Methods for prostate cancer detection and treatment

Abstract

The present invention relates to methods for the detection and treatment of prostate cancer. The present invention relates to a method for detecting prostate cancer, a method for determining whether prostate cancer is stable or progressive, low or high Gleason score, a method for differentiating benign prostatic hyperplasia (BPH) from prostate cancer, a method for determining surgical completeness, and a method for assessing response to prostate cancer therapy.

Description

[0001] This application is a divisional application. The application date of the original application is February 21, 2019, the application number is 201980027332.X (PCT / US2019 / 018878), and the name of the invention is “Methods for detecting and treating prostate cancer”.

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] This application claims priority to and the benefit of U.S. Provisional Application No. 62 / 633,675, filed on February 22, 2018, the contents of which are incorporated herein by reference in their entirety.

[0004] Sequence Listing

[0005] This application contains a sequence listing, which is submitted in ASCII format via EFS-Web and is hereby incorporated by reference in its entirety. The ASCII copy was created on February 19, 2019, is named "LBIO-005_001WO_SeqList.txt", and is 299KB in size. Field of the Invention

[0006] The present invention relates to the detection of prostate cancer. Background of the Invention

[0008] Prostate cancer (PCA) is the fourth most commonly diagnosed cancer worldwide and the second most common cancer in men. Although the incidence and prevalence have been declining, ~200,000 men in the United States will be diagnosed with PCA each year. Multiple factors, including age and family history, genetic susceptibility, and race, contribute to the high incidence of the disease. Although 90% of PCA are diagnosed when local (not spread), the clinical behavior of the tumor is highly variable and ranges from indolence that can be monitored by watchful waiting or active surveillance (e.g., biomarkers and 6-month digital rectal examination) to malignant evolution and androgen-resistant disease, metastatic spread, and death.

[0009] Multiple risk stratification systems have been developed that combine clinical data and pathological information (eg, Gleason score).These risk stratification systems, including the recently developed next generation tools, are only ~70% accurate in predicting outcome.

[0010] Molecular genetic information is increasingly being used to inform pathology and better inform cancer subtypes. This information has been used as a prognostic tool as well as to stratify patients for different therapeutic interventions. Prostate cancer has been examined and mutations, DNA copy number alterations, rearrangements, and gene fusions have been identified. These may be associated with certain pathological features. For example, low-Gleason tumors have few DNA copy number alterations, while high-grade tumors exhibit significant genome-wide copy number alterations. In contrast, somatic point mutations are relatively rare, with mutation frequencies ranging from 1% (IDH1) to 11% (SPOP). The most common abnormalities are androgen-regulated fusions of ERG and other ETS family members (~50% of tumors). However, after prostatectomy, tumors with fusions do not have a significantly different prognosis from fusion-negative tumors. In contrast, androgen receptor variant 7 (AR-V7) has been associated with progression to castration-resistant prostate cancer (CRPC) and is considered to potentially be used as a biomarker for treatment selection. However, overall, the molecular mechanisms underpinning the pathogenesis of PCA are incompletely understood, and molecular-based biomarkers that can be used to predict sensitivity to therapeutic agents are lacking. Therefore, it is critical to develop diagnostic methods that more accurately define the disease state, determine sensitivity to therapies, and can ultimately be used to better monitor disease progression.

[0011] Surveillance remains the cornerstone approach to monitor PCA and detect recurrence at an early stage. After potentially curative resection, surveillance can be performed by measuring blood biomarkers and / or imaging (such as CT) to detect asymptomatic metastatic disease earlier. The current biomarker used for surveillance is prostate-specific antigen (PSA) (also gamma-seminoprotein or kallikrein-3). This glycoproteinase is encoded by the KLK3 gene and secreted by epithelial cells in the prostate. However, it is not the only indicator of prostate cancer, but can also detect prostatitis or benign prostatic hyperplasia (BPH). Using PSA alone can lead to unnecessary biopsies in men without cancer or underdiagnosis of men with significant disease. This is based on a low sensitivity (20-40%) and specificity (70-90%) range, so the positive predictive value is only 25-40%. The United States Preventive Services Task Force (USPSTF) does not recommend the use of PSA for prostate cancer. However, PSA is included in clinical nomograms, such as the UCSF-CAPRA score for prostate cancer risk, which has some utility in predicting disease-free survival after surgery.

[0012] Biomarker-based tools are still needed to accurately diagnose PCA. SUMMARY OF THE INVENTION

[0014] The present disclosure provides a method for detecting prostate cancer in a subject in need thereof, the method comprising: determining the expression levels of at least 38 biomarkers from a test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD 23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPAR The expression levels of each of GC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, Normalized expression levels of each of NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC; inputting each normalized expression level into an algorithm to generate a score; comparing the score to a first predetermined cutoff value; and when the score is equal to or greater than the first predetermined cutoff value, identifying the presence of prostate cancer in the subject, or when the score is less than the first predetermined cutoff value, identifying the absence of prostate cancer in the subject.

[0015] The present disclosure provides a method for detecting prostate cancer in a subject in need thereof, the method comprising: (a) determining the expression levels of at least 38 biomarkers from a test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PP ARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; (b) AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT The expression levels of each of 23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, and FY normalized expression levels of each of CO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (c) inputting each normalized expression level into an algorithm to generate a score; (d) comparing the score to a first predetermined cutoff value;and (e) generating a report, wherein the report identifies the presence of prostate cancer in the subject when the score is equal to or greater than a first predetermined cutoff value, or identifies the absence of prostate cancer in the subject when the score is less than the first predetermined cutoff value.;

[0016] The present disclosure also provides a method for determining whether prostate cancer in a subject is stable or progressive, the method comprising: determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPR C1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2 , PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain the expression levels of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MR Normalized expression levels of each of PS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC; inputting each normalized expression level into an algorithm to generate a score; comparing the score to a first predetermined cutoff value; and when the score is equal to or greater than the first predetermined cutoff value, identifying the prostate cancer as progressive, or when the score is less than the first predetermined cutoff value, identifying the prostate cancer as stable.

[0017] The present disclosure provides a method for determining whether prostate cancer in a subject is stable or progressive, the method comprising: (a) determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS 2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; (b) AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​K The expression levels of each of RT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, and F normalized expression levels of each of YCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (c) inputting each normalized expression level into an algorithm to generate a score; (d) comparing the score to a first predetermined cutoff value;and (e) generating a report, wherein the report identifies the prostate cancer as progressive when the score is equal to or greater than a first predetermined cutoff value, or identifies the prostate cancer as stable when the score is less than the first predetermined cutoff value.;

[0018] The present disclosure also provides a method for determining whether prostate cancer in a subject is of low grade or high grade, the method comprising: determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPR C1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS 2. The expression levels of each of PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, and M Normalized expression levels of each of RPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC; inputting each normalized expression level into an algorithm to generate a score; comparing the score to a first predetermined cutoff value; and when the score is equal to or greater than the first predetermined cutoff value, identifying the prostate cancer as high grade, or when the score is less than the first predetermined cutoff value, identifying the prostate cancer as low grade.

[0019] The present disclosure provides a method for determining whether prostate cancer in a subject is of low grade or high grade, the method comprising: determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPAR The expression levels of each of GC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDU normalized expression levels of each of FS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC; inputting each normalized expression level into an algorithm to generate a score; comparing the score to a first predetermined cutoff value; and generating a report, wherein the report identifies prostate cancer as high grade when the score is equal to or greater than the first predetermined cutoff value, or identifies prostate cancer as low grade when the score is less than the first predetermined cutoff value.

[0020] The present disclosure also provides a method for determining whether prostate cancer in a subject is low Gleason score (≤6) prostate cancer or high Gleason score (≥7) prostate cancer, the method comprising: (a) determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; (b) AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HN The expression levels of each of RNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2 , FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC; (c) inputting each normalized expression level into an algorithm to generate a score; (d) comparing the score to a first predetermined cutoff value;and (e) identifying the prostate cancer as a high Gleason score prostate cancer when the score is equal to or greater than a first predetermined cutoff value, or identifying the prostate cancer as a low Gleason score prostate cancer when the score is less than the first predetermined cutoff value.;

[0021] The present disclosure provides a method for determining whether prostate cancer in a subject is low Gleason score (≤6) prostate cancer or high Gleason score (≥7) prostate cancer, the method comprising: (a) determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, M AN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; (b) AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNR The expression levels of each of NPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, normalized expression levels of each of FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (c) inputting each normalized expression level into an algorithm to generate a score; (d) comparing the score to a first predetermined cutoff value;and (e) generating a report, wherein the report identifies the prostate cancer as a high Gleason score prostate cancer when the score is equal to or greater than a first predetermined cutoff value, or identifies the prostate cancer as a low Gleason score prostate cancer when the score is less than the first predetermined cutoff value.;

[0022] The present disclosure also provides a method for determining the completeness of surgery in a subject with prostate cancer, the method comprising: determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject after surgery with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPR C1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2 , PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain the expression levels of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRP standardized expression levels of each of S25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC; inputting each standardized expression level into an algorithm to generate a score; comparing the score to a first predetermined cutoff value; and when the score is equal to or greater than the first predetermined cutoff value, identifying that prostate cancer is not completely removed, or when the score is less than the first predetermined cutoff value, identifying that prostate cancer is completely removed.

[0023] The present disclosure provides a method for determining the completeness of surgery in a subject with prostate cancer, the method comprising: (a) determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject after surgery with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUF S2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; (b) AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, The expression levels of each of KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, and F normalized expression levels of each of YCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (c) inputting each normalized expression level into an algorithm to generate a score; (d) comparing the score to a first predetermined cutoff value;and (e) generating a report, wherein the report identifies that the prostate cancer was not completely removed when the score is equal to or greater than a first predetermined cutoff value, or identifies that the prostate cancer was completely removed when the score is less than the first predetermined cutoff value.;

[0024] The present disclosure also provides a method for distinguishing benign prostatic hyperplasia from prostate cancer in a subject with prostate enlargement, the method comprising: determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A , PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS 2. The expression levels of each of PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS2 5. A standardized expression level of each of NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC; inputting each standardized expression level into an algorithm to generate a score; comparing the score to a first predetermined cutoff value; and when the score is equal to or greater than the first predetermined cutoff value, identifying the presence of prostate cancer in the subject, or when the score is less than the first predetermined cutoff value, identifying the presence of benign prostatic hyperplasia in the subject.

