Extracelluar Vesicle Biomarkers for Bladder Cancer

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

The research was supported by the NIH grant R44CA239845.

SEQUENCE LISTING, A TABLE, OR A COMPUTER PROGRAM

Not applicable.

FIELD OF THE INVENTION

This invention relates generally to a method to isolate proteins and phosphoproteins from biofluids, such as urine, for biomarker discovery or for clinical detection. More particularly, this invention relates to non-invasive early disease diagnosis, disease monitoring and disease classification. In one aspect, this invention relates to unique proteins and phosphoproteins capable of differentiating bladder cancer urine from healthy urine and inflammation control urine. In another aspect, this invention relates to early-stage detection of bladder cancer by urine test.

BACKGROUND

Bladder cancer is the most common cancer of the urinary tract, affecting close to 400,000 people worldwide (72). Despite not being the highest-incidence, it is the most expensive cancer to treat per patient in the U.S. due to the necessary on-going treatment and monitoring with a re-occurrence rate of 50-80% (73). Most of the current bladder cancer marker tests depend on invasive cystoscopy or the presence of exfoliated cancer cells in urine. The typical sensitivity for current tests is 40-80% (74,75), as a result, low tumor burden (low-grade tumors), tumor heterogeneity, or tumor cells that do not exfoliate into urine leave room for improvement in early detection. While some urine-based tests have been approved by the FDA, none of them are routinely used or incorporated into clinical guidelines due to opportunities for improvement in sensitivity (76). Recent studies found that when given a choice between a bladder cancer molecular marker test with <90% sensitivity or cystoscopy (the invasive current standard test for bladder cancer detection), the patients and urologists chose the invasive cystoscopy (77). So, in order to fulfill the market need, a new non-invasive cancer test needs to perform with >90% sensitivity.

Currently the most widespread method for clinical cancer profiling and diagnosis involves a tumor biopsy, an invasive and painful procedure, and one that certainly is impractical for early-stage detection. As cancer becomes a more chronic disease that requires active monitoring over longer periods of time, tissue biopsies on a continuous basis are no longer a realistic scenario. As a result, “liquid biopsies”—analysis of biofluids such as plasma, serum, urine—have gained much attention as a potentially useful source of diagnostic biomarkers. Liquid biopsies offer numerous advantages for a clinical analysis, including non-invasive collection, a suitable sample source for longitudinal disease monitoring, better screenshot of tumor heterogeneity, higher stability and sample volumes, faster processing times, lower rejection rates and cost. However, the most common focus of liquid biopsy—CTCs and ctDNA—still have room for improvement. Improvements can be made with the heterogeneity and extreme rarity of the circulating tumor cells (CTCs) (1). Similarly, circulating DNA (ctDNA) is highly fragmented and exists in very small, often not detectable amounts. This makes opportunity for early disease detection tests development. For example, in bladder cancer, CTCs were found in only 23% of the preoperative patients and did not have any predictive value for survival (2). As a result, only a limited number of effective clinical CTC or ctDNA diagnostics tests are currently on the market. To date, only two such in vitro diagnostics (IVD) tests have been approved by the FDA—Janssen Diagnostics' CellSearch and Cobas EGFR Mutation Test. However, negative results from these tests still require a follow-up biopsy. The lack of other approvals over the past decade further highlights the need for non-invasive early cancer detection tests with high sensitivity.

A new field has generated a lot of interest over the past few years—profiling of cell-secreted extracellular vesicles (EVs). EVs offer all the same attractive advantages of a liquid biopsy offered by CTCs and ctDNA. These generally include smaller size exosomes derived from multivesicular endosome-based secretions, and microvesicles (MVs) derived from the plasma membrane (3-5). The EVs provide an effective and ubiquitous method for intercellular communication and removal of excess materials and are utilized by every cell type studied to date. As these are shed into every biological fluid and embody a good representation of their parent cell, analysis of the EV cargo has great potential for biomarker discovery and disease diagnosis (6). Notably, researchers have also found many differentiating characteristics of the cancer cell-derived cargo, including gene mutations, active miRNA and proteins, which possess metastatic properties (7-11).

Particularly promising are the findings that these EV-based disease markers can be identified well before the onset of symptoms or physiological detection of a tumor. This makes them favorable candidates for early-stage cancer and other disease detection. In addition, EVs are membrane-covered nanoparticles, which protects the inside contents from external proteases and other enzymes (12-14). Applicants reason that these features make EVs a promising source to advance proteins and phosphoproteins as disease markers, considering that many phosphorylation events directly reflect molecular and physiological status of a tumor (15, 16). Despite some encouraging success in the analysis of exosomes for DNA, RNA and protein content, the methods and data for examination of EV phosphoproteomes are not as far along in development. The studies carried out to date include either data from urine, only 14 phosphoproteins identified from 400 mL urine (17), or 82 phosphoproteins identified through analysis of cell culture media, which may not be directly relevant to clinical biomarkers (18). Besides Applicants' recent publications (19, 20, 80), no other fruitful phosphoprotein analysis studies using plasma, urine or cerebrospinal fluid (CSF) are known.

Current EV/Exosome Analyses.

