Development and use stemness metrics for prostate cancer risk stratification and prognosis

Abstract

The disclosure relates to a method for assessing prostate cancer progression, aggressiveness, and therapy outcomes by employing transcriptome-based metrics. The disclosure also relates to a method of computing a Stemness score by correlating a prostate cancer sample's gene expression profile with a stem cell signature derived from a machine learning algorithm. The disclosure further relates to a method of calculating a PCa-Stem signature score by performing single-sample gene set enrichment analysis on at least twelve genes, including HMMR, AURKB, CENPA, DEPDC1B, HJURP, PBK, MELK, UBE2C, DLGAP5, NEK2, BIRC5, and KLK12. The method may also involve comparing the Stemness and PCa-Stem signature scores to clinical cohort benchmarks to determine prostate cancer stage, aggressiveness, or risk category. Prostate cancer samples characterized by high Stemness or PCa-Stem signature scores may indicate aggressive disease and correlate with poor therapy outcomes.

Description

[0001] Docket No.: 440914 [2085_010 PCT] DEVELOPMENT AND USE STEMNESS METRICS FOR PROSTATE CANCER RISK STRATIFICATION AND PROGNOSIS CROSS-REFERENCE TO RELATED APPLICATIONS This Application claims priority to and the benefit of co-pending U.S. Provisional Patent Application Serial No.65 / 685,643, entitled Development and Use of Stemness Metrics to Measure Prostate Cancer Aggressiveness and Prognosticate Patients’ Clinical Outcome, filed August 21, 2024, which is incorporated herein by reference in its entirety. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT This invention was made with government support under R01CA237027, R01CA240290, and R21CA237939 awarded by the National Institutes of Health, and under PC220137 and PC220273 awarded by the Department of Defense. The government has certain rights in the invention. TECHNICAL FIELD The present disclosure pertains to improvements in cancer diagnosis. More specifically, the subject matter is focused on prognostic tools and biomarkers for evaluating prostate cancer aggressiveness and forecasting clinical outcomes. BACKGROUND Prostate cancer is projected to claim the lives of over 35,000 American men in 2025 (3). Prostate cancer incidence rates increased 3% per year from 2014-2019 driven by advanced prostate cancer diagnosis. The proportion of men diagnosed with metastases has doubled from 2011 to 2019 when the next generation of androgen receptor (AR) pathway inhibitors (ARPIs) such as abiraterone (Zytiga; FDA approval in 2011) and enzalutamide (Xtandi; FDA approval in 2012) were introduced to the clinic. Significant increases in aggressive disease and continued high prostate cancer mortality have been linked to intrinsic prostate cancer cell heterogeneity (e.g., in AR expression) and treatment-induced plasticity (8). The level of malignancy of primary prostate cancer traditionally has been assessed by pathologists using a combined Gleason Score (GS), which gauges histopathological differentiation pattern of prostatic glands. The higher the GS, the more undifferentiated and more malignant the primary prostate cancer is. The GS system, although clinically the ‘gold’ standard in pinpointing prostate cancer diagnosis, is only semi-quantitative, can be subjective (to individual pathologist’s assessment), and is generally not applicable to treatment-failed tumors and distant metastases (which have mostly lost differentiated glandular structures). Notably, whether more advanced primary prostate cancer (GS8-10), treatment failure, and metastatic dissemination are quantitatively associated with increased cancer stemness remains unknown. On the other hand, AR is the master regulator of normal prostatic epithelium differentiation and represents the therapeutic target of prostate cancer treatments including androgen-deprivation therapy (ADT) and Docket No.: 440914 [2085_010 PCT] androgen receptor pathway inhibitors (ARPIs). Inevitably, tumors develop resistance to ADT / ARPIs leading to the state of castration-resistant prostate cancer, which involves many mechanisms including genetic alterations of AR and key genes in AR pathway (10), lineage plasticity (9, 11, 12), and increased stemness. It is unclear how the global pro-differentiation AR signaling activity changes and evolves during the spectrum of prostate cancer initiation and progression and during development of treatment resistance and metastasis. Despite advancements in therapeutic strategies, including androgen receptor (AR)-targeted treatments, clinical outcomes remain highly variable due to the molecular heterogeneity of prostate cancer. Current diagnostic and prognostic tools, such as the Gleason Score (GS), provide a semi- quantitative assessment of tumor differentiation but are limited by subjectivity, lack of applicability to advanced or treatment-resistant tumors, and inability to account for the dynamic molecular changes associated with disease progression. Furthermore, while the loss of differentiation and gain of stem-like traits (stemness) are hallmarks of cancer progression, no established framework exists for quantitatively measuring stemness or the relationship of stemness to AR signaling across the spectrum of prostate cancer development, therapy resistance, and metastasis. This gap in quantitative metrics hinders accurate prognostication and personalized treatment planning. Accordingly, there is a need for robust quantitative frameworks to assess both differentiation-associated pathways—including androgen receptor signaling—and dedifferentiation-related stemness features across prostate cancer stages, thereby enhancing prognostic accuracy and informing personalized treatment decisions. The present disclosure addresses these limitations by introducing novel transcriptome- based metrics, including a Stemness score and a proprietary 12-gene PCa-Stem Signature, to quantitatively assess prostate cancer aggressiveness, therapy response, and patient prognosis. The Stemness score leverages machine learning algorithms to evaluate stem-like features in tumor transcriptomes, while the PCa-Stem Signature employs a linear combination of expression values from 12 genes strongly associated with stemness and poor clinical outcomes. These tools enable systematic quantification of stemness and AR signaling activity (AR-A) across diverse prostate cancer stages. By correlating these metrics with defined prostate cancer stages and / or clinical outcomes, the described approach facilitates stratification of patients based on tumor aggressiveness, therapy resistance, and survival likelihood, thereby supporting personalized therapeutic interventions. This approach represents an advancement over conventional methods by offering objective, reproducible, and clinically actionable metrics for prostate cancer evaluation. The analysis of transcriptomic data across large prostate cancer cohorts informs a correlation between Stemness scores and prostate cancer stages and / or aggressiveness, while the PCa-Stem Signature provides a focused framework for identifying high-risk patients. Together, these advancements address significant gaps in prostate cancer diagnosis and prognosis, enabling more precise treatment strategies and improving patient outcomes. Docket No.: 440914 [2085_010 PCT] SUMMARY OF THE INVENTION A first aspect of the present disclosure is directed to a method of determining the progression and / or stage of prostate cancer in a human subject, comprising: generating a gene expression profile from a prostate cancer tissue sample; computing a Stemness score by determining the Spearman correlation between the subject’s expression profile and a stem cell signature derived from a one-class logistic regression machine learning algorithm trained on pluripotent stem cell transcriptomes and linearly transforming that correlation to a 0–1 scale; calculating a PCa-Stem signature score by performing a single-sample gene set enrichment analysis using expression values of HMMR, AURKB, CENPA, DEPDC1B, HJURP, PBK, MELK, UBE2C, DLGAP5, NEK2, BIRC5, and KLK12; and comparing the resulting Stemness and PCa- Stem signature scores to values derived from clinical prostate cancer cohorts to determine tumor progression or stage. The method may further comprise comparing the scores to benchmark data in figures such as FIG.5A / 5B or FIG. 14B. Accordingly, subjects may be stratified as high-risk when scores exceed predetermined thresholds (for example, 0.4, 0.45, 0.5, 0.6, 0.7, 0.75, 0.775 or the top 33% of scores) and as low-risk otherwise. Gene expression data can be obtained by RNA sequencing, single-cell RNA sequencing, microarray, or other next-generation methods, and tissue without cancerous characteristics is characterized by a Stemness score ≤0.2. Moreover, elevation of at least three PCa-Stem gene transcripts relative to tissue without cancerous characteristics further indicates disease progression. A further aspect of the present disclosure is directed to a method of treating prostate cancer in a human subject, comprising: determining the subject’s cancer stage or progression by generating a gene expression profile, computing Stemness and / or PCa-Stem signature scores, and comparing those scores to values from clinical cohorts; and administering an appropriate treatment regimen selected from androgen deprivation therapy, androgen receptor pathway inhibitors, chemotherapy, radiotherapy, radioligand therapy, immunotherapy, PARP inhibitors, antibody-drug conjugates, chimeric antigen receptor T-cell therapy, cytokine-based therapy, or combinations thereof. In another aspect, the present disclosure is directed to a method of predicting prostate cancer aggressiveness in a human subject, comprising: obtaining transcriptomic data from a tumor sample; calculating a Stemness score via Spearman correlation with a reference stem cell signature weight vector and scaling the correlation to a 0–1 range; calculating a PCa-Stem signature score by single-sample gene set enrichment analysis of HMMR, AURKB, CENPA, DEPDC1B, HJURP, PBK, MELK, UBE2C, DLGAP5, NEK2, BIRC5, and KLK12; and comparing these scores to values from clinical cohorts, wherein aggressiveness is indicated by scores at or above thresholds in the 0.4–0.775 range. Yet another aspect of the present disclosure is directed to a computerized system for prostate cancer prognosis, comprising: a memory storing program code; and a processor configured to receive mRNA expression values for HMMR, AURKB, CENPA, DEPDC1B, HJURP, PBK, MELK, UBE2C, DLGAP5, NEK2, BIRC5, and KLK12 from a prostate-derived sample; perform single-sample gene set enrichment analysis to generate a numeric PCa-Stem signature score; compare the score to one or more thresholds; and output a risk category indicative of poor or favorable clinical outcome. A user interface may display the PCa-Stem Docket No.: 440914 [2085_010 PCT] signature score and corresponding risk category to a clinician. These and other features will be more fully understood upon review of the detailed description and accompanying figures. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 shows that primary prostate cancer (Pri-PCa) exhibits concordant increases of androgen receptor activity (AR-A) and Stemness and both are decreased by neoadjuvant androgen deprivation therapy (nADT). FIG. 1A presents pairwise comparisons of androgen receptor (AR) mRNA levels, canonical AR-A, and Stemness scores showing significant increases in primary tumors (T) as compared to the matched adjacent benign prostate tissues (N) from the same individual in the TCGA-PRAD cohort (n=51). FIG.1B displays similar pairwise comparisons in the Wyatt 2014 cohort (n=12), demonstrating significant increases in AR mRNA levels, canonical AR-A, and Stemness scores in primary tumors (T) compared to matched adjacent benign tissues (N). FIG. 1C shows data from the Long 2020 cohort (n=10), where increases in AR-A and Stemness reached statistical significance whereas the increase in AR mRNA levels approached statistical significance. FIG. 1D illustrates pairwise comparison of AR mRNA levels, AR-A, and Stemness, showing significant decrease of both AR-A and Stemness after nADT (Post-nADT) as compared to the matched samples before nADT (Pre-nADT) from the same individual in the Rajan 2014 cohort (n=7). FIG. 1E presents similar pairwise comparisons in the Sharma 2018 cohort (n=7), showing significant decreases in androgen receptor activity (AR-A) and Stemness after nADT (Post-nADT) compared to pre-treatment samples (Pre-nADT). FIG.1F shows data from the Long 2020 cohort (n=6), where both AR-A and Stemness are significantly decreased after nADT (Post- nADT) compared to matched pre-treatment samples (Pre-nADT). For FIG.1A-FIG.1F, sample sizes are indicated in all plots. Within the plots, results are shown as the mean ± SD and pairs are linked with a grey line. Significance was calculated by two- tailed paired Student’s t-test (ns, not significant; *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001). FIG.2 illustrates continual increases in Stemness with declining androgen receptor activity (AR-A) during spontaneous prostate cancer progression. In FIG.2A, the pie chart displays sample sizes in various Gleason grades in the TCGA untreated primary prostate cancer (PCa) cohort. In FIG. 2B-FIG. 2D, progression of treatment-naïve primary prostate cancer is associated with decreasing AR-A but increasing Stemness. Data is presented as mean ± SD, and Jonckheere- Terpstra’s trend test results are shown as the wedges above the graphs. In FIG. 2E-FIG. 2F, progression in primary prostate cancer (PCa) is accompanied by a gradual loss of the positive correlation between canonical AR-A and Stemness. Scatter plots depict the correlation between AR-A and Stemness (FIG. 2E) in combined primary prostate cancer cases in TCGA with an overall linear regression model (FIG. 2F) and in individual Gleason Score (GS) grades with corresponding linear regressions showing decreasing slopes from low to high GS grades by Jonckheere-Terpstra’s test. The slopes of regression lines with the closest fit are also shown. In FIG.2G, a merged scatter plot illustrates the correlation between AR-A and Stemness across the four Gleason Score (GS) groups with each GS represented by a distinct shade. The Docket No.: 440914 [2085_010 PCT] correlation becomes progressively weaker from GS6 to GS9 / 10, transitioning from positive to nearly zero. The correlation coefficients (r) and p-values for each GS are as follows: GS6 (r = 0.56, P < 0.0001), GS7 (r = 0.37, P < 0.0001), GS8 (r = 0.48, P = 0.022), and GS9 / 10 (r = -10040.02, P = 0.89). Linear regression lines are overlaid for each GS, demonstrating the decline in the correlation co-efficiency as the GS increases. For FIG. 2A-2G, statistical analyses were performed using R and statistical significance was assessed using one-way analysis of variance followed by Tukey’s multiple-comparison test. Jonckheere-Terpstra’s trend test was used to calculate the statistical significance of the trend across different groups. FIG.3 illustrates that therapies and metastatic progression contribute to further divergence between stemness and canonical androgen receptor activity (AR-A), with metastatic castration- resistant prostate cancer (mCRPC) showing the highest stemness but the lowest AR-A. In FIG. 3A-FIG. 3C, hormonal treatment further decreases AR-A while significantly upregulating stemness. Metastatic castration-resistant prostate cancer (mCRPC) demonstrates the lowest pro-differentiation AR-A but the highest stemness. Depicted are androgen receptor (AR) mRNA levels (FIG. 3A), the canonical AR-A (FIG. 3B), and stemness (FIG. 3C) in tumor- adjacent benign tissues, treatment-naïve primary prostate cancer, and advanced primary prostate cancer with post-surgery adjuvant androgen deprivation therapy (Tx) in TCGA, as well as mCRPC (SU2C 2015). Sample sizes are indicated in parentheses. Displayed at the top of the graphs (wedges) are the Jonckheere-Terpstra trend test results. FIG. 3D shows that stemness inversely correlates with AR-A in mCRPC. Shown are scatter plots with linear regression lines that most closely fit the data, illustrating the correlation between AR-A and stemness with Pearson correlation coefficient (r), the slopes of lines, and Jonckheere-Terpstra’s test results of slopes indicated in the wedge at the bottom of the graph. In FIG.3E-FIG.3F, prostate cancer progression is accompanied by loss of AR-A (FIG. 3E) but gain of stemness (FIG.3F) across prostate cancer states. Shown are samples representing the spectrum of prostate cancer development and progression, from normal (organ donors in GTEx) to (tumor-adjacent) benign tissues, to (treatment-naïve) primary prostate cancer, aggressive primary prostate cancer treated with adjuvant ADT (Pri-PCa / ADT), metastatic castration-resistant prostate cancer (mCRPC), and, finally, to (clonally-derived) prostate cancer models including PDX, xenografts and cells lines. From Left to right: Normal (n=116): Normal prostates from GTEx; Benign (n=52): tumor-adjacent benign tissue from TCGA-PRAD; primary prostate cancer (n=422): treatment-naïve localized prostate cancer from TCGA-PRAD; Pri-PCa / ADT (n=70): advanced Pri-PCa treated with adjuvant hormone therapy from TCGA-PRAD; mCRPC (n=59): mCRPC from GSE126078; mCRPC (n=135): mCRPC from SU2C 2015 cohort; mCRPC-Adeno (n=34): adenocarcinoma mCRPC from Beltran 2016 cohort; mCRPC-NE (n=15): neuroendocrine mCRPC from Beltran 2016 cohort; mCRPC (n=42): mCRPC from Westbrook 2022; mCRPC (n=25): mCRPC from Alumkal 2020; PDX-LuCaP (n=39): PCa PDXs from GSE126078; PCa cell lines (n=8): PCa cell lines from CCLE; XG-LAPC9 (n=10) and XG-LNCaP (n=12): Prostate cancer xenografts from GSE88752. Statistical significances between groups were shown in inverted pyramid grid plot as indicated by different shades. Student’s t-test was used to compare each group and adjusted p-value for multiple comparisons was done by Tukey’s range test. See Docket No.: 440914 [2085_010 PCT] Table 3 for the summary of statistical comparisons. J-T trend test was used to assess the statistical significance of the trend across groups. See also Table 1 for cohort and RNA-seq data access information. FIG.3G shows scatter plots in the Bolis 2021 integrated cohort (normal n=174, primary prostate cancer n=714, mCRPC n=335) showing the changing correlation between AR-A and stemness across disease stages: positive in normal to weaker correlations in primary prostate cancer (Pri-PCa) to negative correlations in metastatic castration-resistant prostate cancer (mCRPC). Regression lines are color-coded, and the slope of the best-fit regression line and Pearson’s r are provided: yellow for normal (Pearson’s r=0.42, P <0.0001; slope: 43.87, 95%CI, 29.57 to 58.18); blue for Pri-PCa (r=0.32, P <0.0001; slope: 15.04, 95%CI, 11.76 to 18.32); and green for mCRPC (r=-0.15, P = 0.005; slope: -17.25, 95%CI, -29.24 to -5.26). FIG. 3H is a violin plot showing increasing Stemness among the NCCN Risk Groups containing 82470 microarray samples with localized disease in GRID. J-T trend test was used to assess the statistical significance of the trend across groups. FIG. 3I, FIG. 3J, FIG. 3K, FIG. 3L, and FIG. 3M show increasing stemness during prostate cancer progression in the Taylor 2010 cohort consisting of tumor-adjacent benign tissue (Normal; n=29), primary prostate cancer (Pri-PCa) (n=131) and metastatic prostate cancer (mPCa) (n=19). The AR-A significantly increased in primary prostate cancer vs. Normal and significantly decreased in mPCa compared to primary prostate cancer (FIG.3I) whereas the stemness continued to increase from Benign to primary prostate cancer to mPCa (FIG. 3I). FIG. 3K, FIG. 3L, and FIG. 3M are scatter plots illustrating the correlations between AR-A and stemness in different contexts with corresponding linear regression lines: FIG.3K shows positive correlation in normal (Pearson’s r=0.69, P <0.0001); FIG. 3L shows a gradual loss of positive correlation between stemness and AR-A during PCa progression (Pearson’s r=0.18, P = 0.043); FIG.3M shows a loss of positive correlation between Stemness and AR-A at a more advanced mPCa stage (Pearson’s r=0.18, P = 0.47). All data in dot plots and violin plots in FIG. 3A-FIG. 3M are shown as mean ± SD. Statistical significance was determined using one-way analysis of variance (ANOVA) followed by Tukey’s multiple-comparison test. Jonckheere-Terpstra’s trend (J-T) test was used to calculate the statistical significance of the trend across different groups. Pearson correlation coefficients (Pearson’s r) were used to calculate the correlations between stemness and AR-A. Note: ns, not significant; *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001. FIG.4A, FIG.4B, FIG.4C, and FIG.4D show that high stemness is associated with poor patient overall survival. FIG.4A, FIG.4B, and FIG.4C are Kaplan-Meier plots showing that high Stemness associates with worse overall survival in PCa patients from Spratt 2017 (FIG. 4A), Tosoian 2020 (FIG.4B), and CHAARTED 2021 (FIG.4C) cohorts. P value was determined using the log-rank test. FIG.4D demonstrates that Stemness increases across patient groups with higher Decipher genomic classifier scores. The three cohorts were arranged by their median Decipher genomic classifier scores, and the J-T trend test was used to assess statistical significance of this trend. Docket No.: 440914 [2085_010 PCT] FIG.5A-FIG.5V show that stemness-high prostate cancers are associated with aggressive molecular subtypes and a 12-gene PCa-Stem signature prognosticates poor patient survival. FIG.5A illustrates the profiling strategy used to assess associations between stemness and molecular features in PCa. Patients were ranked by stemness score and stratified into stemness-high (top 33%) and stemness-low (bottom 33%) groups for downstream analyses. Genome-wide functional analyses were performed using transcriptomic, genomic, and clinical data. FIG.5B and FIG.5C are volcano plots showing differentially expressed genes (DEGs) between stemness-high and stemness-low groups in primary prostate cancer (FIG.5B) and metastatic castration-resistant prostate cancer (mCRPC) (FIG.5C) cohorts. Red and blue indicate upregulated and downregulated DEGs, respectively. Genes with FDR > 0.05 are shown in gray. Sample sizes (n) of each group are indicated. FIG. 5D shows a Venn diagram illustrating the 12-gene “PCa-Stem signature” derived from overlapping upregulated differentially expressed genes (fold change ≥ 2, FDR < 0.05) in both primary prostate cancer and metastatic castration-resistant prostate cancer. FIG.5E and FIG.5F show gene set enrichment analysis results, with low stemness associated with immune signaling and inflammatory response (FIG. 5E), while high stemness in both cohorts is enriched for embryonic stem cell traits, aggressiveness, DNA repair, MYC activation, and mTORC1 signaling (FIG.5F). All enrichments are significant (P < 0.05, FDR < 0.05). FIG.5G and FIG.5H show PAM50 subtyping with a higher frequency of LumB subtype in stemness-high versus stemness-low samples in primary prostate cancer (FIG. 5G) and metastatic castration-resistant prostate cancer (FIG.5H), respectively. (****, P < 0.0001, χ2test). FIG. 5I through FIG. 5L show gene set enrichment analysis for the aggressive PCS1 gene expression signature in Stemness-high groups and the less aggressive PCS3 signature in stemness- low groups in primary prostate cancer (FIG. 5I, FIG. 5J) and metastatic castration-resistant prostate cancer (FIG. 5K, FIG. 5L). The Prostate Cancer Subtype (PCS) classification system (45, 46) stratifies tumors into three molecular subtypes based on transcriptomic features: PCS1 (most aggressive), PCS2 (intermediate), and PCS3 (least aggressive). See Table 2 for PCS gene signature. FIG.5M through FIG.5R illustrate a gene set enrichment analysis showing AR signature enrichment in primary prostate cancer Stemness-high (FIG.5M), neuroendocrine prostate cancer (NEPC) signature enrichment in metastatic castration-resistant prostate cancer Stemness-high (FIG.5N), and shared enrichment of cell cycle (FIG.5O, FIG.5P) and lineage plasticity (FIG. 5Q, FIG.5R) programs in both cohorts. See Table 4 for gene sets. FIG.5S and FIG.5U show Kaplan-Meier analyses demonstrating that the 12-gene PCa- Stem signature predicts worse progression-free survival (PFS) in TCGA primary prostate cancer (FIG.5S) and worse overall survival (OS) in SU2C metastatic castration-resistant prostate cancer (FIG.5U). FIG.5T and FIG.5V show multivariable Cox models adjusting for clinicopathologic variables, confirming the PCa-Stem-High subgroup as independently prognostic for progression- free survival (FIG.5T) and overall survival (FIG.5V). Docket No.: 440914 [2085_010 PCT] FIG. 6 shows that stemness-high prostate cancer is associated with higher genomic instability. The figure includes box plots showing that the stemness-high subgroup in both primary prostate cancer (Pri-PCa) and metastatic castration-resistant prostate cancer (mCRPC) displays a higher fraction of altered genome (FIG. 6A), higher tumor mutation burden (TMB) (FIG. 6B), and no age difference compared to stemness-low cases (FIG. 6C). Statistical significance is determined using the two-sided Wilcoxon rank-sum test, with ****, P < 0.0001; ns, not significant. Box plots represent the median, interquartile range (IQR), and whiskers extending to 1.5× the IQR. FIG.6 also presents comparisons of genomic alteration frequencies (Alt. Freq) in cancer- associated genes between stemness-high and stemness-low subgroups in TCGA Pri-PCa (FIG. 6D, FIG.6F) and SU2C metastatic castration-resistant prostate cancer (mCRPC) (FIG.6E, FIG. 6F) cohorts. Statistical significance is determined by two-sided Fisher’s Exact test (*, P < 0.05). FIG.7 shows that MYC activation contributes to increased stemness during spontaneous prostate cancer progression. FIG. 7A and FIG. 7B show pairwise comparisons between MYC mRNA levels and MYC activity (MYC-sig) in primary tumors (T) versus matched adjacent benign tissues (N) in TCGA-PRAD (n=51) and Wyatt (n=12) cohorts. Data is shown as mean ± SD; paired samples are linked by grey lines. Significance was determined using two-tailed paired Student’s t- test (****, P < 0.0001). FIG.7C-7H show that MYC activity increases during prostate cancer progression, peaking in metastatic castration-resistant prostate cancer (mCRPC). In treatment-naïve primary prostate cancer (FIG.7C), MYC-sig is significantly increased in all-grade Gleason Score (GS) tumors as compared to tumor-adjacent benign tissues (N), with the most advanced GS9 / 10 tumors exhibiting the highest MYC-sig. In FIG. 7D, MYC-sig increases from normal to treatment-naïve primary prostate cancer and further in advanced primary prostate cancer treated with adjuvant androgen- deprivation therapy (ADT) (Tx) in TCGA-PRAD, while mCRPC shows the highest MYC-sig in both SU2C 2015 (FIG.7D) and Taylor 2010 (FIG.7D) cohorts. Data is presented as mean ± SD. Statistical significance was assessed by one-way ANOVA with Tukey’s multiple-comparison test (FIG.7C, FIG.7D, and FIG.7E). J-T trend test was used in FIG.7C and FIG.7D to assess the significance of progressive increase. P < 0.01; ***, P < 0.001; ****, P < 0.0001; ns, not significant. FIG. 7F, FIG. 7G, and FIG. 7H are scatter plots showing positive correlations between MYC activity and Stemness in the Taylor 2010 cohort across different stages. Pearson’s r and P-values are indicated. FIG. 8 shows a schematic illustration of bioinformatic datasets and signature score pipelines used for comparisons across the spectrum of prostate cancer (PCa) development and progression. FIG. 8A presents a schematic illustrating the spectrum of prostate cancer (PCa) development, therapy resistance, and metastatic progression. The continuum is shown from a healthy prostate (Normal) through Primary Prostate Cancer (Primary PCa), to Castration-Resistant Prostate Cancer / Metastatic Castration-Resistant Prostate Cancer (CRPC / mCRPC). Each stage is marked by specific changes in cellular composition and architectural organization, including basal and luminal cells, luminal progenitor cells, and neuroendocrine cells. FIG. 8B details the datasets utilized for comparative analyses across these stages, with arrows indicating the relationship of each dataset to specific PCa stages. A total of 26 datasets Docket No.: 440914 [2085_010 PCT] including 3,102 bulk-RNAseq samples and 84,081 microarray samples were used in the current analysis. FIG. 8C shows the schematic presentation of the transcriptome-based stemness quantification method mRNAsi (Stemness). FIG.8D is a schematic presentation of transcriptome- based signature scores for canonical AR signaling activity levels, MYC signaling activity levels and PCa-Stem signature. See Table 2 for gene signature information. FIG. 9 shows transcriptome-based signature scores for canonical androgen receptor activity (AR-A). FIG.9A shows the workflow for validating AR-A signatures from different AR- regulated gene lists. FIG.9B and FIG.9C present a comparison of AR-A gene signatures reported in four studies. The four genes commonly shared in all four AR-A signatures are highlighted in FIG.9C. Genes were listed using updated HGNC symbols (the HUGO gene nomenclature from the Human Genome Organization). Note that in the original publications, some AR-A genes were denoted by their (gene / protein) aliases, as exemplified by CENPN (BM039), C1orf116 (SARG), KLK3 (PSA), PLPP1 (PPAP2A), and PMEPA1 (TMEPAI). FIG. 9D shows that the AR-A signature score highly correlates with mRNA expression levels of canonical AR signaling targets in various prostate cancer related cohorts. See Table 1 for dataset details. FIG.9E, FIG.9F, and FIG.9G demonstrate strong positive linear relationships between the Bluemn (2017) AR-A and the other three AR-A signatures (Hieronymus 2006; Abeshouse 2015, Spratt 2019) using primary prostate cancer cohort (TCGA-PRAD). r indicates Pearson correlation coefficient. FIG. 10 illustrates that the prostate luminal cell compartment harbors higher AR-A but lower stemness as compared to the prostate basal cell compartment. FIG.10A depicts the major cell types in human prostatic glands. Both basal and luminal cell compartments harbor differentiated as well as less mature stem / progenitor cells. LP signifies luminal progenitor; NE signifies neuroendocrine. Cellular markers are highlighted for different cell types. Adapted from reference 8. FIG.10B and FIG.10C show that prostate luminal cells possess higher AR-A but lower stemness as compared to prostate basal cells, and stemness inversely correlates with AR-A in the context of prostatic basal and luminal cells. Results are shown as mean ± SD. The pairs are linked with a grey line. Significance was calculated by two-tailed paired Student’s t-test (*, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001). Pearson correlation coefficients (Pearson’s r) were used to calculate the correlations between Stemness and AR-A. FIG.11 is a matched comparison of AR mRNA levels (FIG.11A), AR-A (FIG.11B), and stemness (FIG.11A) showing significant decrease of both AR-A and stemness in a neo-adjuvant ADT (+nADT) group as compared to the samples without nADT (No-nADT) from stage-, tumor grade-, age-matched prostate cancer individuals (RPCI Nastiuk cohort). Sample sizes are indicated. See Table 1 for cohort and RNAseq data information. Within plots, results are shown as the mean ± SD. Significance was calculated by two-tailed unpaired Student’s t-test (*, P < 0.05; **, P < 0.01; ***, P < 0.001). Docket No.: 440914 [2085_010 PCT] FIG.12 shows that increased stemness represents a “universal” feature of prostate cancer progression and aggressiveness. FIG. 12A demonstrates that increasing stemness accompanies prostate cancer progression and aggressiveness. Stemness scores are shown in various prostate cancer cohorts representing the prostate cancer evolutionary spectrum (from top to bottom): GTEx normal prostate → TCGA tumor-adjacent benign prostate tissue → Treatment-naïve Pri-PCa → aggressive Pri-PCa treated with adjuvant hormone therapy Pri-PCa / ADT → Treatment-failed CRPC → mCRPC → Long-term PCa models including PDX, xenografts (XG), and cell lines. Sample sizes (n) are indicated in the last parentheses. See Table 1 for dataset details and Table 3 for the statistical significance between different groups. FIG. 12B shows a progressive increase in Stemness from primary prostate cancer to advanced metastatic castration-resistant prostate cancer (mCRPC) subtypes. This violin plot displays increasing Stemness scores across four prostate cancer groups, defined by treatment status and pathway activity: treatment-naïve Pri-PCa from TCGA, and three subtypes of mCRPC categorized by AR and NE pathway activities - AR pathway-active (ARPC), neuroendocrine (NEPC), and double-negative (DNPC). These three subtypes were delineated based on established signatures (Bluemn et al., 2017, ref. 14; Labrecque et al., 2019, ref. 33). Displayed from left to right are: Untreated Pri-PCa (n = 422), mCRPC-ARPC (191), mCRPC-NEPC (26), and mCRPC- DNPC (16). Pri-PCa samples originated from TCGA, while mCRPC samples were derived from studies by Sharp et al. (2019, ref.67, GSE118435), Labrecque et al. (2019, ref.33, GSE126078), Nyquist et al. (2020, ref.68, GSE147250), Lim et al. (2021, ref.70, GSE171729), and Beltran et al. (2016, ref. 28, PRJNA282856; dbGaP: phs000909). Normalized RNAseq data were sourced from the Bolis 2021 integrated cohort (ref.27). For dataset details, see Table 1. FIG. 12C-FIG. 12H show that prostate cancer progression in genetically engineered mouse models (GEMMs) is characterized by decreasing AR-A and increasing Stemness in aggressive double knock out (DKO) and triple knock out (TKO) tumors. Stemness and AR-A were analyzed in distinct genetically engineered mouse models (GEMMs) of prostate cancer due to individual or combined deletion of 3 tumor suppressor genes, Pten, Rb1 and Trp53 (Ku et al., 2017, ref.37). Briefly, Pten- / -single knockout (SKO) prostate tumors develop around 9 weeks, and mice rarely develop metastasis with a median lifespan of 48 weeks. In contrast, the Pten- / -; Rb1- / -double knockout (DKO) mice develop highly metastatic PCa that shortens median survival to ~38 weeks. DKO tumors are initially castration-sensitive but eventually become castration-resistant and, notably, castration-resistant DKO tumors turn into an AR– / lophenotype. When Trp53 is further deleted in DKO background, the triple KO (TKO; Pten- / -; Rb1- / -; Trp53- / -) tumors are exclusively AR–NEPC and castration-resistant de novo with high metastatic rate and lifespan of ~16 weeks. AR-A was dramatically reduced (FIG.12C) but stemness was significantly increased (FIG.12D) in the aggressive DKO and TKO prostate cancer compared to indolent single knockout (SKO) prostate tumors. FIG.12E-FIG.12H are scatter plots illustrating the correlations between AR-A and stemness in combined cases in prostate cancer GEMMs (FIG. 12E), single knockout (SKO) (FIG.12F, Pearson’s r=0.95, P = 0.053), double knock out (DKO) (FIG.12G, Pearson’s r= –0.56, P = 0.053), and triple knock out (TKO) (FIG.12C, Pearson’s r= –0.78, P = 0.066). For FIG. 12A-FIG. 12H, all