EP4051807A1

EP4051807A1 - Urinary ev rna biomarkers for urothelial cancer

Info

Publication number: EP4051807A1
Application number: EP20880921.0A
Authority: EP
Inventors: Taku Murakami; Takahiro Osawa
Original assignee: Hokkaido University NUC; Showa Denko Materials Co Ltd; Showa Denko Materials America Inc
Current assignee: Hokkaido University NUC; Resonac Corp; Resonac America Inc
Priority date: 2019-10-31
Filing date: 2020-10-30
Publication date: 2022-09-07
Also published as: WO2021087388A1; JP2022553825A

Abstract

Certain aspects of the present disclosure are generally directed to a minimally-invasive, or non-invasive, method that assesses a patient's condition with regard to urothelial cancer. Several embodiments of the present disclosure relate to methods to characterizing mRNA profiles of extracellular vesicles (EV) such as exosomes and microvesicles from urine samples of a patient to assess, diagnose, or otherwise determine that patient's status with regard to urothelial cancer.

Description

URINARY EV RNA BIOMARKERS FOR UROTHELIAL CANCER INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS [0001] This application claims priority to U.S. Provisional Patent Application No.: 62/928,996, filed October 31, 2019, the entire contents of which is incorporated by reference herein. PARTIES OF JOINT RESEARCH AGREEMENT [0002] The claimed invention was made by, on behalf of, and/or in connection with one or more of the following parties to a joint research agreement between: Hitachi Chemical Co. America, Ltd. (now known as Showa Denko Materials (America), Inc.) and Hokkaido University (now known as National University Corporation Hokkaido University). The agreement was in effect on and before the date the claimed invention was made, and the claimed invention was made as a result of activities undertaken within the scope of the agreement. INCORPORATION BY REFERENCE OF SEQUENCE LISTING IN ASCII TEXT FILE [0003] This application incorporates by reference the Sequence Listing contained in the following ASCII text file being submitted concurrently herewith: File name: 2020-10- 29_Sequence_Listing_HITACHI-131PR; created October 29, 2020, 14.6Kb in size. BACKGROUND Field of the Invention [0004] Certain aspects of the present disclosure are generally directed to a minimally- invasive, or non-invasive, method that assesses a patient’s condition with regard to urothelial cancer. Several embodiments of the present disclosure relate to methods to characterizing mRNA profiles of extracellular vesicles (EV) such as exosomes and microvesicles from urine samples of a patient to assess, diagnose, or otherwise determine that patient’s status with regard to urothelial cancer. Description of the Related Art [0005] In 2015, National Cancer Institute estimated that there will be approximately 74,000 new bladder cancer cases and 14,000 deaths in the United States alone. The majority of bladder cancers and other urothelial cancers (e.g., malignancy in ureters and renal pelvises) are initiated from the transitional epithelium of urinary tract. While urothelial cancers other than bladder cancer account for only 5 to 10% of urothelial cancers, these cancers increase the chance of bladder cancer in the future. [0006] Treatment of urothelial cancer (e.g., bladder cancer) depends on the stage and grade of the cancer. Non-muscle-invasive cancers (pTa, pTis and pT1) can be treated by transurethral tumor removal or intravesical chemotherapy. On the other hand, muscle- invasive cancers (pT2, pT3 and pT4) require more aggressive treatments such as cystectomy and intravenous chemotherapy. Because the recurrence rate for the non-muscle-invasive cancers is 50 to 70%, and even higher for the muscle-invasive cancers, the patients with bladder cancer history require lifelong monitoring of recurrence, making bladder cancer the most expensive cancer in the U.S. from diagnosis to treatment. Furthermore, about 30% of patients with ureter or renal pelvis cancer will develop a bladder cancer after a few years. [0007] The current gold standard of bladder cancer detection is cystoscopy with urine cytology. While cystoscopy with urine cytology has a specificity of about 96%, the sensitivity is only about 44%. For low-grade tumors, the sensitivity of cystoscopy with urine cytology is even lower (4 to 31%). Cystoscopy is an invasive procedure that involves inserting a thin tube with a camera and light into the urethra and advancing the tube to the bladder. [0008] Several FDA-approved test kits are available to screen for urine-based urothelial cancer markers (e.g., BTA stat / BTA trak (Polymedoco, New York), NMP22 BladderChek (Alere, Florida), ImmunoCyt / uCyt+ (Scimedx, New Jersey), UroVysion (Abbott Molecular, Illinois)). These diagnostic kits show very similar sensitivity and specificity to the current gold standard, cystoscopy and cytology. Therefore, better non- invasive biomarkers are still needed, especially ones with higher sensitivity. [0009] Furthermore, high-risk non-muscle invasive bladder cancer (i.e., HR- NMIBC) (high grade pTa, pT1, any pTis in the National Comprehensive Cancer Network (NCCN) categories) patients are at higher risk of both recurrence and progression compared to low-risk (LR) NMIBC (i.e., low grade pTa) (1). Cystoscopy itself does not allow us to distinguish HR- and LR-NMIBC, therefore pathological analyses of tumors following transurethral resection of bladder tumor (TURBT) are crucial. Since HR-NMIBC could progress to muscle invasive bladder cancer (MIBC) (i.e., pT2 and higher stage) or metastasis, it is necessary to detect HR-NMIBC early before progression. In the case when patients are diagnosed as HR-NMIBC, a secondary TURBT is frequently conducted to complete resection of tumors to mitigate recurrence/progression. Therefore, it is clinically beneficial to know, prior to TURBT, if a cancer is low risk such as LR-NMIBC or high risk such as HR-NMIBC and MIBC. The benefits include not only better clinical outcomes but also possible avoidance of the secondary TURBT. SUMMARY [0010] Some embodiments described herein relate to a method for detecting a urothelial cancer in a subject. The method can comprise: a) obtaining a urine sample from the