Methods for determining prostate cancer

Abstract

In general, the disclosure relates to the discovery that proteomics from urine samples can be used to determine the prostate proteome in a patient, which can determine whether the patient has prostate cancer. Certain aspects include analyzing urinary extracellular vesicles (uEVs) separated from a urine sample from a patient.

Description

METHODS FOR DETERMINING PROSTATE CANCER

[0001] This application claims priority of U.S. Provisional Application No. 63 / 525,912 filed July 10, 2023, which is hereby incorporated by reference in its entirety.BACKGROUND OF THE INVENTION

[0002] This invention was made with government support under CA271894, CA092131, and CA214194 awarded by the National Institutes of Health. The government has certain rights in the invention.I. Field of the Invention

[0003] This invention relates to the field of oncology, proteomics, machine learning, and medicine.II. Background

[0004] Human urine is produced when blood is filtered by the kidneys. This filtration retains proteins and nutrients, removes undesired metabolites and regulates pH and water levels. Excreted urine is less than 1% of pre-filtered volume1. After filtration, urine is stored in the bladder for minutes to hours before being voided through the urethra. In males, the urethra runs through the prostate, a gland that produces prostatic fluid2. Because urine can spend significant residence time within the genitourinary tract, it accumulates bioanalytes that reflect the current state of those tissues3. The urine is therefore a remarkably complex biofluid and provides a non-invasive snapshot of both organismal state and genitourinary tissues. Its molecular composition can vary across individuals, and within a single individual over time4. Urine has been widely proposed as a non-invasive longitudinal biomarker matrix5,6, and specific DNA, RNA or protein species have been identified in urine that can serve as biomarkers7 l ().

[0005] Proteins enter the urine in two ways: leakage at the glomeruli of the kidney and throughout the urogenital tract. It is believed that the vast majority of the excreted urinary proteome derives from tissues of the genitourinary tract, rather than leakage from the kidneys. Urogenitary proteins can enter the urine via passive release through cell death, through active translocation and as part of secreted extracellular vesicles (EVs)11,12. EVs are nanosized particles with a lipid bilayer released by cells into the extracellular milieu. They varydramatically in size, ranging from 30 to 2,000 nm in diameter, and are heterogeneous in their mechanisms of biogenesis, molecular composition and function13. EVs play a crucial role in both physiology and in the pathogenesis of diseases, including cancer14. EV and secreted proteomes are hypothesized to be context-driven and tissue-specific15, but their presence, population variability and disease-relevance in urine remains poorly characterized.SUMMARY OF THE INVENTION

[0006] To fill the gap, aspects herein generate comprehensive urinary proteomic profiles, including from 190 treatment-naive men with a range of benign and malignant conditions. Disclosed are simple protocols that use urine to directly sample prostate proteins. This allows the identification of the tissue and subcellular origins of urinary proteins and EVs, and to quantify how the urine proteome changes over time in individuals. In some aspects, urinary EVs, but not those released from prostate cancer cell lines nor secreted urinary proteins, accurately reflect prostatic tissue. In certain aspects, prostate tumor-specific urinary proteins accurately distinguish men with and without prostate cancer, and risk-stratify those already with the disease. In some aspect, canonical EV markers are not effective in urine, context- dependent urine EV cargo can accurately marks specific urinary EV populations.

[0007] In general, the disclosure relates to the discovery that proteomics from urine samples can be used to determine the prostate proteome in a patient, which can determine whether the patient has prostate cancer. The urine sample can be taken after the patient has had a digital rectal examine (DRE), which can be immediately after the DRE or 1, 2, 3, 4, 5, 6, 7 or more, or any range derivable therein, days after the DRE. Urinary extracellular vesicles (uEVs) can be isolated from the urine sample and used as the proxy for the prostate proteosome.

[0008] Disclosed are methods of measuring a urine proteome, methods of measuring a prostate proteome, methods of diagnosing a patient with prostate cancer, methods of measuring prostate tumor-specific urinary proteins, methods of measuring prostate-specific proteins, methods of predicting prostate disease, methods of determining severity and / or a likelihood of prostate cancer for a patient, methods of determining a prostate cancer grade, methods of determining a prostate cancer severity, methods of diagnosing a patient with prostate cancer, methods of prognosing prostate cancer in a patient, methods of determining the likelihood a patient has prostate cancer, methods of treating a patient, methods of treating a prostate cancer patient, methods of reducing the severity of prostate cancer in a patient, and methods ofpreventing prostate cancer in a patient. Any of the methods can comprise 1, 2, 3, 4, 5, 6, or more of any one of the steps disclosed herein.

[0009] Also disclosed are methods of determining severity of prostate cancer in a patient, methods of determining a likelihood of prostate cancer for a patient, methods of prognosing prostate cancer in a patient, and methods of determining a therapeutic intervention strategy for a patient. In certain aspects, the method comprises 1, 2, 3, 4, 5, 6, 7, or more steps including, but not limited to, receiving urinary proteomic dataset obtained from the patient, applying a trained machine learning model to the urinary proteomic dataset to determine the severity of the prostate cancer and / or the likelihood of the patient having prostate cancer, training the trained machine learning model with urinary proteomics training datasets obtained from a plurality of study patients, administering a therapeutic intervention, centrifuging a urine sample obtained from the patient, and separating urinary extracellular vesicles from a urine sample from the patient. In certain aspects, the centrifuging is performed at approximately 17,000 x g, 18,000 x g, 19,000 x g, 20,000 x g, 21,000 x g, 22,000 x g, 23,000 x g (or any range derivable therein). In certain aspects, the centrifuging is performed at approximately 145,000 x g, 146,000 x g, 147,000 x g, 148,000 x g, 149,000 x g, 150,000 x g, 151,000 x g, 152,000 x g, 153,000 x g, 154,000 x g, 155,000 x g (or any range derivable therein). The trained machine learning model can be a machine learning model disclosed herein.

[0010] In certain aspects, the urinary proteomic dataset comprises proteomic data from urinary extracellular vesicles (uEVs) obtained from the patient. In certain aspects, the uEVs have been separated from a urine sample from the patient. In certain aspects, the uEVs were separated by centrifugation. In certain aspects, the centrifugation was performed at approximately 20,000 x g. In certain aspects, the centrifugation was performed at approximately 150,000 x g. In certain aspects, the urinary proteomic dataset is obtained from the patient after the patient has received a digital rectal examination. In certain aspects, the method comprises training the trained machine learning model with urinary proteomics training datasets obtained from a plurality of study patients, wherein the study patients include patients believed to have prostate cancer and study patients believed not to have prostate cancer. In some aspects, the urinary proteomic dataset comprises a measured protein level for one or more of the proteins disclosed herein. In some aspects, the method comprises administering a therapeutic intervention to the patient based on the severity and / or the likelihood of the patient having prostate cancer. In some aspects, the therapeutic intervention comprises active monitoring. In some aspects, the therapeutic intervention comprises a chemotherapy, a radiation therapy, a hormone therapy, an immunotherapy, a bisphosphonate therapy, anultrasound therapy, a photodynamic therapy, a proton therapy, a cell therapy, surgery, or a combination thereof.

[0011] Also disclosed are methods of measuring a prostate proteome in a patient, the method comprising analyzing urinary extracellular vesicles (uEVs) separated from a urine sample from the patient. Also disclosed are methods of measuring a prostate proteome in a patient, the method comprising measuring protein identity, abundance, and / or post- translational modifications of one or more proteins in uEVs obtained from the patient. The methods can comprise 1, 2, 3, 4, or more steps including, but not limited to, analyzing urinary extracellular vesicles (uEVs) separated from a urine sample from the patient, finding protein identity in uEVs obtained from the patient, measuring protein abundance in uEVs obtained from the patient, measuring protein post-translational modifications of one or more proteins in uEVs obtained from the patient, and performing mass-spectrometry on uEVs obtained from the patient.

[0012] Also disclosed are methods for treating a patient predicted to have prostate cancer requiring a therapeutic treatment, methods for treating a patient that is identified as being a candidate for a therapeutic treatment, methods for treating a patient that is identified as being a candidate for a therapeutic treatment using a trained machine learning model, and methods of treating a prostate cancer patient. The method can comprise one or more steps including, but not limited to, administering the therapeutic treatment to the patient. In some aspects, the subject has been determined to have a treatment-responsive prostate cancer based on a trained machine learning model that determines prostate cancer prognosis using a urinary proteomic dataset. In certain aspects, the trained machine learning model was trained with urinary proteomics training datasets obtained from a plurality of study patients, wherein the study patients include patients believed to have prostate cancer and study patients believed not to have prostate cancer. In certain aspects, the therapeutic treatment comprises a chemotherapy, a radiation therapy, a hormone therapy, an immunotherapy, a bisphosphonate therapy, an ultrasound therapy, a photodynamic therapy, a proton therapy, a cell therapy, surgery, or a combination thereof. In certain aspects, the urinary proteomic dataset comprises proteomic data from urinary extracellular vesicles (uEVs) obtained from the patient. In certain aspects, the uEVs were separated from a urine sample from the patient. In certain aspects, the uEVs were separated by centrifugation. In certain aspects, the centrifugation was performed at approximately 20,000 x g. In certain aspects, the centrifugation was performed at approximately 150,000 x g. In certain aspects, the urinary proteomic dataset is obtained from the patient after the patient has received a digital rectal examination. In certain aspects, theurinary proteomic dataset comprises a measured protein level for one or more of protein measured by mass spectrometry.

[0013] Aspects of the present disclosure also concern the disclosure of Khoo et al (Nat Commun. 2024; 15: 5069. (2024)), which is incorporated by reference herein in its entirety.

[0014] In certain aspects, the patient is a human patient. In certain aspects, the patient is a human patient that has, is suspected of having, is diagnosed with having, and / or has symptoms of cancer. In certain aspects, the patient is a human patient that has, is suspected of having, is diagnosed with having, and / or has symptoms of prostate cancer.

[0015] Throughout this application, the term “about” is used to indicate that a value includes the inherent variation of error for the measurement or quantitation method.

[0016] The use of the word “a” or “an” when used in conjunction with the term “comprising” may mean “one,” but it is also consistent with the meaning of “one or more,” “at least one,” and “one or more than one.”

[0017] The phrase “and / or” means “and” or “or”. To illustrate, A, B, and / or C includes: A alone, B alone, C alone, a combination of A and B, a combination of A and C, a combination of B and C, or a combination of A, B, and C. In other words, “and / or” operates as an inclusive or.

[0018] The words “comprising” (and any form of comprising, such as “comprise” and “comprises”), “having” (and any form of having, such as “have” and “has”), “including” (and any form of including, such as “includes” and “include”) or “containing” (and any form of containing, such as “contains” and “contain”) are inclusive or open-ended and do not exclude additional, unrecited elements or method steps.

[0019] The compositions and methods for their use can “comprise,” “consist essentially of,” or “consist of’ any of the ingredients or steps disclosed throughout the specification. Compositions and methods “consisting essentially of’ any of the ingredients or steps disclosed limits the scope of the claim to the specified materials or steps which do not materially affect the basic and novel characteristic of the claimed invention.

[0020] It is contemplated that any embodiment discussed in this specification can be implemented with respect to any method or composition of the invention, and vice versa. Furthermore, compositions of the invention can be used to achieve methods of the invention.

[0021] Other objects, features and advantages of the present invention will become apparent from the following detailed description. It should be understood, however, that the detailed description and the specific examples, while indicating specific embodiments of the invention, are given by way of illustration only, since various changes and modifications withinthe spirit and scope of the invention will become apparent to those skilled in the art from this detailed description.BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present invention. The invention may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein.

[0023] FIGs. 1A-1F show digital rectal exam enriches for prostate tissue-derived proteins, a Matched pre- and post-DRE urine proteomes from clinical International Society of Urological Pathology (cISUP) Grade Group (GG) 1 patients consisting of unfractionated urine (soluble proteins, uSP) and two subtypes of urinary extracellular vesicles (uEV) isolated by differential ultracentrifugation at 20,000 x g (uEV-P20) and 150,000 x g (uEV-P150). b Transmission electron microscopy images of uEVs isolated from pre- and post-DRE urine from a single individual. Scale bar: 200 nm. Images are representative of three biological replicates, c-e Proteomic differences in pre- and post-DRE urines in uSP (c), uEV-P20 (d) and uEV-P150 (e) fractions. Top panel: log2 fold change (log2FC) in protein abundance in urine, grouped by protein detection in prostate tissues18,19. Bonferroni -corrected P-values from two-sided Mann- Whitney U tests. Bottom panel: log2FC in protein abundances. Prostate-specific proteins as per the Human Protein Atlas are in black, f Differences in pre- vs. post-DRE urine (log2FC) for select prostate tissue-specific proteins20. Percentage of 157 prostate cancer (PCa) tissues18,19in which each protein was detected on the left. Background shading denotes P-value < 0.05 from a two-sided Wilcoxon signed-rank test.

[0024] FIGs. 2A-2K show post-DRE uEV fractions harbor distinct protein cargo, a Cohort overview. Clinical ISUP Grade Group (GG); cT: clinical T category; sPSA: Serum Prostate Specific Antigen (ng mL’1); Age: Age at diagnosis (years), b Post-DRE urine fractions analyzed by proteomics, total patients and samples per fraction, c Distribution of particle sizes determined by nanoparticle tracking analysis, mean of n = 22 (uEV-P20) and n = 34 (uEV- P150). d Representative transmission electron microscopy images of uEV fractions from one cISUP GG 1 patient. Scale bar: 200 nm. Images are representative of three biological replicates, e Particle concentration for uEV-P20 and uEV-P150 from 22 and 34 patients, respectively, f Number of proteins quantified by mass spectrometry. Dots represent samples (Samples: 175 uSP, 146 uEV-P20, 148 uEV-P150). P-values for (e) and (f) from two-sided Mann-Whitney Utests, g Fraction of uEV-P20 and uEV-P150-unique proteins from 124 patients with matched uEV fractions, h Number of samples each protein was detected in (n = 96 patients). For each pairwise-comparison, the numbers of proteins present in >90% of samples in one sample type and <10% of the other are labeled on top of each panel, i Differences in shared protein abundance between fractions. Significant differences (FDR < 0.05, two-sided Wilcoxon signed-rank test) are in green (uEV-P20), pink (uEV-P150) or yellow (uSP). n.s.: nonsignificant. Total differentially abundant proteins in bottom comers, n = 96 patients, j Fraction- enriched proteins either unique to one fraction or differentially abundant in one fraction relative to the other two. k Odds ratio (OR) of gene set enrichment for each subcellular localization62for proteins from (j). Grey background shading indicates FDR < 0.05 (Fisher’s exact test). Boxplots are shown with the line indicating the sample median, the box indicating the 25thand 75thpercentiles, and the whiskers indicating ±1.5 x interquartile range (IQR).

[0025] FIGs. 3A-3E show uEV proteome closely reflects the prostate tissue proteome. a Overlap in proteins quantified in each sample type. Samples: Urinary soluble proteins (uSP) = 175, uEV-P150 = 148, uEV-P20 = 146, Tissue = 157; Proteins: uSP = 3,150, uEV-P150 = 3,878, uEV-P20 = 5,462, Tissue = 7,438. b log2 median protein abundance between prostate tissue and uSP (left), uEV-P20 (middle) and uEV-P150 (right). Spearman’s rank correlation and its P-value (two-tailed) are shown, c Analysis strategy to identify tissue-associated genes in RNA-seq of normal or normal adjacent to tumor (NAT) prostate, bladder and kidney from the Genotype-Tissue Expression project (GTEx)25or The Cancer Genome Atlas (TCGA)26 28. P-values from two-sided Mann-Whitney U test adjusted using the Benjamini -Hochberg method (FDR), d Gene set variation analysis scores (GSVA) of sample types based on tissue-specific signatures (prostate: purple, kidney / bladder: grey). Samples: uSP = 175, uEV-P150 = 148, uEV-P20 = 146, Tissue = 157. P-values from two-sided Mann-Whitney U test. Boxplots are shown with the line indicating the sample median, the box indicating the 25thand 75thpercentiles, and the whiskers indicating ± 1.5 x IQR. e Gene Ontology: Cellular Component (GO:CC) gene sets over-represented in each sample type. Only significant gene sets (g:SCS- adjusted P-value < 0.05 from a Fisher’s one-tailed test in g:Profiler64) are visualized.

[0026] FIGs. 4A-4G show prostate cancer cell line EVs do not fully reflect prostate fluid EVs. a Overview of cell line EV (cEV) isolation from conditioned media, b Overlap in proteins quantified between sample types, c-e Spearman’s correlation between log2 mean protein abundance in patient and cell line fractions. Cell line protein abundances are mean of three experimental replicates from five cell lines, f log2FC between EV-P150 and EV-P20 in uEV (x-axis) vs. cEV (y-axis). Significantly differentially abundant proteins in both uEV andcEV (two-sided Mann-Whitney U test FDR < 0.05) are pink (more abundant in EV-P150) or green (more abundant in EV-P20). Grey dots represent non-significant proteins (n.s., FDR > 0.05). g Organellar enrichment for proteins (two-proportion z-test) in each quadrant from (d) (left panel), annotated with differences in fraction (middle panel) between EV-P150 (top right quadrant) and EV-P20 (bottom left quadrant) and P-value (right panel).

[0027] FIGs. 5A-5E show uEV proteome is temporally stable and reflects clinical behavior, a Timepoints for each post-DRE urine collection in a cohort of five patients with cISUP Grade Group (GG) 1 tumors on active surveillance that did not upgrade over the course of urine collection, b Correlation in protein abundance (Spearman’s p) within individuals (Intra, green, 5 patients with cISUP GG 1 tumors), and between individuals (Inter, grey; patients: uSP = 150, uEV-P20 = 126, uEV-P150 = 128). P-values from two-sided Mann- Whitney U tests. Boxplots are shown with the line indicating the sample median, the box indicating the 25thand 75thpercentiles, and the whiskers indicating ± 1.5 x IQR. c-e Significantly differentially abundant proteins between prostate cancers (PCa, red) vs. noncancers (NC, blue) using a two-sided Mann-Whitney U test FDR < 0.05. n.s.: Non-significant, grey. NC patients for uSP and uEV are not matched. Patients: USPNC = 39, uSPpca= 136, uEV- P20NC = 22, uEV-P20pca = 132, UEV-P150NC = 25, uEV-P150pCa= 131.

