Method for prostate cancer detection in saliva

Abstract

The present disclosure relates to methods for detecting prostate cancer, methods for determining whether prostate cancer is stable or progressive, low or high Gleason level, methods for determining surgical completeness, and methods for assessing response to prostate cancer therapy.

Description

Related Applications

[0001] This application claims the benefit and priority of U.S. Provisional Application No. 63 / 379,190, filed on Oct. 12, 2022, the content of which is hereby incorporated by reference in its entirety. Incorporation by Reference of Electronic Sequence Listing

[0002] The Sequence Listing XML associated with this application is provided electronically in XML file format and is hereby incorporated by reference into this specification. The name of the XML file containing the Sequence Listing XML is “LBIO-008_001WO_SeqList.xml”. The XML file is 125,439 bytes and was created on Oct. 11, 2023. Background

[0003] Prostate cancer (PCa) is the fourth most commonly diagnosed cancer globally and the second most common cancer in men. Although the incidence and prevalence have been declining, there will be approximately 200,000 men diagnosed with PCa in the United States 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 is diagnosed when it is localized (non-disseminated), the clinical behavior of tumors is highly variable and ranges from indolent, which can be monitored by watchful waiting or active surveillance (e.g., biomarkers and digital rectal examinations every six months), to malignant evolution and androgen-resistant disease, metastatic dissemination, and death. Symptoms of prostate cancer include problems urinating, blood in the urine or semen, difficulty having an erection, pain in the hips, back (spine), chest (ribs), or other areas due to cancer having spread to the bones, weakness or numbness in the legs or feet, or even loss of bladder or bowel control due to cancer compressing the spinal cord.

[0004] Multiple risk stratification systems that combine clinical data and pathological information (e.g., Gleason score) have been developed. These risk stratification systems, including recently developed next-generation tools, have an accuracy of only about 70% for predicting outcomes.

[0005] Molecular genetic information is increasingly being used to report pathology and better define cancer subtypes. This information has been used as a prognostic tool and to stratify patients for different treatment interventions. Prostate cancer has been examined, and mutations, DNA copy number alterations, rearrangements, and gene fusions have all been identified. These may be associated with some 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 abnormality is androgen-regulated fusion of ERG and other ETS family members (in approximately 50% of tumors). However, after prostatectomy, tumors with fusions do not have a significantly different prognosis compared to fusion-negative tumors. In contrast, androgen receptor variant 7 (AR-V7) is associated with the progression of castration-resistant prostate cancer (CRPC) and is thought to potentially serve as a biomarker for treatment selection. However, overall, the understanding of the molecular mechanisms underlying PCa pathogenesis is incomplete, and there is a lack of molecular-based biomarkers that can be used to predict sensitivity to therapeutic agents. Therefore, it is crucial to develop diagnostic methods that more accurately define the disease state, identify sensitivity to therapies, and ultimately can be used to better monitor disease progression.

[0006] Surveillance remains the fundamental approach for monitoring PCa at an early stage and detecting recurrence. After potentially curative resection, it can be carried out 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 known as gamma-seminoprotein or kallikrein-3). This glycoproteinase is encoded by the KLK3 gene and is secreted by epithelial cells in the prostate. However, it is not a unique indicator of prostate cancer but can also detect prostatitis or benign prostatic hyperplasia (BPH). Using PSA alone leads to unnecessary biopsies in men without cancer or underdiagnosis in men with significant disease. This is based on a low sensitivity (20 - 40%) and specificity (70 - 90%) range, and thus the positive predictive value is only 25 - 40%. The United States Preventive Services Task Force (USPSTF) does not recommend using PSA for prostate cancer screening. 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.

[0007] Saliva is an important test compartment that allows the assessment of biomarkers for viral, bacterial, and fungal parasite infections, as well as the measurement of markers that characterize systemic and non-systemic diseases. Human RNA obtained from cell-free saliva has been evaluated using sequencing and PCR techniques. Cell-free RNA from healthy individuals contains more than 3,000 mRNA species. RNA typically enters the oral cavity as a component of gingival crevicular fluid through secretion (from the parotid, submandibular, and sublingual glands) and from desquamated oral epithelial cells. RNA can be derived from acinar cells or through circulation.

[0008] Saliva has been established as a test compartment for other cancers, such as head and neck tumors. Typically, viral DNA (HPV) is isolated and amplified. This is used to provide a diagnosis of the disease. Recently, tumor RNA has been detected in saliva. For example, a biomarker based on 4-gene RNA has been developed for the diagnosis of oral cancer. The source of the RNA can be from the salivary glands themselves or secondary to cells secreted into the mouth, such as lymphocytes. It is also known that the salivary glands are vascularized and filter blood products. This suggests that blood could also be a source of RNA detectable in saliva. PCa Overview

[0009] The present disclosure provides a method for identifying the presence or absence of prostate cancer in a subject in need thereof, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expressions from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) when the score is greater than or equal to the predetermined cut-off value, identifying the presence of prostate cancer in the subject, or when the score is less than the predetermined cut-off value, determining the absence of prostate cancer in the subject. In some aspects, the predetermined cut-off value is 23% in the range of 0 - 100%.

[0010] The present disclosure provides a method for determining whether prostate cancer is stable or progressive in a subject, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) determining that the prostate cancer is progressive when the score is greater than or equal to the predetermined cut-off value, or determining that the prostate cancer is stable when the score is less than the predetermined cut-off value. In some aspects, the predetermined cut-off value is 50% within the range of 0 - 100%.

[0011] The present disclosure provides a method for determining whether prostate cancer in a subject has a low Gleason score (≤6) or a high Gleason score (≥7), the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) when the score is greater than or equal to the predetermined cut-off value, determining that the prostate cancer in the subject has a high Gleason score (≥7), or when the score is less than the predetermined cut-off value, determining that the prostate cancer in the subject has a low Gleason score (≤6). In some aspects, the predetermined cut-off value is 50% within the range of 0-100%.

[0012] The present disclosure provides a method for determining surgical completeness in a subject with prostate cancer, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject after surgery, wherein the 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) when the score is greater than or equal to the score, identifying that the prostate cancer has not been completely removed, or when the score is less than the predetermined cut-off value, identifying that the prostate cancer has been completely removed. In some aspects, the predetermined cut-off value is 50% within the range of 0-100%.

[0013] The present disclosure provides a method for assessing the response of a subject with prostate cancer to a prostate cancer therapy, the method comprising: (a) at a first time point: (i) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (ii) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; and (iii) inputting each of the normalized expression levels from step (a)(ii) into an algorithm to generate a first score; (b) at a second time point, wherein the second time point is after the first time point and after administration of the therapy to the subject: (i) determining the expression levels of at least 24 biomarkers in a test sample from the subject;(ii) Normalize the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of a housekeeping gene to obtain the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; and (iii) input each of the normalized expression levels from step (b)(ii) into an algorithm to generate a second score; (c) compare the first score and the second score; and (d) when the second score is reduced compared to the first score, identify the subject as responsive to the anti-prostate cancer therapy, or when the second score is not reduced compared to the normalized expression levels from step (a)(ii), identify the subject as non-responsive to the anti-prostate cancer therapy. In some aspects, when the second score is at least 5% lower than the first score, identify the subject as responsive to the anti-neuroendocrine cancer therapy.;

[0014] In some aspects of the foregoing method, the housekeeping gene is selected from ATG4B, RHOA, TOX4, TPT1, and TXNIP. In some aspects, the housekeeping gene is TOX4.

