Tissue-based Genomics Augments Post-prostatectomy Risk Stratification in a Natural History Cohort of Intermediate- and High-Risk Men

Development of a Longitudinal Prostate Cancer Transcriptomic and Clinical Data Linkage

Importance

Although tissue-based gene expression testing has become widely used for prostate cancer risk stratification, its prognostic performance in the setting of clinical care is not well understood.

Objective

To develop a linkage between a prostate genomic classifier (GC) and clinical data across payers and sites of care in the US.

Design, Setting, and Participants

In this cohort study, clinical and transcriptomic data from clinical use of a prostate GC between 2016 and 2022 were linked with data aggregated from insurance claims, pharmacy records, and electronic health record (EHR) data. Participants were anonymously linked between datasets by deterministic methods through a deidentification engine using encrypted tokens. Algorithms were developed and refined for identifying prostate cancer diagnoses, treatment timing, and clinical outcomes using diagnosis codes, Common Procedural Terminology codes, pharmacy codes, Systematized Medical Nomenclature for Medicine clinical terms, and unstructured text in the EHR. Data analysis was performed from January 2023 to January 2024.

Exposure

Diagnosis of prostate cancer.

Main Outcomes and Measures

The primary outcomes were biochemical recurrence and development of prostate cancer metastases after diagnosis or radical prostatectomy (RP). The sensitivity of the linkage and identification algorithms for clinical and administrative data were calculated relative to clinical and pathological information obtained during the GC testing process as the reference standard.

Results

A total of 92 976 of 95 578 (97.2%) participants who underwent prostate GC testing were successfully linked to administrative and clinical data, including 53 871 who underwent biopsy testing and 39 105 who underwent RP testing. The median (IQR) age at GC testing was 66.4 (61.0-71.0) years. The sensitivity of the EHR linkage data for prostate cancer diagnoses was 85.0% (95% CI, 84.7%-85.2%), including 80.8% (95% CI, 80.4%-81.1%) for biopsy-tested participants and 90.8% (95% CI, 90.5%-91.0%) for RP-tested participants. Year of treatment was concordant in 97.9% (95% CI, 97.7%-98.1%) of those undergoing GC testing at RP, and 86.0% (95% CI, 85.6%-86.4%) among participants undergoing biopsy testing. The sensitivity of the linkage was 48.6% (95% CI, 48.1%-49.1%) for identifying RP and 50.1% (95% CI, 49.7%-50.5%) for identifying prostate biopsy.

Conclusions and Relevance

This study established a national-scale linkage of transcriptomic and longitudinal clinical data yielding high accuracy for identifying key clinical junctures, including diagnosis, treatment, and early cancer outcome. This resource can be leveraged to enhance understandings of disease biology, patterns of care, and treatment effectiveness.

Development and Validation of a Multimodality Model Based on Whole-Slide Imaging and Biparametric MRI for Predicting Postoperative Biochemical Recurrence in Prostate Cancer

Purpose To develop and validate a machine learning multimodality model based on preoperative MRI, surgical whole-slide imaging (WSI), and clinical variables for predicting prostate cancer (PCa) biochemical recurrence (BCR) following radical prostatectomy (RP). Materials and Methods In this retrospective study (September 2015 to April 2021), 363 male patients with PCa who underwent RP were divided into training (n = 254; median age, 69 years [IQR, 64-74 years]) and testing (n = 109; median age, 70 years [IQR, 65-75 years]) sets at a ratio of 7:3. The primary end point was biochemical recurrence-free survival. The least absolute shrinkage and selection operator Cox algorithm was applied to select independent clinical variables and construct the clinical signature. The radiomics signature and pathomics signature were constructed using preoperative MRI and surgical WSI data, respectively. A multimodality model was constructed by combining the radiomics signature, pathomics signature, and clinical signature. Using Harrell concordance index (C index), the predictive performance of the multimodality model for BCR was assessed and compared with all single-modality models, including the radiomics signature, pathomics signature, and clinical signature. Results Both radiomics and pathomics signatures achieved good performance for BCR prediction (C index: 0.742 and 0.730, respectively) on the testing cohort. The multimodality model exhibited the best predictive performance, with a C index of 0.860 on the testing set, which was significantly higher than all single-modality models (all P ≤ .01). Conclusion The multimodality model effectively predicted BCR following RP in patients with PCa and may therefore provide an emerging and accurate tool to assist postoperative individualized treatment. Keywords: MR Imaging, Urinary, Pelvis, Comparative Studies Supplemental material is available for this article. © RSNA, 2024.

An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings

Objectives

To evaluate the added benefit of integrating features from pre-treatment MRI (radiomics) and digitized post-surgical pathology slides (pathomics) in prostate cancer (PCa) patients for prognosticating outcomes post radical-prostatectomy (RP) including a) rising prostate specific antigen (PSA), and b) extraprostatic-extension (EPE).

Methods

Multi-institutional data (N = 58) of PCa patients who underwent pre-treatment 3-T MRI prior to RP were included in this retrospective study. Radiomic and pathomic features were extracted from PCa regions on MRI and RP specimens delineated by expert clinicians. On training set (D1, N = 44), Cox Proportional-Hazards models MR, MP and MRaP were trained using radiomics, pathomics, and their combination, respectively, to prognosticate rising PSA (PSA > 0.03 ng/mL). Top features from MRaP were used to train a model to predict EPE on D1 and test on external dataset (D2, N = 14). C-index, Kalplan-Meier curves were used for survival analysis, and area under ROC (AUC) was used for EPE. MRaP was compared with the existing post-treatment risk-calculator, CAPRA (MC).

Results

Patients had median follow-up of 34 months. MRaP (c-index = 0.685 ± 0.05) significantly outperformed MR (c-index = 0.646 ± 0.05), MP (c-index = 0.631 ± 0.06) and MC (c-index = 0.601 ± 0.071) (p < 0.0001). Cross-validated Kaplan-Meier curves showed significant separation among risk groups for rising PSA for MRaP (p < 0.005, Hazard Ratio (HR) = 11.36) as compared to MR (p = 0.64, HR = 1.33), MP (p = 0.19, HR = 2.82) and MC (p = 0.10, HR = 3.05). Integrated radio-pathomic model MRaP (AUC = 0.80) outperformed MR (AUC = 0.57) and MP (AUC = 0.76) in predicting EPE on external-data (D2).

Conclusions

Results from this preliminary study suggest that a combination of radiomic and pathomic features can better predict post-surgical outcomes (rising PSA and EPE) compared to either of them individually as well as extant prognostic nomogram (CAPRA).

Transcriptomic Heterogeneity of Expansile Cribriform and Other Gleason Pattern 4 Prostate Cancer Subtypes

BACKGROUND

Prostate cancers featuring an expansile cribriform (EC) pattern are associated with worse clinical outcomes following radical prostatectomy (RP). However, studies of the genomic characteristics of Gleason pattern 4 subtypes are limited.

OBJECTIVE

To explore transcriptomic characteristics and heterogeneity within Gleason pattern 4 subtypes (fused/poorly formed, glomeruloid, small cribriform, EC/intraductal carcinoma [IDC]) and the association with biochemical recurrence (BCR)-free survival.

DESIGN, SETTING, AND PARTICIPANTS

This was a retrospective cohort study including 165 men with grade group 2-4 prostate cancer who underwent RP at a single academic institution (2016-2020) and Decipher testing of the RP specimen. Patients with Gleason pattern 5 were excluded. IDC and EC patterns were grouped. Median follow-up was 2.5 yr after RP for patients without BCR.

OUTCOMES MEASUREMENTS AND STATISTICAL ANALYSIS

Prompted by heterogeneity within pattern 4 subtypes identified via exploratory analyses, we investigated transcriptomic consensus clusters using partitioning around medoids and hallmark gene set scores. The primary clinical outcome was BCR, defined as two consecutive prostate-specific antigen measurements >0.2 ng/ml at least 8 wk after RP, or any additional treatment. Multivariable Cox proportional-hazards models were used to determine factors associated with BCR-free survival.

RESULTS AND LIMITATIONS

In this cohort, 99/165 patients (60%) had EC and 67 experienced BCR. Exploratory analyses and clustering demonstrated transcriptomic heterogeneity within each Gleason pattern 4 subtype. In the multivariable model controlled for pattern 4 subtype, margin status, Cancer of the Prostate Risk Assessment Post-Surgical score, and Decipher score, a newly identified steroid hormone-driven cluster (hazard ratio 2.35 95% confidence interval 1.01-5.47) was associated with worse BCR-free survival. The study is limited by intermediate follow-up, no validation cohort, and lack of accounting for intratumoral and intraprostatic heterogeneity.

CONCLUSIONS

Transcriptomic heterogeneity was present within and across each Gleason pattern 4 subtype, demonstrating there is additional biologic diversity not captured by histologic subtypes. This heterogeneity can be used to develop novel signatures and to classify transcriptomic subtypes, which may help in refining risk stratification following RP to further guide decision-making on adjuvant and salvage treatments.

PATIENT SUMMARY

We studied prostatectomy specimens and found that tumors with similar microscopic appearance can have genetic differences that may help to predict outcomes after prostatectomy for prostate cancer. Our results demonstrate that further gene expression analysis of prostate cancer subtypes may improve risk stratification after prostatectomy. Future studies are needed to develop novel gene expression signatures and validate these findings in independent sets of patients.

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.

