Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population

ATAD2 Drives Prostate Cancer Progression to Metastasis

Metastasis accounts for the overwhelming majority of cancer deaths. In prostate cancer and many other solid tumors, progression to metastasis is associated with drastically reduced survival outcomes, yet the mechanisms behind this progression remain largely unknown. ATPase family AAA domain containing 2 (ATAD2) is an epigenetic reader of acetylated histones that is overexpressed in multiple cancer types and usually associated with poor patient outcomes. However, the functional role of ATAD2 in cancer progression and metastasis has been relatively understudied. Here, we employ genetically engineered mouse models of prostate cancer bone metastasis, as well as multiple independent human cohorts, to show that ATAD2 is highly enriched in bone metastasis compared with primary tumors and significantly associated with the development of metastasis. We show that ATAD2 expression is associated with MYC pathway activation in patient datasets and that, at least in a subset of tumors, MYC and ATAD2 can regulate each other's expression. Using functional studies on mouse bone metastatic cell lines and innovative organ-on-a-chip bone invasion assays, we establish a functional role for ATAD2 inhibition in reducing prostate cancer metastasis and growth in bone. Implications: Our study highlights ATAD2 as a driver of prostate cancer progression and metastasis and suggests it may constitute a promising novel therapeutic target.

Hypoxia-Associated Gene Signatures Are Not Prognostic in High-Risk Localized Prostate Cancers Undergoing Androgen Deprivation Therapy With Radiation Therapy

PURPOSE

Men with high-risk prostate cancer (PCa) are treated with androgen deprivation therapy (ADT) and radiation therapy, but the disease reoccurs in 30% of patients. Biochemical recurrence of PCa after treatment is influenced by tumor hypoxia. Tumors with high levels of hypoxia are aggressive, resistant to treatment, and have increased metastatic capacity. Gene expression signatures derived from diagnostic biopsies can predict tumor hypoxia and radiosensitivity, but none are in routine clinical use, due to concerns about the applicability of these biomarkers to new patient cohorts. There has been no or limited testing in cohorts of high-risk PCa.

METHODS AND MATERIALS

We generated transcriptomic data for cohorts of patients with high-risk PCa. Patients were treated with ADT followed by external beam radiation therapy with or without a brachytherapy boost. Biomarkers curated from the literature were calculated from pretreatment biopsy gene expression data. The primary endpoint for survival analyses was biochemical recurrence-free survival and the secondary endpoints were distant metastasis-free survival and overall survival.

RESULTS

The performance of the selected biomarkers was poor, with none achieving prognostic significance for biochemical recurrence-free survival or distant metastasis-free survival in any cohort. The brachytherapy boost cohort received shorter durations of ADT than the conventionally fractionated or hypofractionated cohorts (Wilcoxon rank sum test, P = 2.1 × 10-18 and 2.3 × 10-10, respectively) and had increased risk of distant metastasis (log-rank test, P = 8 × 10-4). There were no consistent relationships between biomarker score and outcome for any of the endpoints.

CONCLUSIONS

Hypoxia and radiosensitivity biomarkers were not prognostic in patients with high-risk PCa treated with ADT plus radiation therapy. We speculate that the lack of prognostic capability could be caused by the variable hypoxia-modifying effects of the ADT that these high-risk patients received before and during definitive treatment with radiation therapy. A deeper understanding of biomarker construction, performance, and inter-cohort transferability in relation to patient characteristics, sample handling, and treatment modalities is required before hypoxia biomarkers can be recommended for routine clinical use in the pretreatment setting.

Comprehensive bioinformatic analysis reveals prognostic significance and functional insights of candidate gene expression in colorectal cancer

The purpose of this study was to investigate biomarkers associated with poor clinical outcomes in colorectal cancer (CRC) by utilizing comprehensive datasets from the gene expression omnibus (GEO) databases GSE41258, GSE39582, and GSE44861. We initially identified differentially expressed genes (DEGs) and applied weighted gene co-expression network analysis (WGCNA) to the GSE41258 dataset to reveal key gene modules associated with CRC. Enrichment analyses were conducted to gain insights into the underlying biology of CRC, particularly focusing on pathways linked to the identified gene modules. Our analysis unveiled a distinct module strongly correlated with CRC carcinogenesis, with significant pathways related to extracellular matrix organization and vasculature development. Furthermore, we identified nine candidate genes (CDH11, COL1A1, COL1A2, COL5A1, COL5A2, FAP, SPARC, SULF1, and THY1) as potential crosstalk genes across various datasets. Notably, eight of these candidate genes exhibited a significant correlation with poor overall survival (OS) and recurrence-free survival (RFS) in CRC patients, suggesting their potential as prognostic biomarkers. Experimental validation using short hairpin RNA (shRNA)-mediated knockdown in HCT116 cells demonstrated that silencing of these candidate genes significantly impaired cancer cell proliferation, providing biological evidence supporting their functional roles in CRC progression. Our integrative approach offers a comprehensive understanding of the molecular landscape of CRC and identifies promising biomarkers for further exploration and validation.

Impact of Genomic Classifiers on Risk Stratification and Treatment Intensity in Patients With Localized Prostate Cancer : A Systematic Review

BACKGROUND

Tissue-based genomic classifiers (GCs) have been developed to improve prostate cancer (PCa) risk assessment and treatment recommendations.

PURPOSE

To summarize the impact of the Decipher, Oncotype DX Genomic Prostate Score (GPS), and Prolaris GCs on risk stratification and patient-clinician decisions on treatment choice among patients with localized PCa considering first-line treatment.

DATA SOURCES

MEDLINE, EMBASE, and Web of Science published from January 2010 to August 2024.

STUDY SELECTION

Two investigators independently identified studies on risk classification and treatment choice after GC testing for patients with localized PCa considering first-line treatment.

DATA EXTRACTION

Relevant data extracted by 1 researcher and overread by a second. Risk of bias (ROB) was assessed in duplicate.

DATA SYNTHESIS

Ten studies reported risk reclassification after GC testing. In low ROB observational studies, very low- or low-risk patients with PCa were more likely to have their risk levels classified as the same or lower (GPS, 100% to 88.1%; Decipher, 87.2% to 82.9%; Prolaris, 76.9%). However, 1 randomized trial found that GC testing with GPS reclassified 34.5% of very low-risk and 29.4% of low-risk patients to a higher risk category. Twelve observational studies indicated that treatment decisions after GC testing either remained unchanged or slightly favored active surveillance. In contrast, analyses from a single randomized trial found fewer choices for active surveillance after GPS testing.

LIMITATIONS

Heterogeneity in screening patterns, risk-determination cutoffs, pathology, and clinical practices. Studies on treatment choice were moderate to high ROB.

CONCLUSION

Although GC tests do not consistently influence risk classification or treatment decisions, the differences observed between observational and randomized studies highlight a need for well-designed trials to explore the role of GC tests in patients with newly diagnosed PCa considering first-line treatment.

PRIMARY FUNDING SOURCE

U.S. Department of Veterans Affairs. (PROSPERO: CRD42022347950).

Impact of Gene Expression Classifier Testing on Adjuvant Treatment Following Radical Prostatectomy: The G-MINOR Prospective Randomized Cluster-crossover Trial

BACKGROUND AND OBJECTIVE

Decipher is a tissue-based genomic classifier (GC) developed and validated in the post-radical prostatectomy (RP) setting as a predictor of metastasis. We conducted a prospective randomized controlled cluster-crossover trial assessing the use of Decipher to determine its impact on adjuvant treatment after RP.

METHODS

Eligible patients had undergone RP within 9 mo of enrollment, had pT3-4 disease and/or positive surgical margins, and prostate-specific antigen <0.1 ng/ml. Centers were randomized to a sequence of 3-mo periods of either GC-informed care or usual care (UC). Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) recurrence risk scores were provided to treating physicians and patients in all periods.

KEY FINDINGS AND LIMITATIONS

Impact of GC test results on adjuvant treatment were compared with UC alone. Longitudinal patient-reported urinary and sexual function was assessed. A total of 175 patients were enrolled in 27 periods with GC and 163 in 28 periods with UC. At 18 mo after RP, an average patient in the GC arm received adjuvant treatment 9.7% of the time compared with 8.7% for an average individual in the UC arm (0.99% mean difference, 95% confidence interval [CI] -7.6%, 9.6%, p = 0.8). While controlling for CAPRA-S score, higher GC scores tended to result in an increased likelihood of adjuvant treatment that was not statistically significant (odds ratio [OR] = 1.35 per 0.1 increase in GC score, 95% CI 0.98-1.85, p = 0.066). Using the GC risk groups, reflecting clinical use, a high GC risk was associated with significantly higher odds of receiving adjuvant treatment (OR = 6.9, 95% CI 1.8, 26, p = 0.005) compared with a low GC score, adjusted for CAPRA-S score. There were no differences in patient-reported urinary and sexual function between the study arms. As oncologic outcomes are immature, the present data cannot address whether GC testing provides any cancer control benefit.

CONCLUSIONS AND CLINICAL IMPLICATIONS

GC testing impacts adjuvant therapy administration when viewed through the risk categories presented in the patient report; however, these data do not provide specific support for GC testing in the adjuvant treatment setting.