[0015] In some aspects, the foregoing method has a sensitivity of at least 90%.

[0016] In some aspects, the foregoing method has a specificity of at least 90%.

[0017] In some aspects of the foregoing method, at least one of the at least 24 biomarkers is RNA, cDNA, or protein. In some aspects, when the biomarker is RNA, the RNA is reverse transcribed to produce cDNA and the expression level of the resulting cDNA is detected.

[0018] In some aspects of the foregoing method, the expression level of the biomarker is detected by forming a complex between the biomarker and a labeled probe or primer. In some aspects, the label is a fluorescent label.

[0019] In some aspects, when the biomarker is protein, the protein is detected by forming a complex between the protein and a labeled antibody.

[0020] In some aspects, when the biomarker is RNA or cDNA, the RNA or cDNA is detected by forming a complex between the RNA or cDNA and a labeled nucleic acid probe or primer. In some aspects, the complex between the RNA or cDNA and the labeled nucleic acid probe or primer is a hybridization complex.

[0021] In some aspects of the foregoing method, the first predetermined cut-off value is derived from a plurality of reference samples obtained from subjects who do not have or are not diagnosed with a neoplastic disease. In some aspects, the neoplastic disease is prostate cancer.

[0022] In some aspects of the foregoing method, the algorithm is XGB, RF, glmnet, cforest, CART, treebag, knn, nnet, SVM-radial, SVM-linear, NB or mlp. In some aspects of the foregoing method, the algorithm is Random Forest.

[0023] In some aspects of the foregoing method, the machine learning algorithm is trained using the expression levels or normalized expression levels of at least 24 biomarkers obtained from a plurality of reference samples from subjects who do not have neuroendocrine cancer and the expression levels or normalized expression levels of at least 24 biomarkers from a plurality of reference samples from subjects who have neuroendocrine cancer.

[0024] In some aspects, the foregoing method further comprises treating a subject identified as having prostate cancer with at least one anti-prostate cancer therapy.

[0025] In some aspects, the anti-prostate cancer therapy includes active surveillance, surgery, radiotherapy, cryotherapy, hormone therapy, chemotherapy, vaccine therapy, bone-directed therapy or any combination thereof.

[0026] In some aspects, the radiotherapy includes external beam radiation, brachytherapy, radiopharmaceuticals or any combination thereof, preferably where the radiopharmaceutical includes 177 Lu-PSMA.

[0027] In some aspects, the hormone therapy includes androgen suppression therapy.

[0028] In some aspects, the chemotherapy includes docetaxel, cabazitaxel, mitoxantrone, estramustine or any combination thereof.

[0029] In some aspects, the vaccine therapy includes Sipuleucel-T.

[0030] In some aspects, the bone-directed therapy includes bisphosphonates, denosumab, corticosteroids or a combination thereof.

[0031] In some aspects of the foregoing method, the first time point is before administering the therapy to the subject.

[0032] In some aspects of the foregoing method, the first time point is after administering the therapy to the subject.

[0033] In some aspects, the test sample is saliva.

[0034] In some aspects, the test sample is self - collected saliva placed in a container containing stabilization fluid. Brief Description of the Drawings

[0035] Figure 1 A graph showing the relationship between gene expression in blood and saliva.

[0036] Figure 2A And Figure 2B Is an X - Y scatter plot showing the consistency between ( Figure 2A ) the Ct values in blood and saliva and ( Figure 2B ) the normalized gene expression in blood and saliva. The red line is the linear correlation. The vertical and horizontal lines are the SEM and SD of the mean values from 36 target genes respectively.

[0037] Figure 3 A graph showing the relationship between normalized gene expression in tumor samples and saliva. The red line is the linear correlation. The vertical and horizontal lines are the SEM and SD of the mean values from 24 target genes respectively.

[0038] Figure 4 A graph showing gene expression in age / sex - matched controls: n = 30, and neuroendocrine cancer cases: n = 15. The expression levels of 14 genes were significantly (p < 0.05) elevated, and the expression levels of 5 of the target genes were significantly decreased.

[0039] Figure 5A And Figure 5B Is a graph showing the visualization of 24 putative biomarker genes identified by a random forest algorithm in a derivation cohort of n = 163 control samples and 51 cancer samples. ( Figure 5A ) Expression normalized to TOX4. ( Figure 5B ) Expression normalized to TPT1.

[0040] Figure 6 A graph showing the SalivaPROSTest scores in an independent control group (n = 100) and neuroendocrine cancer (n = 40). The level of NET (61 ± 24) was significantly elevated (p < 0.0001) relative to the control (6 ± 5).

[0041] Figure 7 Graph showing the receiver operating characteristic analysis for the test cohort in the independent group. The AUROC was 0.99. The Youden J index was 0.95. The Z statistic was highly significant (304.7; p < 0.0001).

[0042] Figure 8 Graph showing the measures for the assay for determining prostate cancer. The sensitivity was 95% and the specificity was 100%.

[0043] Figure 9 Graph showing the SalivaPROSTest scores for high-grade PCa (Gleason ≥7) compared to low-grade (Gleason 5+6) tumors. The levels for high-grade tumors (72 ± 25) were significantly elevated compared to low Gleason tumors (46 ± 19) (p < 0.002).

[0044] Figure 10 Graph showing the effect of surgery on the SalivaPROSTest. The levels were elevated before surgery (64 ± 18%). Surgery reduced the levels to 33 ± 11% (p < 0.0001), with no difference from the control levels.

[0045] Figure 11A and Figure 11B Spider graph showing the effect of treatment on the SalivaPROSTest. The levels were elevated before treatment (69 ± 23%). In those who responded to therapy, the levels decreased by -38 ± 31% and -60 ± 19% at two follow-up time points (p < 0.0001). In those who progressed despite therapy, the levels increased by +18 ± 19% and +27 ± 7% (p < 0.05). ( Figure 11A ) Follow-up graph for all patients. ( Figure 11B ) Spider graph for individual responders (blue) and those who progressed (red). DETAILED

[0046] The details of the invention are set forth in the following accompanying description. 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 be apparent from the description and claims. In the specification and the appended claims, the singular forms also include the plural unless the context clearly dictates otherwise. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. All patents and publications cited in this specification are incorporated herein by reference in their entirety.

[0047] The present invention describes methods for quantifying (scoring) molecular signatures of prostate cancer in saliva with high sensitivity and specificity, for purposes including but not limited to detecting prostate cancer, determining whether prostate cancer is stable or progressive, determining the completeness of surgery, and assessing a subject's response to prostate cancer therapy, treating prostate cancer in a subject, or any combination thereof. Without wishing to be bound by theory, the present invention is based on the discovery that the expression levels of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC (normalized by the expression level of a housekeeping gene) are elevated in subjects with prostate cancer compared to healthy subjects.