[0025] The present disclosure provides a method for distinguishing benign prostatic hyperplasia from prostate cancer in a subject with prostate enlargement, the method comprising: (a) determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; (b) AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT The expression levels of each of 15, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7 , FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC; (c) inputting each normalized expression level into an algorithm to generate a score; (d) comparing the score to a first predetermined cutoff value;and (e) generating a report, wherein the report identifies the presence of prostate cancer in the subject when the score is equal to or greater than a first predetermined cutoff value, or identifies the presence of benign prostatic hyperplasia in the subject when the score is less than the first predetermined cutoff value.;

[0026] The present disclosure provides a method for evaluating a subject having prostate cancer in response to a first therapy, the method comprising: (1) at a first time point: (a) determining the expression levels of at least 38 biomarkers from a test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, M (a) comparing the expression levels of the housekeeping genes with those of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU1, and the like. , HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain the expression levels of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, normalized expression levels of each of FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (c) inputting each normalized expression level into an algorithm to generate a first score;(2) at a second time point, wherein the second time point is subsequent to the first time point and subsequent to administration of the first therapy to the subject: (d) determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers; and (e) comparing the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1 to the expression levels of the housekeeping genes. , HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to the expression levels of each of the following: Thus, AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, S (f) inputting each of the normalized expression levels into an algorithm to generate a second score; (3) comparing the first score to the second score; and (4) when the second score is significantly reduced compared to the first score, then identifying the subject as responding to the first therapy, or when the second score is not significantly reduced compared to the first score, then identifying the subject as not responding to the first therapy. ;

[0027] The foregoing method may further include continuing to administer the first therapy to the subject when the second score is significantly reduced compared to the first score. The foregoing method may further include stopping administering the first therapy to the subject when the second score is not significantly reduced compared to the first score. The foregoing method may further include administering the second therapy to the subject when the second score is not significantly reduced compared to the first score.

[0028] The present disclosure provides a method for evaluating a subject having prostate cancer in response to a first therapy, the method comprising: (1) at a first time point: (a) determining the expression levels of at least 38 biomarkers from a test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, M (a) comparing the expression levels of the housekeeping genes with those of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU1, and the like. , HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain the expression levels of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, normalized expression levels of each of FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (c) inputting each normalized expression level into an algorithm to generate a first score;(2) at a second time point, wherein the second time point is after the first time point and after administering the therapy to the subject: (d) determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers; (e) comparing the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, H The expression levels of each of NRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, M AX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, ST (f) inputting each of the normalized expression levels into an algorithm to generate a second score; and (3) comparing the first score to the second score; and (4) generating a report, wherein the report identifies the subject as responsive to the first therapy when the second score is significantly reduced compared to the first score, or identifies the subject as nonresponsive to the first therapy when the second score is not significantly reduced compared to the first score. ;

[0029] The foregoing method may further include continuing to administer the first therapy to the subject when the second score is significantly reduced compared to the first score. The foregoing method may further include stopping administering the first therapy to the subject when the second score is not significantly reduced compared to the first score. The foregoing method may further include administering the second therapy to the subject when the second score is not significantly reduced compared to the first score.

[0030] The present disclosure also provides a method for treating prostate cancer in a subject in need thereof, the method comprising: determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RA D23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPA were selected according to the expression level of the housekeeping genes. The expression levels of each of RGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, and 10-fold increase in the expression of each of the following proteins: , NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC; input each standardized expression level into an algorithm to generate a score; compare the score to a first predetermined cutoff value; and when the score is equal to or greater than the first predetermined cutoff value, administering at least the first therapy to the subject, or when the score is less than the first predetermined cutoff value, identifying the absence of prostate cancer in the subject.

[0031] In the method of the present disclosure, the housekeeping gene can be selected from ALG9, SEPN, YWHAQ, VPS37A, PRRC2B, DOPEY2, NDUFB11, ND4, MRPL19, PSMC4, SF3A1, PUM1, ACTB, GAPD, GUSB, RPLP0, TFRC, MORF4L1, 18S, PPIA, PGK1, RPL13A, B2M, YWHAZ, SDHA, HPRT1, TOX4 and TPT1. The housekeeping gene can be TOX4.

[0032] In the methods of the present disclosure, the predetermined cutoff value may be at least 33% on a scale of 0-100%, or at least 50% on a scale of 0-100%. The first predetermined cutoff value may be at least 33% on a scale of 0-100%, or at least 50% on a scale of 0-100%.

[0033] The methods of the present disclosure may further include administering a therapy to the subject. The methods of the present disclosure may further include administering a first therapy to the subject. The methods of the present disclosure may further include administering a second therapy to the subject.

[0034] In the methods of the present disclosure, the therapy may include active monitoring, radiotherapy, surgery, cryotherapy, hormone therapy, chemotherapy, vaccine therapy, bone-directed therapy, or any combination thereof. The first therapy may include active monitoring, radiotherapy, surgery, cryotherapy, hormone therapy, chemotherapy, vaccine therapy, bone-directed therapy, or any combination thereof. The second therapy may include active monitoring, radiotherapy, surgery, cryotherapy, hormone therapy, chemotherapy, vaccine therapy, bone-directed therapy, or any combination thereof.

[0035] In some aspects, hormone therapy may include androgen suppression therapy. In some aspects, chemotherapy may include docetaxel, cabazitaxel, mitoxantrone, estramustine, or a combination thereof. In some aspects, vaccine therapy may include Sipuleucel-T. In some aspects, bone-directed therapy may include bisphosphonate, denosumab, corticosteroids, or a combination thereof.

[0036] In the method of the present disclosure, the algorithm may be XGB, RF, glmnet, cforest, CART, treebag, knn, nnet, SVM-radial, SVM-linear, NB or mlp. The algorithm may be XGB.

[0037] In the methods of the present disclosure, the first time point can be before or after administration of a therapy to the subject. The first time point can be before or after administration of a first therapy to the subject.

[0038] In the method of the present disclosure, when the second score is at least 25% less than the first score, the second score is significantly reduced compared to the first score.

[0039] The methods of the present disclosure may have a sensitivity of at least 92%. The methods of the present disclosure may have a specificity of at least 95%.

[0040] In the methods of the present disclosure, at least one of the at least 38 biomarkers may be RNA, cDNA, or protein.

[0041] In the methods of the present disclosure, when the biomarker is RNA, the RNA can be reverse transcribed to produce cDNA, and the expression level of the produced cDNA can be detected.

[0042] In the methods of the present disclosure, the expression level of a biomarker can be detected by forming a complex between the biomarker and a labeled probe or primer.

[0043] In the method of the present disclosure, when the biomarker is a protein, the protein can be detected by forming a complex between the protein and a labeled antibody. The label can be a fluorescent label.

[0044] In the method of the present disclosure, when the biomarker is RNA or cDNA, the RNA or cDNA can be detected by forming a complex between the RNA or cDNA and a labeled nucleic acid probe or primer. For example, the label can be a fluorescent label. The complex between the RNA or cDNA and the labeled nucleic acid probe or primer can be a hybridization complex.

[0045] In the methods of the present disclosure, the predetermined cutoff value may be derived from a plurality of reference samples obtained from subjects not having or not diagnosed with a neoplastic disease. The neoplastic disease may be prostate cancer.

[0046] In the methods disclosed herein, the test sample may be blood, serum, plasma or tumor tissue. The reference sample may be blood, serum, plasma or non-tumor tissue.

[0047] In the methods of the present disclosure, the subject may have at least one symptom of prostate cancer. In the methods of the present disclosure, the subject may have a predisposition or family history of prostate cancer.

[0048] In the methods of the present disclosure, the subject may have been previously diagnosed with prostate cancer and is tested for recurrence of prostate cancer.

[0049] In the methods of the present disclosure, the subject may be a human.

[0050] Any of the above aspects may be combined with any other aspect.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as those generally understood by those of ordinary skill in the art to which the present disclosure belongs. In the specification, the singular also includes the plural, unless the context clearly states otherwise; as an example, the terms "a", "an" and "the" should be understood as singular or plural, and the term "or" should be understood as inclusive. For example, "element" means one or more elements. Throughout the specification, the word "comprising" or variants such as "including" or "containing" should be understood to mean including the elements, integers or steps, or groups of elements, integers or steps, but do not exclude any other elements, integers or steps, or groups of elements, integers or steps. Approximately can be understood as within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.05% or 0.01% of the value. Unless the context clearly states otherwise, all numerical values ​​provided herein are modified by the term "about".

[0052] Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, illustrative methods and materials are now described. Other features, objects and advantages of the present invention will become apparent from the specification and claims. In the specification and the appended claims, the singular also includes the plural, unless the context clearly specifies otherwise. Unless otherwise defined, all technical and scientific terms used herein have the same meanings as those of ordinary skill in the art to which the present invention belongs. All patents and disclosures cited in this specification are incorporated herein by reference in their entirety. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1A-1B Figure 2 is a graph showing the visualization of 30 putative marker genes identified by the RandomForest algorithm in the derivation cohort of n=595 tissue samples. Figure 1A )E-GEOD-46691 expression. Figure 1B )expression in E-GEOD-46602.

[0055] Figure 2 Figure 2 is a scatter plot of the mean predictive importance versus the Kruskal Wallis p-value. The vertical and horizontal lines represent the p-value of 0.05 and the median predictive importance value, respectively.