Genomic classifiers and prognosis of localized prostate cancer: a systematic review

BACKGROUND

Refinement of the risk classification for localized prostate cancer is warranted to aid in clinical decision making. A systematic analysis was undertaken to evaluate the prognostic ability of three genomic classifiers, Decipher, GPS, and Prolaris, for biochemical recurrence, development of metastases and prostate cancer-specific mortality in patients with localized prostate cancer.

METHODS

Data sources: MEDLINE, Embase, and Web of Science were queried for reports published from January 2010 to April 2022.

STUDY SELECTION

prospective or retrospective studies reporting prognosis for patients with localized prostate cancer.

DATA EXTRACTION

relevant data were extracted into a customized database by one researcher with a second overreading. Risk of bias was assessed using a validated tool for prognostic studies, Quality in Prognosis Studies (QUIPS). Disagreements were resolved by consensus or by input from a third reviewer. We assessed the certainty of evidence by GRADE incorporating adaptation for prognostic studies.

RESULTS

Data synthesis: a total of 39 studies (37 retrospective) involving over 10,000 patients were identified. Twenty-two assessed Decipher, 5 GPS, and 14 Prolaris. Thirty-four studies included patients who underwent prostatectomy. Based on very low to low certainty of evidence, each of the three genomic classifiers modestly improved upon the prognostic ability for biochemical recurrence, development of metastases, and prostate cancer-specific mortality compared to standard clinical risk-classification schemes.

LIMITATIONS

downgrading of confidence in the evidence stemmed largely from bias due to the retrospective nature of the studies, heterogeneity in treatment received, and era in which patients were treated (i.e., prior to the 2000s).

CONCLUSIONS

Genomic classifiers provide a small but consistent improvement upon the prognostic ability of clinical classification schemes, which may be helpful when treatment decisions are uncertain. However, evidence from current management-era data and of the predictive ability of these tests is needed.

Distinct mesenchymal cell states mediate prostate cancer progression

In the complex tumor microenvironment (TME), mesenchymal cells are key players, yet their specific roles in prostate cancer (PCa) progression remain to be fully deciphered. This study employs single-cell RNA sequencing to delineate molecular changes in tumor stroma that influence PCa progression and metastasis. Analyzing mesenchymal cells from four genetically engineered mouse models (GEMMs) and correlating these findings with human tumors, we identify eight stromal cell populations with distinct transcriptional identities consistent across both species. Notably, stromal signatures in advanced mouse disease reflect those in human bone metastases, highlighting periostin's role in invasion and differentiation. From these insights, we derive a gene signature that predicts metastatic progression in localized disease beyond traditional Gleason scores. Our results illuminate the critical influence of stromal dynamics on PCa progression, suggesting new prognostic tools and therapeutic targets.

Surgical Management and Considerations for Patients with Localized High-Risk Prostate Cancer

OPINION STATEMENT

Localized high-risk (HR) prostate cancer (PCa) is a heterogenous disease state with a wide range of presentations and outcomes. Historically, non-surgical management with radiotherapy and androgen deprivation therapy was the treatment option of choice. However, surgical resection with radical prostatectomy (RP) and pelvic lymph node dissection (PLND) is increasingly utilized as a primary treatment modality for patients with HRPCa. Recent studies have demonstrated that surgery is an equivalent treatment option in select patients with the potential to avoid the side effects from androgen deprivation therapy and radiotherapy combined. Advances in imaging techniques and biomarkers have also improved staging and patient selection for surgical resection. Advances in robotic surgical technology grant surgeons various techniques to perform RP, even in patients with HR disease, which can reduce the morbidity of the procedure without sacrificing oncologic outcomes. Clinical trials are not only being performed to assess the safety and oncologic outcomes of these surgical techniques, but to also evaluate the role of surgical resection as a part of a multimodal treatment plan. Further research is needed to determine the ideal role of surgery to potentially provide a more personalized and tailored treatment plan for patients with localized HR PCa.

The natural history of a delayed detectable PSA after radical prostatectomy

INTRODUCTION

Men with a detectable PSA after radical prostatectomy (RP) are often offered salvage therapy while those with an undetectable PSA are monitored. We aim to better characterize the natural history of men with an initially undetectable PSA who subsequently developed a detectable PSA > 6 months after RP.

METHODS

Retrospective analysis of men who underwent RP for clinically localized prostate cancer at the University of California, San Francisco from 2000 to 2022. The primary outcome was biochemical recurrence, defined as 2 consecutive PSA > = 0.03 ng/mL starting 6 months after surgery. Secondary outcomes were salvage treatment, post-salvage treatment, metastasis free survival (MFS), prostate cancer specific mortality (PCSM), and all-cause mortality (ACM). This cohort was compared to a previously described cohort who had an immediately detectable post-operative PSA.

RESULTS

From our cohort of 3348 patients, we identified 2868 men who had an undetectable post-op PSA. Subsequently, 642 men had a delayed detectable PSA at a median of 25 months (IQR 15, 43) with median follow-up of 72 months after RP. PSA at time of failure was <0.10 ng/mL for 65.7% of men. Of those with a delayed detectable PSA, 46% underwent salvage treatment within 10 years after RP at a median PSA of 0.08 ng/mL (IQR 0.05, 0.14). High CAPRA-S score (HR 1.09, CI 1.02-1.17, p = 0.02) and PSA doubling time (PSA-DT) of <6 months (HR 7.58, CI 5.42-10.6, p < 0.01) were associated with receiving salvage treatment. After salvage treatment, 62% of men had recurrent PSA failure within 10 years. Overall, MFS was 92%, PCSM 3%, and ACM 6% at 10 years. For those who received tertiary treatment for recurrent PSA failure, MFS was 54%, PCSM 23% and ACM 23% at 10 years' time.

CONCLUSIONS

Men who develop a detectable PSA > 6 months post-operatively may have excellent long-term outcomes, even in the absence of salvage therapy.

Clinical-Genomic Risk Group Classification of Suspicious Lesions on Prostate Multiparametric-MRI

The utilization of multi-parametric MRI (mpMRI) in clinical decisions regarding prostate cancer patients' management has recently increased. After biopsy, clinicians can assess risk using National Comprehensive Cancer Network (NCCN) risk stratification schema and commercially available genomic classifiers, such as Decipher. We built radiomics-based models to predict lesions/patients at low risk prior to biopsy based on an established three-tier clinical-genomic classification system. Radiomic features were extracted from regions of positive biopsies and Normally Appearing Tissues (NAT) on T2-weighted and Diffusion-weighted Imaging. Using only clinical information available prior to biopsy, five models for predicting low-risk lesions/patients were evaluated, based on: 1: Clinical variables; 2: Lesion-based radiomic features; 3: Lesion and NAT radiomics; 4: Clinical and lesion-based radiomics; and 5: Clinical, lesion and NAT radiomic features. Eighty-three mpMRI exams from 78 men were analyzed. Models 1 and 2 performed similarly (Area under the receiver operating characteristic curve were 0.835 and 0.838, respectively), but radiomics significantly improved the lesion-based performance of the model in a subset analysis of patients with a negative Digital Rectal Exam (DRE). Adding normal tissue radiomics significantly improved the performance in all cases. Similar patterns were observed on patient-level models. To the best of our knowledge, this is the first study to demonstrate that machine learning radiomics-based models can predict patients' risk using combined clinical-genomic classification.

Genomic Classifier Performance in Intermediate-Risk Prostate Cancer: Results From NRG Oncology/RTOG 0126 Randomized Phase 3 Trial

PURPOSE

Intermediate-risk prostate cancer is a heterogeneous disease state with diverse treatment options. The 22-gene Decipher genomic classifier (GC) retrospectively has shown to improve risk stratification in these patients. We assessed the performance of the GC in men with intermediate-risk disease enrolled in NRG Oncology/RTOG 01-26 with updated follow-up.

METHODS AND MATERIALS

After National Cancer Institute approval, biopsy slides were collected from NRG Oncology/RTOG 01-26, a randomized phase 3 trial of men with intermediate-risk prostate cancer randomized to 70.2 Gy versus 79.2 Gy of radiation therapy without androgen deprivation therapy. RNA was extracted from the highest-grade tumor foci to generate the locked 22-gene GC model. The primary endpoint for this ancillary project was disease progression (composite of biochemical failure, local failure, distant metastasis, prostate cancer-specific mortality, and use of salvage therapy). Individual endpoints were also assessed. Fine-Gray or cause-specific Cox multivariable models were constructed adjusting for randomization arm and trial stratification factors.

RESULTS

Two-hundred fifteen patient samples passed quality control for analysis. The median follow-up was 12.8 years (range, 2.4-17.7). On multivariable analysis, the 22-gene GC (per 0.1 unit) was independently prognostic for disease progression (subdistribution hazard ratio [sHR], 1.12; 95% confidence interval [CI], 1.00-1.26; P = .04), biochemical failure (sHR, 1.22; 95% CI, 1.10-1.37; P < .001), distant metastasis (sHR, 1.28; 95% CI, 1.06-1.55; P = .01), and prostate cancer-specific mortality (sHR, 1.45; 95% CI, 1.20-1.76; P < .001). Ten-year distant metastasis in GC low-risk patients was 4% compared with 16% for GC high-risk patients. In patients with lower GC scores, the 10-year difference in metastasis-free survival rate between arms was -7%, compared with 21% for higher GC patients (P-interaction = .04).