subject; b) isolating an RNA from said urine sample; c) quantifying an expression level of one or more markers selected from the group consisting of CXCR2, GPRC5A, HOXA13, IGFBP5, KRT17, LBR, MALAT1, MDK, MRPL48, MT-ND5, NET1, SLC2A1, and UGCG; and d) identifying the subject as having the urothelial cancer if the expression level of the one or more markers is higher than an expression level of the one or more markers in a urine sample obtained from a non-urothelial cancer subject. [0011] Other embodiments described herein relate to a method of identifying and treating a human patient displaying an indication of urothelial cancer, the method comprising: a) having a urine sample obtained from the human patient; b) isolating an RNA from the urine sample; c) quantifying an expression level of one or more markers selected from the group consisting of CXCR2, GPRC5A, HOXA13, IGFBP5, KRT17, LBR, MALAT1, MDK, MRPL48, MT-ND5, NET1, SLC2A1, and UGCG; d) diagnosing the human patient as having the urothelial cancer if the expression level of the one or more markers is higher than an expression level of the one or more markers in a urine sample obtained from a non-urothelial cancer subject; and e) administering an effective amount of a urothelial cancer medication to the human patient displaying an indication of urothelial cancer, wherein said urothelial cancer medication is selected from the group consisting of chemotherapy, radiation, surgery, and immunotherapy. [0012] These and other features, aspects, and advantages of the present invention will become better understood with reference to the following description and appended claims. BRIEF DESCRIPTION OF THE DRAWINGS [0013] Figures 1A-1F show data related to urinary EV mRNA analysis from various bladder cancers. Figure 1A shows expression data for beta-actin, APOBEC3C, and AQP3. Figure 1B shows data for CDK1, CXCR2, and GAPDH. Figure 1C shows expression data for GPRC5A, HOXA13, and IGFBP5. Figure 1D shows expression data for KRT17, MALAT1, and MDK. Figure 1E shows expression data for MRPL48, MT-ND5, and NET1. Figure 1F shows expression data for NONO and SLC2A1. Gene expression profiles were for Figures 1A-1F were normalized by ALDOB gene and were analyzed by Welch’s t-test. [0014] Figures 2A-2F show data related to urinary EV mRNA analysis from various bladder cancers. Figure 2A shows expression data for beta-actin, APOBEC3C, and AQP3. Figure 2B shows data for CDK1, CXCR2, and GAPDH. Figure 2C shows expression data for GPRC5A, HOXA13, and IGFBP5. Figure 2D shows expression data for KRT17, MALAT1, and MDK. Figure 2E shows expression data for MRPL48, MT-ND5, and NET1. Figure 2F shows expression data for NONO and SLC2A1. Gene expression profiles were for Figures 2A-2F were normalized by UPK1A gene and were analyzed by Welch’s t-test. [0015] Figures 3A-3F show data related to urinary EV mRNA analysis from various bladder cancers. Figure 3A shows expression data for beta-actin, APOBEC3C, and AQP3. Figure 3B shows data for 3CDK1, CXCR2, and GAPDH. Figure 3C shows expression data for GPRC5A, HOXA13, and IGFBP5. Figure 3D shows expression data for KRT17, MALAT1, and MDK. Figure 3E shows expression data for MRPL48, MT-ND5, and NET1. Figure 3F shows expression data for NONO and SLC2A1. Gene expression profiles were for Figures 3A-3F were normalized by NONO gene and were analyzed by Welch’s t-test. [0016] Figures 4A-4F show data related to urinary EV mRNA analysis from various bladder cancers. Figure A shows expression data for beta-actin, APOBEC3C, and AQP3. Figure B shows data for 3CDK1, CXCR2, and GAPDH. Figure 4C shows expression data for GPRC5A, HOXA13, and IGFBP5. Figure 4D shows expression data for KRT17, MALAT1, and MDK. Figure 4E shows expression data for MRPL48, MT-ND5, and NET1. Figure 4F shows expression data for NONO and SLC2A1. Gene expression profiles were for Figures 4A-4F were normalized by ACTB gene and were analyzed by Welch’s t-test. [0017] Figures 5A-5C show data related to various data collected from urine samples from patients with bladder cancers of varying severity. Figure 5A shows urine cytology results. Figure 5B shows ELISA data for presence of NMP22 in the samples. Figure 5C shows results of Bladder Tumor Associated Antigen (BTA stat) immunoassay analyses. [0018] Figures 6A-6D show the percentage of markers selected by Sparse Logistic Regression analysis (SLR). Figure 6A shows marker selection in both low- and high-risk bladder cancer. Figure 6B shows marker selection in high-risk bladder cancer. Figure 6C shows marker selection in low-risk bladder cancer. Figure 6D shows marker selection and associated risk stratification. [0019] Figures 7A-7D show the percentage of markers selected by Sparse Logistic Regression analysis (SLR). Figure 7A shows marker selection in both low- and high-risk bladder cancer. Figure 7B shows marker selection in high-risk bladder cancer. Figure 7C shows marker selection in low-risk bladder cancer. Figure 7D shows marker selection and associated risk stratification. [0020] Figures 8A-8B show data related to determination of predictive formulas and diagnostic performance comparison of Sparse Logistic Regression analysis. Figure 8A shows area under the curve of based on the combination of EV mRNA analysis and cytology. Figure 8B shows area under the curve based on EV mRNA alone. DETAILED DESCRIPTION [0021] Certain aspects of the present disclosure are generally directed to a minimally-invasive, or non-invasive, method that assesses a patient’s condition with regard to urothelial cancer. Each and every feature described herein, and each and every combination of two or more of such features, is included within the scope of the present disclosure provided that the features included in such a combination are not mutually inconsistent. [0022] Extracellular vesicles (EV) such as exosomes and microvesicles are released into the urinary space from all the areas of the nephrons and encapsulate cytoplasmic molecules of the cell of origin. Several studies showed that tumors generate larger EV at higher concentrations. EV from muscle-invasive bladder cancer cells have been shown to cause urothelial cells to undergo epithelial-to-mesenchymal transition. Since