[0056] Figure 3Figure 2 is a graph showing that hierarchical clustering of gene expression identified separate clusters for PCA tumor tissues and control tissues. Gene expression was significantly higher in PCA tissues.

[0057] Figure 4 Diagram showing clustering of prostate-derived cell lines into their tissue of origin (ie, normal, focal, metastatic) to show clustering of normalized gene expression.

[0058] Figure 5 Figure 2 is a graph showing gene expression in controls (n=201), BPH (prostatic hyperplasia, n=26) and prostate cancer cases (n=125). Expression levels identified in cases (63±19%) were significantly (p<0.0001) elevated relative to benign prostatic hyperplasia (BPH: 17±13%) and controls (8±9%).

[0059] Figure 6 Figure 2 is a graph showing the receiver operator curve analysis. The AUROC was 0.97 and the Youden J index was 0.94. The Z statistic was highly significant (38.9; p<0.0001).

[0060] Figure 7 Graph showing that the ProstaTest used to determine PCA measures within the 92-99% range.

[0061] Figure 8 Figure 2 is a Probit risk assessment plot that identifies a ProstaTest score > 30 as having 60% accuracy in predicting prostate cancer in a blood sample. This increases to > 80% at test scores ≥ 34. The dashed line represents the 95% confidence interval.

[0062] Fig. 9 Graph showing receiver operating curve analysis comparing ProstaTest to PSA measurements to predict high grade (≥Gleason 7) compared to low grade (Gleason 5+6) tumors. ProstaTest had an AUROC of 0.98 and a Youden J index of 0.87. For PSA, the AUROC was 0.64 and the Youden J index was 0.42. ProstaTest was significantly better than PSA for predicting grade (Z statistic: 3.05, p=0.002).

[0063] Fig.10 Graph showing the effect of surgery on ProstaTest. Levels were elevated before surgery (80±18%). Surgery reduced levels to 22±7% (p<0.0001), not different from control levels.

[0064] Fig.11 Figure 2 is a graph showing the effect of treatment on ProstaTest. Levels were elevated before treatment (74 ± 18%). In those who responded to hormone therapy, levels decreased to 18 ± 7% (p < 0.0001). In those who required chemotherapy, levels decreased to 27 ± 10% (p < 0.001). DETAILED DESCRIPTION OF THE INVENTION

[0066] The details of the invention are set forth in the accompanying description below.

[0067] Described herein are methods for quantifying (scoring) circulating prostate cancer molecular signatures with high sensitivity and specificity for purposes including, but not limited to, detecting prostate cancer, determining whether prostate cancer is stable or progressive, distinguishing benign prostatic hyperplasia (BPH) from prostate cancer, determining surgical completeness, and assessing response to prostate cancer therapy. Specifically, the present invention is based on the following findings: the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC, normalized by the expression levels of housekeeping genes, are increased in subjects with prostate cancer compared with healthy subjects or subjects with BPH.

[0068] Symptoms of prostate cancer include problems urinating, blood in the urine or semen, difficulty having an erection, pain in the buttocks, back (spine), chest (ribs), or other areas from cancer that has spread to the bones, weakness or numbness in the legs or feet, or even loss of bladder or bowel control from cancer pressing on the spinal cord.

[0069] As described in the Examples, the measurement (ProstaTest) of the prostate cancer transcript in the circulation is diagnosed with prostate cancer, and the reduction of the ProstaTest score in the blood is relevant to the effect of therapeutic intervention such as surgery and chemotherapy. The targeted gene expression spectrum of prostate cancer RNA can be isolated from the peripheral blood of the patient. Expression spectrum is evaluated with an algorithm, and is converted to output (score). It can diagnose and identify active diseases, and provides the assessment of therapeutic response in conjunction with standard clinical assessment and imaging.

[0070] Accordingly, the present disclosure provides a method for detecting prostate cancer in a subject in need thereof, the method comprising: (a) determining the expression levels of at least 38 biomarkers from a test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, R AD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; (b) AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPAR The expression levels of each of GC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUF (c) inputting each standardized expression level into an algorithm to generate a score; (d) comparing the score to a first predetermined cutoff value; and (e) identifying the presence of prostate cancer in the subject when the score is equal to or greater than the first predetermined cutoff value, or identifying the absence of prostate cancer in the subject when the score is less than the first predetermined cutoff value.

[0071] In some aspects of the foregoing methods, step (e) may include generating a report, wherein the report identifies the presence of prostate cancer in the subject when the score is equal to or greater than a first predetermined cutoff value, or identifies the absence of prostate cancer in the subject when the score is less than the first predetermined cutoff value.

[0072] In some aspects of the foregoing methods, the first predetermined cutoff value may be 33% on a scale of 0-100%.

[0073] In some aspects, the foregoing method may further comprise administering to the subject a first therapy. The foregoing method may further comprise administering to the subject a first therapy when the score is equal to or greater than a predetermined cutoff value.

[0074] The present disclosure also provides a method for determining whether prostate cancer in a subject is stable or progressive, the method comprising: (a) determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC 1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; (b) AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, The expression levels of each of PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, (c) inputting each standardized expression level into an algorithm to generate a score; (d) comparing the score to a first predetermined cutoff value; and (e) identifying the prostate cancer as progressive when the score is equal to or greater than the first predetermined cutoff value, or identifying the prostate cancer as stable when the score is less than the first predetermined cutoff value.

[0075] In some aspects of the foregoing methods, step (e) may include generating a report, wherein the report identifies the prostate cancer as progressive when the score is equal to or greater than a first predetermined cutoff value, or identifies the prostate cancer as stable when the score is less than the first predetermined cutoff value.

[0076] In some aspects of the foregoing methods, the first predetermined cutoff value may be 50% on a scale of 0-100%.

[0077] In some aspects, the foregoing method may further comprise administering to the subject a first therapy. The foregoing method may further comprise administering to the subject a first therapy when the score is equal to or greater than a predetermined cutoff value.

[0078] The present disclosure also provides a method for determining whether prostate cancer in a subject is of low grade or high grade, the method comprising: (a) determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC 1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; (b) AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, The expression levels of each of PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, (c) inputting each standardized expression level into an algorithm to generate a score; (d) comparing the score to a first predetermined cutoff value; and (e) identifying the prostate cancer as high grade when the score is equal to or greater than the first predetermined cutoff value, or identifying the prostate cancer as low grade when the score is less than the first predetermined cutoff value.

[0079] In some aspects of the foregoing method, step (e) may include generating a report, wherein the report identifies the prostate cancer as high grade when the score is equal to or greater than a first predetermined cutoff value, or identifies the prostate cancer as low grade when the score is less than the first predetermined cutoff value.

[0080] In some aspects of the foregoing methods, the first predetermined cutoff value may be 50% on a scale of 0-100%.

[0081] In some aspects, the foregoing method may further comprise administering to the subject a first therapy. The foregoing method may further comprise administering to the subject a first therapy when the score is equal to or greater than a predetermined cutoff value.

[0082] In some aspects, low-grade prostate cancer is prostate cancer with a Gleason score of less than or equal to 6. In some aspects, high-grade prostate cancer is prostate cancer with a Gleason score of greater than or equal to 7.

[0083] The Gleason grading system is commonly used as a parameter for the prognosis of prostate cancer in the art, and is usually used in combination with other prognostic factors or tests. For example, a prostate biopsy sample is examined by a microscope, and a pathologist determines the Gleason score based on the structural pattern of the prostate tumor. The Gleason score is based on the degree of loss of normal glandular tissue structure (i.e., the shape, size, and differentiation of the gland). The grade of the most common tumor pattern and the second grade of the next most common tumor pattern are assigned to the sample. There may be a primary or most common pattern, and then there may be an identifiable secondary or second most common pattern; or, there may be only a single grade. The Gleason pattern is associated with the following features: Pattern 1-cancerous prostate closely resembles normal prostate tissue. The glands are small, well-formed, and tightly packed; Pattern 2-tissues still have well-formed glands, but they are larger and have more tissues in between; Pattern 3-tissues still have recognizable glands, but the cells are darker. At high magnification, some of these cells have left the glands and begun to invade the surrounding tissues; Pattern 4-tissues have almost no recognizable glands. Many cells invade the surrounding tissue; Pattern 5 - The tissue has no identifiable glands and the entire surrounding tissue is usually just sheets of cells. Adding the two grades together gives the Gleason score, also called the Gleason sum.

[0084] The present disclosure also provides a method for determining whether prostate cancer in a subject is low Gleason score (≤6) prostate cancer or high Gleason score (≥7) prostate cancer, the method comprising: (a) determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; (b) AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HN The expression levels of each of RNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2 , FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC; (c) inputting each normalized expression level into an algorithm to generate a score; (d) comparing the score to a second predetermined cutoff value;and (e) identifying the prostate cancer as a high Gleason score prostate cancer when the score is equal to or greater than a first predetermined cutoff value, or identifying the prostate cancer as a low Gleason score prostate cancer when the score is less than the first predetermined cutoff value.;

[0085] In some aspects of the foregoing method, step (e) may include generating a report, wherein the report identifies the prostate cancer as a high Gleason score prostate cancer when the score is equal to or greater than a first predetermined cutoff value, or identifies the prostate cancer as a low Gleason score prostate cancer when the score is less than a second predetermined cutoff value.

[0086] In some aspects of the foregoing methods, the first predetermined cutoff value may be 50% on a scale of 0-100%.

[0087] In some aspects, the foregoing method may further comprise administering to the subject a first therapy. The foregoing method may further comprise administering to the subject a first therapy when the score is equal to or greater than a predetermined cutoff value.

[0088] The present disclosure also provides a method for determining the completeness of surgery in a subject with prostate cancer, the method comprising: (a) determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject after surgery with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC 1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; (b) AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, P The expression levels of each of PARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, ND (c) inputting each standardized expression level into an algorithm to generate a score; (d) comparing the score to a first predetermined cutoff value; and (e) when the score is equal to or greater than the first predetermined cutoff value, identifying that prostate cancer has not been completely removed, or when the score is less than the first predetermined cutoff value, identifying that prostate cancer has been completely removed.