[0028] FIGs. 6A-6D show prioritization of urinary proteins for biomarker discovery, a Strategy for urinary protein selection and building models to classify prostate cancers (PCa) vs. non-cancers (NC), and cISUP GG 1 vs. GG > 1. uSP: urinary soluble proteins; U-test: two- sided Mann-Whitney U test; RFE: recursive feature elimination; GLM: generalized linear model; KNN: k-nearest neighbors; LOOCV: leave-one-out cross validation; AUC: area under the receiver operator characteristic (ROC) curve, b ROC curves for multi-protein models (solid line) and serum PSA (dotted line) for classifying PCa vs. NC in the discovery cohort. (Patients: USPNC = 39, uSPpca = 136, UEV-P20NC = 22, uEV-P20pca = 132, UEV-P150NC = 25, uEV- P150pca = 131). c log2 fold changes (log2FC) of proteins in multi-protein models (b) in the discovery cohort (Patients: USPNC = 39, uSPpca = 136, UEV-P20NC = 22, uEV-P20pca = 132, UEV-P150NC = 25, uEV-P150pca= 131) and an independent prospective validation cohort (30 patients each for uSP, uEV-P20 and uEV-P150; PCa = 16, NC = 14). d ROC curves for multiprotein models and serum PSA in classifying PCa vs. NC in an independent validation cohort (30 patients each for uSP, uEV-P20 and uEV-P150; PCa = 16, NC = 14).

[0029] FIGs. 7A-7D show EV cargo is context-dependent, a Proteins differentially abundant in each condition and urine fraction by compartment, b Differences in protein abundance of extracellular vesicle markers from41in matched uEV-P20 (green) and uEV-P150(pink) relative to uSP. n = 96 patients. Boxplots are shown with the line indicating the sample median, the box indicating the 25thand 75thpercentiles, and the whiskers indicating ± 1.5 x IQR. c Fraction-specific proteins were determined by differential protein abundance and frequency of detection (Figure 2j). Proteins in each subset were filtered based on the following criteria. Tumor markers: |log2FC|pca / Nc > 1 and FDR pca / Nc < 0.05; Grade markers: |log2FC|cisup>i / cisup=i > 1 and unadjusted P cisup>i / cisup=i < 0.05; Core markers: | log2FC| pca / Nc < 1 and FDR pca / Nc > 0.05 and |log2FC|cisup>i / cisup=i > 1 and unadjusted PCISUP>I / CISUP=I > 0.05 and > 90% of samples and < 25% least variant proteins. Tumor, grade and core markers were further filtered to select cell surface markers (SPC42> 2). PCa: Prostate cancer; NC: Noncancer; SPC: Surface prediction consensus42. P-values from a two-sided Mann- Whitney U test, d Tumor markers with predicted cell surface localization from (c). Proteins are annotated with the frequency of detection in each fraction (left panel) and differential abundance in PCa vs. NC in uEV (middle panel) and cEV (right panel). Non-cancer (NC) cEVs from RWPE-1 cell line.

[0030] FIGs. 8A-8E show molecular and biophysical characterization of uEVs. a Urinary extracellular vesicle (uEV) isolation by differential ultracentrifugation and filtration. SN: supernatant; DTT: dithiothreitol. b Negative stain transmission electron microscopy images of uEVs isolated from pre- and post-DRE urine from two patients. Scale bar: 200 nm. c Number of uEVs isolated from 14 mL of urine from two men with cISUP Grade Group 1 tumors, d Size distribution of uEV-P20 and uEV-P150 from two matched patients by nanoparticle tracking analysis, e Protein abundance of select prostate proteins in pre- vs. post- DRE urine. P-values from a two-sided Wilcoxon signed-rank test.

[0031] FIGs. 9A-9G show uEV associations with clinical covariates, a Negative stain transmission electron microscopy (TEM) images of uEVs isolated from the post-DRE urine of men with different cISUP Grade Group (GG) tumors. Scale bar: 200 nm. n = 1 patient per cISUP GG. b Size distribution by NTA. Each curve represents the mean size for each cISUP GG. / / GG I = 32 patients, / / GG2 = 21, / / GG I = 12, / / GG4 = 4, / / GG5 = 8. c Number of uEV- P20 and uEV-P150 isolated from 14 mL of post-DRE urine from men with different cISUP Grade Group tumors. Patients: uEV-P20GGl = 7, uEV-P150GGl = 10, uEV-P20GG2= 6, UEV-P20GG2 = 11, uEV-P20GG3 = 5, uEV-P150GG3 = 7, uEV-P20GG4-5 = 4, uEV- P 150GG4-5 = 6. Significantly different groups (two-way ANOVA with post hoc Tukey ’ s HSD < 0.05) are labeled, d Number of uEV particles and age at diagnosis (top) or serum PSA level (bottom), e-g Associations between protein counts and cISUP GG (e), age (f) and serum PSA(g). P-values in (e) from one-way ANOVA. Spearman’s p for (f-g). Patients: GG1 = 54, GG2 = 20, GG3 = 26, GG4 = 11, GG5 = 15.

[0032] FIGs. 10A-10G show uEV proteomes are distinct from uSP proteomes. a Experimental design for assessing reproducibility of uEV isolation from post-DRE urine, b Pairwise comparisons (Spearman’s p) between experimental replicates, clinical groups, and uEV fractions. NC: Non-cancer (two pooled samples); PCa: Prostate cancer (three cISUP Grade Group 1 patients), c Overlap in uEV proteins detected in this study with ExoCarta1and Vesiclepedia2. Proteins unique to the current study are in red. d Fraction of uEV proteins detected in this dataset that were also detected in three other published post-DRE urine small uEV datasets3-5. e-g Median protein abundance showing sample type correlation (Spearman’s p) between e uEV-P20 vs. uEV-P150, f uEV-P20 vs. uSP and g uEV-P150 vs. uSP. n = 96 patients.

[0033] FIGs. 11A-11H show biophysics and proteomics of cell line-derived EVs. a Negative stain TEM images of cell line EVs (cEV) isolated from conditioned media of prostate normal epithelium cell line (RWPE-1) and prostate cancer cell lines (DU145, PC3, LNCaP and 22Rvl). Scale bar: 200 nm. n = 1 experimental replicate per cell line, b Size distribution determined by nanoparticle tracking analysis. Each curve represents the mean size for each biological replicate of cEV-P20 (top) or cEV-P150 (bottom). Experimental replicates: cEV- P20RWPE1 = 2, CEV-P150RWPE1 = 3; cEV-P20DU145 = 2, cEV- P150DU145 = 2; cEV- P20PC3 = 4, cEV-P150PC3 = 3; cEV-P20LNCaP = 3, cEV-P150LNCaP = 4;cEV-P2022Rvl = 3, cEV-P15022Rvl = 3. c Wide-view negative stain TEM images of cEV- P20 (top) and cEV-P150 (bottom) isolated from conditioned media of 22Rvl prostate cancer cell line. Scale bar: 200 nm. n = 1 experimental replicate per cell line, d Number of cEV-P20 and cEV-P150 particles determined by nanoparticle tracking analysis. Experimental replicates: cEV- P20RWPE1 = 2, CEV-P150RWPE1 = 3; cEV-P20DU145 = 2, cEV- P150DU145 = 2; cEV- P20PC3 = 4, cEV-P150PC3 = 3; cEV-P20LNCaP = 3, cEV-P150LNCaP = 4;cEV-P2022Rvl = 3, cEV-P15022Rvl = 3. e Protein counts for each cell line-derived sample type, colored by cell line, n = 3 experimental replicates per cell line. P-values from two- sided Wilcoxon signed- rank test, f Fraction of shared or unique proteins between cEV- P20 and cEV-P150 fractions across cell lines, g Spearman’s p between log2 mean protein abundance in prostate tissue (157 samples), and individual samples from tissue, whole cell lysate (WCL, 5 cell lines), uEVs ( iEV-P20 = 146 samples, / / cEV-P20 = 5 cell lines, iEV- P150 = 148, wcEV-P150 = 5 cell lines), and soluble proteins (uSP, n = 175). Each data point from cell line samples represents the mean of 3 experimental replicates per cell line. P- values from two-sided Mann-Whitney Utest, h z-scores of log2 protein abundance of select EV and non-EV markers (MISEV 20185) in WCL, CEV-P20 and cEV-P150.

[0034] FIGs. 12A-12C show prostate cancer vs. non-cancer proteomes across sample types, a Distribution of estimated variability for proteins in each urine fraction, estimated using intraclass correlation coefficient (ICC), n = 5 patients, b Overlap of proteins significantly upregulated in non-cancer fractions (top panel) and prostate cancer fractions (bottom panel) using a two-sided Mann-Whitney U test with Benjamini -Hochberg correction, FDR < 0.05. c Gene set enrichment analysis Hallmark-enriched gene sets in prostate cancers and non-cancers across sample types. Only gene sets that were significantly enriched (FDR < 0.25) in at least one sample type are shown. Crosses denote non-significant gene sets (FDR > 0.25). uSP: urinary soluble proteins; uEV: urinary extracellular vesicles; NES: normalized enrichment score.

[0035] FIGs. 13A-13D show performance for cISUP Grade Group classifier a Receiver operator characteristic (ROC) curve of multi-protein model performance in classifying cISUP GG>1 or cISUP GG 1. Dotted lines show performance of serum PSA. b log2 fold changes of proteins in the uSP multi-protein model (a) in the discovery cohort and two independent validation cohorts. Patients in Discovery: cISUP GG > 1 = 61, cISUP GG 1 = 50; Validation 1 : cISUP GG > 1 = 114, cISUP GG 1 = 85; Validation 2: cISUP GG > 1 = 42, cISUP GG 1 = 28. c The performance (AUC) for all signature proteins in a univariate model for classifying patients with cISUP GG > 1 or GG 1 tumors with differences in protein abundance and intraclass coefficient (ICC). Grey background in dotmap denotes FDR < 0.05. Crosses denote proteins not detected in half of samples in that urine fraction, d ROC curve of cISUP GG protein model performance in the discovery cohort for classifying prostate cancers (PCa) vs. non-cancers (NC). Dotted lines show performance of serum PSA for uEVs (patients with matched uEV-P20 and uEV-P150) and uSP.

[0036] FIGs. 14A-14G show pathways implicated in EV cargo dysregulation. a Dot plot of log2 odds ratio (OR) of over-representation for each term. Background shading indicates significant terms (FDR < 0.05, Fisher’s exact test), b-d STRING and pathway analysis of proteins in uSP (b), uEV-P20 (c) or uEV-P150 (d) belonging to core or tumor gene sets (i.e., differentially abundant in PCa / NC). Nodes represent proteins, and edges represent protein-protein associations6. Each cluster is annotated with pathway terms and P-value in parentheses, e Summary of uEV-P20 grade markers with predicted cell surface localization from Figure 7c. Proteins are annotated with the frequency of detection in each fraction (left panel), and differential abundance in cISUP GG > 1 vs. GG 1 (right panel), f Summary of coreuEV proteins with predicted cell surface localization from Figure 7c, annotated with the frequency of detection in each fraction (left panel), differential abundance in PCa vs. NC uEVs, cISUP GG uEVs, and PCa vs. NC cEVs. uEV: urinary EV, cEV: cell line EV. g Subcellular annotation? of all disease-specific and core markers for all fractions.

[0037] FIG. 15 is a schematic diagram of an exemplary workflow 100 for the detection of peptide structures associated with a disease state for use in diagnosis and / or treatment in accordance with one or more embodiments.

[0038] FIG. 16A is a schematic diagram of a preparation workflow in accordance with one or more embodiments.

[0039] FIG. 16B is a schematic diagram of data acquisition in accordance with one or more embodiments.

[0040] FIG. 17 is a block diagram of an analysis system in accordance with one or more embodiments.

[0041] FIG. 18 is a block diagram of a computer system in accordance with various embodiments.DETAILED DESCRIPTION OF THE INVENTION

[0042] Urine is a complex biofluid that non-invasively reflects both an individual’s overall physiologic state and of the genitourinary tissues through which it passes. Urine includes both secreted proteins and proteins encapsulated in tissue-derived extracellular vesicles (EVs). To understand the population variability and clinical utility of urine, aspects herein quantified the secreted and EV proteomes. Aspects herein demonstrate that a simple protocol enriches prostatic proteins in urine, and show that secreted and EV proteins can reflect different subcellular compartments. Urinary proteomes can be faithful surrogates of tissue proteomes, but proteins secreted from cell lines are not. In certain aspects, the urinary proteome is longitudinally stable, and accurately distinguishes malignant from benign prostatic lesions, and can risk-stratify prostate tumors. Taken together, aspects herein can quantify the complexity of the urinary proteome, and reveal the synergistic value of distinguishing secreted and EV proteomes for biomarker studies.I. Overview of Exemplary Workflow

[0043] FIG. 15 is a schematic diagram of an exemplary workflow 100 for the detection of peptide structures associated with a disease state for use in diagnosis and / or treatment inaccordance with one or more embodiments. Workflow 100 may include various operations including, for example, sample collection 102, sample intake 104, sample preparation and processing 106, data analysis 108, and output generation 110.

[0044] Sample collection 102 may include, for example, obtaining a biological sample 112 of one or more subjects, such as subject 114. Biological sample 112 may take the form of a specimen obtained via one or more sampling methods. Biological sample 112 may be representative of subject 114 as a whole or of a specific tissue, cell type, or other category or sub-category of interest. Biological sample 112 may be obtained in any of a number of different ways. In various embodiments, biological sample 112 includes a urine sample 116. Biological samples 112 may include nucleotides (e.g., ssDNA, dsDNA, RNA), organelles, amino acids, peptides, proteins, carbohydrates, glycoproteins, or any combination thereof.

[0045] In various embodiments, a single run can analyze a sample (e.g., the sample including a peptide analyte), an external standard (e.g., an NGEP of a serum sample), and an internal standard. As such, abundance values (e.g., abundance or raw abundance) for the external standard, the internal standard, and target peptide analyte can be determined by mass spectrometry in the same run.

[0046] In various embodiments, external standards may be analyzed prior to analyzing samples. In various embodiments, the external standards can be run independently between the samples. In some embodiments, external standards can be analyzed after every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more experiments. In various embodiments, external standard data can be used in some or all of the normalization systems and methods described herein. In additional embodiments, blank samples may be processed to prevent column fouling.

[0047] Sample intake 104 may include one or more various operations such as, for example, aliquoting, registering, processing, storing, thawing, and / or other types of operations. In one or more embodiments, when biological sample 112 includes urine sample 116, sample intake 104 includes aliquoting urine sample 116 to form a set of aliquoted samples that can then be sub-aliquoted to form set of samples 120.

[0048] Sample preparation and processing 106 may include centrifugation of sample 112 and / or urine sample 116.

[0049] Sample preparation and processing 106 may include, for example, one or more operations to form set of peptide structures 122. In various embodiments, set of peptide structures 122 may include various fragments of unfolded proteins that have undergone digestion and may be ready for analysis.

[0050] Further, sample preparation and processing 106 may include, for example, data acquisition 124 based on set of peptide structures 122. For example, data acquisition 124 may include use of, for example, but is not limited to, a liquid chromatography / mass spectrometry (LC / MS) system.

[0051] Data analysis 108 may include, for example, peptide structure analysis 126. In some embodiments, data analysis 108 also includes output generation 110. In other embodiments, output generation 110 may be considered a separate operation from data analysis 108. Output generation 110 may include, for example, generating final output 128 based on the results of peptide structure analysis 126. Final output 128 may be used for determining research, diagnosis, and / or treatment.

[0052] In various embodiments, final output 128 is comprised of one or more outputs. Final output 128 may take various forms. For example, final output 128 may be a report that includes, for example, a diagnosis output, a treatment output (e.g., a treatment design output, a treatment plan output, or combination thereof), analyzed data (e.g., relativized and normalized) or combination thereof. In some embodiments, final output 128 may be an alert (e.g., a visual alert, an audible alert, etc.), a notification (e.g., a visual notification, an audible notification, an email notification, etc.), an email output, or a combination thereof. In some embodiments, final output 128 may be sent to remote system 130 for processing. Remote system 130 may include, for example, a computer system, a server, a processor, a cloud computing platform, cloud storage, a laptop, a tablet, a smartphone, some other type of mobile computing device, or a combination thereof.

[0053] In other embodiments, workflow 100 may optionally exclude one or more of the operations described herein and / or may optionally include one or more other steps or operations other than those described herein (e.g., in addition to and / or instead of those described herein). For example, in one or more embodiments, final output 128 may not be sent to remote system 130 for processing. Instead, a notification or a communication (e.g., email) may be sent to remote system 130 to notify a user(s) or entity that final output 128 is available for retrieval (e.g., download). Accordingly, workflow 100 may be implemented in any of a number of different ways for use in the research, diagnosis, and / or treatment of a disease state.II. Detection and Quantification of Peptide Structures

[0054] FIGs. 16A and 16B are schematic diagrams of a workflow for sample preparation and processing 106 in accordance with one or more embodiments. FIGs. 16A and 16B aredescribed with continuing reference to FIG. 15. Sample preparation and processing 106 may include, for example, preparation workflow 200 shown in FIG. 16A and data acquisition 124 shown in Figure 16B.A. Sample Preparation and Processing

[0055] FIG. 16A is a schematic diagram of preparation workflow 200 in accordance with one or more embodiments. Preparation workflow 200 may be used to prepare a sample, such as a sample of set of samples 120 in FIG. 15, for analysis via data acquisition 124. For example, this analysis may be performed via mass spectrometry (e.g. , LC-MS). In various embodiments, preparation workflow 200 may include denaturation and reduction 202, alkylation 204, and digestion 206.

[0056] In general, polymers, such as proteins, in their native form, can fold to include secondary, tertiary, and / or other higher order structures. Such higher order structures may functionalize proteins to complete tasks (e.g., enable enzymatic activity) in a subject. Further, such higher order structures of polymers may be maintained via various interactions between side chains of amino acids within the polymers. Such interactions can include ionic bonding, hydrophobic interactions, hydrogen bonding, and disulfide linkages between cysteine residues. However, when using analytic systems and methods, including mass spectrometry, unfolding such polymers (e.g., peptide / protein molecules) may be desired to obtain sequence information. In some embodiments, unfolding a polymer may include denaturing the polymer, which may include, for example, linearizing the polymer.