urothelial cancers are located on the urothelium and directly in contact with urine, EV from urothelial cancers may be released into urine, suggesting that, according to several embodiments disclosed herein, urinary EV could be a rich source of urothelial cancer biomarkers. Urinary cells and other markers are released from tumors into urine only after the tumor grows significantly and invades surrounding areas. However, urinary EV are released not only from tumors but also from normal and injured cells. Therefore, molecular signatures of urothelial cancer could be obtained in urine much earlier than the conventional biomarkers of urothelial cancer. [0023] The standard method to isolate urinary EV is a differential centrifugation method using ultracentrifugation. However, use of ultracentrifugation may not be applicable for routine clinical assays at regular clinical laboratories. Several embodiments of the present disclosure employ a urinary EV mRNA assay for biomarker and clinical studies, which enables similar or even superior performances to the standard method in terms of assay sensitivity, reproducibility and ease of use. Several embodiments employ this urinary EV mRNA assay to screen urine samples from urothelial cancer patients with various grades and stages of cancer were screened to identify new biomarkers of urothelial cancer. [0024] As described in more detail below, urinary EV can be isolated from urine by passing urine samples through a vesicle capture filter, thereby allowing the EV to be isolated from urine without the use of ultracentrifugation. In some embodiments, the vesicle capture material has a porosity that is orders of magnitude larger than the size of the captured vesicle. Although the vesicle-capture material has a pore size that is much greater than the size of the EV, the EV are captured on the vesicle-capture material by adsorption of the EV to the vesicle-capture material. The pore size and structure of the vesicle-capture material is tailored to balance EV capture with EV recovery so that mRNA from the EV can be recovered from the vesicle-capture material. In some embodiments, the vesicle-capture material is a multi-layered filter that includes at least two layers having different porosities. In some embodiments, the urine sample passes first through a first layer and then through a second layer, both made of glass fiber. In one embodiment, the first layer has a particle retention rate between 0.6 and 2.7 pm, preferably 1.5 and 1.8 pm, and the second layer has a particle retention rate between 0.1 and 1.6 pm, preferably 0.6 and 0.8 pm. In one embodiment, a particle retention rate of the first layer is greater than that of the second layer, thereby higher particulate loading capacity and faster flow rates can be obtained.

[0025] Several aspects of the present disclosure employ a urinary EV mRNA assay in which EV from urine of a human subject is screened for urothelial cancer biomarkers. Urinary EV mRNA profiles of a healthy volunteer were analyzed by RNA-seq and found to express not only kidney specific genes but also bladder specific genes, suggesting that urinary EV may be useful to detect urothelial diseases (e.g., ureter and renal pelvis cancers) as well as bladder cancer. Because urothelial cancers are directly in contact with urine, it is possible molecular signatures of urothelial cancers may be detected earlier in urinary EV compared to the urinary cell or protein biomarkers that are analyzed under current clinical practice.

[0026] Some embodiments described herein relate to a method for detecting a urothelial cancer in a subject. The method can comprise: a) obtaining a urine sample from the subject; b) isolating an RNA from said urine sample; c) quantifying an expression level of one or more markers selected from the group consisting of CXCR2, GPRC5A, HOXA13, IGFBP5, KRT17, LBR, MALAT1, MDK, MRPL48, MT-ND5, NETl, SLC2A1, and UGCG; and d) identifying the subject as having the urothelial cancer if the expression level of the one or more markers is higher than an expression level of the one or more markers in a urine sample obtained from a non-urothelial cancer subject. [0027] In some embodiments, the expression level NET1 is quantified. [0028] Other embodiments further comprise calculating a score using expression levels of at least two markers selected from the group consisting of CXCR2, GPRC5A, HOXA13, IGFBP5, KRT17, LBR, MALAT1, MDK, MRPL48, MT-ND5, NET1, SLC2A1, and UGCG. [0029] Still other embodiments further comprise detecting a reference gene wherein a said reference gene is used to normalize said expression level of said marker wherein the reference gene is selected from the group consisting of ACTB, ALDOB, DHRS2, GAPDH, GPX3, NONO and UPK1A. [0030] Still other embodiments further comprise a) preparing a urine supernatant by removing cells and large debris; b) isolating urinary extracellular vesicles from the said urine supernatant; and c) isolating an RNA from said urinary extracellular vesicles. [0031] Some embodiments described herein relate to a method of identifying and treating a human patient displaying an indication of urothelial cancer, the method comprising: a) having a urine sample obtained from the human patient; b) isolating an RNA from the urine sample; c) quantifying an expression level of one or more markers selected from the group consisting of CXCR2, GPRC5A, HOXA13, IGFBP5, KRT17, LBR, MALAT1, MDK, MRPL48, MT-ND5, NET1, SLC2A1, and UGCG; d) diagnosing the human patient as having the urothelial cancer if the expression level of the one or more markers is higher than an expression level of the one or more markers in a urine sample obtained from a non-urothelial cancer subject; and e) administering an effective amount of a urothelial cancer medication to the human patient displaying an indication of urothelial cancer, wherein said urothelial cancer medication is selected from the group consisting of chemotherapy, radiation, surgery, and immunotherapy. [0032] In some embodiments for a method of identifying and treating a human patient, the quantifying comprises quantifying a combined expression level of at least three of said markers. In some embodiments, the at least