Given the immaturity of EV analysis, a standardized method for collecting and processing EVs has not yet been developed (21). Most studies rely on differential centrifugation, with ultracentrifugation (UC) as the final step. However, this approach has room for advancement for use in a clinical setting due to opportunities for improvement in reproducibility (11, 22). In addition, multiple publications have shown that the exosome recovery rate after ultracentrifugation is only about 5-25% (23-25). Several other groups have published and commercialized new methods for EV isolation, which include polymer-induced precipitation (26, 27), antibody-based capture (28, 29), affinity filtration (30), size-exclusion chromatography (25, 31), among others. Each one has its own opportunities for enhancement, including recovery rate (usually similar or less than ultracentrifugation) and contamination levels (22, 24, 25, 30, 32-37). While these can certainly be used as a faster alternative to UC, at 5-25% published yields, their efficiency of isolation still leaves much room for improvement. High levels of free protein, which may not be a major concern for RNA/DNA analysis (focus of the majority of exosome researchers), provides another avenue for development of the current methods for the proposed phosphoproteome analysis. While genetic testing is very valuable, it could greatly benefit from an additional layer of biological information.

The ability to detect the genome output—active proteins—can provide useful real-time information about the organism's physiological functions and disease progression, particularly in cancer. Oncologists understand the value of protein testing and immunoassays and can easily interpret the results. Compared to gene panel testing, immunoassays are also relatively inexpensive and are more likely to get reimbursed. Nonetheless, the genetic screens are currently dominating the new biomarkers market and the area of molecular diagnostics because the technology for genomic readout and reproducible quantitation is readily available and highly robust.

Protein phosphorylation is a key control mechanism for cellular regulatory pathways, and one often targeted by drug developers to create inhibitors that block signaling pathways involved in cancer and other diseases. However, due to active phosphatases in biofluids, there are few detectable phosphoproteins available for disease status analysis. No successful urine phosphoproteomics results have been reported, besides a recently accepted manuscript (20).

BRIEF SUMMARY OF THE INVENTION

The invention now will be described more fully hereinafter with reference to the accompanying drawings, which are intended to be read in conjunction with both this summary, the detailed description and any preferred and/or particular embodiments specifically discussed or otherwise disclosed. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided by way of illustration only and so that this disclosure will be thorough, complete and will fully convey the full scope of the invention to those skilled in the art.

Motivated by the urgent need to develop better biomarkers for early non-invasive diagnosis of bladder cancer, we implemented the approach to discover EV biomarkers directly from urine samples. We applied Extracellular Vesicles total recovery and purification (EVtrap) EV enrichment method to urine samples to isolate EVs for subsequent liquid chromatography—mass spectrometry analysis. EVtrap enables the capture of EVs onto functionalized magnetic beads modified with a combination of hydrophilic and lipophilic groups that have a unique affinity toward lipid-coated EVs (Wu et al. 2018). Over 95% recovery yield can be achieved by EVtrap with less contamination from soluble proteins, a significant improvement over current commercially available methods as well as ultracentrifugation.

Processing and enrichment of EVs through EVtrap enabled the removal of soluble proteins, retaining vesicle associated proteins which are more stable in circulation and have enhanced signals from cancer tissues. The protein profiles in EV concentrates are different from protein profiles naturally occurring in patient urine.

In one aspect, this disclosure is related to a robust method for the identification and detection of new biomarkers based on proteins and protein phosphorylation—a true measure of dynamic activity and cellular signaling, for the purposes of disease diagnosis, prognosis, detection, monitoring, patient stratification, drug response analysis, therapy selection, or the like.

The proposed method introduces a novel platform technology to isolate proteins and phosphoproteins from biofluids, such as urine, for biomarker discovery or for clinical detection.

In another aspect, this disclosure is related to a method that successfully demonstrates the feasibility of developing biofluid-derived EV phosphoproteins for cancer profiling. It has tremendous transformative potential for early cancer diagnosis, monitoring and classification based on actual activated pathways using urine as the source.

Further, once fully established, the method of the present disclosure can be implemented by scientists worldwide to analyze the direct signaling networks for a cancer of interest in a non-invasive manner.

Furthermore, once fully established, these new biomarkers can be employed either isolated or as part of a panel of biomarkers as a liquid biopsy in clinical scenarios: (1) as a surveillance test in high-risk patients, such as those with high-risk cystic diseases, hereditary risk of cancer, among others or (2) as a liquid biopsy for the longitudinal monitoring of treatment response in patients with already established cancer diagnosis.

In yet another aspect, this disclosure relates to a biomarker panel for detection and monitoring of bladder cancer. The approach will enable a truly non-invasive test and the first example of using phosphoproteins for early cancer diagnostics, especially in liquid biopsy setting.

Still further, it is envisioned to further apply this innovative procedure to validate and fully develop pre-determined biomarkers panels.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-mentioned and other features of this disclosure, and the manner of attaining them, will become more apparent and the disclosure itself will be better understood by reference to the following description of embodiments of the disclosure taken in conjunction with the accompanying drawings, wherein:

FIG. 1A is the comparison between ultracentrifugation (UC) and EVtrap for exosome capture, illustrated by the detection of CD9 exosome marker using Western Blot (WB).