[0089] In some aspects of the foregoing method, step (e) may include generating a report, wherein the report identifies that the prostate cancer has not been completely removed when the score is equal to or greater than a first predetermined cutoff value, or identifies that the prostate cancer has been completely removed when the score is less than the first predetermined cutoff value.

[0090] In some aspects of the foregoing methods, the first predetermined cutoff value may be 33% on a scale of 0-100%.

[0091] In some aspects, the foregoing method may further comprise administering to the subject a first therapy. The foregoing method may further comprise administering to the subject a first therapy when the score is equal to or greater than a predetermined cutoff value.

[0092] The present disclosure also provides a method for distinguishing benign prostatic hyperplasia from prostate cancer in a subject with prostate enlargement, the method comprising: (a) determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, and VIZI. , NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; (b) AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT The expression levels of each of 15, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7 , FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC; (c) inputting each normalized expression level into an algorithm to generate a score; (d) comparing the score to a first predetermined cutoff value;and (e) identifying the presence of prostate cancer in the subject when the score is equal to or greater than a first predetermined cutoff value, or identifying the presence of benign prostatic hyperplasia in the subject when the score is less than the first predetermined cutoff value.;

[0093] In some aspects of the foregoing methods, step (e) may include generating a report, wherein the report identifies the presence of prostate cancer in the subject when the score is equal to or greater than a first predetermined cutoff value, or identifies the presence of benign prostatic hyperplasia in the subject when the score is less than the first predetermined cutoff value.

[0094] In some aspects of the foregoing methods, the first predetermined cutoff value may be 33% on a scale of 0-100%.

[0095] In some aspects, the foregoing method may further comprise administering to the subject a first therapy. The foregoing method may further comprise administering to the subject a first therapy when the score is equal to or greater than a predetermined cutoff value.

[0096] The present disclosure provides a method for treating prostate cancer in a subject in need thereof, the method comprising: (a) determining the expression levels of at least 38 biomarkers from a test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD 23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; (b) AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARG The expression levels of each of C1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUF (c) inputting each standardized expression level into an algorithm to generate a score; (d) comparing the score to a first predetermined cutoff value; and (e) when the score is equal to or greater than the first predetermined cutoff value, administering at least the first therapy to the subject, or when the score is less than the first predetermined cutoff value, identifying the absence of prostate cancer in the subject.

[0097] The present disclosure also provides a method for evaluating a subject with prostate cancer for a response to a first therapy, the method comprising: (1) at a first time point: (a) determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC and housekeeping genes; (b) AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNP The expression levels of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, U, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain the expression levels of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, normalized expression levels of each of FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (c) inputting each normalized expression level into an algorithm to generate a first score;(2) at a second time point, wherein the second time point is subsequent to the first time point and subsequent to administration of the first therapy to the subject: (d) determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers; and (e) comparing the expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1 to the expression levels of the housekeeping genes. , HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to the expression levels of each of the following: Thus, AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, S (f) inputting each of the normalized expression levels into an algorithm to generate a second score; (3) comparing the first score to the second score; and (4) when the second score is significantly reduced compared to the first score, then identifying the subject as responding to the first therapy, or when the second score is not significantly reduced compared to the first score, then identifying the subject as not responding to the first therapy. ;

[0098] In some aspects of the foregoing method, step (4) may include generating a report, wherein the report identifies the subject as responsive to the first therapy when the second score is significantly reduced compared to the first score, or identifies the subject as nonresponsive to the first therapy when the second score is not significantly reduced compared to the first score.

[0099] In some aspects, the foregoing method may further include continuing to administer the first therapy to the subject when the second score is significantly reduced compared to the first score. The foregoing method may further include stopping administering the first therapy to the subject when the second score is not significantly reduced compared to the first score. The foregoing method may further include administering the second therapy to the subject when the second score is not significantly reduced compared to the first score.

[0100] In some aspects of the aforementioned methods, the second score is significantly reduced compared to the first score when the second score is at least about 10% less than the first score, or at least about 20% less than the first score, or at least about 25% less than the first score, or at least about 30% less than the first score, at least about 40% less than the first score, at least about 50% less than the first score, or at least about 60% less than the first score, or at least about 70% less than the first score, or at least about 75% less than the first score, or at least about 80% less than the first score, or at least about 90% less than the first score, or at least about 95% less than the first score, or at least about 99% less than the first score. In some aspects, when the second score is not significantly reduced compared to the first score, the subject is considered unresponsive to the therapy.

[0101] In some aspects of the foregoing methods, the first time point can be before the first therapy is administered to the subject. The first time point can be after the first therapy is administered to the subject.

[0102] In some aspects of the disclosed methods, housekeeping genes include, but are not limited to, ALG9, SEPN, YWHAQ, VPS37A, PRRC2B, DOPEY2, NDUFB11, ND4, MRPL19, PSMC4, SF3A1, PUM1, ACTB, GAPD, GUSB, RPLP0, TFRC, MORF4L1, 18S, PPIA, PGK1, RPL13A, B2M, YWHAZ, SDHA, HPRT1, TOX4, and TPT1. In some aspects, the housekeeping gene is TOX4.

[0103] In some aspects of the disclosed method, the predetermined cutoff value may be about 33% at a ratio of 0-100%. In some aspects of the disclosed method, the predetermined cutoff value may be about 50% at a ratio of 0-100%. The predetermined cutoff value may be about 60% at a ratio of 0-100%. The predetermined cutoff value may be about 10%, or about 20%, or about 30%, or about 40%, or about 70%, or about 80%, or about 90% at a ratio of 0-100%.

[0104] The methods of the present disclosure may have a sensitivity of at least about 50%, or at least about 60%, or at least about 70%, or at least about 75%, or at least about 80%, or at least about 85%, or at least about 90%, or at least about 95%, or at least about 99%. The methods of the present disclosure may have a sensitivity greater than about 50%, or greater than about 60%, or greater than about 70%, or greater than about 75%, or greater than about 80%, or greater than about 85%, or greater than about 90%, or greater than about 95%, or greater than about 99%.

[0105] The methods of the present disclosure may have a specificity of at least about 50%, or at least about 60%, or at least about 70%, or at least about 75%, or at least about 80%, or at least about 85%, or at least about 90%, or at least about 95%, or at least about 99%. The methods of the present disclosure may have a specificity greater than about 50%, or greater than about 60%, or greater than about 70%, or greater than about 75%, or greater than about 80%, or greater than about 85%, or greater than about 90%, or greater than about 95%, or greater than about 99%.

[0106] The methods of the present disclosure may have an accuracy of at least about 50%, or at least about 60%, or at least about 70%, or at least about 75%, or at least about 80%, or at least about 85%, or at least about 90%, or at least about 95%, or at least about 99%. The methods of the present disclosure may have an accuracy of greater than about 50%, or greater than about 60%, or greater than about 70%, or greater than about 75%, or greater than about 80%, or greater than about 85%, or greater than about 90%, or greater than about 95%, or greater than about 99%.

[0107] In some aspects of the disclosed method, a predetermined cutoff value, such as a first predetermined cutoff value, is derived from a plurality of reference samples obtained from a subject who does not have or is not diagnosed with a neoplastic disease. A plurality of reference samples can be about 2-500, 2-200, 10-100 or 20-80. Each reference sample generates a score using an algorithm, and the first predetermined cutoff value can be, for example, the arithmetic mean of these scores. Each reference sample can be blood, serum, plasma or non-neoplastic tissue. In some aspects, each reference sample is blood. In some aspects, each reference sample has the same type as the test sample.

[0108] In some aspects of the disclosed methods, the test sample may comprise any biological fluid obtained from a subject. In some aspects, the test sample comprises blood, serum, plasma, neoplastic tissue, or any combination thereof. In some aspects, the test sample comprises blood. In some aspects, the test sample comprises serum. In some aspects, the test sample comprises plasma.

[0109] In some aspects of the disclosed methods, the test sample may comprise any biological fluid obtained from a subject. In some aspects, the reference sample comprises blood, serum, plasma, neoplastic tissue, or any combination thereof. In some aspects, the reference sample comprises blood. In some aspects, the reference sample comprises serum. In some aspects, the reference sample comprises plasma.

[0110] Each biomarker disclosed herein may have one or more transcript variants. The methods disclosed herein may measure the expression level of any one transcript variant of each biomarker.

[0111] Expression levels can be measured in a variety of ways, including (but not limited to): measuring the mRNA encoded by the selected gene; measuring the amount of protein encoded by the selected gene; and measuring the activity of the protein encoded by the selected gene.

[0112] Biomarkers can be RNA, cDNA or protein. When the biomarker is RNA, the RNA can be reverse transcribed to produce cDNA (such as by RT-PCR), and the expression level of the produced cDNA can be detected. The expression level of the biomarker can be detected by forming a complex between the biomarker and the labeled probe or primer. When the biomarker is RNA or cDNA, the RNA or cDNA is detected by forming a complex between the RNA or cDNA and the labeled nucleic acid probe or primer. The complex between the RNA or cDNA and the labeled nucleic acid probe or primer can be a hybrid complex.

[0113] Gene expression can also be detected by microarray analysis. Microarray technology can also be used to identify or confirm differential gene expression. Therefore, microarray technology can be used to measure the expression profile biomarkers in fresh or fixed tissues. In this method, the target polynucleotide sequence (including cDNA and oligonucleotide) is plated or arranged on a microchip substrate. The arranged sequence is then hybridized with a specific DNA probe from a target cell or tissue. The source of mRNA is generally the total RNA isolated from a biological sample, and corresponding normal tissues or cell lines can be used to determine differential expression.