[0048] As described herein, measurement of the expression levels of the above-described circulating prostate cancer transcripts (collectively referred to as "SalivaPROSTest transcripts") in saliva samples from a subject can be used to diagnose prostate cancer. In a non-limiting example, the expression levels of SalivaPROSTest transcripts measured from a saliva sample can be input into an algorithm to generate a score (referred to herein as the "ProstaTest score"), which can be used to diagnose the presence of prostate cancer in a subject. Additionally, after administration of one or more anti-prostate cancer therapies (e.g., surgery and chemotherapy), a decrease in the subject's ProstaTest score can be used to determine the responsiveness of the subject to the one or more therapies, optionally in combination with standard clinical evaluations and imaging.

[0049] The present disclosure provides a method for identifying the presence or absence of prostate cancer in a subject in need thereof, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; and (c) identifying the presence or absence of prostate cancer in the subject based on the normalized expression levels from step (b). In some aspects, identifying the presence or absence of prostate cancer in the subject based on the normalized expression levels from step (b) may include comparing the normalized expression levels to corresponding predetermined cut-off values and identifying the presence or absence of prostate cancer in the subject based on the relationship (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to) between the normalized expression levels and the corresponding predetermined cut-off values.

[0050] The present disclosure provides a method for identifying the presence or absence of prostate cancer in a subject in need thereof, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expressions from step (b) into an algorithm to generate a score; and (d) identifying the presence or absence of prostate cancer in the subject based on the score. In some aspects, identifying the presence or absence of prostate cancer in the subject based on the score may include comparing the score with a predetermined cut-off value and identifying the presence or absence of prostate cancer in the subject based on the relationship (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to) between the score and the predetermined cut-off value.

[0051] The present disclosure provides a method for identifying the presence or absence of prostate cancer in a subject in need thereof, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expressions from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) identifying the presence of prostate cancer in the subject when the score is greater than or equal to the predetermined cut-off value, or determining the absence of prostate cancer in the subject when the score is less than the predetermined cut-off value.

[0052] The present disclosure provides a method for identifying the presence or absence of prostate cancer in a subject in need thereof, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expressions from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) identifying the presence of prostate cancer in the subject when the score is greater than the predetermined cut-off value, or determining the absence of prostate cancer in the subject when the score is less than or equal to the predetermined cut-off value.

[0053] In some aspects of the foregoing method, the predetermined cut-off value can be 23% within the range of 0 - 100%.

[0054] The present disclosure provides a method for assessing the risk of a subject having prostate cancer, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining a normalized expression level for each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; and (c) assessing the risk of the subject having prostate cancer based on the normalized expression levels from step (b). In some aspects, assessing the risk of the subject having prostate cancer based on the normalized expression levels from step (b) may include comparing the normalized expression levels to corresponding predetermined cut-off values and assessing the risk of the subject having prostate cancer based on the relationship (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to) between the normalized expression levels and the corresponding predetermined cut-off values.

[0055] The present disclosure provides a method for assessing the risk of a subject having prostate cancer, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expressions from step (b) into an algorithm to generate a score; and (d) assessing the risk of the subject having prostate cancer based on the score. In some aspects, assessing the risk of the subject having prostate cancer based on the score may include comparing the score to a predetermined cut-off value and assessing the risk of the subject having prostate cancer based on the relationship (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to) between the score and the predetermined cut-off value.

[0056] Accordingly, the present disclosure provides a method for assessing the risk of a subject having prostate cancer, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining a normalized expression level for each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expressions from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) when the score is greater than or equal to the predetermined cut-off value, assessing the subject to be at high risk of having prostate cancer, or when the score is less than the predetermined cut-off value, determining the subject to be at low risk of having prostate cancer.

[0057] Accordingly, the present disclosure provides a method for identifying the risk of a subject having prostate cancer, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expressions from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) when the score is greater than the predetermined cut-off value, identifying the subject as being at high risk of having prostate cancer, or when the score is less than or equal to the predetermined cut-off value, determining that the subject is at low risk of having prostate cancer.

[0058] In some aspects of the foregoing method, the predetermined cut-off value can be 23% within the range of 0 - 100%.

[0059] The present disclosure also provides a method for determining whether prostate cancer is stable or progressive in a subject, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; and (c) determining whether the prostate cancer in the subject is stable or progressive based on the normalized expression levels from step (b). In some aspects, determining whether the prostate cancer in the subject is stable or progressive based on the normalized expression levels from step (b) includes comparing the normalized expression levels with corresponding predetermined cut-off values and determining whether the prostate in the subject is stable or progressive based on the relationship (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to) between the normalized expression levels and the corresponding predetermined cut-off values.

[0060] 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 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; and (d) determining whether the prostate cancer in the subject is stable or progressive based on the normalized expression levels from step (b). In some aspects, determining whether the prostate cancer in the subject is stable or progressive based on the score includes comparing the score with a predetermined cut-off value and determining whether the prostate cancer in the subject is stable or progressive based on the relationship (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to) between the score and the predetermined cut-off value.

[0061] The present disclosure also provides a method for determining whether prostate cancer is stable or progressive in a subject, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) determining that the prostate cancer is progressive when the score is greater than or equal to the predetermined cut-off value, or determining that the prostate cancer is stable when the score is less than the predetermined cut-off value.

[0062] The present disclosure also provides a method for determining whether prostate cancer is stable or progressive in a subject, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) determining that the prostate cancer is progressive when the score is greater than the predetermined cut-off value, or determining that the prostate cancer is stable when the score is less than or equal to the predetermined cut-off value.

[0063] In some aspects of the foregoing method, the predetermined cut-off value can be 50% within the range of 0 - 100%.

[0064] In addition, the present disclosure also provides a method for determining whether prostate cancer in a subject has a low Gleason score (≤6) or a high Gleason score (≥7), the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A and XPC and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A and XPC; and (c) determining whether prostate cancer in the subject has a low Gleason score (≤6) or a high Gleason score (≥7) based on the normalized expression levels from step (b). In some aspects, determining whether prostate cancer in the subject has a low Gleason score (≤6) or a high Gleason score (≥7) based on the normalized expression levels from step (b) includes comparing the normalized expression levels with corresponding predetermined cut-off values and determining whether prostate cancer in the subject has a low Gleason score (≤6) or a high Gleason score (≥7) based on the relationship (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to) between the normalized expression levels and the corresponding predetermined cut-off values.

[0065] The present disclosure also provides a method for determining whether prostate cancer in a subject has a low Gleason score (≤6) or a high Gleason score (≥7), the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; and (d) determining whether the prostate cancer in the subject has a low Gleason score (≤6) or a high Gleason score (≥7) based on the score. In some aspects, determining whether the prostate cancer in the subject has a low Gleason score (≤6) or a high Gleason score (≥7) based on the score includes comparing the score with a predetermined cut-off value and determining whether the prostate cancer in the subject has a low Gleason score (≤6) or a high Gleason score (≥7) based on the relationship (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to) between the score and the predetermined cut-off value.