CONCLUSIONS

This study represents the first validation of a biopsy-based gene expression classifier, assessing both its prognostic and predictive value, using data from a randomized phase 3 trial of intermediate-risk prostate cancer. Decipher improves risk stratification and can aid in treatment decision-making in men with intermediate-risk disease.

Predicting Prostate Cancer Molecular Subtype With Deep Learning on Histopathologic Images

Microscopic examination of prostate cancer has failed to reveal a reproducible association between molecular and morphologic features. However, deep-learning algorithms trained on hematoxylin and eosin (H&E)-stained whole slide images (WSI) may outperform the human eye and help to screen for clinically-relevant genomic alterations. We created deep-learning algorithms to identify prostate tumors with underlying ETS-related gene (ERG) fusions or PTEN deletions using the following 4 stages: (1) automated tumor identification, (2) feature representation learning, (3) classification, and (4) explainability map generation. A novel transformer-based hierarchical architecture was trained on a single representative WSI of the dominant tumor nodule from a radical prostatectomy (RP) cohort with known ERG/PTEN status (n = 224 and n = 205, respectively). Two distinct vision transformer-based networks were used for feature extraction, and a distinct transformer-based model was used for classification. The ERG algorithm performance was validated across 3 RP cohorts, including 64 WSI from the pretraining cohort (AUC, 0.91) and 248 and 375 WSI from 2 independent RP cohorts (AUC, 0.86 and 0.89, respectively). In addition, we tested the ERG algorithm performance in 2 needle biopsy cohorts comprised of 179 and 148 WSI (AUC, 0.78 and 0.80, respectively). Focusing on cases with homogeneous (clonal) PTEN status, PTEN algorithm performance was assessed using 50 WSI reserved from the pretraining cohort (AUC, 0.81), 201 and 337 WSI from 2 independent RP cohorts (AUC, 0.72 and 0.80, respectively), and 151 WSI from a needle biopsy cohort (AUC, 0.75). For explainability, the PTEN algorithm was also applied to 19 WSI with heterogeneous (subclonal) PTEN loss, where the percentage tumor area with predicted PTEN loss correlated with that based on immunohistochemistry (r = 0.58, P = .0097). These deep-learning algorithms to predict ERG/PTEN status prove that H&E images can be used to screen for underlying genomic alterations in prostate cancer.

European Association of Urology biochemical recurrence risk groups after radical prostatectomy: External validation and identification of independent risk factors for progression and death

BACKGROUND

The EAU proposed a progression and death risk classification in patients with biochemical recurrence after radical prostatectomy (PR).

OBJECTIVE

To validate the EAU BCR-risk classification in our setting and to find factors related to progression and death.

MATERIAL AND METHODS

Multicenter, retrospective, observational study including 2140 patients underwent RP between 2011 and 2015. Patients with BCR were identified and stratified in low risk (PSA-DT >1yr and pGS <8) or high-risk (PSA-DT ≤1yr or pGS ≥8) grouping. PSA and metastatic free survival (PSA-PFS, MFS), cancer specific survival (CSS) and overall survival (OS) were calculated (Kaplan Meier curves and log-rank test). Independent risk factors were identified (Cox regression).

RESULTS

427 patients experienced BCR (32.3% low-risk and 67.7% high-risk). Median PSA-PFS was 135,0 mo (95% CI 129,63-140,94) and 115,0 mo (95% CI 104,02-125,98) (p<0,001), for low and high-risk groups, respectively. There were also significant differences in MFS and OS. The EAU BCR risk grouping was independent factor for PSA-progression (HR 2.55, p 0.009). Time from PR to BCR, was an independent factor for metastasis onset (HR 0.43, 95% CI 0.18-0.99; p 0.044) and death (HR 0.17, 95% CI 0.26.0.96; 23 p 0.048). Differences in MFS (p 0.001) and CSS (p 0.004) were found for <12, ≥12-<36 and ≥36 months from PR to BCR. Others independent factors were early salvage radiotherapy and PSA at BCR.

CONCLUSIONS

High-risk group is a prognostic factor for biochemical progression, but it has a limited accuracy on MP and death in our setting. The inclusion of other factors could increase its predictive power.

Use of the Decipher genomic classifier among men with prostate cancer in the United States

BACKGROUND

Management of localized or recurrent prostate cancer since the 1990s has been based on risk stratification using clinicopathological variables, including Gleason score, T stage (based on digital rectal exam), and prostate-specific antigen (PSA). In this study a novel prognostic test, the Decipher Prostate Genomic Classifier (GC), was used to stratify risk of prostate cancer progression in a US national database of men with prostate cancer.

METHODS

Records of prostate cancer cases from participating SEER (Surveillance, Epidemiology, and End Results) program registries, diagnosed during the period from 2010 through 2018, were linked to records of testing with the GC prognostic test. Multivariable analysis was used to quantify the association between GC scores or risk groups and use of definitive local therapy after diagnosis in the GC biopsy-tested cohort and postoperative radiotherapy in the GC-tested cohort as well as adverse pathological findings after prostatectomy.

RESULTS

A total of 572 545 patients were included in the analysis, of whom 8927 patients underwent GC testing. GC biopsy-tested patients were more likely to undergo active active surveillance or watchful waiting than untested patients (odds ratio [OR] =2.21, 95% confidence interval [CI] = 2.04 to 2.38, P < .001). The highest use of active surveillance or watchful waiting was for patients with a low-risk GC classification (41%) compared with those with an intermediate- (27%) or high-risk (11%) GC classification (P < .001). Among National Comprehensive Cancer Network patients with low and favorable-intermediate risk, higher GC risk class was associated with greater use of local therapy (OR = 4.79, 95% CI = 3.51 to 6.55, P < .001). Within this subset of patients who were subsequently treated with prostatectomy, high GC risk was associated with harboring adverse pathological findings (OR = 2.94, 95% CI = 1.38 to 6.27, P = .005). Use of radiation after prostatectomy was statistically significantly associated with higher GC risk groups (OR = 2.69, 95% CI = 1.89 to 3.84).

CONCLUSIONS

There is a strong association between use of the biopsy GC test and likelihood of conservative management. Higher genomic classifier scores are associated with higher rates of adverse pathology at time of surgery and greater use of postoperative radiotherapy.In this study the Decipher Prostate Genomic Classifier (GC) was used to analyze a US national database of men with prostate cancer. Use of the GC was associated with conservative management (ie, active surveillance). Among men who had high-risk GC scores and then had surgery, there was a 3-fold higher chance of having worrisome findings in surgical specimens.

Minor intron splicing is critical for survival of lethal prostate cancer

The evolutionarily conserved minor spliceosome (MiS) is required for protein expression of ∼714 minor intron-containing genes (MIGs) crucial for cell-cycle regulation, DNA repair, and MAP-kinase signaling. We explored the role of MIGs and MiS in cancer, taking prostate cancer (PCa) as an exemplar. Both androgen receptor signaling and elevated levels of U6atac, a MiS small nuclear RNA, regulate MiS activity, which is highest in advanced metastatic PCa. siU6atac-mediated MiS inhibition in PCa in vitro model systems resulted in aberrant minor intron splicing leading to cell-cycle G1 arrest. Small interfering RNA knocking down U6atac was ∼50% more efficient in lowering tumor burden in models of advanced therapy-resistant PCa compared with standard antiandrogen therapy. In lethal PCa, siU6atac disrupted the splicing of a crucial lineage dependency factor, the RE1-silencing factor (REST). Taken together, we have nominated MiS as a vulnerability for lethal PCa and potentially other cancers.

A novel prostate cancer subtyping classifier based on luminal and basal phenotypes

BACKGROUND

Prostate cancer (PCa) is a clinically heterogeneous disease. The creation of an expression-based subtyping model based on prostate-specific biological processes was sought.

METHODS

Unsupervised machine learning of gene expression profiles from prospectively collected primary prostate tumors (training, n = 32,000; evaluation, n = 68,547) was used to create a prostate subtyping classifier (PSC) based on basal versus luminal cell expression patterns and other gene signatures relevant to PCa biology. Subtype molecular pathways and clinical characteristics were explored in five other clinical cohorts.

RESULTS

Clustering derived four subtypes: luminal differentiated (LD), luminal proliferating (LP), basal immune (BI), and basal neuroendocrine (BN). LP and LD tumors both had higher androgen receptor activity. LP tumors also had a higher expression of cell proliferation genes, MYC activity, and characteristics of homologous recombination deficiency. BI tumors possessed significant interferon γactivity and immune infiltration on immunohistochemistry. BN tumors were characterized by lower androgen receptor activity expression, lower immune infiltration, and enrichment with neuroendocrine expression patterns. Patients with LD tumors had less aggressive tumor characteristics and the longest time to metastasis after surgery. Only patients with BI tumors derived benefit from radiotherapy after surgery in terms of time to metastasis (hazard ratio [HR], 0.09; 95% CI, 0.01-0.71; n = 855). In a phase 3 trial that randomized patients with metastatic PCa to androgen deprivation with or without docetaxel (n = 108), only patients with LP tumors derived survival benefit from docetaxel (HR, 0.21; 95% CI, 0.09-0.51).

CONCLUSIONS

With the use of expression profiles from over 100,000 tumors, a PSC was developed that identified four subtypes with distinct biological and clinical features.

