Methods for detecting prostate cancer in saliva

Abstract

The present disclosure is directed to methods for detecting prostate cancer, determining whether prostate cancer is stable or progressive, whether the Gleason grade is low or high, determining the completeness of surgery, and assessing response to prostate cancer therapy.

Description

[Technical Field]

[0001] (Related Applications) This application claims priority to and the benefit of U.S. Provisional Application No. 63 / 379,190, filed October 12, 2022, the contents of which are incorporated herein by reference in their entirety.

[0002] Electronic Sequence Listing Reference The Sequence Listing XML associated with this application has been provided electronically in XML file format and is incorporated herein by reference. The XML file containing the Sequence Listing XML is named "LBIO-008_001WO_SeqList.xml." The XML file is 125,439 bytes and was created on October 11, 2023. [Background technology]

[0003] Prostate cancer (PCa) is the fourth most commonly diagnosed cancer worldwide and the second most commonly diagnosed cancer in men. Although incidence and prevalence are declining, approximately 200,000 men are diagnosed with PCa each year in the United States. Multiple factors, including age and family history, genetic susceptibility, and ethnicity, all contribute to the high incidence of this disease. Although 90% of PCa cases are diagnosed while localized (non-disseminated), the clinical behavior of tumors is highly variable and ranges from indolent disease, which can be monitored through watchful waiting or surveillance (e.g., biomarkers and digital rectal examination every six months), to malignant evolution and androgen-resistant disease, metastatic dissemination, and death. Symptoms of prostate cancer include difficulty urinating, hematuria or blood in the urine or semen, erectile dysfunction, pain in the buttocks, back (spine), chest (ribs), or other areas due to cancer that has spread to the bones, weakness or numbness in the legs or feet, or even urinary or fecal incontinence due to cancer compressing the spinal cord.

[0004] Several risk stratification systems have been developed that combine clinical data with pathological information, such as Gleason score. These systems, including more recently developed next-generation tools, are only about 70% accurate in predicting outcome.

[0005] Molecular genetic information is increasingly being used to inform disease state and better subtype cancer. This information is being used both as a prognostic tool and to stratify patients for different therapeutic interventions. Prostate cancer has been investigated, and mutations, DNA copy number changes, rearrangements, and gene fusions have all been identified. These can be correlated with several pathological features. For example, low-grade Gleason tumors have few DNA copy number changes, while high-grade tumors show significant genome-wide copy number changes. In contrast, somatic point mutations are relatively rare, with mutation frequencies ranging from 1% (IDH1) to 11% (SPOP). The most common abnormality is the androgen-regulated fusion of ERG and other ETS family members (approximately 50% of tumors). However, tumors with fusions do not have a significantly different prognosis after prostatectomy than fusion-negative tumors. In contrast, androgen receptor variant 7 (AR-V7) is involved in the progression of castration-resistant prostate cancer (CRPC) and is potentially useful as a treatment selection biomarker. However, overall, our understanding of the molecular mechanisms underlying the pathogenesis of PCa is incomplete, and there are no molecular-based biomarkers that can be used to predict sensitivity to therapeutic agents. Therefore, it is important to develop diagnostic methods that can be used to more precisely define disease states, identify sensitivity to therapy, and ultimately better monitor disease progression.

[0006] Surveillance remains the cornerstone approach for monitoring PCa and detecting early recurrence. Following potentially curative resection, monitoring can be performed via blood biomarker measurements and / or imaging modalities such as CT scans to detect asymptomatic metastatic disease earlier. The current biomarker used for monitoring is prostate-specific antigen (PSA) (also known as gamma-seminoprotein or kallikrein-3). This glycoprotein enzyme is encoded by the KLK3 gene and secreted by epithelial cells in the prostate. However, it is not a specific indicator of prostate cancer and can also detect prostatitis or benign prostatic hyperplasia (BPH). The use of PSA alone can result in either unnecessary biopsies in men without cancer or in insufficient diagnosis in men with significant disease. This is based on low sensitivity (20–40%) and specificity (70–90%) ranges, resulting in a positive predictive value of only 25–40%. The United States Preventive Services Task Force (USPSTF) does not recommend the use of PSA for prostate cancer, however, PSA is included in clinical nomograms, such as the UCSF-CAPRA score for prostate cancer risk, which has some utility in predicting disease-free survival after surgery.

[0007] Saliva is an important test compartment that allows for the evaluation of biomarkers for viral, bacterial, and fungal parasitic infections, as well as the measurement of markers characterizing 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 over 3,000 mRNAs. RNA typically enters the oral cavity via secretion (from the parotid, submandibular, and sublingual glands) as a component of gingival crevicular fluid and from desquamated oral epithelial cells. RNA can originate from acinar cells or through the circulation.

[0008] Saliva has been identified as a test compartment for other cancers, such as head and neck tumors. Typically, viral DNA (HPV) is isolated and amplified, which is used to provide a diagnosis of the disease. Recently, tumor RNA has been detected in saliva. For example, RNA-based biomarkers of four genes have been developed for the diagnosis of oral cancer. The source of the RNA may be from the salivary gland itself or from cells secreted into the oral cavity, such as lymphocytes. Salivary glands are also known to be vascularized and filter blood products. This suggests that blood may also be a source of RNA detectable in saliva. PCa Summary of the Invention

[0009] The present disclosure provides a method of identifying the presence or absence of prostate cancer in a subject in need thereof, comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers being AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETB (b) determining the expression of genes including P1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and housekeeping genes; and (c) determining the expression of genes including 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 to the expression levels of housekeeping genes, thereby obtaining a 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 from step (b) into an algorithm to generate a score; (d) comparing the score to a predetermined cutoff value; and (e) identifying the presence of prostate cancer in the subject if the score is equal to or greater than the predetermined cutoff value, or determining the absence of prostate cancer in the subject if the score is less than the predetermined cutoff value. In some embodiments, the predetermined cutoff value is 23% on a scale of 0 to 100%.