[0066] Accordingly, the present disclosure also provides a method for determining whether prostate cancer in a subject has a low Gleason score (≤6) or a high Gleason score (≥7), the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) when the score is greater than or equal to the predetermined cut-off value, determining that the prostate cancer in the subject has a high Gleason score (≥7), or when the score is less than the predetermined cut-off value, determining that the prostate cancer in the subject has a low Gleason score (≤6).

[0067] Accordingly, the present disclosure also provides a method for determining whether prostate cancer in a subject has a low Gleason score (≤6) or a high Gleason score (≥7), the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) when the score is greater than the predetermined cut-off value, determining that the prostate cancer in the subject has a high Gleason score (≥7), or when the score is less than or equal to the predetermined cut-off value, determining that the prostate cancer in the subject has a low Gleason score (≤6).

[0068] In some aspects of the foregoing method, the predetermined cut-off value can be 50% within the range of 0 - 100%.

[0069] In addition, the present disclosure provides a method for determining surgical completeness in a subject with prostate cancer, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject after surgery, wherein the 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; and (c) identifying incomplete removal of prostate cancer or identifying complete removal of prostate cancer based on the normalized expression levels from step (b). In some aspects, identifying incomplete removal of prostate cancer or identifying complete removal of prostate cancer based on the normalized expression levels from step (b) may include comparing the normalized expression levels with corresponding predetermined cut-off values and identifying incomplete removal of prostate cancer or identifying complete removal of prostate cancer based on the relationship (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to) between the normalized expression levels and the corresponding predetermined cut-off values.

[0070] In addition, the present disclosure provides a method for determining surgical completeness in a subject with prostate cancer, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject after surgery, wherein the 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; and (d) identifying incomplete removal of prostate cancer or identifying complete removal of neuroendocrine cancer based on the score. In some aspects, identifying incomplete removal of prostate cancer or identifying complete removal of prostate cancer based on the score may include comparing the score with a predetermined cut-off value and identifying incomplete removal of prostate cancer or identifying complete removal of neuroendocrine cancer based on the relationship between the score and the corresponding predetermined cut-off value (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to).

[0071] Accordingly, the present disclosure provides a method for determining surgical completeness in a subject with prostate cancer, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject after surgery, wherein the 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each normalized expression level from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) when the score is greater than or equal to the score, identifying that the prostate cancer has not been completely removed, or when the score is less than the predetermined cut-off value, identifying that the prostate cancer has been completely removed.

[0072] Accordingly, the present disclosure provides a method for determining surgical completeness in a subject with prostate cancer, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject after surgery, wherein the 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) when the score is greater than the score, identifying that the prostate cancer has not been completely removed, or when the score is less than or equal to the predetermined cut-off value, identifying that the prostate cancer has been completely removed.

[0073] In some aspects of the foregoing method, the predetermined cut-off value can be 50% within the range of 0 - 100%.

[0074] The response of a subject with prostate cancer to a therapy can also be evaluated by comparing scores determined by the same algorithm at different time points of the therapy. For example, the first time point can be before or after administering the therapy to the subject; the second time point is after the first time point and after administering the therapy to the subject. 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 lower when compared to the first score, the subject is considered responsive to the therapy. In some aspects, the second score is lower when compared to the first score when the second score is at least 5% less than the first score, such as at least 10% less than the first score, at least 15% 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. When the second score is not significantly lower or increased when compared to the first score, the subject is considered non-responsive to the therapy.

[0075] The present disclosure also provides a method for assessing the response of a subject with prostate cancer to a prostate cancer therapy, the method comprising: (a) at a first time point: (i) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (ii) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (b) at a second time point, wherein the second time point is after the first time point and after administering the therapy to the subject: (i) determining the expression levels of at least 24 biomarkers in a test sample from the subject; (ii) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC;(c) Compare the normalized expression levels from step (a)(ii) and step (b)(ii); and (d) identify a subject as responsive to an anti-prostate cancer therapy when the normalized expression level from step (b)(ii) is decreased as compared to the expression level from step (a)(ii), or identify a subject as non-responsive to an anti-prostate cancer therapy when the normalized expression level from step (b)(ii) is not decreased as compared to the normalized expression level from step (a)(ii).;

[0076] The present disclosure also provides a method for assessing the response of a subject with prostate cancer to a prostate cancer therapy, the method comprising: (a) at a first time point: (i) determining the expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (ii) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining a normalized expression level for each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; and (iii) inputting each normalized expression level from step (a)(ii) into an algorithm to generate a first score; (b) at a second time point, wherein the second time point is after the first time point and after administering the therapy to the subject: (i) determining the expression levels of at least 24 biomarkers in a test sample from the subject;(ii) Normalize the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of a housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; and (iii) input each normalized expression level from step (b)(ii) into an algorithm to generate a second score; (c) compare the first score with the second score; and (d) identify the subject as responsive to anti-prostate cancer therapy when the second score is decreased as compared to the first score, or identify the subject as non-responsive to anti-prostate cancer therapy when the second score is not decreased as compared to the normalized expression level from step (a)(ii).;

[0077] General methods and definitions

[0078] The following general methods and definitions may apply to any of the foregoing methods.

[0079] In some aspects, the test sample may comprise saliva.

[0080] Exemplary housekeeping genes include, but are not limited to, ATG4B, RHOA, TOX4, TPT1, and TXNIP. In some aspects, the housekeeping gene is TOX4.

[0081] Each of the biomarkers disclosed herein may have one or more transcript variants. The methods disclosed herein may measure the expression level of any one of the transcript variants of each biomarker.

[0082] In some aspects, determining the expression levels of at least 24 biomarkers in a test sample from a subject may comprise contacting the test sample with a plurality of reagents specific for detecting the expression of at least 24 biological markers.

[0083] Accordingly, the present disclosure provides the use of a plurality of reagents for detecting the expression of at least 36 biomarkers in the manufacture of a kit for identifying the presence or absence of prostate cancer by the methods described herein.

[0084] The present disclosure also provides the use of a plurality of reagents for detecting the expression of at least 36 biomarkers in the manufacture of a kit for identifying the risk of a subject having prostate cancer by the methods described herein.

[0085] The present disclosure also provides the use of a plurality of reagents for detecting the expression of at least 36 biomarkers in the manufacture of a kit for determining whether prostate cancer is stable or progressive in a subject by the methods described herein.

[0086] The present disclosure also provides the use of a plurality of reagents for detecting the expression of at least 36 biomarkers in the manufacture of a kit for determining whether prostate cancer has a low Gleason score (≤6) or a high Gleason score (≥7) in a subject by the methods described herein.

[0087] The present disclosure also provides the use of a plurality of reagents for detecting the expression of at least 36 biomarkers in the manufacture of a kit for determining the completeness of a surgery for removing prostate cancer in a subject by the methods described herein.

[0088] The present disclosure also provides the use of a plurality of reagents for detecting the expression of at least 36 biomarkers in the manufacture of a kit for evaluating the response of a subject having prostate cancer to anti-prostate cancer therapy by the methods described herein.