three of said markers comprise NET1, KRT17 and MDK. [0033] In some embodiments, a method of identifying and treating a human patient further include calculating a score using expression levels of at least two markers selected from the group consisting of CXCR2, GPRC5A, HOXA13, IGFBP5, KRT17, LBR, MALAT1, MDK, MRPL48, MT-ND5, NET1, SLC2A1, and UGCG. [0034] In some embodiments, a method of identifying and treating a human patient further include detecting a reference gene wherein a said reference gene is used to normalize said expression level of said marker wherein the reference gene is selected from the group consisting of ACTB, ALDOB, DHRS2, GAPDH, GPX3, NONO and UPK1A. [0035] In some embodiments, a method of identifying and treating a human patient further include a) preparing a urine supernatant by removing cells and large debris; b) isolating urinary extracellular vesicles from the said urine supernatant; and c) isolating an RNA from said urinary extracellular vesicles. Urinary EV RNA test for bladder cancer detection [0036] When a patient is suspected of having bladder cancer from his/her prior cancer history or clinical presentation such as hematuria, a urine sample can be obtained and sent for urinary EV RNA test for bladder cancer detection. Urine samples can be stored at room temperature or with refrigeration for example at 4°C for up to 7 days, preferably up to 1 day. Alternatively, urine samples can be frozen at below -60°C, preferably at -80°C. Desired volume of urine samples for urinary EV test are less than 50 mL, 40 mL, 30 mL, 20 mL, 10 mL, 5 mL, 3 mL, 2 mL, 1 mL, 500 μL, 200 μL, or 100 μL. Large debris or urinary cells in urine samples can be removed by centrifugation at less than 10000xG, 5000xG, 3500xG, 2500xG, 2000xG, 1500xG, 1000xG, 800xG, 500xG, 400xG, 300xG, 200xG, or 100xG, filtration with smaller than 20 μm pores, 14 μm pores, 12 μm pores, 10 μm pores, 8 μm pores, 5 μm pores, 2 μm pores, 1.2 μm pores, 1.0 μm pores, 0.8 μm pores, 0.6 μm pores, 0.45 μm pores, or 0.22 μm pores, or gravity sedimentation. Urinary EV can be isolated by the method described above, ultracentrifugation, polymer coprecipitation, membrane filtration, depth filtration, or size exclusion chromatography. Urinary EV RNA can be isolated by conventional RNA isolation method such as phenol-chloroform extraction, silica adsorption/elution, and oligo(dT)-immobilized support from the isolated urinary EV. Optionally, urinary EV RNA can be isolated by conventional RNA isolation directly from urine samples without isolation of urinary EV. Expression level of urinary EV RNA marker can be measured by a conventional RNA quantification method such as RNA sequencing (RNA-seq), reverse transcription quantitative polymerase chain reaction (RT-qPCR), isothermal nucleic acid amplification, northern blot, and RNA hybridization. The expression level of a urinary EV RNA marker can be normalized by the expression level of a reference gene to avoid influences by sample quantity, quality and assay procedure. Normalization can be done by subtracting the expression level of a reference gene or more than one refence genes from the expression level of a marker gene. A test is considered positive when expression level of urinary EV RNA marker in a patient urine sample is higher than a certain threshold or a level of the same marker in a group of subjects who do not have bladder cancer or any other cancer. A test is considered negative when expression level of a urinary EV RNA marker in the patient urine sample is lower than the threshold. A positive test result suggests that the patient is having bladder cancer, while a negative test result suggests that the patient is not having bladder cancer at the time when a test urine sample is collected. Repeated urine sample collection and test can increase the accuracy of the test further. To improve the diagnostic performance of a test further, expression levels of two or more EV RNA markers can be measured and used to generate a diagnostic score with or without additional clinical parameters such as hematuria, sex, age, and cancer history or test results such as urine cytology and the diagnostic score can be used in lieu of expression level of individual urinary EV marker. A diagnostic score can be calculated with diagnostic formulas generated by a machine learning approach such as sparse logistic regression, random forest, and support vector machine. A diagnostic score calculated based on a probability of a patient having bladder cancer is even more useful such as logistic regression analysis. A logit function or the log-odds is the logarithm of the odds where p is probability, therefore a higher diagnostic score suggests higher risk of having bladder cancer and a lower diagnostic score suggests lower chance of having bladder cancer. A diagnostic formula can be optimized to detect a certain population of bladder cancer population for clinical needs such as bladder cancer screening, recurrence monitoring, and stratification of low-risk and high-risk bladder cancer. Using the test results, additional confirmatory examination of bladder tumor such as cystoscopy or removal of bladder tumors by surgery could be conducted at doctor’s discretion. Urinary EV RNA marker [0037] Useful urinary EV RNA marker is one selected from ACTB, APOBEC3C, AQP3, CDC42BPB, CDK1, CGNL1, CNTROB, CTXN3, CXCR2, DNAJC24, FAM110B, FAM8A1, FANCD2, FGF1, GAPDH, GAS5, GEMIN5, GPRC5A, GXYLT1, HOXA13, IGFBP5, KRT17, L1CAM, LBR, LEO1, LRRC19, LRRC8D, MAB21L3, MALAT1, MDK, METTL17, MMP15, MRPL48, MT-ND5, NET1, NONO, P4HA1, PARS2, PCAT1, RGN, ROBO1, S100A13, SEMA4A, SLC12A3, SLC16A4, SLC2A1, SPRY4-IT1, TMEM176A, TMEM33, TRHDE, UGCG, and WNT5A. Useful reference gene to normalize urinary EV RNA marker is one selected from ACTB, ALDOB, DHRS2, GAPDH, GPX3, MT-ND5, NONO, and UPK1A. For the detection of bladder cancer including low grade pTa, high grade pTa, any pTis, pT1, pT2 and higher stage, the more useful urinary EV RNA marker is one selected from GPRC5A, GPX3, IGFBP5, KRT17, MALAT1, MDK, MT-ND5, NET1, and SLC2A1 (Table 3). For the detection of high-risk bladder cancer including high grade pTa, any pTis, pT1, pT2 and higher stage, the more useful urinary