSTD2
5.89
32.27
38.16
Ovarian Cancer

MARCKSL1, TFRC
4.31
28.30
32.62
Skin Cancer

MET, MUC1
5.04
28.53
33.57
Lung Cancer

MET, NT5E
8.66
33.25
41.91
Lung Cancer

MET, SDC1
10.20
34.80
45.00
Lung Cancer

MET, sLeX
11.44
26.93
38.37
Lung Cancer

MET, sTn
7.21
29.69
36.90
Lung Cancer

MET, T
10.39
25.47
35.86
Prostate Cancer

MET, TACSTD2
4.07
31.42
35.49
Lung Cancer

MET, TFRC
3.34
31.42
34.76
Colon/Colorectal Cancer

MET, Tn
5.73
25.08
30.80
Prostate Cancer

MSLN, MSLN
10.59
27.72
38.31
Ovarian Cancer

MSLN, MUC1
2.57
32.60
35.17
Lung Cancer

MSLN, MUC1
7.88
27.29
35.17
Ovarian Cancer

MSLN, MUC16
15.74
22.53
38.27
Ovarian Cancer

MSLN, NT5E
11.73
33.27
45.00
Pancreatic Cancer

MSLN, PTK7
3.64
37.28
40.92
Ovarian Cancer

MSLN, SLC34A2
12.51
27.09
39.59
Ovarian Cancer

MSLN, sLeX
7.46
27.03
34.49
Pancreatic Cancer

MSLN, sTn
19.72
22.29
42.01
Ovarian Cancer

MSLN, T
8.18
30.28
38.46
Ovarian Cancer

MSLN, TACSTD2
13.78
23.96
37.74
Ovarian Cancer

MSLN, TFRC
10.04
32.36
42.41
Ovarian Cancer

MSLN, Tn
14.83
22.08
36.91
Ovarian Cancer

MSLN, VTCN1
2.19
31.97
34.16
Ovarian Cancer

MUC1, MUC1
12.33
16.38
28.71
Ovarian Cancer

MUC1, MUC16
18.28
15.89
34.18
Ovarian Cancer

MUC1, MUC4
9.12
19.67
28.79
Lung Cancer

MUC1, NT5E
16.24
23.94
40.18
Lung Cancer

MUC1, PS
7.20
24.40
31.61
Ovarian Cancer

MUC1, SDC1
7.75
37.25
45.00
Lung Cancer

MUC1, SLC34A2
3.99
31.31
35.30
Lung Cancer

MUC1, SLC34A2
7.92
27.38
35.30
Ovarian Cancer

MUC1, sLeX
14.17
19.66
33.83
Lung Cancer

MUC1, sTn
10.21
19.51
29.72
Lung Cancer

MUC1, sTn
13.75
15.97
29.72
Ovarian Cancer

MUC1, T
11.16
21.92
33.08
Ovarian Cancer

MUC1, TACSTD2
20.52
21.29
41.81
Lung Cancer

MUC1, TACSTD2
22.50
19.31
41.81
Ovarian Cancer

MUC1, TFRC
12.52
24.08
36.60
Ovarian Cancer

MUC1, Tn
17.55
14.90
32.45
Ovarian Cancer

MUC1, TNFRSF10B
3.56
28.86
32.42
Pancreatic Cancer

MUC16, SLC34A2
14.32
23.76
38.07
Ovarian Cancer

MUC16, sTn
16.20
21.10
37.30
Lung Cancer

MUC16, sTn
21.59
15.71
37.30
Ovarian Cancer

MUC16, T
21.01
17.03
38.04
Ovarian Cancer

MUC16, TACSTD2
4.38
32.31
36.69
Lung Cancer

MUC16, TACSTD2
18.98
17.71
36.69
Ovarian Cancer

MUC16, Tn
20.25
15.33
35.57
Ovarian Cancer

MUC16, VTCN1
3.27
30.36
33.63
Ovarian Cancer

MUC4, MUC4
7.14
24.33
31.47
Lung Cancer

MUC4, NT5E
12.35
25.88
38.23
Lung Cancer

MUC4, PS
6.63
25.94
32.56
Pancreatic Cancer

MUC4, SDC1
9.48
32.67
42.15
Lung Cancer

MUC4, SLC34A2
7.00
33.51
40.51
Lung Cancer

MUC4, sLeX
11.45
19.92
31.38
Lung Cancer

MUC4, sTn
5.86
26.13
31.99
Lung Cancer

MUC4, TACSTD2
11.99
22.44
34.43
Lung Cancer

MUC4, Tn
6.14
26.85
32.99
Pancreatic Cancer

MUC4, TNFRSF10B
3.28
24.96
28.24
Pancreatic Cancer

NT5E, NT5E
12.93
32.07
45.00
Lung Cancer

NT5E, PS
5.72
28.92
34.64
Ovarian Cancer

NT5E, PTK7
3.96
41.04
45.00
Colon/Colorectal Cancer

NT5E, SDC1
10.46
34.54
45.00
Lung Cancer

NT5E, SLC34A2
9.14
30.42
39.56
Lung Cancer

NT5E, sLeX
20.64
21.27
41.91
Lung Cancer

NT5E, sTn
17.89
27.11
45.00
Lung Cancer

NT5E, T
19.32
25.68
45.00
Lung Cancer

NT5E, TACSTD2
17.83
27.17
45.00
Lung Cancer

NT5E, TFRC
14.57
27.71
42.28
Ovarian Cancer

NT5E, Tn
2.33
33.50
35.83