[0057] In one or more embodiments, denaturation and reduction 202 can be used to disrupt higher order structures (e.g., secondary, tertiary, quaternary, etc. of one or more proteins (e.g., polypeptides and peptides) in a sample (e.g., one of set of samples 120 in FIG. 15). Denaturation and reduction 202 includes, for example, a denaturation procedure and a reduction procedure. In some embodiments, the denaturation procedure may be performed using, for example, thermal denaturation, where heat is used as a denaturing agent. The thermal denaturation can disrupt ionic bonding, hydrophobic interactions, and / or hydrogen bonding.

[0058] In various embodiments, the denaturation procedure may include using one or more denaturing agents, temperature (e.g., heat), or a combination thereof. In one or more embodiments, the denaturation procedure may include using one or more denaturing agents in combination with heat. These one or more denaturing agents may include, for example, but are not limited to, any number of chaotropic salts (e.g., urea, guanidine), surfactants (e.g.,sodium dodecyl sulfate (SDS), beta octyl glucoside, Triton X-100), or combination thereof. In some cases, such denaturing agents may be used in combination with heat when sample preparation workflow further includes a cleanup procedure.

[0059] The resulting one or more denatured (e.g., unfolded, linearized) proteins may then undergo further processing in preparation of analysis. For example, a reduction procedure may be performed in which one or more reducing agents are applied. In various embodiments, a reducing agent can produce an alkaline pH. A reducing agent may take the form of, for example, without limitation, dithiothreitol (DTT), tris(2-carboxyethyl)phosphine (TCEP), or some other reducing agent. The reducing agent may reduce (e.g. , cleave) the disulfide linkages between cysteine residues of the one or more denatured proteins to form one or more reduced proteins.

[0060] In various embodiments, the one or more reduced proteins resulting from denaturation and reduction 202 may undergo a process to prevent the reformation of disulfide linkages between, for example, the cysteine residues of the one or more reduced proteins. This process may be implemented using alkylation 204 to form one or more alkylated proteins. For example, alkylation 204 may be used to add an acetamide group to a sulfur on each cysteine residue to prevent disulfide linkages from reforming. In various embodiments, an acetamide group can be added by reacting one or more alkylating agents with a reduced protein. The one or more alkylating agents may include, for example, one or more acetamide salts. An alkylating agent may take the form of, for example, iodoacetamide (IAA), 2-chloroacetamide, some other type of acetamide salt, or some other type of alkylating agent.

[0061] In some embodiments, alkylation 204 may include a quenching procedure. The quenching procedure may be performed using one or more reducing agents (e.g., one or more of the reducing agents described above).

[0062] In various embodiments, the one or more alkylated proteins formed via alkylation 204 can then undergo digestion 206 in preparation for analysis (e.g., mass spectrometry analysis). Digestion 206 of a protein may include cleaving the protein at or around one or more cleavage sites (e.g., site 205 which may be one or more amino acid residues). For example, without limitation, an alkylated protein may be cleaved at the carboxyl side of the lysine or arginine residues. This type of cleavage may break the protein into various segments, which include one or more peptide structures.

[0063] In various embodiments, digestion 206 is performed using one or more proteolysis catalysts. For example, an enzyme can be used in digestion 206. In some embodiments, the enzyme takes the form of trypsin. In other embodiments, one or more other types of enzymes(e.g., proteases) may be used in addition to or in place of trypsin. These one or more other enzymes include, but are not limited to, LysC, LysN, AspN, GluC, and ArgC. In some embodiments, digestion 206 may be performed using tosyl phenylalanyl chloromethyl ketone (TPCK)-treated trypsin, one or more engineered forms of trypsin, one or more other formulations of trypsin, or a combination thereof. In some embodiments, digestion 206 may be performed in multiple steps, with each involving the use of one or more digestion agents. For example, a secondary digestion, tertiary digestion, etc. may be performed. In one or more embodiments, trypsin is used to digest serum samples. In one or more embodiments, trypsin / LysC cocktails are used to digest plasma samples.

[0064] In some embodiments, digestion 206 further includes a quenching procedure. The quenching procedure may be performed by acidifying the sample (e.g., to a pH <3). In some embodiments, formic acid may be used to perform this acidification.

[0065] In various embodiments, preparation workflow 200 further includes post-digestion procedure 207. Post-digestion procedure 207 may include, for example, a cleanup procedure. The cleanup procedure may include, for example, the removal of unwanted components in the sample that results from digestion 206. For example, unwanted components may include, but are not limited to, inorganic ions, surfactants, etc. In some embodiments, post-digestion procedure 207 further includes a procedure for the addition of heavy-labeled peptide internal standards.Although preparation workflow 200 has been described with respect to a sample created or taken from biological sample 112 that is blood-based (e.g., a whole blood sample, a plasma sample, a serum sample, etc.), sample preparation workflow 200 may be similarly implemented for other types of samples (e.g., tears, urine, tissue, interstitial fluids, sputum, etc.) to produce set of peptides structures 122.B. Peptide Structure Identification and Quantitation

[0066] FIG. 16B is a schematic diagram of data acquisition 124 in accordance with one or more embodiments. In various embodiments, data acquisition 124 can commence following sample preparation 200 described in FIG. 16A. In various embodiments, data acquisition 124 can comprise quantification 208, quality control 210, and peak integration and normalization 212.

[0067] In various embodiments, targeted quantification 208 of peptides can incorporate use of liquid chromatography-mass spectrometry LC / MS instrumentation. For example, LC-MS / MS, or tandem MS may be used. In general, LC / MS (e.g., LC-MS / MS) can combine the physical separation capabilities of liquid chromatograph (LC) with the mass analysis capabilities of mass spectrometry (MS). According to some embodiments described herein, this technique allows for the separation of digested peptides to be fed from the LC column into the MS ion source through an interface.

[0068] In various embodiments, any LC / MS device can be incorporated into the workflow described herein. In various embodiments, an instrument or instrument system suited for identification and targeted quantification 208 may include, for example, a Triple Quadrupole LC / MS™. In various embodiments, targeted quantification 208 is performed using multiple reaction monitoring mass spectrometry (MRM-MS).

[0069] In some cases, targeted quantification 208 includes using a specific collision energy associated for the appropriate fragmentation to consistently see an abundant product ion.

[0070] In various embodiments, quality control 210 procedures can be put in place to optimize data quality. In various embodiments, measures can be put in place allowing only errors within acceptable ranges outside of an expected value. In various embodiments, employing statistical models (e.g., using Westgard rules) can assist in quality control 210. For example, quality control 210 may include, for example, assessing the retention time and abundance of representative peptide structures and spiked-in internal standards, in either every sample, or in each quality control sample.

[0071] Peak integration and normalization 212 may be performed to process the data that has been generated and transform the data into a format for analysis. For example, peak integration and normalization 212 may include converting abundance data for various product ions that were detected for a selected peptide structure into a single quantification metric (e.g., a relative quantity, an adjusted quantity, a normalized quantity, a relative concentration, an adjusted concentration, a normalized concentration, etc.) for that peptide structure. In some embodiments, peak integration and normalization 212 may be performed using one or more of the techniques described in U.S. Patent Publication No. 2020 / 0372973A1 and / or US Patent Publication No. 2020 / 0240996A1, the disclosures of which are incorporated by reference herein in their entireties.III. Peptide Structure Data AnalysisA. Exemplary System for Peptide Structure Data Analysis1. Analysis System for Peptide Structure Data Analysis and Treatment Response Prediction

[0072] FIG. 17 is a block diagram of an analysis system 300 in accordance with one or more embodiments. Analysis system 300 can be used to both detect and analyze various peptide structures that have been associated to various disease states. Analysis system 300 is one example of an implementation for a system that may be used to perform data analysis 108 in FIG. 15. Thus, analysis system 300 is described with continuing reference to workflow 100 as described in Figures 7, 8 A, and / or 8B.

[0073] Analysis system 300 may include computing platform 302 and data store 304. In some embodiments, analysis system 300 also includes display system 306. Computing platform 302 may take various forms. In one or more embodiments, computing platform 302 includes a single computer (or computer system) or multiple computers in communication with each other. In other examples, computing platform 302 takes the form of a cloud computing platform.

[0074] Data store 304 and display system 306 may each be in communication with computing platform 302. In some examples, data store 304, display system 306, or both may be considered part of or otherwise integrated with computing platform 302. Thus, in some examples, computing platform 302, data store 304, and display system 306 may be separate components in communication with each other, but in other examples, some combination of these components may be integrated together. Communication between these different components may be implemented using any number of wired communications links, wireless communications links, optical communications links, or a combination thereof.

[0075] Analysis system 300 includes, for example, treatment management system 308, which may be implemented using hardware, software, firmware, or a combination thereof. In one or more embodiments, treatment management system 308 is implemented using computing platform 302.

[0076] Treatment management system 308 may be used to manage the treatment of a subject diagnosed with prostate cancer. Treatment management system 308 may be used to predict the subject’s response to a treatment for the prostate cancer disease state, determine whether to administer the treatment based on the predicted response, determine whether to modify and / or otherwise change the treatment based on the predicted response, and / or otherwise plan the treatment of the subject.

[0077] Treatment management system 308 receives peptide structure data 310 for processing. Peptide structure data 310 may have been generated using multiple reaction monitoring mass spectrometry. Peptide structure data 310 may be, for example, the peptide structure data that is output from sample preparation and processing 106 in Figures 7, 8 A, and 8B. Accordingly, peptide structure data 310 may correspond to set of peptide structures 122 identified for biological sample 112 and may thereby correspond to biological sample 112. Further, as set of peptide structures 122 corresponds to a set of proteins (e.g., each peptide structure of set of peptide structures 122 being derived from a corresponding protein), peptide structure data 310 therefore corresponds to the set of proteins.

[0078] Peptide structure data 310 can be sent as input into treatment management system 308, retrieved from data store 304 or some other type of storage (e.g., cloud storage), accessed from cloud storage, or obtained in some other manner. In some cases, peptide structure data 310 may be retrieved from data store 304 in response to (e.g., directly or indirectly based on) receiving user input entered by a user via an input device.

[0079] Treatment management system 308 may include scoring system 312, treatment planning system 314, or both. Scoring system 312 may be used to predict the response of a subject (e.g., subject 114) to a treatment. Treatment planning system 314 may be used to determine whether to and / or how to treat the subject based on the predicted response for the subject.

[0080] Scoring system 312 may include, for example, model 315 that is configured to receive peptide structure data 310 for processing. Model 315 may be implemented in any of a number of different ways. Model 315 may be a computational model system that may be implemented using any number of models, functions, equations, algorithms, and / or other mathematical techniques.

[0081] In one or more embodiments, scoring system 312 receives peptide structure data 310 for processing and inputs quantification data 316 identified from peptide structure data 310 for set of peptide structures 318 into model 315. Peptide structure data 310 may comprise a set of quantification metrics for each peptide structure of, for example, set of peptide structures 122 in FIG. 15. A quantification metric for a peptide structure may be comprised of at least one of a relative abundance, a normalized abundance, an adjusted abundance, a relative quantity, an adjusted quantity, a normalized quantity, a relative concentration, an adjusted concentration, or a normalized concentration. Accordingly, quantification data 316 may include one or more quantification metrics for each peptide structure of set of peptide structures 318.

[0082] A peptide structure of set of peptide structures 318 may be an peptide structure that is defined by a peptide sequence. For example, the peptide structure may be a peptide or a portion of a peptide and may be referred to as a quantification peptide.

[0083] Set of peptide structures 318 may be identified as being those most predictive or relevant to the response of a subject to a corresponding treatment(s) based on training or fitting of model 315. In one or more embodiments, set of peptide structures 318 includes at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, or all 22 of the peptide structures disclosed herein.

[0084] Model 315 analyzes quantification data 316 for set of peptide structures 318 to generate response score 320 corresponding to a selected treatment for prostate cancer. The selected treatment may be, for example, an immunotherapy.

[0085] Treatment planning system 314 receives response score 320 from scoring system 312. Treatment planning system 314 uses response score 320 to generate treatment response output 322. Treatment response output 322 may be one example of an output in final output 128 described above with respect to FIG. 15. Treatment response output 322 may include, for example, an identification or categorization of the response of the subject to the treatment, at least one of an identification of a therapeutic (e.g. , immunotherapy) to treat the subj ect, a design for the therapeutic, a treatment plan for administering the therapeutic, or a combination thereof. In various embodiments, treatment response output 322 includes a therapeutic dosage for each therapeutic to be used in treating the subject.

[0086] In one or more embodiments, treatment response output 334 identifies a response classification that indicates a predicted response for the subject to a treatment. For example, response score 320 may be used to predict whether a subject will be a likely responder to treatment or a likely non-responder to treatment.

[0087] In one or more embodiments, treatment planning system 314 uses selected threshold 324 to generate treatment response output 322 based on response score 320. In one or more embodiments, selected threshold 324 is determined based on the initial training / fitting of model 315. For example, treatment planning system 314 may determine that a response score 320 below selected threshold 324 indicates that the subject is a likely responder to treatment, whereas a response score 320 at or above selected threshold 324 indicates that the subject is a likely non-responder to treatment. In one or more embodiments, treatment planning system 314 may determine that a response score 320 at or below selected threshold 324 indicates thatthe subject is a likely responder to treatment, whereas a response score 320 above selected threshold 324 indicates that the subject is a likely non-responder to treatment.

[0088] In one or more embodiments, treatment response output 322 may be sent to remote system 130 for processing in some examples. In other embodiments, treatment response output 322 may be displayed on graphical user interface 326 in display system 306 for viewing by a human operator. The human operator may use treatment response output 322 to manage the prostate cancer treatment of the subject.2. Computer Implemented System

[0089] FIG. 18 is a block diagram of a computer system in accordance with various embodiments. Computer system 400 may be an example of one implementation for computing platform 302 described above in FIG. 17.

[0090] In one or more examples, computer system 400 can include a bus 402 or other communication mechanism for communicating information, and a processor 404 coupled with bus 402 for processing information. In various embodiments, computer system 400 can also include a memory, which can be a random-access memory (RAM) 406 or other dynamic storage device, coupled to bus 402 for determining instructions to be executed by processor 404. Memory also can be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 404. In various embodiments, computer system 400 can further include a read only memory (ROM) 408 or other static storage device coupled to bus 402 for storing static information and instructions for processor 404. A storage device 410, such as a magnetic disk or optical disk, can be provided and coupled to bus 402 for storing information and instructions.

[0091] In various embodiments, computer system 400 can be coupled via bus 402 to a display 412, such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information to a computer user. An input device 414, including alphanumeric and other keys, can be coupled to bus 402 for communicating information and command selections to processor 404. Another type of user input device is a cursor control 416, such as a mouse, a joystick, a trackball, a gesture input device, a gaze-based input device, or cursor direction keys for communicating direction information and command selections to processor 404 and for controlling cursor movement on display 412. This input device 414 typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane. However, it should be understood that input devices 414allowing for three-dimensional (e.g., x, y, and z) cursor movement are also contemplated herein.

[0092] Consistent with certain implementations of the present teachings, results can be provided by computer system 400 in response to processor 404 executing one or more sequences of one or more instructions contained in RAM 406. Such instructions can be read into RAM 406 from another computer-readable medium or computer-readable storage medium, such as storage device 410. Execution of the sequences of instructions contained in RAM 406 can cause processor 404 to perform the processes described herein. Alternatively, hard-wired circuitry can be used in place of or in combination with software instructions to implement the present teachings. Thus, implementations of the present teachings are not limited to any specific combination of hardware circuitry and software.

[0093] The term “computer-readable medium” e.g., data store, data storage, storage device, data storage device, etc.) or “computer-readable storage medium” as used herein refers to any media that participates in providing instructions to processor 404 for execution. Such a medium can take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Examples of non-volatile media can include, but are not limited to, optical, solid state, magnetic disks, such as storage device 410. Examples of volatile media can include, but are not limited to, dynamic memory, such as RAM 406. Examples of transmission media can include, but are not limited to, coaxial cables, copper wire, and fiber optics, including the wires that comprise bus 402.

[0094] Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, or any other tangible medium from which a computer can read.

[0095] In addition to computer readable medium, instructions or data can be provided as signals on transmission media included in a communications apparatus or system to provide sequences of one or more instructions to processor 404 of computer system 400 for execution. For example, a communication apparatus may include a transceiver having signals indicative of instructions and data. The instructions and data are configured to cause one or more processors to implement the functions outlined in the disclosure herein. Representative examples of data communications transmission connections can include, but are not limited to, telephone modem connections, wide area networks (WAN), local area networks (LAN), infrared data connections, NFC connections, optical communications connections, etc.

[0096] It should be appreciated that the methodologies described herein, flow charts, diagrams, and accompanying disclosure can be implemented using computer system 400 as a standalone device or on a distributed network of shared computer processing resources such as a cloud computing network.

[0097] The methodologies described herein may be implemented by various means depending upon the application. For example, these methodologies may be implemented in hardware, firmware, software, or any combination thereof. For a hardware implementation, the processing unit may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, or a combination thereof.

[0098] In various embodiments, the methods of the present teachings may be implemented as firmware and / or a software program and applications written in conventional programming languages such as C, C++, Python, etc. If implemented as firmware and / or software, the embodiments described herein can be implemented on a non-transitory computer-readable medium in which a program is stored for causing a computer to perform the methods described above. It should be understood that the various engines described herein can be provided on a computer system, such as computer system 400, whereby processor 404 would execute the analyses and determinations provided by these engines, subject to instructions provided by any one of, or a combination of, the memory components RAM 406, ROM, 408, or storage device 410 and user input provided via input device 414.IV. Sample Preparation

[0099] In certain aspects, methods involve obtaining a sample from a subject. The methods of obtaining provided herein may include methods of biopsy such as fine needle aspiration, core needle biopsy, vacuum assisted biopsy, incisional biopsy, excisional biopsy, punch biopsy, shave biopsy or skin biopsy. In certain embodiments the sample is obtained from a biopsy from esophageal tissue by any of the biopsy methods previously mentioned. In other embodiments the sample may be obtained from any of the tissues provided herein that include but are not limited to non-cancerous or cancerous tissue and non-cancerous or cancerous tissue from the serum, gall bladder, mucosal, skin, heart, lung, breast, pancreas, blood, liver, muscle, kidney, smooth muscle, bladder, colon, intestine, brain, prostate, esophagus, or thyroid tissue.Alternatively, the sample may be obtained from any other source including but not limited to urine, blood, sweat, hair follicle, buccal tissue, tears, menses, feces, or saliva. In certain aspects of the current methods, any medical professional such as a doctor, nurse or medical technician may obtain a biological sample for testing. Yet further, the biological sample can be obtained without the assistance of a medical professional.

