WO2021087388A1 - Biomarqueurs d'arn ev urinaire pour le cancer urothélial - Google Patents

Biomarqueurs d'arn ev urinaire pour le cancer urothélial Download PDF

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WO2021087388A1
WO2021087388A1 PCT/US2020/058398 US2020058398W WO2021087388A1 WO 2021087388 A1 WO2021087388 A1 WO 2021087388A1 US 2020058398 W US2020058398 W US 2020058398W WO 2021087388 A1 WO2021087388 A1 WO 2021087388A1
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markers
rna
urinary
expression level
urine
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PCT/US2020/058398
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English (en)
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Taku Murakami
Takahiro Osawa
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Showa Denko Materials (America), Inc.
Showa Denko Materials Co., Ltd.
National University Corporation Hokkaido University
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Priority to JP2022525804A priority Critical patent/JP2022553825A/ja
Priority to EP20880921.0A priority patent/EP4051807A1/fr
Publication of WO2021087388A1 publication Critical patent/WO2021087388A1/fr

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • EV extracellular vesicles
  • EV extracellular vesicles
  • 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.
  • EV extracellular vesicles
  • 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 are initiated from the transitional epithelium of urinary tract.
  • 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.
  • Treatment of urothelial cancer depends on the stage and grade of the cancer.
  • Non-muscle-invasive cancers pTa, pTis and pT1
  • muscle- invasive cancers pT2, pT3 and pT4 require more aggressive treatments such as cystectomy and intravenous chemotherapy.
  • 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.
  • 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)).
  • BTA stat / BTA trak Polymedoco, New York
  • NMP22 BladderChek Alere, Florida
  • ImmunoCyt / uCyt+ Scimedx, New Jersey
  • UroVysion Abbott Molecular, Illinois
  • HR- NMIBC high-risk non-muscle invasive bladder cancer
  • LR low-risk NMIBC
  • 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.
  • HR-NMIBC could progress to muscle invasive bladder cancer (MIBC) (i.e., pT2 and higher stage) or metastasis
  • MIBC muscle invasive bladder cancer
  • 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.
  • 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.
  • FIG. 1 Another 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • a minimally-invasive, or non-invasive, method that assesses a patient’s condition with regard to urothelial cancer.
  • 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.
  • 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.
  • the standard method to isolate urinary EV is a differential centrifugation method using ultracentrifugation.
  • 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.
  • 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.
  • the vesicle capture material has a porosity that is orders of magnitude larger than the size of the captured vesicle.
  • 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.
  • the vesicle-capture material is a multi-layered filter that includes at least two layers having different porosities.
  • the urine sample passes first through a first layer and then through a second layer, both made of glass fiber.
  • the first layer has a particle retention rate between 0.6 and 2.7 pm, preferably 1.5 and 1.8 pm
  • the second layer has a particle retention rate between 0.1 and 1.6 pm, preferably 0.6 and 0.8 pm.
  • 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.
  • 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.
  • 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.
  • the expression level NET1 is quantified.
  • 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.
  • 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.
  • 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.
  • 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
  • the quantifying comprises quantifying a combined expression level of at least three of said markers.
  • the at least three of said markers comprise NET1, KRT17 and MDK.
  • 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.
  • 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.
  • 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 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 ⁇ L, 200 ⁇ L, or 100 ⁇ L.
  • 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 ⁇ m pores, 14 ⁇ m pores, 12 ⁇ m pores, 10 ⁇ m pores, 8 ⁇ m pores, 5 ⁇ m pores, 2 ⁇ m pores, 1.2 ⁇ m pores, 1.0 ⁇ m pores, 0.8 ⁇ m pores, 0.6 ⁇ m pores, 0.45 ⁇ m pores, or 0.22 ⁇ m 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.
  • 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.
  • RNA-seq RNA sequencing
  • RT-qPCR reverse transcription quantitative polymerase chain reaction
  • 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.
  • 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 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.
  • the more useful urinary EV RNA marker is one selected from GPRC5A, GPX3, IGFBP5, KRT17, MALAT1, MDK, MT-ND5, NET1, and SLC2A1 (Table 3).
  • the more useful urinary EV RNA marker is one selected from ACTB, CXCR2, GPRC5A, IGFBP5, KRT17, MALAT1, MDK, NET1, and SLC2A1 (Table 4).
  • 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).
  • 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).
  • the more useful urinary EV RNA marker is one selected from ACTB, GAPDH, GPRC5A, GPX3, KRT17, MALAT1, MDK, NET1, and SLC2A1 (Table 7).
  • 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).
  • 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).
  • 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).
  • 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 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 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).
  • 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).
  • C. 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.
  • 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.
  • “comprises” means “includes.”
  • “comprising A or B,” means “including A, B, or A and B,” without excluding additional elements.
  • the definitions provided herein control when the present definitions may be different from other possible definitions.
  • HGNC HUGO Gene Nomenclature Committee
  • IDs HUGO Gene Nomenclature Committee
  • 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.
  • cancer shall be taken to include a disease that is characterized by uncontrolled growth of cells within a subject.
  • cancer and “tumor” are used interchangeably.
  • tumor refers to a benign or non-malignant growth.
  • EXAMPLE 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.
  • RNA-seq 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.
  • GTEx Genotype-Tissue Expression
  • 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,
  • Urine extracellular vesicle marker validation 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • two different scorings were used: Cytology1; Positive (1), suspicious (0) and negative (0) and Cytology2; Positive/suspicious (1) and negative (0).
  • Feature selection 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).
  • 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
  • low-risk bladder cancer i.e., low grade pTa
  • 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.
  • 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.
  • the best marker combinations 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 ( 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

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Abstract

Selon certains aspects, la présente invention concerne de manière générale un procédé minimalement invasif, ou non invasif, qui évalue l'état d'un patient en ce qui concerne le cancer urothélial. Plusieurs modes de réalisation de la présente invention concernent des procédés de caractérisation de profils d'ARNm de vésicules extracellulaires (EV) tels que des exosomes et des microvésicules provenant d'échantillons d'urine d'un patient pour évaluer, diagnostiquer ou déterminer l'état dudit patient par rapport au cancer urothélial.
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100273148A1 (en) * 2004-07-23 2010-10-28 Parry John Guilford Urine markers for detection of bladder cancer
US20180327852A1 (en) * 2015-08-31 2018-11-15 Hitachi Chemical Co., Ltd. Molecular methods for assessing urothelial disease

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100273148A1 (en) * 2004-07-23 2010-10-28 Parry John Guilford Urine markers for detection of bladder cancer
US20180327852A1 (en) * 2015-08-31 2018-11-15 Hitachi Chemical Co., Ltd. Molecular methods for assessing urothelial disease

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