data in dot plots and violin plots are shown as mean ± SD. Statistical significance was determined using one-way analysis of variance (ANOVA) followed by Tukey’s multiple-comparison test. Jonckheere-Terpstra’s trend (J-T) test was used to calculate Docket No.: 440914 [2085_010 PCT] the statistical significance of the trend across different groups. Pearson correlation coefficients (Pearson’s r) were used to calculate the correlations between the Stemness and AR-A. Note: ns, not significant; *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001 FIG. 13 shows that high stemness correlates with poor patient survival. FIG. 13A-FIG. 13C demonstrate that high stemness correlates with poorer overall survival in CHAARTED 2021 cohort (FIG. 13A) or SU2C mCRPC cohort (FIG. 13B- FIG. 13C). The CHAARTED 2021 cohort was divided into stemness-high (n = 80) and stemness-low (n = 80) using stemness median split (FIG.13A). The SU2C 2019 mCRPC cohort was divided into stemness-high and stemness- low using stemness median split (FIG. 13B) or the analysis was done by comparing the 25% of highest stemness samples with the 75% of lowest stemness samples (FIG. 13C). P-value was determined using the Log-Rank test. FIG.13D-FIG.13F demonstrate that high stemness and low AR-A correlate with worse recurrence-free survival in PCa patients in the Taylor 2010 PCa cohort. Stemness (FIG.13D) or AR-A (FIG. 13F) was used to fractionate Taylor cohort for Kaplan-Meier analysis. The Taylor patient cohort was divided into stemness-high (n = 70) and stemness-low (n = 70) using stemness median split (FIG.13E) or AR-A high (n = 70) and AR-A low (n =70) using AR-A median split (FIG. 13F) followed by the Kaplan-Meier analyses. Note: there are 150 PCa samples with transcriptome data in the Taylor cohort but only 140 PCa samples with survival data. P-value was determined using the Log-Rank test. FIG. 14 shows that the PCa-Stem signature and gene expression correlate with prostate cancer (PCa) progression. FIG. 14 is a schematic of PCa continuum, from normal prostate (N) through various stages of primary PCa (Pri-PCa) to treatment-failed mCRPC. Sample groups include: (1) Two subgroups in N (Normal / Benign), including GTEx normal prostate (n=116) and TCGA tumor-adjacent benign prostate tissue (n=52); (2) Five subgroups in Pri-PCa, including Gleason Score 6 (GS6) (n=45), GS7 (n=238), GS8 (n=49), combined GS9 and GS10 (GS9 / 10; n=90), and aggressive Pri-PCa treated with post-surgery adjuvant hormone therapy (Pri-PCa / ADT; n=70); (3) three subtypes of metastatic castration-resistant prostate cancer (mCRPC), including AR-active PCa (ARPC; n=191), double-negative AR-null / neuroendocrine-null PCa (DNPC; n=16), and neuroendocrine PCa (NEPC; n=26). Note that the color-coded entities of normal / benign prostate and progressive PCa stages in FIG.14A also represent the legend for all dot plots in FIG.14B-FIG.14D. FIG. 14B is a dot plot showing that the increasing 12-gene PCa-Stem signature scores correlate positively with the PCa progression described in FIG. 14A. FIG. 14C consists of dot plots showing that the mRNA levels of 11 of the 12 PCa-Stem signature genes (i.e., HMMR, AURKB, CENPA, DEPDC1B, HJURP, PBK, MELK, UBE2C, DLGAP5, NEK2, and BIRC5) except KLK12 (see FIG.5D) exhibited stage-related increases and positively correlated with PCa progression. FIG.14D consists of dot plots showing that the mRNA levels for 14 genes commonly downregulated in Stemness-high groups (i.e., PTGDS, SPARCL1, CLU, GJA1, S100A4, MYH11, LMOD1, FBLN1, SFRP1, IGFBP6, GAS1, TIMP2, MYLK, and FLNA) all individually displayed stage-related downregulation and negatively correlated with PCa progression. Docket No.: 440914 [2085_010 PCT] For all dot plots in FIG.14B-FIG.14D, the Pearson’s correlation coefficients (Corr.) are shown for the relationships between prostate cancer progression scores (derived from trajectory inference analysis in the PCa transcriptome atlas; Bolis 2021; ref. 27) and either PCa-Stem signature scores (FIG.14B) or individual gene expression levels (FIG.14C-FIG.14D). The dot plots in FIG.14B-FIG.14D display the mean ± SD. Trends across groups were assessed using the Jonckheere-Terpstra (J-T) trend test (wedges above). ***, P < 0.001; ****, P < 0.0001; ns, not significant. Data Source: Normalized RNA-seq data across the stages (shown in the legend in FIG. 14A) were obtained from the integrated transcriptomic framework of PCa (Bolis 2021; ref.27). FIG.15 illustrates distinct genomic features of stemness-high prostate cancer (PCa). FIG. 15A is a scatterplot of observed frequencies of genomic alterations in cancer genes reported in primary prostate cancer (TCGA) versus metastatic castration-resistant prostate cancer (SU2C) with data retrieved from cBioPortal. SV (purple) indicates structural embodiments / fusions; MUT (green) indicates mutation; AMP (red) indicates amplification in DNA copy numbers (copy number alterations (CNA) ≥ 2); HOMDEL (blue) indicates homozygous deletion or deep deletion. FIG. 15B is a genomic alteration frequency plot showing that (early-stage) low-grade prostate cancers have prevalent PTEN and RB1 loss and high-grade prostate cancers have frequent TP53 mutation and MYC AMP, whereas advanced prostate cancers have prevalent TP53 mutation, MYC amplification, and AR amplification / mutation. FIG. 15C is a cBioPortal Oncoprint showing the frequencies of significant genetic alteration events along the prostate cancer progression continuum. FIG.15D and FIG.15E show an association of major genomic alterations in prostate cancer with Stemness. Grey, no alterations; Blue, HOMDEL; Green, MUT; Red, AMP; Purple, Fusion. TMPRSS2 / ...†: FUSION (FIG.15C) or TMPRSS2†(in FIG.15D-FIG.15E) stands for combined fusion events in TMPRSS2, ERG, ETV1 and ETV4; FOXA1 / SPOP / ...†:MUT (in FIG. 15C) or FOXA1†(in FIG.15D-FIG.15E) represent combined mutation events in FOXA1, SPOP, IDH1 and CHD1. Note that the data presented represent the major mutation subtypes in primary prostate cancer (TCGA PRAD cohort) reported in Cancer Genome Atlas Research Network 2015 (Abeshouse et al., 2015, ref.18) and alterations (mutations, structural variants and copy number) of unknown significance were excluded. Within the violin plots, the center lines represent median values, and box edges are 75th and 25th percentiles. Significance was calculated by Mann-Whitney test (*, P < 0.05; **, P < 0.01; ****, P < 0.0001). FIG.16 shows the lack of effect of neoadjuvant androgen deprivation therapy (nADT) on MYC signaling activity (MYC-Sig). Matched comparisons show no significant differences in both MYC mRNA and MYC-sig after neo-adjuvant ADT (Post-nADT) as compared to the matched samples before nADT(Pre-nADT) from the same individuals in FIG. 16A (Rajan 2014 cohort); FIG. 16B (Sharma 2018 cohort); and FIG. 16C (Long 2020 cohort); or in FIG. 16D, the RPCI Chatta / Nastiuk cohort (unpublished) comprising prostate cancer samples from patients who received nADT (+nADT) and the patients who did not receive ADT (no-ADT) but otherwise matched (with the +nADT patients) for ages and tumor stage and grade. Sample sizes are indicated in parentheses. Within the plots, results are shown as the mean ± SD. The pairs are linked with a grey line. Significance was calculated by two-tailed paired (FIG. Docket No.: 440914 [2085_010 PCT] 16A-FIG. 16C) or unpaired (FIG. 16D) Student’s t-test (ns, not significant; *, P < 0.05). See Table 2 for MYC-sig gene signature information. FIG. 17 shows a schematic summary of prostate cancer progression and aggressiveness accompanied by gain of oncogenic stemness and loss of prostatic epithelium differentiation defined by canonical AR signaling. The oncogenic dedifferentiation state (stemness; indicated by red arrows) and canonical pro-differentiation AR signaling activity (AR-A; blue arrows) was quantified using transcriptomic data (3,102 RNA-seq and 84,081 microarray samples) collected from 26 datasets encompassing the evolutionary spectrum of prostate cancer development and progression: normal prostate, early-stage and advanced primary tumors, metastatic castration- resistant prostate cancer, and prostate cancer subjected to long-term or neo-adjuvant androgen- deprivation therapy (ADT). The analysis reveals a continual increase in the stemness at all prostate cancer progression stages, i.e., (1) prostate cancer initiation and early tumor growth (compared with normal or matched benign prostate tissues); (2) natural tumor progression (treatment-naïve); and (3) long-term ADT to treatment resistance and metastasis. In contrast, the pro-differentiation AR-A displays a bell-shape trajectory with an increase in early-stage tumors followed by steady decreases in advanced tumors and the lowest in metastatic castration-resistant prostate cancer (mCRPC). The results demonstrate that the increased AR-A represents a driving force of elevated stemness in early prostate cancer but tumor progression is accompanied by a divergence between decreasing AR-A and increasing stemness, and clinical therapies such as ADT further diversify AR-A and stemness. Canonical AR-A shows an inverse correlation with stemness in treatment- failed mCRPC, and stemness tracks faithfully with the treatment-induced reprogramming. On the other hand, akin to the accompanying continually increasing stemness, prostate cancer progression is characterized by continually elevating MYC amplifications and activity, as well as increasing genome instability associated with deletion of tumor suppressors and AR amplifications and mutations (indicated by purple arrows). Importantly, high stemness correlates with poor patient survival suggesting that stemness is prognostic of patient outcomes, and increased stemness represents a ‘universal’ feature of prostate cancer progression and aggressiveness. GLOSSARY / ABBREVIATIONS Androgen-Deprivation Therapy (ADT) - A treatment strategy aimed at reducing androgen levels or blocking androgen receptor signaling to inhibit prostate cancer growth and progression. ADT is commonly used as a first-line therapy for advanced or high-risk prostate cancer. Androgen Receptor (AR) - A nuclear receptor protein that binds to androgens, such as testosterone, to regulate gene expression and support prostate function. In prostate cancer, AR signaling significantly influences tumor progression and therapy resistance. Androgen Receptor Activity (AR-A) - The canonical pro-differentiation signaling activity of the androgen receptor, measured using transcriptome-based gene signature scores derived from AR-regulated genes. Androgen Receptor Pathway Inhibitors (ARPIs) - A class of drugs that target and inhibit the androgen receptor signaling pathway, including agents such as abiraterone and enzalutamide, used in the treatment of prostate cancer to suppress tumor growth and progression. Docket No.: 440914 [2085_010 PCT] AR Pathway-Active mCRPC (ARPC) - A subtype of metastatic castration-resistant prostate cancer (mCRPC) characterized by active androgen receptor (AR) pathway signaling. Cancer Stem Cells (CSCs) - A subpopulation of cancer cells within a tumor that possess stem cell-like properties, including self-renewal and the ability to differentiate into multiple cell types. CSCs are thought to drive tumor initiation, progression, metastasis, therapy resistance, and recurrence. Castration-Resistant Prostate Cancer (CRPC) - A form of prostate cancer that continues to progress despite androgen deprivation therapy (ADT), often characterized by increased stemness and molecular alterations in AR signaling. Cox Proportional Hazards Model - A multivariable statistical model used to evaluate the independent prognostic significance of variables, such as the PCa-Stem Signature, while adjusting for clinicopathologic factors. Dedifferentiation - The process by which cells lose their specialized characteristics and revert to a more stem-like, undifferentiated state, often associated with increased tumor aggressiveness. Double-Negative mCRPC (DNPC) - A subtype of metastatic castration-resistant prostate cancer (mCRPC) lacking both androgen receptor (AR) pathway activity and neuroendocrine (NE) pathway activity. Gleason Score (GS) - A way of describing prostate cancer based on how abnormal the cancer cells in a biopsy sample look under a microscope and how quickly they are likely to grow and spread. Most prostate cancers contain cells that are different grades. The Gleason score is calculated by adding together the two grades of cancer cells that make up the largest areas of the biopsied tissue sample. The Gleason score usually ranges from 6 to 10. The lower the Gleason score, the more the cancer cells look like normal cells and are likely to grow and spread slowly. Higher scores (e.g., GS8-10) indicate poorly differentiated and more aggressive tumors. The Gleason score is used to help plan treatment and determine prognosis (outcome) (Ref.76). Kaplan-Meier Survival Analysis - A statistical method used to estimate survival probabilities over time, often employed to assess the prognostic significance of stemness metrics in prostate cancer cohorts. Metastatic Castration-Resistant Prostate Cancer (mCRPC) - An advanced stage of prostate cancer that has spread to distant sites and is resistant to androgen deprivation therapy, often exhibiting high stemness and reduced AR activity. MYC Signaling Activity (MYC-Sig) - A transcriptome-based metric that quantifies oncogenic activity driven by MYC-regulated genes, often associated with increased stemness and aggressive prostate cancer phenotypes. Neoadjuvant Androgen Deprivation Therapy (nADT) - A pre-surgical treatment strategy that reduces androgen levels to suppress AR signaling and tumor growth, often leading to decreased stemness and AR activity. Neuroendocrine mCRPC (NEPC) - A subtype of metastatic castration-resistant prostate cancer (mCRPC) characterized by neuroendocrine pathway activity and features, often associated with aggressive disease and high stemness. Docket No.: 440914 [2085_010 PCT] Normal Human Prostate (NHP) - A walnut-sized gland, below the bladder and surrounding the urethra, typically characterized by a pseudostratified two-layer epithelium consisting of basal and luminal cells, interspersed with rare neuroendocrine cells, and lacking malignant or preneoplastic changes. Patient-Derived Xenografts (PDX) - In vivo models generated by implanting human tumor tissue into immunodeficient mice. Primary Prostate Cancer (Pri-PCa) - Prostate cancer at the initial site of origin prior to metastasis or treatment resistance. Prostate Cancer (PCa) - Malignant neoplasms arising in the prostate gland. RNA Sequencing (RNA-seq) - High-throughput sequencing of RNA transcripts to analyze the transcriptome and gene expression patterns within biological samples. Single-Sample Gene Set Enrichment Analysis (ssGSEA) - A computational method used to calculate gene signature scores for individual samples based on the enrichment of specific gene sets, such as the PCa-Stem signature. Spearman’s Correlation Coefficient - A statistical measure used to assess the strength and direction of a monotonic relationship between two variables, often applied in calculating the stemness score. Stemness - A set of cellular traits associated with stem cells, including self-renewal and differentiation potential. In cancer, stemness is linked to tumor aggressiveness, therapy resistance, and poor clinical outcomes. The Cancer Genome Atlas (TCGA) – a landmark cancer genomics program that molecularly characterized over 20,000 primary cancer and matched normal samples spanning 33 cancer types. Tumor Mutation Burden (TMB) - The total number of mutations per megabase of DNA in a tumor genome, often used as a marker of genomic instability and aggressiveness in cancer. Z-Score Normalization - A statistical method used to standardize gene expression values by calculating the number of standard deviations a data point is from the mean, enabling comparison across datasets. DETAILED DESCRIPTION This disclosure focuses on new quantitative metrics for evaluating prostate cancer aggressiveness, stage, and predicting patient outcomes. The described approach develops methods including a prostate cancer stemness score and a 12-gene prostate cancer stemness signature, aiming to improve diagnostic accuracy, guide treatment decisions, and support personalized medicine. These methods are particularly significant given the diverse molecular characteristics and progression patterns observed in prostate cancer. One aspect of the method adapts the Stemness Index developed by Malta TM, et al. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell 173, 338-354.e15 (2018) to provide a quantitative framework for assessing prostate cancer (PCa) aggressiveness, therapy resistance, and patient prognosis. The Stemness Index (mRNAsi) is Docket No.: 440914 [2085_010 PCT] calculated using a one-class logistic regression (OCLR) model trained on gene expression profiles of embryonic stem cells and differentiated cells (see FIG. 8C). The OCLR model generates a weight vector for each gene, which is then correlated with the transcriptomic profile of a given tumor sample to produce a stemness score. The resulting stemness score is scaled between 0 and 1, reflecting the similarity of the tumor’s gene expression pattern to that of stem cells, with higher scores indicating greater stem-like characteristics and dedifferentiation (see FIG. 8C). This disclosure describes an adaption of the Stemness Index (hereinafter “Stemness”) to enable systematic quantification of stemness across various stages of prostate cancer progression, from localized primary prostate cancer (Pri-PCa) to metastatic castration-resistant prostate cancer (mCRPC) (see FIG.3F, FIG.12A, and FIG.12B). By integrating these scores with clinical data, the method provides insights into tumor aggressiveness, therapy resistance, and patient prognosis, supporting a robust framework for personalized treatment strategies (see FIG.4; FIG.12). Numerous datasets of prostate cancer cells were analyzed to determine the Stemness associated with specific stages of prostate cancer. The correlation between Stemness scores and different stages of prostate cancer (PCa) reveals a progressive increase in Stemness as the disease advances, as illustrated in FIG. 3F, FIG. 12A, and FIG. 12B. Normal prostate tissues from the GTEx cohort exhibit stemness scores typically clustering between 0.10 and 0.25, reflecting a well- differentiated state. Tumor-adjacent benign tissues and early-stage, treatment-naïve primary prostate cancer (Pri-PCa) samples show intermediate Stemness values, generally ranging from 0.20 to 0.40. As the disease progresses to aggressive primary prostate cancer (Pri-PCa) and post- surgery adjuvant androgen deprivation therapy (Pri-PCa / ADT), Stemness increases further, often falling within the 0.30 to 0.55 range. Elevated Stemness scores, ranging from 0.45 to 0.75, are observed in metastatic castration-resistant prostate cancer (mCRPC), with particularly high values reaching up to 0.95 in long-term models such as patient-derived xenografts, xenografts, and cell lines. FIG.3F further highlights this trend, demonstrating a continual increase in Stemness across the prostate cancer spectrum, from prostate tissues to mCRPC, with mCRPC subtypes such as AR- positive (ARPC), neuroendocrine (NEPC), and double-negative prostate cancer (DNPC) exhibiting the most pronounced stemness. This progressive elevation in Stemness underscores the association of these scores with dedifferentiation, therapy resistance, and increased tumor aggressiveness. The 12-gene PCa-Stem signature is a transcriptome-based molecular tool designed to assess prostate cancer (PCa) aggressiveness and predict patient outcomes. This signature was derived by identifying genes that were significantly upregulated in Stemness-high prostate cancer samples compared to Stemness-low samples across both treatment-naïve primary prostate cancer (Pri-PCa) and metastatic castration-resistant prostate cancer (mCRPC) cohorts (see FIG.5B-FIG. 5D). The resulting 12 genes—HMMR, AURKB, CENPA, DEPDC1B, HJURP, PBK, MELK, UBE2C, DLGAP5, NEK2, BIRC5, and KLK12—were selected based on their consistent association with increased stemness and poor clinical outcomes. To calculate scores for the 12- gene PCa-Stem signature (see Table 2), the Gene Set Variation Analysis (GSVA) package (version 1.50.5; RRID:SCR_021058) (73) in R (version 4.3.3; RRID:SCR_001905) is used to perform a single-sample gene set enrichment analysis (ssGSEA) across different stages of PCa progression using the normalized RNAseq data from Bolis et al., 2021, Ref.27 (Prostate Cancer Transcriptome Atlas. see FIG. 5E–FIG. 5V; FIG. 14). This signature provides a robust framework for identifying high-risk patients and guiding personalized therapeutic strategies. Docket No.: 440914 [2085_010 PCT] Both metrics have been validated across multiple prostate cancer cohorts, demonstrating strong correlations with poor clinical outcomes, including reduced progression-free survival and overall survival (see FIG. 4A-FIG. 4C, FIG. 5, and FIG. 14). For instance, high Stemness consistently correlated with worse patient survival accross various cohorts including the Spratt 2017 (38), Tosoian 2020 (39), CHAARTED 2021 (40), and the mCRPC (SU2C 2019 (41)), as well as Taylor 2010 (33), see FIGs.4A-C and FIGs.13A-F. Higher PCa-Stem signature scores were found to correlate with poorer patient survival in both treatment-naïve primary prostate cancer (Pri-PCa) patients (FIG. 5S–T) and metastatic castration-resistant prostate cancer (mCRPC) patients (FIG.5U–V). Multivariate analysis, adjusted for clinicopathologic factors such as age, Gleason Score (GS), and tumor stage, demonstrated that patients with high PCa-Stem signature scores had significantly worse independent prognoses compared to those with low PCa- Stem signature scores, and high PCa-Stem signature scores were associated with reduced progression-free survival (PFS) in primary prostate cancer (Pri-PCa) patients (FIG. 5T). High PCa-Stem signature scores were also associated with decreased overall survival (OS) in metastatic castration-resistant prostate cancer (mCRPC) patients (FIG.5V). Stages of Prostate Cancer The stages of cancer characterized in the analysis are shown in FIG.8A and FIG.8B. FIG. 8A depicts a normal prostate as a glandular organ with a pseudostratified two-layer epithelium consisting of basal and luminal cells, interspersed with rare neuroendocrine cells. Luminal cells express cytokeratins CK8 and CK18, androgen receptor (AR), and prostate-specific antigen (PSA), and represent the primary secretory cells. Also depicted are rare luminal progenitor cells within the luminal cell layer, which represent less than 2% of the luminal cell population and are characterized by the co-expression of CK5 and CK8 and a low level of CD38 (reviewed in ref.8). Basal cells express CK5 and CK14, interfacing with the stroma through the basal lamina. The basal cell compartment harbors stem / progenitor cells capable of bidirectional differentiation (ref. 8). FIG.10A is an enlarged depiction of a normal human prostatic gland. Localized primary prostate cancer (Pri-PCa) progresses from well-differentiated structures (low Gleason score) to poorly differentiated configurations (high Gleason score), indicating a shift towards a more stem-like state with loss of glandular architecture. This transition involves a reduction in basal cells and an expansion of progenitor-like cells, with the combined Gleason score (GS) reflecting the architectural patterns of cancerous glands. Gleason pattern 1 represents the most well-differentiated glandular structures and Gleason pattern 5 consists of the most poorly differentiated cells lacking glandular structures. Given that prostate cancer is often multifocal, the combined GS is the sum of the two most prevalent patterns, with GS ≤ 7 representing low-grade prostate cancer and GS ≥ 8 representing high-grade prostate cancer. Illustrated in FIG.8A are two representative tumor glands with a decrease / loss in basal cells and expansion of luminal progenitor-like cells. Metastatic castration-resistant prostate cancer (mCRPC) exhibits pronounced Stemness but markedly reduced AR-A. Features of this cancer include increased cellular heterogeneity and the presence of cells with stem-like and neuroendocrine-like characteristics, which drive therapy resistance and disease progression. Docket No.: 440914 [2085_010 PCT] The observed Stemness scores across various prostate cancer (PCa) states and models span the full 0 to 1 range, with distinct groupings corresponding to disease progression and biological context. In prostate tissues from the Genotype-Tissue Expression (GTEx) project, Stemness scores typically cluster at the lower end of the scale, ranging from approximately 0.10 to 0.25. Tumor- adjacent benign tissues and early-stage, treatment-naïve primary PCa samples show intermediate Stemness values, generally between 0.20 and 0.40. As the disease advances to aggressive primary prostate cancer (Pri-PCa) and post-surgery adjuvant hormone therapy (Pri-PCa / ADT), Stemness scores increase further, commonly falling within the 0.30 to 0.55 range. Metastatic castration- resistant prostate cancer (mCRPC) samples exhibit elevated Stemness, with most values between 0.45 and 0.75, and the uppermost scores—ranging from 0.70 up to 0.95—are observed in long- term models such as patient-derived xenografts, xenografts, and cell lines. These empirical ranges, derived from the distribution of datapoints in FIG. 12, provide a framework for interpreting Stemness scores in the context of PCa progression and aggressiveness. In FIG. 5A, high and low stemness in prostate cancer (PCa) are stratified based on the global Stemness score. The top 33% of samples are categorized as “high stemness,” while the bottom 33% are designated as “low stemness.” High stemness tumors exhibit elevated expression of genes associated with embryonic stem cell traits, aggressive molecular pathways, and therapy resistance, including enrichment in DNA repair mechanisms, MYC activation, and mTORC1 signaling. These tumors are strongly correlated with poor clinical outcomes, such as reduced overall survival and increased metastatic potential. Conversely, low stemness tumors demonstrate upregulation of immune signaling and inflammatory response pathways, indicative of a more differentiated state and less aggressive phenotype. Treatment of prostate cancer Current treatments for prostate cancer (PCa) encompass a range of modalities tailored to disease stage and patient risk profile. For localized or low-risk PCa, active surveillance and localized therapies such as radical prostatectomy or radiation therapy are standard approaches. Androgen deprivation therapy (ADT) remains the backbone for advanced and metastatic disease, often combined with androgen receptor pathway inhibitors (ARPIs) such as abiraterone, enzalutamide, apalutamide, or darolutamide to improve survival outcomes. Chemotherapy with docetaxel or cabazitaxel is indicated for metastatic castration-resistant prostate cancer (mCRPC), and radiopharmaceuticals like radium-223 or lutetium-177-PSMA-617 have shown benefit in select patients. Immunotherapy (e.g., sipuleucel-T) and targeted therapies such as PARP inhibitors (olaparib, rucaparib) are also approved for specific molecular subtypes or treatment-refractory cases. Despite these advances, disease progression and therapy resistance remain significant challenges, driving the development of novel agents including radioligand therapies, antibody- drug conjugates, chimeric antigen receptor T-cell (CAR-T) therapies, and cytokine-based treatments. These emerging options are under active investigation and may further expand the therapeutic landscape for PCa. For a comprehensive review of current and emerging treatments, see Atiq M, Chandran E, Karzai F, Madan RA, Aragon-Ching JB. Emerging treatment options for prostate cancer. Expert Rev Anticancer Ther. 2023 Jun;23(6):625-631. doi:10.1080 / 14737140.2023.2208352, including Figures 1 and 2. Docket No.: 440914 [2085_010 PCT] Increasing Stemness correlates with increasing prostate cancer stage and / or aggressiveness Low stemness refers to a tumor state characterized by reduced stem-like features, greater cellular differentiation, and limited plasticity. This state is quantitatively measured using the Stemness score, a transcriptome-based metric scaled from 0 to 1, where scores closer to 0 indicate a more differentiated, less stem cell-like transcriptional program. In the context of prostate cancer (PCa), tumors classified as “low stemness” may fall within the bottom 33% of Stemness scores in a given cohort, corresponding to numerical values generally ranging from approximately 0.10 to 0.25, and in some cases up to 0.30 (see FIG.12). For example, low stemness tumors may be defined as those with scores below 0.25–0.30, representing the lower third (bottom 33%) of the distribution, while the remaining 67% of tumors are considered intermediate or high stemness. Tumors with low stemness are associated with a more differentiated phenotype, lower aggressiveness, increased responsiveness to therapy, and more favorable clinical outcomes, such as improved progression- free survival and overall survival. Low stemness tumors also demonstrate upregulation of immune signaling and inflammatory response pathways, and are less likely to exhibit aggressive molecular subtypes or high genomic instability. High stemness refers to a tumor state characterized by elevated stem-like features, including enhanced self-renewal capacity, dedifferentiation, and cellular plasticity. This state is quantitatively measured using the Stemness score, a transcriptome-based metric scaled from 0 to 1, where scores closer to 1 indicate greater resemblance to stem cell-like transcriptional programs. In the context of prostate cancer (PCa), tumors classified as "high stemness" may fall within the top 33% of Stemness scores in a given cohort, or correspond to numerical values generally ranging from approximately 0.45 to 0.95, and in some model systems, values may reach up to 0.95 or higher (see FIG.3, FIG.12). In another example, high stemness tumors may be defined as those with scores above 0.5, representing the upper third (top 33%) of the distribution, while the remaining 67% of tumors are considered intermediate or low stemness. (FIG.5A). Tumors with high stemness exhibit increased aggressiveness, therapy resistance, and metastatic potential, as well as poor clinical outcomes, such as reduced progression-free survival and overall survival. High stemness is associated with upregulation of oncogenic pathways, including MYC activation and mTORC1 signaling (FIG. 5F). These tumors also display higher genomic instability, including increased tumor mutation burden, and alterations in significant cancer-associated genes (FIG. 6A–B, FIG. 6D–F). Clinically, high stemness serves as a prognostic indicator for identifying high-risk patients and guiding personalized therapeutic strategies. Certain embodiments of high Stemness may have a stemness score above 0.4, above 0.45, above 0.5, above 0.6, above 0.7, above 0.75, or above 0.775. Stemness Values Stemness values across different prostate cancer (PCa) types may demonstrate a progressive increase as the disease advances. The following findings summarize the mean and standard deviation for each group: • Normal prostate tissues exhibit minimal stemness values, with a mean score of 0.132 (standard deviation: 0.051), reflecting a well-differentiated state (FIG.3F; Table 5). Docket No.: 440914 [2085_010 PCT] • Tumor-adjacent benign tissues and early-stage, treatment-naïve primary prostate cancer (Pri-PCa) samples show intermediate stemness values, with mean scores of 0.228 (standard deviation: 0.0049) and 0.306 (standard deviation: 0.060), respectively (FIG.3F; Table 5). • Aggressive primary prostate cancer (Pri-PCa) treated with adjuvant androgen deprivation therapy (Pri-PCa / ADT) demonstrates further elevation, with a mean score of 0.322 (standard deviation: 0.082) (FIG.3F; Table 5). • Metastatic Prostate Cancer (mCRPC) shows evaluated Stemness values compared to earlier stages, with the following means and standard deviations: mCRPC-Adeno (mean 0.363, standard deviation 0.110), mCRPC (mean 0.386, standard deviation 0.066, mCRPC- NE (mean 0.413, standard deviation 0.100), and additional mCRPC cohorts (mean 0.394, standard deviation 0.095; mean 0.480, standard deviation 0.084; mean 0.486, standard deviation 0.070) (FIG.3F and Table 5). These results underscore the correlation between increasing stemness scores and the progression of prostate cancer, highlighting their use as quantitative biomarkers for assessing tumor aggressiveness and therapy resistance. Note that the type of prostate cancer and the cohort are described more fully in FIG.3. Table 5 Normal Benign Pri- Pri-PCa / ADT mCRPC- mCRPC mCRPC (116) (52) PCa(422) (70) Adeno(34) (59) (135) Mean 0.132 0.228 0.306 0.322 0.363 0.386 0.394 Std. Dev. 0.051 0.049 0.060 0.082 0.110 0.066 0.095 mCRPC- mCRPC mCRPC PDX-LuCaP PCa Cell Lines XG-LAPC9 XG- NE(15) (42) (25) (39) (8) (10) LNCaP (12) Mean 0.413 0.480 0.486 0.653 0.669 0.706 0.765 Std. Dev. 0.100 0.084 0.070 0.077 0.093 0.015 0.039 Docket No.: 440914 [2085_010 PCT] PCa-Stem Signature Values The PCa-Stem Signature values, derived from transcriptomic analyses, provide a quantitative measure of prostate cancer (PCa) aggressiveness and progression. The mean and standard deviation of PCa-Stem Signature scores across various prostate cancer states demonstrate a progressive increase correlating with disease advancement, as summarized below: • Normal prostate tissues have a mean PCa-Stem Signature score of 0.306 with a standard deviation of 0.099. • Benign tissues have a mean PCa-Stem Signature score of 0.436 and a standard deviation of 0.119. • Primary prostate cancer (GS6) samples show a mean PCa-Stem Signature score of 0.527 and a standard deviation of 0.078. • GS7 samples have a mean PCa-Stem Signature score of 0.570 and a standard deviation of 0.082. • GS8 samples display a mean PCa-Stem Signature score of 0.570 and a standard deviation of 0.111. • GS9 / 10 samples have a mean PCa-Stem Signature score of 0.647 and a standard deviation of 0.103. • Tx(70) samples show a mean PCa-Stem Signature score of 0.658 and a standard deviation of 0.104. • Metastatic castration-resistant prostate cancer (mCRPC) subtypes display further elevated values: ARPC has a mean of 0.697 and a standard deviation of 0.156, DNPC has a mean of 0.874 and a standard deviation of 0.140, and NEPC has a mean of 0.889 and a standard deviation of 0.064. These findings underscore the utility of PCa-Stem Signature scores as robust biomarkers for assessing tumor aggressiveness and guiding personalized treatment strategies (see FIG. 14 and Table 6). Note that the type of prostate cancer and the cohort are described more fully in FIG.14. Table 6 Normal (116) Benign(52) GS6(45) GS7(238) Mean 0.306 0.436 0.527 0.570 Std. Deviation 0.099 0.119 0.078 0.082 GS8(49) GS9 / 10(90) Tx(70) ARPC(191) DNPC(16) NEPC(26) Mean 0.570 0.647 0.658 0.697 0.874 0.889 Std. Deviation 0.111 0.103 0.104 0.156 0.140 0.064 Note that the above are exemplars of