[0100] A sample may include but is not limited to, tissue, cells, or biological material from cells or derived from cells of a subject. The biological sample may be a heterogeneous or homogeneous population of cells or tissues. The biological sample may be obtained using any method known to the art that can provide a sample suitable for the analytical methods described herein. The sample may be obtained by non-invasive methods including but not limited to: scraping of the skin or cervix, swabbing of the cheek, saliva collection, urine collection, feces collection, collection of menses, tears, or semen.

[0101] The sample may be obtained by methods known in the art. In certain embodiments the samples are obtained by biopsy. In other embodiments the sample is obtained by swabbing, endoscopy, scraping, phlebotomy, or any other methods known in the art. In some cases, the sample may be obtained, stored, or transported using components of a kit of the present methods. In some cases, multiple samples, such as multiple esophageal samples may be obtained for diagnosis by the methods described herein. In other cases, multiple samples, such as one or more samples from one tissue type (for example esophagus) and one or more samples from another specimen (for example serum) may be obtained for diagnosis by the methods. In some cases, multiple samples such as one or more samples from one tissue type (e.g. esophagus) and one or more samples from another specimen (e.g. serum) may be obtained at the same or different times. Samples may be obtained at different times are stored and / or analyzed by different methods. For example, a sample may be obtained and analyzed by routine staining methods or any other cytological analysis methods.

[0102] In some embodiments the biological sample may be obtained by a physician, nurse, or other medical professional such as a medical technician, endocrinologist, cytologist, phlebotomist, radiologist, or a pulmonologist. The medical professional may indicate the appropriate test or assay to perform on the sample. In certain aspects a molecular profiling business may consult on which assays or tests are most appropriately indicated. In further aspects of the current methods, the patient or subject may obtain a biological sample for testing without the assistance of a medical professional, such as obtaining a whole blood sample, a urine sample, a fecal sample, a buccal sample, or a saliva sample.

[0103] In other cases, the sample is obtained by an invasive procedure including but not limited to: biopsy, needle aspiration, endoscopy, or phlebotomy. The method of needle aspiration may further include fine needle aspiration, core needle biopsy, vacuum assisted biopsy, or large core biopsy. In some embodiments, multiple samples may be obtained by the methods herein to ensure a sufficient amount of biological material.

[0104] General methods for obtaining biological samples are also known in the art. Publications such as Ramzy, Ibrahim Clinical Cytopathology and Aspiration Biopsy 2001, which is herein incorporated by reference in its entirety, describes general methods for biopsy and cytological methods. In one embodiment, the sample is a fine needle aspirate of a esophageal or a suspected esophageal tumor or neoplasm. In some cases, the fine needle aspirate sampling procedure may be guided by the use of an ultrasound, X-ray, or other imaging device.

[0105] In some embodiments of the present methods, the molecular profiling business may obtain the biological sample from a subject directly, from a medical professional, from a third party, or from a kit provided by a molecular profiling business or a third party. In some cases, the biological sample may be obtained by the molecular profiling business after the subject, a medical professional, or a third party acquires and sends the biological sample to the molecular profiling business. In some cases, the molecular profiling business may provide suitable containers, and excipients for storage and transport of the biological sample to the molecular profiling business.

[0106] In some embodiments of the methods described herein, a medical professional need not be involved in the initial diagnosis or sample acquisition. An individual may alternatively obtain a sample through the use of an over the counter (OTC) kit. An OTC kit may contain a means for obtaining said sample as described herein, a means for storing said sample for inspection, and instructions for proper use of the kit. In some cases, molecular profiling services are included in the price for purchase of the kit. In other cases, the molecular profiling services are billed separately. A sample suitable for use by the molecular profiling business may be any material containing tissues, cells, nucleic acids, genes, gene fragments, expression products, gene expression products, or gene expression product fragments of an individual to be tested. Methods for determining sample suitability and / or adequacy are provided.

[0107] In some embodiments, the subject may be referred to a specialist such as an oncologist, surgeon, or endocrinologist. The specialist may likewise obtain a biological sample for testing or refer the individual to a testing center or laboratory for submission of the biological sample. In some cases the medical professional may refer the subject to a testingcenter or laboratory for submission of the biological sample. In other cases, the subject may provide the sample. In some cases, a molecular profiling business may obtain the sample.V. Kits

[0108] Certain aspects of the present disclosure also concern kits containing compositions of the disclosure or compositions to implement methods disclosed herein. In some embodiments, kits can be used to evaluate one or more biomarkers. In certain embodiments, a kit contains, contains at least or contains at most 1, 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, 45, 46, 47, 48, 49, 50, 100, 500, 1,000 or more probes, primers or primer sets, synthetic molecules or inhibitors, or any value or range and combination derivable therein. In some embodiments, there are kits for evaluating biomarker activity in a cell.

[0109] Kits may comprise components, which may be individually packaged or placed in a container, such as a tube, bottle, vial, syringe, or other suitable container means.

[0110] Individual components may also be provided in a kit in concentrated amounts; in some embodiments, a component is provided individually in the same concentration as it would be in a solution with other components. Concentrations of components may be provided as lx, 2x, 5x, lOx, or 20x or more.[OHl] Kits for using probes, synthetic nucleic acids, nonsynthetic nucleic acids, and / or inhibitors of the disclosure for prognostic or diagnostic applications are included as part of the disclosure. Specifically contemplated are any such molecules corresponding to any biomarker identified herein, which includes nucleic acid primers / primer sets and probes that are identical to or complementary to all or part of a biomarker, which may include noncoding sequences of the biomarker, as well as coding sequences of the biomarker.

[0112] In certain aspects, negative and / or positive control nucleic acids, probes, and inhibitors are included in some kit embodiments. In addition, a kit may include a sample that is a negative or positive control for methylation of one or more biomarkers.

[0113] It is contemplated that any method or composition described herein can be implemented with respect to any other method or composition described herein and that different embodiments may be combined. The claims originally filed are contemplated to cover claims that are multiply dependent on any filed claim or combination of filed claims.

[0114] Any embodiment of the disclosure involving specific biomarker by name is contemplated also to cover embodiments involving biomarkers whose sequences are at least80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99% identical to the biomarker.

[0115] Embodiments of the disclosure include kits for analysis of a pathological sample by assessing biomarker profile for a sample comprising, in suitable container means, two or more biomarker probes, wherein the biomarker probes detect one or more of the biomarkers identified herein. The kit can further comprise reagents for labeling nucleic acids in the sample. The kit may also include labeling reagents, including at least one of amine-modified nucleotide, poly(A) polymerase, and poly(A) polymerase buffer. Labeling reagents can include an aminereactive dye.

[0116] Aspects of the disclosure include compositions comprising one or more precursor ions having a defined charge and / or defined mass-to-charge (m / z) ratio. Aspects of the disclosure include compositions comprising one or more product ions having a defined mass- to-charge (m / z) ratio, which product ions are produced by converting a peptide structure described herein into a gas phase ion in a mass spectrometry system. Conversion of the peptide structure into a gas phase ion can take place using any of a variety of techniques, including, but not limited to, matrix assisted laser desorption ionization (MALDI); electron ionization (El); electrospray ionization (ESI); atmospheric pressure chemical ionization (APCI); and / or atmospheric pressure photo ionization (APPI).VI. Cancer Therapy

[0117] Certain methods disclosed herein comprise administering a therapy, including a cancer therapy to the patient. The cancer therapy may be chosen based on the protein level measurements, alone or in combination with a calculated score for the patient. In some embodiments, the cancer therapy comprises a local cancer therapy. In some embodiments, the cancer therapy excludes a systemic cancer therapy. In some embodiments, the cancer therapy excludes a local therapy. In some embodiments, the cancer therapy comprises a local cancer therapy without the administration of a system cancer therapy. In some embodiments, the cancer therapy comprises an immunotherapy, which may be an immune checkpoint therapy. Any of these cancer therapies may also be excluded. Combinations of these therapies may also be administered.

[0118] The term “cancer,” as used herein, may be used to describe a solid tumor, metastatic cancer, or non-metastatic cancer. In certain embodiments, the cancer may originate in the bladder, blood, bone, bone marrow, brain, breast, colon, esophagus, duodenum, small intestine,large intestine, colon, rectum, anus, gum, head, kidney, liver, lung, nasopharynx, neck, ovary, pancreas, prostate, skin, stomach, testis, tongue, or uterus. In some embodiments, the cancer is recurrent cancer. In some embodiments, the cancer is Stage I cancer. In some embodiments, the cancer is Stage II cancer. In some embodiments, the cancer is Stage III cancer. In some embodiments, the cancer is Stage IV cancer.

[0119] The cancer may specifically be of the following histological type, though it is not limited to these: neoplasm, malignant; carcinoma; carcinoma, undifferentiated; giant and spindle cell carcinoma; small cell carcinoma; papillary carcinoma; squamous cell carcinoma; lymphoepithelial carcinoma; basal cell carcinoma; pilomatrix carcinoma; transitional cell carcinoma; papillary transitional cell carcinoma; adenocarcinoma; gastrinoma, malignant; cholangiocarcinoma; hepatocellular carcinoma; combined hepatocellular carcinoma and cholangiocarcinoma; trabecular adenocarcinoma; adenoid cystic carcinoma; adenocarcinoma in adenomatous polyp; adenocarcinoma, familial polyposis coli; solid carcinoma; carcinoid tumor, malignant; branchiolo-alveolar adenocarcinoma; papillary adenocarcinoma; chromophobe carcinoma; acidophil carcinoma; oxyphilic adenocarcinoma; basophil carcinoma; clear cell adenocarcinoma; granular cell carcinoma; follicular adenocarcinoma; papillary and follicular adenocarcinoma; nonencapsulating sclerosing carcinoma; adrenal cortical carcinoma; endometroid carcinoma; skin appendage carcinoma; apocrine adenocarcinoma; sebaceous adenocarcinoma; ceruminous adenocarcinoma; mucoepidermoid carcinoma; cystadenocarcinoma; papillary cystadenocarcinoma; papillary serous cystadenocarcinoma; mucinous cystadenocarcinoma; mucinous adenocarcinoma; signet ring cell carcinoma; infiltrating duct carcinoma; medullary carcinoma; lobular carcinoma; inflammatory carcinoma; paget’s disease, mammary; acinar cell carcinoma; adenosquamous carcinoma; adenocarcinoma w / squamous metaplasia; thymoma, malignant; ovarian stromal tumor, malignant; thecoma, malignant; granulosa cell tumor, malignant; androblastoma, malignant; sertoli cell carcinoma; leydig cell tumor, malignant; lipid cell tumor, malignant; paraganglioma, malignant; extra-mammary paraganglioma, malignant; pheochromocytoma; glomangiosarcoma; malignant melanoma; amelanotic melanoma; superficial spreading melanoma; malignant melanoma in giant pigmented nevus; epithelioid cell melanoma; blue nevus, malignant; sarcoma; fibrosarcoma; fibrous histiocytoma, malignant; myxosarcoma; liposarcoma; leiomyosarcoma; rhabdomyosarcoma; embryonal rhabdomyosarcoma; alveolar rhabdomyosarcoma; stromal sarcoma; mixed tumor, malignant; mullerian mixed tumor; nephroblastoma; hepatoblastoma; carcinosarcoma; mesenchymoma, malignant; brenner tumor, malignant; phyllodes tumor, malignant; synovial sarcoma; mesothelioma, malignant;dysgerminoma; embryonal carcinoma; teratoma, malignant; struma ovarii, malignant; choriocarcinoma; mesonephroma, malignant; hemangiosarcoma; hemangioendothelioma, malignant; kaposi’s sarcoma; hemangiopericytoma, malignant; lymphangiosarcoma; osteosarcoma; juxtacortical osteosarcoma; chondrosarcoma; chondroblastoma, malignant; mesenchymal chondrosarcoma; giant cell tumor of bone; ewing's sarcoma; odontogenic tumor, malignant; ameloblastic odontosarcoma; ameloblastoma, malignant; ameloblastic fibrosarcoma; pinealoma, malignant; chordoma; glioma, malignant; ependymoma; astrocytoma; protoplasmic astrocytoma; fibrillary astrocytoma; astroblastoma; glioblastoma; oligodendroglioma; oligodendroblastoma; primitive neuroectodermal; cerebellar sarcoma; ganglioneuroblastoma; neuroblastoma; retinoblastoma; olfactory neurogenic tumor; meningioma, malignant; neurofibrosarcoma; neurilemmoma, malignant; granular cell tumor, malignant; malignant lymphoma; hodgkin’s disease; hodgkin’s; paragranuloma; malignant lymphoma, small lymphocytic; malignant lymphoma, large cell, diffuse; malignant lymphoma, follicular; mycosis fungoides; other specified non-hodgkin’s lymphomas; malignant histiocytosis; multiple myeloma; mast cell sarcoma; immunoproliferative small intestinal disease; leukemia; lymphoid leukemia; plasma cell leukemia; erythroleukemia; lymphosarcoma cell leukemia; myeloid leukemia; basophilic leukemia; eosinophilic leukemia; monocytic leukemia; mast cell leukemia; megakaryoblastic leukemia; myeloid sarcoma; and hairy cell leukemia.A. Immunotherapies

[0120] In some aspects, the therapeutic treatment comprises a cancer immunotherapy. Cancer immunotherapy (sometimes called immuno-oncology, abbreviated IO) is the use of the immune system to treat cancer. Immunotherapies can be categorized as active, passive or hybrid (active and passive). These approaches exploit the fact that cancer cells often have molecules on their surface that can be detected by the immune system, known as tumour- associated antigens (TAAs); they are often proteins or other macromolecules (e.g. carbohydrates). Active immunotherapy directs the immune system to attack tumor cells by targeting TAAs. Passive immunotherapies enhance existing anti-tumor responses and include the use of monoclonal antibodies, lymphocytes and cytokines. Immumotherapies are known in the art, and some are described below.1. Inhibition of co-stimulatory molecules

[0121] In some aspects, the immunotherapy comprises an inhibitor of a co-stimulatory molecule. In some aspects, the inhibitor comprises an inhibitor of B7-1 (CD80), B7-2 (CD86),CD28, ICOS, 0X40 (TNFRSF4), 4-1BB (CD137; TNFRSF9), CD40L (CD40LG), GITR (TNFRSF18), and combinations thereof. Inhibitors include inhibitory antibodies, polypeptides, compounds, and nucleic acids.2. Dendritic cell therapy

[0122] Dendritic cell therapy provokes anti-tumor responses by causing dendritic cells to present tumor antigens to lymphocytes, which activates them, priming them to kill other cells that present the antigen. Dendritic cells are antigen presenting cells (APCs) in the mammalian immune system. In cancer treatment they aid cancer antigen targeting. One example of cellular cancer therapy based on dendritic cells is sipuleucel-T.

[0123] One method of inducing dendritic cells to present tumor antigens is by vaccination with autologous tumor lysates or short peptides (small parts of protein that correspond to the protein antigens on cancer cells). These peptides are often given in combination with adjuvants (highly immunogenic substances) to increase the immune and anti-tumor responses. Other adjuvants include proteins or other chemicals that attract and / or activate dendritic cells, such as granulocyte macrophage colony-stimulating factor (GM-CSF).

[0124] Dendritic cells can also be activated in vivo by making tumor cells express GM- CSF. This can be achieved by either genetically engineering tumor cells to produce GM-CSF or by infecting tumor cells with an oncolytic virus that expresses GM-CSF.

[0125] Another strategy is to remove dendritic cells from the blood of a patient and activate them outside the body. The dendritic cells are activated in the presence of tumor antigens, which may be a single tumor-specific peptide / protein or a tumor cell lysate (a solution of broken down tumor cells). These cells (with optional adjuvants) are infused and provoke an immune response.

[0126] Dendritic cell therapies include the use of antibodies that bind to receptors on the surface of dendritic cells. Antigens can be added to the antibody and can induce the dendritic cells to mature and provide immunity to the tumor. Dendritic cell receptors such as TLR3, TLR7, TLR8 or CD40 have been used as antibody targets.3. CAR-T cell therapy

[0127] Chimeric antigen receptors (CARs, also known as chimeric immunoreceptors, chimeric T cell receptors or artificial T cell receptors) are engineered receptors that combine a new specificity with an immune cell to target cancer cells. Typically, these receptors graft the specificity of a monoclonal antibody onto a T cell. The receptors are called chimeric becausethey are fused of parts from different sources. CAR-T cell therapy refers to a treatment that uses such transformed cells for cancer therapy.

[0128] The basic principle of CAR-T cell design involves recombinant receptors that combine antigen-binding and T-cell activating functions. The general premise of CAR-T cells is to artificially generate T-cells targeted to markers found on cancer cells. Scientists can remove T-cells from a person, genetically alter them, and put them back into the patient for them to attack the cancer cells. Once the T cell has been engineered to become a CAR-T cell, it acts as a “living drug”. CAR-T cells create a link between an extracellular ligand recognition domain to an intracellular signalling molecule which in turn activates T cells. The extracellular ligand recognition domain is usually a single-chain variable fragment (scFv). An important aspect of the safety of CAR-T cell therapy is how to ensure that only cancerous tumor cells are targeted, and not normal cells. The specificity of CAR-T cells is determined by the choice of molecule that is targeted.

[0129] Exemplary CAR-T therapies include Tisagenlecleucel (Kymriah) and Axicabtagene ciloleucel (Yescarta). In some aspects, the CAR-T therapy targets CD19.4. Cytokine therapy

[0130] Cytokines are proteins produced by many types of cells present within a tumor. They can modulate immune responses. The tumor often employs them to allow it to grow and reduce the immune response. These immune-modulating effects allow them to be used as drugs to provoke an immune response. Two commonly used cytokines are interferons and interleukins.

[0131] Interferons are produced by the immune system. They are usually involved in antiviral response, but also have use for cancer. They fall in three groups: type I (IFNa and IFNP), type II (IFNy) and type III (IF NX).