Integration of Genomic Tests in Prostate Cancer Care: Implications for Clinical Practice and Patient Outcomes

Prostate cancer (PCa) is a common malignancy in men and is among the leading causes of cancer-related death worldwide. Genomic tests assess disease aggressiveness and guide treatment, particularly in low- and intermediate-risk PCa. We reviewed the literature on the use of four genomic tests (Prolaris®, Promark®, Oncotype DX®, and Decipher®) in assessing the prognosis of PCa and their use in treatment decision-making. Most of the studies showed that Prolaris® has a strong correlation with biochemical recurrence, metastasis risk, PCa-specific mortality (PCSM), and pathological features. Similarly, three studies on Promark® indicated a connection between results and pathological features in the subsequent prostatectomy, time to metastasis, and biochemical recurrence. Fourteen studies on Oncotype DX® showed a clear correlation between high scores, death, and PCSM. One study found that routine biopsy pathology reports, combined with serum PSA levels, provide a risk assessment comparable to Oncotype DX® testing. Results from 22 studies on Decipher® were controversial. The test was associated with conservative management, suggesting that patients with a high GC score are more likely to need radiation after surgery. Comparative studies indicated that Oncotype DX® is preferable for assessing PCSM, Decipher® for predicting metastasis, and Prolaris® for predicting recurrence. With the incidence rate of PCa dramatically increasing, genomic tests appear to be useful adjunctive precision medicine tools with significant potential in improving prognostic discrimination, facilitating better risk stratification, and guiding personalized treatment, especially in the intermediate-risk patient group. Large-scale, prospective, multi-sectional studies are required to validate the utility of these tests prior to their integration into clinical practice.

Molecular Hallmarks of Prostate-specific Membrane Antigen in Treatment-naïve Prostate Cancer

BACKGROUND AND OBJECTIVE

We characterized tumor prostate-specific membrane antigen (PSMA) levels as a reflection of cancer biology and treatment sensitivities for treatment-naïve prostate cancer.

METHODS

We first correlated PSMA positron emission tomography (PET) maximum standardized uptake values (SUVmax) in primary prostate cancer with tumor FOLH1 (PSMA RNA abundance) to establish RNA as a proxy (n = 55). We then discovered and validated molecular pathways associated with PSMA RNA levels in two large primary tumor cohorts. We validated those associations in independent cohorts (18 total; 5684 tumor samples) to characterize the pathways and treatment responses associated with PSMA.

KEY FINDINGS AND LIMITATIONS

PSMA RNA abundance correlates moderately with SUVmax (ρ = 0.41). In independent cohorts, androgen receptor signaling is more active in tumors with high PSMA. Accordingly, patients with high PSMA tumors experienced longer cancer-specific survival when managed with androgen deprivation therapy for biochemical recurrence (adjusted hazard ratio [AHR] 0.54 [0.34-0.87]; n = 174). PSMA low tumors possess molecular markers of resistance to radiotherapy. Consistent with this, patients with high PSMA tumors experience longer time to recurrence following primary radiotherapy (AHR 0.50 [0.28-0.90]; n = 248). In the SAKK09/10 trial (n = 224), patients with high PSMA tumors who were managed with salvage radiotherapy experienced longer time to progression in the 64-Gy arm (restricted mean survival time [RMST] +7.60 [0.05-15.16]), but this effect was mitigated in the 70-Gy arm (RMST 3.52 [-3.30 to 10.33]). Limitations include using PSMA RNA as a surrogate for PET SUVmax.

CONCLUSIONS AND CLINICAL IMPLICATIONS

PSMA levels in treatment-naïve prostate cancer differentiate tumor biology and treatment susceptibilities. These results warrant validation using PET metrics to substantiate management decisions based on imaging.

Contemporary Update on Clinical and Experimental Prostate Cancer Biomarkers: A Multi-Omics-Focused Approach to Detection and Risk Stratification

Prostate cancer remains a significant health challenge, being the most prevalent non-cutaneous cancer in men worldwide. This review discusses the critical advancements in biomarker discovery using single-omics and multi-omics approaches. Multi-omics, integrating genomic, transcriptomic, proteomic, metabolomic, and epigenomic data, offers a comprehensive understanding of the molecular heterogeneity of prostate cancer, leading to the identification of novel biomarkers and therapeutic targets. This holistic approach not only enhances the specificity and sensitivity of prostate cancer detection but also supports the development of personalized treatment strategies. Key studies highlighted include the identification of novel genes, genetic mutations, peptides, metabolites, and potential biomarkers through multi-omics analyses, which have shown promise in improving prostate cancer management. The integration of multi-omics in clinical practice can potentially revolutionize prostate cancer prognosis and treatment, paving the way for precision medicine. This review underscores the importance of continued research and the application of multi-omics to overcome current challenges in prostate cancer diagnosis and therapy.

Association Between the Decipher Genomic Classifier and Prostate Cancer Outcome in the Real-world Setting

BACKGROUND AND OBJECTIVE

Although the prognostic significance of the Decipher prostate cancer genomic classifier (GC) has been established largely from analyses of archival tissue, less is known about the associations between the results of Decipher testing and oncologic outcomes among patients receiving contemporaneous testing and treatment in the real-world practice setting. Our objective was to assess the associations between the Decipher GC and risks of metastasis and biochemical recurrence (BCR) following prostate biopsy and radical prostatectomy (RP) among patients tested and treated in the real-world setting.

METHODS

A retrospective cohort study was conducted using a novel longitudinal linkage of transcriptomic data from the Decipher GC and real-world clinical data (RWD) aggregated from insurance claims, pharmacy records, and electronic health record data across payors and sites of care. Kaplan-Meier and Cox proportional hazards regressions were used to examine the associations between the GC and study outcomes, adjusting for clinical and pathologic factors.

KEY FINDINGS AND LIMITATIONS

Metastasis from prostate cancer and BCR after radical prostatectomy, Decipher GC continuous score, and risk categories were evaluated. We identified 58 935 participants who underwent Decipher testing, including 33 379 on a biopsy specimen and 25 556 on an RP specimen. The median age was 67 yr (interquartile range [IQR] 62-72) at biopsy testing and 65 yr (IQR 59-69) at RP. The median GC score was 0.43 (IQR 0.27-0.66) among biopsy-tested patients and 0.54 (0.32-0.79) among RP-tested patients. The GC was independently associated with the risk of metastasis among biopsy-tested (hazard ratio [HR] per 0.1 unit increase in GC 1.21 [95% confidence interval {CI} 1.16-1.27], p < 0.001) and RP-tested (HR 1.20 [95% CI 1.17-1.24], p < 0.001) patients after adjusting for baseline clinical and pathologic risk factors. In addition, the GC was associated with the risk of BCR among RP-tested patients (HR 1.12 [95% CI 1.10-1.14], p < 0.001) in models adjusted for age and Cancer of the Prostate Risk Assessment postsurgical score.

CONCLUSIONS AND CLINICAL IMPLICATIONS

This real-world study of a novel transcriptomic linkage conducted at a national scale supports the external prognostic validity of the Decipher GC among patients managed in contemporary practice.

PATIENT SUMMARY

This study looked at the use of the Decipher genomic classifier, a test used to help understand the aggressiveness of a patient's prostate cancer. Looking at the results of 58 935 participants who underwent testing, we found that the Decipher test helped estimate the risk of cancer recurrence and metastasis.

Prostate cancers with distinct transcriptional programs in Black and White men

BACKGROUND

Black men are at a higher risk of prostate cancer (PC) diagnosis and present with more high-grade PC than White men in an equal access setting. This study aimed to identify differential transcriptional regulation between Black and White men with PC.

METHODS

We performed microarray of radical prostatectomy tissue blocks from 305 Black and 238 White men treated at the Durham Veterans Affairs Medical Center. Differential expression, gene set enrichment analysis, master regulator analysis, and network modeling were conducted to compare gene expression by race. Findings were validated using external datasets that are available in the Gene Expression Omnibus (GEO) database. The first was a multi-institutional cohort of 1152 prostate cancer patients (596 Black, 556 White) with microarray data (GEO ID: GSE169038). The second was an Emory cohort of 106 patients (22 Black, 48 White, 36 men of unknown race) with RNA-seq data (GEO ID: GSE54460). Additionally, we analyzed androgen receptor (AR) chromatin binding profiles using paired AR ChIP-Seq datasets from Black and White men (GEO IDs: GSE18440 and GSE18441).

RESULTS

We identified 871 differentially expressed genes between Black and White men. White men had higher activity of MYC-related pathways, while Black men showed increased activity of inflammation, steroid hormone responses, and cancer progression-related pathways. We further identified the top 10 transcription factors (TFs) in Black patients, which formed a transcriptional regulatory network centered on the AR. The activities of this network and the pathways were significantly different in Black vs. White men across multiple cohorts and PC molecular subtypes.

CONCLUSIONS

These findings suggest PC in Black and White men have distinct tumor transcriptional profiles. Furthermore, a highly interactive TF network centered on AR drives differential gene expression in Black men. Additional study is needed to understand the degree to which these differences in transcriptional regulatory elements contribute to PC health disparities.

