Patent Application
20220397576
This application relates to devices and methods for the detection of biomarkers indicative of pancreatic cancer, wherein the biomarkers are detected in biological samples from a patient.
Pancreatic cancer is an almost uniformly fatal disease. Tremendous efforts have been made to elucidate the mechanisms underlying pancreatic cancer in order to develop effective treatments. Although there have been significant scientific advancements, pancreatic cancer survival rates remain stagnant with a 5-year survival rate of 9%. In the United States, 56,770 patients are predicted to be diagnosed with pancreatic cancer and 45,750 individuals will die from the disease in 2019 (Siegel R L, et al. CA Cancer J Clin 2019; 69(1):7-34). Despite the continuous overall decline in the death rates from most cancer forms, both incidence and mortality rates for pancreatic cancer have increased during the past decade (Wu W, et al. Clin Epidemiol 2018; 10:789-97). It is projected that pancreatic cancer will become the second leading cause of cancer related death by the year 2030 (Rahib L, et al. Cancer Res 2014; 74(11):2913-21).
Surgical resection is the only curative treatment option, yet only about 15-20% of patients are eligible for up-front radical surgery (Kommalapati A, et al. Cancers (Basel) 2018; 10(1)). Early detection of resectable tumors is key to reduce pancreatic cancer related deaths (Lennon A M. et al. Cancer Res 2014; 74(13):3381-9). Apart from early diagnosis, molecular markers are also needed to accurately predict the course of the disease or response to therapy (Krantz B A et al. Clin Cancer Res 2018; 24(10):2241-2250). Serum CA 19-9 is the only biomarker used in the routine clinical management of pancreatic cancer. However, CA 19-9 has inadequate sensitivity and specificity for early detection and can only be used for disease monitoring (Poruk K E, et al. Curr Mol Med 2013; 13(3):340-51). Consequently, to improve patient outcomes, novel and improved diagnostic, prognostic and predictive biomarkers are needed to identify instances of pancreatic cancer and to characterize individual pancreatic tumor biology for the selection of optimal treatment.
Some aspects relate to a pancreatic cancer detection device, including:
In some examples, the solid surface includes antibodies configured to selectively bind two target proteins selected from the group.
In some examples, the solid surface includes antibodies configured to selectively bind three target proteins selected from the group.
In some examples, the solid surface includes antibodies configured to selectively bind four target proteins selected from the group.
In some examples, the solid surface includes antibodies configured to selectively bind five target proteins selected from the group.
In some examples, the solid surface comprises antibodies configured to selectively bind A1AT and Carbohydrate antigen 19-9.
Where the solid surface comprises antibodies configured to selectively bind A1AT and Carbohydrate antigen 19-9, the solid surface may comprise antibodies further configured to selectively bind at least one of Complement C2 and Complement component 3. The solid surface may comprise antibodies further configured to selectively bind cartilage oligomeric matrix protein. The solid surface may comprise antibodies further configured to selectively bind at least one of Gamma-glutamyl transpeptidase, C1 inhibitor, and Serum amyloid A.
In some examples, the solid surface comprises antibodies configured to selectively bind A1AT, Carbohydrate antigen 19-9, at least one of Complement C2 and Complement component 3, and Gamma-glutamyl transpeptidase. The solid surface may comprise antibodies further configured to selectively bind at least one of C1 inhibitor, and Serum amyloid A.
In some examples, the solid surface comprises antibodies configured to selectively bind A1AT, Carbohydrate antigen 19-9, and Gamma-glutamyl transpeptidase. The solid surface may comprise antibodies further configured to selectively bind cartilage oligomeric matrix protein. The solid surface may comprise antibodies further configured to selectively bind Serum amyloid A. In some examples, the solid surface comprises antibodies configured to selectively bind A1AT, Carbohydrate antigen 19-9, and cartilage oligomeric matrix protein. The solid surface may comprise antibodies further configured to selectively bind Serum amyloid A.
In some examples, the solid surface comprises antibodies configured to selectively bind Carbohydrate antigen 19-9 and at least one of Complement C2 and Complement component 3.
In some examples, the solid surface includes a lateral flow detection surface.
In some examples, the lateral flow detection surface includes a lateral flow detection test trip.
In some examples, indicating includes visually indicating binding to a user.
Some aspects relate to a method of detecting pancreatic cancer, including:
In some examples, the biological sample includes whole blood.
In some examples, the biological sample includes serum.
In some examples, the method further includes providing a report, the report indicating the likelihood of an incidence of pancreatic cancer in a subject.
Some aspects relate to a pancreatic cancer detection device, including:
In some examples, the solid surface comprises antibodies configured to selectively bind two target proteins selected from the group.
In some examples, the solid surface comprises antibodies configured to selectively bind three target proteins selected from the group.
In some examples, the solid surface comprises antibodies configured to selectively bind four target proteins selected from the group.
In some examples, the solid surface comprises antibodies configured to selectively bind five target proteins selected from the group.
Some aspects relate to a method of developing a protein expression profile in a biological sample obtained from a patient, the method including:
In some examples, the method further includes the step of fractionating the protein digest prior to detecting and quantifying the amount of the one or more fragment peptides.
In some examples, the fractionating step includes liquid chromatography, nano-reverse phase liquid chromatography, high performance liquid chromatography or reverse phase high performance liquid chromatography.
In some examples, the biological is a fresh or a fresh-frozen sample.
In some examples, the biological sample is a formalin fixed tissue.
In some examples, the protein digest includes a protease digest.
In some examples, the protein digest includes a trypsin digest.
In some examples, the mass spectrometry includes tandem mass spectrometry, ion trap mass spectrometry, triple quadrupole mass spectrometry, hybrid ion trap/quadrupole mass spectrometry, MALDI-TOF mass spectrometry, MALDI mass spectrometry, and/or time of flight mass spectrometry.
In some examples, a mode of mass spectrometry used is Selected Reaction Monitoring (SRM), Multiple Reaction Monitoring (MRM), intelligent Selected Reaction Monitoring (iSRM), Parallel Reaction Monitoring (PRM), and/or multiple Selected Reaction Monitoring (mSRM).
In some examples, the one or more fragment peptides are selected from the group consisting of peptides corresponding to SEQ ID NO: 1-46.
In some examples, the formalin fixed tissue is paraffin embedded tissue.
In some examples, the tissue is obtained from a tumor.
In some examples, the tumor is a primary tumor.
In some examples, the tumor is a secondary tumor.
In some examples, quantifying the one or more fragment peptides includes comparing an amount of the one or more fragment peptides in the biological sample to the amount of the same one or more fragment peptides in a different and separate biological sample.
In some examples, quantifying the one or more fragment peptides includes determining an amount of the one or more fragment peptides in the biological sample by comparison to an added internal standard peptide of known amount having the same amino acid sequence of the one or more fragment peptides.
In some examples, the internal standard peptide is an isotopically labeled peptide.
In some examples, the isotopically labeled internal standard peptide comprises one or more heavy stable isotopes selected from the group consisting of 180, 170, 34S, 15N, 13C, 2H and a combination thereof.
In some examples, detecting and quantifying the amount of the one or more fragment peptides in the protein digest indicates the presence of the corresponding protein and an association with cancer in the subject.
In some examples, the method further includes administering to a patient or subject from which the biological sample was obtained a therapeutically effective amount of a cancer therapeutic agent, wherein the cancer therapeutic agent and/or amount of the cancer therapeutic agent administered is based upon detection of and/or amount of the one or more proteins or the one or more fragment peptides selected from SEQ ID NO: 1-46, and wherein the cancer therapeutic agent is a targeted agent that interacts with the one or more proteins that correspond to the one or more fragment peptides selected from SEQ ID NO: 1-46.
In some examples, the cancer therapeutic agent and/or amount of the cancer therapeutic agent administered is based upon multiplex detection of and/or amount of two or more fragment peptides selected from SEQ ID NO: 1-46.
In some examples, the method further includes administering to a patient or subject from which the biological sample was obtained a therapeutically effective amount of a cancer therapeutic agent, wherein the cancer therapeutic agent and/or amount of the cancer therapeutic agent administered is based upon detection of and/or amount of the one or more protein or the one or more fragment peptides selected from SEQ ID NO: 1-46, and wherein the cancer therapeutic agent is an immunomodulatory cancer therapeutic agent whose function is to initiate, enhance, manipulate, and/or otherwise modulate the cancer patient immune response to attack and kill said patient tumor cells.
In some examples, the method further includes combining multiplex detecting and quantitating two or more proteins or two or more fragment peptides corresponding to SEQ ID NO: 1-46 with analysis of other oncoproteins that drive growth of the patient tumor cells, wherein a targeted cancer therapeutic agent that inhibits or modulates the function of the oncoprotein to inhibit growth of the patient tumor cells is administered to the patient in combination with an immunomodulatory cancer therapeutic agent that interacts with one or more of the proteins to initiate, enhance, manipulate, and/or otherwise modulate the cancer patient immune response to attack and kill the patient tumor cells.
Some aspects relate to a method of determining if a subject has an increased risk of suffering from pancreatic cancer, the method including
In some examples, the normal proteomic profile includes the subject's proteomic profile prior to the onset of pan.
In some examples, the normal proteomic profile includes a proteomic profile generated from a population of individuals that do not presently or in the future display memory impairment.
Some aspects relate to a method of monitoring the progression of pancreatic cancer in a subject, the method including
Some aspects relate to a method of monitoring the progression of a treatment for pancreatic cancer in a subject, the method including:
Some aspects relate to a method of determining if a subject has an increased risk of suffering from pancreatic cancer, the method including analyzing at least one sample from the subject to determine levels of individual proteins and comparing the levels of individual proteins with the value of levels of the proteins in one or more normal individuals to determine if the levels of each protein are altered compared to normal levels, wherein a change in the value of the subject's proteins is indicative that the subject has an increased risk of suffering from pancreatic cancer compared to a normal individual.