[0132] Interleukins have an array of immune system effects. IL-2 is an exemplary interleukin cytokine therapy.5. Adoptive T-cell therapy

[0133] Adoptive T cell therapy is a form of passive immunization by the transfusion of T- cells (adoptive cell transfer). They are found in blood and tissue and usually activate when they find foreign pathogens. Specifically they activate when the T-cell's surface receptors encounter cells that display parts of foreign proteins on their surface antigens. These can be either infected cells, or antigen presenting cells (APCs). They are found in normal tissue and in tumor tissue, where they are known as tumor infiltrating lymphocytes (TILs). They are activated by thepresence of APCs such as dendritic cells that present tumor antigens. Although these cells can attack the tumor, the environment within the tumor is highly immunosuppressive, preventing immune-mediated tumour death.

[0134] Multiple ways of producing and obtaining tumour targeted T-cells have been developed. T-cells specific to a tumor antigen can be removed from a tumor sample (TILs) or filtered from blood. Subsequent activation and culturing is performed ex vivo, with the results reinfused. Activation can take place through gene therapy, or by exposing the T cells to tumor antigens.6. Checkpoint Inhibitors and Combination Treatment

[0135] In some aspects, the therapeutic treatment comprises immune checkpoint inhibitors. Certain aspects are further described below. a. PD-1, PDL1, and PDL2 inhibitors

[0136] PD-1 can act in the tumor microenvironment where T cells encounter an infection or tumor. Activated T cells upregulate PD-1 and continue to express it in the peripheral tissues. Cytokines such as IFN-gamma induce the expression of PDL1 on epithelial cells and tumor cells. PDL2 is expressed on macrophages and dendritic cells. The main role of PD-1 is to limit the activity of effector T cells in the periphery and prevent excessive damage to the tissues during an immune response. Inhibitors of the disclosure may block one or more functions of PD-1 and / or PDL1 activity.

[0137] Alternative names for “PD-1” include CD279 and SLEB2. Alternative names for “PDL1” include B7-H1, B7-4, CD274, and B7-H. Alternative names for “PDL2” include B7- DC, Btdc, and CD273. In some aspects, PD-1, PDL1, and PDL2 are human PD-1, PDL1 and PDL2.

[0138] In some aspects, the PD-1 inhibitor is a molecule that inhibits the binding of PD-1 to its ligand binding partners. In a specific aspect, the PD-1 ligand binding partners are PDL1 and / or PDL2. In another aspect, a PDL1 inhibitor is a molecule that inhibits the binding of PDL1 to its binding partners. In a specific aspect, PDL1 binding partners are PD-1 and / or B7- 1. In another aspect, the PDL2 inhibitor is a molecule that inhibits the binding of PDL2 to its binding partners. In a specific aspect, a PDL2 binding partner is PD-1. The inhibitor may be an antibody, an antigen binding fragment thereof, an immunoadhesin, a fusion protein, or oligopeptide. Exemplary antibodies are described in U.S. Patent Nos. 8,735,553, 8,354,509, and 8,008,449, all incorporated herein by reference. Other PD-1 inhibitors for use in the methods and compositions provided herein are known in the art such as described in U. S. PatentApplication Nos. US2014 / 0294898, US2014 / 022021, and US2011 / 0008369, all incorporated herein by reference.

[0139] In some aspects, the PD-1 inhibitor is an anti-PD-1 antibody (e.g., a human antibody, a humanized antibody, or a chimeric antibody). In some aspects, the anti-PD-1 antibody is selected from the group consisting of nivolumab, pembrolizumab, and pidilizumab. In some aspects, the PD-1 inhibitor is an immunoadhesin (e.g., an immunoadhesin comprising an extracellular or PD-1 binding portion of PDL1 or PDL2 fused to a constant region (e.g., an Fc region of an immunoglobulin sequence). In some aspects, the PDL1 inhibitor comprises AMP- 224. Nivolumab, also known as MDX-1106-04, MDX-1106, ONO-4538, BMS- 936558, and OPDIVO®, is an anti-PD-1 antibody described in W02006 / 121168. Pembrolizumab, also known as MK-3475, Merck 3475, lambrolizumab, KEYTRUDA®, and SCH-900475, is an anti-PD-1 antibody described in W02009 / 114335. Pidilizumab, also known as CT-011, hBAT, or hBAT-1, is an anti-PD-1 antibody described in W02009 / 101611. AMP-224, also known as B7-DCIg, is a PDL2-Fc fusion soluble receptor described in W02010 / 027827 and WO2011 / 066342. Additional PD-1 inhibitors include MEDI0680, also known as AMP-514, and REGN2810.

[0140] In some aspects, the immune checkpoint inhibitor is a PDL1 inhibitor such as Durvalumab, also known as MEDI4736, atezolizumab, also known as MPDL3280A, avelumab, also known as MSB00010118C, MDX-1105, BMS-936559, or combinations thereof. In certain aspects, the immune checkpoint inhibitor is a PDL2 inhibitor such as rHIgM12B7.

[0141] In some aspects, the inhibitor comprises the heavy and light chain CDRs or VRs of nivolumab, pembrolizumab, or pidilizumab. Accordingly, in one aspect, the inhibitor comprises the CDR1, CDR2, and CDR3 domains of the VH region of nivolumab, pembrolizumab, or pidilizumab, and the CDR1, CDR2 and CDR3 domains of the VL region of nivolumab, pembrolizumab, or pidilizumab. In another aspect, the antibody competes for binding with and / or binds to the same epitope on PD-1, PDL1, or PDL2 as the above- mentioned antibodies. In another aspect, the antibody has at least about 70, 75, 80, 85, 90, 95, 97, or 99% (or any derivable range therein) variable region amino acid sequence identity with the above-mentioned antibodies. b. CTLA-4, B7-1, and B7-2

[0142] Another immune checkpoint that can be targeted in the methods provided herein is the cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), also known as CD152. Thecomplete cDNA sequence of human CTLA-4 has the Genbank accession number LI 5006. CTLA-4 is found on the surface of T cells and acts as an “off’ switch when bound to B7-1 (CD80) or B7-2 (CD86) on the surface of antigen-presenting cells. CTLA4 is a member of the immunoglobulin superfamily that is expressed on the surface of Helper T cells and transmits an inhibitory signal to T cells. CTLA4 is similar to the T-cell co-stimulatory protein, CD28, and both molecules bind to B7-1 and B7-2 on antigen-presenting cells. CTLA-4 transmits an inhibitory signal to T cells, whereas CD28 transmits a stimulatory signal. Intracellular CTLA- 4 is also found in regulatory T cells and may be important to their function. T cell activation through the T cell receptor and CD28 leads to increased expression of CTLA-4, an inhibitory receptor for B7 molecules. Inhibitors of the disclosure may block one or more functions of CTLA-4, B7-1, and / or B7-2 activity. In some aspects, the inhibitor blocks the CTLA-4 and B7-1 interaction. In some aspects, the inhibitor blocks the CTLA-4 and B7-2 interaction.

[0143] In some aspects, the immune checkpoint inhibitor is an anti-CTLA-4 antibody (e.g., a human antibody, a humanized antibody, or a chimeric antibody), an antigen binding fragment thereof, an immunoadhesin, a fusion protein, or oligopeptide.

[0144] Anti-human-CTLA-4 antibodies (or VH and / or VL domains derived therefrom) suitable for use in the present methods can be generated using methods well known in the art. Alternatively, art recognized anti-CTLA-4 antibodies can be used. For example, the anti- CTLA-4 antibodies disclosed in: US 8,119,129, WO 01 / 14424, WO 98 / 42752; WO 00 / 37504 (CP675,206, also known as tremelimumab; formerly ticilimumab), U.S. Patent No. 6,207,156; Hurwitz et al., 1998; can be used in the methods disclosed herein. The teachings of each of the aforementioned publications are hereby incorporated by reference. Antibodies that compete with any of these art-recognized antibodies for binding to CTLA-4 also can be used. For example, a humanized CTLA-4 antibody is described in International Patent Application No. W02001 / 014424, W02000 / 037504, and U.S. Patent No. 8,017,114; all incorporated herein by reference.

[0145] A further anti-CTLA-4 antibody useful as a checkpoint inhibitor in the methods and compositions of the disclosure is ipilimumab (also known as 10D1, MDX- 010, MDX- 101, and Yervoy®) or antigen binding fragments and variants thereof (see, e.g., WOO 1 / 14424).

[0146] In some aspects, the inhibitor comprises the heavy and light chain CDRs or VRs of tremelimumab or ipilimumab. Accordingly, in one aspect, the inhibitor comprises the CDR1, CDR2, and CDR3 domains of the VH region of tremelimumab or ipilimumab, and the CDR1, CDR2 and CDR3 domains of the VL region of tremelimumab or ipilimumab. In another aspect, the antibody competes for binding with and / or binds to the same epitope on PD-1, B7-1, or B7-2 as the above- mentioned antibodies. In another aspect, the antibody has at least about 70, 75, 80, 85, 90, 95, 97, or 99% (or any derivable range therein) variable region amino acid sequence identity with the above-mentioned antibodies.B. Oncolytic virus

[0147] In some aspects, the therapeutic treatment comprises an oncolytic virus. An oncolytic virus is a virus that preferentially infects and kills cancer cells. As the infected cancer cells are destroyed by oncolysis, they release new infectious virus particles or virions to help destroy the remaining tumour. Oncolytic viruses are thought not only to cause direct destruction of the tumour cells, but also to stimulate host anti-tumour immune responses for long-term immunotherapyC. Polysaccharides

[0148] In some aspects, the therapeutic treatment comprises polysaccharides. Certain compounds found in mushrooms, primarily polysaccharides, can up-regulate the immune system and may have anti-cancer properties. For example, beta-glucans such as lentinan have been shown in laboratory studies to stimulate macrophage, NK cells, T cells and immune system cytokines and have been investigated in clinical trials as immunologic adjuvants.D. Neoantigens

[0149] In some aspects, the therapeutic treatment comprises neoantigen administration. Many tumors express mutations. These mutations potentially create new targetable antigens (neoantigens) for use in T cell immunotherapy. The presence of CD8+ T cells in cancer lesions, as identified using RNA sequencing data, is higher in tumors with a high mutational burden. The level of transcripts associated with cytolytic activity of natural killer cells and T cells positively correlates with mutational load in many human tumors.E. Chemotherapies

[0150] In some aspects, the therapeutic treatment comprises a chemotherapy. Suitable classes of chemotherapeutic agents include (a) Alkylating Agents, such as nitrogen mustards (e.g., mechlorethamine, cylophosphamide, ifosfamide, melphalan, chlorambucil), ethylenimines and methylmelamines (e.g., hexamethylmelamine, thiotepa), alkyl sulfonates (e.g., busulfan), nitrosoureas (e.g., carmustine, lomustine, chlorozoticin, streptozocin) and triazines (e.g., dicarbazine), (b) Antimetabolites, such as folic acid analogs (e.g., methotrexate), pyrimidine analogs (e.g., 5 -fluorouracil, floxuridine, cytarabine, azauridine) and purine analogs and related materials (e.g., 6-mercaptopurine, 6-thioguanine, pentostatin), (c) Natural Products, such as vinca alkaloids (e.g., vinblastine, vincristine), epipodophylotoxins (e.g.,etoposide, teniposide), antibiotics (e.g., dactinomycin, daunorubicin, doxorubicin, bleomycin, plicamycin and mitoxanthrone), enzymes (e.g., L-asparaginase), and biological response modifiers (e.g., Interferon-a), and (d) Miscellaneous Agents, such as platinum coordination complexes (e.g., cisplatin, carboplatin), substituted ureas (e.g., hydroxyurea), methylhydiazine derivatives (e.g., procarbazine), and adreocortical suppressants (e.g., taxol and mitotane). In some aspects, cisplatin is a particularly suitable chemotherapeutic agent.

[0151] Cisplatin has been widely used to treat cancers such as, for example, metastatic testicular or ovarian carcinoma, advanced bladder cancer, head or neck cancer, cervical cancer, lung cancer or other tumors. Cisplatin is not absorbed orally and must therefore be delivered via other routes such as, for example, intravenous, subcutaneous, intratumoral or intraperitoneal injection. Cisplatin can be used alone or in combination with other agents, with efficacious doses used in clinical applications including about 15 mg / m2 to about 20 mg / m2 for 5 days every three weeks for a total of three courses being contemplated in certain aspects. In some aspects, the amount of cisplatin delivered to the cell and / or subject in conjunction with the construct comprising an Egr-1 promoter operably linked to a polynucleotide encoding the therapeutic polypeptide is less than the amount that would be delivered when using cisplatin alone.

[0152] Other suitable chemotherapeutic agents include antimicrotubule agents, e.g., Paclitaxel (“Taxol”) and doxorubicin hydrochloride (“doxorubicin”). The combination of an Egr-1 promoter / TNFa construct delivered via an adenoviral vector and doxorubicin was determined to be effective in overcoming resistance to chemotherapy and / or TNF-a, which suggests that combination treatment with the construct and doxorubicin overcomes resistance to both doxorubicin and TNF-a.

[0153] Doxorubicin is absorbed poorly and is preferably administered intravenously. In certain aspects, appropriate intravenous doses for an adult include about 60 mg / m2 to about 75 mg / m2 at about 21 -day intervals or about 25 mg / m2 to about 30 mg / m2 on each of 2 or 3 successive days repeated at about 3 week to about 4 week intervals or about 20 mg / m2 once a week. The lowest dose should be used in elderly patients, when there is prior bone-marrow depression caused by prior chemotherapy or neoplastic marrow invasion, or when the drug is combined with other myelopoietic suppressant drugs.

[0154] Nitrogen mustards are another suitable chemotherapeutic agent useful in the methods of the disclosure. A nitrogen mustard may include, but is not limited to, mechlorethamine (HN2), cyclophosphamide and / or ifosfamide, melphalan (L-sarcolysin), and chlorambucil. Cyclophosphamide (CYTOXAN®) is available from Mead Johnson andNEOSTAR® is available from Adria), is another suitable chemotherapeutic agent. Suitable oral doses for adults include, for example, about 1 mg / kg / day to about 5 mg / kg / day, intravenous doses include, for example, initially about 40 mg / kg to about 50 mg / kg in divided doses over a period of about 2 days to about 5 days or about 10 mg / kg to about 15 mg / kg about every 7 days to about 10 days or about 3 mg / kg to about 5 mg / kg twice a week or about 1.5 mg / kg / day to about 3 mg / kg / day. Because of adverse gastrointestinal effects, the intravenous route is preferred. The drug also sometimes is administered intramuscularly, by infiltration or into body cavities.

[0155] Additional suitable chemotherapeutic agents include pyrimidine analogs, such as cytarabine (cytosine arabinoside), 5 -fluorouracil (fluouracil; 5-FU) and floxuridine (fluorodeoxyuridine; FudR). 5-FU may be administered to a subject in a dosage of anywhere between about 7.5 to about 1000 mg / m2. Further, 5-FU dosing schedules may be for a variety of time periods, for example up to six weeks, or as determined by one of ordinary skill in the art to which this disclosure pertains.

[0156] Gemcitabine diphosphate (GEMZAR®, Eli Lilly & Co., “gemcitabine”), another suitable chemotherapeutic agent, is recommended for treatment of advanced and metastatic pancreatic cancer, and will therefore be useful in the present disclosure for these cancers as well.

[0157] The amount of the chemotherapeutic agent delivered to the patient may be variable. In one suitable aspect, the chemotherapeutic agent may be administered in an amount effective to cause arrest or regression of the cancer in a host, when the chemotherapy is administered with the construct. In other aspects, the chemotherapeutic agent may be administered in an amount that is anywhere between 2 to 10,000 fold less than the chemotherapeutic effective dose of the chemotherapeutic agent. For example, the chemotherapeutic agent may be administered in an amount that is about 20 fold less, about 500 fold less or even about 5000 fold less than the chemotherapeutic effective dose of the chemotherapeutic agent. The chemotherapeutics of the disclosure can be tested in vivo for the desired therapeutic activity in combination with the construct, as well as for determination of effective dosages. For example, such compounds can be tested in suitable animal model systems prior to testing in humans, including, but not limited to, rats, mice, chicken, cows, monkeys, rabbits, etc. In vitro testing may also be used to determine suitable combinations and dosages, as described in the examples.F. Radiotherapy

[0158] In some aspects, the therapeutic treatment comprises radiation, such as ionizing radiation. As used herein, “ionizing radiation” means radiation comprising particles or photons that have sufficient energy or can produce sufficient energy via nuclear interactions to produce ionization (gain or loss of electrons). An exemplary and preferred ionizing radiation is an x- radiation. Means for delivering x-radiation to a target tissue or cell are well known in the art.

[0159] In some aspects, the amount of ionizing radiation is greater than 20 Gy and is administered in one dose. In some aspects, the amount of ionizing radiation is 18 Gy and is administered in three doses. In some aspects, the amount of ionizing radiation is at least, at most, or exactly 2, 4, 6, 8, 10, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 18, 19, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 40 Gy (or any derivable range therein). In some aspects, the ionizing radiation is administered in at least, at most, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 does (or any derivable range therein). When more than one dose is administered, the does may be about 1, 4, 8, 12, or 24 hours or 1, 2, 3, 4, 5, 6, 7, or 8 days or 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, or 16 weeks apart, or any derivable range therein.

[0160] In some aspects, the amount of IR may be presented as a total dose of IR, which is then administered in fractionated doses. For example, in some aspects, the total dose is 50 Gy administered in 10 fractionated doses of 5 Gy each. In some aspects, the total dose is 50-90 Gy, administered in 20-60 fractionated doses of 2-3 Gy each. In some aspects, the total dose of IR is at least, at most, or about 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, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 125, 130, 135, 140, or 150 (or any derivable range therein). In some aspects, the total dose is administered in fractionated doses of at least, at most, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 20, 25, 30, 35, 40, 45, or 50 Gy (or any derivable range therein. In some aspects, at least, at most, or exactly 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, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 fractionated doses are administered (or any derivable range therein). In some aspects, at least, at most, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 (or any derivable range therein) fractionated dosesare administered per day. In some aspects, at least, at most, or exactly 1, 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, or 30 (or any derivable range therein) fractionated doses are administered per week.G. Surgery

[0161] Approximately 60% of persons with cancer will undergo surgery of some type, which includes preventative, diagnostic or staging, curative, and palliative surgery. Curative surgery includes resection in which all or part of cancerous tissue is physically removed, excised, and / or destroyed and may be used in conjunction with other therapies, such as the treatment of the present aspects, chemotherapy, radiotherapy, hormonal therapy, gene therapy, immunotherapy, and / or alternative therapies. Tumor resection refers to physical removal of at least part of a tumor. In addition to tumor resection, treatment by surgery includes laser surgery, cryosurgery, electrosurgery, and microscopically-controlled surgery (Mohs’ surgery).