PLAIN LANGUAGE SUMMARY

Prostate cancer can behave in an indolent or aggressive manner and vary in how it responds to certain treatments. To differentiate prostate cancer on the basis of biological features, we developed a novel RNA signature by using data from over 100,000 prostate tumors-the largest data set of its kind. This signature can inform patients and physicians on tumor aggressiveness and susceptibilities to treatments to help personalize cancer management.

Distinct mesenchymal cell states mediate prostate cancer progression

Alterations in tumor stroma influence prostate cancer progression and metastatic potential. However, the molecular underpinnings of this stromal-epithelial crosstalk are largely unknown. Here, we compare mesenchymal cells from four genetically engineered mouse models (GEMMs) of prostate cancer representing different stages of the disease to their wild-type (WT) counterparts by single-cell RNA sequencing (scRNA-seq) and, ultimately, to human tumors with comparable genotypes. We identified 8 transcriptionally and functionally distinct stromal populations responsible for common and GEMM-specific transcriptional programs. We show that stromal responses are conserved in mouse models and human prostate cancers with the same genomic alterations. We noted striking similarities between the transcriptional profiles of the stroma of murine models of advanced disease and those of of human prostate cancer bone metastases. These profiles were then used to build a robust gene signature that can predict metastatic progression in prostate cancer patients with localized disease and is also associated with progression-free survival independent of Gleason score. Taken together, this offers new evidence that stromal microenvironment mediates prostate cancer progression, further identifying tissue-based biomarkers and potential therapeutic targets of aggressive and metastatic disease.

Using biological constraints to improve prediction in precision oncology

Many gene signatures have been developed by applying machine learning (ML) on omics profiles, however, their clinical utility is often hindered by limited interpretability and unstable performance. Here, we show the importance of embedding prior biological knowledge in the decision rules yielded by ML approaches to build robust classifiers. We tested this by applying different ML algorithms on gene expression data to predict three difficult cancer phenotypes: bladder cancer progression to muscle-invasive disease, response to neoadjuvant chemotherapy in triple-negative breast cancer, and prostate cancer metastatic progression. We developed two sets of classifiers: mechanistic, by restricting the training to features capturing specific biological mechanisms; and agnostic, in which the training did not use any a priori biological information. Mechanistic models had a similar or better testing performance than their agnostic counterparts, with enhanced interpretability. Our findings support the use of biological constraints to develop robust gene signatures with high translational potential.

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.

Distinct Profiles of DNA Repair Activity Define Favorable-risk Prostate Cancer Subtypes With Divergent Outcome

INTRODUCTION

Understanding if divergent molecular profiles of DNA damage and repair (DDR) pathway activity, a biomarker of disease progression, exist in prostate tumors with favorable-risk features is an unmet need, which this study aim to unearth.

MATERIALS AND METHODS

This was a multicenter registry genome-wide expression profiling study of prospectively collected radical prostatectomy (RP) tumor samples from 2014 to 2016. DDR activity was calculated from average expression of 372 DDR genes. Consensus hierarchical clustering was used to arrive at a robust clustering solution based on DDR gene expression patterns. Genome-wide differential expression between clusters was performed, and outcomes were evaluated across expression patterns.

RESULTS

Of 5239 patients from the prospective registry, 376 had favorable-risk disease (Grade group [GG] 1 to 2, PSA prior to RP <10ng/ml, pT2 or less). DDR activity score was correlated with prognostic genomic signatures that predict for metastatic risk (r = 0.37, P < 2e^-16) and high grade groups (P < .001). High DDR activity (top-quartile) was observed in 28% of patients with favorable-risk disease. In favorable-risk disease, 3 distinct clusters with varied DDR activity emerged with consensus clustering. Cluster I (compared with cluster II-III and GG3-GG5 disease) had the highest expression of all DDR sub-pathways, MYC, PAPR1, AR, and AR activity (P < .001 for all). Furthermore, cluster I was associated with poorer metastasis-free survival (MFS) and Overall survival (OS) compared with other clusters (MFS; HR: 2.43, 95%CI, [1.22-4.83], P = .01; OS; HR: 2.77, 95%CI, [1.18-6.5], P = .01).

CONCLUSIONS

Cluster I is a novel subgroup of favorable-risk disease with high DDR activity, AR activity, PARP1 and chr8q/MYC expression, and poorer MFS and OS.

Prostate Cancer Tumor Volume and Genomic Risk

Background

Despite the historic association of higher prostate cancer volume with worse oncologic outcomes, little is known about the impact of tumor volume on cancer biology.

Objective

To characterize the relationship between tumor volume (measured by percent positive cores [PPC]) and cancer biology (measured by Decipher genomic risk classifier [GC]) in men who underwent prostate biopsy.

Design setting and participants

Prostate biopsies from 52 272 men profiled with Decipher captured in a population-based prospective tumor registry were collected from 2016 to 2021.

Outcome measurements and statistical analysis

The degree of distribution and correlation of PPC with a GC score across grade group (GG) strata were examined using the Mann-Whitney U test, Pearson correlation coefficient, and multivariable linear regression controlled for clinicopathologic characteristics.

Results and limitations

A total of 38 921 (74%) biopsies passed quality control (14 331 GG1, 16 159 GG2, 5661 GG3, 1775 GG4, and 995 GG5). Median PPC and GC increased with sequentially higher GG. There was an increasingly positive correlation (r) between PPC and GC in GG2-5 prostate cancer (r [95% confidence interval {CI}]: 0.07 [0.5, 0.8] in GG2, 0.15 [0.12, 0.17] in GG3, 0.20 [0.15, 0.24] in GG4, and 0.25 [0.19, 0.31] in GG5), with no correlation in GG1 disease (r = 0.01, 95% CI [-0.001, 0.03]). In multivariable linear regression, GC was significantly associated with higher PPC for GG2-5 (all p < 0.05) but was not significantly associated with PPC for GG1. Limitations include retrospective design and a lack of final pathology from radical prostatectomy specimens.

Conclusions

Higher tumor volume was associated with worse GC for GG2-5 prostate cancer, whereas tumor volume was not associated with worse GC for GG1 disease. The finding that tumor volume is not associated with worse cancer biology in GG1 disease encourages active surveillance for most patients irrespective of tumor volume.

Patient summary

We studied the relationship between prostate cancer tumor volume and cancer biology, as measured by the Decipher genomic risk score, in men who underwent prostate biopsy. We found that tumor volume was not associated with worse cancer biology for low-grade prostate cancer. Our findings reassuringly support recent guidelines to recommend active surveillance for grade group 1 prostate cancer in most patients, irrespective of tumor volume.

Transcriptomes of Prostate Cancer with TMPRSS2:ERG and Other ETS Fusions

The most common somatic event in primary prostate cancer is a fusion between the androgen-related TMPRSS2 gene and the ERG oncogene. Tumors with these fusions, which occur early in carcinogenesis, have a distinctive etiology. A smaller subset of other tumors harbor fusions between TMPRSS2 and members of the ETS transcription factor family other than ERG. To assess the genomic similarity of tumors with non-ERG ETS fusions and those with fusions involving ERG, this study derived a transcriptomic signature of non-ERG ETS fusions and assessed this signature and ERG-related gene expression in 1,050 men with primary prostate cancer from three independent population-based and hospital-based studies. Although non-ERG ETS fusions involving ETV1, ETV4, ETV5, or FLI1 were individually rare, they jointly accounted for one in seven prostate tumors. Genes differentially regulated between non-ERG ETS tumors and tumors without ETS fusions showed similar differential expression when ERG tumors and tumors without ETS fusions were compared (differences explained: R2 = 69-77%), including ETS-related androgen receptor (AR) target genes. Differences appeared to result from similarities among ETS tumors rather than similarities among non-ETS tumors. Gene sets associated with ERG fusions were consistent with gene sets associated with non-ERG ETS fusions, including fatty acid and amino acid metabolism, an observation that was robust across cohorts.

IMPLICATIONS

Considering ETS fusions jointly may be useful for etiologic studies on prostate cancer, given that the transcriptome is profoundly impacted by ERG and non-ERG ETS fusions in a largely similar fashion, most notably genes regulating metabolic pathways.

Localization of macrophage subtypes and neutrophils in the prostate tumor microenvironment and their association with prostate cancer racial disparities

BACKGROUND

Black men are two to three times more likely to die from prostate cancer (PCa) than White men. This disparity is due in part to discrepancies in socioeconomic status and access to quality care. Studies also suggest that differences in the prevalence of innate immune cells and heightened function in the tumor microenvironment of Black men may promote PCa aggressiveness.

METHODS

We evaluated the spatial localization of and quantified CD66ce⁺ neutrophils by immunohistochemistry and CD68⁺ (pan), CD80⁺ (M1), and CD163⁺ (M2) macrophages by RNA in situ hybridization on formalin-fixed paraffin-embedded tissues from organ donor "normal" prostate (n = 9) and radical prostatectomy (n = 38) tissues from Black and White men. Neutrophils were quantified in PCa and matched benign tissues in tissue microarray (TMA) sets comprised of 560 White and 371 Black men. Likewise, macrophages were quantified in TMA sets comprised of tissues from 60 White and 120 Black men. The phosphatase and tensin homolog (PTEN) and ETS transcription factor ERG (ERG) expression status of each TMA PCa case was assessed via immunohistochemistry. Finally, neutrophils and macrophage subsets were assessed in a TMA set comprised of distant metastatic PCa tissues collected at autopsy (n = 6) sampled across multiple sites.