Provided herein are lateral flow assay devices and methods of using such devices to detect biomarkers for pancreatic cancer in samples from a subject. One of skill in the art will understand that such lateral flow assay devices may be used to detect any of the biomarkers described herein, for example, such lateral flow assay devices may be used to detect any suitable combination of biomarkers described herein and provide an indication of an instance of pancreatic cancer. Further, although lateral flow devices are described in detail herein, one of skill in the art will understand that other types of devices and systems may be suitable for the detection of pancreatic cancer, therefore this disclosure is not limited to the use of a lateral flow device.
The term “immobilized” or “embedded” interchangeably refers to reversibly or irreversibly immobilized molecules (e.g., analytes or binding agents). In some examples, reversibly immobilized molecules are immobilized in a manner that allows the molecules, or a portion thereof (e.g., at least about 25%, 50%, 60%, 75%, 80% or more of the molecules), to be removed from their immobilized location without substantial denaturation or aggregation. For example, a molecule can be reversibly immobilized in or on an absorbent material (e.g., an absorbent pad) by contacting a solution containing the molecule with the absorbent material, thereby soaking up the solution and reversibly immobilizing the molecule. The reversibly immobilized molecule can then be removed by wicking the solution from the absorbent material, or from one region of the absorbent material to another. In some cases, a molecule can be reversibly immobilized on an absorbent material by contacting a solution containing the molecule with the absorbent material, thereby soaking up the solution, and then drying the solution-containing absorbent material. The reversibly immobilized molecule can then be removed by contacting the absorbent material with another solution of the same or a different composition, thereby solubilizing the reversibly immobilized molecule, and then wicking the solution from the absorbent material, or from one region of the absorbent material to another.
Irreversibly immobilized molecules (e.g., binding agents or analytes) are immobilized such that they are not removed, or not substantially removed, from their location under mild conditions (e.g., pH between about 4-9, temperature of between about 4-65° C.). Exemplary irreversibly immobilized molecules include protein analytes or binding agents bound to a nitrocellulose, polyvinylidene fluoride, nylon or polysulfone membrane by standard blotting techniques (e.g., electroblotting). Other exemplary irreversibly immobilized molecules include protein analytes or binding agents bound to glass or plastic (e.g., a microarray, a microfluidic chip, a glass histology slide or a plastic microtiter plate having wells with bound protein analytes therein).
The term “binding agent” refers to an agent that specifically binds to a molecule such as an analyte. While antibodies are described in many contexts herein, it will be understood by one of skill in the art that other binding agents can be used instead of antibodies as preferred by the user. A wide variety of binding agents are known in the art, including antibodies, aptamers, affimers, lipocalins (e.g., anticalins), thioredoxin A, bilin binding protein, or proteins containing an ankyrin repeat, the Z domain of staphylococcal protein A, or a fibronectin type III domain. Other binding agents include, but are not limited to, biotin/streptavidin, chelating agents, chromatography resins, affinity tags, or functionalized beads, nanoparticles and magnetic particles.
The term “specifically bind” refers to a molecule (e.g., binding agent such as an antibody or antibody fragment) that binds to a target with at least 2-fold greater affinity than non-target compounds, e.g., at least about 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold, 20-fold, 25-fold, 50-fold, 100-fold, 1000-fold, or more than 1000-fold greater affinity.
The term “antibody” refers to a polypeptide comprising a framework region from an immunoglobulin gene, or fragments thereof, that specifically bind and recognize an antigen, e.g., a particular analyte. Typically, the “variable region” contains the antigen-binding region of the antibody (or its functional equivalent) and is most critical in specificity and affinity of binding. Antibodies include for example chimeric, human, humanized antibodies, or single-chain antibodies.
An exemplary immunoglobulin (antibody) structural unit comprises a tetramer. Each tetramer is composed of two identical pairs of polypeptide chains, each pair having one “light” (about 25 kD) and one “heavy” chain (about 50-70 kD). The N-terminus of each chain defines a variable region of about 100 to 110 or more amino acids primarily responsible for antigen recognition. The terms variable light chain (VL) and variable heavy chain (VH) refer to these light and heavy chains respectively.
Antibodies can exist as intact immunoglobulins or as any of a number of well-characterized fragments that include specific antigen-binding activity. Such fragments can be produced by digestion with various peptidases. Pepsin digests an antibody below the disulfide linkages in the hinge region to produce F(ab)′2, a dimer of Fab which itself is a light chain joined to VH-CH1 by a disulfide bond. The F(ab)′2 may be reduced under mild conditions to break the disulfide linkage in the hinge region, thereby converting the F(ab)′2 dimer into an Fab′ monomer. The Fab′ monomer is essentially Fab with part of the hinge region (see Fundamental Immunology (Paul ed., 3d ed. 1993). While various antibody fragments are defined in terms of the digestion of an intact antibody, one of skill will appreciate that such fragments may be synthesized de novo either chemically or by using recombinant DNA methodology. Thus, the term antibody, as used herein, also includes antibody fragments either produced by the modification of whole antibodies, or those synthesized de novo using recombinant DNA methodologies (e.g., single chain Fv) or those identified using phage display libraries.
A example lateral flow device for detecting pancreatic cancer is shown in
Using the methods described herein, one or more lateral flow device(s) may be used to detect one or more protein selected from the group consisting of Apolipoprotein A-I, Immunoglobulin lambda-like polypeptide 5, Alpha-2-HS-glycoprotein, Immunoglobulin lambda constant 2, Alpha-1-acid glycoprotein 1, Immunoglobulin heavy constant gamma 1, Immunoglobulin kappa constant, Immunoglobulin heavy constant alpha 1, Serotransferrin, Serum albumin, Alpha-1-antitrypsin, Brain acid soluble protein 1, Protein S100-A6, Collagen alpha-1(XIV) chain, Histone H1.5, Fibulin-1, Rho GDP-dissociation inhibitor 2, Phospholipase A2, Pancreatic triacylglycerol lipase, Chymotrypsin-like elastase family member 3A, Colipase, Bile salt-activated lipase, Trypsin-2, Carboxypeptidase A1, Protein disulfide-isomerase A2, Trypsin-1, Glutathione S-transferase A2, D-3-phosphoglycerate dehydrogenase, Polyadenylate-binding protein 4, Protein disulfide-isomerase, Translocon-associated protein subunit alpha, Y-box-binding protein 3, Ribosome-binding protein 1, Leucine-rich repeat-containing protein 59, Protein disulfide-isomerase A4, 78 kDa glucose-regulated protein, Hypoxia up-regulated protein 1, N(G), N(G)-dimethylarginine dimethylaminohydrolase 1, Elongation factor 1-beta, Phosphatidylethanolamine-binding protein 1, 40S ribosomal protein S21, 40S ribosomal protein S3a, 40S ribosomal protein S14, 60S ribosomal protein L12, and Protein TFG. Either the complete set of these proteins or a subset thereof may be detected. A subset may include any combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 or 45 of these proteins.
More specifically and as described in greater detail below (including example methods of proteomic profiling to identify such biomarkers), 18 biomarkers found in blood were identified as particularly strong indicators for the detection of pancreatic cancer: A1AT (Alpha-1 antitrypsin), AGP1 (Alpha-1-acid glycoprotein 1), ApoA1 (Apolipoprotein A1), C1 inhib (C1-inhibitor, C1-inh, C1 esterase inhibitor), C2 (Complement C2), C3 (Complement component 3), CA19-9 (Carbohydrate antigen 19-9), Calprotectin CCK18 (caspase-cleaved cytokeratin-18), Ceruloplasmin, COMP (cartilage oligomeric matrix protein), GT (gamma-glutamyl transpeptidase), Haptoglobin, IGF1 (IGF-1, Insulin-like growth factor 1), IGFB3 (IGFBP-3, Insulin-Like Growth Factor Binding Protein 3), Properdin, SAA (Serum amyloid A), and TNF-a (Tumor necrosis factor alpha). As will be understood by one of skill in the art, detection of any subcombination of less than all 18 of these biomarkers may still provide reliable detection of pancreatic cancer. For example, combinations of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 of these biomarkers may be used in an aforementioned lateral flow assay or any other suitable assay to detect pancreatic cancer and notify a user. For example, a smaller subset of 10 biomarkers from the list of 18 biomarkers found in blood may also provide reliable detection of pancreatic cancer, IGFBP3, AGP1, GT, COMP, C1 inhibitor, C3, ApoA1, IGF1, CCK18, CA 19-9. Such a combination has been found to provide a diagnostic accuracy of 100% against healthy controls and a diagnostic accuracy of 90.5% against benign pancreatic disease. In certain examples of a detection assay, a subcombination of 5 biomarkers may be used, such as: A1AT, TNF-alpha, AGP1, C2, CA 19-9. Such a combination has been found to provide a diagnostic accuracy of 100% against healthy controls and a diagnostic accuracy of 79.6% against benign pancreatic disease. In certain examples of a detection assay, a subcombination of 4 biomarkers may be used, such as A1AT, TNF-alpha, AGP1, and CA 19-9. This 4 biomarker combination has been found to provide a diagnostic accuracy of 99.9% against healthy controls and a diagnostic accuracy of 76% against benign pancreatic disease. In certain examples of a detection assay, a subcombination of 3 biomarkers may be used, such as ApoA1, SAA, and CA 19-9. This three marker has been found to provide a a diagnostic accuracy of 93% against healthy controls and a diagnostic accuracy of 77% against benign pancreatic disease. As will be understood by one of skill in the art, smaller combinations of biomarkers may be more easily deployed in a detection assay, such as a lateral flow assay.
A person skilled in the art will appreciate that a number of methods can be used to detect or quantify the DNA/RNA/protein levels of various disease- or health-related biomarkers.