EV RNA marker is one selected from ACTB, CXCR2, GPRC5A, IGFBP5, KRT17, MALAT1, MDK, NET1, and SLC2A1 (Table 4). For the detection of low-risk bladder cancer including low grade pTa, the more useful urinary EV RNA marker for bladder cancer detection is one selected from ACTB, GAPDH, GAS5, GPRC5A, GPX3, HOXA13, IGFBP5, KRT17, MALAT1, MDK, MRPL48, MT-ND5, NET1, and SLC2A1 (Table 5). For the detection of low-/high-grade pTa bladder cancer, the more useful urinary EV RNA marker is one selected from GPRC5A, GPX3, HOXA13, IGFBP5, KRT17, MALAT1, MDK, MRPL48, MT-ND5, NET1, and SLC2A1 (Table 6). For the detection of pT1 and higher bladder cancer, the more useful urinary EV RNA marker is one selected from ACTB, GAPDH, GPRC5A, GPX3, KRT17, MALAT1, MDK, NET1, and SLC2A1 (Table 7). For the detection of high-grade pTa bladder cancer, the more useful urinary EV RNA marker is one selected from AQP3, GPX3, HOXA13, IGFBP5, KRT17, MALAT1, MDK, MRPL48, MT-ND5, NET1, and SLC2A1 (Table 8). For the detection of pTis bladder cancer, the more useful urinary EV RNA marker is one selected from ALDOB, APOBEC3C, CXCR2, GPX3, IGFBP5, KRT17, MALAT1, MDK, MT-ND5, NET1, and UPK1A (Table 9). To distinguish low-risk bladder cancer and high-risk bladder cancer, the more useful urinary EV RNA marker is one selected from ACTB, ALDOB, APOBEC3C, AQP3, CDK1, CXCR2, DHRS2, GAPDH, GAS5, GPX3, KRT17, MALAT1, and MDK (Table 10). [0038] Diagnostic formula can be developed with a combination of EV RNA markers with or without urine cytology result. Useful diagnostic formula or combination of markers for bladder cancer detection include at least one marker selected from the group of ACTB, ALDOB, APOBEC3C, AQP3, CXCR2, DHRS2, GAPDH, GAS5, GPRC5A, GPX3, HOXA13, IGFBP5, KRT17, MALAT1, MDK, MRPL48, MT-ND5, NET1, SEMA4A, and UPK1A (Table 11). Useful diagnostic formula or combination of markers for high-risk bladder cancer detection (i.e., high grade pTa, any pTa, pT1, pT2 and higher) include at least one marker selected from the group of ACTB, APOBEC3C, GPX3, HOXA13, IGFBP5, KRT17, MALAT1, MDK, MRPL48, MT-ND5, NET1, SEMA4A, and UPK1A (Table 12). Useful diagnostic formula or combination of markers for low-risk bladder cancer detection (i.e., low grade pTa) include at least one marker selected from the group of ACTB, APOBEC3C, AQP3, CDK1, DHRS2, GAPDH, GPRC5A, HOXA13, IGFBP5, KRT17, MALAT1, MDK, MRPL48, MT-ND5, NET1, SEMA4A, SLC2A1, and UPK1A (Table 13). Useful diagnostic formula or combination of markers for bladder cancer risk stratification or low-risk bladder cancer and high-risk bladder cancer include at least one marker selected from the group of AQP3, CDK1, CXCR2, DHRS2, GPX3, HOXA13, KRT17, MALAT1, MDK, MRPL48, MT-ND5, SEMA4A, SLC2A1, and UPK1A (Table 14). Some non-limiting embodiments are described in Figures 1-8. [0039] Figure 1. Urinary EV mRNA analysis (1). Gene expression profiles normalized by ALDOB gene were analyzed by Welch’s t-test among Control (CTRL), low grade pTa (pTa LG), high grade pTa (pTa HG), any pTis (pTis with or without concurrent pTa and pT1), pT1 (pT1), pT2 and higher (>pT2) groups. Statistical significance was determined by p<0.05 (*p<0.05, **p<0.005, ***p<0.0005). [0040] Figure 2. Urinary EV mRNA analysis (2). Gene expression profiles normalized by UPK1A gene were analyzed by Welch’s t-test among Control (CTRL), low grade pTa (pTa LG), high grade pTa (pTa HG), any pTis (pTis with or without concurrent pTa and pT1), pT1, pT2 and higher (>pT2) groups. Statistical significance was determined by p<0.05 (*p<0.05, **p<0.005, ***p<0.0005). [0041] Figure 3. Urinary EV mRNA analysis (3). Gene expression profiles normalized by NONO gene were analyzed by Welch’s t-test among Control (CTRL), low grade pTa (pTa LG), high grade pTa (pTa HG), any pTis (pTis with or without concurrent pTa and pT1), pT1, pT2 and higher (>pT2) groups. Statistical significance was determined by p<0.05 (*p<0.05, **p<0.005, ***p<0.0005). [0042] Figure 4. Urinary EV mRNA analysis (4). Gene expression profiles normalized by ACTB gene were analyzed by Welch’s t-test among Control (CTRL), low grade pTa (pTa LG), high grade pTa (pTa HG), any pTis (pTis with or without concurrent pTa and pT1), pT1, pT2 and higher (>pT2) groups. Statistical significance was determined by p<0.05 (*p<0.05, **p<0.005, ***p<0.0005). [0043] Figure 5. Urine cytology, NMP22 and BTA stat analyses. Urine cytology, NMP22 and BTA assay results were compared among Control (CTRL), low grade pTa (pTa LG), high grade pTa (pTa HG), any pTis (pTis with or without concurrent pTa and pT1), pT1, pT2 and higher (>pT2). A. Positive numbers in the Y axis indicate the numbers of samples with cytology positive and suspicious results and negative numbers indicate the numbers of samples with cytology negative results. B. Positive numbers in the Y axis indicate the numbers of samples with NMP22 positive results and negative numbers indicate the numbers of samples with NMP22 negative results. C. Positive numbers in the Y axis indicate the numbers of samples with BTA stat positive results and negative numbers indicate the numbers of samples with BTA stat negative results. [0044] Figure 6. Markers selected frequently in Sparse Logistic Regression analysis (SLR). SLR was conducted and the best combinations of markers were selected from EV RNA markers and cytology scores. The numbers in the Y axis indicate the relative frequency that each marker was selected in the best combinations of markers. [0045] Figure 7. Markers selected frequently in Sparse Logistic Regression analysis (SLR). SLR was conducted and the best combinations of markers were selected from EV RNA markers. The numbers in the Y axis indicate the relative frequency that each marker was selected in the best combinations of markers. [0046] Figure 8. Diagnostic performance comparison of Sparse Logistic Regression analysis. A. The best combinations of markers were selected from EV RNA markers and cytology scores, EV RNA markers excluding NET1 and cytology scores, or EV RNA markers excluding KRT17 and cytology scores. B. The best combinations of markers were selected from EV RNA markers, EV RNA markers excluding NET1, or EV RNA markers excluding KRT17. Diagnostic performance (AUC) of