potential embodiments of the disclosed invention. Other values of Stemness and PCa-Stem Signatures may be calculated. Docket No.: 440914 [2085_010 PCT] Stemness quantitatively measures prostate cancer aggressiveness, plasticity, and progression The current disclosure provides a quantitative co-analysis of global changes and the interrelationship between cancer stemness and pro-differentiation AR signaling activity during prostate cancer (PCa) evolution. By employing gene expression-based general Stemness and the new 12-gene PCa-Stem signature, the analysis demonstrates that while differentiation-regulating AR-A steadily declines in the prostate cancer continuum, increasing Stemness quantitatively measures prostate cancer aggressiveness, plasticity, and progression, and represents a poor prognosticator for patient outcomes. Stemness-high prostate cancer exhibits distinct genomic features with high tumor mutation burdens (TMBs) and is associated with aggressive molecular subtypes. Evidence is presented that elevated Stemness in early prostate cancer might be driven by increased AR-A, but increased MYC-sig activity and other genomic alterations likely propagate the exponentially increased Stemness in metastatic castration-resistant prostate cancer (mCRPC) where AR-A is suppressed. Early studies reported that a cohort of AR-regulated genes declines in high-grade versus low-grade Pri-PCa and that loss of AR expression or inhibition of AR-A promotes a stem-like phenotype (see 6, 34, 54, 55). However, global pro-differentiation AR-A and cancer stemness have not been systematically and quantitatively assessed and co-analyzed in prostate cancer, and it is unclear whether prostate cancer progression is accompanied by increased stemness (6). This disclosure demonstrates that prostate cancer progression is accompanied by a continual increase in Stemness (score) but loss of canonical AR-A, with the two inversely correlated (FIG. 17). Stemness tracks treatment-induced plasticity and prostate cancer aggressiveness, and both high global Stemness and high PCa-Stem signature are associated with poor patient survival. Normal prostatic acini and ducts are lined by AR+secretory luminal cells and AR- basal cells, along with rare neuroendocrine cells. The normal human prostate (NHP) luminal cells exhibit higher AR-A levels compared to basal cells, confirming AR-A as a metric for assessing canonical AR activity and prostatic epithelial differentiation. Basal cells demonstrate increased Stemness, aligning with earlier findings (19, 20, 21, 22, 56) demonstrating that the basal cell compartment contains stem cells. Oncogenic transformation is recognized to impart stem-like characteristics to the initiated cells. Compared to adjacent benign tissue, primary prostate cancers show elevated Stemness, which is associated with and potentially driven by increased AR-A. Loss of PTEN and RB1 may also contribute to elevated Stemness in early prostate tumors. Tumor-adjacent benign tissues exhibit higher Stemness than typical prostate tissue, suggesting that these benign tissues are not normal. In treatment-naïve primary prostate cancer, tumor progression is accompanied by decreasing AR-A but persistently elevated Stemness (FIG.17). The decreasing AR-A is driven by progression-related loss of differentiation, while increasing Stemness is likely driven by MYC amplification and increased MYC activity, with contributions from other oncogenic events such as loss of PTEN and RB1. Long-term ADT / ARPI treatment further attenuates AR-A but substantially upregulates Stemness, such that mCRPC exhibit the most pronounced Stemness, probably driven by continually increasing genomic alterations such as MYC amplification and loss of tumor suppressors. Notably, as AR alterations (AMP / MUT) are specifically induced by ARPIs, Docket No.: 440914 [2085_010 PCT] noncanonical AR-A (i.e., castration-resistant AR activity) likely makes significant contributions to the increased Stemness in mCRPC. The biological correlates measured by the Stemness metric include transcriptional features and relative abundance of stem cells and cancer stem cells (CSCs). The normal human prostate (NHP) basal / stem cells display elevated Stemness compared to luminal cells; ARPI treatment, while shutting down AR signaling, enriches CSCs and upregulates Stemness; and prostate cell lines, xenografts, and patient-derived xenografts (PDX), known to be clonally derived from cancer stem / progenitor cells, demonstrate pronounced Stemness. The metric also measures cellular and lineage plasticity induced by treatment and genetic alterations. Consequently, mCRPC, which harbors more abundant treatment-reprogrammed AR- / locells and prostate cells with stem-like features or in an AR-indifferent state, manifests pronounced Stemness. The analysis further substantiates the aggressive nature of (AR- / lo) CRPC-NEPC and DNPC compared to ARPC, as evidenced by their elevated PCa-Stem signature scores. This suggests a shift in the phenotypic landscape of mCRPC towards more stem-like and aggressive forms, particularly under the pressure of advanced therapies. Likewise, aggressive DKO and TKO murine prostate tumors have undergone lineage transformation and also exhibit elevated Stemness compared to indolent SKO tumors. The Stemness metric likely reports the proliferative status of advanced PCA and mCRPC, as Stemness-elevated tumors in both Pri-PCa and mCRPC are enriched in “Cell-cycle progression” signature and most genes in the 12-gene PCa-Stem Signature are involved in mitosis and cell-cycle progression. In conclusion, the Stemness score measures prostate cancer aggressiveness, as elevated Stemness correlates not only with more advanced and aggressive stages of prostate cancer but also with aggressive PAM50-LumB and PCS1 molecular subtypes. Of clinical significance, both the 12-gene PCa-Stem signature and the global Stemness score correlate with poor patient survival, suggesting the use of the Stemness metric into a predictive biomarker to distinguish aggressive from indolent primary tumors and a prognostic indicator of unfavorable clinical outcomes in mCRPC patients. Notably, 11 of the 12 genes (excluding KLK12) in the newly developed PCa-Stem signature are significantly correlated with PCA progression (FIG.14C), implicating their individual contributions to the increased Stemness in advanced Pri-PCa and mCRPC. The disclosed transcriptome-based gene signature scores have been developed to assess pro-differentiation AR activity (AR-A), MYC activity (MYC-sig), and oncogenic stem-cell features (general Stemness score and the 12-gene PCa-Stem signature). These scores enable global profiling of differentiation and aggressiveness status across the prostate cancer progression spectrum. Adapting and developing Stemness and AR-A metrics for PCa analysis Two transcriptome based algorithms were used to annotate 26 datasets of 3,102 bulk RNA- seq and 84,081 microarray samples encompassing the prostate cancer (PCa) continuum (FIG.8B; Table 1) to determine the global and dynamic changes of and inter-relationship between degree of malignancy (oncogenic dedifferentiation) and level of (luminal) differentiation during prostate cancer (PCa) development and progression (FIG.8A-B). The analysis focused on and compared three progressive prostate cancer stages, from normal prostate to localized primary prostate cancer Docket No.: 440914 [2085_010 PCT] (Pri-PCa), from low-grade to high-grade primary prostate cancer (Pri-PCa), and from treatment- naïve primary prostate cancer (Pri-PCa) to castration-resistant prostate cancer (CRPC) / metastatic castration-resistant prostate cancer (mCRPC) (FIG. 8A). “Progression” was defined as stages beyond primary prostate cancer (Pri-PCa) and as disease entities that are more aggressive in a comparative manner. FIG.8B illustrates how the datasets were utilized for comparative analyses across these prostate cancer stages. FIG. 8C shows the schematic presentation of the transcriptome-based stemness quantification method mRNAsi (Stemness). The disclosed method adapts the Stemness Index (mRNAsi; Stemness for short) (FIG.8C) to gauge the degree of malignancy (or aggressiveness) and employs the Z-score normalized canonical (pro-differentiation) AR activity or AR-A (FIG. 8D) to measure the level of differentiation. Canonical AR-A can be determined by quantifying AR-regulated transcripts under intact androgen / AR signaling conditions, and there are several such AR-regulated gene lists (14, 15, 16, 17, 18; FIG.9A-9B). The AR-A lists were validated based on their enrichment in prostatic epithelium differentiation, correlation of individual gene expression with AR-A signatures in clinical datasets, and overall consistency in correlation among the AR-A scores (FIG. 9B-FIG. 9D). The AR-A scores of the list from Bluemn (14) correlated well with other scores (see FIG. 9E-9G) and the 10 genes in this signature consistently represented AR targets in multiple clinical datasets (FIG.9D; Table 2), AR-A quantification was performed using this list. Normal prostate epithelium shows a negative correlation between Stemness and AR-A The normal human prostate (NHP) comprises 2 major epithelial types: basal and luminal cells (FIG. 10A). Two studies profiled FACS-purified NHP luminal and basal cell populations (19, 20) were annotated for the analysis. The luminal cell population (Trop2+CD49flo) possessed higher AR mRNA levels and AR-A but lower Stemness compared to basal cells (Trop2+CD49fhi) and, in both datasets, AR-A negatively correlated with Stemness (FIG. 10B-C), supporting the conclusion that NHP basal cell compartment harbors primitive stem cells (7, 19, 20, 21, and 22) and AR-A measures the level of prostate epithelial differentiation. Primary prostate cancer (Pri-PCa) exhibits concordantly increased Stemness and AR-A, and neoadjuvant ADT (nADT) decreases both Stemness and AR-A The analysis of AR-A and Stemness in matched pairs of prostate tumor (T) and normal (N; tumor-adjacent benign) tissues in the TCGA-PRAD (18) (n=51 pairs), Wyatt 2014 (23) (n=12 pairs), and Long 2020 (24) (n=10 pairs) cohorts showed concordantly increased AR-A and Stemness (FIG.1A-C). Accompanying elevated AR-A, AR mRNA levels were also increased in primary prostate cancer (Pri-PCa) compared to benign tissue (FIG. 1A-C; note that 130 the increase in AR mRNA levels in the Long 2020 cohort approached statistical significance). The paired T / N comparisons (FIG.1A-C) suggest that early prostate cancer initiation and growth are characterized by concordantly increased Stemness and AR-A. To test whether AR-A might causally drive increased Stemness early during prostate tumorigenesis, 4 PCa nADT (i.e., ~2-6 months of ADT before prostatectomy or radiation) cohorts (24, 25, 26) were investigated. In the first 3 cohorts with matched pairs of pre- and post-nADT samples (from the same patients), nADT concordantly downregulated AR-A (without decreasing AR mRNA levels) as well as the Stemness (FIG.1D-FIG.1F). In the Nastiuk cohort with non-paired post-nADT and no-nADT samples (i.e., Docket No.: 440914 [2085_010 PCT] from different patients but matched for ages and tumor grades and stages), nADT increased AR mRNA levels (FIG.11A) but reduced both AR-A and Stemness (FIG.11B-FIG.11C). Pri-PCa progression is accompanied by reduced AR-A but continually increasing Stemness The dynamic changes of Stemness and AR-A during spontaneous (i.e., therapy-free) Pri- PCa progression were assessed (FIG.2) by sorting out treatment-naïve tumors in TCGA-PRAD. The treatment-naïve tumors in TCGA-PRAD were sorted out by excluding samples from patients who received adjuvant (post-surgery) hormone therapy (i.e., Pri-PCa / ADT or Tx; n=70) (FIG.2A) and grouping the Pri-PCa samples according to increasing Gleason Score (GS) (FIG. 2B-FIG. 2D). When compared with the 52 benign (N) samples, all the four GS groups from Pri-PCa (GS6, GS7, GS8, and 147 GS9 / 10 combined) showed higher AR-A and Stemness (FIG.2C-FIG.2D), consistent with the results in pairwise T / N comparisons (FIG. 1A-FIG. 1C). However, when compared to GS6 PCa, the three higher GS groups showed increasing AR mRNA levels (FIG.2B) but decreasing AR-A (FIG.2C) and increasing Stemness (FIG.2D). Jonckheere-Terpstra’s (J-T) trend test validated a gradual reduction in canonical (pro-differentiation) AR signaling but an increasing trend of Stemness with Pri-PCa progression (FIG. 2C-FIG. 2D). Although a modest positive correlation between AR-A and Stemness in pooled Pri-PCa samples was observed (Pearson’s r=0.27; FIG.2E), this positive correlation gradually diminished with PCa progression when the samples were categorized by tumor grade (FIG. 2F). The linear regression analysis confirmed that the interaction between Stemness and AR-A significantly depended on tumor grade (P=0.0003), with a linear decrease in the slope of the correlation as GS increased (FIG.2G). Collectively, these results indicate that, in contrast to early tumorigenesis, spontaneous prostate cancer progression (i.e., with increasing GS and tumor grade) is marked by a divergence between oncogenic Stemness and pro-differentiation AR-A. Therapies and metastatic progression drive further divergence between attenuated AR-A and increased Stemness Clinical treatment and metastatic progression impact Stemness and AR-A. Untreated Pri- PCa and Pri-PCa / ADT were identified in TCGA and metastatic castration-resistant prostate cancer (mCRPC) was identified in SU2C10 (FIG.3A-FIG.3C). AR-A was elevated in Pri-PCa compared to adjacent benign tissue (N) but reduced in more aggressive Pri-PCa / ADT samples, while the Stemness, in contrast, continually increased from N → Pri-PCa → Pri-PCa / ADT (FIG.3B-FIG. 3C). mCRPC, despite markedly upregulating AR mRNA (FIG.3A), showed the lowest AR-A but highest Stemness (FIG.3B-FIG.3C), indicating a shift from a positive correlation between AR- A and Stemness in Pri-PCa to a negative association in mCRPC (FIG.3D). Stemness and AR-A were tracked across the prostate cancer spectrum by employing normalized RNAseq data from the PCa transcriptome atlas (Bolis 2021), which included normal prostate specimens (n = 174), Pri-PCa (n = 714) and mCRPC (n = 335) (27) (FIG. 3E). AR-A increased from benign to Pri-PCa but declined in Pri-PCa / ADT and continued to decrease in mCRPC, reaching its lowest in mCRPC-NE (28) as supported by J-T test (FIG.3E). In contrast, Stemness exhibited a continually increasing trajectory across the prostate cancer spectrum encompassing Normal / Benign → treatment-naïve Pri-PCa → Pri-PCa / ADT → (m)CRPC (P<0.0001, J-T Trend test; FIG. 3F; FIG. 12A). Notably, the 3 subtypes of mCRPC, i.e., AR- Docket No.: 440914 [2085_010 PCT] positive (ARPC), CRPC-NEPC and AR- / NE- double-negative PCa (DNPC) all showed higher Stemness than Pri-PCa and also displayed increasing Stemness among the 3 subtypes (FIG.12B). Unexpectedly, tumor-adjacent benign prostate tissues showed much higher Stemness than normal prostate in the Genotype-Tissue Expression Project (GTEx) (29) (FIG. 3F; FIG. 12A), the majority of which were from cancer-free organ donors. This surprising finding suggests that the transcriptomes of adjacent benign tissues have apparently skewed towards those of cancer samples, consistent with previous observations that adjacent benign tissues represent an intermediate state between healthy and tumor tissues (30). The Cancer Cell Line Encyclopedia (31, 32) cell lines including PCa cell lines, and PCa-derived PDX (33) and xenografts (34) exhibited the highest Stemness (FIG. 3F; FIG. 12A), supporting that cancer cell lines and xenografts are clonally derived and highly enriched in cancer stem cells. As with the relationship between AR-A and Stemness during spontaneous Pri-PCa progression (FIG.2F-FIG.2G), analysis of the Bolis 2021 PCa transcriptome atlas (27) revealed a similar change from a positive correlation between AR-A and Stemness in Pri-PCa (blue line; Pearson’s r=0.32, P<0.0001; FIG. 3G) to a negative correlation between the two in mCRPC (green line; Pearson's r=-0.15, P=0.005; FIG. 3G). The analysis of the relationship between Stemness and AR-A was expanded to include microarray-derived transcriptomic datasets (35, 36), allowing analysis of data from prostate cancer cohorts generated before the next-generation sequencing (NGS) era. Analyzing the GRID database of over 82,000 prospectively collected biopsy samples of localized PCa from clinical use of the Veracyte Decipher test (35), the Stemness was systematically quantified across the National Comprehensive Cancer Network (NCCN) Risk Groups and increasing Stemness was observed along the NCCN Risk trajectory (FIG.3H). In the Taylor dataset (36), AR-A was the highest in Pri-PCa but significantly decreased in metastatic PCa (mPCa) (FIG.3I), while the Stemness increased progressively from benign to Pri-PCa and reached the highest levels in mPCa (FIG. 3J). Linear regression analysis confirmed a gradual loss of positive correlation between AR-A and Stemness during prostate cancer progression, as illustrated by distinct regression lines for each stage (FIG.3K-FIG.3M). The dynamic changes of Stemness and AR in genetically engineered mouse models (GEMMs) of prostate cancer (PCa) with increasing aggressiveness due to individual or combined deletion of 3 tumor suppressor genes, Pten, Rb1 and Trp5337, were analyzed. Briefly, Pten- / -single knockout (SKO) prostate tumors develop around 9 weeks, and mice rarely develop metastasis with a median lifespan of 48 weeks. In contrast, the Pten- / -; Rb1- / -double knockout (DKO) mice develop highly metastatic prostate cancer that shortens median survival to ~38 weeks. When Trp53 is further deleted in DKO background, the triple KO (TKO; Pten- / -; Rb1- / -; Trp53- / -) tumors are exclusively AR–NEPC and castration-resistant de novo with high metastatic rate and lifespan of ~16 weeks (37). The AR-A was the highest in SKO tumors but much reduced in aggressive DKO and TKO tumors (FIG. 12C). In contrast, Stemness was the lowest in SKO but significantly increased in DKO and TKO (FIG. 12D). Correlation analyses revealed a positive correlation between AR-A and Stemness in SKO that turned to negative correlations in both DKO and TKO tumors (FIG.12E-FIG.12H). These results, collectively, indicate that hormone therapy and metastatic progression further drive marked increases in Stemness with concomitant decreases in pro-differentiation AR- A. Docket No.: 440914 [2085_010 PCT] Stemness is prognostic of poor survival in prostate cancer patients High Stemness consistently correlated with worse patient survival across various cohorts including the Spratt 2017 (38) (P=0.001, log-rank; FIG.4A), Tosoian 2020 (39) (P=0.014, log- rank; FIG.4B), CHAARTED 2021 (40) (P=0.006, log-rank; FIG.4C; P=0.149, log-rank; FIG. 13A), and the mCRPC (SU2C 2019 (41); P=0.06, log-rank; FIG.13B-FIG.13C) as well as Taylor 2010 (36) (P=0.028, log-rank; FIG.13D-FIG.13E). Notably, the CHAARTED cohorts displayed the highest mean Stemness compared to Tosoian and Spratt cohorts, and the CHAARTED cohort showed the highest median Decipher Prostate Genomic Classifier (38, 39, 40) among the three cohorts (FIG.4D). Interestingly, like high Stemness, low AR-A was correlated with poor patient survival in the Taylor dataset (P=0.040, log-rank; FIG.13F). Stemness-high prostate cancers are associated with aggressive molecular subtypes and a 12- gene PCa-Stem signature prognosticates poor patient survival. To identify key drivers of Stemness and explore differences between tumors with high versus low Stemness, Pri-PCa (TCGA-PRAD) and mCRPC (SU2C 2019) samples were stratified separately based on Stemness scores (FIG. 5A). Differential gene expression (DEG) analyses revealed significant differences between the top 33% (Stemness-high) and the bottom 33% (Stemness-low) PCa samples in both treatment-naïve Pri-PCa (FIG. 5B) and treatment-failed mCRPC (FIG.5C), resulting in a 12-gene “PCa-Stem signature” (FIG.5D; Table 2). Gene set enrichment analysis (GSEA) was performed to delineate the molecular differences between Stemness-low and Stemness-high prostate cancer samples (FIG. 5E-FIG. 5F). GSEA revealed that Stemness-high prostate cancer samples were enriched in gene sets associated with embryonic stem cells (ESC), as well as aggressive cancer phenotypes, such as undifferentiated cancer and metastasis, in both Pri-PCa and mCRPC (FIG. 5F). In contrast, Stemness-low prostate cancer samples were enriched in gene signatures linked to lower aggressiveness and inflammatory pathways (FIG. 5E). Additionally, hallmark pathways such as E2F targets, MYC targets, mTORC1 signaling, and DNA repair were upregulated in Stemness- high prostate cancer (FIG. 5F), while TGF-β signaling, TNF-α signaling, and IFN-γ responses were prominent in Stemness-low PCa (FIG.5E). The aggressive PAM50-LumB molecular subtype (42, 43, 44) was significantly more prevalent in Stemness-high prostate cancer, accounting for 74% in Pri-PCa (compared to 15% in Stemness-low PCa; FIG.5G) and 43% in mCRPC (compared to 4% in Stemness-low PCa; FIG. 5H). The PCS1 subtype (45, 46), known for its association with ADT resistance and the highest risk of progression to advanced disease in comparison with PCS2 or PCS3, was predominantly enriched in both Stemness-high Pri-PCa and mCRPC (FIG. 5I-FIG. L). The PAM50-Basal subtype (42, 43) increased in Stemness-high mCRPC but not in Pri-PCa (FIG. 5G-FIG. 5H), aligning with the characteristics of mCRPC, which involves lineage plasticity and acquisition of basal, mesenchymal, neural and stem-like phenotypes when developing ARPI resistance (7, 44, 47). Consequently, Stemness-high samples remained largely AR-dependent (48) in Pri-PCa (FIG. 5M) but transitioned to AR-independent NE subtype (44) in mCRPC (FIG.5N). Stemness-high prostate cancer was also characterized by high proliferation as evidenced by their enrichment in a Docket No.: 440914 [2085_010 PCT] 31-gene cell-cycle progression signature (44) (FIG. 5O-FIG. 5P). The association between Stemness and a signature of lineage plasticity risk after Enza treatment for mCRPC (12) demonstrated that Stemness-high tumors may be at risk for this virulent form of treatment resistance (FIG.5Q-FIG.5R). To further assess the clinical relevance of Stemness, the prognostic impact of the newly developed 12-gene PCa-Stem signature was analyzed (FIG. 5D). Higher PCa-Stem signature correlated with worse patient survival in both Pri-PCa (P<0.0001, log-rank; FIG. 5S-FIG. 5T) and mCRPC (P<0.0001, log-rank; FIG.5U-FIG.5V) patients. Multivariate analysis, adjusted for clinicopathologic parameters such as age, Gleason Score (GS) and stage, showed that patients with high PCa-Stem signature had significantly worse independent prognosis compared to those with a low PCa-Stem signature. This was demonstrated by worse progression-free survival (PFS) in Pri- PCa (multivariable-adjusted HR=1.636 (1.014 – 2.64), P=0.0438; FIG.5T) and overall survival (OS) in mCRPC (multivariable-adjusted HR=4.712 (2.011 – 11.04), P<0.001; FIG. 5V), respectively. Furthermore, the PCa-Stem signature steadily and continually increased across the prostate cancer evolutionary spectrum and correlated with prostate cancer disease progression scores as supported by trajectory inference analysis of the Bolis PCa transcriptome atlas (27) (Pearson’s r=0.72; FIG.14A-FIG.14B). Gene expression analysis revealed that 11 of the 12 genes (except KLK12) in the PCa-Stem signature significantly correlated with the prostate cancer disease progression (FIG.14C). In contrast, among the genes commonly downregulated in Stemness-high PCa samples in both Pri-PCa and mCRPC, some (e.g., PTGDS, SPARCL1, CLU, GJA1 and S100A4) were involved in regulating prostate-specific glandular structures and luminal functions while others (e.g., FBLN1, SFRP1, IGFBP6, GAS1 and TIMP2) played general roles in epithelial differentiation (FIG.14D). These findings indicate that prostate cancers with high Stemness exhibit greater aggressiveness, a higher likelihood of developing lineage plasticity and therapy resistance, and poor survival outcomes. Stemness-high prostate cancers have high tumor mutation burden (TMB) and distinct genomic characteristics The analysis of the data demonstrates that metastatic castration-resistant prostate cancer (mCRPC) had a higher fraction of altered genome (FIG.6A) and higher tumor mutation burden (TMB) (FIG.6B) than primary prostate cancer (Pri-PCa). In both Pri-PCa and mCRPC, Stemness- high PCa displayed a higher fraction of altered genome (FIG. 6A) and TMB (FIG. 6B) than Stemness-low prostate cancer, although there was no difference in patient ages (FIG.6C). In Pri- PCa, Stemness-high samples had a higher prevalence of PTEN deletion (DEL), SPOP, and FOXA1 mutations (MUT) and MYC amplification (AMP) whereas in mCRPC, Stemness-high prostate cancer had more prevalent RB1 DEL and AR MUT (FIG. 6D-FIG. 6F). Globally, both Pri-PCa and mCRPC exhibited mutations in more than a dozen genes, although most of these alterations were <10% (FIG.15A; green symbols), consistent with reports that prostate cancer is genomically heterogeneous with a broad spectrum of genomic alternations of low penetrance (49, 50). On the other hand, homozygous deletion (HOMDEL) events, particularly, loss of PTEN and RB1, occurred frequently in both Pri-PCa and mCRPC (FIG.15A; blue symbols) whereas AMP events in oncogenes such as MYC were more prevalent in mCRPC (FIG. 15A; red symbols). Docket No.: 440914 [2085_010 PCT] Nevertheless, PTEN and RB1 DEL represented among the most prevalent alterations in early-stage (Gleason Stage 6) prostate cancer (FIG.15B-FIG.15C). Analysis of genetic alterations across the spectrum of prostate cancer progression revealed that among the 3 tumor suppressors (RB1, PTEN and TP53), while homozygous RB1 DEL remained relatively constant at 13%, 6%, 10%, and 10% in low-grade (GS6), high-grade (GS9 / 10), treated Pri-PCa and mCRPC, respectively, PTEN HOMDEL (9%, 23%, 30%, and 26%) and TP53 MUT (0%, 21%, 27%, and 37%) continued to increase along this progression (FIG. 15B-FIG. 15C). Among the oncogenic events analyzed, while high levels of TMPRSS2 fusions (~40%) remained unchanged from low-grade to high-grade Pri-PCa to mCRPC, MYC (2%, 12%, 19%, and 24%) and AR (0%, 1%, 6%, and 49%) AMP continued to increase along the progression trajectory (FIG.15B-FIG.15C). In fact, prevalent AR MUT (11%) were observed only in the most aggressive and Stemness-highest mCRPC (FIG. 15B-FIG. 15C), suggesting potentially high noncanonical AR-A in mCRPC. The data demonstrated that RB1 DEL, FOXA1 (together with SPOP, IDH1, and CHD1) MUT, and especially MYC AMP were significantly associated with increased Stemness in Pri-PCa (FIG. 15D). In mCRPC, only RB1 DEL exhibited a statistically significant association with higher Stemness (FIG.15E). MYC further drives Stemness during spontaneous prostate cancer progression AR AMP and MUT are exclusively treatment-induced as there were virtually no AR genomic alterations in treatment-naïve Pri-PCa (FIG. 15B-FIG. 15C). Early during prostate cancer development, increased AR-A may drive elevated Stemness (FIG. 1D-FIG. 1F); however, treatment-naïve Pri-PCa displayed continually increasing Stemness but decreasing AR-A (FIG. 2). The question is what could be driving persistently high Stemness during Pri-PCa progression with decreasing AR-A and in the absence of AR genomic alterations. Stemness-high Pri-PCa, compared to Stemness-low Pri-PCa, were enriched in MSigDB Hallmark MYC_TARGETS (MYC_Targets_V1 and MYC_Targets_V2; FIG.5F) and had more prevalent MYC AMP (FIG. 6D and FIG.6F). High-grade (GS9 / 10) Pri-PCa also had more MYC AMP events than low-grade (GS6) Pri-PCa (12% vs. 2%; P=0.006, c2 test; FIG. 15C). Importantly, MYC AMP was most prominently associated with increased Stemness in Pri-PCa (FIG. 15D). This data supports the hypothesis that MYC may represent a critical Stemness driver during spontaneous Pri-PCa progression (in absence of therapeutic pressure). To test this hypothesis, MYC signaling activity (51) (MYC-sig; see Table 2 for gene signature information) was quantified across the prostate cancer progression spectrum. The quantification showed that 1) both MYC mRNA and MYC-sig increased in Pri-PCa compared to matched benign samples (FIG. 7A-FIG. 7B); 2) MYC-sig continued to go up during Pri-PCa progression (FIG.7C); 3) unlike nADT-induced concomitant decline in both AR-A and Stemness (FIG.1D-FIG.1F; FIG.11), nADT showed no discernible effect on MYC-sig (FIG. 16A-FIG. 16D); and 4) MYC-sig continued to increase across the disease spectrum with mCRPC exhibiting the highest MYC activity (FIG.7D-FIG.7E) and with MYC-sig positively correlated with the Stemness (FIG. 7F- FIG. 7H). These results, together, support MYC as a Stemness driver during PCa progression. Method PCa clinical datasets and data collection Docket No.: 440914 [2085_010 PCT] This disclosure utilized a total of 26 published and in-house prostate cancer (PCa) related datasets, including various cohorts encompassing the evolutionary spectrum of prostate cancer (FIG.8A-FIG.8B): normal prostate, tumor-adjacent benign tissue, treatment-naïve primary PCa (Pri-PCa) with increasing Gleason Score (GS), PCa treated with neo-adjuvant ADT (nADT) or long-term ADT, metastatic castration-resistant PCa (mCRPC), and PCa cell lines, PCa patient- derived xenografts (PDX), and xenografts. Clinical information, genomic data, and gene expression profiling data were downloaded and detailed information on each dataset is listed in Table 1. Dataset descriptions include dataset source references, PubMed IDs (PMIDs; RRID:SCR_004846), DOI links, sample type and source (e.g., normal prostate tissue, adjacent benign prostate tissue or primary culture from PCa patient, primary or metastatic prostate tumor, xenografts, cell lines), sample sizes, data types used in the current study (clinicopathologic data, RNAseq, microarray, whole exome sequencing [WES]), and public data access portals. Data Access and Repository Information Data referenced in this disclosure were retrieved from the following public repositories and databases (persistent identifiers where applicable): 1. GTEx Portal (RRID:SCR_013042; https: / / gtexportal.org / ) 2. Gene Expression Ombnibus (GEO; RRID: SCR_005012; https: / / www.ncbi.nlm.nih.gov / geo / ) 3. cBioPortal for Cancer Genomics (RRID:SCR_014555; https: / / www.cbioportal.org / ) 4. Zenodo Repository (RRID:SCR_004129; https: / / zenodo.org / ) 5. Decipher GRID Database (RRID:SCR_006552; ClinicalTrials.gov identifier: NCT02609269) 6. European Genome-phenome Archive (EGA; RRID:SCR_004944; https: / / ega-archive.org / ) 7. European Nucleotide Archive (ENA; RRID:SCR_006515; https: / / www.ebi.ac.uk / ena / browser / home) 8. UCSC Xena Functional Genomics Portal (RRID:SCR_018938; https: / / xenabrowser.net / ) Accession numbers and portal links for the 26 datasets described below are summarized in Table 1. The 26 datasets are briefly described below: 1. GTEx 2013 (PMID: 23715323): The Genotype-Tissue Expression (GTEx) Project repository collected high-throughput and clinical data of normal tissue from many organs. RNAseq data of 245 normal prostate tissue samples was downloaded from the GTEx portal (RRID:SCR_013042; ref.29; DOI: 10.1038 / ng.2653; https: / / www.gtexportal.org / , version: v8). 2. Smith 2015 (PMID: 26460041): RNAseq data of 5 prostate basal cell samples (Trop2+CD49fhi) and corresponding 5 luminal cell samples (Trop2+CD49flo) FACS-purified from benign human prostate tissue in 5 PCa patients (n=5) was retrieved from the GEO database GSE82071 (ref.20; DOI: 10.1073 / pnas.1518007112). Docket No.: 440914 [2085_010 PCT] 3. Liu 2016 (PMID: 27926864): RNAseq data of 3 FACS-purified human prostate basal cell samples (CD45-EpCAM+CD49fhiCD38lo) with corresponding luminal (CD45- EpCAM+CD49floCD38hi) and luminal progenitor (CD45-EpCAM+CD49floCD38lo) cell populations purified from the benign prostate tissues of 3 PCa patients (n=3) was retrieved from the supplementary data from Liu et al., 2016 (ref. 56). The microarray-based transcriptomic profiles of the same samples were retrieved from the GEO database GSE89050 (ref. 56; DOI: 10.1016 / j.celrep.2016.11.010). 4. Zhang 2016 (PMID: 26924072): RNAseq data of 3 human prostate basal cell (Trop2+CD49fhi) with the corresponding 3 luminal cell (Trop2+CD49flo) samples FACS-purified from benign prostate tissues of 3 PCa patients (n=3) was retrieved from the GEO database GSE67070 (ref.19; DOI: 10.1038 / ncomms10798). 5. TCGA PRAD 2015 and Pan-Cancer Atlas 2018: The Cancer Genome Atlas (TCGA) (https: / / www.cancer.gov / tcga) repository collected high-throughput molecular and clinical data from primary cancer and matched normal / benign samples spanning 33 human cancer types, including prostate adenocarcinoma (PRAD). For each cancer type, TCGA published an initial “marker papers” summarizing analyses performed. Supplemental and associated data files supporting these “marker papers” are available through the NCI Genomic Data Commons (GDC) website (https: / / gdc.cancer.gov / about-data / publications). For prostate cancer, TCGA PRAD 2015 (PMID: 26544944; ref. 18; DOI: 10.1016 / j.cell.2015.10.025) is the marker paper on prostate cancer published in 2015, which includes the dataset of 333 primary prostate adenocarcinomas (281 treatment-naïve Pri-PCa, 49 PCa with post-surgery adjuvant ADT, and 52 matched normal / benign prostate tissue). Genomic data were obtained from cBioPortal for Cancer Genomics (RRID:SCR_014555; https: / / www.cbioportal.org), specifically the TCGA-PRAD Firehose Legacy study (https: / / www.cbioportal.org / study / summary?id=prad_tcga). RNAseq expression data was obtained from Zhang et al., 2020 (PMID: 32350277; ref. 16; DOI: 10.1038 / s41467-020-15815-7). The expanded TCGA PRAD 2018 (Pan-Cancer Altas) cohort includes 494 patients with Pri-PCa (422 treatment-naïve Pri-PCa, 70 PCa with post-surgery adjuvant hormone treatment, 2 PCa with chemotherapy) and 52 matched adjacent normal / benign prostate tissue. Clinicopathologic features (age, Gleason scores, PSA levels, tumor stages) and genomic alterations data (mutation frequency, copy number alterations [CNAs], structural variants [SVs], tumor mutation burden [TMB], fraction genome altered) were retrieved from cBioPortal for Cancer Genomics PRAD PanCancer Atlas study (https: / / www.cbioportal.org / study / summary?id=prad_tcga_pan_can_atlas_2018) and from the Pan-Cancer Atlas companion site (welcome to the Pan-Cancer Atlas webpage: https: / / www.cell.com / pb-assets / consortium / pancanceratlas / pancani3 / index.html). Survival data were obtained from Liu et al., 2018 (65) (PMID: 29625055; DOI: 10.1016 / j.cell.2018.02.052). Normalized RNAseq data were downloaded via NCI GDC hub at UCSC Xena Functional Genomics Portal (https: / / gdc.xenahubs.net, version 2019-07-20; DOI: 10.1038 / s41587-020-0546- 8). Docket No.: 440914 [2085_010 PCT] 6. Rajan 2014 (PMID: 24054872): Clinicopathologic (Gleason scores) and RNAseq data of 7 matched pre- and post-nADT PCa samples were obtained, respectively, from Table 1 in Rajan et al., 2014 (ref.25; DOI: 10.1016 / j.eururo.2013.08.011) and the GEO database GSE48403. 