Ovarian Cancer

NT5E, TNFRSF10B
16.31
28.69
45.00
Pancreatic Cancer

PS, PS
6.65
19.35
26.00
Lung Cancer

PS, SDC1
7.58
33.67
41.25
Colon/Colorectal Cancer

PS, SLC34A2
2.19
30.28
32.47
Ovarian Cancer

PS, sLeX
5.80
25.53
31.34
Colon/Colorectal Cancer

PS, STn
6.90
25.60
32.49
Ovarian Cancer

PS, T
8.03
25.08
33.11
Ovarian Cancer

PS, TACSTD2
5.54
28.71
34.25
Ovarian Cancer

PS, TFRC
2.37
31.57
33.94
Ovarian Cancer

PTK7, SLC34A2
7.08
37.92
45.00
Ovarian Cancer

PTK7, T
7.29
29.28
36.57
Ovarian Cancer

SDC1, sLeX
14.05
30.95
45.00
Lung Cancer

SDC1, sTn
5.44
32.74
38.17
Colon/Colorectal Cancer

SDC1, T
7.38
33.65
41.03
Ovarian Cancer

SDC1, TACSTD2
9.40
35.60
45.00
Lung Cancer

SDC1, Tn
5.65
31.07
36.73
Prostate Cancer

SDC1, TNFRSF10B
9.69
35.31
45.00
Pancreatic Cancer

SLC34A2, SLC34A2
7.12
37.88
45.00
Lung Cancer

SLC34A2, sLeX
6.85
26.98
33.83
Lung Cancer

SLC34A2, sTn
5.02
29.58
34.60
Ovarian Cancer

SLC34A2, T
9.81
27.95
37.76
Ovarian Cancer

SLC34A2, TACSTD2
4.36
29.62
33.99
Lung Cancer

SLC34A2, Tn
6.81
27.99
34.80
Ovarian Cancer

SLC34A2, VTCN1
3.20
29.76
32.95
Ovarian Cancer

sLeX, STn
12.91
22.27
35.18
Colon/Colorectal Cancer

sLeX, T
10.05
18.70
28.75
Ovarian Cancer

sLeX, TACSTD2
19.06
20.31
39.37
Lung Cancer

sLeX, TFRC
12.22
24.00
36.22
Colon/Colorectal Cancer

sTn, T
14.17
14.95
29.12
Lung Cancer

sTn, T
10.75
18.37
29.12
Ovarian Cancer

sTn, TACSTD2
12.01
24.92
36.93
Lung Cancer

sTn, TFRC
18.69
22.37
41.05
Ovarian Cancer

sTn, Tn
4.11
29.46
33.57
Leukemia

sTn, Tn
7.02
26.54
33.57
Prostate Cancer

sTn, VTCN1
4.81
27.09
31.91
Ovarian Cancer

T, T
4.97
22.08
27.05
Lung Cancer

T, TACSTD2
11.28
26.55
37.83
Lung Cancer

T, TACSTD2
13.92
23.91
37.83
Ovarian Cancer

T, TFRC
8.62
28.21
36.83
Lung Cancer

T, Tn
12.21
20.29
32.50
Prostate Cancer

T, TNFRSF10B
4.97
27.50
32.47
Lung Cancer

TACSTD2, TACSTD2
21.11
23.89
45.00
Ovarian Cancer

TACSTD2, TFRC
16.94
28.06
45.00
Ovarian Cancer

TACSTD2, Tn
15.88
18.52
34.40
Ovarian Cancer

TFRC, TFRC
10.80
25.47
36.27
Skin Cancer

TFRC, TNFRSF10B
9.11
25.48
34.59
Skin Cancer

Tn, VTCN1
5.59
26.62
32.21
Ovarian Cancer

dCt: delta Ct between positive and negative cell-line; abs Ct: absolute Ct; (+): positive cell line.

Table 6 shows Ct values from characterization of certain 2-biomarker combinations in various cancer-specific cell lines and in a negative control group (e.g., no extracellular vesicles). Ct values were read from qPCR where the numeric value corresponds to the number of PCR cycles (i.e., higher values indicate less signal). As shown in Table 6, certain 2-biomarker combinations may be particularly useful for detection of a particular cancer. For example, surface biomarkers with high delta Ct values between the cancer-specific cell line and the negative control group (e.g., delta Ct values greater than 2, including, e.g., greater than 3, greater than 4, greater than 5, or higher) may be particularly useful for detection of a particular cancer. In some embodiments, biomarker combinations as shown in Table 6 may be useful for detection of various cancers.