Gene expression can be measured using, for example, low-to-mid-plex techniques, including but not limited to reporter gene assays, Northern blot, fluorescent in situ hybridization (FISH), and reverse transcription PCR (RT-PCR). Gene expression can also be measured using, for example, higher-plex techniques, including but not limited, serial analysis of gene expression (SAGE), DNA microarrays. Tiling array, RNA-Seq/whole transcriptome shotgun sequencing (WTSS), high-throughput sequencing, multiplex PCR, multiplex ligation-dependent probe amplification (MLPA), DNA sequencing by ligation, and Luminex/XMAP.
A person skilled in the art will appreciate that a number of methods can be used to detect or quantify the level of RNA products of the disease- or health-related biomarkers within a sample, including arrays, such as microarrays, RT-PCR (including quantitative PCR), nuclease protection assays and Northern blot analyses.
Centralized testing platforms may be used to combine fluidics, optics, and digital signal processing with microsphere technology to deliver multiplexed assay capabilities to perform protein or nucleic acid assays quickly, cost-effectively, and accurately. For example, the Luminex (Austin, Tex.) xMAP® Technology is a centralized testing platform that enables multiplexing of biological tests (assays), reducing time, labor, and costs over traditional methods such as ELISA, western blotting, PCR, and traditional arrays. For example, such systems may perform discrete assays on the surface of color-coded beads known as microspheres, which are then read in a compact analyzer. Using multiple lasers or LEDs and high-speed digital-signal processors, the analyzer reads multiplex assay results by reporting the reactions occurring on each individual microsphere.
Due to robust multiplexing, centralized testing potentially delivers more data in less time than other bioassay products, with comparable results to ELISA and microarray. Centralized testing several other distinct advantages over traditional methods including (a) Speed/High Throughput—because each microsphere serves as an individual test, a large number of different bioassays can be performed and analyzed simultaneously; (b) Versatility—a centralized testing system can perform bioassays in several different formats, including nucleic acids and antigen-antibody binding, along with enzyme, receptor-ligand, and other protein interactions; (c) Flexibility—the technology can be customized for the user's specific needs or updated periodically by attaching a specific probe to a uniquely colored microsphere; (d) Accuracy—the technology generates real-time analysis and accurate quantification of the biological interactions; and (e) Reproducibility-high volume production of microspheres within a single lot allows assay standardization that solid-phased planar arrays cannot provide.
Centralized testing platforms both a high plex capability and high throughput at the same time. Traditional ELISA, real-time PCR and other technologies that excel at high throughput applications (greater than 1000 samples per day) lack the ability to multiplex more than five tests at a time. On the other hand, microarray technology excels in high density screening (greater than 250-plex tests), but lacks the reproducibility needed for high throughput applications. For applications requiring a throughput of up to 1000 samples per day and multiplexing from one to 500 tests per sample.
Various methods exist in the art for profiling the biomarkers associated with a particular disease state, for example pancreatic cancer. One of skill in the art will understand that the methods and techniques described below may be used to characterize a number of disease states.
Selected reaction monitoring (SRM) is a method used in tandem mass spectrometry in which an ion of a particular mass is selected in the first stage of a tandem mass spectrometer and an ion product of a fragmentation reaction of the precursor ion is selected in the second mass spectrometer stage for detection (E. de Hoffmann (1996) Journal of Mass Spectrometry. 31(2): 129-137). Multiple reaction monitoring (MRM) is the application of selected reaction monitoring to multiple product ions from one or more precursor ions (Murray, et al. (2013) Pure and Applied Chemistry. 85 (7): 1515-1609; and Kondrat, R. W. et al. (1978) Analytical Chemistry. 50(14): 2017-2021).
Parallel reaction monitoring (PRM) is an ion monitoring technique based on high-resolution and high-precision mass spectrometry. The principle of this technique is comparable to SRM/MRM, but it is more convenient in assay development for absolute quantification of proteins and peptides. It is most suitable for quantification of multiple proteins in complex samples with an attomole-level detection. PRM is based on Q-Orbitrap as the representative quadrupole-high resolution mass spectrum platform. Unlike SRM, which performs one transition at a time, PRM performs a full scan of each transition by a precursor ion, that is, parallel monitoring of all fragments from the precursor ion. PRM technology not only has the SRM/MRM target quantitative analysis capabilities, but also has the qualitative ability. The mass accuracy can reach to ppm level, which can eliminate the background interference and false positive better than SRM/MRM, and improves the detection limit and sensitivity in complex background effectively. It provides a full scan of product ions, without the need to select the ion pair and optimize the fragmentation energy, and it is easier to establish the assay. In addition, it provides a wider linear range: increased to 5-6 orders of magnitude.
Examples of methods are provided for carrying out parallel reaction monitoring (PRM) or specific mass spectrometry-SRM/MRM assays useful for developing a molecular profile for a patient, by precisely quantifying specific protease-digested peptides derived from a collection of proteins having a variety of functions and cellular locations in proteomic lysates prepared directly from patient tissue, e.g., a tumor tissue. The process and assays can be used for understanding the molecular landscape of a patient's tumor and to guide selection of optimal cancer therapeutic agents that either directly kill the tumor cells or induce, initiate, support, and/or otherwise manipulate an active and successful immune response to the patient's own tumor cells, leading to improved patient survival. Cells from a biological sample of a cancer patient, such as, for example, fresh tissues, fresh-frozen tissues, or formalin-fixed paraffin embedded (FFPE) tumor tissue, can be collected using, for example, the methodology of tissue microdissection. “Fresh-frozen” tissues for mass spectrometry analysis may include tumor specimens and normal pancreas controls. In some cases, evidence suggests superiority of fresh-frozen over FFPE for mass spectrometry (Bauden M, et al. Lab Invest. 2017 March; 97(3):279-288). However, fresh-frozen tissues may be rare and not easily attainable compared to FFPE. For tissue microarrays and immunohistochemistry, we have typically used FFPE.
A lysate for mass spectrometry analysis can be prepared from the collected cells using, for example, the Liquid Tissue® reagents and protocol (e.g. see U.S. Pat. No. 7,473,532). The lysate can be analyzed using PRM or specific SRM/MRM assays as described in more detail below, where the assays are performed individually or in multiplex, and using protein detection/quantitation data from these SRM/MRM assays to develop a molecular profile for the patient/subject. These methods and the resulting PRM or SRM/MRM assay data can be used to determine an improved or optimal treatment regimen for the patient using therapeutic agents that directly function to inhibit protein function to kill tumor cells and inhibit their growth. In addition, the PRM or SRM/MRM assay data can be used to determine an improved or optimal treatment regimen for the patient using therapeutic agents that function to initiate, modulate, effect, enhance, and/or otherwise manipulate the cancer patient immune system to kill the tumor cells by directly interacting with one or more of the proteins detected and/or quantitated by the presently described SRM/MRM assays.
Determining a patient molecular profile by the described PRM or SRM/MRM assays may be performed on a variety of patient-derived samples including but not limited to blood, urine, sputum, pleural effusion, inflammatory fluid surrounding a tumor, normal tissue, and/or tumor tissue. In a particular example, the sample is FFPE tissue, for example FFPE tumor tissue.
FFPE tissue is the most widely and advantageously available form of tissue, including tumor tissue, from cancer patients. Formaldehyde/formalin fixation of surgically removed tissue is by far the most common method of preserving cancer tissue samples worldwide and is the accepted convention in standard pathology practice. Aqueous solutions of formaldehyde are referred to as formalin. “100%” formalin consists of a saturated solution of formaldehyde (about 40% by volume or 37% by mass) in water, with a small amount of stabilizer, usually methanol, to limit oxidation and degree of polymerization. The most common way in which tissue is preserved is to soak whole tissue for extended periods of time (8 hours to 48 hours) in aqueous formaldehyde, commonly termed 10% neutral buffered formalin, followed by embedding the fixed whole tissue in paraffin wax for long term storage at room temperature. Molecular analytical methods that can analyze formalin fixed cancer tissue are the most accepted and heavily utilized methods for analysis of cancer patient tissue.
The most widely-used methodology presently applied to analyze protein expression in cancer patient tissue, especially FFPE tissue, is immunohistochemistry (IHC). IHC methodology uses an antibody to detect the protein of interest. The results of an IHC test are most often interpreted by a pathologist or histotechnologist. This interpretation is subjective and does not provide quantitative data that may be predictive of sensitivity to therapeutic agents that target specific proteins. Each pathologist running a test also may use different criteria to decide whether the results are positive or negative. In most cases, this happens when the test results are borderline, i.e. the results are neither strongly positive nor strongly negative. In other cases, cells present in one area of the cancer tissue section can test positive while cells in a different area of the cancer tissue section can test negative. Inaccurate test results may mean that patients diagnosed with cancer do not receive the best possible care. If all or a specific region/cells of tumor tissue is truly positive for a specific protein but test results classify it as negative, physicians are unlikely to administer the correct therapeutic treatment to the patient. If tumor tissue is truly negative for expression of a specified protein but test results classify it as positive, physicians may use a specific therapeutic treatment even though the patient is not only unlikely to receive any benefit but also will be exposed to the agent's secondary risks. Accordingly, there is great clinical value in the ability to precisely detect and correctly evaluate quantitative levels of specific proteins in tumor tissue so that the patient will have the greatest chance of receiving a successful treatment regimen while reducing unnecessary toxicity and other side effects.
Precise detection and correct evaluation of quantitative levels of specific proteins in tumor tissue may be effectively determined in a mass spectrometer by PRM or SRM/MRM methodology. This methodology detects and quantitates unique fragment peptides from specific proteins, including cancer biomarkers, in which the SRM/MRM signature chromatographic peak area of each peptide is determined within a complex peptide mixture present in a lysate. One method of preparing a complex biomolecule sample directly from formalin-fixed tissue is described in U.S. Pat. No. 7,473,532. In a particular example, the proteolytic enzyme trypsin may be used to fragment proteins in a sample. Quantitative levels of proteins can then be determined by the PRM or SRM/MRM methodology whereby the PRM or SRM/MRM signature chromatographic peak area of an individual specified peptide from each protein in a biological sample can be compared to the PRM or SRM/MRM signature chromatographic peak area of a known amount of a “spiked” internal standard for each of the individual fragment peptides.