[0010] The present disclosure provides a method for determining whether prostate cancer in a subject is stable or progressive, comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers being AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETB (b) determining the expression of genes including 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 housekeeping genes; (c) inputting each of the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score to a predetermined cutoff value; and (e) determining that the prostate cancer is progressive if the score is equal to or greater than the predetermined cutoff value, or determining that the prostate cancer is stable if the score is less than the predetermined cutoff value. In some embodiments, the predetermined cutoff value is 50% on a scale of 0 to 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), comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers being AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR3 (b) determining the expression of genes encoding 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, as well as housekeeping genes; (c) inputting each normalized expression level from step (b) into an algorithm to generate a score; (d) comparing the score to a predetermined cutoff value; and (e) determining that the prostate cancer in the subject has a high Gleason score (≧7) if the score is equal to or greater than the predetermined cutoff value, or determining that the prostate cancer in the subject has a low Gleason score (≦6) if the score is less than the predetermined cutoff value. In some embodiments, the predetermined cutoff value is 50% on a scale of 0 to 100%.

[0012] The present disclosure provides a method for determining the completeness of surgery in a subject with prostate cancer, comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject after surgery, the 24 biomarkers being AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC1 (b) determining the expression of genes including 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 housekeeping genes. 2. Normalizing the expression levels of each of UNC45A and XPC to the expression levels of housekeeping genes, thereby obtaining normalized expression levels 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 the normalized expression levels from step (b) into an algorithm to generate a score; (d) comparing the score to a predetermined cutoff value; and (e) identifying the prostate cancer as not having been completely eliminated if the score is equal to or greater than the predetermined cutoff value, or identifying the prostate cancer as having been completely eliminated if the score is less than the predetermined cutoff value. In some embodiments, the predetermined cutoff value is 50% on a scale of 0 to 100%.

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

[0014] In some embodiments of the foregoing methods, the housekeeping gene is selected from the group consisting of ATG4B, RHOA, TOX4, TPT1, and TXNIP. In some embodiments, the housekeeping gene is TOX4.

[0015] In some embodiments, the above-described methods have a sensitivity of at least 90%.

[0016] In some embodiments, the above-described methods have a specificity of at least 90%.

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

[0018] In some aspects of the foregoing methods, 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 embodiments, when the biomarker is a protein, the protein is detected by forming a complex between the protein and a labeled antibody.

[0020] In some embodiments, 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 embodiments, the complex between the RNA or cDNA and a labeled nucleic acid probe or primer is a hybridization complex.

[0021] In some embodiments of the foregoing methods, the first predetermined cutoff 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. In some embodiments, the neoplastic disease is prostate cancer.

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

[0023] In some embodiments of the foregoing methods, the machine learning algorithm is trained using expression levels or normalized expression levels of at least 24 biomarkers obtained from a plurality of reference samples obtained from subjects without neuroendocrine cancer and expression levels or normalized expression levels of at least 24 biomarkers from a plurality of reference samples obtained from subjects with neuroendocrine cancer.

[0024] In some embodiments, the aforementioned methods further comprise treating the subject identified as having prostate cancer with at least one anti-prostate cancer therapy.

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

[0026] In some aspects, the radiation therapy comprises external beam radiation, brachytherapy, a radiopharmaceutical, or any combination thereof, preferably the radiopharmaceutical is 177 Contains Lu-PSMA.

[0027] In some embodiments, the hormone therapy comprises androgen deprivation therapy.

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

[0029] In some embodiments, the vaccine therapy comprises Sipuleucel-T.

[0030] In some aspects, the bone-directed therapy comprises a bisphosphonate, denosumab, a corticosteroid, or a combination thereof.

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

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

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

[0034] In some embodiments, the test sample is self-collected saliva in a container with a stabilizing fluid. [Brief explanation of the drawings]

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

[0036] [Figure 2A] XY scatter plots showing the concordance of Ct values ​​between blood and saliva (Figure 2A) and the concordance of normalized gene expression between blood and saliva (Figure 2B). The red line represents the linear correlation. The vertical and horizontal lines represent the SEM and SD of the mean values ​​from 36 target genes, respectively. [Figure 2B] XY scatter plots showing the concordance of Ct values ​​between blood and saliva (Figure 2A) and the concordance of normalized gene expression between blood and saliva (Figure 2B). The red line represents the linear correlation. The vertical and horizontal lines represent the SEM and SD of the mean values ​​from 36 target genes, respectively.

[0037] [Figure 3] 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] Figure 1 shows gene expression in age- and sex-matched controls (n=30) and neuroendocrine cancer cases (n=15). Expression levels of 14 of the target genes were significantly increased (p<0.05) and 5 were significantly decreased.

[0039] [Figure 5A] Graphs showing visualization of 24 putative marker genes identified by the Random Forest algorithm in a derived cohort of n=163 control samples and n=51 cancer samples. (FIG. 5A) Expression normalized to TOX4. (FIG. 5B) Expression normalized to TPT1. [Figure 5B]Graphs showing visualization of 24 putative marker genes identified by the Random Forest algorithm in a derived cohort of n=163 control samples and n=51 cancer samples. (FIG. 5A) Expression normalized to TOX4. (FIG. 5B) Expression normalized to TPT1.

[0040] [Figure 6] Graph showing SalivaPROSTest scores in an independent set of controls (n=100) and neuroendocrine carcinomas (n=40). Levels were significantly elevated in NETs (61±24) versus controls (6±5) (p<0.0001).

[0041] [Figure 7] Receiver operating curve analysis of test partitions in an independent set. 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] 1 is a graph showing the evaluation index of the assay for determining prostate cancer. The sensitivity was 95% and the specificity was 100%.

[0043] [Figure 9] Graph showing SalivaPROSTest scores in high-grade PCa (Gleason >= 7) compared to low-grade (Gleason 5+6) tumors. Levels were significantly elevated in higher-grade tumors (72±25) versus low-grade Gleason tumors (46±19) (p<0.002).

[0044] [Figure 10] 1 is a graph showing the effect of surgery on SalivaPROSTest. Pre-surgery levels are elevated (64±18%). Surgery reduced levels to 33±11% (p<0.0001), which was not different from control levels.