7. Sharma 2018 (PMID: 30314329): Clinicopathologic (Gleason scores) and RNAseq data of 7 matched pre- and post-nADT PCa samples from the responder group (also defined as “Low Impact Group” in the source reference) was obtained, respectively, from supplementary Table 1 in Sharma et al., 2018 (ref. 26; DOI: 10.3390 / cancers10100379) and the GEO database GSE111177. 8. Long 2020 (PMID: 32951005; DOI: 10.1016 / j.eururo.2013.08.011): RNAseq data of 6 matched pre- and post-nADT PCa samples was obtained from the GEO database GSE82071. Additionally, RNAseq data from 10 locally advanced PCa samples along with their paired adjacent benign prostate tissues were obtained from the GEO database GSE82071. 9. Nastiuk (RPCI) 2020 (submitted) (61): Clinicopathologic data (Gleason scores) and RNAseq data of 43 Pri-PCa samples from post-nADT PCa patients and 43 tumor samples from age-, stage-, clinically-matched PCa patients without nADT was obtained from Jamroze et al., 2024 (work led by Drs. K. Nastiuk and G. Chatta at the Roswell Park Comprehensive Cancer Center). RNAseq data and clinicopathologic information from the Nastiuk 2020 cohort (unpublished) were provided in-house by Dr. K. Nastiuk and are available upon reasonable request. 10. Gerhauser 2018 (PMID: 30537516; DOI: 10.1016 / j.ccell.2018.10.016) (62): RNAseq data of 118 primary tumor samples and 9 adjacent benign prostate tissue from early-onset PCa patients was obtained from the European Genome-Phenome Archive (EGA) (https: / / ega- archive.org), accession number EGAS00001002923. 11. Wyatt 2014 (PMID: 25155515; DOI: 10.1186 / s13059-014-0426-y) (23): Clinicopathologic (Gleason scores) and RNAseq data of 12 primary PCa samples from treatment- naïve PCa patients and 12 adjacent benign prostate tissue were obtained from the European Nucleo-tide Archive (ENA) (https: / / www.ebi.ac.uk / ena / browser / home), accession number PRJEB6530. 12. Spratt 2017 (PMID: 28358655; DOI: 10.1200 / JCO.2016.70.2811) (38): Transcriptomic profiles (Microarray) and clinicopathologic data of 855 radical prostatectomy specimens from a multi-institutional study of intermediate- and high-risk localized PCa patients were obtained from Veracyte GRID (https: / / decipherbio.com / grid / ). 13. Tosoian 2020 (PMID: 32231245; DOI: 10.1038 / s41391-020-0226-2) (39): Transcriptomic profiles (Microarray) and clinicopathologic data of 405 radical prostatectomy and biopsy specimens from high-risk localized PCa patients were obtained from Veracyte GRID (https: / / decipherbio.com / grid / ). 14. CHAARTED correlatives 2021 (PMID: 34129855; DOI: 10.1016 / j.annonc.2021.06.003) (40): Transcriptomic profiles (Microarray) and clinicopathologic Docket No.: 440914 [2085_010 PCT] data of 160 biopsy specimens from a Phase 3 trial of metastatic hormone-sensitive PCa patients were obtained from the NCTN Data Archive (https: / / nctn-data-archive.nci.nih.gov), accession number NCT00309985-D14. 15. Decipher GRID Biopsy (PMID: 37060201; DOI: 10.1002 / cncr.34790) (35): Transcriptomic profiles from 82,470 prospectively collected prostate biopsy samples were retrieved from the Decipher GRID database (RRID: SCR_006552; https: / / decipherbio.com / grid; ClinicalTrials.gov identifier: NCT02609269; https: / / clinicaltrials.gov / ct2 / show / NCT02609269). Patient data were de-identified from clinical use of the Decipher prostate genomic classifier in accordance with the Safe Harbor method described in the HIPAA Privacy Rule 45 CFR 164.514(b) and (c) (Veracyte, San Diego, CA). The samples, comprising a large cohort of localized prostate cancer (PCa), were utilized to compare the distribution of Stemness Index values across National Comprehensive Cancer Network (NCCN) risk groups. Clinicopathologic Breakdown and Risk Group Analysis: Detailed clinicopathologic review revealed that the majority of the cohort presents with low-grade localized PCa. The TNM staging breakdown includes: • T1 stage: Approximately 50% of samples, indicating early-stage cancer localized within the prostate. • T2 stage: Approximately 8%, representing cancer confined within the prostate but more extensive than T1. • T3 and T4 stages: Less than 1%, suggesting advanced local spread. • N0: Approximately 98%, indicating no regional lymph node involvement. • N1: Approximately 2%, suggesting regional lymph node involvement. The Decipher GRID database (RRID:SCR_006552) categorizes patients into NCCN risk groups based on TNM stage, PSA level, and Gleason score: • Very Low and Low Risk: T1–T2a, N0, M0, PSA <10 ng / mL, and Gleason score ≤6; often managed with active surveillance or less aggressive treatment. • Intermediate Risk – Favorable: Typically T2b–T2c, N0, M0, PSA 10–20 ng / mL, and Gleason score 7; managed with less aggressive treatment but closer monitoring. • Intermediate Risk – Unfavorable: Similar clinical features to favorable but with higher disease burden or adverse factors requiring careful monitoring. • High Risk: Usually involves T3a, N0, M0, PSA >20 ng / mL, or Gleason score 8– 10; often managed aggressively with surgery, radiation, and / or hormonal therapy. • Very High Risk: Typically includes T3b–T4, any N, M0, and high Gleason scores or PSA levels (PSA >20 ng / mL); patients are at significant risk for metastatic progression and are treated aggressively, often with multimodal therapies. Observations on Disease Severity and Treatment Naïvety: Analysis of the cohort indicates that only approximately 13% of cases fall into the High and Very High risk categories (8,376 + 2,399 = 10,775 samples), suggesting that the majority Docket No.: 440914 [2085_010 PCT] (approximately 87%) are below Intermediate Risk (≤GS7). This distribution implies that most patients were treatment-naïve and likely androgen deprivation therapy (ADT)-free due to less aggressive disease status. 16. Bolis 2021 (PMID: 34857732; DOI: 10.1038 / s41467-021-26840-5) (27): RNAseq and clinicopathologic data of 1,223 clinical samples from an integrated cohort consisting of normal prostate specimens (n = 174), Pri-PCa (n = 714) and mCRPC (n = 335) was obtained from the Zenodo repository (Record ID: 5546618; https: / / zenodo.org / records / 5546618). This resource of Prostate Cancer Transcriptome Atlas (https: / / prostatecanceratlas.org) was built and integrated from the following studies / datasets: (1) Genotype-Tissue Expression Database (GTEx; PMID: 23715323; ref.29); (2) The Cancer Genome Atlas (TCGA, TCGA-PRAD; PMID: 26544944; ref. 18); (3) Atlas of RNA-sequencing profiles of normal human tissues (GSE120795; PMID: 31015567; ref. 68); (4) Integrative epigenetic taxonomy of PNPCa (GSE120741; PMID: 30464211; ref. 69); (5) Prognostic markers in locally advanced lymph node-negative prostate cancer (PRJNA477449); (6) The long noncoding RNA landscape of NEPC and its clinical implications (PRJEB21092; PMID: 29757368; ref. 70); (7) Integrative clinical sequencing analysis of metastatic CRPC reveals a high frequency of clinical actionability (PRJNA283922; dbGaP: phs000915; PMID: 26000489; ref.10); (8) CSER—exploring precision cancer medicine for sarcoma and rare cancers (PRJNA223419; dbGaP: phs000673; PMID: 28783718; ref.71); (9) Molecular basis of NEPC (Beltran 2016; PRJNA282856; dbGaP: phs000909; PMID: 26855148; ref.28); (10) Heterogeneity of androgen receptor splice variant-7 (AR-V7) protein expression and response to therapy in CRPC72 (GSE118435; PMID: 30334814; ref.72); (11) RNAseq of human PCa cell lines and mCRPC tumor (GSE14750; PMIDs: 32460015; ref. 68; 33658518; ref. 69; 34244513; ref.70; | GSE171729; PMID: 34244513; ref.70); and (12) Molecular profiling stratifies diverse phenotypes of treatment-refractory metastatic CRPC (PRJNA520923; GEO: GSE126078; PMID: 31361600; ref.33). 17. Taylor 2010 (PMID: 20579941; DOI: 10.1016 / j.ccr.2010.05.026) (36): Transcriptomic profiles (Microarray) and clinicopathologic (recurrence-free survival) data of 29 adjacent benign prostate tissue, 131 Pri-PCa, and 19 mPCa were obtained from the GEO database (GSE21034) and the cBioPortal for Cancer Genomics PRAD MSKCC 2010 study (https: / / www.cbioportal.org / study / summary?id=prad_mskcc). 18. SU2C 2015 (PMID: 26000489; DOI: 10.1016 / j / cell.2015.05.001) (10): Clinical and genomic data of 150 mCRPC samples were obtained from the cBioPortal for Cancer Genomics PRAD SU2C 2015 study (https: / / www.cbioportal.org / study / summary?id=prad_su2c_2015). RNAseq data of 98 mCRPC samples was retrieved from Zhang et al., 2020 (PMID: 32350277; DOI: 10.1038 / s41467-020-15815-7; ref.16). 19. SU2C 2019 (PMID: 31061129; DOI: 10.1073 / pnas.1902651116) (41): This dataset includes clinicopathologic and genomic data for 444 mCRPC samples. RNAseq data of 266 mCRPC samples (prepared by using poly(A) enrichment for RNAseq library construction) were obtained from the cBioPortal for Cancer Genomics PRAD SU2C 2019 study (https: / / www.cbioportal.org / study / summary?id=prad_su2c_2019). In parallel with TCGA-PRAD, clinicopathologic features (age, Gleason scores, PSA levels), genomic alterations (mutation frequency, CNAs, SVs, TMB, fraction genome altered), and patient outcome data (overall Docket No.: 440914 [2085_010 PCT] survival) were also retrieved from cBioPortal (RRID:SCR_014555). These datasets were used for comparative analysis between Stemness-high and Stemness-low groups, and to explore genomic drivers and clinical associations as described in the Methods. 20. Labrecque 2019 (PMID: 31361600; DOI: 10.1172 / JCI128212) (33): RNAseq data of 98 mCRPC samples and 39 PDX-LuCaP was obtained from the GEO database GSE126078. 21. Alumkal 2020 (PMID: 32424106; DOI: 10.1073 / pnas.1922207117): RNAseq data of 25 mCRPC samples before treatment with enzalutamide (ENZA) was obtained from Supplementary dataset S01 of Alumkal et al., 2020 (ref.13). 22. Westbrook 2022 (PMID: 36109521; DOI: 10.1038 / s41467-022-32701-6): RNAseq data of 42 mCRPC samples consisting of 21 matched pre / post Enzalutamide treatment samples was retrieved from Westbrook et al., 2022 (ref.12). 23. Beltran 2016 (PMID: 26855148; DOI: 10.1038 / nm.4045) (28): RNAseq data of mCRPC samples, including 34 castration-resistant adenocarcinoma (mCRPC-Adeno) and 15 neuroendocrine histologies (mCRPC-NEPC) samples, was obtained from cBioPortal for the Cancer Genomics (https: / / www.cbioportal.org / study / summary?id=nepc_wcm_2016). 24. Goodrich 2017 (PMID: 28059767; DOI: 10.1126 / science.aah4199) (37): RNAseq data of murine prostate tumors from genetically engineering mouse models (GEMMs) of PCa of different genotypes, including 4 SKO (single knockout: PBCre4:Ptenf / f), 13 DKO (double knockout PBCre4:Ptenf / f:Rb1f / f), and 6 TKO (PBCre4:Ptenf / f:Rb1f / f:Trp53f / f) was obtained from the GEO database GSE90891 (f = a floxed allele of the indicated genes). 25. CCLE 2018 (PMID: 22460905; DOI: 10.1038 / nature11003) (31): RNAseq data of 1,019 human cell lines were obtained from the Xena Functional Genomics Portal (https: / / xenabrowser.net, version: 2018-05-30; ref. 71). Among them, eight prostate cancer cell lines — DU145 (RRID:CVCL_0105), LNCaP clone FGC (RRID:CVCL_1379), MDA PCa 2b (RRID:CVCL_4745), NCI-H660 (RRID:CVCL_0459), PC-3 (RRID:CVCL_0035), VCaP (RRID:CVCL_2235), 22Rv1 (RRID:CVCL_1045), and PRECLH (PrEC LH; RRID:CVCL_V626) were included for analysis. 26. XG-LNCaP and XG-LAPC9 (PMID: 30190514; DOI: 10.1038 / s41467-018- 06067-7) (34): RNAseq data of 12 LNCaP xenografts (XG-LNCaP) and 10 LAPC9 xenografts (XG-LAPC9) was obtained from the GEO database GSE88752. Sex as a biological variable All clinical datasets analyzed consisted exclusively of male patients, consistent with the male-specific nature of prostate cancer. Preclinical models, including xenografts, patient-derived xenografts, and genetically engineered mouse models (GEMMs), were also male-derived. All prostate cell lines utilized in the study were of male origin, with the exception of FIG.12A, where a broader cohort of 1,019 cell lines from the Cancer Cell Line Encyclopedia (CCLE), comprising Docket No.: 440914 [2085_010 PCT] both male- and female-derived samples across multiple cancer types, was analyzed. Analyses of CCLE data in FIG.12A were performed without stratification by sex. Transcriptomic data normalization and transcriptome-based signature scores To ensure consistency and compatibility across diverse datasets, normalization strategies tailored to the specific data types and analytical approaches were employed. Canonical AR activity score (AR-A) and MYC activity score (MYC-sig), and PCa-Stem Signature: Canonical pro-differentiation AR-A can be determined by RNA-seq based measurements of AR-regulated transcripts under intact androgen / AR signaling conditions. Similarly, MYC-sig can be determined by MYC-regulated transcripts. The Z-score normalization method (77) was used to determine AR-A and MYC-sig. Briefly, the numeric AR or MYC activity score was calculated based on a linear combination of expression values (Z-scores) of experimentally validated AR targets (14) or MYC targets (51) (Table 2, GSEA Molecular Signature Database 2022.1 version, MSigDB). The target gene lists were detailed in Table 2. The expression value for each AR or MYC target genes (Table 2) was converted to Z-score by Z = (x − μ) / σ, where x is the expression value of the target gene in a specific sample, μ is the mean, and σ is the standard deviation across all samples of a gene. Finally, the combined Z-scores were summed across all genes to represent the expression score of canonical AR or MYC target genes. Analysis of gene expression signatures using Z-score normalization (AR-A and MYC-sig) was confined within individual datasets, obviating the need for cross-dataset comparisons. To further minimize batch effects and ensure data integrity and comparability across prostate cancer stages, normalized RNAseq data from a published integrated data framework, the Prostate Cancer Transcriptome Atlas (27) (https: / / prostatecanceratlas.org), was used. In this harmonized resource, raw RNAseq data with high-quality sequencing reads from different datasets were re-mapped to the human reference genome GRCh38, adjusted for library size, and normalized using the variance stabilizing transformation pipeline using DESeq2 (version 1.28.1; RRID:SCR_015687) (Ref.27). Transcriptome-based quantitative measurement of oncogenic stemness (the Stemness Index): The mRNA-based Stemness Index (mRNAsi) was adapted to determine the degree of oncogenic dedifferentiation (Stemness) (ref.6). Spearman correlations were computed between the stemness model’s weight vector and the transcriptome expression profile of the queried samples, assuming paired observations, at least ordinal scale data, and a monotonic relationship without significant outliers. Spearman correlation calculations were performed using the ‘stats’ package (version 4.3.3) in R (version 4.3.3; RRID:SCR_001905). The resulting correlation coefficients were then linearly transformed to scale the mRNAsi scores between 0 and 1 using the following formula: Stemness Index = (Spearman correlation + 0.2619) / 0.4151. Spearman correlation is advocated for calculating the Stemness Index because it is more robust with respect to potential cross-dataset batch effects that may arise (6). Integrative analysis of genomic alterations in primary PCa (TCGA) and mCRPC (SU2C) Docket No.: 440914 [2085_010 PCT] To explore molecular and clinical features associated with Stemness, integrative analysis of genomic, transcriptomic, clinicopathologic, and outcome data was performed using cBioPortal for Cancer Genomics (RRID:SCR_014555), which provides interactive access to multidimensional cancer genomics datasets and integrated visualization tools (74). Specifically, data was analyzed from primary prostate cancer (Pri-PCa; TCGA-PRAD 2018, Pan-Cancer Atlas) and metastatic castration-resistant PCa (mCRPC; SU2C 2019) cohorts. Clinicopathologic variables (e.g., age, Gleason scores, PSA levels, tumor stage), genomic alterations (e.g., mutation frequency, TMB, fraction genome altered, CNAs, and SVs), and patient outcome data (e.g., overall survival, progression-free survival) were retrieved from cBioPortal (RRID:SCR_014555) as described in the “PCa clinical datasets and data collection” section. Transcriptomic data used for Stemness score calculation were obtained as described in the same section. For comparative analysis, mCRPC samples were stratified into top and bottom 33% by Stemness score. In TCGA, only treatment-naïve Pri-PCa samples were used for a similar 33% stratification, excluding 70 patients who had received adjuvant (post-surgery) hormone therapy to reflect spontaneous disease progression. Differential expression analysis was conducted using DESeq2 (75) (version 1.42.1; RRID:SCR_015687), and gene-level and genome-wide alteration frequencies were assessed across stratified groups and disease stages (e.g., Pri-PCa vs. mCRPC) using OncoPrint visualizations, bar plots, and correlation analysis. Associations with clinical outcomes were evaluated as described in the “Statistical analysis” section. Statistical analysis Statistical analyses were performed using GraphPad Prism software (version 10.4.1 (532); RRID:SCR_002798) and R (version 4.3.3; RRID:SCR_001095). For comparisons between two groups, independent sample or paired t-tests were used to compare group means, assuming normality and equal variance. For multi-group comparisons, Tukey’s range test was employed following one-way ANOVA (adjusted p-values listed in Table 3). The Wilcoxon rank-sum test was applied for comparisons of altered genome fraction, TMB, or age between two groups. For differential expression analyses and comparisons involving multiple hypotheses (e.g., RNAseq-based gene comparisons), p-values were further adjusted using the false discovery rate (FDR) method to control for multiple testing. Pearson’s correlation coefficient was used to evaluate linear associations between two continuous variables, assuming normally distributed paired observations. Linear regression modeling was applied to examine the relationship between two continuous variables, with the slope of the best-fit regression line reported where applicable. Spearman’s rank correlation was employed to assess monotonic relationships between continuous or ordinal variables, assuming paired observations and monotonic trends without significant outliers. The Jonckheere-Terpstra (J-T) trend test was used to assess monotonic association between an ordinal variable and a continuous variable. Chi-square test or Fisher’s exact test was performed to evaluate the associations between categorical variables. Docket No.: 440914 [2085_010 PCT] For the analysis of patient progression-free survival (PFS), recurrence-free survival (RFS) and overall survival (OS), the standard Kaplan-Meier method was used for the estimation of survival fractions and log-rank tests were used for group comparisons. Cox proportional hazards models were used for multivariable analysis and for assessing the prognostic significance of gene signatures (Stemness, PCa-Stem signature, etc.). For the mRNA-based gene signatures (Stemness, PCa-Stem signature, AR-A), patients were stratified into high and low signature groups based on median split in cohorts including Spratt 2017 (FIG.4C), Tosoian 2020 (FIG.4C), CHAARTED 2021 (FIG. 13C), Taylor 2010 (FIG. 13C), TCGA-PRAD 2018 (FIG. 13C), and SU2C 2019 (FIG.13C). For certain analyses, including SU2C 2019 (FIG.13C) and CHAARTED 2021 (FIG. 13C), patients were alternatively stratified by comparing the top 25% versus the bottom 75% of signature scores to assess whether patients with very high Stemness levels exhibited a higher risk of disease progression. All statistical tests were two-sided, and P < 0.05 was considered statistically significant. Data Availability All datasets analyzed in this study are publicly available from repositories including GEO (RRID:SCR_005012), cBioPortal (RRID:SCR_014555), the NCI GDC Portal, Xena Functional Genomics Portal (RRID:SCR_018938), EGA (RRID:SCR_004944), ENA (RRID:SCR_006515), and Zenodo (RRID:SCR_004129), as detailed in Table 1. Generating a gene expression profile or transcriptome data RNA sequencing (RNA-seq) is a high-throughput technique utilized to analyze the transcriptome, providing a comprehensive view of gene expression patterns and RNA dynamics within biological samples. RNA-seq is employed to generate transcriptomic profiles that serve as the foundation for calculating stemness metrics, such as the mRNA-based Stemness Index (mRNAsi). The process involves isolating RNA from tumor samples, converting the RNA into complementary DNA (cDNA) through reverse transcription, and sequencing the cDNA fragments to produce millions of short reads. These reads are then aligned to a reference genome, and gene expression levels are quantified. The resulting data are processed using machine learning algorithms, such as the One- Class Logistic Regression (OCLR) model, to compute the Stemness Index by correlating the transcriptomic profile of each sample with a reference stem cell signature. This approach enables the identification of stem-like features in tumors, providing insights into cancer aggressiveness, therapy resistance, and patient prognosis. Single-cell RNA sequencing (scRNA-seq) technology enables the analysis of gene expression at the resolution of individual cells, providing insights into cellular heterogeneity, rare cell populations, and dynamic biological processes within complex tissues. The scRNA-seq workflow typically begins with the isolation of single cells from tissue samples using methods such as fluorescence-activated cell sorting (FACS), microfluidic platforms, or droplet-based systems. Once isolated, each cell’s RNA is captured and reverse transcribed into complementary DNA (cDNA), often incorporating molecular identifiers and cell-specific barcodes to distinguish transcripts from individual cells. The cDNA is then amplified, and sequencing libraries are prepared by ligating sequencing adapters. High-throughput sequencing platforms are used to Docket No.: 440914 [2085_010 PCT] sequence the libraries, generating large datasets of short reads. Following sequencing, bioinformatics pipelines demultiplex the reads based on cell barcodes and UMIs, align the reads to a reference genome, and quantify gene expression for each cell. Quality control steps are applied to filter out low-quality cells and technical artifacts. Downstream analyses include normalization, dimensionality reduction, clustering, and cell type annotation, as well as differential gene expression and trajectory inference to explore cellular states and transitions (see Jovic et al., Clin. Transl. Med.2022;12:e694). Next-generation sequencing (NGS) and microarray methods are advanced technologies that enable comprehensive transgenomic sequencing, providing high-resolution analysis of genomic and transcriptomic landscapes in cancer and other biological contexts. The process of transgenomic sequencing using NGS typically begins with the extraction of nucleic acids (DNA or RNA) from biological samples, such as tissue biopsies or blood. The extracted nucleic acids are then fragmented into smaller pieces, and sequencing adapters are ligated to the ends of these fragments to facilitate amplification and subsequent sequencing. Library preparation is followed by clonal amplification, often through PCR or bridge amplification, to generate sufficient quantities of each fragment for detection. The prepared libraries are then loaded onto a sequencing platform, where massively parallel sequencing reactions generate millions to billions of short sequence reads. These reads are computationally aligned to a reference genome, enabling the identification of genetic embodiments, such as single nucleotide polymorphisms (SNPs), insertions, deletions, copy number variations, and structural rearrangements. In parallel, microarray-based transgenomic sequencing involves the hybridization of fluorescently labeled nucleic acid samples to a microarray chip containing thousands of immobilized oligonucleotide probes. Each probe is designed to target a specific genomic region or transcript, allowing for the simultaneous interrogation of gene expression levels, detection of known mutations, or assessment of methylation status across the genome. After hybridization, the microarray is scanned to measure fluorescence intensity at each probe location, which is quantitatively analyzed to determine the presence or abundance of specific sequences. Both NGS and microarray approaches require rigorous data processing and bioinformatic analysis, including quality control, normalization, embodiment calling, and annotation. NGS, in particular, provides an unbiased, high-throughput platform for the discovery of novel genetic alterations and transcriptomic changes, while microarrays offer a cost-effective solution for targeted analyses. 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Androgen receptor splice embodiment-7 expression emerges with castration resistance in prostate cancer. J Clin Invest 129, 192-208 (2019). Nyquist MD, et al. Combined TP53 and RB1 Loss Promotes Prostate Cancer Resistance to a Spectrum of Therapeutics and Confers Vulnerability to Replication Stress. Cell Rep 31, 107669 (2020). Brady L, et al. Inter- and intra-tumor heterogeneity of metastatic prostate cancer determined by digital spatial gene expression profiling. Nat Commun 12, 1426 (2021). Lim Y, et al. Multiplexed functional genomic analysis of 5' untranslated region mutations across the spectrum of prostate cancer. Nat Commun 12, 4217 (2021). Goldman MJ, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol 38, 675-678 (2020). Docket No.: 440914 [2085_010 PCT] 72. Lee E, Chuang HY, Kim JW, Ideker T, Lee D. Inferring pathway activity toward precise disease classification. PLoS Comput Biol 4, e1000217 (2008). 73. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013). 74. Gao J, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6, pl1 (2013). 75. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550 (2014). 76. NCI Dictionary of Cancer Terms (https: / / www.cancer.gov / publications / dictionaries / cancer-terms / def / gleason-score). Table 1 provides a summary of the datasets utilized in this study, detailing the sources, sample types, and clinical contexts analyzed. Table 2 outlines the gene signatures employed in the study, including their composition and relevance to prostate cancer (PCa) progression and stemness evaluation. Table 3 summarizes the statistical significance of differences observed in the study, as determined using Tukey's multiple comparisons test, with results provided across three sheets. Table 4 compiles the gene signatures and gene sets utilized in the Gene Set Enrichment Analysis, highlighting their functional associations and contributions to the study's findings. Table 1: Datasets Information Sample # Histology (Sample Type) Source Datasets Reference 1 Normal Prostate Tissue GTEx 2013 GTEx Consortium, Nat Genet. 2013;45(6):580-585. 2 Benign Prostate Primary Smith BA et al., PNAS culture Smith 2015 2015;112(47):E6544-6552. 3 Benign Prostate Primary Liu X et al., Cell reports culture Liu 2016 2016;17(10):2596-2606. 4 Benign Prostate Primary Zhang D et al., Nat Commun. culture Zhang 2016 2016;7(1):10798. Abeshouse A et al., Cell 2015;163(4):1011-1025; TCGA PRAD Pan-Cancer Atlas 2018: 5 PCa, Benign Tissue 2015; gdc.cancer.gov Pan-Cancer Driver and mutations: Bailey MH et Atlas 2018 al. Cell, 2018;173(2):371-385. Survival data: Liu J. et al., Cell 2018;173(2):400-416. 6 PCa (nADT) Tissue Rajan 2014 Rajan P et al., Eur Urol. 2014;66(1):32-39 7 PCa (nADT) Tissue Sharma 2018 Sharma NV et al., Cancers (Basel). 2018;10(10). Docket No.: 440914 [2085_010 PCT] PCa (nADT), Benign Tissue Long 2020 Long X et al., Cell Death Dis.2020;11(9):779. PCa (nADT) Tissue RPCI Nastiuk Jamroze A et al. (Chatta G., Nastiuk 2020 KL) 2024 (Submitted) PCa, Benign Tissue Gerhauser Gerhauser C et al., Cancer cell 2018 2018;34(6):996-1011. PCa, Benign Tissue Wyatt 2014 Wyatt AW et al., Genome biology 2014;15(8) 1-14. Primary PCa tumor Spr Spratt DE et al., J Clin Oncol. tissue- att 2017 2017;35(18):1991-1998. FFPE Primary PCa tumor Tosoian JJ et al., Prostate Cance e- Tosoia r tissu n 2020 Prostatic Dis.2020;23(4):646-653. FFPE Primary PCa tumor CHAARTED issue- cor Hamid AA et al., Ann Oncol.2021 t relatives 2021 Sep;32(9):1157-1166. FFPE Primary tumor Decipher GRI Weiner AB et al. Cancer vol. PCa D tissue- Biopsy 129,14 (2023): 2169-2178. FFPE doi:10.1002 / cncr.34790 PCa, CRPC, Benign, ormal Prostate Tissue Bolis M et al., Nat Commun. N Bolis 2021 2021;12(1):7033. PCa, mPCa, Benign Tissue Taylor 2010 Taylor BS et al., Cancer Cell 2010;18(1):11-22. mCRPC Tissue SU2C 2015 Robinson D et al., Cell 2015,161(5): 1215-1228. mCRPC Tissue SU2C 2019 Abida W et al., PNAS 2019;116(23):11428-11436. mCRPC Tissue, PDX Labrecque Labrecque MP et al., J Clin Invest. 2019 2019;129(10):4492-4505. mCRPC Tissue Alumkal 2020 Alumkal JJ et al., PNAS 2020;117(22):12315-12323. mCRPC Tissue Westbrook Westbrook TC et al., Nat commun. 2022 2022,13(1): 5345. mCRPC (NEPC, Tissue B Beltran H et al., Nature medicine Adeno-mCRPC) eltran 2016 2016;22(3):298-305. Docket No.: 440914 [2085_010 PCT]4NEPC GEMM Ku SY et al., Scien tissu Goodrich 2017 ce e 2017;355(6320):78-83. Barretina J et al., Nature5Various cell lines Cell lines CCLE 2018 2012;483(7391):603-607; Ghandi M et al. Nature 2019;569(7757):503– 508. 6 PCa cell line Xenografts XG-LNCaP Li Q et al., Nat Commun. 2018;9(1):3600 6 PCa cell line Xenografts XG-LAPC9 Li Q et al., Nat Commun. 2018;9(1):3600#Sample information Data type Data Access Portal Normal human prostate from RNAseq; 1 healthy donors (245 from GTEx Clinicopathologic GTEx portal (gtexportal.org); Xena portal (GTEx v8)) data portal (xenabrowser.net) 5 human prostate basal (Trop2+ 2 CD49f High); 5 luminal (Trop2+ CD49f Low) from either benign RNAseq GEO: GSE82071 or cancer sample from patients 3 human prostate basal (EpCAM+, CD45-, CD49f Hi, CD38 low); 3 luminal (EpCAM+, 3 CD45-, CD49f Low, CD38 High); Microarray; 3 luminal progenitor (EpCAM+, RNASeq GEO: GSE89050 CD45-, CD49f Low, CD38 Low) from benign samples collected from patients 3 human prostate basal (Trop2+ CD49f High); 3 luminal (Trop2+ 4 CD49f Low) from benign RNAseq GEO: GSE67070 samples collected from prostate cancer patients TCGA PRAD 2015: Adjacent benign prostate (52); treatment- naïve Pri-PCa (281); PCa with post-surgery adjuvant ADT (Hormone Tx PCa (n=49)); RNAseq; WES; 5 PRAD (Pan-Cancer Atlas 2018): Clinicopathologic Xena portal; cBioportal; Adjacent benign prostate (52); data firebrowse.org; gdac.broadinstitute.org treatment-naïve pri-PCa (422); PCa with post-surgery adjuvant ADT (Hormone Tx PCa (n=70)); Chemotherapy PCa (2) 6 7 matched pre / post neo-adjuvant RNAseq; ADT PCa samples Clinicopathologic GEO: GSE48403 data 7 matched pre / post neo-adjuvant RNAseq; 7 ADT PCa samples from Clinicopathologic GEO: GSE111177 responder group (defined as data Docket No.: 440914 [2085_010 PCT] "Low Impact Group" in source reference) 6 matched pre / post neo-adjuvant ADT PCa samples; PCa samples (10) and paired adjacent benign RNAseq; prostate tissues (10) obtained Clinicopathologic GEO: GSE150368 from five locally advanced PCa data patients RNAseq data and clinicopathologic information from the Nastiuk 2020 Post neo-adjuvant ADT PCa RNAseq; cohort (unpublished) were provided in- (43); Age-, Stage-, clinically- Clinicopathologic house by Dr. K. Nastiuk at Roswell matched nADT-free PCa (43) data Park Comprehensive Cancer Center and are available from the corresponding author upon reasonable request. Adjacent benign prostate (9); RNAseq; Early onset PCa (118) Clinicopathologic EGA: EGAS00001002923. data Adjacent benign prostate (12); RNAseq; Clinicopathologic The European Nucleotide Archive treatment-naïve PCa (12) data (ENA): PRJEB6530 Radical prostatectomy specimens from a multi- Microarray; instutional study of intermediate Clinicopathologic Veracyte GRID and high risk localized disease data (https: / / decipherbio.com / grid / ) patients (855) Radical prostatectomy and Microarray; biopsy specimens from high risk Clinicopathologic Veracyte GRID localized disease patients (405) data (https: / / decipherbio.com / grid / ) Biopsy specimens from a Phase Microarray; 3 trial of metastatic hormone- Clinicopathologic NCTN Data Archive: NCT00309985- sensitive patients (160) data D14 Biopsy specimens from a prospectively collected from clinical use of the Decipher test, retrieved from the GRID database (82,470). Transcriptomic profiles from 82,470 prospectively collected prostate biopsy samples were Microarray; analyzed from the Decipher Clinicopathologic Veracyte GRID GRID database data (https: / / decipherbio.com / grid / ) (ClinicalTrials.gov identifier: NCT02609269). The data were de-identified according to the Safe Harbor method outlined in the HIPAA Privacy Rule 45 CFR 164.514(b) and (c) (Veracyte, San Diego, CA). These samples, Docket No.: 440914 [2085_010 PCT] which comprise a large cohort of localized prostate cancer (PCa), were utilized to compare the distribution of the Stemness Index values, focusing on differences across NCCN risk groups. Clinicopathologic Breakdown and Risk Group Analysis: The detailed clinicopathologic data reveal that a majority of the cohort presents with low-grade localized PCa. The breakdown of TNM staging includes: • T1 stage: Approximately 50% of the total samples, indicating early-stage cancer localized within the prostate. • T2 stage: About 8%, representing cancer confined within the prostate but more extensive than T1. • T3 and T4 stages: Less than 1%, suggesting advanced local spread. • N0: Roughly 98%, indicating no regional lymph node involvement. • N1: Approximately 2%, suggesting regional lymph node involvement. The Decipher GRID database categorizes patients into NCCN risk groups as follows, which guide treatment suggestions based on TNM stage, PSA level, and Gleason score: • Very Low and Low Risk: T1- T2a, N0, M0, PSA <10 ng / mL, and Gleason score ≤6. Often managed with active surveillance or less aggressive treatment. • Intermediate Risk - Favorable: Typically T2b-T2c, N0, M0, PSA 10-20 ng / mL, and Gleason score 7. Managed with less aggressive treatment but with more careful monitoring. • Intermediate Risk - Unfavorable: Similar to favorable but might include a higher Docket No.: 440914 [2085_010 PCT] volume of disease or other adverse features that make the prognosis less certain. Also managed with less aggressive treatment but requires more careful monitoring. • High Risk: Usually involves T3a, N0, M0, PSA >20 ng / mL, or Gleason score 8-10. Often requires aggressive treatment, including a combination of surgery, radiation, and hormonal therapy. • Very High Risk: Typically includes T3b-T4, any N, M0, and high Gleason scores or PSA levels (PSA >20 ng / mL). Patients are at significant risk for metastatic disease and are treated aggressively, often with multi-modal therapies. Observations on Disease Severity and Treatment Naivety: Analysis of the cohort indicates that only 13% of cases fall into the High and Very High risk categories (8,376 + 2,399 = 10,775), suggesting that the majority (approximately 87%) are below Intermediate Risk (~≤ GS7). This distribution implies that most of these lower-grade patients are likely ADT-free due to their less aggressive disease status.