top combinations of markers were compared by Welch’s t-test. Statistical significance was determined by p<0.05 (*p<0.05, **p<0.005, ***p<0.0005). Definitions [0047] Throughout this specification the word “comprise,” or variations such as “comprises” or “comprising,” will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. [0048] The following explanations of terms and methods are provided to better describe the present disclosure and to guide those of ordinary skill in the art in the practice of the present disclosure. The singular forms “a,” “an,” and “the” refer to one or more than one, unless the context clearly dictates otherwise. For example, the term “comprising a nucleic acid molecule” includes single or plural nucleic acid molecules and is considered equivalent to the phrase “comprising at least one nucleic acid molecule.” The term “or” refers to a single element of stated alternative elements or a combination of two or more elements, unless the context clearly indicates otherwise. As used herein, “comprises” means “includes.” Thus, “comprising A or B,” means “including A, B, or A and B,” without excluding additional elements. Unless otherwise specified, the definitions provided herein control when the present definitions may be different from other possible definitions. [0049] Unless explained otherwise, all technical and scientific terms used herein have the same meaning as commonly understood to one of ordinary skill in the art to which this disclosure belongs. All HUGO Gene Nomenclature Committee (HGNC) identifiers (IDs) mentioned herein are incorporated by reference in their entirety. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present disclosure, suitable methods and materials are described below. The materials, methods, and examples are illustrative only and not intended to be limiting. [0050] The term “cancer” denotes a malignant neoplasm that has undergone characteristic anaplasia with loss of differentiation, increased rate of growth, invasion of surrounding tissue, and is capable of metastasis. The term “cancer” shall be taken to include a disease that is characterized by uncontrolled growth of cells within a subject. In some embodiments, the terms “cancer” and “tumor” are used interchangeably. In some embodiments, the term “tumor” refers to a benign or non-malignant growth. EXAMPLE [0051] This non-limiting example describes patient recruitment and urine sample collection, and extracellular vesicle RNA Marker discovery and validation. Patient recruitment and urine sample collection [0052] The study protocol was approved by institutional review boards at Hokkaido University Hospital, Sapporo City General Hospital, Hokkaido Cancer Center, Teine Keijinkai Hospital, and Sapporo Keiyukai Hospital. Up to 604 urine samples (231 patients) were collected prior to TURBT and follow-ups for 3 years after the last TURBT with informed consents. Tumor staging and grading were done with cystoscopy and following pathological examination. Urine cytology, NMP22 (Abbott, IL) and BTA stat (Polymedco, NY) tests were performed following the manufacturers’ protocols for marker performance comparison. Extracellular vesicle RNA Marker discovery [0053] In order to obtain urinary extracellular vesicle (EV) RNA biomarker candidates useful to detect low-risk and high-risk bladder cancer, differential gene expression analysis of urinary EV (bladder cancer N=12 vs. no bladder cancer control N=7) was conducted by RNA-seq and compared with our previous study data (2) and the expression profiles of tumors and urinary precipitates (cells) from bladder cancer patients. Tumor RNA- seq data was obtained from the Cancer Genome Atlas (TCGA) (Bladder tumor RNA-seq, Tumor, N=405; matched normal bladder tissue, N=19) and the Genotype-Tissue Expression (GTEx) (normal bladder tissue RNA-seq, N=12) databases. Urinary cell data was obtained from NIH GEO database (GSE68020, urothelial cancer, N=30, vs. no bladder cancer control, N=20). From the differential gene expression analysis and literature research (3–7), at least 56 marker candidates and reference genes (ACTB, ALDOB, APOBEC3C, AQP3, CDC42BPB, CDK1, CGNL1, CNTROB, CTXN3, CXCR2, DHRS2, DNAJC24, FAM110B, FAM8A1, FANCD2, FGF1, GAPDH, GAS5, GEMIN5, GPRC5A, GPX3, GXYLT1, HOXA13, IGFBP5, KRT17, L1CAM, LBR, LEO1, LRRC19, LRRC8D, MAB21L3, MALAT1, MDK, METTL17, MMP15, MRPL48, MT-ND5, NET1, NONO, P4HA1, PARS2, PCAT1, RGN, ROBO1, S100A13, SEMA4A, SLC12A3, SLC16A4, SLC2A1, SPRY4-IT1, TMEM176A, TMEM33, TRHDE, UGCG, UPK1A, WNT5A) were selected for further validation in a larger cohort by RT-qPCR. Urine extracellular vesicle marker validation [0054] Urinary EV mRNA was isolated using ExoComplete (Hitachi Chemical Diagnostics, CA) and assayed by RT-qPCR. Primer sequences are listed in Table 2. RT- qPCR data was normalized by that of ACTB, ALDOB, DHRS2, GAPDH, GPX3, MT-ND5, NONO, or UPK1A as a reference gene using the delta Ct method, i.e. a threshold cycle value of each marker was subtracted by a threshold cycle value of a reference gene. [0055] Diagnostic categories of the participants were low grade pTa (N=44), high grade pTa (N=51), pT1 (N=39), any pTis (with or without concurrent pTa and pT1) (N=22), pT2 or higher stages (N=12), other non-bladder cancer (N=4) and control (benign tumors or no tumor, N=139) (Table 1). Urine samples collected during the follow-ups 12 months after the last TURBT were considered as control if the patients do not show any recurrence during the study period. [0056] Diagnostic performance was obtained by ROC curve analysis against control using R and pROC package. For urine cytology, two scoring system were used to calculate area under the curve (AUC). Cytology 1: score “1” was assigned to cytology positive results and “0” to Cytology suspicious and negative results. Cytology 2: score “1” to cytology positive and suspicious results and “0” to cytology negative results. For BTA stat and NMP22, thresholds specified in the manufacturers’ protocols were used. For each of urinary EV RNA markers, an optimum threshold was obtained at the nearest point of the ROC curve to the top-left corner, and used to characterize diagnostic performance. [0057] Urinary EV NET1 was significantly