[0045] [Figure 11A] Figure 11A shows spider plot graphs showing the effect of treatment on the SalivaPROSTest test. Pre-treatment levels are elevated (69±23%). In patients who responded to therapy, levels decreased by -38±31% and -60±19% at the two follow-up time points (p<0.0001). In patients who progressed despite therapy, levels increased by +18±19% and +27±7%, respectively (p<0.05). (Figure 11A) Follow-up plot for all patients. (Figure 11B) Spider plots for individual responders (blue) and progressors (red). [Figure 11B] Figure 11A shows spider plot graphs showing the effect of treatment on the SalivaPROSTest test. Pre-treatment levels are elevated (69±23%). In patients who responded to therapy, levels decreased by -38±31% and -60±19% at the two follow-up time points (p<0.0001). In patients who progressed despite therapy, levels increased by +18±19% and +27±7%, respectively (p<0.05). (Figure 11A) Follow-up plot for all patients. (Figure 11B) Spider plots for individual responders (blue) and progressors (red). DETAILED DESCRIPTION OF THE INVENTION

[0046] Details of the invention are set forth in the accompanying description below. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, exemplary methods and materials are described herein. Other features, objects, and advantages of the present invention will become apparent from the specification and claims. In this specification and the appended claims, the singular forms "a," "an," and "the" include the plural forms 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 these inventions belong. All patents and publications cited herein are incorporated by reference in their entirety.

[0047] This paper describes a method for quantifying (scoring) salivary prostate cancer molecular signatures with high sensitivity and specificity, for purposes including but not limited to detecting prostate cancer, determining whether prostate cancer is stable or progressive, determining the completeness of surgery, and evaluating the response of a subject 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 levels of housekeeping genes are elevated in subjects with prostate cancer compared to healthy subjects.

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

[0049] The present disclosure provides a method for identifying the presence or absence of prostate cancer in a subject in need thereof, comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers including 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 housekeeping genes; and (b) determining the expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers including 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 housekeeping genes. 3A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC to the expression levels of housekeeping genes, thereby obtaining normalized 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, respectively; 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 level from step (b) may include comparing the normalized expression level to a corresponding predetermined cutoff value, and identifying the presence or absence of prostate cancer in the subject based on the relationship between the normalized expression level and the corresponding predetermined cutoff value (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to).

[0050] The present disclosure provides a method for identifying the presence or absence of prostate cancer in a subject in need thereof, comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers including 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 housekeeping genes; and (b) determining the expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers including 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 housekeeping genes. (c) inputting the respective normalized expression levels 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 embodiments, identifying the presence or absence of prostate cancer in the subject based on the score may include comparing the score to a predetermined cutoff value and identifying the presence or absence of prostate cancer in the subject based on the relationship of the score to the predetermined cutoff value (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to).

[0051] The present disclosure provides a method of identifying the presence or absence of prostate cancer in a subject in need thereof, comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers being AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETB (b) determining the expression of genes including P1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and housekeeping genes; and (c) determining the expression of genes including 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 to the expression levels of housekeeping genes, thereby obtaining a 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 from step (b) into an algorithm to generate a score; (d) comparing the score to a predetermined cutoff value; and (e) identifying the presence of prostate cancer in the subject if the score is equal to or greater than the predetermined cutoff value, or determining the absence of prostate cancer in the subject if the score is less than the predetermined cutoff value.

[0052] The present disclosure provides a method of identifying the presence or absence of prostate cancer in a subject in need thereof, comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers being AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETB (b) determining the expression of genes including P1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC, and housekeeping genes; and (c) determining the expression of genes including 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 to the expression levels of housekeeping genes, thereby obtaining a 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 from step (b) into an algorithm to generate a score; (d) comparing the score to a predetermined cutoff value; and (e) identifying the presence of prostate cancer in the subject if the score is greater than the predetermined cutoff value, or determining the absence of prostate cancer in the subject if the score is equal to or less than the predetermined cutoff value.

[0053] In some embodiments of the foregoing methods, the predetermined cutoff value may be 23% on a scale of 0 to 100%.

[0054] The present disclosure provides a method for identifying a subject's risk of having prostate cancer, comprising: (a) determining expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers including 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 housekeeping genes; and (b) determining expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers including 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 housekeeping genes. (c) normalizing the expression level of each of EPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC to the expression level of a housekeeping gene, thereby obtaining a 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 risk of the subject having prostate cancer based on the normalized expression levels from step (b). In some aspects, identifying the risk of the subject having prostate cancer based on the normalized expression level from step (b) may include comparing the normalized expression level to a corresponding predetermined cutoff value, and identifying 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) the normalized expression level to the corresponding predetermined cutoff value.

[0055] The present disclosure provides a method for identifying a subject's risk of having prostate cancer, comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers including 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 housekeeping genes; and (b) determining the expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers including AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SE (c) inputting the respective normalized expression levels from step (b) into an algorithm to generate a score; and (d) identifying the subject's risk of having prostate cancer based on the score. In some embodiments, identifying the risk that the subject has prostate cancer based on the score may include comparing the score to a predetermined cutoff value and determining the risk that the subject has prostate cancer based on the relationship of the score to the predetermined cutoff value (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to).

[0056] Accordingly, the present disclosure provides a method for identifying a subject's risk of having prostate cancer, comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers being AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC1 (b) determining the expression of genes including 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 housekeeping genes; (c) inputting each normalized expression level from step (b) into an algorithm to generate a score; (d) comparing the score to a predetermined cutoff value; and (e) identifying the subject as having a high risk of having prostate cancer if the score is equal to or greater than the predetermined cutoff value, or determining that the subject has a low risk of having prostate cancer if the score is less than the predetermined cutoff value.

[0057] Accordingly, the present disclosure provides a method for identifying a subject's risk of having prostate cancer, comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers being AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC1 (b) determining the expression of genes including 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 housekeeping genes; (c) inputting each normalized expression level from step (b) into an algorithm to generate a score; (d) comparing the score to a predetermined cutoff value; and (e) identifying the subject as having a high risk of having prostate cancer if the score is greater than the predetermined cutoff value, or determining that the subject has a low risk of having prostate cancer if the score is equal to or less than the predetermined cutoff value.

[0058] In some embodiments of the foregoing methods, the predetermined cutoff value may be 23% on a scale of 0 to 100%.