[0002] Docket No.: 440914 [2085_010 PCT] Bolis 2021 dataset provides normalized RNAseq counts of 1223 clinical samples from an integrated cohort consisting of normal prostate specimens (n = 174), Pri-PCa (n = 714) and mCRPC (n = 335). RNAseq data were downloaded from Zenodo repository (ID: 5546618). As described in the reference (PMID: 34857732), this resource of Prostate Cancer Transcriptome Atlas (https: / / prostatecanceratlas.org) was build and integrated from the following studies / datasets: (1) Genotype-Tissue Expression Database (GTEx; PMID: 23715323); (2) The Cancer Genome Atlas (TCGA, TCGA-PRAD; PMID: 26544944) (3) Atlas of RNA-sequencing profiles of normal human tissues (GSE120795; PMID: 31015567); (4) Integrative epigenetic taxonomy of PNPCa Zenodo repository, (GSE120741; PMID: 30464211); https: / / zenodo.org / records / 5546618 (5) Prognostic markers in locally advanced node- Docket No.: 440914 [2085_010 PCT] expression and response to therapy in CRPC (GSE118435; PMID: 30334814); (11) Molecular profiling stratifies diverse phenotypes of treatment- refractory metastatic CRPC (PRJNA520923; GEO: GSE126078; PMID: 31361600); (12) Combined TP53 and RB1 Loss Promotes Prostate Cancer Resistance to a Broad Spectrum of Cancer Therapeutics and Confers Vulnerability to Replication Stress (GSE147250; PMID: 32460015); (13) Inter- and intra-tumor heterogeneity of metastatic prostate cancer determined by digital spatial gene expression profiling (GSE147250; PMID: 33658518); (14) Multiplexed functional genomic analysis of 5' untranslated region mutations across the spectrum of prostate cancer (GSE171729; PMID: 34244513). Adjacent benign prostate (29); Microarray; Pri-PCa (131); mPCa (19) Clinicopathologic GEO: GSE21034. cBioPortal data 98 mCRPC samples RNAseq cBioportal (www.cbioportal.org). 266 mCRPC samples RNAseq; WES cBioportal (www.cbioportal.org). 98 mCRPC samples; 39 PDX- LuCaP RNAseq GEO: GSE126078 25 mCRPC samples before ENZA Tx: 7 nonresponders ( PSA decline <50%) and 18 RNAseq PMID: 32424106 (supplementary) responders ( PSA decline ≥50%). 42 mCRPC samples: 21 matched pre / post Enzalutamide RNAseq PMID: 36109521 (supplementary) treatment mCRPC 15 mCRPC-NE; 34 mCRPC- Adeno RNAseq cBioportal 4 SKO (PBCre4:Ptenf / f); 13 DKO (PBCre4:Ptenf / f:Rb1f / f); 6 TKO RNAseq GEO: GSE90891 (PBCre4:Ptenf / f:Rb1f / f:Trp53f / f) 1019 human cell lines, including 8 prostate cell lines: DU145, RNAseq depmap portal (depmap.org); Xena LNCaPcloneFGC, MDAPCA2B, portal (xenabrowser.net) Docket No.: 440914 [2085_010 PCT] NCIH660, PC3, VCaP, 22RV1; PRECLH 12 LNCaP Xenografts: 4 26 androgen-dependent PCa; 4 Primary CRPC.; 4 Secondary RNAseq GEO: GSE88752 CRPC (Enzalutamide-resistant) 10 LAPC9 Xenografts: 5 26 androgen-dependent; 5 CRPC RNAseq GEO: GSE88752 (Enzalutamide-resistant) Table 2: Gene Signature Information 1. AR activity genes (AR-A) Reference: Bluemn, E. G., ... & Nelson, P. S. (2017). Androgen receptor pathway- independent prostate cancer is sustained through FGF signaling. Cancer cell, 32(4), 474-489. AR-A (Bluemn 2017) Ensembl ID HGNC Symbol HGNC gene name Aldehyde Dehydrogenase 1 Family 1 ALDH1A3 ENSG00000184254 ALDH1A3 Member A3 2 FKBP5 ENSG00000096060 FKBP5 FKBP Prolyl Isomerase 5 3 KLK2 ENSG00000167751 KLK2 Kallikrein Related Peptidase 2 4 KLK3 ENSG00000142515 KLK3 Kallikrein Related Peptidase 3 5 NKX3-1 ENSG00000167034 NKX3-1 NK3 homeobox 1 Prostate androgen-regulated transcript 6 PART1 ENSG00000152931 PART1 1 7 PLPP1 ENSG00000067113 PLPP1 Phospholipid Phosphatase 1 Prostate Transmembrane Protein, 8 PMEPA1 ENSG00000124225 PMEPA1 Androgen Induced 1 9 STEAP4 ENSG00000127954 STEAP4 STEAP4 Metalloreductase 10 TMPRSS2 ENSG00000184012 TMPRSS2 Transmembrane Serine Protease 2 AR-A List for Previous name, Aliases Mouse Ortholog NEPC GEMMs 1 ALDH6 Aldh1a3 Aldh1a3 2 FKBP51; FKBP54; PPIase; P54; Ptg-10 Fkbp5 Fkbp5 3 *Egfbp2 pbsn 4 PSA; APS *Klk1 5 NKX3A; NKX3.1; BAPX2 Nkx3-1 Nkx3-1 6 7 PPAP2A; PAP-2a; LPP1 Ppap2a Ppap2a 8 TMEPAI; STAG1 Pmepa1 Pmepa1 TNFAIP9; FLJ23153; TIARP; STAMP2; 9 SchLAH Steap4 Steap4 10 PRSS10 Tmprss2 Tmprss2 Docket No.: 440914 [2085_010 PCT] 2. MYC signaling activity genes Reference: Liberzon, A., ... & Tamayo, P. (2015). The molecular signatures database hallmark gene set collection. Cell systems, 1(6), 417-425. MYC-sig (Liberzon Ensembl ID HGNC Symbol HGNC gene name 2015) aminoacyl tRNA synthetase 1 AIMP2 ENSG00000106305 AIMP2 complex interacting multifunctional protein 2 2 BYSL ENSG00000112578 BYSL bystin like 3 CBX3 ENSG00000122565 CBX3 chromobox 3 4 CDK4 ENSG00000135446 CDK4 cyclin dependent kinase 4 5 DCTPP1 ENSG00000179958 DCTPP1 dCTP pyrophosphatase 1 6 DDX18 ENSG00000088205 DDX18 DEAD-box helicase 18 7 DUSP2 ENSG00000158050 DUSP2 dual specificity phosphatase 2 8 EXOSC5 ENSG00000077348 EXOSC5 exosome component 5 9 FARSA ENSG00000179115 FARSA phenylalanyl-tRNA synthetase subunit alpha # GNL3 ENSG00000163938 GNL3 G protein nucleolar 3 Docket No.: 440914 [2085_010 PCT] # GRWD1 ENSG00000105447 GRWD1 glutamate rich WD repeat containing 1 # HK2 ENSG00000159399 HK2 hexokinase 2 # HSPD1 ENSG00000144381 HSPD1 heat shock protein family D (Hsp60) member 1 # HSPE1 ENSG00000115541 HSPE1 heat shock protein family E (Hsp10) member 1 # IMP4 ENSG00000136718 IMP4 IMP U3 small nucleolar ribonucleoprotein 4 # IPO4 ENSG00000196497 IPO4 importin 4 # LAS1L ENSG00000001497 LAS1L LAS1 like ribosome biogenesis factor # MAP3K6 ENSG00000142733 MAP3K6 mitogen-activated protein kinase kinase kinase 6 # MCM4 ENSG00000104738 MCM4 minichromosome maintenance complex component 4 # MCM5 ENSG00000100297 MCM5 minichromosome maintenance complex component 5 # MPHOSPH10 ENSG00000124383 MPHOSPH10 M-phase phosphoprotein 10 # MRTO4 ENSG00000053372 MRTO4 MRT4 homolog, ribosome maturation factor # MYBBP1A ENSG00000132382 MYBBP1A MYB binding protein 1a # MYC ENSG00000136997 MYC MYC proto-oncogene, bHLH transcription factor Docket No.: 440914 [2085_010 PCT] NADH:ubiquinone # NDUFAF4 ENSG00000123545 NDUFAF4 oxidoreductase complex assembly factor 4 # NIP7 ENSG00000132603 NIP7 nucleolar pre-rRNA processing protein NIP7 # NOC4L ENSG00000184967 NOC4L nucleolar complex associated 4 homolog # NOLC1 ENSG00000166197 NOLC1 nucleolar and coiled-body phosphoprotein 1 # NOP16 ENSG00000048162 NOP16 NOP16 nucleolar protein # NOP2 ENSG00000111641 NOP2 NOP2 nucleolar protein # NOP56 ENSG00000101361 NOP56 NOP56 ribonucleoprotein # NPM1 ENSG00000181163 NPM1 nucleophosmin 1 # PA2G4 ENSG00000170515 PA2G4 proliferation-associated 2G4 # PES1 ENSG00000100029 PES1 pescadillo ribosomal biogenesis factor 1 # PHB1 ENSG00000167085 PHB1 prohibitin 1 # PLK1 ENSG00000166851 PLK1 polo like kinase 1 # PLK4 ENSG00000142731 PLK4 polo like kinase 4 # PPAN ENSG00000130810 PPAN peter pan homolog Docket No.: 440914 [2085_010 PCT] # PPRC1 ENSG00000148840 PPRC1 PPARG related coactivator 1 # PRMT3 ENSG00000185238 PRMT3 protein arginine methyltransferase 3 # PUS1 ENSG00000177192 PUS1 pseudouridine synthase 1 # RABEPK ENSG00000136933 RABEPK Rab9 effector protein with kelch motifs # RCL1 ENSG00000120158 RCL1 RNA terminal phosphate cyclase like 1 # RRP12 ENSG00000052749 RRP12 ribosomal RNA processing 12 homolog ribosomal RNA processing 9, U3 # RRP9 ENSG00000114767 RRP9 small nucleolar RNA binding protein # SLC19A1 ENSG00000173638 SLC19A1 solute carrier family 19 member 1 # SLC29A2 ENSG00000174669 SLC29A2 solute carrier family 29 member 2 # SORD ENSG00000140263 SORD sorbitol dehydrogenase # SRM ENSG00000116649 SRM spermidine synthase # SUP V3L1 ENSG00000156502 SUPV3L1 Suv3 like RNA helicase # TBRG4 ENSG00000136270 TBRG4 transforming growth factor beta regulator 4 # TCOF1 ENSG00000070814 TCOF1 treacle ribosome biogenesis factor 1 Docket No.: 440914 [2085_010 PCT] # TFB2M ENSG00000162851 TFB2M transcription factor B2, mitochondrial # TMEM97 ENSG00000109084 TMEM97 transmembrane protein 97 # UNG ENSG00000076248 UNG uracil DNA glycosylase # UTP20 ENSG00000120800 UTP20 UTP20 small subunit processome component # WDR43 ENSG00000163811 WDR43 WD repeat domain 43 # WDR74 ENSG00000133316 WDR74 WD repeat domain 74 3. PCa-Stem signature Reference: This study. Differential gene expression analyses revealed significant differences between the top 33% (Stemness-high) and the bottom 33% (Stemness-low) PCa samples in both treatment-naïve Pri-PCa (Fig.5B) and treatment-failed mCRPC (Fig.5C), resulting in a 12-gene ‘PCa-Stem signature’. MYC-sig HGNC (Liberzon 2015) Ensembl ID Symbol HGNC gene name aminoacyl tRNA synthetase complex interacting 1 AIMP2 ENSG00000106305 AIMP2 multifunctional protein 2 2 BYSL ENSG00000112578 BYSL bystin like 3 CBX3 ENSG00000122565 CBX3 chromobox 3 4 CDK4 ENSG00000135446 CDK4 cyclin dependent kinase 4 5 DCTPP1 ENSG00000179958 DCTPP1 dCTP pyrophosphatase 1 6 DDX18 ENSG00000088205 DDX18 DEAD-box helicase 18 7 DUSP2 ENSG00000158050 DUSP2 dual specificity phosphatase 2 8 EXOSC5 ENSG00000077348 EXOSC5 exosome component 5 phenylalanyl-tRNA synthetase 9 FARSA ENSG00000179115 FARSA subunit alpha 10 GNL3 ENSG00000163938 GNL3 G protein nucleolar 3 Docket No.: 440914 [2085_010 PCT] glutamate rich WD repeat GRWD1 ENSG00000105447 GRWD1 containing 1 HK2 ENSG00000159399 HK2 hexokinase 2 heat shock protein family D HSPD1 ENSG00000144381 HSPD1 (Hsp60) member 1 heat shock protein family E HSPE1 ENSG00000115541 HSPE1 (Hsp10) member 1 IMP U3 small nucleolar IMP4 ENSG00000136718 IMP4 ribonucleoprotein 4 IPO4 ENSG00000196497 IPO4 importin 4 LAS1 like ribosome biogenesis LAS1L ENSG00000001497 LAS1L factor mitogen-activated protein kinase MAP3K6 ENSG00000142733 MAP3K6 kinase kinase 6 minichromosome maintenance MCM4 ENSG00000104738 MCM4 complex component 4 minichromosome maintenance MCM5 ENSG00000100297 MCM5 complex component 5 MPHOSPH10 ENSG00000124383 MPHOSPH10 M-phase phosphoprotein 10 MRT4 homolog, ribosome MRTO4 ENSG00000053372 MRTO4 maturation factor MYBBP1A ENSG00000132382 MYBBP1A MYB binding protein 1a MYC proto-oncogene, bHLH MYC ENSG00000136997 MYC transcription factor NADH:ubiquinone oxidoreductase complex NDUFAF4 ENSG00000123545 NDUFAF4 assembly factor 4 nucleolar pre-rRNA processing NIP7 ENSG00000132603 NIP7 protein NIP7 nucleolar complex associated 4 NOC4L ENSG00000184967 NOC4L homolog nucleolar and coiled-body NOLC1 ENSG00000166197 NOLC1 phosphoprotein 1 NOP16 ENSG00000048162 NOP16 NOP16 nucleolar protein NOP2 ENSG00000111641 NOP2 NOP2 nucleolar protein NOP56 ENSG00000101361 NOP56 NOP56 ribonucleoprotein NPM1 ENSG00000181163 NPM1 nucleophosmin 1 PA2G4 ENSG00000170515 PA2G4 proliferation-associated 2G4 pescadillo ribosomal biogenesis PES1 ENSG00000100029 PES1 factor 1 PHB1 ENSG00000167085 PHB1 prohibitin 1 PLK1 ENSG00000166851 PLK1 polo like kinase 1 PLK4 ENSG00000142731 PLK4 polo like kinase 4 PPAN ENSG00000130810 PPAN peter pan homolog PPRC1 ENSG00000148840 PPRC1 PPARG related coactivator 1 protein arginine PRMT3 ENSG00000185238 PRMT3 methyltransferase 3 PUS1 ENSG00000177192 PUS1 pseudouridine synthase 1 Docket No.: 440914 [2085_010 PCT] Rab9 effector protein with kelch 42 RABEPK ENSG00000136933 RABEPK motifs RNA terminal phosphate cyclase 43 RCL1 ENSG00000120158 RCL1 like 1 ribosomal RNA processing 12 44 RRP12 ENSG00000052749 RRP12 homolog ribosomal RNA processing 9, U3 small nucleolar RNA binding 45 RRP9 ENSG00000114767 RRP9 protein solute carrier family 19 member 46 SLC19A1 ENSG00000173638 SLC19A1 1 solute carrier family 29 member 47 SLC29A2 ENSG00000174669 SLC29A2 2 48 SORD ENSG00000140263 SORD sorbitol dehydrogenase 49 SRM ENSG00000116649 SRM spermidine synthase 50 SUPV3L1 ENSG00000156502 SUPV3L1 Suv3 like RNA helicase transforming growth factor beta 51 TBRG4 ENSG00000136270 TBRG4 regulator 4 treacle ribosome biogenesis 52 TCOF1 ENSG00000070814 TCOF1 factor 1 transcription factor B2, 53 TFB2M ENSG00000162851 TFB2M mitochondrial 54 TMEM97 ENSG00000109084 TMEM97 transmembrane protein 97 55 UNG ENSG00000076248 UNG uracil DNA glycosylase UTP20 small subunit 56 UTP20 ENSG00000120800 UTP20 processome component 57 WDR43 ENSG00000163811 WDR43 WD repeat domain 43 58 WDR74 ENSG00000133316 WDR74 WD repeat domain 74 4. Prostate Cancer Classification System (PCS) PCS1-3 subtype-enriched genes Reference: You, S., ... & Freeman, M. R. (2016). Integrated classification of prostate cancer reveals a novel luminal subtype with poor outcome. Cancer research, 76(17), 4948-4958. PCS3 (219 PCS1 (86 genes) PCS2 (123 genes) genes) 1 BUB1 PPAP2A ACTC1 2 KIF4A AGR2 ACTG2 3 CCNA2 NKX3-1 ATP1A2 4 CENPE EPCAM RARRES2 5 CKS2 RAB3B CXCL1 6 DLGAP5 ST6GAL1 PGM5 7 KIF2C TMEFF2 TAGLN 8 BUB1B AMD1 KCNAB1 9 CDK1 TOM1L1 SPON1 10 CDC6 ARG2 ANGPT1 Docket No.: 440914 [2085_010 PCT] CENPA CACNA1D DES KIF23 GUCY1A3 DPT CCNB1 UAP1 FOSB CDC20 KLK3 GABRP CENPF F5 MYLK HMMR HLA-DMB PPP1R12B RRM2 KCNN2 NBL1 AURKA ABCC4 DDR2 CCNB2 STK39 RARRES1 PTTG1 SH3RF1 RBP1 KNTC1 TARP TGM4 KIF14 COL9A2 TPM2 ZWINT DPP4 ADAMTS1 NUSAP1 GHR CXCL13 CDCA5 MYO6 TNC ASPM COLEC12 NCAM1 MCM4 ACPP ACTA2 KIAA0101 ALDH1A3 ANPEP TPX2 ANK3 ATF3 UHRF1 DHCR24 COL4A6 DTL DSC2 CRYAB BIRC5 ERG FOXF1 ECT2 F3 FLNA FABP5 ACSL3 CXCL2 LMNB1 FMOD ITGA5 TOP2A GCNT1 ITGA7 TTK GJB1 KRT17 TYMS HPN MAL CDC45 KLK2 MYH11 PKMYT1 LCP1 PENK MELK TACSTD2 SERPINB5 KIF20A MSMB PMP22 CIT MYBPC1 PTGDS ESRP1 PGM3 S100A6 HJURP PPP3CA CXCL12 NCAPG SORD SMTN CENPM SPOCK1 SRD5A2 NETO2 TMPRSS2 TPM1 HES6 PLA2G7 ZFP36 EZH2 SLC4A4 NR4A3 UGT2B15 CLDN8 SRPX ANLN ZMPSTE24 ALDH1A2 NCAPG2 CRISP3 IER3 KIF15 PDLIM5 SOCS3 NUF2 IQGAP2 PDLIM7 ALB PDIA5 PAGE4 AR CUX2 MYL9 FOXM1 NEDD4L SORBS1 Docket No.: 440914 [2085_010 PCT] HMGB2 SLC39A6 DKK1 KIF11 RDH11 LMOD1 STMN1 FAM198B EFEMP2 MKI67 PLA1A SPOCK3 TK1 FNIP2 FXYD6 PRC1 DNASE2B SMOC1 CCNE2 ENPP5 PTRF EXO1 ELOVL5 AOX1 TROAP TRPM8 RND3 TACC3 STEAP4 ASPA UBE2C ACSM1 ATP2B4 UBE2T TMEM178A CFB RACGAP1 OR51E1 BMP5 CDCA8 CREB3L4 SERPING1 CEP55 SLC38A11 C1S PBK REPS2 CAV1 MLF1IP FBP1 CAV2 FAM111B SLC30A4 CCND2 HOXC6 HOXB13 CES1 CDKN3 SLC27A2 CLU CELSR3 KIAA1324 CNN1 SHMT2 TMEM45B COL6A1 SPP1 ANXA3 COL6A2 RAD54L ENTPD5 COL16A1 SPAG5 FOLH1 COX7A1 POLQ HGD CSRP1 HILPDA NEFH CYP3A5 HN1 NPY CYP4B1 PLA2G2A CYP27A1 RAB27B DEFB1 ACSM3 CFD SLC12A2 DPYSL3 SOAT1 FBLN1 TSPAN8 EFEMP1 TM9SF2 FGFR2 FAM189A2 FHL1 TSPAN1 FHL2 TMSB15A FLNC AMACR GABRE MPC2 GAS1 SLC17A5 GSN STEAP1 GSTM1 GPR160 GSTM2 MMADHC GSTM5 CRYL1 GSTP1 HSD17B11 ID1 GOLM1 ID3 CYP39A1 IGFBP6 Docket No.: 440914 [2085_010 PCT] DHRS7 CYR61 GALNT7 IL6 DNAJC10 KCNJ8 RBM47 KCNMB1 CSGALNACT1 KRT5 TDRD1 KRT13 SMOC2 KRT15 ZNF614 LAMA4 CWH43 LAMB3 OR51E2 LCN2 C15orf48 LGALS1 TMTC4 LTF SEC11C MAOB GLYATL1 MATN2 CPNE4 MEIS1 FOLH1B MEIS2 ZNF615 MFAP4 ROR2 OGNPCDH7 PCP4 SERPINF1 FXYD1 PLN PRKCB MASP1 PTNPYGM S100A2 S100A4 CCL2 CX3CL1 SELE SGCA SLC2A5 SLC14A1 SMARCD3 STAC SVIL TGFB1I1 TGFB3 TIMP2 CLEC3B TNS1 TRIP6 SCGB1A1 VCLRNF112 Docket No.: 440914 [2085_010 PCT] ACOX2 SPARCL1 LTBP4 PPAP2B TP63 AOC3 PDE5A HEPH RCAN2EFSSPEG MRVI1 WFDC2 OLFM4 FAXDC2 ADIRF RBPMS EMILIN1 LDB3 FAM107A FILIP1L SCRG1 PALLD SYNM VSIG2 TRIM29 KANK2 KRT23 CLIP3 HSPB8 PCOLCE2 DKK3 HSPB7 PDZRN4 RASL12 ASB2 LIMS2 WFDC1 TMEM35 POPDC2 NDNF C1orf54 FHOD3 CCDC3 CRISPLD2 C2orf40 TUBB6 C16orf45 Docket No.: 440914 [2085_010 PCT] 203 NEXN 204 CHRDL1 205 MYOCD 206 PPP1R14A 207 PRKCDBP 208 AHNAK2 209 MRGPRF 210 PCAT4 211 HSPB6 212 TCEAL2 213 RBFOX3 214 DACT3 215 ANKRD35 216 SYNPO2 217 MSRB3 218 C11orf96 219 MIR143HG Table 3: Summary of statistical significance of differences using Tukey's multiple Comparison Test Related to Fig.3E. PCa progression is accompanied by loss of AR-A. Summary of Statistical significance between different groups. Tukey's multiple comparisons test P Value Adjusted P Value (Tukey's (Unpaired t-Test) multiple comparisons test) Normal Prostate (GTEx)(116) vs. Adjacent Benign 0.0007 0.006 (TCGA-PRAD)(52) Normal Prostate (GTEx)(116) vs. Untread Pri-PCa <0.0001 <0.0001 (TCGA)(422) Normal Prostate (GTEx)(116) vs. Hormone Tx <0.0001 <0.0001 PCa (TCGA)(70) Normal Prostate (GTEx)(116) vs. mCRPC (GSE126078)(59) 0.588 0.9971 Normal Prostate (GTEx)(116) vs. mCRPC (SU2C 0.9351 >0.9999 2015)(135) Normal Prostate (GTEx)(116) vs. mCRPC-Adeno 0.0325 0.2918 (Beltran 2016)(34) Normal Prostate (GTEx)(116) vs. mCRPC-NE <0.0001 <0.0001 (Beltran 2016)(15) Adjacent Benign (TCGA-PRAD)(52) vs. Untread <0.0001 <0.0001 Pri-PCa (TCGA)(422) Adjacent Benign (TCGA-PRAD)(52) vs. Hormone 0.0009 0.047 Tx PCa (TCGA)(70) Adjacent Benign (TCGA-PRAD)(52) vs. mCRPC 0.0231 0.0284 (GSE126078)(59) Adjacent Benign (TCGA-PRAD)(52) vs. mCRPC 0.0067 0.0032 (SU2C 2015)(135) Docket No.: 440914 [2085_010 PCT] P Value Adjusted P Value (Tukey's Tukey's multiple comparisons test (Unpaired t-Test) multiple comparisons test) Adjacent Benign (TCGA-PRAD)(52) vs. mCRPC- 0.0002 <0.0001 Adeno (Beltran 2016)(34) Adjacent Benign (TCGA-PRAD)(52) vs. mCRPC- <0.0001 <0.0001 NE (Beltran 2016)(15) Untread Pri-PCa (TCGA)(422) vs. Hormone Tx 0.0002 0.5386 PCa (TCGA)(70) Untread Pri-PCa (TCGA)(422) vs. mCRPC <0.0001 <0.0001 (GSE126078)(59) Untread Pri-PCa (TCGA)(422) vs. mCRPC (SU2C <0.0001 <0.0001 2015)(135) Untread Pri-PCa (TCGA)(422) vs. mCRPC-Adeno <0.0001 <0.0001 (Beltran 2016)(34) Untread Pri-PCa (TCGA)(422) vs. mCRPC-NE <0.0001 <0.0001 (Beltran 2016)(15) Hormone Tx PCa (TCGA)(70) vs. mCRPC <0.0001 <0.0001 (GSE126078)(59) Hormone Tx PCa (TCGA)(70) vs. mCRPC (SU2C <0.0001 <0.0001 2015)(135) Hormone Tx PCa (TCGA)(70) vs. mCRPC-Adeno <0.0001 <0.0001 (Beltran 2016)(34) Hormone Tx PCa (TCGA)(70) vs. mCRPC-NE <0.0001 <0.0001 (Beltran 2016)(15) mCRPC (GSE126078)(59) vs. mCRPC (SU2C 0.5741 0.9913 2015)(135) mCRPC (GSE126078)(59) vs. mCRPC-Adeno 0.0537 0.0959 (Beltran 2016)(34) mCRPC (GSE126078)(59) vs. mCRPC-NE <0.0001 <0.0001 (Beltran 2016)(15) mCRPC (SU2C 2015)(135) vs. mCRPC-Adeno 0.1152 0.3093 (Beltran 2016)(34) mCRPC (SU2C 2015)(135) vs. mCRPC-NE <0.0001 <0.0001 (Beltran 2016)(15) mCRPC-Adeno (Beltran 2016)(34) vs. mCRPC- <0.0001 <0.0001 NE (Beltran 2016)(15) Related to Fig.3F. PCa progression is accompanied by gain of Stemness. Summary of Statistical significance between different groups. P Value Adjusted P Value (Tukey's Tukey's multiple comparisons test (Unpaired t-Test) multiple comparisons test) Normal Prostate (GTEx)(116) vs. Adjacent Benign <0.0001 <0.0001 (TCGA-PRAD)(52) Docket No.: 440914 [2085_010 PCT] P Value Adjusted P Value (Tukey's Tukey's multiple comparisons test (Unpaired t-Test) multiple comparisons test) Normal Prostate (GTEx)(116) vs. Untread Pri-PCa <0.0001 <0.0001 (TCGA)(422) Normal Prostate (GTEx)(116) vs. Hormone Tx <0.0001 <0.0001 PCa (TCGA)(70) Normal Prostate (GTEx)(116) vs. mCRPC-Adeno <0.0001 <0.0001 (Beltran 2016)(34) Normal Prostate (GTEx)(116) vs. mCRPC <0.0001 <0.0001 (GSE126078)(59) Normal Prostate (GTEx)(116) vs. mCRPC (SU2C <0.0001 <0.0001 2015)(135) Normal Prostate (GTEx)(116) vs. mCRPC-NE <0.0001 <0.0001 (Beltran 2016)(15) Normal Prostate (GTEx)(116) vs. mCRPC <0.0001 <0.0001 (Westbrook 2022)(42) Normal Prostate (GTEx)(116) vs. mCRPC <0.0001 <0.0001 (Alumkal 2020)(25) Normal Prostate (GTEx)(116) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) Normal Prostate (GTEx)(116) vs. CCLE Prostate <0.0001 <0.0001 Cell Lines (8) Normal Prostate (GTEx)(116) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) Normal Prostate (GTEx)(116) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) Adjacent Benign (TCGA-PRAD)(52) vs. Untread <0.0001 <0.0001 Pri-PCa (TCGA)(422) Adjacent Benign (TCGA-PRAD)(52) vs. Hormone <0.0001 <0.0001 Tx PCa (TCGA)(70) Adjacent Benign (TCGA-PRAD)(52) vs. mCRPC- <0.0001 <0.0001 Adeno (Beltran 2016)(34) Adjacent Benign (TCGA-PRAD)(52) vs. mCRPC <0.0001 <0.0001 (GSE126078)(59) Adjacent Benign (TCGA-PRAD)(52) vs. mCRPC <0.0001 <0.0001 (SU2C 2015)(135) Adjacent Benign (TCGA-PRAD)(52) vs. mCRPC- <0.0001 <0.0001 NE (Beltran 2016)(15) Adjacent Benign (TCGA-PRAD)(52) vs. mCRPC <0.0001 <0.0001 (Westbrook 2022)(42) Adjacent Benign (TCGA-PRAD)(52) vs. mCRPC <0.0001 <0.0001 (Alumkal 2020)(25) Adjacent Benign (TCGA-PRAD)(52) vs. PDX- <0.0001 <0.0001 LuCaP (GSE126078)(39) Adjacent Benign (TCGA-PRAD)(52) vs. CCLE <0.0001 <0.0001 Prostate Cell Lines (8) Adjacent Benign (TCGA-PRAD)(52) vs. XG- <0.0001 <0.0001 LAPC9 (GSE88752)(10) Adjacent Benign (TCGA-PRAD)(52) vs. XG- <0.0001 <0.0001 LNCaP (GSE88752)(12) Docket No.: 440914 [2085_010 PCT] P Value Adjusted P Value (Tukey's Tukey's multiple comparisons test (Unpaired t-Test) multiple comparisons test) Untread Pri-PCa (TCGA)(422) vs. Hormone Tx 0.0555 0.9079 PCa (TCGA)(70) Untread Pri-PCa (TCGA)(422) vs. mCRPC-Adeno <0.0001 0.0008 (Beltran 2016)(34) Untread Pri-PCa (TCGA)(422) vs. mCRPC <0.0001 <0.0001 (GSE126078)(59) Untread Pri-PCa (TCGA)(422) vs. mCRPC (SU2C <0.0001 <0.0001 2015)(135) Untread Pri-PCa (TCGA)(422) vs. mCRPC-NE <0.0001 <0.0001 (Beltran 