[0162] Upon excision of part or all of cancerous cells, tissue, or tumor, a cavity may be formed in the body. Treatment may be accomplished by perfusion, direct injection, or local application of the area with an additional anti-cancer therapy. Such treatment may be repeated, for example, every 1, 2, 3, 4, 5, 6, or 7 days, or every 1, 2, 3, 4, and 5 weeks or every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 months. These treatments may be of varying dosages as well.VII. Kits

[0163] Certain aspects of the disclosure also encompass kits for performing the methods of the disclosure. Kits may comprise a container with a label. Suitable containers include, for example, bottles, vials, and test tubes. The containers may be formed from a variety of materials such as glass or plastic. The container may hold a composition which includes a probe that is useful for prognostic or non-prognostic applications, such as described above. The label on the container may indicate that the composition is used for a specific prognostic or non-prognostic application, and may also indicate directions for either in vivo or in vitro use, such as those described above. The kit may comprise the container described above and one or more other containers comprising materials desirable from a commercial and user standpoint, including buffers, diluents, filters, needles, syringes, and package inserts with instructions for use.

[0164] Further kit aspects relate to kits comprising the therapeutic compositions of the disclosure. The kits may be useful in the treatment methods of the disclosure and comprise instructions for use.Examples

[0165] The following examples are included to demonstrate preferred embodiments of the invention. It should be appreciated by those of skill in the art that the techniques disclosed in the examples which follow represent techniques discovered by the inventor to function well in the practice of the invention, and thus can be considered to constitute preferred modes for its practice. However, those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit and scope of the invention.Example 1: Experimental ResultsDigital rectal examination enriches for prostate proteins

[0166] The urine proteome is believed to derive almost exclusively from tissues of the genitourinary tract: the kidney, bladder and (in men) prostate11. Perturbation of the prostate gland using digital rectal examination (DRE) enriches for prostate-specific RNAs in urine16. While the mechanism is unknown, it is thought to occur by expelling prostatic fluid into the urethra, where it can be collected as part of first-catch urine. DREs are routine, minimally- invasive physical manipulations performed millions of times annually by oncologists and primary care physicians, and thus might provide a simple approach to enrich prostate-derived proteins in urine.

[0167] The inventors therefore collected matched pre- and post-DRE urines from ten men (Figure la) and applied differential ultracentrifugation to separate urine soluble proteins (uSP) from urinary extracellular vesicles (uEVs). The inventors further isolated two uEV populations based on size17: one at 20,000 x g (termed uEV-P20) and the other at 150,000 x g (uEV-P150; Fig. 8a). To determine if a DRE influenced biophysical characteristics of EVs, the inventors quantified EV diameter, number and morphology by nanoparticle tracking and by transmission electron microscopy (Figure lb and Fig. 8b-8d). uEV biophysical characteristics were unchanged before or after a DRE (Fig. 8b-8d).

[0168] To evaluate if a DRE increased the abundance of prostate-derived proteins, the inventors measured the proteomes of each urine fraction using mass spectrometry. Prostate tissue-derived proteins were curated from three independent tissue proteomics datasets18 20, then annotated in urine. While the total number of urine proteins was unchanged by a DRE (4,064 ± 604 pre-DRE vs. 4,362 ± 511 post-DRE; P = 0.31; two-sided Wilcoxon signed-rank test), prostate tissue-derived proteins were significantly more abundant in post-DRE urine (P < 2.2 x 10'16; Figures Ic-e). These included classic prostate marker proteins like PSMA (FOLH1) and PSA (KLK3 ° (Figures If and Fig. 8e). Thus, a DRE significantly enriches theurine for proteins of prostate origin but does not influence EV biophysical characteristics, suggesting the latter may be relatively tissue-independent.The subcellular origins o f urinary proteins

[0169] To investigate urinary protein heterogeneity, the inventors next collected post-DRE urines from 190 men: 64 men with no cancer diagnosis and 126 with untreated prostate cancers. The prostate cancer patients reflected the full risk spectrum of primary disease, with biopsy ISUP Grade Groups (GG) ranging from low (GG 1) to high (GG 5; Figure 2a). The inventors isolated uSP, uEV-P20 and uEV-P150 from post-DRE urine and quantified their proteomes (Figure 2). uEV biophysics and urine protein counts were largely independent of disease status, age or serum PSA levels (Fig. 9a-9g). The inventors observed an increasing trend in uEV particle count in cISUP GG 1-3 followed by a decrease in particle count in cISUP GG 4-5, although this finding requires further validation in larger cohorts due to the low numbers of patients in each group (Fig. 9c). The uEV-P20 and uEV-P150 fractions were biophysically similar in size, morphology and particle count (Figures 2c-e).

[0170] Despite these biophysical similarities, the proteomes of different urine fractions were significantly different. The uEV-P20 fraction had the most detectable proteins (Figure 2f) and was the most biophysically and proteomically diverse, suggesting heterogeneity in vesicular type or origin (Figure 2g). uEV isolation was highly reproducible, with larger proteomic differences observed between patients and clinical groups compared to within samples (Figs. 10a- 10b). Of the 6,518 proteins detected in uEVs, 60% were identified as EV cargo in previous studies (Figs. I Oc- I Od)l 7‘21 24uEV fractions were more similar to one another than to the soluble protein fraction, consistent with the detected EV proteins being true cargo rather than co-isolated urinary proteins (Figs. lOe-lOg). To determine the differential origins and biology represented by each fraction, the inventors performed differential proteome analysis. The inventors identified proteins detected in one fraction but not the others (Figure 2h) and proteins detected at different abundances across fractions (Figure 2i). Each fraction was defined by presence of ~50 proteins and differential abundance of -500 others (Figure 2j). Fraction-specific proteins tended to arise from specific subcellular compartments. Urine soluble proteins were typically secreted or derived from the Golgi apparatus, while uEV-P20 proteins derived from mitochondria or endoplasmic reticulum and uEV-P150 proteins from the plasma membrane (Figure 2k). uEVs but not cEVs reflect the prostate tissue proteome

[0171] The inventors next quantified how well each of the three urine fraction proteomes reflects the proteome of prostate tissue18,19. A majority (67%) of all proteins detected in prostatetissue were detected in one or more of the three urine fractions (Figure 3a). EV fractions were a much richer source of prostate-derived proteins than urine soluble proteins. Only 116 prostate-derived proteins were identified in unfractionated urine, whereas 2,439 were identified only in one or both of EV fractions and 4,968 in both EV and non-EV urine. Protein abundances were well-correlated between urinary and tissue proteomes (Figure 3b), with uEV-P20 being the best surrogate for prostate tissue.

[0172] To further quantify the tissue provenance of urinary proteins, the inventors used RNA-seq data from normal tissues (GTEx25) and from normal tissue adjacent to tumors (NAT, TCGA26 28) to identify transcripts enriched in prostate, kidney or bladder (Figure 3c). Differentially abundant transcripts were then used as signatures of tissue origin to quantify the contribution of each tissue to each sample. In uEVs, prostate proteins were very significantly more abundant than non-prostate proteins (P < 1 x 1 O’5). In contrast, the soluble urine fraction (uSP) showed the inverse trend: it was significantly depleted in prostate-derived proteins (P < 2.2 x 10'16; Figure 3d). The soluble protein fraction (uSP) was enriched in functions classically associated with blood (lipoprotein, blood microparticle; Figure 3e). The soluble proteome was also highly enriched in cell surface proteins, likely from the shedding of extracellular domains29,30. Proteins involved in multivesicular body biogenesis were over-represented in uEV-P150, suggesting an enrichment of exosomes31. Consistent with the univariate protein analysis, uEV-P20 fraction more closely reflected prostate tissue, harboring proteins originating from mitochondria, ribosome and extracellular matrix19. These data suggest that the prostate primarily sheds EVs into urine, and that these can provide a robust non-invasive proxy of its proteome.

[0173] Cell line conditioned media has been widely used to study secreted cellular components32,33. To ascertain if cell line-derived EVs (cEVs) accurately reflect tumor tissue, the inventors isolated and proteomically characterized cEVs from five prostate cell lines (Figure 4a). EVs from urine and from cell line conditioned media displayed similar biophysical characteristics (Fig. 1 la-1 Id). More proteins were detected in cEV-P20 than in cEV-P150, but fewer than in whole-cell lysates (Fig. 1 le-1 If). Cell line whole-cell lysates closely resembled tumor tissue in both protein composition (Figure 4b) and abundance (Figure 4c). By contrast uEVs and cEVs differed, particularly in abundance (Figures 4d-e). Urinary EVs were better surrogates for prostate tissue than EVs derived from cell lines (Fig. 11g). Commonly used EV markers like CD9, CD81 and CD6334’35were amongst the proteins more abundant in EV-P150 than EV-P20 in both cell lines and urines (Figure 4f and Fig. 1 Ih), while other EV markers like FLOT1 were discordant between cell lines and urine (Figure 4f). PSA protein expression inwhole-cell lysates was consistent with the androgen sensitivity of the cell lines, with androgen insensitive cell lines DU145, PC3 and RWPE1 not expressing PSA and androgen sensitive cell lines 22Rvl and LNCaP expressing PSA36. In cEVs, only EVs isolated from LNCaP had detectable PSA. Mitochondrial proteins were over-represented in cEV-P20 and plasma membrane proteins in cEV-P150 (Figure 4g). Thus, post-DRE EV-associated proteins more accurately reflect prostate tissue than prostate cancer cell line EVs or, particularly, post-DRE urine soluble proteins.Biomarker potential of urinary proteomes

[0174] For a biomarker to be useful, it needs to be robust to a variety of types of errors. While urine is not prone to spatial biases, it is unclear to what extent an individual’s urine proteome is stable over time. To quantify the temporal stability of post-DRE urine, the inventors evaluated longitudinal samples in five prostate cancer patients over several years. From each patient, the inventors collected post-DRE urine at multiple timepoints; all patients were managed by active surveillance without any indication of clinical progression (Figure 5a). The inventors used variance analysis to quantify which proteins were more variable between samples of a single patient (intra) or across individuals (inter). On average, proteomes were more similar within patients than between them, for both EV cargo and urine-soluble proteins (Figure 5b). To identify proteins that might be particularly useful as biomarkers, the inventors identified those that were longitudinally stable using the intraclass correlation coefficient (ICC)37. The higher a protein’s ICC, the less its variance in protein abundance is caused by random fluctuations over time. A subset of proteins was highly longitudinally stable in each fraction (Fig. 12a), and these comprise excellent candidate biomarkers because they are robust to physiological variability over several years.

[0175] Next, the inventors sought to evaluate the biomarker potential of urine-soluble and uEV proteins for predicting prostatic disease. Men with benign prostatic conditions (noncancer [NC]) included individuals with elevated serum PSA levels and benign prostatic hyperplasia as well as patients with no diagnosed prostate cancer on transrectal ultrasound- guided 12-core biopsy. Patients with and without prostate cancer had similar serum PSA abundances (P = 0.34). Despite this similarity, thousands of soluble (Figure 5c) and EV proteins (Figures 5d-e) differed between patients with and without cancer. The specific differentially abundant proteins varied between urinary fractions (Fig. 12b). Of these proteins that were differentially abundant in prostate cancer and non-cancer uEVs, 541 proteins were uniquely detected in the dataset compared to other datasets of post-DRE urine-derived uEVs from patients with prostatic disease17,21’22. In addition, only 21 proteins were unique to a singledisease subgroup (i.e. non-cancers or specific cISUP GG). These results suggest that differences in disease groups are reflected in the differential abundance of proteins in the uEV proteome.

[0176] Pathways associated with malignancy in prostate tissue also differed from those in urine (Fig. 12c). For example, proteins regulated by androgen response, such as PSA, had increased abundance in tumor tissue compared to NAT tissue, but had the opposite trend in urine soluble proteins and uEVs. Consistent with other studies38,39, PSA (KLK3) protein abundance was consistently significantly reduced in all fractions of urine from prostate cancer patients relative to men without a cancer diagnosis, despite being increased in serum and in tumor regions18(Figures 5c-e). Therefore, the data suggest that pathways associated with malignancy are reflected in different subsets of the prostate urinary proteome.

[0177] To create biomarkers of prostatic disease, the inventors focused on proteins that were frequently detected (in >50% of samples), enriched in a urinary fraction, prostate-derived, and longitudinally stable (ICC > 0.4) (Figure 6a). This filtering strategy retained 226 uSP proteins, 280 uEV-P20 proteins and 235 uEV-P150 proteins. The inventors used statistical machine learning to create and validate classifiers independently for each urine fraction. First, the inventors created classifiers that distinguish cancer from non-cancer based solely on urine proteins; these had AUCs ranging from 0.92-1, significantly outperforming serum PSA (Figure 6b). In an independent, prospective validation cohort of 30 patients with proteomic data from all three urinary fractions, proteins had concordant effect sizes and validation AUCs ranging from 0.71-0.81 (Figures 6c-d). Next, the inventors applied the same methodology to the more challenging task of distinguishing low-grade from high-grade cancer. This is an important clinical question, as low-grade disease is typically managed by active surveillance and higher- grade disease by definitive local therapy. Urine distinguished low- from high-grade prostate cancer with AUCs ranging from 0.73-0.79 (Fig. 13a), again matching or exceeding serum PSA. In two independent validation cohorts of 199 and 75 patients , effect sizes of uSP proteins between the discovery and validation cohorts were largely concordant (Fig. 13b). Intriguingly, signatures of grade also informed on disease status, reflecting an overlap in determinants of initiation and progression, as seen in studies of prostate cancer genetic drivers40(Fig. 13c). Proteins within these signatures exhibit longitudinal stability and were univariately associated with disease status (Fig. 13d). These data suggest that the urine proteome is an untapped source of biomarkers for genitourinary disease.Markers of prostate tumor uEVs

[0178] Post-DRE urine can non-invasively sample the prostate tissue proteome and has significant biomarker potential. However, the soluble fraction and the protein cargo of different size EVs differ in their origin and association with disease phenotypes. As examples, SPOCK1 protein was significantly lower in the soluble fraction of urine from cancer patients but not in uEVs, while PCYOX1 shows the inverse (Fig. 13d). This interplay between subcellular origin, clinical phenotype and urine fraction is summarized in Figure 7a and suggests that protein cargo is selectively packaged into EVs in processes that are dysregulated during malignant transformation (Fig. 14a).

[0179] This heterogeneity in origins and association with clinico-epidemiologic characteristics highlights the importance of accurately isolating specific EV populations. The gold-standard intensive centrifugation used here is expensive and time-consuming, so affinitybased isolation methods are strongly preferred for rapid translational and clinical studies. Affinity methods require identification of proteins characteristic of specific EV subpopulations41. The inventors evaluated the performance of 13 EV markers that are ubiquitous to multiple human cancer tissues and fluids but not yet evaluated in prostate urinary EVs41. Four of these were elevated in uEV-P150 relative to soluble urine proteins and five were elevated in uEV-P20. Others were depleted in uEVs, and none showed the large effect sizes or small inter-sample variability needed for ideal affinity-based markers (Figure 7b). Thus, canonical EV protein markers do not appear to be optimal for uEV identification and isolation.

[0180] The inventors therefore sought to identify protein markers to distinguish prostate- derived uEVs. The inventors selected proteins that were both distinct to one urine fraction (Figure 2j) and are known to be present in prostate tissue. The inventors separated these into three protein subsets: those associated with presence of a tumor, those associated with tumor grade and disease-invariant “core” proteins (Figure 7c). These subsets were functionally distinct, with prostate-specific secreted proteins such as KLK3 and ACPP (Cluster CUSP8) being specific to the urine soluble protein fraction but not uEVs, and GTP binding proteins specific to the uEV-P150 fraction (Cluster CPISO8)15(Figs. 14b-14d).

[0181] Finally, to identify specific actionable markers for uEV affinity studies, the inventors selected predicted cell surface proteins from each subset42(Figure 7c and Figs. 14e- 14f). The resulting five uEV-P20 and ten uEV-P150 tumor markers that differed in patients with cancer and benign disease include the classical EV marker CD6334,35(Figure 7d). Grade markers (Fig. 14e) contain markers such as ITGB2 and SLC4A1 in the uEV-P20 fraction. Disease-invariant core markers of uEV-P150 include classical EV markers CD9 and CD81, which were frequently detected in uEV-P150 (Fig. 14f). These data indicate that the EVproteome is context-specific (Fig. 14g), and that protocols for rapid, specific isolation of EV subpopulations may differ from those useful in plasma or some other tissues.Example 2: Discussion

[0182] Urine contains a complex mixture of proteins that differ in their form of release and tissue of origin, resulting in a dynamic range of concentrations spanning ten orders of magnitude4. To better define the urinary proteome, the inventors used fractionation to distinguish urine-soluble proteins from proteins carried in urinary extracellular vesicles (uEVs). uEVs of different sizes and densities contained proteins from different subcellular origins, suggesting distinct biogenesis43,44. uEVs, particularly the uEV-P20 population, appear to derive heavily from prostate tissue, and to accurately reflect its proteome. By contrast, neither the soluble urine proteome nor cell line-derived EVs did so. This highlights the need to prioritize patient-derived EVs in translational studies. This is particularly key for diseases where models that faithfully recapitulate aspects of the natural history of cancer are lacking, such as hormone sensitivity and hypoxia in prostate cancer.

[0183] Prior work has rigorously quantified the role of factors such as sample collection, processing45,46and storage47on urine proteomes. The inventors show that digital rectal examinations are a simple way to enrich for prostate proteins and EVs in first-catch urine. While post-DRE urine proteomes can vary over time4,48, a subset of specific proteins are temporally stable over many months and are well suited for non-invasive sampling. The analysis of temporal stability of the urine proteome was restricted to prostate cancer patients that all have cISUP GG 1 tumors, similar serum PSA levels, and who did not have clinical upgrading over a period of 1-2 years. Follow-up studies utilizing larger, clinically heterogeneous cohorts will be needed to verify if the temporally stable proteins identified in low-grade patients generalize to other patients with other tumor grades. The inventors found that the protein composition of urinary proteomes uniquely differs as a function of tumor grade, and when comparing prostate cancers to non-cancers. Consistent with data from other groups, prostate proteins such as PSA were elevated in the urine and urinary EVs of patients without cancer compared to those with prostate cancer, while serum PSA levels display the opposite trend38,39,49. This is likely due to the disruption of prostate cellular architecture with increasing tumor grade, which results in elevated levels of PSA in the bloodstream and consequently decreased levels of PSA in urine2,38. These differences in the secreted proteome between groups likely reflect altered signaling and intercellular communication by cancer cells, or as a compensatory mechanism for altered tumor metabolism or cellular stress50,51. Therefore, theinventors show that EV cargo is context dependent and is associated with different cellular phenotypes, such as disease.