FIG. 1B is the comparison between ultracentrifugation (UC) and EVtrap for exosome capture, illustrated by the quantitation of the WB data in FIG. 1A as a percent recovery from the control sample (n=5).

FIG. 2A is the quantitative exosome capture comparison by CD9 Western Blot between ultracentrifugation (100K UC), EVtrap and three commercial methods.

FIG. 2B is the silver stain total protein contamination comparison of the same samples from FIG. 2A.

FIG. 3 is the test of EVtrap procedure reproducibility carried out by two researchers over 5 days.

FIG. 4A is the LC-MS total proteome analysis of 100K UC and EVtrap samples, illustrated by (A) the quantitation of 13 common exosome proteins and (B) for 5 free urine proteins.

FIG. 4B is the LC-MS total proteome analysis of 100K UC and EVtrap samples, illustrated by (A) the quantitation of 13 common exosome proteins and (B) for 5 free urine proteins.

FIG. 4C is the LC-MS total proteome analysis of 100K UC and EVtrap samples, illustrated by the fold increase in total proteome intensity of known exosome markers compared to UC sample.

FIG. 5A is the LC-MS phosphoproteomic analysis of 100K UC and EVtrap samples, illustrated by the total number of unique phosphopeptides and phosphoproteins identified.

FIG. 5B is the LC-MS phosphoproteomic analysis of 100K UC and EVtrap samples, illustrated by the fold increase in total phosphoproteome intensity from FIG. 5A LC-MS data (EVtrap vs. UC).

FIG. 6A is the total quantitative data of identified and quantified proteins and phosphoproteins, with the inclusion of proteins that are increased at least 4-fold in bladder cancer urine compared to healthy and inflammation controls.

FIG. 6B is the quantitative data of total EV markers, proteins and phosphoproteins that are increasing in bladder cancer urine compared to healthy and inflammation controls.

FIG. 6C is the log-scale heatmap analysis of select proteins capable of differentiating bladder cancer urine from healthy and inflammation controls.

FIG. 7 is the box-and-whisker plots for select proteins and phosphoproteins capable of differentiating bladder cancer urine from healthy and inflammation controls (log 2 intensity scale). Horizontal line represents Not Detectable (N.D.) in those samples.

FIG. 8 is the ROC curve for Protein Marker A of FIG. 7.

FIG. 9 is the illustration of the EVtrap magnetic capture of EVs.

FIG. 10 is the box-and-whisker plots for reverse phase protein assay (RPPA) data of selected 4 protein markers capable of differentiating bladder cancer urine from healthy and inflammation controls (normal urine n=24; bladder cancer urine n=20). The data was normalized to the CD9 RPPA signal within each sample.

FIG. 11A is the volcano plot analysis of urine EV proteins upregulated in bladder cancer urine compared to healthy controls.

FIG. 11B is the log-scale heatmap analysis of select proteins capable of differentiating bladder cancer urine from healthy controls.

FIG. 11C is the volcano plot analysis of urine EV phosphoproteins upregulated in bladder cancer urine compared to healthy controls.

FIG. 11D is the log-scale heatmap analysis of select phosphoproteins capable of differentiating bladder cancer urine from healthy controls.

FIG. 12 is the box-and-whisker plots for select proteins and phosphoproteins capable of differentiating bladder cancer urine from healthy controls (log 2 intensity scale).

FIG. 13A shows Ingenuity Pathway Analysis (IPA) of the bladder cancer urine EV phosphoproteomics data, revealing the top cancer networks upregulated.

FIG. 13B shows IPA ontology illustration of the upregulated and downregulated proteins and phosphoproteins from our data known to be linked to bladder cancer.

FIG. 14A is the volcano plot analysis of urine EV proteins upregulated and downregulated in bladder cancer urine compared to healthy controls from the new validation cohort of 176 patients.

FIG. 14B is the log-scale heatmap analysis of select proteins capable of differentiating bladder cancer urine from healthy controls from the new validation cohort of 176 patients.

FIG. 15 shows the accuracy of the training and test sets of samples for top bladder cancer biomarkers validated from the new 176-patient cohort of samples.

FIG. 16 shows the ROC curve analysis of the top markers validated in this experiment from the new 176 cohort group of patients.

DETAILED DESCRIPTION OF THE INVENTION

The following detailed description includes references to the accompanying drawings, which forms a part of the detailed description. The drawings show, by way of illustration, specific embodiments in which the invention may be practiced. These embodiments, which are also referred to herein as “examples,” are described in enough detail to enable those skilled in the art to practice the invention. The embodiments may be combined, other embodiments may be utilized, or structural, and logical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense.

Before the present invention of this disclosure is described in such detail, however, it is to be understood that this invention is not limited to particular variations set forth and may, of course, vary. Various changes may be made to the invention described and equivalents may be substituted without departing from the true spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation, material, composition of matter, process, process act(s) or step(s), to the objective(s), spirit or scope of the present invention. All such modifications are intended to be within the scope of the disclosure made herein.