In some embodiments, a plurality of (including, e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or more) biomarker combinations as shown in Table 6 can be used in pan-cancer detection. In some embodiments, pan-cancer detection may encompass (i) at least one breast cancer-associated biomarker combination as shown in Table 6, (ii) at least one colon/colorectal cancer-associated biomarker combination as shown in Table 6, (iii) at least one lung cancer-associated biomarker combination as shown in Table 6, (iv) at least one ovarian cancer-associated biomarker combination as shown in Table 6, and (v) at least one prostate cancer-associated biomarker combination as shown in Table 6. In some embodiments, pan-cancer detection for female subjects may encompass (i) at least one breast cancer-associated biomarker combination as shown in Table 6, (ii) at least one colon/colorectal cancer-associated biomarker combination as shown in Table 6, (iii) at least one lung cancer-associated biomarker combination as shown in Table 6, and (iv) at least one ovarian cancer-associated biomarker combination as shown in Table 6. In some embodiments, pan-cancer detection for male subjects may encompass (i) at least one colon/colorectal cancer-associated biomarker combination as shown in Table 6, (ii) at least one lung cancer-associated biomarker combination as shown in Table 6, and (iii) at least one prostate cancer-associated biomarker combination as shown in Table 6. In some embodiments, certain biomarker combinations used in pan-cancer detection may share at least one surface biomarker, which in some embodiments may be used as a capture biomarker.

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Im, H., Shao, H., Park, Y. I., Peterson, V. M., Castro, C. M., Weissleder, R. and Lee, H., 2014. Label-free detection and molecular profiling of exosomes with a nano-plasmonic sensor. Nature biotechnology, 32(5), p. 490.

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EQUIVALENTS

Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. It is to be understood that the invention encompasses all variations, combinations, and permutations in which one or more limitations, elements, clauses, descriptive terms, etc., from one or more of the listed claims is introduced into another claim dependent on the same base claim (or, as relevant, any other claim) unless otherwise indicated or unless it would be evident to one of ordinary skill in the art that a contradiction or inconsistency would arise. Further, it should also be understood that any embodiment or aspect of the invention can be explicitly excluded from the claims, regardless of whether the specific exclusion is recited in the specification. The scope of the present invention is not intended to be limited to the above Description, but rather is as set forth in the claims that follow.

Number	Date	Country
63224374	Jul 2021	US
63224378	Jul 2021	US
63224379	Jul 2021	US
63224380	Jul 2021	US
63224381	Jul 2021	US
63224382	Jul 2021	US
63224385	Jul 2021	US
63224390	Jul 2021	US

COMPOSITIONS AND METHODS FOR CANCER DETECTION

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