In one example, the “spiked” internal standard is a synthetic version of the same exact protein-derived fragment peptide where the synthetic peptide contains one or more amino acid residues labeled with one or more heavy isotopes, such as 2H, 18O, 17O, 15N, 13C, or combinations thereof. Such isotope labeled internal standards are synthesized so that mass spectrometry analysis generates a predictable and consistent PRM or SRM/MRM signature chromatographic peak that is different and distinct from the native fragment peptide chromatographic signature peak and which can be used as comparator peak. Thus when the internal standard is “spiked” in known amounts into a protein or peptide preparation from a biological sample and analyzed by mass spectrometry, the PRM or SRM/MRM signature chromatographic peak area of the native peptide is compared to the PRM or SRM/MRM signature chromatographic peak area of the internal standard peptide, and this numerical comparison indicates either the absolute molarity and/or absolute weight of the native peptide present in the original proteomic preparation from the biological sample. Quantitative data for fragment peptides are displayed according to the amount of proteomic lysate analyzed per sample.
In order to develop and perform the PRM or SRM/MRM assay for a fragment peptide for a given protein, additional information beyond simply the peptide sequence may be utilized by the mass spectrometer. This additional information can be used to direct and instruct the mass spectrometer (e.g., a triple quadrupole mass spectrometer) to perform the correct and focused analysis of a specific fragment peptide. The additional information about a target peptide in general may include one or more of the mono isotopic mass of each peptide, its precursor charge state, the precursor m/z value, the m/z transition ions, and the ion type of each transition ion. A PRM or SRM/MRM assay may be effectively performed on a triple quadrupole mass spectrometer or an ion trap/quadrupole hybrid instrument. These types of mass spectrometers can analyze a single isolated target peptide within a very complex protein lysate containing hundreds of thousands to millions of individual peptides from all the proteins contained within a cell. This additional information provides the mass spectrometer with the correct directives to allow analysis of a single isolated target peptide within a very complex protein lysate. PRM or SRM/MRM assays also can be developed and performed on other types of mass spectrometer, including MALDI, ion trap, ion trap/quadrupole hybrid, or triple quadrupole instruments.
The foundation for a single PRM or SRM/MRM assay to detect and quantitate a specific protein in a biological sample is identification and analysis of one or more fragment peptides derived from the larger, full length version of the protein. This is because mass spectrometers are highly efficient, proficient, and reproducible instruments when analyzing very small molecules such as a single fragment peptide while mass spectrometers cannot efficiently, proficiently, or reproducibly detect and quantitate full length, intact proteins.
A candidate peptide for developing a single PRM or SRM/MRM assay for an individual protein may theoretically be any individual peptide that results from complete protease digestion, as for example digestion with trypsin, of the intact full length proteins. Many peptides are unsuitable for reliable detection and quantitation of any given protein—indeed, for some proteins no suitable peptide has yet been found. Accordingly, it is impossible to predict which is the most advantageous peptide to assay by PRM or SRM/MRM for a given protein, and therefore the specifically-defined assay characteristics about each peptide must be empirically discovered and determined. This is especially true when identifying the best PRM or SRM/MRM peptide for analysis in a protein lysate such as a lysate from FFPE tissue. The presently described PRM or SRM/MRM assays designate one or more protease digested peptides (e.g., tryptic digested peptides) for each protein whereby each peptide has been discovered to be an advantageous peptide for PRM or SRM/MRM assays.
The presently described PRM or SRM/MRM assays detect and quantitate proteins that can be used to develop a molecular profile of the patient tumor tissue microenvironment. These proteins provide a wide variety of functions and are found in a wide variety of locations within the cell. These proteins include, but are not limited to growth factors, growth factor receptors, extracellular matrix proteins, nuclear transcription factors, epithelial cell differentiation factors, cell signaling proteins, immune cell differentiation factors, cell/cell recognition factors, self vs. tumor recognition factors, immune cell activation factors, immune cell inhibiting factors, and immune checkpoint proteins. Each of these individual proteins within this collection of proteins can be, and are, expressed by a wide variety of cells in the cancer patient including, but not limited to, all varieties of solid tissue cells such as epithelial tumor cells, normal epithelial cells, normal fibroblasts, tumor-associated fibroblasts, normal endothelial cells, tumor-associated endothelial cells, normal mesenchymal cells, and tumor-associated mesenchymal cells. Each of these proteins can be expressed by a wide variety of blood-born white blood cells including but not limited to all varieties of lymphocytes, such as B cells, T cells, macrophages, dendrites, mast cells, natural killer cells, eosinophils, neutrophils, and basophils. It is well known that in many cases each of these individual proteins can be expressed by both solid tissue cells and blood-born tissue cells.
The presently described PRM or SRM/MRM assays detect and quantitate expression of unique proteins expressed by many different cell types demonstrating many different functions and residing in many different locations within the cell. Each of the assays describes at least one optimal peptide that was found to be useful for reliable and reproducible detection and measurement of a single protein, where each assay can be performed individually or in multiplex with other peptides for other proteins.
The peptides found in Tables 1 and 2 were derived from their respective designated proteins by protease digestion of all the proteins within a complex lysate prepared from cells procured from human tissue. The lysate was then analyzed by mass spectrometry to determine those peptides derived from a designated protein that are detected and analyzed by mass spectrometry. Identification of a specific preferred subset of peptides for mass spectrometric analysis is based on discovery under experimental conditions of which peptide or peptides from a protein ionize in mass spectrometry analyses of lysates, and thus demonstrate the ability of the peptide to result from the protocol and experimental conditions used in preparing a lysate to be analyzed by the methodology of mass spectrometry.
The method for measuring the level of a designated protein in a biological sample described herein (or fragment peptides as surrogates thereof) may be used as a diagnostic indicator of pancreatic cancer in a patient or subject. The results from measurements of the level of a designated protein may be employed to determine the diagnostic stage/grade/status of a pancreatic cancer by correlating (e.g., comparing) the level of the protein found in a tissue with the level of that protein found in normal and/or cancerous or precancerous tissues. The results from measurements of the level of a designated protein also may be employed to determine which cancer therapeutic agents to treat a pancreatic cancer patient with and thus the most optimal cancer treatment regimen.
The tissue protein expression landscape is highly complex whereby multiple proteins expressed by multiple types of solid tissue cells and localized/non-localized immune cells require multiple assays for multiple therapeutic agent indications. This level of protein assay complication can be analyzed by the presently described PRM or SRM/MRM assays. These assays are designed to substantially simultaneously (or at substantially the same time or substantially together) detect and quantify many different proteins having a variety of molecular functions, where the proteins include, but are not limited to soluble proteins, membrane-bound proteins, nuclear factors, differentiation factors, proteins that modulate cell-to-cell interactions, secreted proteins, immune checkpoint proteins, growth factors, growth factor receptors, cell signaling proteins, immune inhibitory proteins, cytokines, and lymphocyte-activating/inhibiting factors.
Tissue microdissection can advantageously be used to procure pure populations of tumor cells from patient tumor tissue for protein expression analysis using the PRM or SRM/MRM assays in order to determine the molecular profile that specifically defines tumor cell status for the patient. Tissue microdissection of tumor tissue can be performed using the process of laser induced forward transfer of cells and cell populations, e.g., utilizing DIRECTOR® technology.
The presently described PRM or SRM/MRM assays detect and quantitate expression of specific proteins in lysates prepared from solid tissue, e.g., tumor tissue. However, unless pure populations of cells are collected and analyzed these assays may not accurately provide detailed information about which cells express which proteins. In some cases, this is important because aberrant protein expression is common in the tumor microenvironment, as for example when tumor cells express immune inhibitory factors that are usually expressed solely by normal cells, normal lymphocytic cells, and/or tumor infiltrating lymphocytes (TILs). Thus, when expression of candidate therapeutic protein targets has been detected and quantified by the described PRM or SRM/MRM assays, a follow-up assay may be necessary to provide missing cellular localization information. The method to achieve cellular expression context is immunohistochemistry. Understanding which proteins are expressed within the tumor microenvironment and which cells express these proteins may advantageously inform optimal treatment decisions to modulate the patient's own immune response to seek out and kill the tumor cells. The presently described PRM or SRM/MRM assays and analysis process provide the ability to detect and quantify protein targets of cancer therapeutic agents directly in patient tumor tissue.
An advantageous approach for tumor cell killing is to use a combination therapy whereby immunomodulatory agents are used in combination with tumor cell targeting agents synergistically for optimal patient response. PRM or SRM/MRM assays can be used to determine the quantitative expression status in patient tumor tissue of oncoprotein targets for which inhibitory therapeutic agents have been developed.
In some examples, combining multiplex detecting and quantitating of two or more fragment peptides corresponding to SEQ ID NO: 1-46 with analysis of other oncoproteins that drive growth of the patient tumor cells can be advantageous. This can allow a targeted cancer therapeutic agent that inhibits or modulates the function of the oncoprotein to inhibit growth of the patient tumor cells to be administered to the patient in combination with an immunomodulatory cancer therapeutic agent that interacts with one or more of the proteins to initiate, enhance, manipulate, and/or otherwise modulate the cancer patient immune response to attack and kill the patient tumor cells.
Because both nucleic acids and protein can be analyzed from the same biomolecular preparation it is possible to generate additional information about drug treatment decisions from the nucleic acids in the same sample analyzed with the presently described PRM or SRM/MRM assays. A specific protein can be found by the presently described PRM or SRM/MRM assays to be expressed by certain cells at increased levels while at the same time information about the mutation status of specific genes and/or the nucleic acids and proteins they encode (e.g., mRNA molecules and their expression levels or splice variations) can be obtained. Those nucleic acids can be examined, for example, by one or more, two or more, or three or more of: sequencing methods, polymerase chain reaction methods, restriction fragment polymorphism analysis, identification of deletions, insertions, and/or determinations of the presence of mutations, including but not limited to, single base pair polymorphisms, transitions, transversions, or combinations thereof.