Unless otherwise indicated, the words and phrases presented in this document have their ordinary meanings to one of skill in the art. Such ordinary meanings can be obtained by reference to their use in the art and by reference to general and scientific dictionaries.

References in the specification to “one embodiment” indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

The following explanations of certain terms are meant to be illustrative rather than exhaustive. These terms have their ordinary meanings given by usage in the art and in addition include the following explanations.

Unless otherwise stated, a reference to a compound or component includes the compound or component by itself, as well as in combination with other compounds or components, such as mixtures of compounds.

As used herein, the term “and/or” refers to any one of the items, any combination of the items, or all of the items with which this term is associated.

As used herein, the singular forms “a,” “an,” and “the” include plural reference unless the context clearly dictates otherwise.

As used herein, the terms “include,” “for example,” “such as,” and the like are used illustratively and are not intended to limit the present invention.

As used herein, the terms “preferred” and “preferably” refer to embodiments of the invention that may afford certain benefits, under certain circumstances. However, other embodiments may also be preferred, under the same or other circumstances.

Furthermore, the recitation of one or more preferred embodiments does not imply that other embodiments are not useful and is not intended to exclude other embodiments from the scope of the invention.

It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element without departing from the teachings of the disclosure.

All publications, patents and patent applications cited herein, whether supra or infra, are hereby incorporated by reference in their entirety to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference.

While the invention has been described above in terms of specific embodiments, it is to be understood that the invention is not limited to these disclosed embodiments. Upon reading the teachings of this disclosure many modifications and other embodiments of the invention will come to mind of those skilled in the art to which this invention pertains, and which are intended to be and are covered by both this disclosure and the appended claims. It is indeed intended that the scope of the invention should be determined by proper interpretation and construction of the appended claims and their legal equivalents, as understood by those of skill in the art relying upon the disclosure in this specification and the attached drawings.

Extracellular Vesicles Total Recovery and Purification (EVtrap) Technology

Total EV and/or exosome capture and purification has been the focus of many recent studies, with particular consideration toward simple and easy protocol. The vast majority of the exosome analysis projects are based on the differential centrifugation. Although viable, this approach leaves room for improvements for the detection of EV biomarkers in a high-throughput automatable environment. Here, this disclosure introduces a novel non-antibody affinity beads-based capture method for effective EV isolation, termed EVtrap (Extracellular Vesicles total recovery and purification), directly from urine. It enables purification of the complete EV profile based on the lipid bilayer structure of these vesicles and the unique combination of the hydrophilic and aromatic lipophilic groups on the synthesized beads. The introductory manuscript of this technology was published in September 2018 (20).

Comparison of EV capture efficiency by Western Blot.

For the initial method validation, this disclosure compared the EVtrap approach with the “gold standard” ultracentrifugation method using 500 μL of urine for each test.

After the ultracentrifugation step (100K UC), the pellet was boiled and loaded on the gel directly or washed with PBS once or twice and centrifuged again. The supernatant from 100K UC step was concentrated and loaded on the gel in the same proportion.

The EVtrap method was carried out also using 500 μL urine: after 1-hour incubation, the supernatant (unbound fraction) was collected, concentrated and loaded on the gel. The captured EVs were eluted by incubation for 10 minutes with triethylamine. For recovery yield quantitation: the complete EV (exosome) population from 500 μL urine was concentrated and used as the exosome control.

All samples described above were loaded on the same gel and detected by Western Blot using a primary antibody for CD9 (common exosome marker). This experiment was carried out 5 separate times. A representative Western Blot is shown in FIG. 1A, and the quantitative values for each CD9 band signal are listed in the bar graph in FIG. 1B. As the results show, ultracentrifugation step indeed captures only a portion of the exosomes—14% on average—a recovery rate similar to other studies (23, 24). Detection of the UC supernatant further confirmed the incomplete capture, as it is expected to see a large percentage of EVs remaining in the supernatant (38). In contrast, the EVtrap method resulted in no detectable CD9-containing exosomes in the supernatant (unbound fraction), with ˜99% of the exosomes being captured and recovered. As quantified in FIG. 1B, the EVtrap capture and elution reproducibility is outstanding, producing a standard deviation of 3.8%.

For additional recovery and purity assessment, besides ultracentrifugation, this disclosure also sought to compare other frequently used approaches. This disclosure used three common commercially available methods: including Qiagen's membrane affinity spin method, Hitachi's size-based filtration tube and Invitrogen's polymer-based exosome precipitation. Direct urine was used in each case and 500 μL equivalent was run after capture on two different gels and detected by anti-CD9 antibody (FIG. 2A) or silver stain for purity assessment (FIG. 2B). As the results demonstrate, the alternative methods produced somewhat similar exosome recovery signal compared to 100K ultracentrifugation, matching the previously published results for these methods. When compared to 100K UC pellet, the polymer-based EV precipitation even produced 2.5× higher exosome yield, although the contamination level was also much higher (FIG. 2B). Nonetheless, EVtrap produced significantly higher exosome recovery yield, with lower level of contamination, compared to the other approaches.