[0089] The expression level can be measured in a variety of ways, including but not limited to measuring the mRNA encoded by a selected gene; measuring the amount of protein encoded by a selected gene; measuring the activity of the protein encoded by a selected gene; or any combination thereof.

[0090] The biomarker 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 resulting cDNA can be detected. The expression level of the biomarker can be detected by forming a complex between the biomarker and a 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 a labeled nucleic acid probe or primer. The complex between the RNA or cDNA and the labeled nucleic acid probe or primer can be a hybridization complex.

[0091] As those skilled in the art should appreciate, gene expression can be detected by microarray analysis. Microarray technology can also be used to identify or confirm differential gene expression. Thus, microarray technology can be used to measure expression profile biomarkers in fresh or fixed tissue. In this method, polynucleotide sequences of interest (including cDNA and oligonucleotides) are plated or arrayed on a microchip substrate. The arrayed sequences are then hybridized with specific DNA probes from cells or tissues of interest. The source of mRNA is typically total RNA isolated from a biological sample, and a corresponding normal tissue or cell line can be used to determine differential expression.

[0092] In some aspects of microarray technology, PCR-amplified cDNA clone inserts are applied to a substrate in a dense array. In some aspects, at least 10,000 nucleotide sequences are applied to the substrate. Microarrayed genes immobilized on a microchip at 10,000 units each are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes can be generated by reverse transcription of RNA extracted from tissues of interest via incorporation of fluorescent nucleotides. The labeled cDNA probes applied to the chip specifically hybridize to each DNA spot on the array. After stringent washing to remove non-specifically bound probes, the microarray chip is scanned by a device (such as a confocal laser microscope) or by another detection method (such as a CCD camera). Quantifying the hybridization of each arrayed unit enables the evaluation of the corresponding mRNA abundance. With two-color fluorescence, separately labeled cDNA probes generated from two RNA sources are hybridized to the array in pairs. Thus, the relative abundance of transcripts corresponding to each designated gene from the two sources is determined simultaneously. Microarray analysis can be performed using commercially available equipment according to the manufacturer's protocol.

[0093] In some aspects, RNAseq can be used to detect biomarkers (i.e., SalivaPROSTest transcripts and / or housekeeping genes) in saliva samples. As those skilled in the art should appreciate, the first step in gene expression analysis by RNAseq is to extract RNA from a saliva sample and then reverse transcribe the RNA template into cDNA to generate an RNA library. Sequencing adapters are added. The cDNA is then sequenced using a sequencing platform. The data are analyzed and expressed as transcripts per million.

[0094] In some aspects, biomarkers (i.e., SalivaPROS Test transcripts and / or housekeeping genes) can be detected in saliva samples using qRT-PCR. As those skilled in the art should appreciate, the first step in gene expression analysis by RT-PCR is to extract RNA from a biological sample, then reverse transcribe the RNA template into cDNA and amplify it by PCR reaction. Depending on the purpose of the expression analysis, specific primers, random hexamers, or oligo-dT primers are typically used to initiate the reverse transcription reaction step. Two commonly used reverse transcriptases are avian myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MLV-RT).

[0095] In some aspects where 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, chemiluminescent label, 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, a biomarker can be detected in an ELISA, where the biomarker antibody is bound to a solid phase and an enzyme-antibody conjugate is used to detect and / or quantify the biomarker present in the sample. Alternatively, a Western blot assay can be used, where the solubilized and separated biomarker is 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 the sample.

[0096] In some aspects, the methods described herein can have at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% specificity, sensitivity, and / or accuracy.

[0097] In some aspects, the methods described herein can have at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% specificity (e.g., specificity for identifying the presence or absence of prostate cancer, specificity for identifying whether prostate cancer is stable or progressive, specificity for identifying surgical completeness in a subject with prostate cancer, or specificity for assessing the response of a subject with prostate cancer to anti-prostate cancer therapy).

[0098] In some aspects, the methods described herein can have a sensitivity of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% (e.g., the specificity for identifying the presence or absence of prostate cancer, the specificity for identifying whether prostate cancer is stable or progressive, the specificity for identifying surgical completeness in a subject with prostate cancer, or the specificity for assessing the response of a subject with prostate cancer to anti - prostate cancer therapy).

[0099] In some aspects, the methods described herein can have an accuracy of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% (e.g., the specificity for identifying the presence or absence of prostate cancer, the specificity for identifying whether prostate cancer is stable or progressive, the specificity for identifying surgical completeness in a subject with prostate cancer, or the specificity for assessing the response of a subject with prostate cancer to anti - prostate cancer therapy).

[0100] Any algorithm that can generate a score for a sample by evaluating where the sample values fall on a prediction model generated using different techniques (e.g., decision trees) can be used in the methods disclosed herein. The algorithm analyzes the data (i.e., expression levels) and then assigns a score. In some aspects, the algorithm can be a machine - learning algorithm. Exemplary algorithms that can be used in the methods disclosed herein can include, but are not limited to, XGB, RF, glmnet, cforest, CART, treebag, knn, nnet, SVM - radial, SVM - linear, NB, and mlp. In some aspects, the algorithm can be XGB (also known as XGBoost). XGB is an implementation of gradient - boosted decision trees designed for speed and performance. In some aspects, the algorithm can be a random forest. In some aspects, the random forest algorithm can be a grid - search - optimized random forest. Random forest is an implementation of an ensemble learning method for classification, regression, and other tasks that operates by constructing a large number of decision trees at training time.

[0101] In some aspects of the methods of the present disclosure, machine learning algorithms can be trained using: a) the expression levels or normalized expression levels of at least 24 biomarkers in at least one biological sample (e.g., saliva) from at least one subject without prostate cancer; and b) the expression levels or normalized expression levels of at least 24 biomarkers in at least one biological sample (e.g., saliva) from at least one subject with prostate cancer. That is, in some aspects, machine learning algorithms are trained using the expression levels or normalized expression levels of at least 24 biomarkers obtained from multiple reference samples from subjects without prostate cancer and the expression levels or normalized expression levels of at least 24 biomarkers from multiple reference samples from subjects with prostate cancer.

[0102] In some aspects, one or more predetermined cut-off values can be derived from multiple reference samples obtained from subjects without or not diagnosed with a neoplastic disease. The multiple reference samples can be from about 2 to about 500 samples, about 2 to about 200 samples, about 10 to about 100 samples, or about 20 to about 80 samples.

[0103] In some aspects, determining a predetermined cut-off value can include inputting the normalized expression levels of the SalivaPROSTest transcripts from each reference sample into the same algorithm used in the methods described above, thereby generating multiple scores from the multiple reference samples. The predetermined cut-off value can then be determined by taking the arithmetic mean of these scores. In some aspects, the reference samples can comprise saliva. In some aspects, the reference samples are of the same type as the test samples.

[0104] In some aspects of the methods of the present disclosure, at least one receiver operating characteristic (ROC) curve can be used to calculate and / or select a predetermined cut-off value. In some aspects of the methods of the present disclosure, as would be appreciated by a person skilled in the art, any method known in the art can be used to calculate and / or select a predetermined cut-off value to have any of the characteristics described herein (e.g., a particular sensitivity, specificity, accuracy, or any combination thereof).