RESULTS

CD66ce⁺ neutrophils were minimal in normal prostates, but were increased in PCa compared to benign tissues, in low grade compared to higher grade PCa, in PCa tissues from White compared to Black men, and in PCa with PTEN loss or ERG positivity. CD163⁺ macrophages were the predominant macrophage subset in normal organ donor prostate tissues from both Black and White men and were significantly more abundant in organ donor compared to prostatectomy PCa tissues. CD68,⁺  CD80,⁺ and CD163⁺ macrophages were significantly increased in cancer compared to benign tissues and in cancers with ERG positivity. CD68⁺ and CD163⁺ macrophages were increased in higher grade cancers compared to low grade cancer and CD80 expression was significantly higher in benign prostatectomy tissues from Black compared to White men.

CONCLUSIONS

Innate immune cell infiltration is increased in the prostate tumor microenvironment of both Black and White men, however the composition of innate immune cell infiltration may vary between races.

Transcriptomic Features of Cribriform and Intraductal Carcinoma of the Prostate

BACKGROUND

Cribriform (CF) and/or intraductal carcinoma (IDC) are associated with more aggressive prostate cancer (CaP) and worse outcomes.

OBJECTIVE

The transcriptomic features that typify CF/IDC are not well described and the capacity for clinically utilized genomic classifiers to improve risk modeling for CF/IDC remains undefined.

DESIGN, SETTING, AND PARTICIPANTS

We performed a retrospective review of CaP patients who had Decipher testing at a single high-volume institution. Index lesions from radical prostatectomy specimens were identified by genitourinary pathologists who simultaneously reviewed prostatectomy specimens for the presence of CF and IDC features. Patients were grouped based on pathologic features, specifically the absence of CF/IDC (CF-/IDC-), CF positive only (CF+/IDC-), and CF/IDC positive (CF+/IDC+).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Clinical, pathologic, and genomic categorical variables were assessed using the Pearson chi-square test, while quantitative variables were assessed with the Kruskal-Wallis test. Multivariable logistic regression was used to identify the predictors of high-risk Decipher scores (>0.60). A gene set enrichment analysis was performed to identify genes and gene networks associated with CF/IDC status.

RESULTS AND LIMITATIONS

A total of 463 patients were included. Patients who were CF+/IDC+ had the highest Decipher risk scores (CF+/IDC+: 0.79 vs CF+/IDC-: 0.71 vs CF-/IDC-: 0.56, p < 0.001). On multivariate logistic regression, predictors of high-risk Decipher scores included the presence of CF, both alone (CF+/IDC-; odds ratio [OR]: 5.45, p < 0.001) or in combination with positive IDC status (CF+/IDC+; OR: 6.87, p < 0.001). On the gene set enrichment analysis, MYC pathway upregulation was significantly enriched in tumor samples from CF/IDC-positive patients (normalized enrichment score [NES]: 1.65, p = 0.046). Other enriched pathways included E2F targets (NES: 1.69, p = 0.031) and oxidative phosphorylation (NES: 1.68, =0 .033).

CONCLUSIONS

This is the largest series identifying an association between a clinically validated genomic classifier and the presence of CF and IDC at radical prostatectomy. Tumors with CF and intraductal features were associated with aggressive transcriptomic signatures.

PATIENT SUMMARY

Genomic-based tests are becoming readily available for the management of prostate cancer. We observed that Decipher, a commonly used genomic test in prostate cancer, correlates with unfavorable features in tissue specimens.

Comprehensive methylome sequencing reveals prognostic epigenetic biomarkers for prostate cancer mortality

BACKGROUND

Prostate cancer is a clinically heterogeneous disease with a subset of patients rapidly progressing to lethal-metastatic prostate cancer. Current clinicopathological measures are imperfect predictors of disease progression. Epigenetic changes are amongst the earliest molecular changes in tumourigenesis. To find new prognostic biomarkers to enable earlier intervention and improved outcomes, we performed methylome sequencing of DNA from patients with localised prostate cancer and long-term clinical follow-up.

METHODS

We used whole-genome bisulphite sequencing (WGBS) to comprehensively map and compare DNA methylation of radical prostatectomy tissue between patients with lethal disease (n = 7) and non-lethal (n = 8) disease (median follow-up 19.5 years). Validation of differentially methylated regions (DMRs) was performed in an independent cohort (n = 185, median follow-up 15 years) using targeted multiplex bisulphite sequencing of candidate regions. Survival was assessed via univariable and multivariable analyses including clinicopathological measures (log-rank and Cox regression models).

RESULTS

WGBS data analysis identified cancer-specific methylation patterns including CpG island hypermethylation, and hypomethylation of repetitive elements, with increasing disease risk. We identified 1420 DMRs associated with prostate cancer-specific mortality (PCSM), which showed enrichment for gene sets downregulated in prostate cancer and de novo methylated in cancer. Through comparison with public prostate cancer datasets, we refined the DMRs to develop an 18-gene prognostic panel. Applying this panel to an independent cohort, we found significant associations between PCSM and hypermethylation at EPHB3, PARP6, TBX1, MARCH6 and a regulatory element within CACNA2D4. Strikingly in a multivariable model, inclusion of CACNA2D4 methylation was a better predictor of PCSM versus grade alone (Harrell's C-index: 0.779 vs. 0.684).

CONCLUSIONS

Our study provides detailed methylome maps of non-lethal and lethal prostate cancer and identifies novel genic regions that distinguish these patient groups. Inclusion of our DNA methylation biomarkers with existing clinicopathological measures improves prognostic models of prostate cancer mortality, and holds promise for clinical application.

The prostate tissue‐based telomere biomarker as a prognostic tool for metastasis and death from prostate cancer after prostatectomy

Current biomarkers are inadequate prognostic predictors in localized prostate cancer making treatment decision‐making challenging. Previously, we observed that the combination of more variable telomere length among prostate cancer cells and shorter telomere length in prostate cancer‐associated stromal cells – the telomere biomarker – is strongly associated with progression to metastasis and prostate cancer death after prostatectomy independent of currently used pathologic indicators. Here, we optimized our method allowing for semi‐automated telomere length determination in single cells in fixed tissue, and tested the telomere biomarker in five cohort studies of men surgically treated for clinically localized disease (N = 2,255). We estimated the relative risk (RR) of progression to metastasis (N = 311) and prostate cancer death (N = 85) using models appropriate to each study's design adjusting for age, prostatectomy stage, and tumor grade, which then we meta‐analyzed using inverse variance weights. Compared with men who had less variable telomere length among prostate cancer cells and longer telomere length in prostate cancer‐associated stromal cells, men with the combination of more variable and shorter telomere length had 3.76 times the risk of prostate cancer death (95% confidence interval [CI] 1.37–10.3, p = 0.01) and had 2.23 times the risk of progression to metastasis (95% CI 0.99–5.02, p = 0.05). The telomere biomarker was associated with prostate cancer death in men with intermediate risk disease (grade groups 2/3: RR = 9.18, 95% CI 1.14–74.0, p = 0.037) and with PTEN protein intact tumors (RR = 6.74, 95% CI 1.46–37.6, p = 0.015). In summary, the telomere biomarker is robust and associated with poor outcome independent of current pathologic indicators in surgically treated men.

The Prognostic Value of Liquid Biopsies for Benefit of Salvage Radiotherapy in Relapsed Oligometastatic Prostate Cancer

Simple Summary

Around 30% of patients with oligometastatic prostate cancer relapse will benefit from local PET/CT-guided ablative radiotherapy (RT) with improved progression-free and ADT (Androgene Deprivation Therapy)-free survivals. Therefore, there is an urgent need for predictive testing for therapeutic benefits prior to initiation. Various tests have already been established on tumor specimens for the prediction of prostate cancer’s behavior or therapy outcome. However, in imaging-proven relapse tumor tissue from the local recurrence or metastases is often not available. Hence, there is a need for a liquid biopsy-based testing. We aimed to assess the prognostic value of CTCs- associated mRNA and blood-derived RNA for the benefit of PSMA PET-guided salvage RT in oligometastatic prostate cancer relapses. Significant correlations were found between the relative transcript levels of several investigated genes and clinicopathological parameters. Furthermore, distinct “transcriptional signatures” were found in patients with temporary and long-term benefits from RT.

Abstract

To assess the prognostic value of “liquid biopsies” for the benefit of salvage RT in oligometastatic prostate cancer relapse, we enrolled 44 patients in the study between the years 2016 and 2020. All the patients were diagnosed as having an oligometastatic prostate cancer relapse on prostate-specific membrane antigen (PSMA)-targeted PET-CT and underwent irradiation at the Department of Radiotherapy at the Hannover Medical School. Tumor cells and total RNA, enriched from the liquid biopsies of patients, were processed for the subsequent quantification analysis of relative transcript levels in real-time PCR. In total, 54 gene transcripts known or suggested to be associated with prostate cancer or treatment outcome were prioritized for analysis. We found significant correlations between the relative transcript levels of several investigated genes and the Gleason score, PSA (prostate-specific antigen) value, or UICC stage (tumor node metastasis -TNM classification of malignant tumors from Union for International Cancer Control). Furthermore, a significant association of MTCO2, FOXM1, SREBF1, HOXB7, FDXR, and MTRNR transcript profiles was found with a temporary and/or long-term benefit from RT. Further studies on larger patients cohorts are necessary to prove our preliminary findings for establishing liquid biopsy tests as a predictive examination method prior to salvage RT.