In order to enable reliable analysis of clinical samples, the method must be highly reproducible by different users and over time. This disclosure tested the coefficient of variation (CV) of EVtrap capture by running 5 separate experiments using 0.5 mL urine on 5 different days carried out by two independent researchers. The eluted samples were then loaded on the same gel and the exosomal CD9 signal was quantified. FIG. 3 shows <5% CV for EVtrap isolation, demonstrating outstanding day-to-day reproducibility, as is necessary for clinical sample analysis.

While Western Blot-based detection (as used in preliminary evaluation studies) does allow simple analysis of EV markers, there is opportunity for improvement since it tends to work for a few targets at a time, with good antibodies available. Mass spectrometry (MS) analysis enables the detection and quantitation of hundreds or thousands of proteins in a single experiment, while uncovering previously unknown targets. Hence, MS is the method of choice for current cancer biomarker discovery efforts, often coupled with liquid chromatography (LC).

Comparison of EV capture efficiency by LC-MS.

For the LC-MS analysis, this disclosure used 200 μL of urine as the starting material. As a control, ultracentrifugation (100K UC) pellet was used directly for protein extraction. The EVtrap method was then carried out on the supernatant from the 100K UC sample to analyze the exosomes left after the ultracentrifugation step. For the sample comparison, the EVtrap method was also carried out on the 10K supernatant and on 200 μL direct urine.

Using a single 90-min LC-MS gradient, the EVtrap method as disclosed herein was able to identify over 16,000 unique peptides from approximately 2,000 unique proteins. By comparison, ultracentrifugation method produced approximately 7,200 unique peptides from approximately 1,100 unique proteins.

EVtrap Capture Efficiency.

This disclosure further utilized label-free quantitation to compare all of the proteins identified by each method. In this experiment, this disclosure identified and quantified 94 out of 100 common exosome markers published in ExoCarta (39-41). All of them showed a significant increase after EVtrap capture compared to ultracentrifugation. This is noteworthy because many other studies have shown that different methods enrich different exosome populations with various success rates (34, 42). With EVtrap, it appears that the complete EV profile is recovered. As an example of the data, this disclosure listed a few common exosome markers in a bar chart in FIGS. 4A-B. The average increase of all detected exosome markers captured by EVtrap is almost 17-fold higher compared to the UC sample (FIG. 4C). For comparison, this disclosure also quantified any contamination free urine proteins. As shown, highly abundant urine proteins were detected at the levels similar to exosome markers, indicating generally good specificity of EVtrap to enrich EVs with low contamination. It is envisioned that the contamination levels can be further reduced by the extensive washing steps.

EVtrap and Phosphoproteome Analysis.

With the ability of EVtrap to enable improved LC-MS analysis of urinary EVs, this disclosure further sought to apply this approach for phosphoproteome analysis. Despite the increasing interest in biofluid EVs, methods for EV phosphoproteome analysis provide opportunity for improvement in reporting, except a couple of recent publications (19, 20). Indeed, this disclosure's preliminary data have demonstrated that the suggested published EV workflows (43-47) provide opportunities for improvement for subsequent phosphoproteome analysis. It is therefore understood that other methods for EV isolation may be utilized when optimized, such as the commonly used commercial EV isolation methods: Qiagen ExoEASY (membrane filtration), Cell Guidance System Exo-spin (size-exclusion chromatography (SEC)), Thermo Fisher Total Exosome Isolation Reagent (TEIR) (precipitation), JSR Life Sciences ExoCAP (antibody immunoprecipitation (IP)) and 101Bio PureExo kit (precipitation).

For preliminary phosphoproteomic analyses, this disclosure used 10 mL of urine for each treatment, including EVtrap and ultracentrifugation (100K UC). FIG. 5A shows the phosphoproteome data identified. The UC sample produced 165 unique phosphopeptides from 105 unique phosphoproteins, which seems to be a higher urine phosphoproteome ID number than reported by others. However, when EVtrap was used for capture, this disclosure saw a statistically significant increase in phosphoproteome identification levels. This disclosure identified almost 2,000 unique phosphopeptides from over 860 unique phosphoproteins using only 10 mL of urine and a single 60-min LC-MS run. Most phosphoproteins were not detected by MS after ultracentrifugation. FIG. 5B lists the total increase in signal of the identified phosphoproteins, demonstrating approximately 41-fold increase in EV phosphoproteome signal after EVtrap capture compared to UC. These data show that EVtrap can process urine directly, a highly useful feature for routine clinical analysis. With 10-mL urine being sufficient to identify hundreds of phosphoproteins, the volume is also convenient.

Example 1: Biomarker Discovery for Bladder Cancer

EVtrap capture was utilized for urine EV proteome and phosphoproteome analysis. This disclosure utilized the EVtrap method for the discovery of novel urinary biomarkers from bladder cancer patients, for the purposes of bladder cancer diagnosis, prognosis, detection, monitoring, patient stratification, drug response analysis, therapy selection, or the like. Here, both urine EV proteomics and phosphoproteomics were carried out to examine both sets of potential biomarkers.