2016)(15) Untread Pri-PCa (TCGA)(422) vs. mCRPC <0.0001 <0.0001 (Westbrook 2022)(42) Untread Pri-PCa (TCGA)(422) vs. mCRPC <0.0001 <0.0001 (Alumkal 2020)(25) Untread Pri-PCa (TCGA)(422) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) Untread Pri-PCa (TCGA)(422) vs. CCLE Prostate <0.0001 <0.0001 Cell Lines (8) Untread Pri-PCa (TCGA)(422) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) Untread Pri-PCa (TCGA)(422) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) Hormone Tx PCa (TCGA)(70) vs. mCRPC-Adeno 0.037 0.251 (Beltran 2016)(34) Hormone Tx PCa (TCGA)(70) vs. mCRPC <0.0001 <0.0001 (GSE126078)(59) Hormone Tx PCa (TCGA)(70) vs. mCRPC (SU2C <0.0001 <0.0001 2015)(135) Hormone Tx PCa (TCGA)(70) vs. mCRPC-NE 0.0003 0.0005 (Beltran 2016)(15) Hormone Tx PCa (TCGA)(70) vs. mCRPC <0.0001 <0.0001 (Westbrook 2022)(42) Hormone Tx PCa (TCGA)(70) vs. mCRPC <0.0001 <0.0001 (Alumkal 2020)(25) Hormone Tx PCa (TCGA)(70) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) Hormone Tx PCa (TCGA)(70) vs. CCLE Prostate <0.0001 <0.0001 Cell Lines (8) Hormone Tx PCa (TCGA)(70) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) Hormone Tx PCa (TCGA)(70) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) mCPRC-Adeno (Beltran 2016)(34) vs. mCRPC 0.1993 0.9565 (GSE126078)(59) mCPRC-Adeno (Beltran 2016)(34) vs. mCRPC 0.0929 0.5141 (SU2C 2015)(135) mCPRC-Adeno (Beltran 2016)(34) vs. mCRPC- 0.132 0.5365 NE (Beltran 2016)(15) Docket No.: 440914 [2085_010 PCT] P Value Adjusted P Value (Tukey's Tukey's multiple comparisons test (Unpaired t-Test) multiple comparisons test) mCPRC-Adeno (Beltran 2016)(34) vs. mCRPC <0.0001 <0.0001 (Westbrook 2022)(42) mCPRC-Adeno (Beltran 2016)(34) vs. mCRPC <0.0001 <0.0001 (Alumkal 2020)(25) mCPRC-Adeno (Beltran 2016)(34) vs. PDX- <0.0001 <0.0001 LuCaP (GSE126078)(39) mCPRC-Adeno (Beltran 2016)(34) vs. CCLE <0.0001 <0.0001 Prostate Cell Lines (8) mCPRC-Adeno (Beltran 2016)(34) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) mCPRC-Adeno (Beltran 2016)(34) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) mCRPC (GSE126078)(59) vs. mCRPC (SU2C 0.5444 >0.9999 2015)(135) mCRPC (GSE126078)(59) vs. mCRPC-NE 0.2076 0.9883 (Beltran 2016)(15) mCRPC (GSE126078)(59) vs. mCRPC <0.0001 <0.0001 (Westbrook 2022)(42) mCRPC (GSE126078)(59) vs. mCRPC (Alumkal <0.0001 <0.0001 2020)(25) mCRPC (GSE126078)(59) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) mCRPC (GSE126078)(59) vs. CCLE Prostate <0.0001 <0.0001 Cell Lines (8) mCRPC (GSE126078)(59) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) mCRPC (GSE126078)(59) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) mCRPC (SU2C 2015)(135) vs. mCRPC-NE 0.4693 0.9994 (Beltran 2016)(15) mCRPC (SU2C 2015)(135) vs. mCRPC <0.0001 <0.0001 (Westbrook 2022)(42) mCRPC (SU2C 2015)(135) vs. mCRPC (Alumkal <0.0001 <0.0001 2020)(25) mCRPC (SU2C 2015)(135) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) mCRPC (SU2C 2015)(135) vs. CCLE Prostate <0.0001 <0.0001 Cell Lines (8) mCRPC (SU2C 2015)(135) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) mCRPC (SU2C 2015)(135) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) mCRPC-NE (Beltran 2016)(15) vs. mCRPC 0.0142 0.0926 (Westbrook 2022)(42) mCRPC-NE (Beltran 2016)(15) vs. mCRPC 0.0102 0.0962 (Alumkal 2020)(25) mCRPC-NE (Beltran 2016)(15) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) Docket No.: 440914 [2085_010 PCT] P Value Adjusted P Value (Tukey's Tukey's multiple comparisons test (Unpaired t-Test) multiple comparisons test) mCRPC-NE (Beltran 2016)(15) vs. CCLE Prostate <0.0001 <0.0001 Cell Lines (8) mCRPC-NE (Beltran 2016)(15) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) mCRPC-NE (Beltran 2016)(15) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) mCRPC (Westbrook 2022)(42) vs. mCRPC 0.7869 >0.9999 (Alumkal 2020)(25) mCRPC (Westbrook 2022)(42) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) mCRPC (Westbrook 2022)(42) vs. CCLE Prostate <0.0001 <0.0001 Cell Lines (8) mCRPC (Westbrook 2022)(42) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) mCRPC (Westbrook 2022)(42) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) mCRPC (Alumkal 2020)(25) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) mCRPC (Alumkal 2020)(25) vs. CCLE Prostate <0.0001 <0.0001 Cell Lines (8) mCRPC (Alumkal 2020)(25) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) mCRPC (Alumkal 2020)(25) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) PDX-LuCaP (GSE126078)(39) vs. CCLE Prostate 0.6014 >0.9999 Cell Lines (8) PDX-LuCaP (GSE126078)(39) vs. XG-LAPC9 0.0364 0.6813 (GSE88752)(10) PDX-LuCaP (GSE126078)(39) vs. XG-LNCaP <0.0001 0.0001 (GSE88752)(12) CCLE Prostate Cell Lines (8) vs. XG-LAPC9 0.232 0.9981 (GSE88752)(10) CCLE Prostate Cell Lines (8) vs. XG-LNCaP 0.0049 0.1463 (GSE88752)(12) XG-LAPC9 (GSE88752)(10) vs. XG-LNCaP 0.0002 0.7947 (GSE88752)(12) Related to Fig. S5A. PCa progression is accompanied by gain of Stemness. Summary of Statistical significance between different groups. Docket No.: 440914 [2085_010 PCT] P Value Adjusted P Value (Tukey's Tukey's multiple comparisons test (Unpaired t-Test) multiple comparisons test) Normal Prostate (GTEx)(245) vs. Adjacent Benign <0.0001 <0.0001 (TCGA-PRAD)(52) Normal Prostate (GTEx)(245) vs. Adjacent Benign <0.0001 <0.0001 (Gerhauser 2018)(9) Normal Prostate (GTEx)(245) vs. Early onset PCa <0.0001 <0.0001 (Gerhauser 2018)(118) Normal Prostate (GTEx)(245) vs. Low-Grade PCa <0.0001 <0.0001 (TCGA)(185) Normal Prostate (GTEx)(245) vs. High-Grade PCa <0.0001 <0.0001 (TCGA)(237) Normal Prostate (GTEx)(245) vs. Untread Pri-PCa <0.0001 <0.0001 (TCGA)(422) Normal Prostate (GTEx)(245) vs. Hormone Tx <0.0001 <0.0001 PCa (TCGA)(70) Normal Prostate (GTEx)(245) vs. mCPRC-Adeno <0.0001 <0.0001 (Beltran 2016)(34) Normal Prostate (GTEx)(245) vs. mCRPC (SU2C <0.0001 <0.0001 2015)(98) Normal Prostate (GTEx)(245) vs. mCRPC (SU2C <0.0001 <0.0001 2019)(266) Normal Prostate (GTEx)(245) vs. mCRPC <0.0001 <0.0001 (GSE126078)(98) Normal Prostate (GTEx)(245) vs. mCRPC-NE <0.0001 <0.0001 (Beltran 2016)(15) Normal Prostate (GTEx)(245) vs. mCRPC <0.0001 <0.0001 (Westbrook 2022)(42) Normal Prostate (GTEx)(245) vs. mCRPC <0.0001 <0.0001 (Alumkal 2020)(25) Normal Prostate (GTEx)(245) vs. CCLE Cell Lines <0.0001 <0.0001 (1019) Normal Prostate (GTEx)(245) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) Normal Prostate (GTEx)(245) vs. CCLE Prostate <0.0001 <0.0001 Cell Lines (8) Normal Prostate (GTEx)(245) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) Normal Prostate (GTEx)(245) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) Adjacent Benign (TCGA-PRAD)(52) vs. Adjacent 0.7546 0.7546 Benign (Gerhauser 2018)(9) Adjacent Benign (TCGA-PRAD)(52) vs. Early <0.0001 <0.0001 onset PCa (Gerhauser 2018)(118) Adjacent Benign (TCGA-PRAD)(52) vs. Low- <0.0001 <0.0001 Grade PCa (TCGA)(185) Adjacent Benign (TCGA-PRAD)(52) vs. High- <0.0001 <0.0001 Grade PCa (TCGA)(237) Adjacent Benign (TCGA-PRAD)(52) vs. Untread <0.0001 <0.0001 Pri-PCa (TCGA)(422) Adjacent Benign (TCGA-PRAD)(52) vs. Hormone <0.0001 <0.0001 Tx PCa (TCGA)(70) Docket No.: 440914 [2085_010 PCT] P Value Adjusted P Value (Tukey's Tukey's multiple comparisons test (Unpaired t-Test) multiple comparisons test) Adjacent Benign (TCGA-PRAD)(52) vs. mCPRC- <0.0001 <0.0001 Adeno (Beltran 2016)(34) Adjacent Benign (TCGA-PRAD)(52) vs. mCRPC <0.0001 <0.0001 (SU2C 2015)(98) Adjacent Benign (TCGA-PRAD)(52) vs. mCRPC <0.0001 <0.0001 (SU2C 2019)(266) Adjacent Benign (TCGA-PRAD)(52) vs. mCRPC <0.0001 <0.0001 (GSE126078)(98) Adjacent Benign (TCGA-PRAD)(52) vs. mCRPC- <0.0001 <0.0001 NE (Beltran 2016)(15) Adjacent Benign (TCGA-PRAD)(52) vs. mCRPC <0.0001 <0.0001 (Westbrook 2022)(42) Adjacent Benign (TCGA-PRAD)(52) vs. mCRPC <0.0001 <0.0001 (Alumkal 2020)(25) Adjacent Benign (TCGA-PRAD)(52) vs. CCLE <0.0001 <0.0001 Cell Lines (1019) Adjacent Benign (TCGA-PRAD)(52) vs. PDX- <0.0001 <0.0001 LuCaP (GSE126078)(39) Adjacent Benign (TCGA-PRAD)(52) vs. CCLE <0.0001 <0.0001 Prostate Cell Lines (8) Adjacent Benign (TCGA-PRAD)(52) vs. XG- <0.0001 <0.0001 LAPC9 (GSE88752)(10) Adjacent Benign (TCGA-PRAD)(52) vs. XG- <0.0001 <0.0001 LNCaP (GSE88752)(12) Adjacent Benign (Gerhauser 2018)(9) vs. Early 0.0280 0.028 onset PCa (Gerhauser 2018)(118) Adjacent Benign (Gerhauser 2018)(9) vs. Low- 0.0002 0.0002 Grade PCa (TCGA)(185) Adjacent Benign (Gerhauser 2018)(9) vs. High- 0.0002 0.0002 Grade PCa (TCGA)(237) Adjacent Benign (Gerhauser 2018)(9) vs. Untread 0.0002 0.0002 Pri-PCa (TCGA)(422) Adjacent Benign (Gerhauser 2018)(9) vs. 0.0010 0.001 Hormone Tx PCa (TCGA)(70) Adjacent Benign (Gerhauser 2018)(9) vs. 0.0002 0.0002 mCPRC-Adeno (Beltran 2016)(34) Adjacent Benign (Gerhauser 2018)(9) vs. mCRPC <0.0001 <0.0001 (SU2C 2015)(98) Adjacent Benign (Gerhauser 2018)(9) vs. mCRPC <0.0001 <0.0001 (SU2C 2019)(266) Adjacent Benign (Gerhauser 2018)(9) vs. mCRPC <0.0001 <0.0001 (GSE126078)(98) Adjacent Benign (Gerhauser 2018)(9) vs. <0.0001 <0.0001 mCRPC-NE (Beltran 2016)(15) Adjacent Benign (Gerhauser 2018)(9) vs. mCRPC <0.0001 <0.0001 (Westbrook 2022)(42) Adjacent Benign (Gerhauser 2018)(9) vs. mCRPC <0.0001 <0.0001 (Alumkal 2020)(25) Adjacent Benign (Gerhauser 2018)(9) vs. CCLE <0.0001 <0.0001 Cell Lines (1019) Docket No.: 440914 [2085_010 PCT] P Value Adjusted P Value (Tukey's Tukey's multiple comparisons test (Unpaired t-Test) multiple comparisons test) Adjacent Benign (Gerhauser 2018)(9) vs. PDX- <0.0001 <0.0001 LuCaP (GSE126078)(39) Adjacent Benign (Gerhauser 2018)(9) vs. CCLE <0.0001 <0.0001 Prostate Cell Lines (8) Adjacent Benign (Gerhauser 2018)(9) vs. XG- <0.0001 <0.0001 LAPC9 (GSE88752)(10) Adjacent Benign (Gerhauser 2018)(9) vs. XG- <0.0001 <0.0001 LNCaP (GSE88752)(12) Early onset PCa (Gerhauser 2018)(118) vs. Low- <0.0001 <0.0001 Grade PCa (TCGA)(185) Early onset PCa (Gerhauser 2018)(118) vs. High- <0.0001 <0.0001 Grade PCa (TCGA)(237) Early onset PCa (Gerhauser 2018)(118) vs. <0.0001 <0.0001 Untread Pri-PCa (TCGA)(422) Early onset PCa (Gerhauser 2018)(118) vs. <0.0001 <0.0001 Hormone Tx PCa (TCGA)(70) Early onset PCa (Gerhauser 2018)(118) vs. <0.0001 <0.0001 mCPRC-Adeno (Beltran 2016)(34) Early onset PCa (Gerhauser 2018)(118) vs. <0.0001 <0.0001 mCRPC (SU2C 2015)(98) Early onset PCa (Gerhauser 2018)(118) vs. <0.0001 <0.0001 mCRPC (SU2C 2019)(266) Early onset PCa (Gerhauser 2018)(118) vs. <0.0001 <0.0001 mCRPC (GSE126078)(98) Early onset PCa (Gerhauser 2018)(118) vs. <0.0001 <0.0001 mCRPC-NE (Beltran 2016)(15) Early onset PCa (Gerhauser 2018)(118) vs. <0.0001 <0.0001 mCRPC (Westbrook 2022)(42) Early onset PCa (Gerhauser 2018)(118) vs. <0.0001 <0.0001 mCRPC (Alumkal 2020)(25) Early onset PCa (Gerhauser 2018)(118) vs. CCLE <0.0001 <0.0001 Cell Lines (1019) Early onset PCa (Gerhauser 2018)(118) vs. PDX- <0.0001 <0.0001 LuCaP (GSE126078)(39) Early onset PCa (Gerhauser 2018)(118) vs. CCLE <0.0001 <0.0001 Prostate Cell Lines (8) Early onset PCa (Gerhauser 2018)(118) vs. XG- <0.0001 <0.0001 LAPC9 (GSE88752)(10) Early onset PCa (Gerhauser 2018)(118) vs. XG- <0.0001 <0.0001 LNCaP (GSE88752)(12) Low-Grade PCa (TCGA)(185) vs. High-Grade 0.0999 0.0999 PCa (TCGA)(237) Low-Grade PCa (TCGA)(185) vs. Untread Pri- 0.3073 0.3073 PCa (TCGA)(422) Low-Grade PCa (TCGA)(185) vs. Hormone Tx 0.0132 0.0132 PCa (TCGA)(70) Low-Grade PCa (TCGA)(185) vs. mCPRC-Adeno <0.0001 <0.0001 (Beltran 2016)(34) Low-Grade PCa (TCGA)(185) vs. mCRPC (SU2C <0.0001 <0.0001 2015)(98) Docket No.: 440914 [2085_010 PCT] P Value Adjusted P Value (Tukey's Tukey's multiple comparisons test (Unpaired t-Test) multiple comparisons test) Low-Grade PCa (TCGA)(185) vs. mCRPC (SU2C <0.0001 <0.0001 2019)(266) Low-Grade PCa (TCGA)(185) vs. mCRPC <0.0001 <0.0001 (GSE126078)(98) Low-Grade PCa (TCGA)(185) vs. mCRPC-NE <0.0001 <0.0001 (Beltran 2016)(15) Low-Grade PCa (TCGA)(185) vs. mCRPC <0.0001 <0.0001 (Westbrook 2022)(42) Low-Grade PCa (TCGA)(185) vs. mCRPC <0.0001 <0.0001 (Alumkal 2020)(25) Low-Grade PCa (TCGA)(185) vs. CCLE Cell <0.0001 <0.0001 Lines (1019) Low-Grade PCa (TCGA)(185) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) Low-Grade PCa (TCGA)(185) vs. CCLE Prostate <0.0001 <0.0001 Cell Lines (8) Low-Grade PCa (TCGA)(185) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) Low-Grade PCa (TCGA)(185) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) High-Grade PCa (TCGA)(237) vs. Untread Pri- 0.3759 0.3759 PCa (TCGA)(422) High-Grade PCa (TCGA)(237) vs. Hormone Tx 0.1754 0.1754 PCa (TCGA)(70) High-Grade PCa (TCGA)(237) vs. mCPRC-Adeno <0.0001 <0.0001 (Beltran 2016)(34) High-Grade PCa (TCGA)(237) vs. mCRPC (SU2C <0.0001 <0.0001 2015)(98) High-Grade PCa (TCGA)(237) vs. mCRPC (SU2C <0.0001 <0.0001 2019)(266) High-Grade PCa (TCGA)(237) vs. mCRPC <0.0001 <0.0001 (GSE126078)(98) High-Grade PCa (TCGA)(237) vs. mCRPC-NE <0.0001 <0.0001 (Beltran 2016)(15) High-Grade PCa (TCGA)(237) vs. mCRPC <0.0001 <0.0001 (Westbrook 2022)(42) High-Grade PCa (TCGA)(237) vs. mCRPC <0.0001 <0.0001 (Alumkal 2020)(25) High-Grade PCa (TCGA)(237) vs. CCLE Cell <0.0001 <0.0001 Lines (1019) High-Grade PCa (TCGA)(237) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) High-Grade PCa (TCGA)(237) vs. CCLE Prostate <0.0001 <0.0001 Cell Lines (8) High-Grade PCa (TCGA)(237) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) High-Grade PCa (TCGA)(237) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) Docket No.: 440914 [2085_010 PCT] P Value Adjusted P Value (Tukey's Tukey's multiple comparisons test (Unpaired t-Test) multiple comparisons test) Untread Pri-PCa (TCGA)(422) vs. Hormone Tx 0.0392 0.0392 PCa (TCGA)(70) Untread Pri-PCa (TCGA)(422) vs. mCPRC-Adeno <0.0001 <0.0001 (Beltran 2016)(34) Untread Pri-PCa (TCGA)(422) vs. mCRPC (SU2C <0.0001 <0.0001 2015)(98) Untread Pri-PCa (TCGA)(422) vs. mCRPC (SU2C <0.0001 <0.0001 2019)(266) Untread Pri-PCa (TCGA)(422) vs. mCRPC <0.0001 <0.0001 (GSE126078)(98) Untread Pri-PCa (TCGA)(422) vs. mCRPC-NE <0.0001 <0.0001 (Beltran 2016)(15) Untread Pri-PCa (TCGA)(422) vs. mCRPC <0.0001 <0.0001 (Westbrook 2022)(42) Untread Pri-PCa (TCGA)(422) vs. mCRPC <0.0001 <0.0001 (Alumkal 2020)(25) Untread Pri-PCa (TCGA)(422) vs. CCLE Cell <0.0001 <0.0001 Lines (1019) Untread Pri-PCa (TCGA)(422) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) Untread Pri-PCa (TCGA)(422) vs. CCLE Prostate <0.0001 <0.0001 Cell Lines (8) Untread Pri-PCa (TCGA)(422) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) Untread Pri-PCa (TCGA)(422) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) Hormone Tx PCa (TCGA)(70) vs. mCPRC-Adeno 0.041 0.041 (Beltran 2016)(34) Hormone Tx PCa (TCGA)(70) vs. mCRPC (SU2C <0.0001 <0.0001 2015)(98) Hormone Tx PCa (TCGA)(70) vs. mCRPC (SU2C <0.0001 <0.0001 2019)(266) Hormone Tx PCa (TCGA)(70) vs. mCRPC <0.0001 <0.0001 (GSE126078)(98) Hormone Tx PCa (TCGA)(70) vs. mCRPC-NE 0.0012 <0.0001 (Beltran 2016)(15) Hormone Tx PCa (TCGA)(70) vs. mCRPC <0.0001 <0.0001 (Westbrook 2022)(42) Hormone Tx PCa (TCGA)(70) vs. mCRPC <0.0001 <0.0001 (Alumkal 2020)(25) Hormone Tx PCa (TCGA)(70) vs. CCLE Cell <0.0001 <0.0001 Lines (1019) Hormone Tx PCa (TCGA)(70) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) Hormone Tx PCa (TCGA)(70) vs. CCLE Prostate <0.0001 <0.0001 Cell Lines (8) Hormone Tx PCa (TCGA)(70) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) Hormone Tx PCa (TCGA)(70) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) Docket No.: 440914 [2085_010 PCT] P Value Adjusted P Value (Tukey's Tukey's multiple comparisons test (Unpaired t-Test) multiple comparisons test) mCPRC-Adeno (Beltran 2016)(34) vs. mCRPC 0.1401 0.1401 (SU2C 2015)(98) mCPRC-Adeno (Beltran 2016)(34) vs. mCRPC 0.2282 0.2282 (SU2C 2019)(266) mCPRC-Adeno (Beltran 2016)(34) vs. mCRPC 0.0502 0.0502 (GSE126078)(98) mCPRC-Adeno (Beltran 2016)(34) vs. mCRPC- 0.1503 0.1503 NE (Beltran 2016)(15) mCPRC-Adeno (Beltran 2016)(34) vs. mCRPC 0.0003 0.0003 (Westbrook 2022)(42) mCPRC-Adeno (Beltran 2016)(34) vs. mCRPC 0.0005 0.0005 (Alumkal 2020)(25) mCPRC-Adeno (Beltran 2016)(34) vs. CCLE Cell <0.0001 <0.0001 Lines (1019) mCPRC-Adeno (Beltran 2016)(34) vs. PDX- <0.0001 <0.0001 LuCaP (GSE126078)(39) mCPRC-Adeno (Beltran 2016)(34) vs. CCLE <0.0001 <0.0001 Prostate Cell Lines (8) mCPRC-Adeno (Beltran 2016)(34) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) mCPRC-Adeno (Beltran 2016)(34) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) mCRPC (SU2C 2015)(98) vs. mCRPC (SU2C 0.8901 0.8901 2019)(266) mCRPC (SU2C 2015)(98) vs. mCRPC 0.3818 0.3818 (GSE126078)(98) mCRPC (SU2C 2015)(98) vs. mCRPC-NE (Beltran 2016)(15) 0.3696 0.3696 mCRPC (SU2C 2015)(98) vs. mCRPC (Westbrook 0.0001 0.0001 2022)(42) mCRPC (SU2C 2015)(98) vs. mCRPC (Alumkal 0.0003 0.0003 2020)(25) mCRPC (SU2C 2015)(98) vs. CCLE Cell Lines <0.0001 <0.0001 (1019) mCRPC (SU2C 2015)(98) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) mCRPC (SU2C 2015)(98) vs. CCLE Prostate Cell <0.0001 <0.0001 Lines (8) mCRPC (SU2C 2015)(98) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) mCRPC (SU2C 2015)(98) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) mCRPC (SU2C 2019)(polyA)(266) vs. mCRPC 0.3248 0.3248 (GSE126078)(98) mCRPC (SU2C 2019)(polyA)(266) vs. mCRPC-NE 0.4458 0.4458 (Beltran 2016)(15) mCRPC (SU2C 2019)(polyA)(266) vs. mCRPC 0.0006 0.0006 (Westbrook 2022)(42) Docket No.: 440914 [2085_010 PCT] P Value Adjusted P Value (Tukey's Tukey's multiple comparisons test (Unpaired t-Test) multiple comparisons test) mCRPC (SU2C 2019)(polyA)(266) vs. mCRPC 0.0027 0.0027 (Alumkal 2020)(25) mCRPC (SU2C 2019)(polyA)(266) vs. CCLE Cell <0.0001 <0.0001 Lines (1019) mCRPC (SU2C 2019)(polyA)(266) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) mCRPC (SU2C 2019)(polyA)(266) vs. CCLE <0.0001 <0.0001 Prostate Cell Lines (8) mCRPC (SU2C 2019)(polyA)(266) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) mCRPC (SU2C 2019)(polyA)(266) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) mCRPC (GSE126078)(98) vs. mCRPC-NE 0.6834 0.6834 (Beltran 2016)(15) mCRPC (GSE126078)(98) vs. mCRPC 0.0027 0.0027 (Westbrook 2022)(42) mCRPC (GSE126078)(98) vs. mCRPC (Alumkal 0.0041 0.0041 2020)(25) mCRPC (GSE126078)(98) vs. CCLE Cell Lines <0.0001 <0.0001 (1019) mCRPC (GSE126078)(98) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) mCRPC (GSE126078)(98) vs. CCLE Prostate Cell <0.0001 <0.0001 Lines (8) mCRPC (GSE126078)(98) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) mCRPC (GSE126078)(98) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) mCRPC-NE (Beltran 2016)(15) vs. mCRPC 0.157 0.1565 (Westbrook 2022)(42) mCRPC-NE (Beltran 2016)(15) vs. mCRPC 0.0996 0.0996 (Alumkal 2020)(25) mCRPC-NE (Beltran 2016)(15) vs. CCLE Cell <0.0001 <0.0001 Lines (1019) mCRPC-NE (Beltran 2016)(15) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) mCRPC-NE (Beltran 2016)(15) vs. CCLE Prostate <0.0001 <0.0001 Cell Lines (8) mCRPC-NE (Beltran 2016)(15) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) mCRPC-NE (Beltran 2016)(15) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) mCRPC (Westbrook 2022)(42) vs. mCRPC 0.7861 0.7861 (Alumkal 2020)(25) mCRPC (Westbrook 2022)(42) vs. CCLE Cell <0.0001 <0.0001 Lines (1019) mCRPC (Westbrook 2022)(42) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) Docket No.: 440914 [2085_010 PCT] P Value Adjusted P Value (Tukey's Tukey's multiple comparisons test (Unpaired t-Test) multiple comparisons test) mCRPC (Westbrook 2022)(42) vs. CCLE Prostate <0.0001 <0.0001 Cell Lines (8) mCRPC (Westbrook 2022)(42) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) mCRPC (Westbrook 2022)(42) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) mCRPC (Alumkal 2020)(25) vs. CCLE Cell Lines <0.0001 <0.0001 (1019) mCRPC (Alumkal 2020)(25) vs. PDX-LuCaP <0.0001 <0.0001 (GSE126078)(39) mCRPC (Alumkal 2020)(25) vs. CCLE Prostate Cell <0.0001 0.0049 Lines (8) mCRPC (Alumkal 2020)(25) vs. XG-LAPC9 <0.0001 <0.0001 (GSE88752)(10) mCRPC (Alumkal 2020)(25) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) CCLE Cell Lines (1019) vs. PDX-LuCaP 0.15 0.15 (GSE126078)(39) CCLE Cell Lines (1019) vs. CCLE Prostate Cell 0.3438 0.3438 Lines (8) CCLE Cell Lines (1019) vs. XG-LAPC9 0.0716 0.0716 (GSE88752)(10) CCLE Cell Lines (1019) vs. XG-LNCaP 0.0011 0.0011 (GSE88752)(12) PDX-LuCaP (GSE126078)(39) vs. CCLE Prostate 0.6018 0.6018 Cell Lines (8) PDX-LuCaP (GSE126078)(39) vs. XG-LAPC9 0.0363 0.0363 (GSE88752)(10) PDX-LuCaP (GSE126078)(39) vs. XG-LNCaP <0.0001 <0.0001 (GSE88752)(12) CCLE Prostate Cell Lines (8) vs. XG-LAPC9 0.2314 0.2314 (GSE88752)(10) CCLE Prostate Cell Lines (8) vs. XG-LNCaP 0.0049 0.0049 (GSE88752)(12) XG-LAPC9 (GSE88752)(10) vs. XG-LNCaP 0.0002 0.0002 (GSE88752)(12)