Predicting prostate cancer progression with a Multi-lncRNA expression-based risk score and nomogram integrating ISUP grading

Prostate cancer is a highly heterogeneous disease; therefore, estimating patient prognosis accurately is challenging due to the lack of biomarkers with sufficient specificity and sensitivity. One of the current challenges lies in integrating genomic and transcriptomic data with clinico-pathological features and in incorporating their application in everyday clinical practice. Therefore, we aimed to model a risk score and nomogram containing long non-coding RNA (lncRNA) expression and clinico-pathological data to better predict the probability of prostate cancer progression. We performed bioinformatics analyses to identify lncRNAs differentially expressed across various prostate cancer stages and associated with progression-free survival. This information was further integrated into a prognostic risk score and nomogram containing transcriptomic and clinico-pathological features to estimate the risk of disease progression. We used RNA-seq data from 5 datasets from public repositories (total n = 178) comprising different stages of prostate cancer: pre-treatment primary prostate adenocarcinomas, post-treatment tumors and metastatic castration resistant prostate cancer. We found 30 lncRNAs with consistent differential expression in all comparisons made using two R-based packages. Multivariate progression-free survival analysis including the ISUP group as covariate, revealed that 7/30 lncRNAs were significantly associated with time-to-progression. Next, we combined the expression of these 7 lncRNAs into a multi-lncRNA score and dichotomized the patients into low- or high-score. Patients with a high-score showed a 4-fold risk of disease progression (HR = 4.30, 95 %CI = 2.66-6.97, p = 3.1e-9). Furthermore, we modelled a combined risk-score containing information on the multi-lncRNA score and ISUP group. We found that patients with a high-risk score had nearly 8-fold risk of progression (HR = 7.65, 95 %CI = 4.05-14.44, p = 3.4e-10). Finally, we created and validated a nomogram to help uro-oncologists to better predict patient's risk of progression at 3- and 5-years post-diagnosis. In conclusion, the integration of lncRNA expression data and clinico-pathological features of prostate tumors into predictive models might aid in tailored disease risk assessment and treatment for patients with prostate cancer.

The impact of genomic biomarkers on a clinical risk prediction model for upgrading/upstaging among men with favorable-risk prostate cancer

BACKGROUND

The challenge of distinguishing indolent from aggressive prostate cancer (PCa) complicates decision-making for men considering active surveillance (AS). Genomic classifiers (GCs) may improve risk stratification by predicting end points such as upgrading or upstaging (UG/US). The aim of this study was to assess the impact of GCs on UG/US risk prediction in a clinicopathologic model.

METHODS

Participants had favorable-risk PCa (cT1-2, prostate-specific antigen [PSA] ≤15 ng/mL, and Gleason grade group 1 [GG1]/low-volume GG2). A prediction model was developed for 864 men at the University of California, San Francisco, with standard clinical variables (cohort 1), and the model was validated for 2267 participants from the Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE) registry (cohort 2). Logistic regression was used to compute the area under the receiver operating characteristic curve (AUC) to develop a prediction model for UG/US at prostatectomy. A GC (Oncotype Dx Genomic Prostate Score [GPS] or Prolaris) was then assessed to improve risk prediction.

RESULTS

The prediction model included biopsy GG1 versus GG2 (odds ratio [OR], 5.83; 95% confidence interval [CI], 3.73-9.10); PSA (OR, 1.10; 95% CI, 1.01-1.20; per 1 ng/mL), percent positive cores (OR, 1.01; 95% CI, 1.01-1.02; per 1%), prostate volume (OR, 0.98; 95% CI, 0.97-0.99; per mL), and age (OR, 1.05; 95% CI, 1.02-1.07; per year), with AUC 0.70 (cohort 1) and AUC 0.69 (cohort 2). GPS was associated with UG/US (OR, 1.03; 95% CI, 1.01-1.06; p < .01) and AUC 0.72, which indicates a comparable performance to the prediction model.

CONCLUSIONS

GCs did not substantially improve a clinical prediction model for UG/US, a short-term and imperfect surrogate for clinically relevant disease outcomes.

Predictive Models for Assessing Patients' Response to Treatment in Metastatic Prostate Cancer: A Systematic Review

Background and objective

The treatment landscape of metastatic prostate cancer (mPCa) has evolved significantly over the past two decades. Despite this, the optimal therapy for patients with mPCa has not been determined. This systematic review identifies available predictive models that assess mPCa patients' response to treatment.

Methods

We critically reviewed MEDLINE and CENTRAL in December 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. Only quantitative studies in English were included with no time restrictions. The quality of the included studies was assessed using the PROBAST tool. Data were extracted following the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews criteria.

Key findings and limitations

The search identified 616 citations, of which 15 studies were included in our review. Nine of the included studies were validated internally or externally. Only one study had a low risk of bias and a low risk concerning applicability. Many studies failed to detail model performance adequately, resulting in a high risk of bias. Where reported, the models indicated good or excellent performance.

Conclusions and clinical implications

Most of the identified predictive models require additional evaluation and validation in properly designed studies before these can be implemented in clinical practice to assist with treatment decision-making for men with mPCa.

Patient summary

In this review, we evaluate studies that predict which treatments will work best for which metastatic prostate cancer patients. We found that existing studies need further improvement before these can be used by health care professionals.

Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment

The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.

Analysis of the Gene Networks and Pathways Correlated with Tissue Differentiation in Prostate Cancer

Prostate cancer (PCa) is the most prevalent non-cutaneous cancer in men. Early PCa detection has been made possible by the adoption of screening methods based on the serum prostate-specific antigen and Gleason score (GS). The aim of this study was to correlate gene expression with the differentiation level of prostate adenocarcinomas, as indicated by GS. We used data from The Cancer Genome Atlas (TCGA) and included 497 prostate cancer patients, 52 of which also had normal tissue sample sequencing data. Gene ontology analysis revealed that higher GSs were associated with greater responses to DNA damage, telomere lengthening, and cell division. Positive correlation was found with transcription factor activator of the adenovirus gene E2 (E2F) and avian myelocytomatosis viral homolog (MYC) targets, G2M checkpoints, DNA repair, and mitotic spindles. Immune cell deconvolution revealed high M0 macrophage counts and an increase in M2 macrophages dependent on the GS. The molecular pathways most correlated with GSs were cell cycle, RNA transport, and calcium signaling (depleted). A combinatorial approach identified a set of eight genes able to differentiate by k-Nearest Neighbors (kNN) between normal tissues, low-Gleason tissues, and high-Gleason tissues with high accuracy. In conclusion, our study could be a step forward to better understanding the link between gene expression and PCa progression and aggressiveness.

Validation of the Combined Clinical Cell-Cycle Risk Score to Prognosticate Early Prostate Cancer Metastasis From Biopsy Specimens and Comparison With Other Routinely Used Risk Classifiers

PURPOSE

We aim to independently validate the prognostic utility of the combined cell-cycle risk (CCR) multimodality threshold to estimate risk of early metastasis after definitive treatment of prostate cancer and compare this prognostic ability with other validated biomarkers.

METHODS

Patients diagnosed with localized prostate cancer were enrolled into a single-institutional registry for the prospective observational cohort study. The primary end point was risk of metastasis within 3 years of diagnostic biopsy. Secondary end points included time to definitive treatment, time to subsequent therapy, and metastasis after completion of initial definitive treatment. Multivariable cause-specific Cox proportional hazards regression models were produced accounting for competing risk of death and stratified on the basis of the CCR active surveillance and multimodality (MM) thresholds. Time-dependent areas under the receiver operating characteristic curve were calculated.

RESULTS

The cohort consisted of 554 men with prostate cancer and available CCR score from biopsy. The CCR score was prognostic for metastasis (hazard ratio [HR], 2.32 [95% CI, 1.17 to 4.59]; P = .02), with scores above the MM threshold having a higher risk than those below the threshold (HR, 5.44 [95% CI, 2.72 to 10.91]; P < .001). The AUC for 3-year risk of metastasis on the basis of CCR was 0.736. When men with CCR above the MM threshold received MM therapy, their 3-year risk of metastasis was significantly lower than those receiving single-modality therapy (3% v 14%). Similarly, a CCR score above the active surveillance threshold portended a faster time to first definitive treatment.

CONCLUSION

CCR outperforms other commonly used biomarkers for prediction of early metastasis. We illustrate the clinical utility of the CCR active surveillance and multimodality thresholds. Molecular genomic tests can inform patient selection and personalization of treatment for localized prostate cancer.

How to Integrate Prostate Cancer Biomarkers in Urology Clinical Practice: An Update

Nowadays, the management of prostate cancer has become more and more challenging due to the increasing number of available treatment options, therapeutic agents, and our understanding of its carcinogenesis and disease progression. Moreover, currently available risk stratification systems used to facilitate clinical decision-making have limitations, particularly in providing a personalized and patient-centered management strategy. Although prognosis and prostate cancer-specific survival have improved in recent years, the heterogenous behavior of the disease among patients included in the same risk prognostic group negatively impacts not only our clinical decision-making but also oncological outcomes, irrespective of the treatment strategy. Several biomarkers, along with available tests, have been developed to help clinicians in difficult decision-making scenarios and guide management strategies. In this review article, we focus on the scientific evidence that supports the clinical use of several biomarkers considered by professional urological societies (and included in uro-oncological guidelines) in the diagnosis process and specific difficult management strategies for clinically localized or advanced prostate cancer.

Gallen**, Eppenberger-Castori S. Critical Evaluation of Transcripts and Long Noncoding RNA Expression Levels in Prostate Cancer Following Radical Prostatectomy

INTRODUCTION

The clinical course of prostate cancer (PCa) is highly variable, ranging from indolent behavior to rapid metastatic progression. The Gleason score is widely accepted as the primary histologic assessment tool with significant prognostic value. However, additional biomarkers are required to better stratify patients, particularly those at intermediate risk.

METHODS

In this study, we analyzed the expression of 86 cancer hallmark genes in 171 patients with PCa who underwent radical prostatectomy and focused on the outcome of the 137 patients with postoperative R0-PSA0 status.

RESULTS

Low expression of the IGF1 and SRD52A, and high expression of TIMP2, PLAUR, S100A2, and CANX genes were associated with biochemical recurrence (BR), defined as an increase of prostate-specific antigen above 0.2 ng/mL. Furthermore, the analysis of the expression of 462 noncoding RNAs (ncRNA) in a sub-cohort of 39 patients with Gleason score 7 tumors revealed that high levels of expression of the ncRNAs LINC00624, LINC00593, LINC00482, and cd27-AS1 were significantly associated with BR. Our findings provide further evidence for tumor-promoting roles of ncRNAs in PCa patients at intermediate risk. The strong correlation between expression of LINC00624 and KRT8 gene, encoding a well-known cell surface protein present in PCa, further supports a potential contribution of this ncRNA to PCa progression.