[0114] In some embodiments of microarray technology, cDNA clone inserts amplified by PCR are applied to substrates in dense arrays. In some embodiments, at least 10,000 nucleotide sequences are applied to substrates. Microarrayed genes are fixed on microchips with 10,000 units each, suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes can be produced by reverse transcription of RNA extracted from target tissues by incorporating fluorescent nucleotides. The labeled cDNA probes applied to the chip specifically hybridize with each DNA spot on the array. After strict washing to remove non-specifically bound probes, the microarray chip is scanned by a device (such as confocal laser microscopy) or by another detection method (such as a CCD camera). The quantification of the hybridization of each array unit allows the evaluation of the corresponding mRNA abundance. Using dual-color fluorescence, the cDNA probes labeled separately produced from two RNA sources are hybridized in pairs with the array. Therefore, the relative abundance of transcripts corresponding to each specified gene from two sources is determined simultaneously. Microarray analysis can be performed by commercially available equipment according to the manufacturer's protocol.

[0115] In some embodiments, qRT-PCR can be used to detect biomarkers in biological samples. The first step of gene expression profiling by RT-PCR is to extract RNA from the biological sample, then reverse transcribe the RNA template into cDNA, and amplify it by PCR reaction. Depending on the target of expression profiling, specific primers, random hexamer or oligo-dT primers are usually used to trigger the reverse transcription reaction step. Two commonly used reverse transcriptases are avian myeloblastosis virus reverse transcriptase (avilo myeloblastosis virus reverse transcriptase) (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (Moloney murine leukemia virus reverse transcriptase) (MLV-RT).

[0116] When the biomarker is a protein, the protein can be detected by forming a complex between the protein and a labeled antibody. The label can be any label, such as a fluorescent label, a chemiluminescent label, a radioactive label, etc. Exemplary methods for protein detection include, but are not limited to, enzyme immunoassay (EIA), radioimmunoassay (RIA), Western blot analysis, and enzyme-linked immunosorbent assay (ELISA). For example, biomarkers can be detected by ELISA, in which biomarker antibodies are bound to a solid phase, and enzyme-antibody conjugates are used to detect and / or quantify the biomarkers present in the sample. Alternatively, a western blot assay can be used, in which the dissolved and separated biomarkers are bound to nitrocellulose paper. The combination of a highly specific stable liquid conjugate and a sensitive chromogenic substrate allows for rapid and accurate identification of samples.

[0117] In some aspects of the disclosed methods, the methods described herein may have a specificity, sensitivity, and / or accuracy of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0118] In some aspects of the disclosed methods, the labeled probe, labeled primer, labeled antibody, or labeled nucleic acid can comprise a fluorescent label.

[0119] Any algorithm that can generate a score for a sample by evaluating where the sample value falls on a prediction model generated using different techniques (e.g., decision trees) can be used for the methods disclosed herein. The algorithm analyzes the data (i.e., expression level) and then assigns a score. In some embodiments, the algorithm may be a machine learning algorithm. Exemplary algorithms that can be used for the methods disclosed herein may include, but are not limited to, XGBoost (XGB), random forests (RF), glmnet, cforest, Classification and Regression Trees for Machine Learning (CART), treebag, K-Nearest Neighbors (kNN), neural networks (nnet), radial support vector machines (Support Vector Machine radial) (SVM-radial), linear support vector machines (Support Vector Machine linear) (SVM-linear), naive Bayes (NB), multilayer perceptron (mlp) or any combination thereof.

[0120] In some aspects of the disclosed methods, the algorithm may be XGB (also known as XGBoost). XGB is an implementation of gradient boosted decision trees designed for speed and performance.

[0121] In some aspects of the disclosed methods, a therapy, such as the first therapy or the second therapy, may include active surveillance, surgery, radiation therapy, cryotherapy, hormone therapy, chemotherapy, vaccine therapy, bone-directed therapy, immunotherapy, or any combination thereof.

[0122] In some aspects of the disclosed methods, active monitoring may include a doctor's visit with a prostate-specific antigen blood test and a digital rectal exam approximately every 6 months. Active monitoring may also include a prostate biopsy, which may be performed annually.

[0123] In some aspects of the disclosed methods, surgery may include a radical prostatectomy.

[0124] In some aspects of the disclosed methods, radiation therapy may include external beam radiation and brachytherapy.

[0125] Cryotherapy, also called cryosurgery or cryoablation, uses very cold temperatures to freeze and kill prostate cancer cells.

[0126] In some aspects of the disclosed methods, hormone therapy may include androgen deprivation therapy or androgen suppression therapy. The purpose is to reduce the level of male hormones called androgens in the body, or to prevent them from affecting prostate cancer cells. Hormone therapy may include orchiectomy. Hormone therapy may also include the administration of compounds that reduce androgen levels, such as luteinizing hormone-releasing hormone (LHRH) agonists, LHRH antagonists, and CYP17 inhibitors. Known LHRH agonists include (but are not limited to) leuprolide, goserelin, triptorelin, and histrelin. Known LHRH antagonists include degarelix. Known CYP17 inhibitors include abiraterone. Hormone therapy may also include the administration of anti-androgens, such as flutamide, bicalutamide, nilutamide, and enzalutamide. Hormonal therapy may also include administration of androgen suppressing drugs such as estrogen and ketoconazole.

[0127] In some aspects of the disclosed methods, chemotherapy can comprise docetaxel, cabazitaxel, mitoxantrone, estramustine, or a combination thereof.

[0128] In some aspects of the disclosed methods, the vaccine treatment may comprise Siprussin-T.

[0129] If cancer has grown outside the prostate, preventing or slowing the spread of cancer to the bones is the main goal of treatment. Bone-directed treatments may include bisphosphonates (such as zoledronic acid), denosumab, corticosteroids, external radiation therapy, radiopharmaceuticals (such as strontium-89, samarium-153, or radium-223), and pain medications.

[0130] It is also possible to evaluate the response of a subject with prostate cancer to therapy by comparing the scores determined by the same algorithm at different time points of therapy. For example, the first time point may be before or after giving the subject therapy; the second time point may be after the first time point and after giving the subject therapy. A first score is generated at the first time point, and a second score is generated at the second time point. When the second score is significantly reduced compared to the first score, it is considered that the subject is responsive to therapy. In some embodiments, when the second score is at least 10% less than the first score, for example, at least 20% less than the first score, at least 25% less than the first score, at least 40% less than the first score, at least 50% less than the first score, at least 75% less than the first score or at least 90% less than the first score, the second score is significantly reduced compared to the first score. When the second score is not significantly reduced compared to the first score, it is considered that the subject is unresponsive to therapy.

[0131] The sequence information of prostate cancer biomarkers and housekeeping genes is shown in Table 1 .

[0132] Table 1. Prostate cancer biomarker / housekeeping sequence information

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

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[0152]

[0153]

[0154]

[0155]

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[0159]

[0160]

[0161]

[0162]

[0163]

[0164]

[0165]

[0166]

[0167]

[0168]

[0169]

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176]

[0177]

[0178]

[0179]

[0180]

[0181]

[0182]

[0183]

[0184]

[0185]

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[0200]

[0201]

[0202]

[0203] definition

[0204] The articles "a" and "an" used in this disclosure refer to one or to more than one (ie, to at least one) of the grammatical object of the article. For example, "an element" means one element or more than one element.

[0205] Unless otherwise stated, the term "and / or" is used in this disclosure to mean "and" or "or".

[0206] The terms "polynucleotide" and "nucleic acid molecule" as used herein are used interchangeably, and refer to a polymeric form of nucleotides (ribonucleotides or deoxynucleotides or modified forms of either type of nucleotides) of at least 10 bases or base pairs in length, and are meant to include single-stranded and double-stranded forms of DNA. The nucleic acid molecules or nucleic acid sequences used as probes in microarray analysis as used herein preferably comprise nucleotide chains, more preferably DNA and / or RNA. In other embodiments, the nucleic acid molecules or nucleic acid sequences comprise other types of nucleic acid structures, such as DNA / RNA helices, peptide nucleic acids (PNA), locked nucleic acids (LNA) and / or ribozymes. Therefore, the term "nucleic acid molecule" as used herein also encompasses chains comprising non-natural nucleotides, modified nucleotides and / or non-nucleotide building blocks that exhibit the same functions as natural nucleotides.

[0207] As used herein, the terms "hybridize," "hybridizing," "hybridizes," and the like used in the context of polynucleotides refer to conventional hybridization conditions, such as hybridization in 50% formamide / 6XSSC / 0.1% SDS / 100 μg / ml ssDNA, wherein the hybridization temperature is above 37 degrees Celsius, and the temperature for washing with 0.1XSSC / 0.1% SDS is above 55°C, and preferably stringent hybridization conditions.

[0208] As used herein, the term "standardization" or "standardizer" refers to expressing differential values ​​according to a standard value to adjust for effects caused by technical variations due to sample handling, sample preparation, and measurement methods rather than biological variations in the concentration of biomarkers in the sample. For example, when measuring the expression of a differentially expressed protein, the absolute value of the protein expression can be expressed according to the absolute value of the expression of a standard protein whose expression is substantially constant.

[0209] The terms "diagnosis" and "diagnostics" also encompass the terms "prognosis" and "prognostics," respectively, as well as the application of such procedures over two or more time points to monitor diagnosis and / or prognosis over time, and statistical modeling based thereon. In addition, the term diagnosis includes: a. prediction (determining whether a patient will likely develop aggressive disease (hyperproliferative / invasive)), b. prognosis (predicting whether a patient will likely have a better or worse outcome at a preselected time in the future); c. treatment selection, d. therapeutic drug monitoring, and e. recurrence monitoring.

[0210] "Accuracy" refers to the closeness of agreement between a measured or calculated quantity (a test-reported value) and its actual (or true) value. Clinical accuracy is related to the proportion of true results (true positives (TP) or true negatives (TN)) relative to misclassified results (false positives (FP) or false negatives (FN)) and can be expressed as sensitivity, specificity, positive predictive value (PPV) or negative predictive value (NPV), or as a measure such as likelihood, odds ratio, etc.