elevated in low grade pTa, high grade pTa, pTis, pT1 and >pT2 compared to the control group when the expression level of NET1 is normalized by that of ACTB (Figure 4), ALDOB (Figure 1), DHRS2, GAPDH, GPX3, NONO (Figure 3), or UPK1A (Figure 2). The diagnostic performance of NET1 for bladder cancer outperformed any of the conventional markers such as urine cytology, BTA stat and NMP22 as shown in Table 3, Table 4, Table 5, Table 6, Table 7, Table 8 and Table 9. [0058] Urinary EV KRT17 was significantly elevated more in high-risk bladder cancer including high grade pTa, pTis, pT1 and >pT2 compared to the control group (Figure 1, Figure 2, Figure 3, Figure 4). The diagnostic performance of KRT17 for bladder cancer outperformed any of the conventional markers such as urine cytology, BTA stat and NMP22 as shown in Table 3, Table 4, Table 5, Table 6, Table 7, Table 8 and Table 9 and useful to distinguish low risk and high-risk bladder cancer as shown in Table 10. [0059] Urinary EV MDK was significantly elevated more in low grade pTa, high grade pTa, pTis, pT1 and >pT2 compared to the control group (Figure 1, Figure 2, Figure 3, Figure 4). The diagnostic performance of MDK for bladder cancer outperformed any of the conventional markers such as urine cytology, BTA stat and NMP22 as shown in Table 3, Table 4, Table 5, Table 6, Table 7, Table 8 and Table 9. [0060] Sparse logistic regression analysis was conducted by R and glmnet package. Raw threshold cycle values of the 23 genes including ACTB, ALDOB, APOBEC3C, AQP3, CDK1, CXCR2, DHRS2, GAPDH, GAS5, GPRC5A, GPX3, HOXA13, IGFBP5, KRT17, MALAT1, MDK, MRPL48, MT-ND5, NET1, NONO, SEMA4A, SLC2A1, and UPK1A were used for feature selection with or without urine cytology result. For urine cytology, two different scorings were used: Cytology1; Positive (1), suspicious (0) and negative (0) and Cytology2; Positive/suspicious (1) and negative (0). Feature selection (formula screening) was conducted with 10-fold cross validation and 50,000 bootstrap re-sampling to determine if the sample is bladder cancer positive (score 1) or negative (score 0) in various bladder cancer populations such as low-risk and high-risk bladder cancer (i.e., low grade pTa, high grade pTa, any pTis, pT1, pT2 and higher), high-risk bladder cancer (i.e., high grade pTa, any pTis, pT1, pT2 and higher), and low-risk bladder cancer (i.e., low grade pTa). For bladder cancer risk stratification, the same feature selection method was employed except determining if the sample is high-risk bladder cancer (score 1) or low-risk bladder cancer (score 0). Formulas selected 10 times or more were applied for further validation and parameter optimization. For each formula, logistic regression analysis was conducted with 10-fold cross validation and 500 bootstrap re-sampling without feature selection to optimize the parameters (formula confirmation). The best formulas for bladder cancer with or without urine cytology result were shown in Table 11 and most frequently selected markers including KRT17 and NET1 are shown in Figure 6A and Figure 7A. The best formulas for high-risk bladder cancer were shown in Table 12 and most frequently selected markers including KRT17 and NET1 are shown in Figure 6B and Figure 7B. The best formulas for low-risk bladder cancer were shown in Table 13 and most frequently selected markers including NET1, MDK and HOXA13 are shown in Figure 6C and Figure 7C. The best formulas for bladder cancer risk stratification or to distinguish low-risk and high-risk bladder cancer were shown in Table 14 and most frequently selected markers including HOXA13, KRT17 and AQP3 are shown in Figure 6D and Figure 7D. [0061] In order to figure out how critical to include NET1 or KRT17 in formula development, feature selection was conducted as shown above except excluding NET1 or KRT17 and diagnostic performance of formulas selected 10 times or more were compared (Figure 8A, B). Diagnostic performance of top formulas was significantly lower without NET1 or KRT17, meaning those markers are critical to develop formulas to detect bladder cancer.

Tables: Table 1. Urine samples analyzed in the study and their associated pathologically confirmed diagnostic results.

Table 2. Primer sequences of urinary EV RNA marker and reference genes.

Table 3. Diagnostic performance of individual marker and reference gene combinations in bladder cancer population including low grade pTa, high grade pTa, any pTis, pT1, pT2 and higher stages. Only the marker/reference combinations with the highest AUCs and conventional assays are shown in the table. Table 4. Diagnostic performance of individual marker and reference gene combinations in high-risk bladder cancer population including high grade pTa, any pTis, pT1, pT2 and higher stages. Only the marker/reference combinations with the highest AUCs and conventional assays are shown in the table. Table 5. Diagnostic performance of individual marker and reference gene combinations in low-risk bladder cancer population including low grade pTa. Only the marker/reference combinations with the highest AUCs and conventional assays are shown in the table. Table 6. Diagnostic performance of individual marker and reference gene combinations in low and high grade pTa bladder cancer population. Only the marker/reference combinations with the highest AUCs and conventional assays are shown in the table. Table 7. Diagnostic performance of individual marker and reference gene combinations in pT1 and higher bladder cancer population. Only the marker/reference combinations with the highest AUCs and conventional assays are shown in the table. Table 8. Diagnostic performance of individual marker and reference gene combinations in high grade pTa bladder cancer population. Only the marker/reference combinations with the highest AUCs and conventional assays are shown in the table. Table 9. Diagnostic performance of individual marker and reference gene combinations in pTis bladder cancer population. Only the marker/reference combinations with the highest AUCs and conventional assays are shown in the table. Table 10. Diagnostic performance of individual marker and reference gene combinations to distinguish low-risk and high-risk bladder