[0059] The disclosure also provides a method for determining whether prostate cancer in a subject is stable or progressive, comprising: (a) determining expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers including 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 housekeeping genes; and (b) determining 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, XPC, and housekeeping genes. (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 level from step (b) comprises comparing the normalized expression level to a corresponding predetermined cutoff value, and determining whether the prostate cancer in the subject is stable or progressive based on the relationship between the normalized expression level and the corresponding predetermined cutoff value (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to).

[0060] The present disclosure also provides a method for determining whether prostate cancer in a subject is stable or progressive, comprising: (a) determining expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers including 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 housekeeping genes; and (b) determining 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, XPC, and housekeeping genes. (c) inputting each normalized expression level from step (b) into an algorithm to generate a score; and (d) determining whether prostate cancer in the subject is stable or progressive based on the normalized expression levels from step (b). In some embodiments, determining whether the prostate cancer in the subject is stable or progressive based on the score comprises comparing the score to a predetermined cutoff value and determining whether the prostate cancer in the subject is stable or progressive based on the relationship of the score to the predetermined cutoff value (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to).

[0061] The present disclosure also provides a method for determining whether prostate cancer in a subject is stable or progressive, comprising: (a) determining expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers are AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SET (b) determining the expression of genes including 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 housekeeping genes; (c) inputting each normalized expression level from step (b) into an algorithm to generate a score; (d) comparing the score to a predetermined cutoff value; and (e) determining that the prostate cancer is progressive if the score is equal to or greater than the predetermined cutoff value, or determining that the prostate cancer is stable if the score is less than the predetermined cutoff value.

[0062] The present disclosure also provides a method for determining whether prostate cancer in a subject is stable or progressive, comprising: (a) determining expression levels of at least 24 biomarkers in a test sample from the subject, wherein the at least 24 biomarkers are AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SET (b) determining the expression of genes including 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 housekeeping genes; (c) inputting each normalized expression level from step (b) into an algorithm to generate a score; (d) comparing the score to a predetermined cutoff value; and (e) determining that the prostate cancer is progressive if the score is greater than the predetermined cutoff value, or determining that the prostate cancer is stable if the score is equal to or less than the predetermined cutoff value.

[0063] In some embodiments of the foregoing methods, the predetermined cutoff value may be 50% on a scale of 0 to 100%.

[0064] Additionally, 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), comprising: (a) determining expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers including 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 housekeeping genes; and (b) determining 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, and housekeeping genes. 3A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC to the expression levels of housekeeping genes, thereby obtaining normalized 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, respectively; and (c) determining whether the 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 the prostate cancer in the subject has a low Gleason score (≦6) or a high Gleason score (≧7) based on the normalized expression level from step (b) comprises comparing the normalized expression level to a corresponding predetermined cutoff 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 between the normalized expression level and the corresponding predetermined cutoff value (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to).

[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), comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers being AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC (b) determining the expression of genes encoding 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, as well as housekeeping genes; (c) inputting the respective 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 embodiments, 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 to a predetermined cutoff 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 of the score to the predetermined cutoff value (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to).

[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), comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers being AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN (b) determining the expression of genes encoding 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, as well as housekeeping genes; The expression levels 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 were normalized to the expression levels of housekeeping genes, thereby determining the normalized expression levels 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 to a predetermined cutoff value; and (e) determining that the prostate cancer in the subject has a high Gleason score (≧7) if the score is equal to or greater than the predetermined cutoff value, or determining that the prostate cancer in the subject has a low Gleason score (≦6) if the score is less than the predetermined cutoff value.

[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), comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject, the at least 24 biomarkers being AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN (b) determining the expression of genes encoding 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, as well as housekeeping genes; (c) inputting each normalized expression level from step (b) into an algorithm to generate a score; (d) comparing the score to a predetermined cutoff value; and (e) determining that the prostate cancer in the subject has a high Gleason score (≧7) if the score is greater than the predetermined cutoff value, or determining that the prostate cancer in the subject has a low Gleason score (≦6) if the score is equal to or less than the predetermined cutoff value.

[0068] In some embodiments of the foregoing methods, the predetermined cutoff value may be 50% on a scale of 0 to 100%.

[0069] Further, the disclosure provides a method for determining the completeness of surgery in a subject with prostate cancer, comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject after surgery, the 24 biomarkers including 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 housekeeping genes; and (b) determining the expression levels of at least 24 biomarkers in a test sample from the subject after surgery, the 24 biomarkers including 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 housekeeping genes. (c) identifying the prostate cancer as not completely eliminated or identifying the prostate cancer as completely eliminated based on the normalized expression levels from step (b). In some aspects, identifying that the prostate cancer has not been completely eliminated or that the prostate cancer has been completely eliminated based on the normalized expression level from step (b) may include comparing the normalized expression level to a corresponding predetermined cutoff value, and identifying that the prostate cancer has not been completely eliminated or that the prostate cancer has been completely eliminated based on a relationship between the normalized expression level and the corresponding predetermined cutoff value (e.g., greater than, greater than or equal to, less than, less than or equal to, or equal to).

[0070] Further, the disclosure provides a method for determining the completeness of surgery in a subject with prostate cancer, 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 housekeeping genes; and (b) 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 housekeeping genes. , SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, and XPC to the expression levels of housekeeping genes, thereby obtaining normalized 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, respectively; (c) inputting the normalized expression levels from step (b) into an algorithm to generate a score; and (d) identifying the prostate cancer as not having been completely eliminated or identifying the neuroendocrine cancer as having been completely eliminated based on the score. In some aspects, identifying the prostate cancer as not being completely removed or identifying the prostate cancer as being completely removed based on the score may include comparing the score to a predetermined cutoff value and identifying the prostate cancer as not being completely removed or identifying the neuroendocrine cancer as being completely removed based on a relationship between the score and the corresponding predetermined cutoff 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 the completeness of surgery in a subject with prostate cancer, comprising: (a) determining the expression levels of at least 24 biomarkers in a test sample from the subject after surgery, the 24 biomarkers being AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, and SETBP1. , SLC18A2, SMC4, SPARC, SQLE, STRIP1, STX12, UNC45A, XPC and housekeeping genes, and (b) determining 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, XPC and housekeeping genes. (c) inputting each normalized expression level from step (b) into an algorithm to generate a score; (d) comparing the score to a predetermined cutoff value; and (e) identifying that the prostate cancer has not been completely eliminated if the score is equal to or greater than the predetermined cutoff value, or that the prostate cancer has been completely eliminated if the score is less than the predetermined cutoff value.