[0211] As used herein, the term "biological sample" refers to any sample of biological origin that may contain one or more biomarkers. Examples of biological samples include tissues, organs, or body fluids, such as whole blood, plasma, serum, tissue, lavage fluid, or any other sample used to detect disease.

[0212] The term "subject" as used herein refers to a mammal, preferably a human. In some embodiments, the subject has at least one symptom of prostate cancer. In some embodiments, the subject has a predisposition or family history of prostate cancer. The subject may also have been previously diagnosed with prostate cancer and tested for recurrence of cancer. In some embodiments, the subject suffers from benign prostatic hyperplasia.

[0213] As used herein, "treating" or "treatment" with respect to a disorder may mean preventing the disorder, slowing the onset or rate of development of the disorder, reducing the risk of developing the disorder, preventing or delaying the development of symptoms associated with the disorder, reducing or stopping symptoms associated with the disorder, causing complete or partial regression of the disorder, or some combination thereof.

[0214] The level of a biomarker may change due to treatment of a disease. Changes in the level of a biomarker can be measured by the present disclosure. Changes in the level of a biomarker can be used to monitor the progression of a disease or treatment.

[0215] "Altered," "varied," or "significantly different" refers to a detectable change or difference from a reasonably comparable state, profile, measurement, or the like. Such changes may be all or nothing. They may be incremental and need not be linear. They may be by an order of magnitude. The change may be an increase or decrease of 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 100% or more, or any value between 0%-100%. Alternatively, the change may be 1-fold, 1.5-fold, 2-fold, 3-fold, 4-fold, 5-fold or more, or any value between 1-fold and 5-fold. The change may be statistically significant with a p-value of 0.1, 0.05, 0.001, or 0.0001.

[0216] The term "stable disease" refers to the diagnosis of the presence of prostate cancer, however, the prostate cancer has been treated and remains stable, ie, not progressive, as determined by imaging data and / or best clinical judgment.

[0217] The term "progressive disease" refers to the diagnosis of the presence of a highly active prostate cancer state, i.e., it has not been treated and is unstable, or has been treated and has not responded to therapy, or has been treated and active disease persists, as determined by imaging data and / or best clinical judgment.

[0218] The term "neoplastic disease" refers to any abnormal growth of cells or tissues, benign (non-cancerous) or malignant (cancerous). For example, the neoplastic disease may be prostate cancer.

[0219] The term "neoplastic tissue" refers to an abnormal growth of cells.

[0220] The term "non-neoplastic tissue" refers to a normal growing mass of cells.

[0221] The term "immunotherapy" may refer to activating immunotherapy or suppressing immunotherapy. As will be appreciated by those skilled in the art, activating immunotherapy refers to the use of therapeutic agents that induce, enhance or promote immune responses (including, for example, T cell responses), while suppressing immunotherapy refers to the use of therapeutic agents that interfere, suppress or suppress immune responses (including, for example, T cell responses). Activating immunotherapy may include the use of checkpoint inhibitors. Activating immunotherapy may include administering to a subject a therapeutic agent that activates stimulatory checkpoint molecules. Stimulatory checkpoint molecules include, but are not limited to, CD27, CD28, CD40, CD122, CD137, OX40, GITR, and ICOS. Therapeutic agents that activate stimulatory checkpoint molecules include, but are not limited to, MEDI0562, TGN1412, CDX-1127, and lipocalin.

[0222] The term "antibody" herein is used in the broadest sense and encompasses various antibody structures, including, but not limited to, monoclonal antibodies, polyclonal antibodies, multispecific antibodies (e.g., bispecific antibodies), and antibody fragments, as long as they exhibit the desired antigen-binding activity. An antibody that binds to a target refers to an antibody that is capable of binding to a target with sufficient affinity so that the antibody can be used as a diagnostic and / or therapeutic agent for targeting a target. In one embodiment, the extent to which an anti-target antibody binds to an unrelated non-target protein is less than about 10% of the binding of the antibody to the target, as measured, for example, by a radioimmunoassay (RIA) or a biacore assay. In certain embodiments, the dissociation constant (Kd) of an antibody that binds to a target is <1 μM, <100 nM, <10 nM, <1 nM, <0.1 nM, <0.01 nM, or <0.001 nM (e.g., 108 M or less, e.g., 108 M-1013 M, e.g., 109 M-1013 M). In certain embodiments, an anti-target antibody binds to an epitope of the target that is conserved between different species.

[0223] "Blocking antibodies" or "antagonistic antibodies" are antibodies that partially or completely block, inhibit, interfere with, or neutralize the normal biological activity of the antigen to which it binds. For example, antagonistic antibodies can block signal transduction through immune cell receptors (e.g., T cell receptors), thereby restoring the functional response of T cells to antigen stimulation (e.g., proliferation, cytokine production, target cell killing) from a dysfunctional state.

[0224] "Agonistic antibodies" or "activating antibodies" are antibodies that simulate, promote, stimulate or enhance the normal biological activity of the antigen to which they bind. Agonistic antibodies can also enhance or initiate signal transduction through the antigen to which they bind. In some embodiments, agonistic antibodies cause or activate signal transduction in the absence of a natural ligand. For example, agonistic antibodies can increase memory T cell proliferation, increase cytokine production by memory T cells, inhibit regulatory T cell function, and / or inhibit regulatory T cells from inhibiting effector T cell function, such as effector T cell proliferation and / or cytokine production.

[0225] "Antibody fragment" refers to a molecule other than an intact antibody, which comprises a portion of an intact antibody that binds to the antigen to which the intact antibody binds. Examples of antibody fragments include, but are not limited to, Fv, Fab, Fab', Fab'-SH, F(ab')2; diabodies; linear antibodies; single-chain antibody molecules (e.g., scFv); and multispecific antibodies formed from antibody fragments.