cancer. Table 11. Marker combinations for bladder cancer diagnostics and their diagnostic performance. The best marker combinations (formulas) were selected from EV RNA markers and cytology score (A) or EV RNA markers only (B). A. EV RNA with cytology F^ormula _(screening) (optimization APOBEC3C+Cytology2+HOXA13+KRT17+MALAT1+MDK+MT-ND5+NET1+SEMA4A +_UPK1A ^{0.877 0.890} B. EV RNA only Table 12. Marker combinations for high-risk bladder cancer diagnostics and their diagnostic performance. The best marker combinations (formulas) were selected from EV RNA markers and cytology score (A) or EV RNA markers only (B). A. EV RNA with cytology (_screening) (optimization B. EV RNA only Table 13. Marker combinations for low-risk bladder cancer diagnostics and their diagnostic performance. The best marker combinations (formulas) were selected from EV RNA markers and cytology score (A) or EV RNA markers only (B). A. EV RNA with cytology (_screening) (optimization B. EV RNA only Table 14. Marker combinations for bladder cancer risk stratification and their diagnostic performance. The best marker combinations (formulas) were selected from EV RNA markers and cytology score (A) or EV RNA markers only (B). A. EV RNA with cytology B. EV RNA only Reference: 1. Ravvaz K, Walz ME, Weissert JA, Downs TM. Predicting Nonmuscle Invasive Bladder Cancer Recurrence and Progression in a United States Population. J Urol. 2017 Oct 1;198(4):824–31. 2. Murakami T, Yamamoto CM, Akino T, Tanaka H, Fukuzawa N, Suzuki H, et al. Bladder cancer detection by urinary extracellular vesicle mRNA analysis. Oncotarget.2018 Aug 13;9(67):32810–21. 3. O’Sullivan P, Sharples K, Dalphin M, Davidson P, Gilling P, Cambridge L, et al. A Multigene Urine Test for the Detection and Stratification of Bladder Cancer in Patients Presenting with Hematuria. J Urol.2012 Sep 1;188(3):741–7. 4. Sin MLY, Mach KE, Sinha R, Wu F, Trivedi DR, Altobelli E, et al. Deep Sequencing of Urinary RNAs for Bladder Cancer Molecular Diagnostics. Clin Cancer Res.2017 Jul 15;23(14):3700–10. 5. Du L, Duan W, Jiang X, Zhao L, Li J, Wang R, et al. Cell-free lncRNA expression signatures in urine serve as novel non-invasive biomarkers for diagnosis and recurrence prediction of bladder cancer. J Cell Mol Med.2018;22(5):2838–45. 6. Zhan Y, Du L, Wang L, Jiang X, Zhang S, Li J, et al. Expression signatures of exosomal long non-coding RNAs in urine serve as novel non-invasive biomarkers for diagnosis and recurrence prediction of bladder cancer. Mol Cancer.2018 Sep 29;17(1):142. 7. Song Y, Jin D, Ou N, Luo Z, Chen G, Chen J, et al. Gene Expression Profiles Identified Novel Urine Biomarkers for Diagnosis and Prognosis of High-Grade Bladder Urothelial Carcinoma. Front Oncol [Internet].2020 [cited 2020 Aug 20];10. Available from: https://www.frontiersin.org/articles/10.3389/fonc.2020.00394/full

Claims

WHAT IS CLAIMED IS: 1. A method for detecting a urothelial cancer in a subject comprising: obtaining a urine sample from the subject; isolating an RNA from said urine sample; quantifying an expression level of one or more markers selected from the group consisting of CXCR2, GPRC5A, HOXA13, IGFBP5, KRT17, LBR, MALAT1, MDK, MRPL48, MT-ND5, NET1, SLC2A1, and UGCG; and identifying the subject as having the urothelial cancer if the expression level of the one or more markers is higher than an expression level of the one or more markers in a urine sample obtained from a non-urothelial cancer subject. 2. The method of Claim 1, wherein the expression level of NET1 is quantified. 3. The method of Claim 1, further comprising: calculating a score using expression levels of at least two markers selected from the group consisting of CXCR2, GPRC5A, HOXA13, IGFBP5, KRT17, LBR, MALAT1, MDK, MRPL48, MT-ND5, NET1, SLC2A1, and UGCG. 4. The method of Claim 1, further comprising: detecting a reference gene wherein a said reference gene is used to normalize said expression level of said marker wherein the reference gene is selected from the group consisting of ACTB, ALDOB, DHRS2, GAPDH, GPX3, NONO and UPK1A. 5. The method of Claim 1, further comprising: preparing a urine supernatant by removing cells and large debris; isolating urinary extracellular vesicles from the said urine supernatant; and isolating an RNA from said urinary extracellular vesicles. 6. A method of identifying and treating a human patient displaying an indication of urothelial cancer, the method comprising: having a urine sample obtained from the human patient; isolating an RNA from the urine sample; quantifying an expression level of one or more markers selected from the group consisting of CXCR2, GPRC5A, HOXA13, IGFBP5, KRT17, LBR, MALAT1, MDK, MRPL48, MT-ND5, NET1, SLC2A1, and UGCG; diagnosing the human patient as having the urothelial cancer if the expression level of the one or more markers is higher than an expression level of the one or more markers in a urine sample obtained from a non-urothelial cancer subject; and administering an effective amount of a urothelial cancer medication to the human patient displaying an indication of urothelial cancer, wherein said urothelial cancer medication is selected from the group consisting of chemotherapy, radiation, surgery, and immunotherapy. 7. The method of Claim 6, wherein quantifying comprises quantifying a combined expression level of at least three of said markers. 8. The method of Claim 7, wherein the at least three of said markers comprise NET1, KRT17 and MDK. 9. The method of Claim 6, further comprising: calculating a score using expression levels of at least two markers selected from the group consisting of CXCR2, GPRC5A, HOXA13, IGFBP5, KRT17, LBR, MALAT1, MDK, MRPL48, MT-ND5, NET1, SLC2A1, and UGCG. 10. The method of Claim 6, further comprising: detecting a reference gene wherein a said reference gene is used to normalize said expression level of said marker wherein the reference gene is selected from the group consisting of ACTB, ALDOB, DHRS2, GAPDH, GPX3, NONO and UPK1A. 11. The method of Claim 6, further comprising: preparing a urine supernatant by removing cells and large debris; isolating urinary extracellular vesicles from the said urine supernatant; and isolating an RNA from said urinary extracellular vesicles.