CONCLUSION

While larger and further studies are needed to define the role of these genes/ncRNA in PCa, our findings pave the way toward the identification of a subgroup of patients at intermediate risk who may benefit from adjuvant treatments and new therapeutic agents.

ALAN is a computational approach that interprets genomic findings in the context of tumor ecosystems

Gene behavior is governed by activity of other genes in an ecosystem as well as context-specific cues including cell type, microenvironment, and prior exposure to therapy. Here, we developed the Algorithm for Linking Activity Networks (ALAN) to compare gene behavior purely based on patient -omic data. The types of gene behaviors identifiable by ALAN include co-regulators of a signaling pathway, protein-protein interactions, or any set of genes that function similarly. ALAN identified direct protein-protein interactions in prostate cancer (AR, HOXB13, and FOXA1). We found differential and complex ALAN networks associated with the proto-oncogene MYC as prostate tumors develop and become metastatic, between different cancer types, and within cancer subtypes. We discovered that resistant genes in prostate cancer shared an ALAN ecosystem and activated similar oncogenic signaling pathways. Altogether, ALAN represents an informatics approach for developing gene signatures, identifying gene targets, and interpreting mechanisms of progression or therapy resistance.

The combination of prostate MRI PI-RADS scoring system and a genomic classifier is associated with pelvic lymph node metastasis at the time of radical prostatectomy

OBJECTIVE

Pelvic lymph node metastasis (PLNM) at the time of radical prostatectomy (RP) portends an unfavorable prognosis in prostate cancer patients. Conventional and advanced imaging remains limited in its ability to detect PLNM. We sought to evaluate the combination of a genomic classifier Decipher with Prostate Imaging Reporting and Data System (PI-RADS) scores in improving the detection of PLNM.

METHODS

A retrospective review was performed of patients whom underwent RP, Decipher analysis, and pre-operative prostate MRI. Categorical variables were compared using Pearson chi-squareχ2 tests. Quantitative variables were assessed with Wilcoxon rank-sum tests. Multivariable logistic regression was used to identify predictors of PLNM on final pathology.

RESULTS

In total, 202 patients were included in the analysis, 23 of whom (11%) had PLNM. Patients with PLNM had higher median Decipher scores (0.73) than those without PLNM (0.61; p = 0.003). Patients with PLNM were more likely to demonstrate PI-RADS scores ≥ 4 (96%) than those without PLNM (74%; p = 0.012). Logistic regression demonstrated an interaction between Decipher score with PI-RADS score ≥4 (OR = 20.41; 95% CI, 2.10-198.74; p = 0.009) The combination demonstrated an area under the curve (AUC) of 0.73 (95% CI, 0.63-0.82; p < 0.001) for predicting PLNM.

CONCLUSION

The combination of elevated Decipher genomic score (≥ 0.6) and clinically significant PI-RADS score (≥ 4) is associated with PLNM at the time of RP in a modern high-risk cohort of patients with PCaprostate cancer.

ADVANCES IN KNOWLEDGE

Prostate MRI and genomic testing may help identify patients with adverse pathology.

High intratumoral plasma cells content in primary prostate cancer defines a subset of tumors with potential susceptibility to immune-based treatments

BACKGROUND

Data on advanced prostate cancer (PCa) suggest more prior systemic therapies might reduce tumor immune responsiveness. In treatment-naïve primary PCa, recent work correlated intratumoral plasma cell content with enhanced tumor immune-responsiveness. We sought to identify features of localized PCa at a high risk of recurrence following local treatment with high plasma cell content to help focus future immune-based neoadjuvant trials.

METHODS

We performed retrospective analyses of molecular profiles from three independent cohorts of over 1300 prostate tumors. We used Wilcoxon Rank Sum to compare molecular pathways between tumors with high and low intratumoral plasma cell content and multivariable Cox proportional hazards regression analyses to assess metastasis-free survival.

RESULTS

We validated an expression-based signature for intratumoral plasma cell content in 113 primary prostate tumors with both RNA-expression data and digital image quantification of CD138+ cells (plasma cell marker) based on immunohistochemisty. The signature showed castration-resistant tumors (n = 101) with more prior systemic therapies contained lower plasma cell content. In high-grade primary PCa, tumors with high plasma cell content were associated with increased predicted response to immunotherapy and decreased response to androgen-deprivation therapy. Master regulator analyses identified upregulated transcription factors implicated in immune (e.g. SKAP1, IL-16, and HCLS1), and B-cell activity (e.g. VAV1, SP140, and FLI-1) in plasma cell-high tumors. Master regulators overactivated in tumors with low plasma cell content were associated with shorter metastasis-free survival following radical prostatectomy.

CONCLUSIONS

Markers of plasma cell activity might be leveraged to augment clinical trial targeting and selection and better understand the potential for immune-based treatments in patients with PCa at a high risk of recurrence following local treatment.

Lymphatic Dissemination in Prostate Cancer: Features of the Transcriptomic Profile and Prognostic Models

Radical prostatectomy is the gold standard treatment for prostate cancer (PCa); however, it does not always completely cure PCa, and patients often experience a recurrence of the disease. In addition, the clinical and pathological parameters used to assess the prognosis and choose further tactics for treating a patient are insufficiently informative and need to be supplemented with new markers. In this study, we performed RNA-Seq of PCa tissue samples, aimed at identifying potential prognostic markers at the level of gene expression and miRNAs associated with one of the key signs of cancer aggressiveness-lymphatic dissemination. The relative expression of candidate markers was validated by quantitative PCR, including an independent sample of patients based on archival material. Statistically significant results, derived from an independent set of samples, were confirmed for miR-148a-3p and miR-615-3p, as well as for the CST2, OCLN, and PCAT4 genes. Considering the obtained validation data, we also analyzed the predictive value of models based on various combinations of identified markers using algorithms based on machine learning. The highest predictive potential was shown for the "CST2 + OCLN + pT" model (AUC = 0.863) based on the CatBoost Classifier algorithm.

An overview of prostate cancer (PCa) diagnosis: Potential role of miRNAs

Prostate cancer is the second most frequently diagnosed cancer among men worldwide, with the estimated sixth leading cause of cancer death. Despite major advancements in clinical biology and imaging, digital rectal examination (DRE), prostate-specific antigen (PSA), and biopsies indication remain the keystone for screening. Several kits are used to detect genomic changes and non-coding RNAs in the sample. However, its indication remains controversial for screening purposes. There is an urged need for non-invasive biomarkers to implement precision medicine. Recent research shows that miRNAs have an important role in the diagnostic, prognostic, and therapeutic agents as non-invasive biomarkers. Though prostate cancer data remains controversial in other cancer types, such as breast cancer, miR-21 expression is upregulated. Here, we reported a prolonged revision of miRNAs as prostate cancer prognostic, diagnostic, and predictive tools, including data on androgen receptor (AR) signaling, epithelial-mesenchymal transition (EMT) process, and cancer stem cells (CSCs) regulation. The combined utilization of miRNAs with other tests will help patients and clinicians to select the most appropriate personalized treatment and to avoid overdiagnosis and unnecessary biopsies. Future clinical applications of our reported novel miRNAs have a substantial role in the primary diagnosis of prostate cancer to help treatment decisions.

GENOMICS OF PROSTATE CANCER: CLINICAL UTILITY AND CHALLENGES

The studying of prostate cancer genomics is important for understanding prostate cancer biology, it can provide clinically relevant stratification into subtypes, the development of new prognostic and predictive markers in the context of precision medicine, and the development of new targeted therapies. Recent studies have provided detailed insight into genomics, epigenomics and proteomics of prostate cancer, both primary and metastatic castration-resistant (mCRPC). Many mutations have been discovered, both those that occur early in the carcinogenesis and progression as well as those responsible for the resistance to therapy occurring later under the influence of treatment. A large number of characteristic mutated signaling pathways has been identified, e.g. the mutations in DNA repair pathway were found in 23% of mCRPC, which suggests potential response to PARP inhibitors. Multifocality and intralesional genomic heterogeneity of prostate cancer make the clinical application of genomics complicated. Although a great progress was made in understanding prostate cancer genomic, and clinical studies related to its routine application are ongoing, prostate cancer genomics still needs to find its standard wide routine application in patients with prostate cancer.