[0226] Administering chemotherapy to a subject may include administering a therapeutically effective dose of at least one chemotherapeutic agent. Chemotherapeutic agents include, but are not limited to, 13-cis retinoic acid, 2-CdA, 2-chlorodeoxyadenosine, 5-azacytidine, 5-fluorouracil, 5-FU, 6-mercaptopurine, 6-MP, 6-TG, 6-thioguanine, Abemaciclib, Abiraterone acetate, Abraxane, Accutane, Actinomycin-D, Adcetris, Ado-Trastuzumab Emtansine、Adriamycin、Adrucil、Afatinib、Afinitor、Agrylin、Ala-Cort、Aldesleukin、Alemtuzumab、Alecensa、Alectinib、Alimta、Alitretinoin、Alkaban-AQ、Alkeran、All-transretinoic Acid、Alpha-interferon Interferon、Altretamine、Alunbrig、Amethopterin、Amifostine、Aminoglutethimide、Anagrelide、Anandron、Anastrozole、Apalutamide、Arabinosylcytosine、Ara-C、Aranesp、Aredia、Arimidex、Aromasin、Arranon、Arsenic Trioxide、Arzerra、Asparaginase、atezolizumab、Atra、Avastin、Avelumab、AxicabtageneCiloleucel, Axitinib, Azacitidine, Bavencio, Bcg, Beleodaq, Belinostat, Bendamustine, Bendeka, Besponsa, Bevacizumab, Bexarotene, Bexxar, Bicalutamide, Bicnu, Blenoxane, Bleomycin, Blinatumomab, Blincyto, Bortezomib, Bosulif, Bosutinib, Brentuximab Vedotin, Brigatinib, Busulfan, Busulfex, C225, Cabazitaxel, Cabozantinib, Calcium Leucovorin, Campath, Camptosar, Camptothecin-11, Capecitabine, Caprelsa, Carac, Carboplatin, Carfilzomib, Carmustine, Carmustine Wafers Wafer、Casodex、CCI-779、Ccnu、Cddp、Ceenu、Ceritinib、Cerubidine、Cetuximab、Chlorambucil、Cisplatin、Citrovorum Factor、Cladribine、Clofarabine、Clolar、Cobimetinib、Cometriq、Cortisone、Cosmegen、Cotellic、Cpt-11、Crzotinib、Cyclophosphamide、Cyramza、Cytadren、Cytarabine、Cytarabine liposomeLiposomal, Cytosar-U, Cytoxan, Dabrafenib, Dacarbazine, Dacogen, Dactinomycin, Daratumumab, Darbepoetin Alfa, Darzalex, Dasatinib, Daunomycin, Daunorubicin, Daunorubicin Cytarabine (liposomal), daunorubicin hydrochloride, Daunorubicin liposomal Liposomal, DaunoXome, Decadron, Decitabine, Degarelix, Delta-Cortef, Deltasone, Denileukin Diftitox, Denosumab, DepoCyt, Dexamethasone, Dexamethasone Acetate, Dexamethasone Sodium Phosphate, Dexasone, Dexrazoxane, Dhad, Dic, Diodex, Docetaxel, Doxil, Doxorubicin, Doxorubicin LiposomalLiposomal, Droxia, DTIC, Dtic-Dome, Duralone, Durvalumab, Eculizumab, Efudex, Ellence, Elotuzumab, Eloxatin, Elspar, Eltrombopag, Emcyt, Empliciti, Enasidenib, Enzalutamide, Epirubicin, Epoetin Alfa, Erbitux, Eribulin, Erivedge, Erleada, Erlotinib, Erwinia L-asparaginase L-asparaginase、Estramustine、Ethyol、Etopophos、Etopophos、Etoposide Phosphate、Eulexin、Everolimus、Evista、Exemestane、Fareston、Farydak、Faslodex、Femara、Filgrastim、Firmagon、Floxuridine、Fludara、Fludarabine、Fluoroplex、Fluorouracil、Fluorouracil Cream、Fluoxymesterone、Flutamide、Folinic Acid Acid, Folotyn, Fudr, Fulvestrant, G-Csf, Gazyva, Gefitinib, Gemcitabine, Gemtuzumab ozogamicin, Gemzar, Gilotrif, Gleevec, Gleostine, Gliadel WafersWafer、Gm-Csf、Goserelin、Granix、Granulocyte-Colony Stimulating Factor、Granulocyte Macrophage Colony Stimulating Factor、Halaven、Halotestin、Herceptin、Hexadrol、Hexalen、Hexamethylmelamine、Hmm、Hycamtin、Hydrea、Hydrocort Acetate、Hydrocortisone、Hydrocortisone Sodium Phosphate、Hydrocortisone Sodium Succinate、Hydrocortone Phosphate, Hydroxyurea, Ibrance, Ibritumomab, Ibritumomab Tiuxetan, Ibrutinib, Iclusig, Idamycin, Idarubicin, Idelalisib, Idhifa, Ifex, IFN-α, Ifosfamide, IL-11, IL-2, Imbruvica, Imatinib mesylate, Imfinzi, Imidazole Carboxamide, Imlygic, Inlyta, Inotuzumab Ozogamicin), interferon-α, interferon α-2b (PEG conjugate), interleukin-2, interleukin-11, IntronA (interferon alpha-2b), Ipilimumab, Iressa, Irinotecan, Irinotecan (liposomal), Isotretinoin, Istodax, Ixabepilone, Ixazomib, Ixempra, Jakafi, Jevtana, Kadcyla, Keytruda, uda), Kidrolase, Kisqali, Kymriah, Kyprolis, Lanacort, Lanreotide, Lapatinib, Lartruvo, L-Asparaginase, Lbrance, Lcr, Lenalidomide, Lenvatinib, Lenvima , Letrozole, Leucovorin, Leukeran, Leukine, Leuprolide, Leurocristine, Leustatin, Liposomal Ara-C, Liquid Pred, Lomustine, Lonsurf, L-PAM, L-Sarcolysin, Lupron, Lupron Depot Mix Lupron Depot, Lynparza, Marqibo, Matulane, Maxidex, Mechlorethamine, Mechlorethamine Hydrochloride, Medralone, Medrol, Megace, Megestrol, Megestrol Acetate, Mekinist, Mercaptopurine, Mesna, Mesnex, Methotrexate, Methotrexate SodiumSodium, Methylprednisolone, Meticorten, Midostaurin, Mitomycin, Mitomycin-C, Mitoxantrone, M-Prednisolone, MTC, MTX, Mustargen, Mustine, Mutamycin, Myleran, Mylocel, Mylotarg, Navelbin e), Necitumumab, Nelarabine, Neosar, Neratinib, Nerlynx, Neulasta, Neumega, Neupogen, Nexavar, Nilandron, Nilotinib, Nilutamide, Ninlaro, Nipent, Niraparib, Nitrogen mustardMustard、Nivolumab、Nolvadex、Novantrone、Nplate、Obinutuzumab、Octreotide、OctreotideAcetate、Odomzo、Ofatumumab、Olaparib、Olaratumab、Omar Omacetaxine, Oncospar, Oncovin, Onivyde, Ontak, Onxal, Opdivo, Oprelvekin, Orapred, Orasone, Osimertinib, Otrexup, Oxaliplatin, Paclitaxel l), Paclitaxel protein-bound, Palbociclib, Pamidronate, Panitumumab, Panobinostat, Panretin, Paraplatin, Pazopanib, Pediapred, PegInterferon, Pegaspargase, Pegfilgrastim, Peg-Intron, PEG-L-asparaginase, Pembrolizumab, Pemetrexed, Pentostatin, Perjeta, Pertuzumab, PhenylalanineMustard, Platinol, Platinol-AQ, Pomalidomide, Pomalyst, Ponatinib, Portrazza, Pralatrexate, Prednisolone, Prednisone, Prelone, Procarbazine, Procrit, Proleukin, Prolia, Prolifeprospan 20 with carmustine implant, Promacta, Provenge, Purinethol, Radium 223 dichloride 223Dichloride), Raloxifene, Ramucirumab, Rasuvo, Regorafenib, Revlimid, Rheumatrex, Ribociclib, Rituxan, Rituxan Hycela, Rituximab, Rituximab Hyaluronidase Hyalurodinase, Roferon-A (interferon α-2a), Romidepsin, Romiplostim, Rubex, Rubidomycin Hydrochloride, Rubraca, Rucaparib, Ruxolitinib, Rydapt, Sandostatin, Sandostatin LARLAR), Sargramostim, Siltuximab, Sipuleucel-T, Soliris, Solu-Cortef, Solu-Medrol, Somatuline, Sonidegib, Sorafenib, Sprycel, Sti-571, Stivarga, Streptozocin, SU11248, Sunitinib, Sutent, Sylvant, Synribo, Tafinlar, Tagrisso, Talimogene Laherparepvec, Tamoxifen, Tarceva, Targretin, Tasigna, Taxol, Taxotere, Tecentriq, Temodar, Temozolomide, Temsirolimus, Teniposide, Tespa, Thalidomide, Thalomid, TheraCys, Thioguanine, Thioguanine tablets Tabloid, Thiophosphoramide, Thioplex, Thiotepa, Tice, Tisagenlecleucel, Toposar, Topotecan, Toremifene, Torisel, Tositumomab, Trabectedin, Trametinib, Trastuzumab, Treanda, Trelstar, Tretinoin, Trexall, Trifluridine / Tipiracil, Triptorelin pamoatepamoate), arsenic trioxide (Trisenox), thiotepa (Tspa), T-VEC, Tykerb, valrubicin, Valstar, vandetanib, VCR, Vectibix, vinblastine (Velban), Velcade, Vemurafenib, Venclexta, Venetoclax, VePesid, Verzenio, Vesanoid, leuprorelin acetate implant (Viadur), Vidaza, Vinblastine, Vinblastine Sulfate, Vincristine Sulfate Injection (VincasarPfs), Vincristine, Vincristine Liposomal (Vincristine Liposomal, Vinorelbine, Vinorelbine Tartrate, Vismodegib, Vlb, VM-26, Vorinostat, Votrient, VP-16, Vumon, Vyxeos, Xalkori Capsules, Xeloda, Xgeva, Xofigo, Xtandi, Yervoy, Yescarta, Yondelis, Zaltrap, Zanosar, Zarxio, Zejula, Zelboraf, Zevalin, Zinecard, Ziv-aflibercept, Zoladex, Zoledronic Acid, Zolinza, Zometa, Zydelig, Zykadia, Zytiga, or any combination thereof.

[0227] The terms "effective amount" and "therapeutically effective amount" of an agent or compound are used in the broadest sense to refer to a non-toxic but sufficient amount of the active agent or compound to provide the desired effect or benefit.

[0228] The term "benefit" is used in the broadest sense and refers to any desired effect, and specifically includes clinical benefits as defined herein. Clinical benefits can be measured by evaluating various endpoints, such as inhibiting the progression of the disease to some extent, including slowing down and completely stopping; reducing the number of disease attacks and / or symptoms; reducing the size of lesions; inhibiting (i.e., reducing, slowing down or completely stopping) the infiltration of disease cells into adjacent peripheral organs and / or tissues; inhibiting (i.e., reducing, slowing down or completely stopping) the spread of the disease; reducing autoimmune reactions, which may (but not necessarily) lead to the regression or ablation of the disease lesion; alleviating one or more symptoms associated with the disorder to some extent; increasing the length of disease-free survival after treatment, such as progression-free survival; improving overall survival rate; higher response rate; and / or reducing the mortality rate at a given time point after treatment.

[0229] The terms "cancer" and "cancerous" refer to or describe the physiological condition in mammals that is generally characterized by uncontrolled cell growth. The definition includes both benign and malignant cancers. Examples of cancer include, but are not limited to, carcinoma, lymphoma, blastoma, sarcoma, and leukemia. More specific examples of such cancers include adrenocortical carcinoma, bladder urothelial carcinoma, invasive breast cancer, cervical squamous cell carcinoma, endocervical adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, lymphoid tumors diffuse large B-cell lymphoma, esophageal cancer, glioblastoma multiforme, head and neck squamous cell carcinoma, kidney chromophobe cell carcinoma, renal clear cell carcinoma, renal papillary cell carcinoma, acute myeloid leukemia, brain low-grade glioma, hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma, paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin melanoma, gastric adenocarcinoma, testicular germ cell tumor, thyroid cancer, thymoma, uterine carcinosarcoma, uveal melanoma. Other examples include breast cancer, lung cancer, lymphoma, melanoma, liver cancer, colorectal cancer, ovarian cancer, bladder cancer, kidney cancer, or gastric cancer. Other examples of cancer include neuroendocrine cancer, non-small cell lung cancer (NSCLC), small cell lung cancer, thyroid cancer, endometrial cancer, bile duct cancer, esophageal cancer, anal cancer, salivary gland cancer, vulvar cancer, or cervical cancer.

[0230] The term "tumor" refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all precancerous and cancerous cells and tissues. The terms "cancer," "cancerous," "cell proliferative disorder," "proliferative disorder," and "tumor" as used herein are not mutually exclusive.

[0231] As used herein, the term "about" when used in conjunction with a numerical value and / or range generally refers to those numerical values ​​and / or ranges that are close to the numerical value and / or range. In some cases, the term "about" may mean within ±10% of the value. For example, in some cases, "about 100 [units]" may mean within ±10% of 100 (e.g., from 90 to 110). Example

[0232] The present disclosure is further illustrated by the following examples, which should not be construed as limiting the present disclosure in scope or spirit to the specific procedures described herein. It should be understood that these examples are provided to illustrate certain embodiments and are not intended to limit the scope of the present disclosure. It should be further understood that various other embodiments, modifications thereof, and equivalent forms that may be presentable to those skilled in the art may have to be resorted to without departing from the spirit of the present disclosure and / or the scope of the appended claims.