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

[0073] In some embodiments of the foregoing methods, the predetermined cutoff value may be 50% on a scale of 0 to 100%.

[0074] The response of a subject with prostate cancer to therapy can also be evaluated by comparing the scores determined by the same algorithm at different time points during therapy.For example, the first time point can be before or after administering therapy to the subject, and the second time point is after the first time point and after administering therapy to the subject.At the first time point, a first score is generated, and at the second time point, a second score is generated.If the second score is reduced compared to the first score, the subject is considered to be responsive to therapy.In some embodiments, if the second score is at least 5% lower than the first score, for example, at least 10% lower than the first score, at least 15% lower than the first score, at least 25% lower than the first score, at least 40% lower than the first score, at least 50% lower than the first score, at least 75% lower than the first score, or at least 90% lower than the first score, the second score is reduced compared to the first score. If the second score is not significantly decreased or is increased compared to the first score, the subject is considered to be non-responsive to the therapy.

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

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

[0077] General Methods and Definitions

[0078] The following general methods and definitions may be applied to any of the aforementioned methods.

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

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

[0081] Each of the biomarkers disclosed herein can have one or more transcript variants, and the methods disclosed herein can measure the expression level of any one of the transcript variants for each biomarker.

[0082] In some embodiments, determining the expression levels of the at least 24 biomarkers in a test sample from a subject may include contacting the test sample with a plurality of agents specific for detecting the expression of the at least 24 biomarkers.

[0083] Thus, the present disclosure provides for the use of multiple agents 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 agents for detecting the expression of at least 36 biomarkers in the manufacture of a kit for identifying the risk that a subject has prostate cancer by the methods described herein.

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

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

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

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

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

[0090] Biomarkers can be RNA, cDNA, or protein. If the biomarker is RNA, the RNA can be reverse transcribed (e.g., by RT-PCR) to generate cDNA, and the expression level of the generated cDNA is detected. The expression level of the biomarker can be detected by forming a complex between the biomarker and a labeled probe or primer. If 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 a labeled nucleic acid probe or primer can be a hybridization complex.

[0091] As will be understood by those skilled in the art, gene expression can be detected by microarray analysis. Differential gene expression can also be identified or confirmed using microarray technology. Thus, expression profile biomarkers can be measured in either fresh or fixed tissue using microarray technology. In this method, the 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 derived from cells or tissues of interest. The source of mRNA is typically total RNA isolated from biological samples, and corresponding normal tissues or cell lines can be used to determine differential expression.

[0092] In some embodiments of the microarray technique, PCR-amplified inserts of cDNA clones are applied to a high-density array substrate. In some embodiments, at least 10,000 nucleotide sequences are applied to the substrate. Microarrayed genes immobilized on a microchip with 10,000 elements each are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes can be generated through the incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest. The labeled cDNA probes applied to the chip specifically hybridize to each DNA spot on the array. After stringent washing to remove nonspecifically bound probes, the microarray chip is scanned by a device such as a confocal laser microscope or another detection method such as a CCD camera. Quantification of hybridization of each arrayed element allows assessment of the abundance of the corresponding mRNA. Using dual-color fluorescence, separately labeled cDNA probes generated from two sources of RNA are hybridized to the array in pairs. Thus, the relative abundance of transcripts from the two sources corresponding to each specific gene is determined simultaneously. Microarray analysis can be performed by commercially available equipment according to the manufacturer's protocols.

[0093] In some embodiments, biomarkers (i.e., SalivaPROSTest transcripts and / or housekeeping genes) can be detected in saliva samples using RNA sequencing. As will be understood by those skilled in the art, the first step in gene expression profiling by RNA sequencing is to extract RNA from saliva samples, and then reverse transcribe the RNA template into cDNA to create an RNA library. Sequencing adapters are added. Then, the cDNA is sequenced using a sequencing platform. Data is analyzed and expressed as transcripts per million.

[0094] In some embodiments, biomarkers (i.e., SalivaPROSTest transcripts and / or housekeeping genes) can be detected in saliva samples using qRT-PCR. As will be understood by those skilled in the art, the first step in gene expression profiling by RT-PCR is to extract RNA from a biological sample, then reverse transcribe the RNA template into cDNA and amplify it by PCR. The reverse transcription step is generally primed using specific primers, random hexamers, or oligo-dT primers, depending on the goal of expression profiling. Two commonly used reverse transcriptases are avian myeloblastosis virus reverse transcriptase (AMV-RT) and murine leukemia virus reverse transcriptase (MLV-RT).

[0095] In some embodiments 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, a chemiluminescent label, a radioactive label, etc. Exemplary methods for protein detection include, but are not limited to, enzyme immunoassays (EIA), radioimmunoassays (RIA), Western blot analysis, and enzyme-linked immunoabsorbent assays (ELISA). For example, biomarkers can be detected in ELISAs, in which biomarker antibodies are bound to a solid phase and enzyme-antibody conjugates are used to detect and / or quantify biomarkers present in a sample. Alternatively, Western blot assays can be used, in which solubilized and separated biomarkers are bound to nitrocellulose paper. The combination of highly specific and stable liquid conjugates with sensitive chromogenic substrates allows for rapid and accurate identification of samples.

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

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

[0098] In some aspects, the methods described herein may have a sensitivity (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 the completeness of surgery in a subject with prostate cancer, or specificity for assessing the response of a subject with prostate cancer to an anti-prostate cancer therapy) of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0099] In some aspects, the methods described herein may have an accuracy (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 the completeness of surgery in a subject with prostate cancer, or specificity for assessing the response of a subject with prostate cancer to an anti-prostate cancer therapy) of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0100] Any algorithm that can generate a score for a sample by evaluating where the sample value falls on a predictive model generated using different techniques, such as a decision tree, 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 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 forests are an implementation of ensemble learning methods for classification, regression, and other tasks that work by building a large number of decision trees during training.