Randomized, Open-Label Phase 2 Study of Apalutamide plus Androgen Deprivation Therapy versus Apalutamide Monotherapy versus Androgen Deprivation Monotherapy in Patients with Biochemically Recurrent Prostate Cancer

Purpose. This randomized phase 2 study sought to assess the treatment effect of a finite duration of apalutamide with and without androgen deprivation therapy (ADT) in biochemically recurrent prostate cancer (BCR PC). Materials and Methods. Patients with BCR PC after primary definitive therapy and prostate-specific antigen (PSA) doubling time ≤12 months were randomized to open-label apalutamide (240 mg/d) alone, apalutamide plus ADT, or ADT alone (1 : 1:1 ratio) for 12 months followed by a 12-month observation period (NCT01790126). Mean changes from baseline in Functional Assessment of Cancer Therapy-Prostate (FACT-P) at 12 months (primary endpoint) and other prespecified assessments of health-related quality of life (HRQoL), PSA nadir, time to PSA progression, time to testosterone recovery, recovered testosterone >150 ng/dL without PSA progression at 24 months, and molecular markers were evaluated. Results. In 90 enrolled patients (apalutamide plus ADT (n = 31), apalutamide (n = 29), ADT (n = 30)), FACT-P at 12 months was not significantly different between apalutamide, ADT and apalutamide, and ADT groups. Addition of apalutamide to ADT prolonged time to PSA progression but this change did not reach statistical significance (hazard ratio (HR): 0.56, 95% confidence interval (CI): 0.23–1.36, $P=0.196$ ); time to testosterone recovery was similar in the ADT-containing groups. In apalutamide plus ADT, apalutamide, and ADT groups, 37.9%, 37.0%, and 19.2% of patients, respectively, had testosterone >150 ng/dL at 24 months without confirmed PSA progression. Of the few biomarkers expressed in blood, EPHA3 was significantly associated with shorter time to PSA progression ( $P=0.02$ ) in the overall population. Conclusions. HRQoL was similar in patients treated with apalutamide alone, ADT alone, or their combination, although apalutamide plus ADT did not demonstrate statistically significant noninferiority in change from baseline in overall HRQoL. The aggregated efficacy and safety outcomes support further evaluation of apalutamide plus ADT in BCR PC.

Docetaxel chemotherapy plus androgen-deprivation therapy in high-volume disease metastatic hormone-sensitive prostate cancer in Chinese patients: an efficacy and safety analysis

OBJECTIVE

To investigate the efficacy and safety of docetaxel chemotherapy combined with androgen-deprivation therapy for patients with high-volume disease metastatic hormone-sensitive prostate cancer.

METHODS

153 cases of high-volume disease metastatic hormone-sensitive prostate cancer in Minhang Hospital between January 2018 and December 2019 were analyzed retrospectively, including the number of patients, age, initial PSA level, Gleason score, TNM stage and ECOG score. 90 patients in the endocrine therapy group received continuous ADT, and 63 patients in the combined chemotherapy group received docetaxel plus ADT. The progression-free survival time (time from initiation of prostate cancer treatment to progression to CRPC), PSA response rate, and adverse reactions were compared between the two groups.

RESULTS

All 153 cases were closely followed up for a period of 12.3-35.3 months, with a median follow-up time of 23.5 months. The median time to reach the lowest point of PSA in the two groups was 6.3 months and 7.9 months (P = 0.018) in the combination chemotherapy group and the ADT group alone, with 27 (42.9%) and 12 (13.3%) cases in the two groups Within 12 months of treatment, PSA decreased to below 0.2 ng/ml (P = 0.02), and progression-free survival was 16.9 months (6.5-28.5 months) and 11.2 months (4.3-22.7 months) in the two groups. (P < 0.001). There were 18 cases (28.6%) and 54 cases (60%) in the two groups with disease progression (P < 0.001). There were 6 cases (9.5%) and 15 cases (16.7%) in the combination chemotherapy group and the ADT group died of prostate cancer and related complications, respectively. All 63 cases in the combined chemotherapy group completed 6 cycles of chemotherapy. 39 (61.9%) cases experienced varying degrees of neutropenia, of which 12 (19%) experienced grade 3-4 neutropenia, with 6 cases (9.5%) developed febrile neutropenia. 30 cases (47.6%) had toxic reactions in the digestive system, and 3 case (4.3%) had grade 3 liver dysfunction. 27 cases (42.8%) had skin and mucosal toxicity. 9 cases (14.3%) had mild fluid retention. No blood and digestive toxicity were observed in the ADT group. 33 cases (52.4%) and 48 (53.3%) of the two groups had symptoms of afternoon hot flashes and fatigue, (P = 0.961).

CONCLUSION

Docetaxel chemotherapy combined with endocrine therapy could be one of effective treatments for delaying castration resistance of HVD-mHSPC, which could prolong PFS effectively and obtain a higher PSA response rate, high safety under close monitoring, and controllable adverse reactions.

Differential Gene Expression and Weighted Correlation Network Dynamics in High-Throughput Datasets of Prostate Cancer

Precision oncology is an absolute need today due to the emergence of treatment resistance and heterogeneity among cancerous profiles. Target-propelled cancer therapy is one of the treasures of precision oncology which has come together with substantial medical accomplishment. Prostate cancer is one of the most common cancers in males, with tremendous biological heterogeneity in molecular and clinical behavior. The spectrum of molecular abnormalities and varying clinical patterns in prostate cancer suggest substantial heterogeneity among different profiles. To identify novel therapeutic targets and precise biomarkers implicated with prostate cancer, we performed a state-of-the-art bioinformatics study, beginning with analyzing high-throughput genomic datasets from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) suggests a set of five dysregulated hub genes (MAF, STAT6, SOX2, FOXO1, and WNT3A) that played crucial roles in biological pathways associated with prostate cancer progression. We found overexpressed STAT6 and SOX2 and proposed them as candidate biomarkers and potential targets in prostate cancer. Furthermore, the alteration frequencies in STAT6 and SOX2 and their impact on the patients' survival were explored through the cBioPortal platform. The Kaplan-Meier survival analysis suggested that the alterations in the candidate genes were linked to the decreased overall survival of the patients. Altogether, the results signify that STAT6 and SOX2 and their genomic alterations can be explored in therapeutic interventions of prostate cancer for precision oncology, utilizing early diagnosis and target-propelled therapy.

Androgen receptor reprogramming demarcates prognostic, context-dependent gene sets in primary and metastatic prostate cancer

The androgen receptor (AR) is a prostate master transcription factor. It binds to genetic enhancers, where it regulates gene activity and plays a fundamental role in prostate pathophysiology. Previous work has demonstrated that AR-DNA binding is systematically and consistently reprogrammed during prostate tumorigenesis and disease progression. We charted these reprogrammed AR sites and identified genes proximal to them. We were able to devise gene lists based on AR status within specific histological contexts: normal prostate epithelium, primary prostate tumor, and metastatic prostate cancer. We evaluated expression of the genes in these gene sets in subjects from two distinct clinical cohorts-men treated with surgery for localized prostate cancer and men with metastatic prostate cancer. Among men with localized prostate cancer, expression of genes proximal to AR sites lost in the transition from normal prostate to prostate tumor was associated with clinical outcome. Among men with metastatic disease, expression of genes proximal to AR sites gained in metastatic tumors was associated with clinical outcome. These results are consistent with the notion that AR is fundamental to both maintaining differentiation in normal prostate tissue and driving de-differentiation in advanced prostate cancer. More broadly, the study demonstrates the power of incorporating context-dependent epigenetic data into genetic analyses.

Variation in Molecularly Defined Prostate Tumor Subtypes by Self-identified Race

Background

Socioeconomic and health care utilization factors are major drivers of prostate cancer (PC) mortality disparities in the USA; however, tumor molecular heterogeneity may also contribute to the higher mortality among Black men.

Objective

To compare differences in PC subtype frequency and genomic aggressiveness by self-identified race.

Design setting and participants

Five molecular subtype classifiers were applied for 426 Black and 762 White PC patients in the Decipher Genomics Resource Information Database (GRID).

Outcome measurements and statistical analysis

Differences in subtype frequency and tumor genomic risk (Decipher score >0.6) by race were evaluated using χ² tests and multivariable-adjusted logistic regression models.

Results and limitations

Subtype frequencies differed by race for four classifiers. Subtypes characterized by the presence of SPOP mutations, SPINK1 overexpression, and neuroendocrine differentiation were more common among Black men. ERG and ETS fusion-positive subtypes were more frequent among White men, with no clear differences for subtypes reflecting luminal versus basal lineage. The hypothesized low-risk Kamoun S2 subtype was associated with a lower Decipher score among White men only (p = 0.01 for heterogeneity), while the aggressive You PCS1 subtype was associated with a higher Decipher score among White men only (p = 0.001 for heterogeneity). The Tomlins ERG⁺ subtype was associated with a higher Decipher score relative to all other subtypes among Black men, with no association among White men (p = 0.007 for heterogeneity).

Conclusions

The frequency of PC molecular subtypes differed by self-identified race. Additional studies are required to evaluate whether our observations suggest differences in the tumor genomic risk of progression by self-identified race.

Patient summary

We studied five classifiers that identify subtypes of prostate tumors and found that subtypes differed in frequency between Black and White patients. Further research is warranted to evaluate how differences in tumor subtypes may contribute to disparities in prostate cancer mortality.

The promising role of new molecular biomarkers in prostate cancer: from coding and non-coding genes to artificial intelligence approaches

Background

Risk stratification or progression in prostate cancer is performed with the support of clinical-pathological data such as the sum of the Gleason score and serum levels PSA. For several decades, methods aimed at the early detection of prostate cancer have included the determination of PSA serum levels. The aim of this systematic review is to provide an overview about recent advances in the discovery of new molecular biomarkers through transcriptomics, genomics and artificial intelligence that are expected to improve clinical management of the prostate cancer patient.

Methods

An exhaustive search was conducted by Pubmed, Google Scholar and Connected Papers using keywords relating to the genetics, genomics and artificial intelligence in prostate cancer, it includes “biomarkers”, “non-coding RNAs”, “lncRNAs”, “microRNAs”, “repetitive sequence”, “prognosis”, “prediction”, “whole-genome sequencing”, “RNA-Seq”, “transcriptome”, “machine learning”, and “deep learning”.