[0184] Further proteomic interrogation of urinary EVs in individuals of different ancestries, ages and in different clinical scenarios52could offer new insights into genitourinary biology. However, the complexity of EV isolation by differential ultracentrifugation and requirement for high starting material can be limiting17. Affinity-based capture of specific EV subpopulations using cell surface markers is appealing53. Intriguingly, uEVs appears to be characterized by cell surface markers different from other EV subpopulations41. Thus new protocols to rapidly isolate EVs from urine are urgently needed to maximize its utility for biomarker and other translational studies. In this study the inventors used differential ultracentrifugation, which is a less specific EV isolation approach compared to affinity-based capture or differential gradient ultracentrifugation which can isolate EV populations with higher specificity54. As such, the selectivity, protein composition and protein abundance in EVs may differ depending on the choice of EV isolation method. Future experiments will be needed to verify if these putative markers are able to capture similar EV subpopulations.

[0185] Since the unique dataset is one of the largest urine proteomics studies, the inventors were able to leverage urinary fraction-specificity, frequency of detection in prostate tissues and longitudinal stability to prioritize proteins as new biomarker candidates for prostatic diseases. The proteomics dataset generated in this study comprises one of the larger-sized discovery cohorts for urinary proteomics studies totaling >100 patients for prostate cancer biomarker discovery in urine, even for urinary EVs, and is well powered to detect log2 fold changes in protein abundances of 1. However, while many of the proteomic differences between groups (e.g. cISUP GG) are concordant in independent cohorts, these differences are small. Further validation of the urinary signatures using robust quantitation by targeted proteomics assays with stable isotope labeled standards52,55in larger, racially diverse validation cohorts is needed. The full data resource presented here is accessible via an interactive portal (http: / / kislingerlab.uhnres.utoronto.ca / ev / home / ) to facilitate investigation into the urinary secreted and EV proteomes.Example 3: Methods for Aspects HereinHuman subjects

[0186] Samples were obtained from men following informed consent and use of Institutional Review Board approved protocols at Eastern Virginia Medical School (EVMS, Norfolk, Virginia, USA, IRB# 06-12-FB-0343), Sunnybrook Health Sciences Centre (SHSC,Toronto, Ontario, Canada, Project #2457) and the Research Ethics Review Board at the University Health Network (UHN, Toronto, Ontario, Canada, 10-0159 and 19-5009). Men with benign prostatic conditions (non-cancer [NC]) included individuals with elevated serum PSA (sPSA) levels and benign prostatic hyperplasia (BPH; 44 patients; median sPSA 6.3 ng mL’1, range 1.7 - 11.9 ng mL’1) or no diagnosed prostate cancer on transrectal ultrasound-guided 12- core biopsy (Biopsy-negative; 20 patients; median sPSA 5.2 ng mL’1, range 0.5 - 31.5 ng mL'x). Selection criteria for men with benign prostatic conditions included a diagnostic sPSA level < 20 ng mL’1and post-surgery sPSA level <0.1 ng mL’1to exclude highly metastatic men.Urine collection

[0187] The first 15 mL of first-catch urine collected post digital rectal exam (DRE) (post- DRE urine) was collected by performing a gentle massage of the prostate gland during DRE prior to biopsy2. For the DRE cohort, which comprised of ten men with clinical ISUP GG 1 tumors, mid-stream urine was collected an hour before the DRE massage (pre-DRE urine). Matched post-DRE urine was also collected for these ten men. The longitudinal cohort comprised of five men with cISUP GG 1 tumors who are on active surveillance and did not upgrade in the period of 12-16 months after their first DRE. Serial post-DRE urine was collected for each patient at three timepoints. Each timepoint was 3-12 months apart. For assessing the reproducibility of uEV isolation in prostate cancer patients, the first 50 mL of first-catch post-DRE urine was collected from three men with cISUP GG 1 tumors. For assessing the reproducibility of uEV isolation in men without prostate cancer, the inventors pooled post-DRE urines from 10 men with benign prostatic hyperplasia and pooled post-DRE urines from 10 men with elevated serum PSA but no prostate cancer detected on needle biopsy. Pre- and post-DRE urine was centrifuged at 2,000 x g for 15 min at 4°C to pellet cellular debris, and the resulting urine supernatant was stored at -80°C.Cell lines

[0188] Commercial human prostate cell lines DU145 (ATCC #HTB-81), PC3 (ATCC #CRL-1435), 22Rvl (ATCC #CRL-2505), LNCaP (ATCC #CRL-1740) and RWPE1 (ATCC #CRL-3607) were a gift from Dr. Stanley Liu, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada. All cell lines are immortalized cell lines from males. Cell line identity was confirmed by short tandem repeat testing. Mycoplasma negativity was confirmed using the Universal Mycoplasma Testing kit (ATCC). Cells were seeded in two T500 Nunc™ TripleFlasks™ (total area = 1,000 cm2) with 100 mL of media or ten 15 cm plates (total area = 1,480 cm2) in 20 mL of media each and cultured in a 37°C incubator with 5% CO2. RPMI media (Gibco) supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin-glutamine (PSG) was used for the prostate cancer cell lines (DU145, PC3, 22Rvl and LNCaP) and Keratinocyte-serum free media supplemented with 0.05 mg mL'1bovine pituitary extract, 5 ng mL'1epidermal growth factor, and 1% Penicillin-Streptomycin-Glutamine was used for the RWPE1 cell line.EV isolation from urine

[0189] Urinary extracellular vesicles (uEV) were isolated by differential ultracentrifugation17. Briefly, 14 mL of frozen urine supernatant was thawed at 4°C, then diluted to a volume of 35 mL with isotonic buffer (250 mM sucrose, 10 mM HEPES, 1 mM EDTA, pH 7.4). The urine was centrifuged at 20,000 x g for 30 min at 4°C (k-factor 1,790) in an Optima XPN-80 ultracentrifuge (Beckman Coulter) equipped with a SW32 Ti swinging bucket rotor (Rmin 67, Rmax 153, Beckman Coulter) to pellet EVs. The 20,000 g pellet (P20) was treated with 500 mM of dithiothreitol (DTT) at 37°C for 30 min to reduce the uromodulin network and centrifuged a second time at 20,000 x g for 30 min at room temperature. The P20 pellet was resuspended in 1 mL of cold PBS and centrifuged at 18,210 x g for 30 mins (Eppendorf Centrifuge 5430 R, FA-45-48-11 rotor, k-factor 198). The supernatant from the first and second centrifugation steps were combined and centrifuged at 150,000 x g for 2 hours at 4°C (SW32TI swinging bucket rotor, k-factor 239) in an ultracentrifuge to pellet EVs. The 150,000 x g pellet (Pl 50) was resuspended in high pH buffer then passed twice through a 0.22 pm filter. Samples were centrifuged again at 150,000 x g for 2 hours at 4°C to pellet uEV- P150. The P20 and P150 pellets containing uEV-P20 and uEV-P150, respectively, were resuspended in 100 pL of 50% 2,2,2-trifluoroethanol (Sigma Aldrich) in PBS, flash-frozen in liquid nitrogen, and stored at -80°C until proteomics analysis.EV isolation from cell culture conditioned media

[0190] All cell lines were grown to 70-80% confluency then washed three times with phosphate-buffered saline (PBS) and serum-starved for 48 hours prior collection of conditioned media. The cell line conditioned media containing EVs (cEV) was collected and centrifuged at 500 x g for 10 min then 2,000 x g at 4°C for 30 min to clear cell debris. The supernatant was concentrated to a volume of 4-5 mL (if using EVs for biophysical studies) or to a volume of 20-30 mL (if using EVs for proteomics) in a 100 kDa MWCO ultrafiltration concentrator (Millipore). EVs were isolated from conditioned media by differential ultracentrifugation in a SW32Ti swinging bucket rotor as described above. Conditioned media was topped off with PBS as required. Unlike the EV isolation protocol for urine described above, the first P20 pellet was not treated with DTT as cell line conditioned media is not expected to contain uromodulin. cEVs were collected from the 20,000 x g (cEV-P20) and 150,000 x g (cEV-P150) pellets.Urine proteomics

[0191] Proteomic profiles of the soluble protein fraction were generated from 250 pL of urine supernatant (following 2,000 x g centrifugation). Urine was prepared for proteomics using the MStem protocol56. For each sample, 2 pmol of Saccharomyces cerevisiae invertase was added as a sample preparation control. Proteins in each sample were reduced with 5 mM DTT and incubated for 30 min at 60°C. To prevent re-formation of disulfide bonds, 25 mM iodoacetamide was added and samples were incubated at room temperature for 30 min in the dark. Liquid in the following steps were passed through the MStern wells using vacuum suction unless otherwise stated. The polyvinylidene fluoride membrane (Millipore Sigma, MSIP4510) was equilibrated with 50 pL of 70% ethanol, then washed twice with 100 mM ammonium bicarbonate (ABC). Samples were added to the wells and passed through the membrane by vacuum suction. Each well was washed twice with 100 pL of ABC to remove salts, then proteins were digested with 1 pg of mass spectrometry grade Trypsin / Lys-C enzyme mix (Promega) in 50 pL of digestion buffer (100 mM ABC, pH 8.0, 1 mM CaCh, 5% acetonitrile). To ensure that the proteins are in contact with the digestion buffer, the digestion buffer was passed through the membrane by centrifugation, and the flow-through was reapplied on top of the membrane. Protein digestion was performed at 37°C for four hours. Samples were resuspended in the well by gentle pipetting every two hours. Peptides were collected by centrifugation, and remnant membrane-bound peptides were eluted with 50 pL of 50% acetonitrile and combined with the previous flow through. Samples were dried in a SpeedVac vacuum concentrator (Thermo). Dried peptides were resuspended in 0.1% trifluoroacetic acid in water and desalted using homemade solid phase extraction stage tips containing 3 plugs of 3M™ Empore ™ C18 membrane57. Peptides were quantified by NanoDrop (Thermo Scientific). 2 pg of peptides were loaded on column.Extracellular vesicle proteomics

[0192] EVs or whole cell lysates in 50% 2,2,2-trifluoroethanol (Sigma Aldrich) were lysed by freeze-thaw, then incubated at 60°C for 1 hour to extract proteins. Then, proteins were reduced with 5 mM of DTT, alkylated with 25 mM of iodoacetamide, and digested overnight at 37°C with a 2 pg Trypsin / Lys-C enzyme mix (Promega). The next day, the enzymatic digest was quenched with 1% formic acid, and samples were desalted with homemade C18 StageTips (see above) prior to LC-MS analysis. iRT peptide standards (Biognosys) were spiked into reconstituted peptides at a 1 : 100 dilution according to manufacturer’s instructions.Mass spectrometry and data processins

[0193] Peptides were separated on a 50 cm C18 reverse phase EASY-Spray LC column (Thermo ES803) with trap column (Acclaim™ PepMap™ 100 C18) interfaced with an EASY- nanoLC 1000 system over a 2 h gradient (EVs and urine) or 4 h gradient (whole cell lysates). Mass spectrometry was performed on a Q-Exactive HF, Orbitrap Fusion Tribrid or Orbitrap Fusion Lumos mass spectrometer coupled to an EASY-Spray ESI source (Thermo Scientific). All datasets were acquired in data-dependent acquisition mode. Raw files for each urine fraction and cohort were searched separately in MaxQuant58(v.1.5.8.3 for uSP samples and v.1.6.2.3 for uEV samples) at a single site using a UniProt human protein sequence database (complete human proteome with isoforms). For cohorts with samples processed or acquired in replicates, protein intensities were combined in MaxQuant. Searches were performed with trypsin cleavage at lysine and arginine, maximum of two missed cleavages, peptide length 7 - 25 amino acids, and carbamidom ethylation of cysteine as a fixed modification. Variable modifications were set as oxidation of methionine and acetylation of the protein N-terminus. The false discovery rate for the target-decoy search was set to 1% for protein and peptide. Peptide detection was performed with an initial precursor and fragment mass deviation threshold of 10 and 20 parts per million respectively. Intensity -based absolute quantification (iBAQ), label-free quantitation, and match between runs (matching and alignment time windows set as 0.7 and 20 min respectively) were enabled. The peptides.txt output files from each MaxQuant search were parsed into an in-house database for protein grouping59. Protein abundances (gene-centric) were determined from peptide abundances using the iBAQ algorithm60. Reverse hits (false positives from target-decoy search) were removed, and proteins detected with two or more peptides were carried forward. Raw iBAQ intensities were normalized using median normalization. Median-normalized values were used for all analyses unless stated otherwise. All further data analysis was performed in the R statistical environment (v.4.2.1).EV isolation for biophysical studies

[0194] EVs were isolated from urines or cell line conditioned media for nanoparticle tracking analysis (NT A) and transmission electron microscopy (TEM) as described above, with the following changes. 5 mL of fluid was used for EV isolation using a SW55 Ti swinging bucket rotor (Rmin 61, Rmax 109, Beckman Coulter). To keep the k-factor consistent with the SW32Ti rotor at each centrifugation step and taking into account the time needed for the rotor to achieve its desired speed (approximately 5 min), centrifugation time were reduced to 20 min and 1 hour for the 20,000 x g (k-factor 699) and 150,000 x g (k-factor 120) centrifugation steps,respectively. EV pellets were resuspended in 100-200 pL of cold, 0.22 pm filtered PBS, and stored at 4°C for no more than 16 hours prior to NTA or TEM analysis.Transmission electron microscopy

[0195] TEM was performed at SickKids Nanoscale Biomedical Imaging Facility and Princess Margaret Cancer Centre. Samples were deposited on formvar carbon-coated grids, washed once with water, and stained twice with uranyl acetate. Images were acquired on a Tecnai 20, Hitachi HT7800, and Talos™ F200X G2 transmission electron microscope. Images were processed with ImageJ (v.1.53t) for visualization.Nanoparticle trackins analysis

[0196] NTA for 34 men with uEV-P20 and uEV-P150 was performed using a NanoSight LM10 system configured with a 405 nm laser and a high sensitivity sCMOS camera. Camera settings were as follows: screen gain 3.0; camera level 11; 25 frames per second; slider gain 146. Each sample was diluted in particle-free PBS and introduced manually. Analysis was performed with NTA software (v.3.1 build 3.1.46). One technical replicate was captured per sample. For each replicate, three 30 s videos were captured, with approximately 20-200 particles in the field of view for each measurement. Ambient temperature was set at 22°C.

[0197] NTA for the DRE cohort (pre-DRE vs. post-DRE urine from two matched men) and cEVs (cEV-P20 and cEV-P150) was performed using a NanoSight NS300 system (Malvern) configured with a 405 nm laser and a high sensitivity sCMOS camera. Each sample was diluted in 0.22 pm filtered PBS and introduced with a syringe pump at 60 pL min'1. Analysis was performed with NTA software (v.3.4). For every sample, two to four technical replicates were captured. For each technical replicate, three 30 second videos were captured, with approximately 20-200 particles in the field of view for each measurement. Ambient temperature was set at 22°C.

[0198] Raw data files (“ / / / c / / a / 77c-ExperimentSummary.csv”) were parsed as follows for quantification and statistical analysis. Raw particle counts for each size bin were corrected for dilution factors, then grouped to biological replicates. Each data point represents the mean of all measurements for each biological replicate . For cEVs, an experimental replicate is defined as cEVs isolated from the same cell line at different passages. For uEVs, a biological replicate is defined as uEVs isolated from a specific biofluid (pre-DRE or post-DRE urine) from one individual. For visualizing particle concentration vs. size distribution for each replicate (FIGs. 8D, 9C and 9D) particle concentration was scaled with min-max [0,1] normalization with formula Equation (1). x' = (x — min (x)) / (max (x) — min (x) ) (1)Quantification and statistical analysis

[0199] Where appropriate, quantitative analyses are described in the relevant sections of the Methods. Unless stated otherwise, bioinformatic and statistical analyses and plotting were performed using R (v.4.2.1). Data were visualized using R packages BoutrosLab.plotting.general (v.7.0.3), ggplot2 (v.3.4.0), ggbeeswarm (v.0.6.0) ggpubr (v.0.4.0), and ComplexHeatmap (v.2.12.1). Qualitative variables were compared by Fisher Exact Test, and quantitative variables by two-sided Mann-Whitney U test for unpaired comparisons (wilcox. tesQ two-sided Wilcoxon signed-rank test for pairwise comparisons wilcox.tesf) and the Kruskal-Wallis test for multiple group comparisons. Iog2 fold change (log2FC) is calculated from the difference in medians. The specific statistical tests used are indicated in the figure legends. Multiple test P-values were adjusted using Benj amini -Hochberg method for independent tests unless stated otherwise. Correlation coefficients were determined by the Spearman method (cor. test). P-values for Spearman’s correlation were computed by asymptotic t approximation using an Edgeworth series. Statistical significance was set at P- value < 0.05. Missing values in protein-level data were replaced by random numbers drawn from the lower tail of the gaussian distribution 1.8 standard deviations from the mean (width = 0.2 standard deviations), unless stated otherwise61.Tissue speci ficity in DRE urines

[0200] To determine if DRE enriches for prostate tumor-derived proteins, proteomic profiles for the soluble protein (uSP) and uEV fractions were generated from urines collected pre- and post-DRE. Paired Student’s t-tests were used to identify differentially abundant proteins in pre- and post-DRE. To determine which proteins are anticipated to be derived from prostate tumors, proteins in this study were annotated based on detection in more than 10 tissue samples of two prostate cancer tissue proteomic datasets: Sinha et. al.19(76 men with prostate cancer; 76 tumor samples) and Khoo et. aid (40 men with Prostate cancer; 81 samples [41 tumor and 40 NAT]); 7,438 proteins. To determine which proteins had elevated expression in human prostate tissue, the Human Protein Atlas (v.21.1, updated 2022-05-31) Human Tissue Specific Proteome20from prostate was used to annotate proteins detected in this study. Proteins were included if they belonged to the ‘Tissue enriched’, ‘Group enriched’ or Tissue enhanced’ categories for the tissue of interest, totaling 127 genes for prostate tissue.Sample type correlations

[0201] Median protein abundances across samples were used for all comparisons using Spearman’s rank correlation (cor. test). P-values for Spearman’s correlation were computed by asymptotic t approximation using an Edgeworth series. Samples: uEV-P20 = 146, uEV-P150= 148, Tissue = 157, uSP = 175. Proteins used for each comparison: uEV-P20 vs. uEV-P150 = 3,593; uSP vs. uEV-P20 = 2,839; uSP vs. uEV-P150 = 2,309; Tissue vs. uSP = 2,626; Tissue vs. uEV-P20 = 4,735; Tissue vs. uEV-P150 = 3,410.Identifying and annotating sample type-enriched proteins

[0202] To identify proteins enriched inuSP, uEV-P20 or uEV-P150 fractions, the inventors considered both unique and differentially abundant proteins in each fraction. The set of fraction-unique proteins were defined as proteins detected in >90% of samples of one fraction and detected in less than 10% of samples of the other two fractions. Only samples with matched uSP, uEV-P20 and uEV-P150 fractions were used (288 samples from 96 patients). To identify differentially abundant proteins, proteins present in >20% of samples were used (Proteins: uSP = 2,909; uEV-P20 = 4,841, uEV-P150 = 3,389). A two-tailed, paired two-sided Wilcoxon signed-rank test was used to compare protein abundance in uSP vs. uEV-P20 (2,430 shared proteins), uSP vs. uEV-P150 (1,950 shared proteins), and uEV-P20 vs. uEV-P150 (2,939 shared proteins). Proteins were considered fraction-elevated if they were differentially abundant in both comparisons (Figure 2i). For example, proteins were considered uSP-unique if they were detected in >90% of uSP samples, in <10% of uEV-P20 samples, and in <10% of uEV-P150 samples (Figure 2h). Proteins were considered ‘uSP-elevated’ if they were more abundant in uSP vs. uEV-P20 and uSP vs. uEV-P150 comparisons (FDR < 0.05 and |log2FC| > 0, intersect: 516 uSP-enriched proteins) (Figure 2i). This process resulted in a total of 606 uSP-enriched proteins (Figure 2j).