[0003] Docket No.: 440914 [2085_010 PCT] Table 4: Gene signature and Gene sets Information used in Gene Set Enrichment Analysis (GSEA) No. Gene Signature or Gene Figur Gene Set Gene Set No. of Brief Description Set es Enrichment Source: Genes Category MSigDB, in the Literature Gene (Pubmed Se ID) 1 Benporath_ES_2 eFig. stemness GSEA 40 Genes overexpressed in human embryonic 6I MSigDB, stem cells according to a meta-analysis of 8 C2 profiling studies. (curated MSigDB standard name: Benporath_ES_2 gene sets); Pubmed 18443585 2 Bhattacharya_ESC eFig. stemness GSEA 84 The 'stemness' signature: genes up-regulated 6I MSigDB, and common to 6 human embryonic stem cell C2 lines tested. (curated MSigDB standard name: gene BHATTACHARYA_EMBRYONIC_STEM_CE sets); LL Pubmed 15070671 3 Chandran_Metastasis_U eFig. Cancer GSEA 209; Genes up-regulated in metastatic tumors from p; 6I, 6J aggressiven MSigDB, 313 the whole panel of patients with prostate Chandran_Metastasis_D ess C2 cancer; n (curated Genes down-regulated in metastatic tumors gene from the whole panel of patients with prostate sets); Pubmed 17430594 4 Lee_Metastasis_Up eFig. Cancer GSEA 16 Components of RNA post-transcriptional 6I aggressiven MSigDB, modification machinery up-regulated in ess C2 MDA-MB-435 cells (breast cancer) whose (curated metastatic potential has been reduced by gene expression of NME1 [GeneID=4830] sets); MSigDB standard name: Pubmed LEE_METASTASIS_AND_RNA_PROCESSI 18245461 NG_UP 5 Wang_Metastasis_Up eFig. Cancer GSEA 22 Genes whose expression in primary ER(+) 6I aggressiven MSigDB, [GeneID=2099] breast cancer tumors ess C2 positively (curated correlates with developing distant metastases. gene MSigDB standard name: sets); WANG_METASTASIS_OF_BREAST_CANC Pubmed ER_ESR1_UP 15721472 6 Wallace_PCa_Cancer_U eFig. Cancer GSEA 20 Genes up-regulated in prostate tumor vs p 6I aggressiven MSigDB, normal tissue samples. MSigDB standard ess C2 name: WALLACE_PROSTATE_CANCER_UP (curated gene sets); Pubmed 18245496 7 Tomlins_PCa_Cancer_U eFig. Cancer GSEA 40; Genes up-regulated in prostate cancer vs p; 6I, aggressiven MSigDB, 40 benign prostate tissue, based on a meta- Tomlins_PCa_Cancer_D ess C2 analysis of five gene expression profiling n 6J (curated studies. gene MSigDB standard name: TOMLINS sets); PROSTATE CANCER UP; Pubmed 17173048 8 Rhodes_undifferentiated eFig. Cancer GSEA 69 Genes commonly up-regulated in _Cancer 6I aggressiven MSigDB, undifferentiated cancer (more ess C2 aggressiveness) relative to well-differentiated Docket No.: 440914 [2085_010 PCT] No. Gene Signature or Gene Figur Gene Set Gene Set No. of Brief Description Set es Enrichment Source: Genes Category MSigDB, in the Literature Gene (Pubmed Se ID) (curated (less aggressiveness) cancer, based on the gene meta-analysis of the OncoMine gene sets); expression database. Pubmed MSigDB standard name: 15184677 RHODES_UNDIFFERENTIATED_CANCER 9 Liu_Prostate_Cancer_D eFig. Cancer GSEA 493 Genes down-regulated in prostate cancer n 6J aggressiven MSigDB, samples vs. benign tissue. ess C2 MSigDB standard name: (curated LIU_PROSTATE_CANCER_DN gene sets); Pubmed 16618720 10 Smid_Breast_Cancer_D eFig. Cancer GSEA 485 Genes down-regulated in aggressive n 6J aggressiven MSigDB, subtypes of breast cancer vs. normal-like ess C2 subtype of (curated breast cancer. gene MSigDB standard name: sets); SMID_BREAST_CANCER_NORMAL_LIKE_ Pubmed UP 18451135 11 DNA_REPAIR eFig. GSEA GSEA 150 Genes involved in DNA repair. 6I Hallmark MSigDB, MSigDB standard name: Pathway Hallmark; HALLMARK_DNA_REPAIR Pubmed 26771021 12 E2F_TARGETS eFig. GSEA GSEA 200 Genes encoding cell cycle related targets of 6I Hallmark MSigDB, E2F transcription factors. MSigDB standard Pathway Hallmark; name: HALLMARK_E2F_TARGETS Pubmed 26771021 13 IFN_GAMMA_RESPON eFig. GSEA GSEA 200 Genes up-regulated in response to IFNG. SE 6J Hallmark MSigDB, MSigDB standard name: Pathway Hallmark; HALLMARK_INTERFERON_GAMMA_RESP Pubmed ONSE 26771021 14 INFLAMMATORY_RES eFig. GSEA GSEA 200 Genes defining inflammatory response. PONSE 6J Hallmark MSigDB, MSigDB standard name: Pathway Hallmark; HALLMARK_INFLAMMATORY_RESPONSE Pubmed 26771021 15 MTORC1_SIGNALING eFig. GSEA GSEA 200 Genes up-regulated through activation of 6I Hallmark MSigDB, mTORC1 complex. MSigDB standard name: Pathway Hallmark; HALLMARK_MTORC1_SIGNALING Pubmed 26771021 16 MYC_TARGETS_V1 eFig. GSEA GSEA 200 A subgroup of genes regulated by MYC - 6I Hallmark MSigDB, version 1 (v1). MSigDB standard name: Pathway Hallmark; HALLMARK _MYC_TARGETS _V1 Pubmed 26771021 17 MYC_TARGETS_V2 eFig. GSEA GSEA 58 A subgroup of genes regulated by MYC - 6I Hallmark MSigDB, version 2 (v2). MSigDB standard name: Pathway Hallmark; HALLMARK_MYC_TARGETS_V2 Pubmed 26771021 18 TGF_BETA_SIGNALING eFig. GSEA GSEA 54 Genes up-regulated in response to TGFB1. 6J Hallmark MSigDB, MSigDB standard name: Pathway Hallmark; HALLMARK_TGF_BETA_SIGNALING Pubmed 26771021 Docket No.: 440914 [2085_010 PCT] No. Gene Signature or Gene Figur Gene Set Gene Set No. of Brief Description Set es Enrichment Source: Genes Category MSigDB, in the Literature Gene (Pubmed Se ID) 19 TNFA_SIGNALING_VIA eFig. GSEA GSEA 200 Genes regulated by NF-kB in response to _NFKB 6J Hallmark MSigDB, TNF. Pathway Hallmark; MSigDB standard name: Pubmed HALLMARK_TNFA_SIGNALING_VIA_NFKB 26771021 20 You_PCA_subtype: eFig. PCa Pubmed 86; Integrated classification of prostate cancer PCS1; 6L, subtypes 27302169 219 based on human PCa transcriptome profiles You_PCA_subtype: 6M from a large virutal cohort (n=1 ,321) PCS3 developed a novel classfication system consisting of 3 distinct subtypes (named PCS1, PCS2, PCS3). PCS1 and PCS2 tumors reflect luminal subtypes, while PCS3 represents a basal subtype. PCS1 tumors progress more rapidly to metastatic disease in comparison to PCS2 or PCS3. Wilcoxon rank-sum test and subsequent false discovery rate (FDR) correction with Storey’s method (25) were employed to identify differentially expressed genes between the subtypes. Genes were selected with FDR<0.001 and fold change ≥1.5, resulting in 428 SEGs. Among 428 subtype enriched genes, 86 for PCS1, 123 for PCS2, and 219 for PCS3. 21 Tang_mCRPC-AR- eFig. mCRPC Pubmed 93; Integrated classfication of castration- dependent; 6N subtypes 35617398 93 resistant prostate cancer based on Tang_mCRPC-NE- Chromatin (accessbility) profiles (ATAC- subtype seq), transcriptomic profiles (RNA-seq), genomic alterations (DNA sequncing) in 40 metastatic prostate cancer models, including 22 organoids, 6 PDXs, 7 cell lines, and 5 derived CRPC cell lines from Park et al Science 2018, the study revealed four subtypes using ATAC-seq clusering, AR- dependent, neuroendocrine, and two AR- negative / low groups (Wnt-dependent, stem cell-like). Gene signatures of the four epigenetically defined subgroups were generated using RNA-seq data. 22 Coleman_Cell cycle eFig. Cell Cycle Pubmed 31 This gene signature is curated in a published progression 6O Progression 35552660 study Pubmed 21310658, where the authors ; initially selected 126 cell cycle progression 21310658 (CCP) genes from the Gene Expression Omnibus database and tested their performance with RNA extracted from 96 commercially available FFPE prostate tumour sections (Asterand, Detroit, MI, USA) obtained from anonymous patients and subsequently the authors selected genes for inclusion in the signature on the basis of their correlation with the mean expression of the entire set of CCP genes. The final signature consisted of 31 CCP genes (FOXM1, CDC20, CDKN3, CDC2, KIF11, KIAA0101, NUSAP1, CENPF, ASPM, BUB1B, RRM2, DLGAP5, BIRC5, KIF20A, PLK1, TOP2A, TK1, PBK, ASF1B, C18orf24, RAD54L, PTTG1, CDCA3, MCM10, PRC1, DTL, CEP55, RAD51, CENPM, CDCA8, and ORC6L). These highly correlated genes were used to provide a robust and highly reproducible measurement of cell proliferation and were not intended to capture information related to other factors (eg, Docket No.: 440914 [2085_010 PCT] No. Gene Signature or Gene Figur Gene Set Gene Set No. of Brief Description Set es Enrichment Source: Genes Category MSigDB, in the Literature Gene (Pubmed Se ID) invasive potential). The signature was assessed retrospectively in a cohort of patients from the USA who had undergone radical prostatectomy, and in a cohort of randomly selected men with clinically localised prostate cancer diagnosed by use of a transurethral resection of the prostate (TURP) in the UK who were managed conservatively. The CCP score was predictive of outcome in both cohorts. The authors also suggested that Expression of CCP genes is higher in actively growing cells, and presumably by measuring the expression of CCP genes, we are able to indirectly measure the growth rate and inherent 23 Alumkal_Lineage eFig. Lineage Pubmed 14 This gene signature is curated in a published plasticity risk 6P Plasticity 36109521 study using the transcriptomes of matched Risk in biopsies from men with metastatic CRPC CRPC obtained prior to the androgen receptor (AR) signaling inhibitor Enzalutamide (enza) treatment and at progression (n = 21). To determine if any of the progression tumors in the cohort underwent lineage plasticity after enza, the authors determined the Aggarwal cluster and Labrecque classifier designation. Twelve of 21 matched pairs did not change their Aggarwal cluster designation. However, three of the 21 progression tumors (hereafter referred to as converters) had gene expression profiles consistent with cluster 2, suggesting enza-induced conversion to an alternate lineage. By identifying genes significantly upregulated in the baseline tumors from converters vs. non-converters, the authors identified a 14-gene signature highly activated in the three baseline tumors from converters. This signature indicates the susceptibility to transcriptional conversion and lineage plasticity