Genetic Testing and Its Clinical Application in Prostate Cancer Management: Consensus Statements from the Hong Kong Urological Association and Hong Kong Society of Uro-Oncology

Background

In recent years, indications for genetic testing in prostate cancer (PC) have expanded from patients with a family history of prostate and/or related cancers to those with advanced castration-resistant disease, and even to early PC patients for determination of the appropriateness of active surveillance. The current consensus aims to provide guidance to urologists, oncologists and pathologists working with Asian PC patients on who and what to test for in selected populations.

Methods

A joint consensus panel from the Hong Kong Urological Association and Hong Kong Society of Uro-Oncology was convened over a series of 5 physical and virtual meetings. A background literature search on genetic testing in PC was performed in PubMed, ClinicalKey, EBSCOHost, Ovid and ProQuest, and three working subgroups were formed to review and present the relevant evidence. Meeting agendas adopted a modified Delphi approach to ensure that discussions proceed in a structured, iterative and balanced manner, which was followed by an anonymous voting on candidate statements. Of 5 available answer options, a consensus statement was accepted if ≥ 75% of the panelists chose “Accept Completely” (Option A) or “Accept with Some Reservation” (Option B).

Results

The consensus was structured into three parts: indications for testing, testing methods, and therapeutic implications. A list of 35 candidate statements were developed, of which 31 were accepted. The statements addressed questions on the application of PC genetic testing data and guidelines to Asian patients, including patient selection for germline testing, selection of gene panel and tissue sample, provision of genetic counseling, and use of novel systemic treatments in metastatic castration-resistant PC patients.

Conclusion

This consensus provides guidance to urologists, oncologists and pathologists working with Asian patients on indications for genetic testing, testing methods and technical considerations, and associated therapeutic implications.

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.

Effect of Clinical Parameters on Risk of Death from Cancer after Radical Prostatectomy in Men with Localized and Locally Advanced Prostate Cancer

Background: The study aimed to assess predictors and to identify patients at increased risk of prostate-cancer-specific mortality (CSM) after radical prostatectomy (RP). Methods: A total of 2421 men with localized and locally advanced PCa who underwent RP in 2001−2017 were included in the study. CSM predictors were assessed using multivariate competing risk analysis. Death from other causes was considered a competing event. Cumulative CSM and other-cause mortality (OCM) were calculated in various combinations of predictors. Results: During the median 8 years (interquartile range 4.4−11.7) follow-up, 56 (2.3%) of registered deaths were due to PCa. Cumulative 10 years CSM and OCM was 3.6% (95% CI 2.7−4.7) and 15.9% (95% CI 14.2−17.9), respectively. The strongest predictors of CSM were Grade Group 5 (GG5) (hazard ratio (HR) 19.9, p < 0.0001), lymph node invasion (HR 3.4, p = 0.001), stage pT3b-4 (HR 3.1, p = 0.009), and age (HR 1.1, p = 0.0007). In groups created regarding age, stage, and GG, cumulative 10 years CSM ranged from 0.4−84.9%, whereas OCM varied from 0−43.2%. Conclusions: CSM after RP is related to GGs, pathological stage, age, and combinations of these factors, whereas other-cause mortality is only associated with age. Created CSM and OCM plots can help clinicians identify patients with the most aggressive PCa who could benefit from more intensive or novel multimodal treatment strategies.

Novel Transcriptomic Interactions Between Immune Content and Genomic Classifier Predict Lethal Outcomes in High-grade Prostate Cancer

Grade group 4 and 5 (GG-45) prostate cancer (PCa) patients are at the highest risk of lethal outcomes, yet lack genomic risk stratification for prognosis and treatment selection. Here, we assess whether transcriptomic interactions between tumor immune content score (ICS) and the Decipher genomic classifier can identify most lethal subsets of GG-45 PCa. We utilized whole transcriptome data from 8071 tumor tissue (6071 prostatectomy and 2000 treatment-naïve biopsy samples) to derive four immunogenomic subtypes using ICS and Decipher. When compared across all grade groups, GG-45 samples had the highest proportion of most aggressive subtype-ICS^High/Decipher^High. Subsequent analyses within the GG-45 patient samples (n = 1420) revealed that the ICS^High/Decipher^High subtype was associated with increased genomic radiosensitivity. Additionally, in a multivariable model (n = 335), ICS^High/Decipher^High subtype had a significantly higher risk of distant metastasis (hazard ratio [HR] = 5.41; 95% confidence interval [CI], 2.76-10.6; p ≤  0.0001) and PCa-specific mortality (HR = 10.6; 95% CI, 4.18-26.94; p ≤  0.0001) as compared with ICS^Low/Decipher^Low. The novel immunogenomic subtypes establish a very strong synergistic interaction between ICS and Decipher in identifying GG-45 patients who experience the most lethal outcomes. PATIENT SUMMARY: In this analysis, we identified a novel interaction between the total immune content of prostate tumors and genomic classifier to identify the most lethal subset of patients with grade groups 4 and 5. Our results will aid in the subtyping of aggressive prostate cancer patients who may benefit from combined immune-radiotherapy modalities.

Association of B7-H3 expression with racial ancestry, immune cell density, and androgen receptor activation in prostate cancer

Background

B7 homolog 3 (B7‐H3) is an immunomodulatory molecule that is highly expressed in prostate cancer (PCa) and belongs to the B7 superfamily, which includes PD‐L1. Immunotherapies (antibodies, antibody‐drug conjugates, and chimeric antigen receptor T cells) targeting B7‐H3 are currently in clinical trials; therefore, elucidating the molecular and immune microenvironment correlates of B7‐H3 expression may help to guide trial design and interpretation. The authors tested the interconnected hypotheses that B7‐H3 expression is associated with genetic racial ancestry, immune cell composition, and androgen receptor signaling in PCa.

Methods

An automated, clinical‐grade immunohistochemistry assay was developed by to digitally quantify B7‐H3 protein expression across 2 racially diverse cohorts of primary PCa (1 with previously reported transcriptomic data) and pretreatment and posttreatment PCa tissues from a trial of intensive neoadjuvant hormonal therapy.

Results

B7‐H3 protein expression was significantly lower in self‐identified Black patients and was inversely correlated with the percentage African ancestry. This association with race was independent of the significant association of B7‐H3 protein expression with ERG/ETS and PTEN status. B7‐H3 messenger RNA expression, but not B7‐H3 protein expression, was significantly correlated with regulatory (FOXP3‐positive) T‐cell density. Finally, androgen receptor activity scores were significantly correlated with B7‐H3 messenger RNA expression, and neoadjuvant intensive hormonal therapy was associated with a significant decrease in B7‐H3 protein expression.

Conclusions

The current data underscore the importance of studying racially and molecularly diverse PCa cohorts in the immunotherapy era. This study is among the first to use genetic ancestry markers to add to the emerging evidence that PCa in men of African ancestry may have a distinct biology associated with B7‐H3 expression.

Lay Summary

B7‐H3 is an immunomodulatory molecule that is highly expressed in prostate cancer and is under investigation in clinical trials.

The authors determined that B7‐H3 protein expression is inversely correlated with an individual's proportion of African ancestry.

The results demonstrate that B7‐H3 messenger RNA expression is correlated with the density of tumor T‐regulatory cells.

Finally, in the first paired analysis of B7‐H3 protein expression before and after neoadjuvant intensive hormone therapy, the authors determined that hormone therapy is associated with a decrease in B7‐H3 protein levels, suggesting that androgen signaling may positively regulate B7‐H3 expression.

These results may help to guide the design of future clinical trials and to develop biomarkers of response in such trials.

B7‐H3 protein expression was significantly lower in self‐identified Black patients and was inversely correlated with the percentage African ancestry. Androgen receptor activity scores were significantly correlated with B7‐H3 messenger RNA expression, and neoadjuvant intensive hormonal therapy was associated with a significant decrease in B7‐H3 protein expression, consistent with a presumed androgen receptor binding site upstream of the B7‐H3 promoter.

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.

Prostate cancer biomarkers: a practical review based on different clinical scenarios

Traditionally, diagnosis and staging of prostate cancer (PCa) have been based on prostate-specific antigen (PSA) level, digital rectal examination (DRE), and transrectal ultrasound (TRUS) guided prostate biopsy. Biomarkers have been introduced into clinical practice to reduce the overdiagnosis and overtreatment of low-grade PCa and increase the success of personalized therapies for high-grade and high-stage PCa. The purpose of this review was to describe available PCa biomarkers and examine their use in clinical practice. A nonsystematic literature review was performed using PubMed and Scopus to retrieve papers related to PCa biomarkers. In addition, we manually searched websites of major urological associations for PCa guidelines to evaluate available evidence and recommendations on the role of biomarkers and their potential contribution to PCa decision-making. In addition to PSA and its derivates, thirteen blood, urine, and tissue biomarkers are mentioned in various PCa guidelines. Retrospective studies have shown their utility in three main clinical scenarios: (1) deciding whether to perform a biopsy, (2) distinguishing patients who require active treatment from those who can benefit from active surveillance, and (3) defining a subset of high-risk PCa patients who can benefit from additional therapies after RP. Several validated PCa biomarkers have become commercially available in recent years. Guidelines now recommend offering these tests in situations in which the assay result, when considered in combination with routine clinical factors, is likely to affect management. However, the lack of direct comparisons and the unproven benefits, in terms of long-term survival and cost-effectiveness, prevent these biomarkers from being integrated into routine clinical use.