[0101] In some embodiments of the methods of the present disclosure, a machine learning algorithm 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 who does not have 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 who has prostate cancer. That is, in some embodiments, the machine learning algorithm is trained using the expression levels or normalized expression levels of at least 24 biomarkers obtained from multiple reference samples obtained from subjects who do not have prostate cancer, and the expression levels or normalized expression levels of at least 24 biomarkers from multiple reference samples obtained from subjects who have prostate cancer.

[0102] In some embodiments, the one or more predetermined cutoff values ​​can be derived from a plurality of reference samples obtained from subjects who do not have or have not been diagnosed with a neoplastic disease. The plurality of reference samples can be 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 embodiments, determining the predetermined cutoff value can include inputting the normalized expression level of SalivaPROSTest transcript from each reference sample into the same algorithm used in the above method, thereby generating multiple scores from multiple reference samples.Then, the predetermined cutoff value can be determined by taking the arithmetic mean of these scores.In some embodiments, the reference sample can include saliva.In some embodiments, the reference sample is the same type as the test sample.

[0104] In some aspects of the disclosed methods, the predetermined cutoff value can be calculated and / or selected using at least one receiver operating characteristic (ROC) curve. In some aspects of the disclosed methods, the predetermined cutoff value can be calculated and / or selected to have any of the characteristics described herein (e.g., a particular sensitivity, specificity, accuracy, or any combination thereof) using any method known in the art, as will be understood by one of skill in the art.

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

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

[0107] In some aspects, the described methods further include treating subjects identified as not responding to the anti-prostate cancer therapy with a different anti-prostate cancer therapy. In some aspects, the described methods further include continuing to treat subjects identified as responding to the anti-prostate cancer therapy with the same anti-prostate cancer therapy.

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

[0109] As will be appreciated by those skilled in the art, surveillance may include doctor visits with prostate-specific antigen blood tests and digital rectal examinations approximately every six months. As will be appreciated by those skilled in the art, surveillance may also include prostate biopsies, which may be performed annually.

[0110] As will be appreciated by those skilled in the art, surgery for prostate cancer patients may include a radical prostatectomy.

[0111] As will be appreciated by those skilled in the art, radiation therapy for prostate cancer can include external beam radiation, brachytherapy, and radiopharmaceuticals. As will be appreciated by those skilled in the art, radiopharmaceuticals include: 177 It may comprise Lu-PSMA.

[0112] As will be appreciated by those skilled in the art, cryotherapy (also called cryosurgery or cryoablation) can involve the use of very low temperatures to freeze and kill prostate cancer cells.

[0113] As will be understood by those skilled in the art, hormone therapy is also referred to as androgen deprivation therapy or androgen suppression therapy. Without wishing to be bound by theory, the goal is to reduce the level of male hormones in the body, called androgens, or to stop them from affecting prostate cancer cells. Hormone therapy may include orchiectomy. Hormone therapy may include the administration of 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, leuprolide, goserelin, triptorelin, and histrelin. Known LHRH antagonists include degarelix. Known CYP17 inhibitors include abiraterone. Hormonal therapy may also include administration of antiandrogens, including, but not limited to, flutamide, bicalutamide, nilutamide, and enzalutamide. Hormonal therapy may also include administration of androgen suppressants, including, but not limited to, estrogen and ketoconazole.

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

[0115] As will be appreciated by those skilled in the art, the vaccine treatment may include Sipuleucel-T.

[0116] As will be appreciated by those skilled in the art, when prostate cancer grows outside the prostate, the primary goal of treatment is to prevent or slow the spread of cancer to bone. Bone-directed treatments can include bisphosphonates (e.g., zoledronic acid), denosumab, corticosteroids, external beam radiation therapy, radiopharmaceuticals (e.g., strontium-89, samarium-153, lutetium-177, or radium-223), and analgesics.

[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. One of skill in the art will understand that in addition to the specific sequences shown in Table 1, other isoforms and variants of prostate cancer biomarkers can be measured in the methods of the present disclosure to obtain expression levels of the biomarkers or housekeeping genes.

[0118] [Table 1]

[0119] definition

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

[0121] The term "and / or" is used in this disclosure to mean either "and" or "or," unless otherwise indicated.

[0122] As used herein, the terms "polynucleotide" and "nucleic acid molecule" are used interchangeably to refer to a polymeric form of nucleotides at least 10 bases or 10 base pairs in length, either ribonucleotides or deoxynucleotides, or modified forms of either type of nucleotide, and are intended to include single-stranded and double-stranded forms of DNA. As used herein, nucleic acid molecules or nucleic acid sequences that function as probes in microarray analysis preferably comprise a strand of nucleotides, more preferably DNA and / or RNA. In some embodiments, nucleic acid molecules or nucleic acid sequences include other types of nucleic acid structures, such as, for example, DNA / RNA helices, peptide nucleic acids (PNAs), locked nucleic acids (LNAs), and / or ribozymes. Thus, as used herein, the term "nucleic acid molecule" also encompasses strands containing non-natural nucleotides, modified nucleotides, and / or non-nucleotide components that exhibit the same function as natural nucleotides.

[0123] As used herein, the terms "hybridize," "hybridizing," "hybridizes," and the like, when used in the context of polynucleotides, are intended to refer to conventional hybridization conditions, preferably stringent hybridization conditions, such as hybridization in 50% formamide / 6xSSC / 0.1%SDS / 100 μg / ml ssDNA, with a hybridization temperature above 37 degrees Celsius and a wash temperature in 0.1xSSC / 0.1% SDS above 55°C.

[0124] As used herein, the term "normalization" or "normalization factor" refers to the expression of a differential value in terms of a standard value to adjust for effects resulting from technical variations due to sample handling, sample preparation, and measurement methodology, rather than biological variations in biomarker concentration in a sample. For example, when measuring the expression of a differentially expressed protein, the absolute value of the protein's expression can be expressed in terms of the absolute value of the expression of a standard protein whose expression is substantially constant.

[0125] The terms "diagnosis" and "diagnostics" also encompass the terms "prognosis" and "prognostics," respectively, and the application of such procedures across two or more time points to monitor diagnosis and / or prognosis over time, and statistical modeling based thereon. Furthermore, the term diagnosis includes a. prediction (determining whether a patient is likely to develop invasive disease (hyperproliferative / invasive)), b. prognosis (predicting whether a patient is likely to have a better or worse outcome at a preselected time point in the future), c. therapy selection, d. therapeutic drug monitoring, and e. recurrence monitoring.