For the first experiment this disclosure pooled together 27 healthy, 10 inflammation, 4 low-grade and 21 high-grade bladder cancer urine samples into 4 separate samples analyzed in triplicate. In this large pooled experiment, 259 proteins and 222 phosphoproteins were quantified as significantly increased (>4-fold) in bladder cancer. The statistically significant protein and phosphorite changes were identified by P-value as significant based on a two-sample t-test with a permutation-based 0.05 FDR cutoff. The overall quantitation numbers, EV markers, EV protein and phosphoprotein intensity differences and select phosphoproteins log-scale intensity heatmap are shown in FIGS. 6A-6C. As denoted on the heatmap, this disclosure was able to group together select proteins and phosphoproteins into clusters that can effectively differentiate low-grade and high-grade bladder cancer from healthy control samples or patients with other non-cancerous bladder indications. It will be understood by one skilled in the art that other relevant conditions for an EV biomarker can be used as control samples.

This disclosure then carried out the follow-up EVtrap+LCMS experiments using individual urine samples. For the phosphoproteome analysis, this disclosure processed 23 healthy urine samples, 4 inflammation/infection samples and 18 bladder cancer samples. For the proteome evaluation, the distribution was 23 healthy, 4 inflammation, and 11 bladder cancer samples. In total, each individual sample was analyzed in triplicate by quantitative mass spectrometry, producing a total of 2,769 quantified unique proteins and over 11,000 quantified unique phosphorites. This disclosure carried out linear regression statistical analysis at P-value <0.05 to narrow down the panel of potential biomarkers to those with highest statistical significance and largest intensity changes (majority were >10-fold increased).

FIG. 7 shows the box-and-whisker plot examples of a few of the select potential biomarkers. The scale is set at log intensity for easy interpretation, so the average difference in signal for most is 10-70× increase in bladder cancer compared to the two control groups. For most of these markers, the signal was not detectable in the majority of the control samples. The F-ratio values and P-values are included under each plot. Both proteomics and phosphoproteomics datasets were effective at finding differentiated markers, although phosphoproteomics was more likely to find unique bladder cancer proteins that were present at very low amounts.

From these data, this disclosure generated an initial list containing 83 proteins and phosphoproteins that were consistently present in bladder cancer urine but at very low amounts or not detectable in any control samples. We carried out additional LC-MS analyses of 29 new healthy urine EV samples and 39 new bladder cancer urine EV samples. These efforts identified over 7,000 proteins and over 5,000 phosphoproteins. Finally, the panel of potential biomarkers from all of the above experiments contained 594 proteins and phosphoproteins that have the potential to differentiate bladder cancer from non-cancer urine samples.

We identified 289 upregulated proteins and 78 upregulated phosphoproteins that increased in abundance at least 4-fold in bladder cancer at P-value <0.05. These results are visualized in FIG. 11A with the volcano plot, and FIG. 11B with the heatmap of the EV proteins that were significantly up- or downregulated in bladder cancer compared to healthy controls. Likewise, FIG. 11C shows the volcano plot, and FIG. 11D the heatmap of the EV phosphoproteins that were significantly up- or downregulated in bladder cancer compared to healthy controls.

From this dataset, in FIG. 12 we selected several proteins and phosphoproteins to create box-and-whisker plots for quantitative LC-MS data of markers capable of differentiating bladder cancer urine from healthy controls (normal urine n=29; bladder cancer urine n=39). The scale is set at log intensity for easy interpretation, so the average difference in signal for most is at least 10× increase in bladder cancer compared to the control group.

Analysis of Gene Ontology pathways of these data showed a significant enrichment of cancer networks in upregulated proteins and phosphoproteins (FIG. 13A). Bladder cancer was one of those significantly enriched networks, with hundreds of proteins correlated with bladder cancer (FIG. 13B). This further underscores that urinary exosomes have a unique ability to serve as a surrogate for bladder tissue samples, as many known bladder cancer-related markers are also upregulated in urine EVs. In addition, we found several significantly upregulated kinases and kinase pathways, further underscoring the importance of protein phosphorylation in bladder cancer development and progression.

Finally, we carried out a validation experiment for bladder cancer biomarkers using a new cohort of 74 control urine samples and 102 urine samples from bladder cancer individuals. As before, the EVs from urine were enriched using EVtrap, and the resulting EV proteins were identified and quantified using LC-MS. This validation experiment confirmed many of the bladder cancer EV protein biomarkers previously identified.

The most recent results for the significantly upregulated and downregulated EV proteins in bladder cancer are visualized in FIG. 14A with the volcano plot and FIG. 15B with the heatmap.

Example 2: Bladder Cancer Biomarkers Panel for Diagnosis by Urine Test

The samples from Example 1 were subsequently split into a training set (70% of the samples) and test set (30%). The algorithm was trained using the training set, and then checked on the test set to see its accuracy. As shown in FIG. 15, the accuracy for bladder cancer detection in the training set was the perfect 1, and for the test set an outstanding 0.94.

We also did ROC curve analysis of the top markers validated in this experiment, and found that the best combination of markers can result in AUC of 98% (FIG. 16). Overall, these data confirm the ability of using the novel urine EV markers discovered by the method described in Example 1 and listed in Table Ito successfully differentiate bladder cancer samples from non-cancer controls. Table I lists unique proteins and phosphoproteins capable of differentiating bladder cancer urine from healthy urine and inflammation control urine.