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

Simple Summary

Most men with prostate cancer will live as long as those who do not have prostate cancer. However, some men will die early of their disease due to a particular type of prostate cancer associated with recurrence and metastasis: intraductal carcinoma of the prostate. In this review, we discuss the associations between intraductal carcinoma of the prostate and metastasis, and the contemporary knowledge about the molecular alterations of intraductal carcinoma of the prostate.

Abstract

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.

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.

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.

Molecular Characterization of Prostate Cancers in the Precision Medicine Era

Prostate cancer (PCa) therapy has been recently revolutionized by the approval of new therapeutic agents in the metastatic setting. However, the optimal therapeutic strategy in such patients should be individualized in the light of prognostic and predictive molecular factors, which have been recently studied: androgen receptor (AR) alterations, PTEN-PI3K-AKT pathway deregulation, homologous recombination deficiency (HRD), mismatch repair deficiency (MMRd), and tumor microenvironment (TME) modifications. In this review, we highlighted the clinical impact of prognostic and predictive molecular factors in PCa patients' outcomes, identifying biologically distinct subtypes. We further analyzed the relevant methods to detect these factors, both on tissue, i.e., immunohistochemistry (IHC) and molecular tests, and blood, i.e., analysis of circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA). Moreover, we discussed the main pros and cons of such techniques, depicting their present and future roles in PCa management, throughout the precision medicine era.

Subpathologies and genomic classifier for treatment individualization of post-prostatectomy radiotherapy

PURPOSE/OBJECTIVE

Risk-stratification for post-prostatectomy radiotherapy (PORT) using conventional clinicopathologic indexes leads to substantial over- and under-treatment. Better patient selection could spare unnecessary toxicities and improve outcomes. We investigated the prognostic utility of unfavorable subpathologies intraductal carcinoma and cribriform architecture (IDC/CA), and a 22-gene Decipher genomic classifier (GC) in prostate cancer (PCa) patients receiving PORT.

MATERIAL/METHODS

A cohort of 302 men who received PORT at 2 academic institutions was pooled. PORT was predominately delivered as salvage (62% of cases); 20% received HT+PORT. Specimens were centrally reviewed for IDC/CA presence. In 104 cases, GC scores were determined. Endpoints were biochemical relapse-free (bRFR) and metastasis-free (mFR) rates.

RESULTS

After a median follow-up of 6.49-years, 135 (45%) and 40 (13%) men experienced biochemical relapse and metastasis, respectively. IDC/CA were identified in 160 (53%) of cases. Men harboring IDC/CA experienced inferior bRFR (HR 2.6, 95%CI 1.8-3.2, P<0.001) and mFR (HR 3.1, 95%CI 1.5-6.4, P = 0.0014). Patients with GC scores, 22 (21%) were stratified low-, 30 (29%) intermediate-, and 52 (50%) high-risk. GC low-risk was associated with superior bRFR (HR 0.25, 95%CI 0.1-0.5, P<0.001) and mFR (HR 0.15, 95%CI 0.03-0.8, P = 0.025). On multivariable analyses, IDC/CA and GC independently predicted for bRFR, corresponding to improved discrimination (C-index = 0.737 (95%CI 0.662-0.813)).

CONCLUSIONS

IDC/CA subpathologies and GC predict for biochemical relapse and metastasis beyond conventional clinicopathologic indexes in the PORT setting. Patients harboring IDC/CA are at higher risk of relapse after maximal local therapies, thus warranting consideration for treatment intensification strategies. Conversely, for men with absence of IDC/CA and low GC scores, de-intensification strategies could be explored.

Transcriptional profiling of primary prostate tumor in metastatic hormone-sensitive prostate cancer and association with clinical outcomes: correlative analysis of the E3805 CHAARTED trial

BACKGROUND

The phase III CHAARTED trial established upfront androgen-deprivation therapy (ADT) plus docetaxel (D) as a standard for metastatic hormone-sensitive prostate cancer (mHSPC) based on meaningful improvement in overall survival (OS). Biological prognostic markers of outcomes and predictors of chemotherapy benefit are undefined.

PATIENTS AND METHODS

Whole transcriptomic profiling was performed on primary PC tissue obtained from patients enrolled in CHAARTED prior to systemic therapy. We adopted an a priori analytical plan to test defined RNA signatures and their associations with HSPC clinical phenotypes and outcomes. Multivariable analyses (MVAs) were adjusted for age, Eastern Cooperative Oncology Group status, de novo metastasis presentation, volume of disease, and treatment arm. The primary endpoint was OS; the secondary endpoint was time to castration-resistant PC.

RESULTS

The analytic cohort of 160 patients demonstrated marked differences in transcriptional profile compared with localized PC, with a predominance of luminal B (50%) and basal (48%) subtypes, lower androgen receptor activity (AR-A), and high Decipher risk disease. Luminal B subtype was associated with poorer prognosis on ADT alone but benefited significantly from ADT + D [OS: hazard ratio (HR) 0.45; P = 0.007], in contrast to basal subtype which showed no OS benefit (HR 0.85; P = 0.58), even in those with high-volume disease. Higher Decipher risk and lower AR-A were significantly associated with poorer OS in MVA. In addition, higher Decipher risk showed greater improvements in OS with ADT + D (HR 0.41; P = 0.015).

CONCLUSION

This study demonstrates the utility of transcriptomic subtyping to guide prognostication in mHSPC and potential selection of patients for chemohormonal therapy, and provides proof of concept for the possibility of biomarker-guided selection of established combination therapies in mHSPC.

Tumour markers of prostate cancer: The post-PSA era

Although PSA-based prostate cancer (PCa) screening had a positive impact in reducing PCa mortality, it also led to overdiagnosis, overtreatment and to a significant number of unnecessary biopsies. In the post-PSA era, new biomarkers have emerged that can complement the information given by PSA, towards a better cancer diagnostic specificity, and also allow a better estimate of the aggressiveness of the disease and its clinical outcome. That means those markers have the potential to assist the clinician in the decision-making processes, such as whether or not to perform a biopsy, and to make the best treatment choice among the new therapeutic options available, including active surveillance (AS) in lower risk disease. In this article, we will review several of those more recent diagnostic markers (4Kscore®, [-2]proPSA and Prostate Health Index (PHI), SelectMDx®, ConfirmMDx®, Progensa® Prostate Cancer Antigen 3, Mi-Prostate Score, ExoDx™ Prostate Test, the Stockholm-3 test and ERSPC risk calculators) and prognostic markers (OncotypeDX® Genomic Prostate Score, Prolaris®, Decipher® and ProMark®). We will also address some new liquid biopsy approaches - circulating tumour cells and cell-free DNA (cfDNA) - with a potential role in metastatic castration-resistant PCa and will briefly give some future perspectives, mostly outlooking epigenetic markers.

Degree of Preservation of Neurovascular Bundles in Radical Prostatectomy and Recurrence of Prostate Cancer

BACKGROUND

Reports on possible benefits for continence with nerve-sparing (NS) radical prostatectomy have expanded the indications beyond preservation of erectile function. It is unclear whether NS surgery affects oncological outcomes.

OBJECTIVE

To determine whether the degree of NS during radical prostatectomy influences oncological outcomes.

DESIGN SETTING AND PARTICIPANTS

Of 4003 patients enrolled in a prospective, controlled trial comparing open and robotic radical prostatectomy during 2008-2011, we evaluated 2401 patients who received robotic radical prostatectomy at seven Swedish centres. Patients were followed for 8 yr.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Data for recurrence and positive surgical margin status were assessed using validated patient questionnaires, patient interviews, and clinical record forms before and at 3, 12, and 24 mo and 6 and 8 yr after surgery. Cox and logistic regressions were used to model the effect on recurrence and positive surgical margins (PSM), respectively.

RESULTS AND LIMITATIONS

A total of 481 men had PSM and 467 experienced recurrence during follow-up. Median follow-up for men without recurrence was 6.6 yr. There were no statistically significant differences in recurrence rate between degrees of NS. The PSM rate was significantly higher with a higher degree of NS: interfascial NS, odds ratio (OR) 2.32 (95% confidence interval [CI] 1.69-3.16); intrafascial NS, OR 3.23 (95% CI 2.17-4.80). Recurrence rates were higher for patients with pT2 disease and PSM (hazard ratio [HR] 3.32, 95% CI 2.43-4.53) than for patients with pT3 disease without PSM (HR 2.08, 95% CI 1.66-2.62). The lack of central review of pathological specimens is a limitation.

CONCLUSIONS

A higher degree of NS significantly increased the risk of PSM but did not significantly increase the risk of cancer recurrence. Combined with the known functional benefits of NS surgery, these results underscore the need to identify an individualised balance.

PATIENT SUMMARY

In this report we looked at the effect of a nerve-sparing approach during removal of the prostate on cancer outcomes for patients having robot-assisted surgery at seven Swedish hospitals. We found that a high degree of nerve-sparing increased the rate of cancer positivity at the margins of surgical specimens and that positive surgical margins increased the risk of recurrence of prostate cancer.

Transcriptional landscape of PTEN loss in primary prostate cancer

BACKGROUND

PTEN is the most frequently lost tumor suppressor in primary prostate cancer (PCa) and its loss is associated with aggressive disease. However, the transcriptional changes associated with PTEN loss in PCa have not been described in detail. In this study, we highlight the transcriptional changes associated with PTEN loss in PCa.