[0126] "Accuracy" refers to the closeness of fit of a measured or calculated quantity (test-reported value) to its actual (or true) value. Clinical accuracy relates to the proportion of true outcomes (true positives (TP) or true negatives (TN)) to misclassified outcomes (false positives (FP) or false negatives (FN)), and can be described as sensitivity, specificity, positive predictive value (PPV) or negative predictive value (NPV), or as likelihood, odds ratio, among other measures.

[0127] The term "biological sample" as used herein refers to any sample of biological origin that potentially contains one or more biomarkers. Examples of biological samples include tissues, organs, or bodily fluids, such as whole blood, plasma, serum, tissue, lavage fluid, or any other specimen used in the detection of disease.

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

[0129] "Treating" a disease or condition or treatment thereof refers to carrying out a protocol or treatment plan, which may include administering one or more therapeutic agents to a patient in an effort to alleviate the signs or symptoms of the disease or the recurrence of the disease. Desirable effects of treatment include slowing the rate of disease progression, improvement or palliative treatment of the disease state, and remission, prolonged survival, improved quality of life, or improved prognosis. Furthermore, "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 a minimal effect on the patient.

[0130] As used herein, "prevent," "preventing," and the like refer to halting the onset of a disease, condition, or disorder, or one or more symptoms or complications thereof.

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

[0132] "Altered," "changed," or "significantly different" refers to a detectable change or difference from reasonably comparable states, profiles, measurements, etc. Such changes can be all or nothing. They can be incremental and need not be linear. They can be orders of magnitude changes. Changes 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. Changes can be statistically significant at a p-value of 0.1, 0.05, 0.001, or 0.0001.

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

[0134] The term "progressive disease" refers to a diagnosis of the presence of a highly active state of prostate cancer, i.e., prostate cancer that is untreated and not stable, or that is treated and not responsive to therapy, or that is treated and remains active disease, as determined by imaging data and / or best clinical judgment.

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

[0136] The term "neoplastic tissue" refers to a mass of abnormally proliferating cells.

[0137] The term "non-neoplastic tissue" refers to 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 refers to a numerical value and / or range that is close to the recited numerical value and / or range. In some cases, the term "about" can mean within ±10% of the recited value. For example, in some cases, "about 100 units" can mean within ±10% of 100 (e.g., 90 to 110). [Example]

[0139] The present disclosure is further illustrated by the following examples, which should not be construed as limiting the scope or spirit of the disclosure to the specific procedures described herein. It should be understood that the examples are provided to illustrate certain embodiments, and that no limitation to the scope of the disclosure is intended thereby. It should further be understood that various other embodiments, modifications, and equivalents thereof that may suggest themselves to those skilled in the art may be resorted to without departing from the spirit of the present disclosure and / or the scope of the appended claims.

[0140] Example 1. Derivation of a 24-marker gene panel

[0141] A panel for blood evaluation was previously developed and patented, containing 38 marker genes. The SalivaPROSTest transcript panel was derived from evaluating gene expression in matched blood and saliva samples from 51 prostate cancer patients, including expression of biomarkers previously identified in blood samples from prostate cancer patients (see U.S. Patent Application Publication No. 2019 / 0259471(A1)). While all previously identified genes were detectable in blood, only 24 of these were detectable in more than 40% of saliva samples (Figure 1). These 24 genes were highly correlated, both in terms of measured values ​​(Ct values) 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 normalized values, the Pearson r value was 0.67 (p = 0.0003, Figure 2B).

[0142] These genes were demonstrated to be highly expressed in prostate cancer tumor tissue and were significantly correlated with salivary gene expression (r = 0.64, p = 0.0004), identifying saliva as an effective liquid biopsy tool (Figure 3).

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

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

[0145] [Table 2]

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

[0147] Example 2. Clinical utility

[0148] The SalivaPROSTest score was significantly elevated (p<0.001) in PCa (61±24%) compared with control men (8±9%), including men with benign prostatic hyperplasia (BPH) (Figure 6). Data (receiver operating curve analysis and evaluation index) regarding the test's utility for distinguishing prostate cancer patients (n=40) from controls (n=100) in the validation study are included in Figure 7. The score demonstrated an area under the curve (AUROC) of 0.99. The evaluation index had a sensitivity of 95% and a specificity of 100% (Figure 8). The Youden index J was 0.95, and the Z statistic for distinguishing non-malignant prostate disease from controls was 304.7.

[0149] SalivaPROSTest scores were significantly elevated in high-grade (Gleason score ≥ 7: 72 ± 25%) compared to low-grade (Gleason 5+6) PCa (46 ± 19%) (p<0.002). Data are included in Figure 9.

[0150] Specific evaluation of the pre- and post-operative prostate carcinoma cohort identified that complete removal of tumor and no evidence of disease was associated with a significant decrease in SalivaPROSTest score (p<0.0001) (Figure 10). Levels were not significantly different from controls. Evaluation of a separate cohort confirmed that patients who received and responded to therapy had significantly lower scores than patients diagnosed with disease (p<0.001) (Figure 11). Therapies included ADT and 177 Lu-PSMA therapy was included. Therefore, the tool can accurately identify treatment response in prostate cancer disease.

[0151] equivalent While the present invention has been described in conjunction with the specific embodiments set forth above, many alternatives, modifications, and other variations thereof will be apparent to those skilled in the art. All such alternatives, modifications, and variations are intended to be within the spirit and scope of the present invention.

Claims

1. 1. A method of identifying the presence or absence of prostate cancer in a subject in need thereof, 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 housekeeping genes; (b) The expression levels of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQL, STRIP1, STX12, UNC45A, and XPC relative to the expression levels of the housekeeping genes. and normalizing the expression levels of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQL, STRIP1, STX12, UNC45A, and XPC, respectively; (c) inputting each normalized expression from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cutoff value; (e) identifying the presence of prostate cancer in the subject if the score is equal to or greater than the predetermined cutoff value, or determining the absence of prostate cancer in the subject if the score is less than the predetermined cutoff value.

2. 2. The method of claim 1, wherein the predetermined cutoff value is 23% on a scale of 0 to 100%.