Results

New advances, including the search for changes in novel biomarkers such as mRNAs, microRNAs, lncRNAs, and repetitive sequences, are expected to contribute to an earlier and accurate diagnosis for each patient in the context of precision medicine, thus improving the prognosis and quality of life of patients. We analyze several aspects that are relevant for prostate cancer including its new molecular markers associated with diagnosis, prognosis, and prediction to therapy and how bioinformatic approaches such as machine learning and deep learning can contribute to clinic. Furthermore, we also include current techniques that will allow an earlier diagnosis, such as Spatial Transcriptomics, Exome Sequencing, and Whole-Genome Sequencing.

Conclusion

Transcriptomic and genomic analysis have contributed to generate knowledge in the field of prostate carcinogenesis, new information about coding and non-coding genes as biomarkers has emerged. Synergies created by the implementation of artificial intelligence to analyze and understand sequencing data have allowed the development of clinical strategies that facilitate decision-making and improve personalized management in prostate cancer.

A transcriptomic model for homologous recombination deficiency in prostate cancer

BACKGROUND

Tumors with mutations associated with homologous recombination deficiency (HRD) are uncommon in prostate cancer (PCa) and variably responsive to PARP inhibition. To better identify tumors with HRD, we developed a transcriptomic signature for HRD in PCa (HRD-P).

METHODS

By using an established mutational signature, we created and validated HRD-P in six independent PCa cohorts (primary PCa, n = 8224; metastatic castration-resistant PCa [mCRPC], n = 328). Molecular and clinical features were compared between HRD-P+ tumors and those with single HR-gene mutations.

RESULTS

HRD-P+ tumors were more common than tumors with single HR-gene mutations in primary (201/491, 41% vs 32/491 6.5%) and mCRPC (126/328, 38% vs 82/328, 25%) cases, and HRD-P+ was more predictive of genomic instability suggestive of HRD. HRD-P+ was associated with a shorter time to recurrence following surgery and shorter overall survival in men with mCRPC. In a prospective trial of mCRPC treated with olaparib (n = 10), all three men with HRD-P+ experienced prolonged (>330 days) PSA progression-free survival.

CONCLUSION

These results suggest transcriptomics can identify more patients that harbor phenotypic HRD than single HR-gene mutations and support further exploration of transcriptionally defined HRD tumors perhaps in conjunction with genomic markers for therapeutic application.

Adjuvant or Salvage Radiation Therapy for Prostate Cancer after Prostatectomy: Current Status, Controversies and Perspectives

Simple Summary

The management of patients with biochemical recurrence after prostatectomy has undergone significant changes in recent years. Currently, close monitoring of prostate-specific antigen (PSA) with early salvage radiotherapy (RT) in case of recurrence is the standard of care based on several randomized trials and a meta-analysis that has demonstrated its non-inferiority to adjuvant RT. Uncertainties remain regarding the management of patients at very high risk of recurrence, including appropriate selection criteria for adjuvant hormone therapy, and the role of imaging in refining the treatment strategy. This review explains this paradigm shift, raises points of controversy, and suggests ways to think about the future.

Abstract

Nearly one-third of the patients who undergo prostatectomy for prostate cancer have a biochemical recurrence (BCR) during follow-up. While several randomized trials have shown that adjuvant radiation therapy (aRT) improves biochemical control, this strategy has not been widely used because of the risk of toxicity and the fear of overtreating patients who would not have relapsed. In addition, the possibility of close PSA monitoring in the era of ultrasensitive assays enables to anticipate early salvage strategies (sRT). Three recent randomized trials and their meta-analysis have confirmed that aRT does not improve event-free survival compared to sRT, imposing the latter as the new standard of treatment. The addition of androgen deprivation therapy (ADT) to RT has been shown to improve biochemical control and metastasis-free survival, but the precise definition of to whom it should be proposed is still a matter of debate. The development of genomic tests or the use of artificial intelligence will allow more individualized treatment in the future. Therapeutic intensification with the combination of new-generation hormone therapy and RT is under study. Finally, the growing importance of metabolic imaging (PET/CT) due to its performance especially for low PSA levels will help in further personalizing management strategies.

A non-coding RNA balancing act: miR-346-induced DNA damage is limited by the long non-coding RNA NORAD in prostate cancer

BACKGROUND

miR-346 was identified as an activator of Androgen Receptor (AR) signalling that associates with DNA damage response (DDR)-linked transcripts in prostate cancer (PC). We sought to delineate the impact of miR-346 on DNA damage, and its potential as a therapeutic agent.

METHODS

RNA-IP, RNA-seq, RNA-ISH, DNA fibre assays, in vivo xenograft studies and bioinformatics approaches were used alongside a novel method for amplification-free, single nucleotide-resolution genome-wide mapping of DNA breaks (INDUCE-seq).

RESULTS

miR-346 induces rapid and extensive DNA damage in PC cells - the first report of microRNA-induced DNA damage. Mechanistically, this is achieved through transcriptional hyperactivation, R-loop formation and replication stress, leading to checkpoint activation and cell cycle arrest. miR-346 also interacts with genome-protective lncRNA NORAD to disrupt its interaction with PUM2, leading to PUM2 stabilisation and its increased turnover of DNA damage response (DDR) transcripts. Confirming clinical relevance, NORAD expression and activity strongly correlate with poor PC clinical outcomes and increased DDR in biopsy RNA-seq studies. In contrast, miR-346 is associated with improved PC survival. INDUCE-seq reveals that miR-346-induced DSBs occur preferentially at binding sites of the most highly-transcriptionally active transcription factors in PC cells, including c-Myc, FOXA1, HOXB13, NKX3.1, and importantly, AR, resulting in target transcript downregulation. Further, RNA-seq reveals widespread miR-346 and shNORAD dysregulation of DNA damage, replication and cell cycle processes. NORAD drives target-directed miR decay (TDMD) of miR-346 as a novel genome protection mechanism: NORAD silencing increases mature miR-346 levels by several thousand-fold, and WT but not TDMD-mutant NORAD rescues miR-346-induced DNA damage. Importantly, miR-346 sensitises PC cells to DNA-damaging drugs including PARP inhibitor and chemotherapy, and induces tumour regression as a monotherapy in vivo, indicating that targeting miR-346:NORAD balance is a valid therapeutic strategy.

CONCLUSIONS

A balancing act between miR-346 and NORAD regulates DNA damage and repair in PC. miR-346 may be particularly effective as a therapeutic in the context of decreased NORAD observed in advanced PC, and in transcriptionally-hyperactive cancer cells.

Intraductal Carcinoma of the Prostate as a Cause of Prostate Cancer Metastasis: A Molecular Portrait

Intraductal carcinoma of the prostate (IDC-P) is one of the most aggressive types of prostate cancer (PCa). IDC-P is identified in approximately 20% of PCa patients and is associated with recurrence, metastasis, and PCa-specific death. The main feature of this histological variant is the colonization of benign glands by PCa cells. Although IDC-P is a well-recognized independent parameter for metastasis, mechanisms by which IDC-P cells can spread and colonize other tissues are not fully known. In this review, we discuss the molecular portraits of IDC-P determined by immunohistochemistry and genomic approaches and highlight the areas in which more research is needed.

Antizyme Inhibitor 1 regulates matrikine expression and enhances the metastatic potential of aggressive primary prostate cancer

Molecular drivers of metastasis in patients with high-risk localized prostate cancer (PCa) are poorly understood. Therefore, we aim to study molecular drivers of metastatic progression in high-risk PCa patients. A retrospective matched case-control study of two clinico-pathologically identical groups of high-risk PCa patients was undertaken. One group developed metastatic recurrence (n=19) while the other did not (n=25). The primary index tumor was identified by a uro-pathologist, followed by DNA and RNA extraction for somatic copy number aberration (CNA) analysis and whole-transcriptome gene expression analysis. In vitro and in vivo studies included cell line manipulation and xenograft models. The integrative CNA and gene expression analyses identified an increase in AZIN1 gene expression within a focal amplification of 8q22.3, which was associated with metastatic recurrence of high-risk PCa patients in four independent cohorts. The effects of AZIN1 knockdown were evaluated, due to its therapeutic potential. AZIN1 knockdown effected proliferation and metastatic potential of PCa cells and xenograft models. RNA sequencing after AZIN1 knockdown in PCa cells revealed upregulation of genes coding for collagen subunits. The observed effect on cell migration after AZIN1 knockdown was mimicked when exposing PCa cells to bio-active molecules deriving from COL4A1 and COL4A2. Our integrated CNA and gene expression analysis of primary high-risk PCa identified the AZIN1 gene as a novel driver of metastatic progression, by altering collagen subunit expression. Future research should further investigate its therapeutic potential in preventing metastatic recurrence. Implications: AZIN1 was identified as driver of metastatic progression in high-risk PCa through matrikine regulation.

Prognostic Genomic Tissue-Based Biomarkers in the Treatment of Localized Prostate Cancer

In localized prostate cancer clinicopathologic variables have been used to develop prognostic nomograms quantifying the probability of locally advanced disease, of pelvic lymph node and distant metastasis at diagnosis or the probability of recurrence after radical treatment of the primary tumor. These tools although essential in daily clinical practice for the management of such a heterogeneous disease, which can be cured with a wide spectrum of treatment strategies (i.e., active surveillance, RP and radiation therapy), do not allow the precise distinction of an indolent instead of an aggressive disease. In recent years, several prognostic biomarkers have been tested, combined with the currently available clinicopathologic prognostic tools, in order to improve the decision-making process. In the following article, we reviewed the literature of the last 10 years and gave an overview report on commercially available tissue-based biomarkers and more specifically on mRNA-based gene expression classifiers. To date, these genomic tests have been widely investigated, demonstrating rigorous quality criteria including reproducibility, linearity, analytical accuracy, precision, and a positive impact in the clinical decision-making process. Albeit data published in literature, the systematic use of these tests in prostate cancer is currently not recommended due to insufficient evidence.