METHODS

Using a meta-analysis approach, we leveraged two large PCa cohorts with experimentally validated PTEN and ERG status by Immunohistochemistry (IHC), to derive a transcriptomic signature of PTEN loss, while also accounting for potential confounders due to ERG rearrangements. This signature was expanded to lncRNAs using the TCGA quantifications from the FC-R2 expression atlas.

RESULTS

The signatures indicate a strong activation of both innate and adaptive immune systems upon PTEN loss, as well as an expected activation of cell-cycle genes. Moreover, we made use of our recently developed FC-R2 expression atlas to expand this signature to include many non-coding RNAs recently annotated by the FANTOM consortium. Highlighting potential novel lncRNAs associated with PTEN loss and PCa progression.

CONCLUSION

We created a PCa specific signature of the transcriptional landscape of PTEN loss that comprises both the coding and an extensive non-coding counterpart, highlighting potential new players in PCa progression. We also show that contrary to what is observed in other cancers, PTEN loss in PCa leads to increased activation of the immune system. These findings can help the development of new biomarkers and help guide therapy choices.

Radiation Therapy After Radical Prostatectomy: What Has Changed Over Time?

The role and timing of radiotherapy (RT) in prostate cancer (PCa) patients treated with radical prostatectomy (RP) remains controversial. While recent trials support the oncological safety of early salvage RT (SRT) compared to adjuvant RT (ART) in selected patients, previous randomized studies demonstrated that ART might improve recurrence-free survival in patients at high risk for local recurrence based on adverse pathology. Although ART might improve survival, this approach is characterized by a risk of overtreatment in up to 40% of cases. SRT is defined as the administration of RT to the prostatic bed and to the surrounding tissues in the patient with PSA recurrence after surgery but no evidence of distant metastatic disease. The delivery of salvage therapies exclusively in men who experience biochemical recurrence (BCR) has the potential advantage of reducing the risk of side effects without theoretically compromising outcomes. However, how to select patients at risk of progression who are more likely to benefit from a more aggressive treatment after RP, the exact timing of RT after RP, and the use of hormone therapy and its duration at the time of RT are still open issues. Moreover, what the role of novel imaging techniques and genomic classifiers are in identifying the most optimal post-operative management of PCa patients treated with RP is yet to be clarified. This narrative review summarizes most relevant published data to guide a multidisciplinary team in selecting appropriate candidates for post-prostatectomy radiation therapy.

Comparative analysis of 1152 African-American and European-American men with prostate cancer identifies distinct genomic and immunological differences

Racial disparities in prostate cancer have not been well characterized on a genomic level. Here we show the results of a multi-institutional retrospective analysis of 1,152 patients (596 African-American men (AAM) and 556 European-American men (EAM)) who underwent radical prostatectomy. Comparative analyses between the race groups were conducted at the clinical, genomic, pathway, molecular subtype, and prognostic levels. The EAM group had increased ERG (P < 0.001) and ETS (P = 0.02) expression, decreased SPINK1 expression (P < 0.001), and basal-like (P < 0.001) molecular subtypes. After adjusting for confounders, the AAM group was associated with higher expression of CRYBB2, GSTM3, and inflammation genes (IL33, IFNG, CCL4, CD3, ICOSLG), and lower expression of mismatch repair genes (MSH2, MSH6) (p < 0.001 for all). At the pathway level, the AAM group had higher expression of genes sets related to the immune response, apoptosis, hypoxia, and reactive oxygen species. EAM group was associated with higher levels of fatty acid metabolism, DNA repair, and WNT/beta-catenin signaling. Based on cell lines data, AAM were predicted to have higher potential response to DNA damage. In conclusion, biological characteristics of prostate tumor were substantially different in AAM when compared to EAM.

Walter Rayford, Alp Tuna Beksac et al. investigated gene expression alterations in African-American and European-American men who underwent radical prostatectomy for prostate cancer. The observed differences include higher expression of inflammation genes and lower expression of mismatch repair genes in African-American men.

The 2019 Genitourinary Pathology Society (GUPS) White Paper on Contemporary Grading of Prostate Cancer

CONTEXT.—

Controversies and uncertainty persist in prostate cancer grading.

OBJECTIVE.—

To update grading recommendations.

DATA SOURCES.—

Critical review of the literature along with pathology and clinician surveys.

CONCLUSIONS.—

Percent Gleason pattern 4 (%GP4) is as follows: (1) report %GP4 in needle biopsy with Grade Groups (GrGp) 2 and 3, and in needle biopsy on other parts (jars) of lower grade in cases with at least 1 part showing Gleason score (GS) 4 + 4 = 8; and (2) report %GP4: less than 5% or less than 10% and 10% increments thereafter. Tertiary grade patterns are as follows: (1) replace "tertiary grade pattern" in radical prostatectomy (RP) with "minor tertiary pattern 5 (TP5)," and only use in RP with GrGp 2 or 3 with less than 5% Gleason pattern 5; and (2) minor TP5 is noted along with the GS, with the GrGp based on the GS. Global score and magnetic resonance imaging (MRI)-targeted biopsies are as follows: (1) when multiple undesignated cores are taken from a single MRI-targeted lesion, an overall grade for that lesion is given as if all the involved cores were one long core; and (2) if providing a global score, when different scores are found in the standard and the MRI-targeted biopsy, give a single global score (factoring both the systematic standard and the MRI-targeted positive cores). Grade Groups are as follows: (1) Grade Groups (GrGp) is the terminology adopted by major world organizations; and (2) retain GS 3 + 5 = 8 in GrGp 4. Cribriform carcinoma is as follows: (1) report the presence or absence of cribriform glands in biopsy and RP with Gleason pattern 4 carcinoma. Intraductal carcinoma (IDC-P) is as follows: (1) report IDC-P in biopsy and RP; (2) use criteria based on dense cribriform glands (>50% of the gland is composed of epithelium relative to luminal spaces) and/or solid nests and/or marked pleomorphism/necrosis; (3) it is not necessary to perform basal cell immunostains on biopsy and RP to identify IDC-P if the results would not change the overall (highest) GS/GrGp part per case; (4) do not include IDC-P in determining the final GS/GrGp on biopsy and/or RP; and (5) "atypical intraductal proliferation (AIP)" is preferred for an intraductal proliferation of prostatic secretory cells which shows a greater degree of architectural complexity and/or cytological atypia than typical high-grade prostatic intraepithelial neoplasia, yet falling short of the strict diagnostic threshold for IDC-P. Molecular testing is as follows: (1) Ki67 is not ready for routine clinical use; (2) additional studies of active surveillance cohorts are needed to establish the utility of PTEN in this setting; and (3) dedicated studies of RNA-based assays in active surveillance populations are needed to substantiate the utility of these expensive tests in this setting. Artificial intelligence and novel grading schema are as follows: (1) incorporating reactive stromal grade, percent GP4, minor tertiary GP5, and cribriform/intraductal carcinoma are not ready for adoption in current practice.

Tissue-Based Biomarkers for the Risk Stratification of Men With Clinically Localized Prostate Cancer

Risk stratification of men with clinically localized prostate cancer has historically relied on basic clinicopathologic parameters such as prostate specific antigen level, grade group, and clinical stage. However, prostate cancer often behaves in ways that cannot be accurately predicted by these parameters. Thus, recent efforts have focused on developing tissue-based genomic tests that provide greater insights into the risk of a given patient's disease. Multiple tests are now commercially available and provide additional prognostic information at various stages of the care pathway for prostate cancer. Indeed, early evidence suggests that these assays may have a significant impact on patient and physician decision-making. However, the impact of these tests on oncologic outcomes remains less clear. In this review, we highlight recent advances in the use of tissue-based biomarkers in the treatment of prostate cancer and identify the existing evidence supporting their clinical use.

Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study

Existing tools for post-radical prostatectomy (RP) prostate cancer biochemical recurrence (BCR) prognosis rely on human pathologist-derived parameters such as tumor grade, with the resulting inter-reviewer variability. Genomic companion diagnostic tests such as Decipher tend to be tissue destructive, expensive, and not routinely available in most centers. We present a tissue non-destructive method for automated BCR prognosis, termed "Histotyping", that employs computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide. Patients from two institutions (n = 214) were used to train Histotyping for identifying high-risk patients based on six features of glandular morphology extracted from RP specimens. Histotyping was validated for post-RP BCR prognosis on a separate set of n = 675 patients from five institutions and compared against Decipher on n = 167 patients. Histotyping was prognostic of BCR in the validation set (p < 0.001, univariable hazard ratio [HR] = 2.83, 95% confidence interval [CI]: 2.03-3.93, concordance index [c-index] = 0.68, median years-to-BCR: 1.7). Histotyping was also prognostic in clinically stratified subsets, such as patients with Gleason grade group 3 (HR = 4.09) and negative surgical margins (HR = 3.26). Histotyping was prognostic independent of grade group, margin status, pathological stage, and preoperative prostate-specific antigen (PSA) (multivariable p < 0.001, HR = 2.09, 95% CI: 1.40-3.10, n = 648). The combination of Histotyping, grade group, and preoperative PSA outperformed Decipher (c-index = 0.75 vs. 0.70, n = 167). These results suggest that a prognostic classifier for prostate cancer based on digital images could serve as an alternative or complement to molecular-based companion diagnostic tests.