[0203] Fraction-enriched proteins were annotated with subcellular localization information from nine main categories (Secreted, Vesicles, Plasma membrane, Mitochondria, Cytosol, Nuclear Membrane, Nucleoplasm, Nucleoli and Golgi apparatus) from Human Protein Atlas’ Subcellular location data62(v.22.0, proteinatlas.org). “Nuclear membrane”, “Nucleoplasm” and “Nucleoli” categories were collapsed into one category called “Nucleus”. Fisher’s Exact Test was used to test for over- or under-representation in each category for each fraction. The magnitude of the enrichment was estimated using the odds ratio (epitools v.0.5-10.1), with the union of proteins detected in fluids (uSP, uEV-P20 and uEV-P150; 6,540 proteins) used as a custom background.Sample type tissue enrichment scores

[0204] To score samples based on their tissue content, Gene Set Variation Analysis (v.1.44.5)63was used to score samples using two custom gene signature sets - prostate and non-prostate (bladder + kidney). For the signature gene sets, proteins that were enriched in each tissue type in the Genotype-Tissue Expression (GTEx) Project Bulk Tissue RNA-Seq dataset25(V8; retrieved 2017-06-05; nprostate = 245 samples; nKidney = 89 samples; r iadder = 21 samples) and The Cancer Genome Atlas (TCGA) normal tissue adjacent to tumor from males26 28(NAT; TCGA v.2016_01_28; HPRAD = 52 samples, 19,821 genes; WBLCA = 10 samples, 18,951 genes; WKIRP = 22 samples, 19,518 genes; WKIRC = 52 samples, 19,667 genes). For KIRP and KIRC NAT samples, the inventors selected duplicated samples that had Spearman’s p > 0.99 and took the mean intensity, leaving a total of 71 KIRP / KIRC samples (19,829 genes). For each dataset, tissue-enriched genes were determined using two-sided Mann-Whitney U tests of each tissue of interest vs. the other two tissue types (i.e., GTExprostate vs. GTExBiadder + Kidney; log2FC > 3 and FDR < 0.05). For the gene set signature, concordant genes in GTEx25and TCGA NAT2628were selected (Genes: prostate = 46, bladder or kidney = 41).Pathway analysis - sample type comparison

[0205] Proteins detected in >10 samples of each sample type (Proteins: tissue = 7,438, uSP = 3,150, uEV-P20 = 5,462, uEV-P150 = 3,878) were used for pathway analysis using gprofiler2 (v.0.2.1) and g:GOSt() against Gene Ontology:Cellular Component gene sets. Pathway enrichment was performed using default parameters (organism = “hsapiens”, significant = TRUE, user threshold = 0.05, correction method = “g_SCS”, custom bg = NULL, sources = “GOCC”). P-values were determined using Fisher’s one-tailed test and adjusted for multiple correction using the g_SCS method64. Significantly enriched pathways were visualized using EnrichmentMap (v.3.3.4) in Cytoscape (v.3.9.1).Cell line proteomics data

[0206] Cells and EVs were collected from three separate passages for each cell line, termed experimental replicates, for proteomics. For each cell line and sample type, only proteins that were present in a minimum of two of three replicates were carried forward for analyses.Identi fying temporally stable proteins

[0207] The inventors generated proteomic profiles of uSP, uEV-P20 and uEV-P150 fractions from post-DRE urine from a longitudinal cohort composed of five patients. These patients had cISUP GG 1 tumors and were on active surveillance. None of the men upgraded in the time that the urines were collected. For statistical analysis of the longitudinal cohort, only reproducibly detected proteins were included for analysis. For each urine fraction, the inventors selected proteins that were detected in at least two timepoints for each patient and detected in at least two patients. This resulted in a total of 1,664 uSP proteins, 3,365 uEV-P20 proteins and 1,990 uEV-P150 proteins. The similarity of intra-patient and inter-patient proteomes were determined using Spearman’s correlation, calculated using the cor() function (use = “pairwise. complete. obs”) in stats package in R (v.4.2.1).

[0208] Intra- and inter-individual variance in protein intensities was assessed using linear mixed-effects regression using the lme4 package (v.1.1-31), and the intra-class correlation coefficient (ICC) was measured, which represents the proportion of inter-individual variance relative to total intra- and interindividual variance explained by a model37. Proteins for which a model cannot be fitted due to random effect variances of close to zero. This resulted in a total of 1,664 uSP proteins, 3,365 uEV-P20 proteins and 1,990 uEV-P150 proteins with estimated ICC values.Prostate cancer vs. non-cancer comparisons

[0209] Proteins detected in >50% of each sample type were considered for differential abundance analysis. This resulted in a total of 2, 156 uSP proteins, 3,431 uEV-P20 proteins and 2,255 uEV-P150 proteins. Two-sided Mann-Whitney U test was used for comparisons. For each sample type, gene set enrichment analysis (GSEA v.4.3.2) was performed on a pre-ranked list of proteins based on differential abundance (log2 fold change) in prostate cancers and noncancers. Enrichment analysis was performed against the human MSigDB Hallmarks gene set (v.2022.1) with gene set sizes from 25-500 and 1,000 permutations.Feature selection and machine learning

[0210] To generate predictors that distinguishes men with prostate cancers and non-cancers (NC), proteomics data from uSP, uEV-P20, and uEV-P150 fractions were trained separately. Patients: USPNC = 39, uSPpca= 136, UEV-P20NC = 22, uEV-P20pca = 132, UEV-P150NC = 25, uEV-P150pca= 131. The same cohorts were used to generate predictors that distinguishes men with cISUP GG 1 from men with cISUP GG > 1 (uSP: 50 GG 1 vs. 61 GG > 1; uEV-P20: 41 GG 1 vs. 63 GG > 1; uEV-P150: 40 GG 1 vs. 63 GG > 1). To develop predictive models, all datasets were divided into two groups: feature selection (50% of dataset) and training (50% of dataset). Within each urinary fraction, proteins that were detected in more than 50% of all samples and temporally stable (intraclass coefficient > 0.4 from serial post-DRE urine collected from three cISUP GG 1 patients at three timepoints) were passed into feature selection. Three methods were used to select the top 2 to 15 features within each dataset. For each feature, log2FC protein abundance was calculated and the significance level was assessed using a two- sided Mann-Whitney U test (wilcox.tesf). Features with the smallest -ra / ues were selected as the first set of top features. Features with the highest log2FC and P-value < 0.001 were also selected as the second set of top features. Ten times repeated five-fold cross validated (rfeControl) was applied to get the third set of top features. Seven machine-learning algorithms were applied to the top features in the biomarker identification, including generalized linear models, random forest, k-nearest neighbour classification, naive bayes, ridge, lasso, and elastic-net-regularized generalized linear model. Receiver operating characteristic (ROC) analysis with leave-one-out cross-validation was used to evaluate model performance with the use of ‘pROC package (v.1.18.0). Models with the highest area under the ROC were chosen and were fit to the entire dataset to get the final model. Machine learning algorithms were performed using the caret package (v.6.0.91) in R (v.3.6.1).Identi fying context-driven urine proteins

[0211] For each urine fraction (uEV-P20, uEV-P150 and uSP), the inventors sought to identify proteins that were distinct to each urine fraction, prostate-derived, and indicative of disease state (prostate cancer vs. non-cancer or cISUP GG>1 vs. cISUP GG 1). The inventors also sought to identify fraction-specific, prostate-derived proteins that were stable in protein abundance, independent of disease state (i.e., core proteome). Of the fraction-enriched proteins previously identified in Figure 2j (uEV-P20: 535 proteins, uEV-P150: 644 proteins, uSP: 606 proteins), the inventors filtered for proteins that were detected in >50% of prostate tissue samples (i.e., in more than 78 prostate tissues)18,19. The inventors called these proteins fraction- enriched, prostate-derived proteins (uEV-P20: 496 proteins, uEV-P150: 544 proteins, uSP: 409 proteins). From this set of genes, the inventors identified proteins that were stable across disease conditions, making up the core proteome - detected in >90% of samples in each fraction, not differentially abundant in prostate cancers vs. non-cancers (|log2FC| < 0.5), not differentially abundant in cISUP GG > 1 vs. cISUP GG 1 (|log2FC| < 0.5), and in the bottom 25% least variable proteins. This resulted in 70 uEV-P20 proteins, 115 uEV-P150 proteins, and 83 uSP proteins. The inventors also identified proteins that were differentially abundant in prostate cancers vs. non-cancers (i.e., “Tumor markers”; |log2FC| > 1 and FDR < 0.05) and in cISUP GG >1 vs. cISUP GG 1 (i.e., “Grade markers”; |log2FC| > 1 and unadjusted P-value < 0.05). From each of these groups - Core, Tumor, and Grade markers - the inventors identified proteins with predicted cell surface localization (Surface Prediction Consensus [SPC] score42> 2) that could serve as potential markers for each of these groups.Data Availability

[0212] The mass spectrometry raw data generated in this study have been deposited in the UCSD MassIVE database under accession code MSV000092061 [https: / / massive.ucsd. edu / ProteoSAFe / dataset.isp?task=28fc0fb96blf4al7870b85c5dl789f6e ]. All relevant data for the EV experiments have been submitted to the EV-TRACK knowledgebase65(EV-TRACK ID: EV230578). An accompanying website for this study provides an interactive browser for interrogation of multiple datasets and clinical cohorts, available at http: / / kislingerlab.uhnres.utoronto.ca / ev / home / . Published proteomics datasetsused in this study17 19,21can be accessed at UCSD’s MassIVE database under accession codes MSV000088000[https: / / massive.ucsd.edu / ProteoSAFe / datasetj sp?task=998b7dl444784bd3bd56e288ffb597d c] and MSV000081552[https: / / massive.ucsd. edu / ProteoSAFe / dataset.jsp?task=cd9b5c4ab5944516bfcee2d0e95a4d5 0] or PRIDE repository PXDO 15289[https: / / proteomecentral.proteomexchange.org / cgi / GetDataset?ID=PXD015289] andPXD026974 [https: / / proteomecentral.proteomexchange.org / cgi / GetDataset?ID=PXD026974]. Source data are provided with this paper.* * *

[0213] All of the methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the compositions and methods of this invention have been described in terms of preferred embodiments, it will be apparent to those of skill in the art that variations may be applied to the methods and in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit and scope of the invention. More specifically, it will be apparent that certain agents which are both chemically and physiologically related may be substituted for the agents described herein while the same or similar results would be achieved. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined by the appended claims.REFERENCESThe following references, to the extent that they provide exemplary procedural or other details supplementary to those set forth herein, are specifically incorporated herein by reference.1. Ridley, J. W. Fundamentals of the Study of Urine and Body Fluids. (Springer, Cham, Switzerland, 2018).2. Drake, R. R. et al. Clinical collection and protein properties of expressed prostatic secretions as a source for biomarkers of prostatic disease. J. Proteomics 72, 907-917 (2009).3. Simerville, J. A., Maxted, W. C. & Pahira, J. J. Urinalysis: a comprehensive review. Am. Fam. Physician 71, 1153-1162 (2005).4. Nagaraj, N. & Mann, M. Quantitative analysis of the intra- and inter-individual variability of the normal urinary proteome. J. Proteome Res. 10, 637-645 (2011).5. Tomlins, S. A. et al. 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Claims

WHAT IS CLAIMED IS:

1. A method of determining severity and / or a likelihood of prostate cancer for a patient, the method comprising: receiving urinary proteomic dataset obtained from the patient; and applying a trained machine learning model to the urinary proteomic dataset to determine the severity of the prostate cancer and / or the likelihood of the patient having prostate cancer.

2. The method of claim 1, wherein the urinary proteomic dataset comprises proteomic data from urinary extracellular vesicles (uEVs) obtained from the patient.

3. The method of claim 2, wherein the uEVs were separated from a urine sample from the patient.

4. The method of claim 3, wherein the uEVs were separated by centrifugation.

5. The method of claim 4, wherein the centrifugation was performed at approximately 20,000 x g.

6. The method of claim 4, wherein the centrifugation was performed at approximately 150,000 x g.

7. The method of any one of claims 1 to 6, wherein the urinary proteomic dataset is obtained from the patient after the patient has received a digital rectal examination.

8. The method of any one of claims 1 to 7, further comprising training the trained machine learning model with urinary proteomics training datasets obtained from a plurality of study patients, wherein the study patients include patients believed to have prostate cancer and study patients believed not to have prostate cancer.

9. The method of any one of claims 1 to 8, wherein the urinary proteomic dataset comprises a measured protein level for one or more of the proteins disclosed herein.

10. The method of any one of claims 1 to 9, further comprising administering a therapeutic intervention to the patient based on the severity and / or the likelihood of the patient having prostate cancer.

11. The method of claim 10, wherein the therapeutic intervention comprises active monitoring.

12. The method of claim 10, wherein the therapeutic intervention comprises a chemotherapy, a radiation therapy, a hormone therapy, an immunotherapy, a bisphosphonate therapy, anultrasound therapy, a photodynamic therapy, a proton therapy, a cell therapy, surgery, or a combination thereof.

13. A method of measuring a prostate proteome in a patient, the method comprising analyzing urinary extracellular vesicles (uEVs) separated from a urine sample from the patient.

14. The method of claim 13, wherein the uEVs were separated by centrifugation.

15. The method of claim 13, wherein the uEVs were separated by centrifugation at 20,000 x g.

16. The method of claim 13, wherein the uEVs were separated by centrifugation at 150,000 x g-17. The method of claim 13, wherein urinary extracellular vesicles (uEVs) separated from the urine sample comprises centrifuging the urine sample.

18. The method of claim 17, wherein the centrifuging occurs at approximately 20,000 x g.

19. The method of claim 17, wherein the centrifuging occurs at approximately 150,000 x g.

20. The method of any one of claims 13 to 19, wherein the urine sample is obtained from the patient after the patient has received a digital rectal examination.

21. The method of any one of claim 13 to 20, wherein the analyzing uEVs comprises measuring a level of one or more proteins from the uEVs.

22. The method of claim 21, wherein the one or more proteins are one or more proteins disclosed herein.

23. The method of any one of claims 13 to 22, further comprising administering a therapeutic intervention to the patient.

24. The method of claim 23, wherein the therapeutic intervention comprises active monitoring.

25. The method of claim 23, wherein the therapeutic intervention comprises a chemotherapy, a radiation therapy, a hormone therapy, an immunotherapy, a bisphosphonate therapy, an ultrasound therapy, a photodynamic therapy, a proton therapy, a cell therapy, surgery, or a combination thereof.

26. A method for treating a patient predicted to have prostate cancer requiring a therapeutic treatment, the method comprising:administering the therapeutic treatment to the patient, wherein the subject has been determined to have a treatment-responsive prostate cancer based on a trained machine learning model that determines prostate cancer prognosis using a urinary proteomic dataset.

27. The method of claim 26, wherein the trained machine learning model was trained with urinary proteomics training datasets obtained from a plurality of study patients, wherein the study patients include patients believed to have prostate cancer and study patients believed not to have prostate cancer.

28. The method of claim 26 or 27, wherein the therapeutic treatment comprises a chemotherapy, a radiation therapy, a hormone therapy, an immunotherapy, a bisphosphonate therapy, an ultrasound therapy, a photodynamic therapy, a proton therapy, a cell therapy, surgery, or a combination thereof.

29. The method of any one of claims 26 to 28, wherein the urinary proteomic dataset comprises proteomic data from urinary extracellular vesicles (uEVs) obtained from the patient.

30. The method of claim 29, wherein the uEVs were separated from a urine sample from the patient.

31. The method of claim 30, wherein the uEVs were separated by centrifugation.

32. The method of claim 31, wherein the centrifugation was performed at approximately 20,000 x g.

33. The method of claim 31, wherein the centrifugation was performed at approximately 150,000 x g.

34. The method of any one of claims 26 to 33, wherein the urinary proteomic dataset is obtained from the patient after the patient has received a digital rectal examination.

35. The method of any one of claims 26 to 34, wherein the urinary proteomic dataset comprises a measured protein level for one or more of protein measured by mass spectrometry.

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