[0105] In some aspects, the methods described herein can further include treating the subject with an anti-prostate cancer therapy.

[0106] Thus, in some aspects, the methods described herein further comprise treating a subject identified as having prostate cancer with an anti-prostate cancer therapy. In some aspects, the methods described herein further comprise treating a subject identified as having progressive prostate cancer with at least one anti-prostate cancer therapy. In some aspects, the methods described herein further comprise treating a subject identified as being at high risk of prostate cancer with at least one anti-prostate cancer therapy. In some aspects, the methods described herein further comprise treating a subject in whom prostate cancer has not been completely removed by surgery with at least one anti-prostate cancer therapy.

[0107] In some aspects, the methods described further comprise treating a subject identified as non-responsive to one anti-prostate cancer therapy with a different anti-prostate cancer therapy. In some aspects, the methods described further comprise continuing to treat a subject identified as responsive to the anti-prostate cancer therapy with the same anti-prostate cancer therapy.

[0108] In some aspects, the anti-prostate cancer therapy can include active surveillance, surgery, radiation therapy, cryotherapy, hormone therapy, chemotherapy, vaccine therapy, bone-directed therapy, or any combination thereof. The anti-prostate cancer therapy can include any therapy known in the art to be effective in treating neuroendocrine cancer.

[0109] As would be appreciated by one of skill in the art, active surveillance can include doctor visits approximately every 6 months with prostate-specific antigen blood tests and digital rectal examinations. As would be appreciated by one of skill in the art, active surveillance can also include prostate biopsies, which can be performed annually.

[0110] As would be appreciated by one of skill in the art, surgery for prostate cancer patients can include radical prostatectomy.

[0111] As would be appreciated by one of skill in the art, radiation therapy for prostate cancer can include external beam radiation, brachytherapy, and radiopharmaceuticals. As would be appreciated by one of skill in the art, radiopharmaceuticals can include 177 Lu-PSMA.

[0112] As would be appreciated by one of skill in the art, cryotherapy (also known as cryosurgery or cryoablation) can include using extremely cold temperatures to freeze and kill prostate cancer cells.

[0113] As those skilled in the art will appreciate, hormone therapy is also known as androgen deprivation therapy or androgen suppression therapy. Without wishing to be bound by theory, the aim is to reduce the level of male hormones (known as androgens) in the body or to prevent their effects on prostate cancer cells. Hormone therapy may include orchiectomy. Hormone therapy may include administering compounds that reduce androgen levels, including but not limited to luteinizing hormone-releasing hormone (LHRH) agonists, LHRH antagonists, and CYP17 inhibitors. Known LHRH agonists include but are not limited to leuprorelin, goserelin, triptorelin, and histrelin. Known LHRH antagonists include degarelix. Known CYP17 inhibitors include abiraterone. Hormone therapy may also include administering antiandrogens, including but not limited to flutamide, bicalutamide, nilutamide, and enzalutamide. Hormone therapy may include administering androgen-suppressing drugs, including but not limited to estrogen and ketoconazole.

[0114] As those skilled in the art will appreciate, chemotherapy may include docetaxel, cabazitaxel, mitoxantrone, estramustine, or any combination thereof.

[0115] As those skilled in the art will appreciate, vaccine therapy may include Sipuleucel-T.

[0116] As those skilled in the art will appreciate, if prostate cancer has spread outside the prostate, preventing or slowing the spread of cancer to the bones is a primary goal of treatment. Bone-directed therapy may include bisphosphonates (such as zoledronic acid), denosumab, corticosteroids, external radiation therapy, radiopharmaceuticals (such as strontium-89, samarium-153, lutetium-177, or radium-223), and pain relievers.

[0117] Sequence information for prostate cancer biomarkers and housekeeping genes is shown in Table 1. Table 1 shows representative sequences for each of the prostate cancer biomarkers and housekeeping genes discussed herein. Those skilled in the art will appreciate that in addition to the specific sequences shown in Table 1, other subtypes and variants of prostate cancer biomarkers may be measured in the methods of the present disclosure to obtain the expression levels of the biomarkers or housekeeping genes.

[0118] Table 1. Prostate Cancer Biomarker / Housekeeping Gene Sequence Information

[0119] Definitions

[0120] As used in this disclosure, the articles "a" and "an" refer to one or more than one (i.e., at least one) of the grammatical objects of the article. For example, "an element" means one element or more than one element.

[0121] Unless otherwise specified, the term "and / or" as used in this disclosure is meant to mean "and" or "or".

[0122] As used herein, the terms "polynucleotide" and "nucleic acid molecule" are used interchangeably to mean a polymeric form of nucleotides (ribonucleotides or deoxyribonucleotides or modified forms of either type of nucleotide) of at least 10 bases or base pairs in length, and to mean both single- and double-stranded forms of DNA. As used herein, a nucleic acid molecule or nucleic acid sequence used as a probe in microarray analysis preferably comprises a nucleotide chain, more preferably DNA and / or RNA. In some aspects, the nucleic acid molecule or nucleic acid sequence comprises other types of nucleic acid structures, such as DNA / RNA helices, peptide nucleic acids (PNAs), locked nucleic acids (LNAs), and / or ribozymes. Thus, the term "nucleic acid molecule" as used herein also encompasses a chain comprising unnatural nucleotides, modified nucleotides, and / or non-nucleotide building blocks that exhibit the same function as natural nucleotides.

[0123] As used herein, the terms "hybridize", "hybridizing", "hybridizes", etc. when used in the context of polynucleotides mean conventional hybridization conditions, such as hybridization in 50% formamide / 6×SSC / 0.1% SDS / 100 μg / ml ssDNA, where the hybridization temperature is above 37 degrees Celsius, and the washing temperature in 0.1×SSC / 0.1% SDS is above 55°C, and preferably mean stringent hybridization conditions.

[0124] As used herein, the term "normalize" or "normalizer" refers to expressing a difference value in terms of 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 biomarker concentration in the sample. For example, when measuring the expression of a differentially expressed protein, the absolute value of that protein expression can be expressed in terms of the absolute value of a standard protein expression that is substantially constant.

[0125] The terms "diagnosis" and "diagnostics" also respectively encompass the terms "prognosis" and "prognostics", as well as the application of such procedures at 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 an invasive disease (hyperproliferative / infiltrative)), b. prognosis (predicting whether a patient will likely have a better or worse outcome at a preselected future time); c. therapy selection, d. therapeutic drug monitoring, and e. recurrence monitoring.

[0126] "Accuracy" refers to the degree of agreement between a measured or calculated quantity (test report value) and its actual (or true) value. Clinical accuracy is related to the proportion of true results (true positive (TP) or true negative (TN)) relative to misclassified results (false positive (FP) or false negative (FN)), and can be expressed as sensitivity, specificity, positive predictive value (PPV), or negative predictive value (NPV), or as measures such as likelihood, odds ratio.