[0233] Example 1.38 - Derivation of marker genomes

[0234] Two microarray datasets (E-GEOD-46691 and E-GEOD-46602, Table 2) were used as inference cohorts (n = 595 samples). The random forest algorithm was applied to each dataset to identify the most important transcript sets that predicted phenotypic diversity within each set. Each microarray dataset contained a set of 22011 and 54675 probes, respectively. The random forest-driven marker selection algorithm identified n = 129 transcripts as predictive of disease progression across the two datasets. Of these, n = 30 presented high predictive importance scores in both datasets ( Figure 1A-1B ). Three microarray datasets (E-GEOD-62116, E-GEOD-62667, E-GEOD-72220, Table 2) were then used to validate the putative prostate cancer signature. Predicted significance and Kruskal-Wallis p-values ​​were obtained for each transcript in the validation cohort (n = 564 samples) and averaged across the three datasets ( Figure 2 ). Literature review identified ARv7 variants, ERG-TRMS22 fusion genes, and AR1 / AR2 signaling as additional gene sets for inclusion and evaluation.

[0235] Transcripts in a preliminary data set of blood samples from prostate cancer (n=20) and matched normal blood (n=20) were evaluated and the expression of 37 genes was confirmed as markers for PCA (Table 3). These genes were demonstrated to be highly expressed in PCA tumor tissue compared to normal prostate and can be used to effectively distinguish tumors from controls (Table 3). It also distinguished 7 different PCA cell lines: 22Rv1 and E006AA-hT (focal); VCaP; PC-3; LNCaP; DU145 and MDA PCa2b (both metastatic) from two normal prostate epithelial cell lines PWR-1E and RWPE-1 ( Figure 4 ). These data demonstrate that the candidate target transcripts are produced by neoplastically transformed prostate epithelial cells and are detectable in the blood.

[0236] An artificial intelligence model for prostate cancer disease was established using standardized gene expression of these 37 markers in whole blood from control (n=100) and PCA (n=21) samples. The data set was randomly divided into training and test partitions for model creation and validation, respectively. 12 algorithms (XGB, RF, glmnet, cforest, CART, treebag, knn, nnet, SVM-radial, SVM-linear, NB and mlp) were evaluated. The best performing algorithm (XGB-"gradient boosting") best predicted the training data. In the test set, XGB generates a probability score for the predicted sample. Each probability score reflects the "certainty" of the algorithm as to whether the unknown sample belongs to the "control" or "PCA" category. For example, the unknown sample S1 may have the following probability vector [control=20%, PCA=80%]. The sample would be considered a PCA sample.

[0237] Table 2. Overview of all public microarray datasets used to derive prostate cancer-specific gene signatures.

[0238]

[0239]

[0240] Table 3: 37 PCA marker gene sets (excluding housekeeping genes)

[0241]

[0242]

[0243]

[0244]

[0245] Example 2. Clinical utility

[0246] ProstaTest scores were significantly (p<0.001) elevated in PCA (63±19%) compared with men with BPH (17±13%) and controls (8±9%). Figure 5 There were no differences in levels between controls and hyperplasia. Data (receiver operating curve analysis and metrics) on the utility of the test to distinguish prostate cancer patients (n=125) from controls (n=201) in validation are included in Figure 6 The score showed an area under the curve (AUROC) of 0.97. The measurements were: sensitivity: 92% and specificity: 99% ( Figure 7 ). Youden index J was 0.94 and the Z statistic for distinguishing controls was 38.9.

[0247] The Probit-risk assessment plot identified ProstaTest scores >30 as being 50% accurate in predicting PCA in blood samples ( Figure 8 ). This increased to 60% at ProstaTest scores ≥ 32 and to > 80% at scores > 34. Thus, this tool can accurately distinguish controls from prostate cancer disease.

[0248] ProstaTest scores were significantly (p<0.001) elevated in high-grade (Gleason score ≥7: 70±19%) PCA compared with low-grade (Gleason 5+6) PCA (41±7%). Data (receiver operating curve analysis and metrics) on the utility of the test to discriminate high-grade from low-grade scores are included in Fig. 9 The score showed an area under the curve (AUROC) of 0.98. The measures were: sensitivity: 100% and specificity: 88%. The Youden index J was 0.87. In contrast, the PSA level showed an AUROC of 64%. The sensitivity and specificity were 54% and 87%, respectively. The Z statistic for distinguishing ProstaTest from PSA was 3.05 (p=0.002).

[0249] Specific evaluation of the PCA cohort before and after surgery identified that complete tumor removal and absence of disease evidence were associated with a significant decrease in ProstaTest (p<0.0001) ( Fig.10 ). Levels were not significantly different from controls or those with prostatic hyperplasia. Evaluation of an independent cohort identified that patients who received and responded to treatment exhibited scores significantly lower than those diagnosed with disease (p<0.001)( Fig.11 ). Therapies include hormone and chemotherapy. Therefore, this tool can accurately identify treatment response in prostate cancer disease.

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[0281] Equivalent

[0282] Although the present invention has been described in conjunction with the above specific embodiments, many alternatives, modifications and other variations will be apparent to those skilled in the art. All such alternatives, modifications and variations are intended to fall within the spirit and scope of the present invention.

Claims

1. Use of multiple reagents for detecting the expression of 38 biomarkers in the preparation of a kit for determining whether prostate cancer in a subject is stable or progressive by the following method, the method comprising: determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and a housekeeping gene, wherein the housekeeping gene is TOX4; The expression levels of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were compared with the expression levels of the housekeeping genes. The normalized expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were obtained. inputting each normalized expression level into an algorithm to generate a probability score, wherein the algorithm is a prediction model generated using the XGB algorithm; comparing the probability score to a first predetermined cutoff value; and When the probability score is equal to or greater than a first predetermined cutoff value, the prostate cancer is identified as progressive, or when the probability score is less than the first predetermined cutoff value, the prostate cancer is identified as stable.

2. Use of multiple reagents for detecting the expression of 38 biomarkers in the preparation of a kit for determining whether prostate cancer in a subject is low grade or high grade by the following method, the method comprising: determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and a housekeeping gene, wherein the housekeeping gene is TOX4; The expression levels of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were compared with the expression levels of the housekeeping genes. The normalized expression levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were obtained. inputting each normalized expression level into an algorithm to generate a probability score, wherein the algorithm is a prediction model generated by the XGB algorithm; comparing the probability score to a first predetermined cutoff value; and When the probability score is equal to or greater than a first predetermined cutoff value, the prostate cancer tumor is identified as high grade, or when the probability score is less than the first predetermined cutoff value, the prostate cancer tumor is identified as low grade.

3. Use of multiple reagents for detecting the expression of 38 biomarkers in the preparation of a kit for evaluating the response of a subject with prostate cancer to therapy, the method comprising: (1) At the first point in time: (a) determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers, wherein the at least 38 biomarkers include AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, XPC, and a housekeeping gene, wherein the housekeeping gene is TOX4; (b) The expression levels of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC were compared with those of the housekeeping genes. The levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain the normalized expression level of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (c) inputting each normalized expression level into an algorithm to generate a first probability score, wherein the algorithm is a prediction model generated using an XGB algorithm; (2) at a second time point, wherein the second time point is subsequent to the first time point and after administration of the therapy to the subject: (d) determining the expression levels of at least 38 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents that specifically detect the expression of at least 38 biomarkers; (e) comparing the expression levels of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A and XPC with respect to the expression levels of the housekeeping genes The levels of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC were normalized to obtain the normalized expression level of each of AAMP, ANO7, AR, AR-V7, C16orf89, CHTOP, COL1A1, EDC4, FGFR2, FXYD7, FYCO1, HNRNPU, HPN, KRT15, ​​KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPARGC1A, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC14A1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, TMPRSS2_1, TMPRSS2_2, TRIM29, UNC45A, and XPC; (f) inputting each normalized expression level into the algorithm to generate a second probability score; (3) comparing the first probability score with the second probability score; and (4) When the second score is significantly reduced compared to the first score, the subject is identified as responding to the therapy, or when the second score is not significantly reduced compared to the first score, the subject is identified as not responding to the therapy.

4. The use according to any one of claims 1-2, wherein the first predetermined cut-off value is at least 33% on a scale of 0-100%.

5. Use according to claim 4, wherein the first predetermined cut-off value is at least 50% on a scale of 0-100%.

6. The use according to any one of claims 1 to 3, comprising: a) a sensitivity of at least 92%; or b) A specificity of at least 95%.

7. The use according to any one of claims 1 to 3, wherein at least one of the 38 biomarkers is RNA, cDNA or protein.

8. The use according to claim 7, wherein when the biomarker is RNA, the RNA is reverse transcribed to produce cDNA, and the expression level of the produced cDNA is detected.

9. The use according to claim 7, wherein the expression level of the biomarker is detected by forming a complex between the biomarker and a labeled probe or primer.

10. The use according to any one of claims 1-2, wherein the predetermined cut-off value is derived from a plurality of blood samples obtained from subjects not having prostate cancer.

11. The use according to claim 3, wherein the at least one therapy comprises active surveillance, surgery, radiotherapy, cryotherapy, hormone therapy, chemotherapy, vaccine therapy, bone-directed therapy, or a combination thereof.

12. The use according to claim 11, wherein when the at least one therapy comprises hormone therapy, the hormone therapy comprises androgen suppression therapy.

13. The use according to claim 11, wherein when the at least one therapy comprises chemotherapy, the chemotherapy comprises docetaxel, cabazitaxel, mitoxantrone, estramustine or a combination thereof.

14. The use according to claim 11, wherein when the at least one therapy comprises a vaccine therapy, the vaccine therapy comprises Siprussin-T.

15. The use of claim 11, wherein when the at least one therapy comprises a bone-directed therapy, the bone-directed therapy comprises a bisphosphonate, denosumab, a corticosteroid, or a combination thereof.

16. The use of claim 3, wherein the first time point is prior to administering the therapy to the subject.

17. The use of claim 3, wherein the first time point is after administration of the therapy to the subject.

18. The use according to claim 3, wherein the second score is significantly reduced compared to the first score when the second score is at least 25% less than the first score.

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