Disclosed herein are methods of determining if a subject has an increased risk of suffering from pancreatic cancer. The methods may comprise analyzing at least one plasma and/or blood sample from the subject to determine a value of the subject's proteomic profile and comparing the value of the subject's proteomic profile with the value of a normal proteomic profile. A change in the value of the subject's proteomic profile, over or under normal values is indicative that the subject has an increased risk of suffering from pancreatic cancer compared to a normal individual.
As used herein, the term subject or “test subject” indicates a mammal, in particular a human or non-human primate. The test subject may or may not be in need of an assessment of a predisposition to pancreatic cancer. For example, the test subject may have a condition or may have been exposed to conditions that are associated with pancreatic cancer prior to applying the methods described herein. In another example, the test subject has not been identified as a subject that may have a condition or may have been exposed to injuries or conditions that are associated with pancreatic cancer prior to applying the methods and apparatuses disclosed herein.
As used herein, the term “increased risk” is used to mean that the test subject has an increased chance of developing or acquiring pancreatic cancer compared to a normal individual. The increased risk may be relative or absolute and may be expressed qualitatively or quantitatively. For example, an increased risk may be expressed as simply determining the subject's proteomic profile and placing the patient in an “increased risk” category, based upon previous population studies. Alternatively, a numerical expression of the subject's increased risk may be determined based upon the proteomic profile. As used herein, examples of expressions of an increased risk include but are not limited to, odds, probability, odds ratio, p-values, attributable risk, relative frequency, positive predictive value, negative predictive value, and relative risk.
In one example, the increased risk of a patient can be determined from p-values that are derived from association studies. Specifically, associations with specific profiles can be performed using regression analysis by regressing the proteomic profile with pancreatic cancer. In addition, the regression may or may not be corrected or adjusted for one or more factors. The factors for which the analyses may be adjusted include, but are not limited to age, sex, weight, ethnicity, geographic location, general health of the subject, alcohol or drug consumption, caffeine or nicotine intake and the subject's apolipoprotein E (ApoE) genotype.
Increased risk can also be determined from p-values that are derived using logistic regression. Binomial (or binary) logistic regression is a form of regression which is used when the dependent is a dichotomy and the independents are of any type. Logistic regression can be used to predict a dependent variable on the basis of continuous and/or categorical independents and to determine the percent of variance in the dependent variable explained by the independents; to rank the relative importance of independents; to assess interaction effects; and to understand the impact of covariate control variables. Logistic regression applies maximum likelihood estimation after transforming the dependent into a “logit” variable (the natural log of the odds of the dependent occurring or not). In this way, logistic regression estimates the probability of a certain event occurring.
As used herein, the phrase “proteomic profile” means the combination of a subject's proteins found in the peripheral blood or portions thereof, such as but not limited to plasma or serum. The proteomic profile is a collection of measurements, such as but not limited to a quantity or concentration, for individual proteins taken from a test sample of the subject. Examples of test samples or sources of components for the proteomic profile include, but are not limited to, biological fluids, which can be tested by suitable methods described herein, and include but are not limited to whole blood, such as but not limited to peripheral blood, serum, plasma, cerebrospinal fluid, urine, amniotic fluid, lymph fluids, and various external secretions of the respiratory, intestinal and genitourinary tracts, tears, saliva, milk, white blood cells, myelomas and the like. Test samples to be assayed also include but are not limited to tissue specimens including normal and abnormal tissue.
Techniques to assay levels of individual components of the proteomic profile from test samples are well known to the skilled technician, and the methods and apparatuses of this disclosure are is not limited by the means by which the components are assessed. In one example, levels of the individual components of the proteomic profile are assessed using mass spectrometry in conjunction with ultra-performance liquid chromatography (UPLC), high-performance liquid chromatography (HPLC), and UPLC to name a few. Other methods of assessing levels of the individual components include biological methods, such as but not limited to ELISA assays.
The assessment of the levels of the individual components of the proteomic profile can be expressed as absolute or relative values and may or may not be expressed in relation to another component, a standard an internal standard or another molecule of compound known to be in the sample. If the levels are assessed as relative to a standard or internal standard, the standard may be added to the test sample prior to, during or after sample processing.
To assess levels of the individual components of the proteomic profile, a sample is taken from the subject. The sample may or may not processed prior assaying levels of the components of the proteomic profile. For example, whole blood may be taken from an individual and the blood sample may be processed, e.g., centrifuged, to isolate plasma or serum from the blood. The sample may or may not be stored, e.g., frozen, prior to processing or analysis.
In one example, the individual levels of each of the proteins are lower than those compared to normal levels. In another example, the individual levels of some of the proteins are lower than those compared to normal levels.
In other examples, the individual levels of each of the proteins are higher than those compared to normal levels. In another example, the individual levels of some of the proteins are higher than those compared to normal levels.
The levels of depletion or augmentation of the proteins compared to normal levels can vary. In one example, the levels of any one or more of the proteins is at least 1.05, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55 or 60 times lower than normal levels. In another example, the levels of any one or more of the proteins is at least 1.05, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55 or 60 times higher than normal levels. The number of “times” the levels of a protein is lower or higher over normal can be a relative or absolute number of times. In the alternative, the levels of the proteins may be normalized to a standard and these normalized levels can then be compared to one another to determine if a protein is lower or higher.
The subject's proteomic profile may be compared to the profile that is deemed to be a normal proteomic profile. The proteomic profile of an individual or group of individuals without pancreatic cancer can be used to establish a “normal proteomic profile.” In one example, a normal proteomic profile can be ascertained from the same subject having no signs (clinical or otherwise) of pancreatic cancer. In one example, a “normal” proteomic profile is assessed in the same subject from whom the sample is taken prior to the onset of pancreatic cancer. That is, the term “normal” with respect to a proteomic profile can be used to mean the subject's baseline proteomic profile prior to the onset of pancreatic cancer. The proteomic profile can then be reassessed periodically and compared to the subject's baseline proteomic profile.
Thus, the present disclosure also includes methods of monitoring the progression of pancreatic cancer in a subject, with the methods comprising determining the subject's proteomic profile more than once over a period of time. For example, some examples may include determining the subject's proteomic profile two, three, four, five, six, seven, eight, nine, 10 or even more times over a period of time, such as a year, two years, three, years, four years, five years, six years, seven years, eight years, nine years or even 10 years or longer. The methods of monitoring a subject's risk of having pancreatic cancer would also include examples in which the subject's proteomic profile is assessed during and after treatment of pancreatic cancer. In other words, also disclosed are includes methods of monitoring the efficacy of treatment of proteomic impairment by assessing the subject's proteomic profile over the course of the treatment and after the treatment.
In another example, a normal proteomic profile is assessed in a sample from a different subject or patient (from the subject being analyzed) and this different subject does not have or is not suspected of having pancreatic cancer. In still another example, the normal proteomic profile is assessed in a population of healthy individuals, the constituents of which display no pancreatic cancer. Thus, the subject's proteomic profile can be compared to a normal proteomic profile generated from a single normal sample or a proteomic profile generated from more than one normal sample.
Of course, measurements of the individual components, e.g., concentration, of the normal proteomic profile can fall within a range of values, and values that do not fall within this “normal range” are said to be outside the normal range. These measurements may or may not be converted to a value, number, factor or score as compared to measurements in the “normal range.” For example, a measurement for a specific protein that is below the normal range, may be assigned a value or −1, −2, −3, etc., depending on the scoring system devised.
In one example, the “proteomic profile value” can be a single value, number, factor or score given as an overall collective value to the individual molecular components of the profile. For example, if each component is assigned a value, such as above, the proteomic value may simply be the overall score of each individual value. For example, if 10 of the components of the proteomic profile are used to create the profile value, and five of the components are assigned values of“−2” and five are assigned values of“−1,” the proteomic profile in this example would be −15, with a normal value being, for example, “0.” In this manner, the proteomic profile value could be useful single number or score, the actual value or magnitude of which could be an indication of the actual risk of pancreatic cancer, e.g., the “more negative” or the “more positive” the value, the greater the risk of pancreatic cancer.
In another example the “proteomic profile value” can be a series of values, numbers, factors or scores given to the individual components of the overall profile. In another example, the “proteomic profile value” may be a combination of values, numbers, factors or scores given to individual components of the profile as well as values, numbers, factors or scores collectively given to a group of components. In another example, the proteomic profile value may comprise or consist of individual values, number, factors or scores for specific component as well as values, numbers, factors or scores for a group on components.
In another example, individual values from the proteins can be used to develop a single score, such as a “combined proteomic index,” which may utilize weighted scores from the individual component values reduced to a diagnostic number value. The combined proteomic index may also be generated using non-weighted scores from the individual component values. When the “combined proteomic index” exceeds (or is less than) a specific threshold level, the individual has a high risk of pancreatic cancer, whereas the maintaining a normal range value of the “combined proteomic index” would indicate a low or minimal risk of pancreatic cancer. In this example, the threshold value would be set by the combined proteomic index from normal subjects.
In another example, the value of the proteomic profile can be the collection of data from the individual measurements and need not be converted to a scoring system, such that the “proteomic profile value” is a collection of the individual measurements of the individual components of the profile.
If it is determined that a subject has an increased risk of pancreatic cancer, the attending health care provider may subsequently prescribe or institute a treatment program. Therefore, methods of screening individuals as candidates for treatment of pancreatic cancer are also provided herein. The attending healthcare worker may begin treatment, based on the subject's proteomic profile, before there are perceivable, noticeable or measurable signs of pancreatic cancer in the individual.