TABLE I

Biomarkers (proteins and phosphoproteins) capable of differentiating bladder

cancer urine from healthy urine and inflammation control urine

CYFIP1
SEMG1
IGKV2D-40
PRPS1
IGKV1-17
PDIA5

EMILIN2
IGKC
EPX
EIF2S1
PSME1
YKT6

IRF2BPL
PON1
AXL
PLG
ANK1
SF3A3

FGB
FUS
CEACAM8
F12
HTT
NAAA

APBB1IP
RPS19
GK
A2M
TFPI2
PSD3

PTPRC
PSMD8
FRK
C3
MARCKSL1
PRG4

CFH
VASP
EIF1AX
C5
PXN
HNF4A

VIM
PLTP
S100A10
APOA1
BASP1
GPX2

MSN
SNRPE
ARHGAP1
APOC1
AMPD3
GBE1

ARHGAP4
EWSR1
EPS8
KLKB1
HSP90AA2P
GIT2

NUMA1
SRSF1
DDX39B
HRG
SF3A1
BOLA3

PDCD4
ILF3
CTTN
PROS1
RASAL3
RRM2B

LRRK2
APOF
EIF4H
C8A
REPS2
CPD

NEK9
HABP2
MAPK14
HNRNPC
ATXN2L
RPS27

MED9
NONO
CDC37
C7
HSPH1
L3MBTL3

SAA2-SAA4
SF1
OCC1
C6
KCTD12
TMEM163

IGHV2-26
MAPRE1
S100A13
CPN1
PNN
ASPSCR1

ZNF233
ZYX
C14orf166
VCL
RTN4
UBE2Z

APOA2
CPSF6
PSMD9
ITIH2
NUDC
PEAK3

APOC4-APOC2
HP1BP3
ZNF207
ITIH1
HBG1
EPHA1

APOL1
FKBP15
GMFG
TKT
ACSL1
PATL1

HMGB3
SLC25A24
C2
STIP1
CXorf38
RAN

FCN3
DNMBP
RPL7
HDGF
SRPRA
OTUD5

F13A1
SND1
WARS
S100A12
CARM1
LARP7

HPR
FUBP1
EIF4B
LMNB2
PSMB6
UBASH3B

C1QA
BIN2
APEX1
FAM49B
RACK1
ARAP1

C1QB
SEPT9
CFP
TLN1
CCSER1
DDX1

C1QC
HAPB2
ADSS
WIPF1
UBXN11
SEPT6

FN1
MAP2K2
SUB1
MECP2
ZNF613
TRIM24

MMP1
TJP2
PAFAH1B2
LCP2
TMEM43
TLE4

C4BPA
TPD52L2
SRSF3
SRSF6
CTXN1
FLII

APOB
BUB3
ITIH3
IQCB1
BCAP31
WDR33

SERPIND1
BCAS1
BAX
RCSD1
PSMB9
SPTA1

C8B
KRAS
AP1B1
CHRDL2
SERPINB9
EP300

C8G
FGA
CBX3
PRAM1
H2AC21
ZFYVE20

PLEK
FGG
PSMD6
SRRM2
KIFAP3
CTNND1

NCF1
S100A9
TGFBI
INPP5D
CLPB
L1CAM

STMN1
HSP90AB1
SRSF7
SLC26A4
LRMP
SRSF10

ORM2
MMP2
FERMT3
NCF1B
MCMBP
MAP2K3

NFKB1
TACSTD2
CNN2
KDM1A
GSDMD
C1orf35

NCF2
CDH1
CFHR5
STK17B
PTGES2
NFX1

EIF2S2
SRC
APMAP
NADK
RPS10
PMS2

LMNB1
YES1
PADI4
OXSR1
TRIM9
IPCEF1

C4BPB
EPCAM
PA2G4
SLC4A1
LBR
JUP

FLNA
S100A1
FLNB
SLC2A3
RPL32
SIRT2

CBL
MAPK1
APOM
TOP2A
LDHC
ACKR3

S100A4
MARCKS
HP
TRP
DIAPH3
ADAR

IPO11
ADGRG6
LPA
COL1A1
LYAR
SUPT5H

NFASC
PYCR3
KPNB1
HAPLN3
DCPS
LSM4

CUL4A
CHAMP1
ACAA1
MYH13
CUL5
HNRNPM

SEC62
GMDS
TGM2
NDUFB11
TRIOBP
EIF4G2

RPS14
FARSA
UAP1L1
HK2
EIF4A3
SF3B2

ATP5IF1
TMEM33
SLC6A13
ABHD3
HAO2
DHX57

SCAMP4
POLR1B
SEPTIN6
ANGPTL4
RCC2
STARD10

HNRNPUL2
DDX46
LDLR
AKR1C2
CBX5
ATN1

TNFAIP8
MRPL58
EHD2
RNASE3
HBD
SRF

TMX1
MTM1
EPB42
IDI1
ALDH3A2
BDP1

ATG3
CNOT1
DEK
SEC24C
PLOD3
NOLC1

EPB41L3
MLKL
DNAJC17
SEC23B
TSNAX
CASP9

ACOX1
NECTIN1
EXOC3
DDX17
NCL
NFIA

SUCLG1
CUL2
PSTPIP1
DEPDC1B
ABCC11
SMC3

PSPC1
GAR1
RPL10
PCDHA3
WDR5
FLVCR1

MCM5
DDI2
FAM98A
EGFLAM
TM9SF2
RP2

VAPA
AIMP1
RIC8A
MYO15B
EFCAB13
PRPF38B

RAB2B
SRP14
CIB1
RELCH
TBC1D24
MCM3

RBBP4
BSG
MTREX
SNX27
DIAPH1
ARHGAP9

SAA4
DMTN
NCKAP1L
IRF4
HSD17B2
UBAP2

G3BP1
CORO1B
PRMT1
ENAH
CCDC124
BRD9

TENM1
SKP1
SF3A2
BZW1
GATA5
HNRNPA0

SLC4A11
ZBTB5
CHID1
CSTF3
CTSO
KMT2C

PRDX3
DDX23
CRAT
SNRPB2
WDR13
NRG1

RPL28
TMEM41B
PLEKHA6
ATP2B4
PSMF1
GLYR1

HMGCR
BLOC1S1
WAS
MYO5C
HBA1
MAPRE3

LACTB
SELENOS
GIGYF2
BRI3
DEFB1
SNRPC

TRBC2
PUS1
CAMP
H2BC14
TTC21B
LTB4R

PPP6C
DNAH9
GMIP
SPAG17
GAPVD1
LRWD1

SCARF2
SCG3
PURB
PDE9A
TNKS1BP1
MYLK3

ADSL
DYNC2H1
UBTF
XPO4