Molecular risk classifier score and biochemical recurrence risk are associated with cribriform pattern type in Gleason 3+4=7 prostate cancer

PURPOSE

Among Gleason pattern 4 types, cribriform pattern is associated with the worst outcomes. We hypothesized that larger cribriform patterns would be associated with increased Decipher scores and higher biochemical recurrence (BCR) risk in Gleason 3+4=7 prostatectomy patients.

MATERIALS AND METHODS

The slide from patients who underwent prostatectomy from January 2016 to March 2020 on which Decipher was performed was re-reviewed for Gleason score and cribriform patterns, with large cribriform defined as cribriform acini with greater than 12 lumens and simple cribriform as 12 or fewer lumens. Differences in Decipher score were analyzed in a generalized linear model controlling for pathology stage and tumor margin status. A multivariable Cox proportional hazards model was performed for BCR-free survival.

RESULTS

Of 337 cases, 118 were Gleason 3+4=7. The mean Decipher scores in 3+4=7 cases without cribriform, with simple cribriform, and with large cribriform were 0.41, 0.54, and 0.62, respectively. In a multivariable model with pathology stage, margin tumor length, and percentage pattern 4 as covariates, compared to cases without cribriform, simple cribriform was associated with 0.10 increase in Decipher (p=0.03) and 4.7-fold hazard ratio of BCR (95% confidence interval [CI], 0.4-56.5; p=0.22) and large cribriform was associated with 0.17 increase in Decipher (p<0.001) and 16.0-fold hazard ratio of BCR (95% CI, 1.4-181.2; p=0.02).

CONCLUSIONS

Among Gleason 3+4=7 carcinomas, large cribriform was associated with higher Decipher scores and greater BCR risk. Our results support that large cribriform is an aggressive pattern 4 subtype and should be considered a contraindication for active surveillance.

The long noncoding RNA H19 regulates tumor plasticity in neuroendocrine prostate cancer

Neuroendocrine (NE) prostate cancer (NEPC) is a lethal subtype of castration-resistant prostate cancer (PCa) arising either de novo or from transdifferentiated prostate adenocarcinoma following androgen deprivation therapy (ADT). Extensive computational analysis has identified a high degree of association between the long noncoding RNA (lncRNA) H19 and NEPC, with the longest isoform highly expressed in NEPC. H19 regulates PCa lineage plasticity by driving a bidirectional cell identity of NE phenotype (H19 overexpression) or luminal phenotype (H19 knockdown). It contributes to treatment resistance, with the knockdown of H19 re-sensitizing PCa to ADT. It is also essential for the proliferation and invasion of NEPC. H19 levels are negatively regulated by androgen signaling via androgen receptor (AR). When androgen is absent SOX2 levels increase, driving H19 transcription and facilitating transdifferentiation. H19 facilitates the PRC2 complex in regulating methylation changes at H3K27me3/H3K4me3 histone sites of AR-driven and NEPC-related genes. Additionally, this lncRNA induces alterations in genome-wide DNA methylation on CpG sites, further regulating genes associated with the NEPC phenotype. Our clinical data identify H19 as a candidate diagnostic marker and predictive marker of NEPC with elevated H19 levels associated with an increased probability of biochemical recurrence and metastatic disease in patients receiving ADT. Here we report H19 as an early upstream regulator of cell fate, plasticity, and treatment resistance in NEPC that can reverse/transform cells to a treatable form of PCa once therapeutically deactivated.

Deep Prostate-specific Antigen Response following Addition of Apalutamide to Ongoing Androgen Deprivation Therapy and Long-term Clinical Benefit in SPARTAN

BACKGROUND

Apalutamide plus androgen deprivation therapy (ADT) significantly improved metastasis-free survival (MFS), overall survival (OS), and time to prostate-specific antigen (PSA) progression in the placebo-controlled SPARTAN study of high-risk nonmetastatic castration-resistant prostate cancer (nmCRPC).

OBJECTIVE

To assess the relationships between PSA kinetics, outcomes, and molecular subtypes in SPARTAN.

DESIGN, SETTING, AND PARTICIPANTS

The authors conducted a post hoc analysis of nmCRPC patients randomized to receive apalutamide (n = 806) or placebo (n = 401) plus ADT and a subset stratified by molecular classifiers.

INTERVENTION

Apalutamide 240 mg/d.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The association between PSA kinetics and MFS, OS, time to PSA progression, and molecular subtypes was evaluated using the landmark analysis and Kaplan-Meier methods.

RESULTS AND LIMITATIONS

By 3 mo, PSA decreased in most apalutamide-treated patients and increased in most placebo-treated patients. After apalutamide, the median time to PSA nadir, confirmed ≥50% PSA reduction, ≥90% PSA reduction, and PSA ≤0.2 ng/ml were 7.4, 1.0, 1.9, and 2.8 mo, respectively. By 6 mo, 90%, 57%, and 32% of apalutamide patients had ≥50% PSA reduction, ≥90% PSA reduction, and PSA ≤0.2 ng/ml, respectively, while only 1.5% of placebo patients experienced ≥50% PSA reduction. PSA reductions were observed within 3 mo and up to 12 mo of apalutamide treatment, and were similar across molecular subtypes. Deep PSA responses (≥90% PSA reduction or PSA ≤0.2 ng/ml) at landmark 6-mo apalutamide treatment were significantly associated with improved time to PSA progression (hazard ratio {HR} [95% confidence interval {CI}] 0.25 [0.18-0.33] or 0.13 [0.08-0.21]), MFS (0.41 [0.29-0.57] or 0.3 [0.19-0.47]), and OS (0.45 [0.35-0.59] or 0.26 [0.18-0.38]; p < 0.001 for all).

CONCLUSIONS

Apalutamide plus ADT produced rapid, deep, and durable PSA responses by 6-mo treatment regardless of assessed molecular prognostic markers. An early PSA response with apalutamide was associated with clinical benefits, supporting prognostic value of PSA monitoring.

PATIENT SUMMARY

In this report, we describe how prostate-specific antigen (PSA) levels relate to outcomes in patients with nonmetastatic castration-resistant prostate cancer treated with apalutamide plus androgen deprivation therapy (ADT). We found that treatment with apalutamide plus ADT resulted in rapid, deep, and durable PSA responses in the majority of patients, including those with high-risk molecular subtypes, which were associated with improved survival.

G3BP1 inhibits Cul3SPOP to amplify AR signaling and promote prostate cancer

SPOP, an E3 ubiquitin ligase, acts as a prostate-specific tumor suppressor with several key substrates mediating oncogenic function. However, the mechanisms underlying SPOP regulation are largely unknown. Here, we have identified G3BP1 as an interactor of SPOP and functions as a competitive inhibitor of Cul3SPOP, suggesting a distinctive mode of Cul3SPOP inactivation in prostate cancer (PCa). Transcriptomic analysis and functional studies reveal a G3BP1-SPOP ubiquitin signaling axis that promotes PCa progression through activating AR signaling. Moreover, AR directly upregulates G3BP1 transcription to further amplify G3BP1-SPOP signaling in a feed-forward manner. Our study supports a fundamental role of G3BP1 in disabling the tumor suppressive Cul3SPOP, thus defining a PCa cohort independent of SPOP mutation. Therefore, there are significantly more PCa that are defective for SPOP ubiquitin ligase than previously appreciated, and these G3BP1high PCa are more susceptible to AR-targeted therapy.

The Prognostic Potential of Human Prostate Cancer-Associated Macrophage Subtypes as Revealed by Single-Cell Transcriptomics

Macrophages in the tumor microenvironment are causally linked with prostate cancer development and progression, yet little is known about their composition in neoplastic human tissue. By performing single cell transcriptomic analysis of human prostate cancer resident macrophages, three distinct populations were identified in the diseased prostate. Unexpectedly, no differences were observed between macrophages isolated from the tumorous and nontumorous portions of the prostatectomy specimens. Markers associated with canonical M1 and M2 macrophage phenotypes were identifiable, however these were not the main factors defining unique subtypes. The genes selectively associated with each macrophage cluster were used to develop a gene signature which was highly associated with both recurrence-free and metastasis-free survival. These results highlight the relevance of tissue-specific macrophage subtypes in the tumor microenvironment for prostate cancer progression and demonstrates the utility of profiling single-cell transcriptomics in human tumor samples as a strategy to design gene classifiers for patient prognostication. IMPLICATIONS: The specific macrophage subtypes present in a diseased human prostate have prognostic value, suggesting that the relative proportions of these populations are related to patient outcome. Understanding the relative contributions of these subtypes will not only inform patient prognostication, but will enable personalized immunotherapeutic strategies to increase beneficial populations or reduce detrimental populations.

Tissue- and Liquid-Based Biomarkers in Prostate Cancer Precision Medicine

Worldwide, prostate cancer (PC) is the second-most-frequently diagnosed male cancer and the fifth-most-common cause of all cancer-related deaths. Suspicion of PC in a patient is largely based upon clinical signs and the use of prostate-specific antigen (PSA) levels. Although PSA levels have been criticised for a lack of specificity, leading to PC over-diagnosis, it is still the most commonly used biomarker in PC management. Unfortunately, PC is extremely heterogeneous, and it can be difficult to stratify patients whose tumours are unlikely to progress from those that are aggressive and require treatment intensification. Although PC-specific biomarker research has previously focused on disease diagnosis, there is an unmet clinical need for novel prognostic, predictive and treatment response biomarkers that can be used to provide a precision medicine approach to PC management. In particular, the identification of biomarkers at the time of screening/diagnosis that can provide an indication of disease aggressiveness is perhaps the greatest current unmet clinical need in PC management. Largely through advances in genomic and proteomic techniques, exciting pre-clinical and clinical research is continuing to identify potential tissue, blood and urine-based PC-specific biomarkers that may in the future supplement or replace current standard practices. In this review, we describe how PC-specific biomarker research is progressing, including the evolution of PSA-based tests and those novel assays that have gained clinical approval. We also describe alternative diagnostic biomarkers to PSA, in addition to biomarkers that can predict PC aggressiveness and biomarkers that can predict response to certain therapies. We believe that novel biomarker research has the potential to make significant improvements to the clinical management of this disease in the near future.