Similarly, methods disclosed herein may also be of use for monitoring the effectiveness of a treatment for pancreatic cancer. Once a treatment regimen has been established, with or without the use of the methods and apparatuses disclosed herein, to assist in a diagnosis of pancreatic cancer, the methods of monitoring a subject's proteomic profile over time can be used to assess the effectiveness of a pancreatic cancer treatment. Specifically, the subject's proteomic profile can be assessed over time, including before, during and after treatments for pancreatic cancer. The proteomic profile can be monitored, with, for example, a decline or an increase in the values of the profile over time being indicative that the treatment may or may not be as effective as desired.
The study described herein provides an example of a method for identifying a prognostic biomarker for use in diagnosing pancreatic cancer, however, one of skill in the art will understand that such a method may be applicable to all manner of disease states. In this example, global protein sequencing of fresh frozen pancreatic cancer and healthy pancreas tissue samples was conducted by MS to discover potential protein biomarkers. Selected candidate proteins were further verified by targeted proteomics using parallel reaction monitoring (PRM). The expression of biomarker candidates was validated by immunohistochemistry in a large tissue microarray (TMA) cohort of 141 patients with resectable pancreatic cancer. Kaplan-Meier and Cox proportional hazard modelling was used to investigate the prognostic utility of candidate protein markers.
In the initial MS-discovery phase, 165 proteins were identified as potential biomarkers. In the subsequent MS-verification phase, a panel of 45 candidate proteins was verified by the development of a PRM assay. We found brain acid soluble protein 1 (BASP1) to be significantly upregulated in pancreatic cancer and have identified it as a new biomarker for pancreatic cancer possessing largely unknown biological and clinical functions and selected this marker for further analysis. We conducted external validation by tissue microarray (TMA) and immunohistochemistry in a large cohort showed that BASP1 overexpression significantly correlated to survival and response to chemotherapy in patients with pancreatic cancer.
Bioinformatic analysis and pathway analysis linked to clinical data indicated that BASP1 interacts with Wilms tumor protein (WT1) in pancreatic cancer. TMA-based immunohistochemistry analysis showed that BASP1 was an independent predictor of prolonged survival (HR 0.468, 95% CI 0.257-0.852, p=0.013) and predicted favorable response to adjuvant chemotherapy, whereas WT1 indicated a worsened survival (HR 1.636, 95% CI 1.083-2.473, p=0.019) and resistance to chemotherapy. Interaction analysis showed that patients with negative BASP1 and high WT1 expression had the poorest outcome (HR 3.536, 95% CI 1.336-9.362, p=0.011). Bioinformatic analysis and clinical data from our study provides a basis for using BASP1 and its putative interaction partner WT1 as biomarkers for predicting outcomes in pancreatic cancer patients.
The methodological workflow of the present study is illustrated in
For MS analysis, fresh frozen pancreatic cancer tissue samples (n=10 for MS discovery, n=8 for targeted MS) were prospectively collected from patients undergoing pancreaticoduodenectomy due to tumors located in the head of the pancreas between July 2013 and April 2015 at the Department of Surgery, Slane University Hospital, Lund, Sweden. Age and gender-matched fresh frozen normal pancreas (n=10) from organ donors free of any pancreatic disease were obtained from Lund University Diabetes Center and used as healthy controls (HC). Written informed consent was obtained from participating patients. For tissue microarray (TMA) and immunohistochemistry (IHC) analysis, FFPE tissue samples (n=143) were included from a retrospective cohort of pancreatic cancer patients who underwent surgery with curative intent from 1995 to 2017 at Slane University Hospital in Lund and Malmo, Sweden. Following antibody optimization and staining, biomarker expression could be evaluated in 141 of the 143 (98.6%) of tumor samples included in the TMA. All samples were re-evaluated by a pancreatic pathologist to confirm the diagnosis and uniformity of staging. The REMARK guidelines were followed where applicable (McShane L M, Altman D G, Sauerbrei W, Taube S E, Gion M, Clark G M. Statistics Subcommittee of the NCIEWGoCD: REporting recommendations for tumour MARKer prognostic studies (REMARK). Br J Cancer 2005; 93(4):387-91).
Individual fresh frozen tissue samples were pulverized in liquid N2 and thoroughly homogenized in an extraction buffer consisting of 500 mM Tris-C1 [pH 8], 6 M guanidine-HCl in 50 mM ammonium bicarbonate (AMBIC) along with protease and phosphatase inhibitor cocktail. The obtained extracts were then subjected to 4 freeze-thaw cycles, followed by ultrasonic bath for 20 min at 0° C. The soluble proteins were then reduced with 15 mM dithiothreitol (DTT) for 60 min at 60° C., alkylated using 50 mM iodoacetamide (IAA) for 30 min at room temperature in the dark, precipitated with a sample to ethanol (99.5%) ratio of 1:9 at −20° C. The protein precipitates were dissolved in 50 mM AMBIC and digested at 37° C. overnight using Mass Spec Grade Trypsin/Lys-C Mix (Promega, Madison, Wis., USA), with an enzyme to protein ratio of 1:100. The digested samples were dried and dissolved in 50 μl 0.1% Formic Acid (mobile phase A), and the concentration was specified using Pierce quantitative colorimetric peptide assay from Thermo Scientific (Rockford, Ill., USA). Finally, to enable normalization and as a control of the chromatographic performance, 25 fmol peptide retention time mixture (PRTC) (Thermo Fisher) consisting of 15 peptides was added to each sample.
The analytical platform, including a high-performance nanoflow liquid chromatography (HPLC) system (EASY-nLCTM™ 1000) and a Plus Hybrid Quadrupole-Orbitrap mass spectrometer (Q Exactive™) equipped with a nanospray ion source (EASY-Spray™), were manufactured by Thermo Fisher Scientific (Bremen, Germany). Individual samples containing 1 μg of peptide mixture in mobile phase A were injected at a flowrate of 300 nl min-1, separated by a 132 min gradient of 5-22% acetonitrile (ACN) in mobile phase A, followed by an 18 min gradient of 22-38% ACN in mobile phase A. Subsequent separation was conducted by a two-column system including the EASY-Spray analytical column (25 cm×75 μm ID, particle size 2 μm, pore size 100 Å, PepMap C18) tandem with the Acclaim pre-column (2 cm×75 μm ID, particle size 3 μm, pore size 100 Å, PepMap C18). The Orbitrap system was operated in the positive data-dependent acquisition (DDA) mode with an automatic switch between the full scanMS and MS/MS acquisition. On the precursors with the highest intensity, 15 data-dependent higher energy collision dissociation MS/MS scans were implemented. For the peptide detection, a full MS survey scan was performed in the Orbitrap detector. The MS scans with a resolution of 70,000 at 200 m/z, recording window between 400.0 and 1600.0 m/z, and automatic gain control (AGC) target value of 1×10{circumflex over ( )}6 with a maximum injection time of 100 ms. The resolution of the data dependent MS/MS scans was fixed of 17,500 at 200 m/z, values for the AGC target of 5×10{circumflex over ( )}5 and maximum injection time was 80 ms. The normalized collision energy was set on 27.0% for all scans.
PRM analysis was performed to verify differentially expressed proteins.
One or 2 unique peptides of each targeted protein were selected from the discovery measurements, depending on detection frequencies >50%, missed cleavage=0 and p-value <0.05, along with peptide intensities and ranking of peptide spectrum matches. Finally, a spectral library of 81 selected proteins (from the 165 differentially expressed proteins as well as the proteins only detectable in one condition) including 150 peptides was created. Owing to inadequate tissue sample volume, we had to exclude 2 pancreatic cancer subjects from the PRM phase. The proteins extracted from 18 fresh frozen samples (8 pancreatic cancer samples vs. 10 healthy controls) were reduced, alkylated, and digested as described previously in sample preparation. One microgram of the sample was injected into the LC-MS/MS system, and the PRM assay was set in a time-scheduled acquisition mode with a retention time+/−5 min and resolution at 35000 (AGC target to 5×10{circumflex over ( )}5, maximum injection time of 50 ms). The chromatographic peak width was 30 s, normalized collision energy on 26.0%, and the isolation window of 2 m/z. Skyline software was used for relative quantification in the PRM study (Henderson C M, et al. Clin Chem 2018; 64(2):408-10).
Each sample was measured in duplicate by LC-MS/MS in a randomized order. The raw files generated from the duplicates were combined and evaluated using Proteome Discoverer software (Thermo Fisher) Version 1.4 focusing on high confidence peptides only. The spectra selection settings: minimum and maximum precursor mass at 350 Da and 5000 Da, respectively; signal-to-noise (s/n) threshold 1.5. Parameters for SEQUEST HT (Tabb D L. The SEQUEST family tree. J Am Soc Mass Spectrom 2015; 26(11):1814-9) were set as follows: precursor mass tolerance of 10 ppm (p.p.m); fragment mass tolerance of 0.02 Da; trypsin as the enzyme; one missed cleavage site was accepted. Based on the UniProtKB human database (Chen C, et al. Methods Mol Biol 2017; 1558:3-39), dynamic modifications were included, such as: methyl (+14.016 Da; K, R), dimethyl (+28.031 Da; K, R), acetyl (+42.011 Da; K), trimethyl (+42.047 Da; K, R), glygly (+114.043 Da; K), oxidation (+15.995 Da; M), and the fixed modification carbamidomethyl (+57.021 Da; C). The percolator was applied for the processing node, and the false discovery rate (FDR) value was set to 0.01. To quantify the peptides, the precursor ions area detector was used in the search engine (Proteome discoverer; Thermo Scientific), protein groups identified>2 peptides from all samples were considered for further analysis and only unique peptides were used for protein quantification.
Archival FFPE pancreatic cancer specimens from the larger validation cohort were subjected to TMA. Employing an automated tissue array instrument (Minicore®3, Alphelys, Plaisir, France), 4 cores of cancer tissue from each specimen (diameter at 2 mm, selected by a pathologist) were extracted and fixed into paraffin blocks. After quality control, the TMA blocks were sectioned into 3 μm thick slides for IHC analysis.