VAV1
RANBP10

SNRPA1
TUBB2A
SNRPD2
PFAS
GALNT18
PRPF19

RPL22L1
HMGB2
SNRPGP15
ZNF268
KRT82
ZFR

PPM1A
NOP56
DNAH14
PARP14
GALNT16
DDX42

ABCE1
PRELP
PRG3
NBEAL2
TPP2
TM9SF3

SMU1
NOP58
PSMA1
PSMD4
PGM2L1
H6PD

FMC1
XPO1
ARHGEF2
NRBP1
CCDC22
PKN1

ARHGAP27
APOC4
POSTN
IMPDH1
KIF13B
SPN

HBG2
B2M
CSE1L
PRKDC
HBB
VWA7

EIF3M
ROCK2
TCEAL9
APOC2
ADSS2
PRSS2

SLC25A13
SRRM1
TMF1
FARSB
CD5L
CD47

RIPOR2
LMLN
MAP4
THBS2
CARS1
SH3KBP1

PRDM7
TRA2B
ATXN10
MPP1
PREP
TMA7

LY75
UMPS
RAC2
HDGFL2
DNM1L
CD5L

FTSJ1
ZNF276
ILF2
SH3BP1
MACF1
COX7C

RRAS2
HINT3
CHST3
IL18
SPINT1
GNG2

PPA2
VNN1
YOD1
SLC47A1
NPHS2
CEACAM5

AQP1
TMEM27
SMR3B
ENTPD6
CA12
MASP1

DNASE1L1
GAS1
ACADVL
AK3
IDE
HMOX1

DBT
CAMK2D
IQCC
CD109
EGF
SERPINA10

DNAJB2
CYSTM1
MAN1B1
TSTA3
PPP1R12A
PPBP

MUC20
ARL6IP5
NEBL
C21orf33
TMEM106B
SEMA3C

In this disclosure, the applicants have demonstrated the feasibility and clinical ability of the newly discovered urine biomarkers to discriminate between cancer and non-cancer samples. This disclosure discovered hundreds of proteins and phosphoproteins that appear to change significantly in bladder cancer urine EVs compared to healthy or inflamed urine samples. This disclosure narrowed this list down to about 590 potential markers that demonstrate the most statistically significant differentiation between the cases. Most of them show at least 4-fold change on a highly consistent and reproducible level at p-value <0.0001.

This highlights the primary advantage of using phosphoproteomic analysis for biomarker discovery. It is not necessarily the case that these proteins are always differentially phosphorylated in urine EVs. But rather that they are very low-abundant signaling proteins which are loaded into EVs and are commonly phosphorylated. Typically, they are not detectable by standard proteomic analysis. Phosphoproteome enrichment thus removes the common higher abundant proteins and peptides, while isolating and bringing to the forefront these phosphorylated low-abundant targets. The ability to increase the volume of urine utilized for phosphoproteomics from 0.2 mL to about 10 mL also contributes to the detection and quantitation of these targets. Therefore, even though multiple biomarkers were discovered through phosphoproteomic experiments, it is most likely the whole protein amount changes in the EVs. And, thus, these targets can be detectable by regular antibody assays without having to develop specific phospho-antibodies.

While this disclosure has been described as having an exemplary design, the present disclosure may be further modified within the spirit and scope of this disclosure. This application is therefore intended to cover any variations, uses, or adaptations of the disclosure using its general principles. Further, this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this disclosure pertains.

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Extracelluar Vesicle Biomarkers for Bladder Cancer

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