Artificial intelligence in urological oncology: An update and future applications

There continues to be rapid developments and research in the field of Artificial Intelligence (AI) in Urological Oncology worldwide. In this review we discuss the basics of AI, application of AI per tumour group (Renal, Prostate and Bladder Cancer) and application of AI in Robotic Urological Surgery. We also discuss future applications of AI being developed with the benefits to patients with Urological Oncology.

Genomic Strategies to Personalize Use of Androgen Deprivation Therapy With Radiotherapy

The use of combination RT and androgen deprivation therapy in many prostate cancer curative-intent treatment scenarios is supported by level 1 evidence. However, in our current clinical paradigm, we have no ability to determine a priori which patients truly benefit from combination therapy and therefore apply the combination RT and androgen deprivation therapy intensification strategy to all patients, which results in overtreatment or undertreatment of the majority of our patients. Genomics has the ability to more deeply and objectively characterize the disease, in turn refining our prognostication capabilities and enabling the individualization of treatments. We review the commercially available prostate cancer genomic tests, focusing on those able to predict patient outcomes following radiotherapy or guide radiotherapy treatment decisions.

Expression of ISL1 and its partners in prostate cancer progression and neuroendocrine differentiation

INTRODUCTION AND OBJECTIVES

ISL1 serves as a biomarker of metastasis and neuroendocrine neoplasia in multiple tumors. However, the expression and relation of ISL1 to other biomarkers in prostate cancer have not been fully elucidated. Here, we characterize the expression of ISL1 and its partners in PCa and document its association to disease progression and post castration resistance neuroendocrine differentiation.

METHODS

The expression of ISL1 was interrogated in > 6000 primary samples from the Decipher GRID registry and 250 mCRPC samples to assess its prognostic value and relation to neuroendocrine differentiation.

RESULTS

ISL1 was highly correlated to MEIS genes and other genes related to cell motility. ISL1 down-regulation in PCa was associated with cancer progression, aggressive primary tumors, and metastatic outcome. We found that ISL1 is highly correlated to MEIS genes across multiple primary PCa and mCRPC cohorts. The expression of ISL1 and MEIS genes were significantly and inversely related to metastasis-free survival and lethal disease, and were downregulated in CRPC and hormone naïve metastatic tumors but showed upregulation in neuroendocrine tumors. Co-immunoprecipitation showed MEIS2 and ISL1 interacting with each supporting their role in modulating transcriptional regulation and nominating this complex for potential targeted therapy.

CONCLUSIONS

ISL1 complex with MEIS2 serves a critical role in prostate tumor progression and its upregulation in mCRPC/NE provides a rationale for assessing the role of ISL1 and its associated protein in treatment resistance.

Reference-free transcriptome signatures for prostate cancer prognosis

BACKGROUND

RNA-seq data are increasingly used to derive prognostic signatures for cancer outcome prediction. A limitation of current predictors is their reliance on reference gene annotations, which amounts to ignoring large numbers of non-canonical RNAs produced in disease tissues. A recently introduced kind of transcriptome classifier operates entirely in a reference-free manner, relying on k-mers extracted from patient RNA-seq data.

METHODS

In this paper, we set out to compare conventional and reference-free signatures in risk and relapse prediction of prostate cancer. To compare the two approaches as fairly as possible, we set up a common procedure that takes as input either a k-mer count matrix or a gene expression matrix, extracts a signature and evaluates this signature in an independent dataset.

RESULTS

We find that both gene-based and k-mer based classifiers had similarly high performances for risk prediction and a markedly lower performance for relapse prediction. Interestingly, the reference-free signatures included a set of sequences mapping to novel lncRNAs or variable regions of cancer driver genes that were not part of gene-based signatures.

CONCLUSIONS

Reference-free classifiers are thus a promising strategy for the identification of novel prognostic RNA biomarkers.

Prostate-specific Membrane Antigen and Fluciclovine Transporter Genes are Associated with Variable Clinical Features and Molecular Subtypes of Primary Prostate Cancer

¹⁸F-Fluciclovine-based positron emission tomography (PET) imaging is recommended in the USA for biochemical recurrence (BCR) after prostate cancer treatment. However, prostate-specific membrane antigen (PSMA)-based PET imaging is more common worldwide, supported by international guidelines, and is now approved by the Food and Drug Administration in the USA for initial staging of primary prostate cancer. Little is known about the molecular profiles of lesions detected by PSMA-targeted PET/computed tomography (CT) versus ¹⁸F-fluciclovine PET/CT. We examined the expression of PSMA (FOLH1) and the fluciclovine transporter genes LAT1-4 and ASCT1/2 in a combined cohort of more than 18 000 radical prostatectomy specimens and their associations with clinical outcomes. Expression of PSMA and all but one fluciclovine transporter gene was higher in prostate cancer than in benign tissue. PSMA expression was associated with Gleason score (GS) ≥8 and lymph node involvement (LNI), and had a positive linear correlation with Decipher risk score. By contrast, expression of the fluciclovine transporters LAT2, LAT3, and ASCT2 was negatively associated with GS ≥ 8, LNI, and high Decipher score. The top decile of PSMA expression was associated with poorest metastasis-free survival (MFS), while the bottom deciles of LAT3 and ASCT2 expression were associated with poorest MFS. PATIENT SUMMARY: We measured the expression of genes that encode the targets for two different radiotracers in PET (positron emission tomography) scans of the prostate. We found that PSMA gene expression (PSMA-based tracer) is associated with worse clinical outcomes, while expression of ASCT2, LAT2, and LAT3 genes (fluciclovine tracer) is associated with better outcomes.

Validation of a 22-Gene Genomic Classifier in Patients With Recurrent Prostate Cancer: An Ancillary Study of the NRG/RTOG 9601 Randomized Clinical Trial

Question

Can a genomic biomarker estimate the risk of prostate cancer clinical end points in men who received salvage radiation for rising prostate-specific antigen levels after surgery?

Findings

In this ancillary study of 352 men randomized to placebo or hormone therapy in the NRG/RTOG 9601 clinical trial of salvage radiation, the Decipher genomic classifier was independently associated with the risk of metastasis, prostate cancer–specific mortality, and overall survival.

Meaning

These findings suggest that the Decipher genomic classifier is a promising biomarker to risk stratify men to better enable hormone therapy treatment decisions for biochemical recurrence of their prostate cancer after surgery.

Importance

Decipher (Decipher Biosciences Inc) is a genomic classifier (GC) developed to estimate the risk of distant metastasis (DM) after radical prostatectomy (RP) in patients with prostate cancer.

Objective

To validate the GC in the context of a randomized phase 3 trial.

Design, Setting, and Participants

This ancillary study used RP specimens from the phase 3 placebo-controlled NRG/RTOG 9601 randomized clinical trial conducted from March 1998 to March 2003. The specimens were centrally reviewed, and RNA was extracted from the highest-grade tumor available in 2019 with a median follow-up of 13 years. Clinical-grade whole transcriptomes from samples passing quality control were assigned GC scores (scale, 0-1). A National Clinical Trials Network–approved prespecified statistical plan included the primary objective of validating the independent prognostic ability of GC for DM, with secondary end points of prostate cancer–specific mortality (PCSM) and overall survival (OS). Data were analyzed from September 2019 to December 2019.

Intervention

Salvage radiotherapy (sRT) with or without 2 years of bicalutamide.

Main Outcomes and Measures

The preplanned primary end point of this study was the independent association of the GC with the development of DM.

Results

In this ancillary study of specimens from a phase 3 randomized clinical trial, GC scores were generated from 486 of 760 randomized patients with a median follow-up of 13 years; samples from a total of 352 men (median [interquartile range] age, 64.5 (60-70) years; 314 White [89.2%] participants) passed microarray quality control and comprised the final cohort for analysis. On multivariable analysis, the GC (continuous variable, per 0.1 unit) was independently associated with DM (hazard ratio [HR], 1.17; 95% CI, 1.05-1.32; P = .006), PCSM (HR, 1.39; 95% CI, 1.20-1.63; P < .001), and OS (HR, 1.17; 95% CI, 1.06-1.29; P = .002) after adjusting for age, race/ethnicity, Gleason score, T stage, margin status, entry prostate-specific antigen, and treatment arm. Although the original planned analysis was not powered to detect a treatment effect interaction by GC score, the estimated absolute effect of bicalutamide on 12-year OS was less when comparing patients with lower vs higher GC scores (2.4% vs 8.9%), which was further demonstrated in men receiving early sRT at a prostate-specific antigen level lower than 0.7 ng/mL (−7.8% vs 4.6%).

Conclusions and Relevance

This ancillary validation study of the Decipher GC in a randomized trial cohort demonstrated association of the GC with DM, PCSM, and OS independent of standard clinicopathologic variables. These results suggest that not all men with biochemically recurrent prostate cancer after surgery benefit equally from the addition of hormone therapy to sRT.

Trial Registration

ClinicalTrials.gov identifier: NCT00002874

Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives

Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.