Claims

Docket No.: 440914 [2085_010 PCT] CLAIMS 1. A method of determining the progression and / or stage of prostate cancer in a human subject, comprising: (a) Generating a gene expression profile from a prostate cancer tissue sample of the subject; (b) Obtaining a Stemness score through use of a one-class logistic regression (OCLR) and applying the Stemness score in assessing prostate cancer progression and / or stage by a computation comprising (1) determining the Spearman correlation between the subject’s gene expression profile and a stem cell signature derived from the OCLR machine learning algorithm, wherein said algorithm has been trained on transcriptomic profiles of non-transformed pluripotent stem cells and their ectoderm, mesoderm, and endoderm progenitors, and (2) linearly transforming the Spearman correlation to a scale of 0 to 1; and / or (c) Calculating a prostate cancer-specific stemness signature (PCa-Stem signature) score by performing a single-sample gene set enrichment analysis (ssGSEA) using the expression values of HMMR, AURKB, CENPA, DEPDC1B, HJURP, PBK, MELK, UBE2C, DLGAP5, NEK2, BIRC5, and KLK12 from the gene expression profile; and (d) Comparing the resulting Stemness score to stemness values derived from at least one database of gene expression profiles of clinical cohorts of patients with prostate cancer and / or comparing the PCa-Stem signature score to gene expression values derived from at least one database of gene expression profiles of clinical cohorts of patients with prostate cancer, thereby determining the progression and / or stage of the subject’s prostate cancer.

2. The method of claim 1, wherein determining the progression and / or stage of the subject’s prostate cancer further comprises stratifying the subject as high-risk when the PCa-Stem signature score exceeds a predetermined threshold value, and as low-risk when the PCa- Stem signature score is below the threshold value.

3. The method of claim 1 or 2, wherein the gene expression profile is obtained by RNA sequencing, single-cell RNA sequencing, microarray methods and / or next-generation sequencing of the prostate cancer tissue.

4. The method of any of claims 1-3, wherein determining the progression and / or state of the subject’s prostate cancer further comprises stratifying the subject as high-risk when the resulting value of the Stemness score exceeds a predetermined threshold value, and as low-risk when the Stemness score is below the threshold value.

5. The method of claim 4, wherein the subject is stratified as high risk when the predetermined threshold value is a Stemness score equal or greater to 0.36 ± 0.11 , based on analysis of the database of gene expression profiles of clinical cohorts of patients with prostate cancer.Docket No.: 440914 [2085_010 PCT] 6. The method of claim 5 wherein the predetermined threshold value is a Stemness score equal to or greater than 0.39 ± 0.67, based on analysis of the database of gene expression profiles of clinical cohorts of patients with prostate cancer.

7. The method of claim 6, wherein the predetermined threshold value is a Stemness score equal to or greater than 0.41 ± 0.10, based on analysis of the database of gene expression profiles of clinical cohorts of patients with prostate cancer.

8. The method of claim 7, wherein the predetermined threshold value is a Stemness score equal to or greater than 0.48 ± 0.08, based on analysis of the database of gene expression profiles of clinical cohorts of patients with prostate cancer.

9. The method of claim 8, wherein the predetermined threshold value is a Stemness score equal to or greater than 0.49 ± 0.07, based on analysis of the database of gene expression profiles of clinical cohorts of patients with prostate cancer.

10. The method of claim 9, wherein the predetermined threshold value is a Stemness score equal to or greater than 0.65 ± 0.078, based on analysis of the database of gene expression profiles of clinical cohorts of patients with prostate cancer.

11. The method of claim 10, wherein the predetermined threshold value is a Stemness score equal to or greater than either 0.67 ± 0.09, 0.70 ± 0.02, or 0.77 ± 0.04, based on analysis of the database of gene expression profiles of preclinical models.

12. The method of claim 4, wherein the subject is stratified as high risk when the predetermined threshold is computed by stratifying the top 33% of stemness scores and the database of gene expression profiles consist of Pri-PCa (TCGA-PRAD) and mCRPC (SU2C 2019).

13. The method of claim 4, wherein the subject is stratified as low risk when the predetermined threshold value is a Stemness score less than or equal to 0.32 ± 0.08, based on analysis of the database of gene expression profiles of clinical cohorts of patients with prostate cancer.

14. The method of claim 13, wherein the subject is stratified as low risk when the predetermined threshold value is a Stemness score less than or equal to 0.30 ± 0.06, based on analysis of the database of gene expression profiles of clinical cohorts of patients with prostate cancer.

15. The method of claim 1, wherein the prostate cancer tissue is characterized as normal when the Stemness score is less than or equal to 0.13 ± 0.05 or 0.022 ± 0.05 relative to reference values from normal prostate cohorts or benign prostate cohorts.

16. The method of claims 1-15, wherein the mRNA expression levels of at least three of the genes HMMR, AURKB, CENPA, DEPDC1B, HJURP, PBK, MELK, UBE2C, DLGAP5, NEK2, and BIRC5 are elevated compared to the mRNA expressed levels of those genes in normal prostate tissue, indicating progression of the prostate cancer.Docket No.: 440914 [2085_010 PCT] 17. The method of claim 2, wherein the patient is high risk when the PCa-Stem signature score is 0.53 ± 0.08 or 0.57 ± 0.08 or greater, wherein the score is based on analysis of the database of gene expression profiles of clinical cohorts of patients with prostate cancer.

18. The method of claim 1, wherein the stage or progression of the prostate cancer is determined by comparing the Stemness score to the Stemness scores in FIG.3F (Table 5) and / or by comparing the PCa-Stem signature to the PCa-Stem signatures in FIG.14B (Table 6).

19. The method of any of claims 1-18, wherein the database of gene expression profiles of clinical cohorts of patients with prostate cancer comprises at least one of the following datasets: GTEx 2013, Smith 2015, Liu 2016, Zhang 2016, TCGA PRAD 2015, TCGA PRAD 2018 (Pan-Cancer Atlas), Rajan 2014, Sharma 2018, Long 2020, Nastiuk (RPCI) 2020, Gerhauser 2018, Wyatt 2014, Spratt 2017, Tosoian 2020, CHAARTED correlatives 2021, Decipher GRID Biopsy, Bolis 2021, Taylor 2010, SU2C 2015, SU2C 2019, Labrecque 2019, Alumkal 2020, Westbrook 2022, Beltran 2016, Goodrich 2017, CCLE 2018, and XG-LNCaP and XG-LAPC9.

20. The method of any of claims 1-19, consisting of administering a treatment to the patient based on the progression and / or stage of the prostate cancer based on the Stemness score and / or PCa-Stem signature, wherein the treatment is selected from the group consisting of androgen deprivation therapy, androgen receptor pathway inhibitors, chemotherapy, radiotherapy, radioligand therapy, immunotherapy, targeted therapy with PARP inhibitors, antibody-drug conjugates, chimeric antigen receptor T-cell therapy, cytokine- based therapy, and combinations thereof.

21. A method for diagnosing the stage of prostate cancer or predicting cancer therapy outcome in a subject, the method comprising the steps of: a. Determining a Stemness score of a tumor sample from the subject; and / or b. Determining a PCa-Stem signature by determining the mRNA expression of genes in the tumor sample, wherein the genes consist of HMMR, AURKB, CENPA, DEPDC1B, HJURP, PBK, MELK, UBE2C, DLGAP5, NEK2, BIRC5, and KLK12, wherein a high Stemness score and / or PCa-Stem signature indicates an aggressive form of cancer and / or a poor prognosis.

22. The method of claim 21, wherein the high Stemness score is equal to or greater than 0.36 ± 0.11, based on analysis of a database of gene expression profiles of clinical cohorts of patients with prostate cancer.

23. The method of claim 22, wherein the high Stemness score is equal to or greater than 0.39 ± 0.67, based on analysis of a database of gene expression profiles of clinical cohorts of patients with prostate cancer.Docket No.: 440914 [2085_010 PCT] 24. The method of claim 23, wherein the predetermined threshold value is a Stemness score equal to or greater than 0.41 ± 0.10, based on analysis of the database of gene expression profiles of clinical cohorts of patients with prostate cancer.

25. The method of claim 24, wherein the predetermined threshold value is a Stemness score equal to or greater than 0.48 ± 0.08, based on analysis of the database of gene expression profiles of clinical cohorts of patients with prostate cancer.

26. The method of claim 25, wherein the predetermined threshold value is a Stemness score equal to or greater than 0.49 ± 0.07, based on analysis of the database of gene expression profiles of clinical cohorts of patients with prostate cancer.

27. The method of claim 26, wherein the predetermined threshold value is a Stemness score equal to or greater than 0.65 ± 0.07, based on analysis of the database of gene expression profiles of clinical cohorts of patients with prostate cancer.

28. The method of claim 27, wherein the predetermined threshold value is a Stemness score equal to or greater than either 0.67 ± 0.09, 0.70 ± 0.02, or 0.77 ± 0.04, based on analysis of the database of gene expression profiles of clinical cohorts of patients with prostate cancer.

29. The method of claims 22-28, wherein the mRNA expression levels of at least three of the genes HMMR, AURKB, CENPA, DEPDC1B, HJURP, PBK, MELK, UBE2C, DLGAP5, NEK2, and BIRC5 are elevated compared to the mRNA expressed levels of those genes in normal prostate tissue, indicating the stage of the prostate cancer.

30. The method of claim 21, wherein the stage of the prostate cancer or the prognosis is determined by comparing the Stemness score to the Stemness scores in FIG.3F (Table 5) and / or by comparing the PCa-Stem signature to the PCa-Stem signatures in FIG.14B (Table 6).

31. The method of any of claims 22-30, wherein the database of gene expression profiles of clinical cohorts of patients with prostate cancer comprises at least one of the following datasets: GTEx 2013, Smith 2015, Liu 2016, Zhang 2016, TCGA PRAD 2015, TCGA PRAD 2018 (Pan-Cancer Atlas), Rajan 2014, Sharma 2018, Long 2020, Nastiuk (RPCI) 2020, Gerhauser 2018, Wyatt 2014, Spratt 2017, Tosoian 2020, CHAARTED correlatives 2021, Decipher GRID Biopsy, Bolis 2021, Taylor 2010, SU2C 2015, SU2C 2019, Labrecque 2019, Alumkal 2020, Westbrook 2022, Beltran 2016, Goodrich 2017, CCLE 2018, and XG-LNCaP and XG-LAPC9.

32. The method of any of claims 22-31, wherein the gene expression profile is obtained by RNA sequencing, single-cell RNA sequencing, microarray methods and / or next- generation sequencing of the prostate tumor sample.

33. The method of any of claims 22-32, further consisting of administering a treatment to the patient based on the Stemness score and / or PCa-Stem signature, wherein the treatment is selected from the group consisting of androgen deprivation therapy, androgen receptor pathway inhibitors, chemotherapy, radiotherapy, radioligand therapy, immunotherapy,Docket No.: 440914 [2085_010 PCT] targeted therapy with PARP inhibitors, antibody-drug conjugates, chimeric antigen receptor T-cell therapy, cytokine-based therapy, and combinations thereof.

34. A method of treating prostate cancer in a subject in need thereof comprising administering to said subject an appropriate treatment regimen based on determining the stage and / or progression of the subject’s prostate cancer, wherein the appropriate treatment regimen is selected from the group consisting of: androgen deprivation therapy, androgen receptor pathway inhibitors, chemotherapy, radiotherapy, radioligand therapy, immunotherapy, targeted therapy with PARP inhibitors, antibody-drug conjugates, chimeric antigen receptor T-cell therapy, cytokine- based therapy, and combinations thereof, and wherein the determining is carried out by a method comprising: providing a biological sample of the subject; generating a gene expression profile from the biological sample; and Obtaining a Stemness score through use of a one-class logistic regression (OCLR) and applying the Stemness score in assessing prostate cancer progression and / or stage by a computation comprising (1) determining the Spearman correlation between the subject’s gene expression profile and a stem cell signature derived from the OCLR machine learning algorithm, wherein said algorithm has been trained on transcriptomic profiles of non-transformed pluripotent stem cells and their ectoderm, mesoderm, and endoderm progenitors, and (2) linearly transforming the Spearman correlation to a scale of 0 to 1; and / or Calculating a prostate cancer-specific stemness signature (PCa-Stem signature) score by performing a single-sample gene set enrichment analysis (ssGSEA) using the expression values of HMMR, AURKB, CENPA, DEPDC1B, HJURP, PBK, MELK, UBE2C, DLGAP5, NEK2, BIRC5, and KLK12 from the gene expression profile; and comparing the Stemness score and / or the PCa-Stem signature value to a stemness score and / or a PCa-stem Signature derived from a database of gene expression profiles of clinical cohorts of patients with prostate cancer, thereby determining the stage and / or progression of the subject’s prostate cancer.

35. A method of predicting prostate cancer aggressiveness in a subject, comprising: (a) obtaining transcriptomic data from a prostate cancer tissue sample of the subject; (b) calculating a Stemness score by determining a Spearman’s correlation coefficient between the transcriptomic data and a reference stem cell signature weight vector, and scaling the Spearman’s correlation coefficient to a value between 0 and 1;Docket No.: 440914 [2085_010 PCT] (c) calculating a prostate cancer-specific stemness signature (PCa-Stem signature) score by performing a single-sample gene set enrichment analysis (ssGSEA) using the expression values of HMMR, AURKB, CENPA, DEPDC1B, HJURP, PBK, MELK, UBE2C, DLGAP5, NEK2, BIRC5, and KLK12 from the transcriptomic data; and (d) Comparing the Stemness score and / or the PCa-Stem signature score to stemness scores and / or prostate cancer stemness signature scores derived from a database of transcriptomic data of clinical cohorts of patients with prostate cancer, thereby predicting the aggressiveness of the subject’s prostate cancer.

36. The method of claim 35, wherein predicting aggressiveness comprises stratifying the subject as high-risk when the PCa-Stem signature score exceeds a predetermined threshold value, and as low-risk when the score is below the threshold value.

37. The method of claim 35 or 36, wherein the transcriptomic data is obtained by RNA sequencing, single-cell RNA sequencing, microarray methods and / or next-generation sequencing of the prostate tumor tissue.

38. The method of any of claims 35-37, wherein the prostate cancer is determined to be aggressive when the Stemness score is greater than or equal to 0.36 ± 011, based on analysis of transcriptomic data of clinical cohorts of patients with prostate cancer.

39. The method of claim 38, wherein the prostate cancer is determined to be aggressive when the Stemness score is greater than or equal to 0.39 ± 0.67, based on analysis of transcriptomic data of clinical cohorts of patients with prostate cancer.

40. The method of claim 39, wherein the prostate cancer is determined to be aggressive when the Stemness score is greater than or equal to 0.41 ± 0.10, based on analysis of transcriptomic data of clinical cohorts of patients with prostate cancer.

41. The method of claim 40, wherein the prostate cancer is determined to be aggressive when the Stemness score is greater than or equal to 0.48 ± 0.08, based on analysis of transcriptomic data of clinical cohorts of patients with prostate cancer.

42. The method of claim 41, wherein the prostate cancer is determined to be aggressive when the Stemness score is greater than or equal to 0.49 ± 0.07, based on analysis of transcriptomic data of clinical cohorts of patients with prostate cancer patients with prostate cancer.

43. The method of claim 42, wherein the prostate cancer is determined to be aggressive when the Stemness score is greater than or equal to 0.65 ± 0.078, based on analysis of transcriptomic data of clinical cohorts of patients with prostate cancer patients with prostate cancer.

44. The method of claim 43, wherein the prostate cancer is determined to be aggressive when the Stemness score is greater than or equal to 0.67 ± 0.09, 0.70 ± 0.02, or 0.77 ± 0.04, based on analysis of the database of gene expression profiles of preclinical models.Docket No.: 440914 [2085_010 PCT] 45. The method of claims 35-44, wherein the mRNA expression levels of at least three of the genes HMMR, AURKB, CENPA, DEPDC1B, HJURP, PBK, MELK, UBE2C, DLGAP5, NEK2, and BIRC5 are elevated compared to the mRNA expressed levels of those genes in normal prostate tissue, indicating that the prostate cancer is aggressive.

46. The method of any of claims 35-45, wherein the patient is high risk when the PCa-Stem signature score is 0.53 ± 0.08 or 0.57 ± 0.08 or greater, wherein the score is based on analysis of the database of gene expression profiles of clinical cohorts of patients with prostate cancer.

47. The method of any of claims 35-46, wherein the database of gene expression profiles of clinical cohorts of patients with prostate cancer comprises at least one of the following datasets: GTEx 2013, Smith 2015, Liu 2016, Zhang 2016, TCGA PRAD 2015, TCGA PRAD 2018 (Pan-Cancer Atlas), Rajan 2014, Sharma 2018, Long 2020, Nastiuk (RPCI) 2020, Gerhauser 2018, Wyatt 2014, Spratt 2017, Tosoian 2020, CHAARTED correlatives 2021, Decipher GRID Biopsy, Bolis 2021, Taylor 2010, SU2C 2015, SU2C 2019, Labrecque 2019, Alumkal 2020, Westbrook 2022, Beltran 2016, Goodrich 2017, CCLE 2018, and XG-LNCaP and XG-LAPC9.

48. The method of any of claims 35-47, consisting of administering a treatment to the patient based on the Stemness score and / or PCa-Stem signature, wherein the treatment is selected from the group consisting of androgen deprivation therapy, androgen receptor pathway inhibitors, chemotherapy, radiotherapy, radioligand therapy, immunotherapy, targeted therapy with PARP inhibitors, antibody-drug conjugates, chimeric antigen receptor T-cell therapy, cytokine-based therapy, and combinations thereof.

49. A computerized system for prostate cancer prognosis, comprising: a memory that stores program code configured to: receive mRNA expression values for each of HMMR, AURKB, CENPA, DEPDC1B, HJURP, PBK, MELK, UBE2C, DLGAP5, NEK2, BIRC5, and KLK12 from a prostate- derived sample of a patient; perform a single-sample gene set enrichment analysis (ssGSEA) using the expression values of HMMR, AURKB, CENPA, DEPDC1B, HJURP, PBK, MELK, UBE2C, DLGAP5, NEK2, BIRC5, and KLK12 from the gene expression profile to generate a numeric PCa-Stem signature score; and output a risk category indicative of poor or favorable clinical outcome according to the generated score; and a processor operatively coupled to the memory and configured to execute the program code.

50. The system of claim 49, wherein the program code is further configured to compare the PCa-Stem Signature score to at least one threshold and to output the risk category as “high-risk” when the score exceeds the threshold and “low-risk” when the score does not exceed the threshold.

51. The system of claim 50, further comprising a user interface that displays the PCa-Stem Signature score and the corresponding risk category to a clinician.

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