IHC was performed as described previously (Hu D, Ansari D, Zhou Q, Sasor A, Hilmersson K S, Bauden M, et al. Calcium-activated chloride channel regulator 1 as a prognostic biomarker in pancreatic ductal adenocarcinoma. BMC Cancer 2018; 18(1):1096). Briefly, after de-paraffinization, rehydration and antigen-retrieval, TMA-slides were incubated with primary antibodies (rabbit anti-human BASP1 (dilution 1:100; Cat No. HPA045218, Atlas Antibodies); mouse anti-humanWT1 (clone 6F-H2, Ready-to-Use, Cat No. IS05530-2, DAKO)) overnight at 4° C. Next, slides were incubated with second antibody (for BASP1, biotinylated goat anti-rabbit (dilution 1:200; Cat No. BA-1000, Vector Laboratories, Burlingame, Calif.); for WT1, biotinylated horse anti-mouse (dilution 1:200, Vector Laboratories, Cat No. BA-2000)) followed by staining with avidin-biotin-peroxidase complex (Vectastain Elite ABCHRP Kit, Cat No. PK-6100, Vector Laboratories, Burlingame, Calif.). The sections were then incubated with chromogen diaminobenzidine (DAB) (Cat No. SK-4100, Vector Laboratories, Burlingame, Calif.) and counter stained with haematoxylin and mounted with xylene based medium. The IHC scoring was performed by an experienced pancreas pathologist (A.S.) who was blinded to the clinical information. Scoring was based on the percentage of positive tumor cells and the staining intensity. IHC results were scored as follows: 0=negative; 1=weak; 2=moderate; and 3=strong. For tumors that showed heterogeneous staining, the predominant pattern was taken into account for scoring.
The human pancreatic cancer cell line, PANC-1, was purchased from ATCC-LGC Standards (Manassas, Va., USA). The cells were maintained in Dulbecco's modified Eagle's medium (DMEM; Life Technologies, CA, USA) supplemented with 10% fetal bovine serum and antibiotics (100 U/ml penicillin and 100 μg/ml streptomycin) in a humidified 5% CO2 atmosphere at 37° C.
For investigating intracellular localization, PANC-1 cells were cultured (8×10{circumflex over ( )}3 cells/well) in eight-well chamber slides (Lab-Tek II Chamber Slide System, Nunc). After 48 h, the cells were fixed with 4% formaldehyde, then permeabilized with 1% Triton X-100, blocked with 5% goat serum and incubated with mouse anti-human WT1 (clone 6FH2, Ready-to-Use; Cat No. IS05530-2, DAKO) at room temperature for 2 h. After washing, cells were moved into dark environment, Goat anti-Mouse Alexa Fluor 594 (dilution 1:500; Cat No. A11032, Invitrogen) was added at room temperature for 1 h. Subsequently, the cells were blocked with 5% donkey serum and incubated with rabbit anti-human BASP1 (dilution 1:50; Cat No. HPA045218, Atlas Antibodies) at room temperature for 2 h. Following washing, Donkey-anti-Rabbit Alexa Fluor 488 (dilution 1:500; Cat No. A21206, Invitrogen) was added at room temperature for 1 h. Finally, the cells were incubated with DAPI to stain the nuclei. Positive staining was visualized using a Nikon Eclipse 80i microscope with a Nikon DS-Qi1 camera and analyzed using NIS-Elements software (Nikon Instruments Inc.; Melville, N.Y., USA).
Perseus software (Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein M Y, Geiger T, et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods 2016; 13(9):731-40) version 1.6.0.7 was used for the statistical analysis of the MS results. The protein intensities were log 2 transformed and normalized by subtracting the median intensity of all the proteins per sample. Replacing the missing values from a normal distribution was performed though data imputation by using the following settings: width 0.3 and downshift 0. A Two-Sample Student's t-test (two-tailed) followed by permutation-based FDR correction was performed to compare protein levels between the groups. The settings included S0=2, which is a parameter used to calculate the relative difference (ratio of change in protein expression to standard deviation) between group means. It defines the within groups variance, the relative importance of the resulted p-values, and the difference between means of log 2 intensities (Tusher V G, et al. Proc Natl Acad Sci 2001; 98(9):5116-21). Finally, the proteins with FDR adjusted p-value (or q-value) of 0.01 were considered as differentially expressed.
For bioinformatic analysis of networks involving the biological relationship between BASP1 and WT1, the Ingenuity Pathway Analysis software (IPA, Qiagen, Inc. Redwood City, Calif., USA) was used. This toolset builds upon a literature-derived relationship knowledge base. A network involving all direct interactors of these proteins was built and analyzed for pathway enrichment and functional annotations. Additionally, differentially expressed proteins between pancreatic cancer and healthy controls samples from MS discovery were mapped onto the BASP1/WT1 network. Subcellular localization of significantly up- and down-regulated proteins in pancreatic cancer versus healthy control samples was manually assessed using UniProt (On the World-Wide Web at uniprot.org/). PANTHER (Mi H, et al. Nucleic Acids Res 2010; 38(suppl_1): D204-10), also on the World-Wide Web at pantherdb.org/) was employed to identify gene ontology terms of the significantly differentially expressed proteins.
For IHC analysis, the correlation between the expression levels of protein biomarkers and clinicopathological parameters was estimated using the Mann-Whitney U test for continuous variables and Fisher's exact test or χ2 for categorical variables. Kaplan-Meier analysis was used to calculate the cumulative probability of overall survival (OS), log-rank tests were used to evaluate the differences. Prognostic factors were calculated using univariable and multivariable analysis (Cox proportional hazards regression model). A value of p<0.05 was considered statistically significant.
Statistical evaluation was conducted with Perseus software version 1.6.0.7, SPSS version 23.0 (SPSS Inc., Chicago, Ill., USA), GraphPad Prism v.7 (La Jolla, Calif., USA), and R (Team R C. R: A language and environment for statistical computing; 2013) programming language version 3.5.1 (R Foundation for Statistical Computing, on the World-Wide Web at: r-project.org/).
Representative fresh frozen pancreatic cancer (n=10) and healthy control (n=10) tissue samples were analyzed using a LC-MS/MS platform. A total of 4138 proteins were identified and 2950 proteins were quantified with one or more unique peptides (see Table A).
Among the quantified proteins, 2264 proteins were present in the pancreatic cancer group and 2354 proteins in the healthy control group, respectively. To demonstrate the general pattern of protein abundance variation within and between different groups, a two-dimensional Principal Component Analysis (PCA) was performed based on all quantified proteins by an online tool ClustVis (Metsalu T, et al. Nucleic Acids Res 2015; 43(W1):W566-70). Using the log 2-ratio of each sample over the mean of all samples, a complete separation of the pancreatic cancer and healthy control groups was observed (
By employing the criteria of FDR adjusted p-value (or q-value) of 0.01, S0=2, the number of peptides N1 and the fold change N2 as a cut-off, a total of 165 proteins with two or more unique peptides were significantly differentially expressed between the two experimental groups (
To verify the differential expression changes of potential protein biomarkers from MS discovery, PRM was employed based on the same samples from the MS discovery phase (n=8 in the pancreatic cancer group and n=10 in the healthy control group). Eighty-one proteins with one or two unique peptides for each protein were selected and a panel of 45 proteins were successfully detected and quantified. Among these proteins, 17 proteins were significantly up-regulated (p<01), while 28 proteins were down-regulated in pancreatic cancer versus healthy controls, respectively (Tables 1 and 2, below). From the panel of 45 verified candidates, 16 extracellular proteins emerged that could theoretically be detected in serum and potentially be applied in noninvasive diagnosis and/or prognosis prediction, including S100A6, TF, FBLN1, HYOU1, PNLIP, P4HB, AHSG, PLA2G1B, AGP1, PRSS1, PRSS2, APOA1, ALB, SERPINAL CLPS, and COL14A1 as previously reported by our group (Zhou Q, et al. Alpha-1-acid glycoprotein 1 is a diagnostic and prognostic biomarker for pancreatic cancer; 2019). Subsequently, a consensus clustering heatmap was created based on the 45 verified proteins and a clear discrimination between pancreatic cancer and healthy controls was observed (
BASP1 is a neuron enriched Ca(2+)-dependent calmodulin-binding protein with unknown function in pancreatic cancer. BASP1 was established as a top-ranked protein, being significantly up-regulated in the pancreatic cancer group by a fold change of 11.24, p=9E-08 (
In order to obtain an unbiased overview of the BASP1 functional relationships in a biological context, Ingenuity Pathway Analysis (IPA) was used to create a network involving all proteins with direct relationships (e.g. physical interaction or direct activation) to BASP1. This analysis, building upon a literature-derived relationship knowledge base, yielded a network including 412 proteins that were significantly enriched and involved in several canonical pathways (e.g., pancreatic adenocarcinoma signaling, regulation of the epithelial-mesenchymal transition pathway, ILK signaling, as well as tumorigenic conditions (e.g. apoptosis, cell migration, angiogenesis). Furthermore, among the top upstream regulators automatically identified by the IPA algorithm for the BASP1 interactor set, several well known tumor-related signaling proteins emerged (e.g., TP53, TNF, TGFB1, EGF, HRAS).