[0127] 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 for the detection of disease.

[0128] As used herein, the term "subject" refers to a mammal, preferably a human. In some aspects, the subject has at least one symptom of prostate cancer. In some aspects, the subject has a predisposition or family history of developing prostate cancer. The subject may also have been previously diagnosed with prostate cancer and tested for cancer recurrence. In some aspects, the subject has benign prostatic hyperplasia.

[0129] "Treating" or "treatment" of a disease or condition refers to implementing a protocol or treatment plan, which may include administering to a patient one or more therapeutic agents in an attempt to alleviate the signs or symptoms of the disease or the recurrence of the disease. Desirable treatment effects include reducing the rate of disease progression, improving or alleviating the disease state, and alleviating, increasing survival rate, improving quality of life, or improving prognosis. Additionally, "treating" or "treatment" does not require complete alleviation of signs or symptoms, does not require a cure, and specifically includes protocols or treatment plans that have only marginal effects on the patient.

[0130] As used herein, "prevent", "preventing", etc. describe preventing the onset of a disease, condition, or disorder, or one or more of its symptoms or complications.

[0131] Biomarker levels can change due to the treatment of a disease. Changes in biomarker levels can be measured by the present disclosure. Changes in biomarker levels can be used to monitor the progression of a disease or a therapy.

[0132] "Altered", "changed", or "significantly different" means a detectable change or difference from a reasonably comparable state, profile, measurement, etc. Such changes can be all-or-nothing. They can be incremental and need not be linear. They can be on the order of magnitude. The change can 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% and 100%. Alternatively, the change can 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 can be statistically significant, with a p-value of 0.1, 0.05, 0.001 or 0.0001.

[0133] The term "stable disease" means the presence of a diagnosis of prostate cancer, however, the prostate cancer has been treated and remains in a stable state, i.e., not a progressive prostate cancer as determined by imaging data and / or best clinical judgment.

[0134] The term "progressive disease" means the presence of a diagnosis of prostate cancer in a highly active state, i.e., not treated and unstable, or treated and non-responsive to therapy, or treated and still an active disease as determined by imaging data and / or best clinical judgment.

[0135] The term "neoplastic disease" means any abnormal growth of cells or tissues, which can be benign (non-cancerous) or malignant (cancerous). For example, a neoplastic disease can be prostate cancer.

[0136] The term "neoplastic tissue" means a mass of abnormally growing cells.

[0137] The term "non-neoplastic tissue" means a mass of normally growing cells.

[0138] As used herein, the term "about", when used in conjunction with a numerical value and / or range, generally means those numerical values and / or ranges near the stated numerical value and / or range. In some cases, the term "about" can mean within ±10% of the stated value. For example, in some cases, "about 100 [units]" can mean within ±10% of 100 (e.g., from 90 to 110). Examples

[0139] 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 thereby intended to limit the scope of the present disclosure. It should be further understood that, without departing from the spirit and / or scope of the appended claims, various other embodiments, modifications thereof, and equivalents thereof that may present themselves to those skilled in the art can be resorted to.

[0140] Example 1. Origin of the 24-marker gene panel

[0141] Previously, a panel was developed and patented for blood assessment. It included 38 marker genes. The SalivaPROSTest transcript panel was derived from the evaluation of gene expression in matched blood and saliva samples collected from 51 prostate cancer patients, including the expression of biomarkers previously identified in blood samples from prostate cancer patients (see US2019-0259471A1). All previously identified genes could be detected in blood, but only 24 of them could be detected in >40% of saliva samples ( Figure 1 ). These 24 genes were highly correlated both in terms of measurement (Ct value) and when expressed as normalized values. The correlation between blood and saliva Ct values was r = 0.85 (p < 0.0001, Figure 2A ), and for the normalized values, the Pearson r value was 0.67 (p = 0.0003; Figure 2B ).

[0142] These genes were confirmed to be highly expressed in prostate cancer tumor tissues and had a significant correlation with saliva gene expression (r = 0.64, p = 0.0004), which determined that saliva could be effectively used as a liquid biopsy ( Figure 3 ).

[0143] Evaluation of transcripts in a preliminary dataset of saliva samples from age-matched (mean age 76 years) prostate cancer (n = 15) and normal saliva (n = 30) confirmed the expression of the 24 genes as prostate cancer markers ( Figure 4 ). These data confirmed that the candidate target transcripts were produced by neoplastically transformed prostate cells and could be detected in saliva.

[0144] Using the normalized gene expression of these 24 markers in saliva from control (n = 163) and PCa (n = 51) samples, an artificial intelligence model for prostate cancer disease was constructed. The dataset was randomly divided into training and test partitions for model creation and validation, respectively. Twelve algorithms (XGB, RF, glmnet, cforest, CART, treebag, knn, nnet, SVM - radial, SVM - linear, NB, and mlp) were evaluated. The best - performing algorithm (RF – "random forest") best predicted the training data. In the test set, RF produced probability scores for the predicted samples. Each probability score reflects the "certainty" of the algorithm that the unknown sample belongs to the "control" or "PCa" category. For example, an unknown sample S1 may have the following probability vector [control = 20%, PCa = 80%]. This sample would be considered a PCa sample.

[0145] Table 2. 24 - gene combination of PCa markers (excluding housekeeping genes)

[0146] Using the IVIS algorithm, the 24 marker genes identified by the random forest algorithm were visualized in a derivation cohort of n = 163 control samples and 51 cancer samples ( Figure 5A -B).

[0147] Example 2. Clinical utility

[0148] Compared with control men (8 ± 9%), including those with benign prostatic hyperplasia, the SalivaPROSTest score in PCa (61 ± 24%) was significantly (p < 0.001) elevated ( Figure 6 ). Utility data (receiver operating characteristic analysis and metrics) for the test that distinguished patients with prostate cancer (n = 40) from controls (n = 100) in validation are included in Figure 7 . The score showed an area under the curve (AUROC) of 0.99. The metrics were: sensitivity: 95% and specificity: 100% ( Figure 8 ). The Youden index J was 0.95 and the Z - statistic for distinguishing non - malignant prostate disease and controls was 304.7.

[0149] Compared with low - grade (Gleason 5 + 6) PCa (46 ± 19%), the SalivaPROSTest score in high - grade (Gleason score ≥7: 72 ± 25%) was significantly (p < 0.002) elevated. The data are included in Figure 9 .

[0150] Specific evaluations of the prostate cancer cohort before and after surgery identified that complete removal of the tumor and no signs of disease were associated with a significant decrease in the SalivaPROS Test score (p < 0.0001)( Figure 10 ). The levels were not significantly different from the controls. Evaluation of the individual cohort identified that patients who underwent therapy and responded to it had significantly lower scores than those diagnosed with the disease (p < 0.001) (Figure 11). Therapies included ADT and 177 Lu-PSMA therapy. Thus, the tool can accurately identify treatment response in prostate cancer disease. Equivalent

[0151] Although the present invention has been described in connection with the above specific embodiments, many alternatives, modifications, and other variations will be apparent to those of ordinary skill in the art. All such alternatives, modifications, and variations are intended to fall within the spirit and scope of the present invention.