3. 1. A method for determining whether prostate cancer in a subject is stable or progressive, 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 housekeeping genes; (b) The expression levels of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQL, STRIP1, STX12, UNC45A, and XPC relative to the expression levels of the housekeeping genes. and normalizing the expression levels of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQL, STRIP1, STX12, UNC45A, and XPC, respectively; (c) inputting each normalized expression level from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cutoff value; (e) determining that the prostate cancer is progressive if the score is equal to or greater than the predetermined cutoff value, or determining that the prostate cancer is stable if the score is less than the predetermined cutoff value.

4. 4. The method of claim 3, wherein the predetermined cutoff value is 50% on a scale of 0 to 100%.

5. 1. A method for determining whether prostate cancer in a subject has a low Gleason score (≦6) or a high Gleason score (≧7), 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, and XPC, and housekeeping genes; (b) The expression levels of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQL, STRIP1, STX12, UNC45A, and XPC relative to the expression levels of the housekeeping genes. and normalizing the expression levels of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQL, STRIP1, STX12, UNC45A, and XPC, respectively; (c) inputting each normalized expression level from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cutoff value; (e) determining that the prostate cancer in the subject has a high Gleason score (≧7) if the score is equal to or greater than the predetermined cutoff value, or determining that the prostate cancer in the subject has a low Gleason score (≦6) if the score is less than the predetermined cutoff value.

6. The method of claim 5, wherein the predetermined cutoff value is 50% on a scale of 0 to 100%.

7. 1. A method for determining the completeness of surgery in a subject with prostate cancer, comprising: (a) determining 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 housekeeping genes; (b) The expression levels of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQL, STRIP1, STX12, UNC45A, and XPC relative to the expression levels of the housekeeping genes. and normalizing the expression levels of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQL, STRIP1, STX12, UNC45A, and XPC, respectively; (c) inputting each normalized expression level from step (b) into an algorithm to generate a score; (d) comparing the score with a predetermined cutoff value; (e) identifying the prostate cancer as not being completely removed if the score is equal to or greater than the predetermined cutoff value, or identifying the prostate cancer as being completely removed if the score is less than the predetermined cutoff value.

8. 8. The method of claim 7, wherein the predetermined cutoff value is 50% on a scale of 0 to 100%.

9. 1. A method for assessing the response of a subject having prostate cancer to an anti-prostate cancer therapy, comprising: (a) at a first time, (i) 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 housekeeping genes; (ii) The expression levels of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQL, STRIP1, STX12, UNC45A, and XPC are compared to the expression levels of the housekeeping genes. and normalizing the expression levels of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQL, STRIP1, STX12, UNC45A, and XPC, respectively; (iii) inputting each normalized expression level from step (a)(ii) into an algorithm to generate a first score; (b) at a second time point that is after the first time point and after administering the therapy to the subject; (i) determining the expression levels of said at least 24 biomarkers in a saliva sample from said subject; (ii) The expression levels of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQL, STRIP1, STX12, UNC45A, and XPC are compared to the expression levels of the housekeeping genes. and normalizing the expression levels of AAMP, CHTOP, EDC4, FYCO1, HNRNPU, HPN, KRT23, MAN2B2, MAX, MRPS25, NDUFS2, PPRC1, RAD23A, REPIN1, SDR39U1, SETBP1, SLC18A2, SMC4, SPARC, SQL, STRIP1, STX12, UNC45A, and XPC, respectively; (iii) inputting each normalized expression level from step (b)(ii) into an algorithm to generate a second score; (c) comparing the first score with the second score; (d) identifying the subject as responsive to the anti-prostate cancer therapy if the second score is reduced compared to the first score, or identifying the subject as not responsive to the anti-prostate cancer therapy if the second score is not reduced compared to the normalized expression level from step (a)(ii).

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

11. The method of any one of claims 1 to 10, wherein the housekeeping gene is selected from the group consisting of ATG4B, RHOA, TOX4, TPT1 and TXNIP.

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

13. The method of any one of claims 1 to 12, having a sensitivity of at least 90%.

14. The method of any one of claims 1 to 13, having a specificity of at least 90%.

15. The method of any one of claims 1 to 14, wherein at least one of the at least 24 biomarkers is RNA, cDNA, or protein.

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

17. 17. The method of any one of claims 1 to 16, wherein the expression level of the biomarker is detected by forming a complex between the biomarker and a labeled probe or primer.

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

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

20. 16. The method of 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. 21. The method of claim 20, wherein the label is a fluorescent label.

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

23. 23. The method of any one of claims 1 to 22, 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. 24. The method of claim 23, wherein the neoplastic disease is prostate cancer.

25. 25. The method of any one of claims 1 to 24, wherein the algorithm is XGB, RF, glmnet, cforest, CART, treebag, knn, nnet, SVM-radial, SVM-linear, NB, or mlp.

26. 26. The method of claim 25, wherein the algorithm is a random forest.

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

28. 28. The method of any one of claims 1 to 27, further comprising treating the subject identified as having prostate cancer with at least one anti-prostate cancer therapy.

29. 29. The method of any one of claims 1 to 28, wherein the anti-prostate cancer therapy comprises active surveillance, surgery, radiation therapy, cryotherapy, hormone therapy, chemotherapy, vaccine therapy, bone-directed therapy, or any combination thereof.

30. The radiation therapy comprises external beam radiation, brachytherapy, a radiopharmaceutical, or any combination thereof, preferably the radiopharmaceutical comprises: １７７ 30. The method of claim 29, comprising Lu-PSMA.

31. 30. The method of claim 29, wherein the hormone therapy comprises androgen deprivation therapy.

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

33. 30. The method of claim 29, wherein the vaccine therapy comprises Sipuleucel-T.

34. 30. The method of claim 29, wherein the bone-directed therapy comprises a bisphosphonate, denosumab, a corticosteroid, or a combination thereof.

35. 35. The method of any one of claims 1 to 34, wherein the first time point is before administering the therapy to the subject.

36. 36. The method of any one of claims 1 to 35, wherein the first time point is after administering the therapy to the subject.

37. A method according to any one of claims 1 to 36, wherein the saliva sample is self-collected saliva in a container with a stabilising fluid.

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