The pathway analysis may suggest that the link between BASP1 and pancreatic cancer is via WT1, and there are 21 proteins from the pancreatic adenocarcinoma signaling pathway that interact with WT1 (enrichment p-value 3E-16,
The expression levels of BASP1 and WT1 were assessed in a larger cohort of pancreatic cancer patients by TMA-IHC. The clinical characteristics of the pancreatic cancer patients are shown in Table 3, below. Based on the validation cohort, 141 patients were successfully scored for BASP1 and 139 patients for WT1, respectively. Both markers were evaluable in 137 patients. In the BASP1 cohort (n=141), 15 (10.6%) tissue samples from pancreatic cancer patients showed negative staining (Score 0) and 126 (89.4%) samples displayed positive staining, where 25 (17.7%) samples were scored as weak (Score 1), 66 (46.8%) as moderate (Score 2), and 35 (24.8%) as strong (Score 3). The majority of the staining was observed accentuated in the cytoplasm/plasma membrane (PM), accompanied by weak nuclear staining (
In order to study the dual expression patterns of BASP1 and WT1 in human pancreatic cancer cell line, immunofluorescence staining of BASP1 and WT1 in PANC-1 cell line was also performed. In accordance with the IHC results, BASP1 was mostly expressed in cytoplasm and PM, while WT1 was detected in the cytoplasm and mostly with perinuclear localization (
Kaplan-Meier analysis showed that pancreatic cancer patients with positive BASP1 expression had significantly prolonged overall survival (OS) compared to patients with negative BASP1 expression (median survival, 27.7 vs. 13.3 months, respectively, p=0.022,
In the BASP1 cohort, patients with high expression of BASP1 (Score 3) exhibited significantly improved OS when they received adjuvant chemotherapy compared to those without adjuvant chemotherapy (median survival, 40.5 vs. 7.2 months, respectively, p=0.020,
Kaplan-Meier analysis revealed that patients in the high WT1 expression (Score 3) group had significantly shorter OS compared to those in the low WT1 expression (Score 0, 1, and 2) group (median survival, 22.2 vs. 25.7 months, respectively, p=0.028,
In pancreatic cancer patients with strong expression of WT1, adjuvant chemotherapy displayed no significant impact on OS (p=0.335,
Patients with Negative BASP1 and High WT1 Expression have the Poorest Outcome
To examine the potential biological cross-talk between BASP1 and WT1 in terms of patient survival, subgroup functionality analysis of these prognostic markers was performed. For patients with negative expression of BASP1, the high WT1 expression group had significantly reduced OS compared to the low WT1 expression group (median survival, 9.4 vs. 20.4 months, respectively, p=0.022,
Moreover, for patients with high WT1 expression, the positive BASP1 expression group presented significantly prolonged OS compared to the BASP1 negative group (median survival, 25.8 vs. 9.4 months, respectively, p=0.00012,
As described above, particularly in relation to the examples described above in Tables 1-4,
Carbohydrate antigen 19-9 (CA 19-9) is the sole blood-based biomarker approved by the FDA for clinical management of pancreatic cancer. However, CA 19-9 has a limited sensitivity (79%) and specificity (82%) for diagnosis of pancreatic cancer. For example, CA 19-9 levels can be elevated in several benign conditions and 5-7% of the population who are Lewis antigen negative do not express CA 19-9. Hence, CA 19-9 generally is not recommended as a screening test, but only for disease monitoring during treatment. Thus, as explained above, new markers are needed to enhance pancreatic cancer diagnosis, preferably by non-invasive methods. As will be described below, proteomic technology may be used to identify blood-based biomarkers that can aid in the detection of early-stage pancreatic cancer. These proteins can be combined with CA 19-9 to enhance diagnostic performance and they can be measured as an inexpensive, accurate and portable method of detecting pancreatic cancer.
As explained in the non-limiting example above, a number of proteins relevant to distinguishing pancreatic cancer tissue (fresh-frozen) versus normal controls were identified (2950 quantified proteins, see Table A). Additionally, certain combinations of differentially expressed biomarkers may be used to more easily distinguish between healthy tissue and pancreatic cancer, using biomarkers collected from a particular human source such as blood, serum, plasma, healthy or non-healthy tissue. In brief, 300 samples were tested, including 100 pancreatic cancer samples and 200 healthy samples.
From the study, the following 18 protein markers, also described above in relation to
An analysis of all 18 markers provides a diagnostic accuracy of 98.6% against healthy controls. Consequently, in certain examples of a method for detecting pancreatic cancer, some or all of these biomarkers may be used.
As described above in relation to
The analysis was conducted using the statistical programming language R. Spearman's rank correlation between each pair of variables was calculated using the cor function in base R to identify any potential issues with multicollinearity prior to calculating any models. No correlations above 70% were observed. The performance of each individual biomarker, in the form of Receiver Operator Characteristic (ROC) curves, was calculated using the R function pROC for a variety of comparisons between the diagnostic groups listed above. For multivariate analysis, logistic regressions were conducted using the R function lrm. This was done for the comparison Pancreatic Cancer versus Healthy (PCvH), on all 12,615 possible combinations of the variables up to panels of five.
For each combination of variables for each of the above comparisons, the following values were calculated:
and the results tabulated. Logistic Regression models were held to be valid if the p-value for the model, and the p-value for each variable within the model, were all less than 0.05. Models not meeting the above criteria were discarded, and the remainder ordered in decreasing order of AUC. 749 valid models were found, with AUCs ranging from 61.1% to 98.1%.
Cross-validation (k=3, n=100) was performed on these models by dividing into randomly selected thirds, training on two thirds and testing on the final third. This was done for each third in turn. This process was repeated 100 times and the AUC calculated. An average of these 300 AUCs was then computed as the cross-validated AUC. For each of these eleven models the cross-validated AUC was calculated to be between 0.44% and 1.20% lower than the AUC of the model for that panel calculated on all of the data, which is a good result for models trained on this kind of data. The high performance of the models does not therefore rely on overtraining.
Referring to
Any 2 or more marker combination comprising at least:
This combination provided a diagnostic accuracy of at least 95.8% against healthy controls.
Any 3 or more marker combination comprising at least:
At least one of Complement C2 and Complement component 3 This combination provided a diagnostic accuracy of at least 96.6% against healthy controls.
Any 4 or more marker combination comprising at least:
This combination provided a diagnostic accuracy of at least 97.5% against healthy controls.
Any 5 or more marker combination comprising at least:
This combination provided a diagnostic accuracy of at least 97.5% against healthy controls.
5 marker combination
This combination provided a diagnostic accuracy of 98.1% against healthy controls.
Any 4 or more marker combination comprising at least:
This combination provided a diagnostic accuracy of at least 97.0% against healthy controls.
Any 5 or more marker combination comprising at least:
This combination provided a diagnostic accuracy of at least 97.1% against healthy controls.
Any 3 or more marker combination comprising at least:
This combination provided a diagnostic accuracy of at least 96.6% against healthy controls.
Any 4 or more marker combination comprising at least:
This combination provided a diagnostic accuracy of at least 96.9% against healthy controls.
Any 5 or more marker combination comprising at least:
Any 3 or more marker combination comprising at least:
This combination provided a diagnostic accuracy of at least 96.5% against healthy controls.
Any 2 or more marker combination comprising at least:
This combination provided a diagnostic accuracy of at least 92.8% against healthy controls.
From a follow up study, the following 15 protein markers in patients' blood were found to be particularly strong indicators of pancreatic cancer:
In some examples of a method for detecting pancreatic cancer, individual biomarkers listed in Tables 5 and 6, or a combination of biomarkers thereof, may be used. In some examples, 1 of the biomarkers may be used. In other examples, 2, 3, 4, 5, 6, 7, 9, 9, 10 or more of these markers may be used in combination as a method of detecting pancreatic cancer.
While the present description sets forth specific details of various examples, it will be appreciated that the description is illustrative only and should not be construed in any way as limiting. Furthermore, various applications of such examples and modifications thereto, which may occur to those who are skilled in the art, are also encompassed by the general concepts described herein. Each and every feature described herein, and each and every combination of two or more of such features, is included within the scope of the present invention provided that the features included in such a combination are not mutually inconsistent.
All figures, tables, and appendices, as well as patents, applications, and publications, referred to above, are hereby incorporated by reference.
Some examples have been described in connection with the accompanying drawings. However, it should be understood that the figures are not drawn to scale. Distances, angles, etc. are merely illustrative and do not necessarily bear an exact relationship to actual dimensions and layout of the devices illustrated. Components can be added, removed, and/or rearranged. Further, the disclosure herein of any particular feature, aspect, method, property, characteristic, quality, attribute, element, or the like in connection with various examples can be used in all other examples set forth herein. Additionally, it will be recognized that any methods described herein may be practiced using any device suitable for performing the recited steps.
For purposes of this disclosure, certain aspects, advantages, and novel features are described herein. It is to be understood that not necessarily all such advantages may be achieved in accordance with any particular example. Thus, for example, those skilled in the art will recognize that the disclosure may be embodied or carried out in a manner that achieves one advantage or a group of advantages as taught herein without necessarily achieving other advantages as may be taught or suggested herein.
Although these inventions have been disclosed in the context of certain preferred examples and examples, it will be understood by those skilled in the art that the present inventions extend beyond the specifically disclosed examples to other alternative examples and/or uses of the inventions and obvious modifications and equivalents thereof. In addition, while several variations of the inventions have been shown and described in detail, other modifications, which are within the scope of these inventions, will be readily apparent to those of skill in the art based upon this disclosure. It is also contemplated that various combination or sub-combinations of the specific features and aspects of the examples may be made and still fall within the scope of the inventions. It should be understood that various features and aspects of the disclosed examples can be combined with or substituted for one another in order to form varying modes of the disclosed inventions. Further, the actions of the disclosed processes and methods may be modified in any manner, including by reordering actions and/or inserting additional actions and/or deleting actions. Thus, it is intended that the scope of at least some of the present inventions herein disclosed should not be limited by the particular disclosed examples described above. The limitations in the claims are to be interpreted broadly based on the language employed in the claims and not limited to the examples described in the present specification or during the prosecution of the application, which examples are to be construed as non-exclusive.
| Number | Date | Country | Kind |
|---|---|---|---|
| 1930339-5 | Oct 2019 | SE | national |
| 2030164-4 | May 2020 | SE | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/SE2020/050984 | 10/15/2020 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 62923336 | Oct 2019 | US |