Claims

1. A method for identifying the presence or absence of prostate cancer in a subject in need thereof, the method comprising: (a) determining the expression levels of at least 24 biomarkers in a saliva sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) normalizing the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) inputting each of the normalized expressions from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cut-off value; and (e) identifying the presence of prostate cancer in the subject when the score is greater than or equal to the predetermined cut-off value, or determining the absence of prostate cancer in the subject when the score is less than the predetermined cut-off value.

2. The method according to claim 1, wherein the predetermined cut-off value is 23% within the range of 0 - 100%.

3. 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 24 biomarkers in a saliva sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) Normalize the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) Input each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) Compare the score with a predetermined cut-off value; and (e) When the score is greater than or equal to the predetermined cut-off value, determine that the prostate cancer is progressive, or when the score is less than the predetermined cut-off value, determine that the prostate cancer is stable.

4. The method according to claim 3, wherein the predetermined cut-off value is 50% within the range of 0 - 100%.

5. A method for determining whether prostate cancer in a subject has a low Gleason score (≤6) or a high Gleason score (≥7), the method comprising: (a) Determine the expression levels of at least 24 biomarkers in a saliva sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC and a housekeeping gene; (b) Normalize the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) Input each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) Compare the score with a predetermined cut-off value; and (e) When the score is greater than or equal to the predetermined cut-off value, determine that the prostate cancer in the subject has a high Gleason score (≥7), or when the score is less than the predetermined cut-off value, determine that the prostate cancer in the subject has a low Gleason score (≤6).

6. The method according to claim 5, wherein the predetermined cut-off value is 50% within the range of 0 - 100%.

7. A method for determining surgical completeness in a subject with prostate cancer, the method comprising: (a) Determine the expression levels of at least 24 biomarkers in a saliva sample from the subject after the surgery, wherein the 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (b) Normalize the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; (c) Input each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) Compare the score with a predetermined cut-off value; and (e) When the score is greater than or equal to the score, identify that the prostate cancer has not been completely removed, or when the score is less than the predetermined cut-off value, identify that the prostate cancer has been completely removed.

8. The method according to claim 7, wherein the predetermined cut-off value is 50% within the range of 0 - 100%.

9. A method for evaluating the response of a subject with prostate cancer to a prostate cancer therapy, the method comprising: (a) At a first time point: (i) Determine the expression levels of at least 24 biomarkers in a saliva sample from the subject, wherein the at least 24 biomarkers include AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and a housekeeping gene; (ii) Normalize the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; and (iii) Input each of the normalized expression levels from step (a)(ii) into an algorithm to generate a first score; (b) At a second time point, wherein the second time point is after the first time point and after administering the therapy to the subject: (i) Determine the expression levels of at least 24 biomarkers in a saliva sample from the subject; (ii) Normalize the expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC relative to the expression level of the housekeeping gene, thereby obtaining the normalized expression level of each of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC; and (iii) Input each of the normalized expression levels from step (b)(ii) into an algorithm to generate a second score; (c) Compare the first score and the second score; and (d) When the second score is decreased compared to the first score, identify the subject as responsive to the anti - prostate cancer therapy, or when the second score is not decreased compared to the normalized expression levels from step (a)(ii), identify the subject as non - responsive to the anti - prostate cancer therapy.

10. The method according to claim 9, wherein when the second score is at least 5% less than the first score, the subject is identified as being responsive to the anti-neuroendocrine cancer therapy.

11. The method according to any one of the preceding claims, wherein the housekeeping gene is selected from ATG4B, RHOA, TOX4, TPT1, and TXNIP.

12. The method according to claim 11, wherein the housekeeping gene is TOX4.

13. The method according to any one of the preceding claims, which has a sensitivity of at least 90%.

14. The method according to any one of the preceding claims, which has a specificity of at least 90%.

15. The method according to any one of the preceding claims, wherein at least one of the at least 24 biomarkers is RNA, cDNA, or protein.

16. The method according to claim 15, wherein when the biomarker is RNA, the RNA is reverse transcribed to produce cDNA, and the expression level of the resulting cDNA is detected.

17. The method according to any one of the preceding claims, wherein the expression level of the biomarker is detected by forming a complex between the biomarker and a labeled probe or primer.

18. The method according to claim 15, wherein when the biomarker is protein, the protein is detected by forming a complex between the protein and a labeled antibody.

19. The method according to claim 18, wherein the label is a fluorescent label.

20. The method according to claim 15, wherein when the biomarker is RNA or cDNA, the RNA or cDNA is detected by forming a complex between the RNA or cDNA and a labeled nucleic acid probe or primer.

21. The method according to claim 20, wherein the label is a fluorescent label.

22. The method according to claim 20 or claim 21, wherein the complex between the RNA or cDNA and the labeled nucleic acid probe or primer is a hybridization complex.

23. The method according to any one of the preceding claims, wherein the first predetermined cut-off value is derived from a plurality of reference samples obtained from subjects who do not have or have not been diagnosed with a neoplastic disease.

24. The method according to claim 23, wherein the neoplastic disease is prostate cancer.

25. The method according to any one of the preceding claims, wherein the algorithm is XGB, RF, glmnet, cforest, CART, treebag, knn, nnet, SVM-radial, SVM-linear, NB, or mlp.

26. The method according to claim 25, wherein the algorithm is random forest.

27. The method according to claim 26, wherein the machine learning algorithm is trained using the expression levels or normalized expression levels of at least 24 biomarkers obtained from a plurality of reference samples from subjects without neuroendocrine cancer and the expression levels or normalized expression levels of at least 24 biomarkers from a plurality of reference samples from subjects with neuroendocrine cancer.

28. The method according to any one of the preceding claims, further comprising treating the subject identified as having prostate cancer with at least one anti-prostate cancer therapy.

29. The method according to any one of the preceding claims, wherein the anti-prostate cancer therapy comprises active surveillance, surgery, radiotherapy, cryotherapy, hormone therapy, chemotherapy, vaccine therapy, bone-directed therapy, or any combination thereof.

30. The method according to claim 29, wherein the radiotherapy comprises external beam radiotherapy, brachytherapy, radiopharmaceuticals, or any combination thereof, preferably wherein the radiopharmaceutical comprises 177 Lu-PSMA.

31. The method according to claim 29, wherein the hormone therapy comprises androgen suppression therapy.

32. The method according to claim 29, wherein the chemotherapy comprises docetaxel, cabazitaxel, mitoxantrone, estramustine, or any combination thereof.

33. The method according to claim 29, wherein the vaccine therapy comprises Sipuleucel-T.

34. The method according to claim 29, wherein the bone-directed therapy comprises bisphosphonates, denosumab, corticosteroids, or a combination thereof.

35. The method according to any one of the preceding claims, wherein the first time point is before administering the therapy to the subject.

36. The method according to any one of the preceding claims, wherein the first time point is after administering the therapy to the subject.

37. The method according to any one of the preceding claims, wherein the saliva sample is self-collected saliva placed in a container with a stabilizing liquid.

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