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

Clinical implications of genomic evaluations for prostate cancer risk stratification, screening, and treatment: a narrative review

New classification systems based on molecular features have been introduced to improve precision medicine for prostate cancer (PCa). This review covers the increasing risk of PCa and the differences in response to targeted therapy that are related to specific gene variations. We believe that genomic evaluations will be useful for guiding PCa risk stratification, screening, and treatment. We searched the PubMed and MEDLINE databases for articles related to genomic testing for PCa that were published in 2020 or earlier. There is increasing evidence that germline mutations in DNA repair genes, such as BRCA1/2 or ATM, are closely related to the development and aggressiveness of PCa. Targeted prostate-specific antigen screening based on the presence of germline alterations in DNA repair genes is recommend to achieve an early diagnosis of PCa. In cases of localized PCa, even if it has a favorable risk classification, patients under active surveillance with these gene alterations are likely to develop aggressive PCa. Thus, active treatment may be preferable to active surveillance for these patients. In cases of metastatic castration–resistant PCa, BRCA1/2 and DNA mismatch repair genes may be useful biomarkers for predicting the response to androgen receptor–targeting agents, poly (ADP-ribose) polymerase inhibitors, platinum chemotherapy, prostate-specific membrane antigen–targeted therapy, immunotherapy, and radium-223. Genomic evaluations may allow for risk stratification of patients with PCa based on their molecular features, which may help guide precision medicine for treating PCa.

Report From the International Society of Urological Pathology (ISUP) Consultation Conference on Molecular Pathology of Urogenital Cancers. I. Molecular Biomarkers in Prostate Cancer

The combined clinical and molecular heterogeneity of prostate cancer necessitates the use of prognostic, predictive, and diagnostic biomarkers to assist the clinician with treatment selection. The pathologist plays a critical role in guiding molecular biomarker testing in prostate cancer and requires a thorough knowledge of the current testing options. In the setting of clinically localized prostate cancer, prognostic biomarkers such as Ki-67 labeling, PTEN loss or mRNA-based genomic signatures can be useful to help determine whether definitive therapy is required. In the setting of advanced disease, predictive biomarkers, such as the presence of DNA repair deficiency mediated by BRCA2 loss or mismatch repair gene defects, may suggest the utility of poly-ADP ribosylase inhibition or immune checkpoint blockade. Finally, androgen receptor-related biomarkers or diagnostic biomarkers indicating the presence of small cell neuroendocrine prostate cancer may help guide the use of androgen receptor signaling inhibitors and chemotherapy. In this review, we examine the current evidence for several prognostic, predictive and diagnostic tissue-based molecular biomarkers in prostate cancer management. For each assay, we summarize a recent survey of the International Society of Urology Pathology (ISUP) members on current testing practices and include recommendations for testing that emerged from the ISUP Working Group on Molecular Pathology of Prostate Cancer and the 2019 Consultation Conference on Molecular Pathology of Urogenital Cancers.

The development of apalutamide for the treatment of prostate cancer

INTRODUCTION

Prostate cancer progresses, despite androgen-deprivation therapy, the backbone of its treatment. This progression is mainly related to the androgen receptor (AR)-related mechanisms of resistance, and, several AR-targeted therapies have demonstrated benefit in metastatic and nonmetastatic disease. Apalutamide is a third-generation AR-targeted therapy which competitively blocks the AR and prevents AR dimerization, nuclear internalization, thereby avoiding cancer progression. Early studies have demonstrated that apalutamide was safe and demonstrated clinical benefit. Phase II and phase III studies had confirmed preliminary results of clinical benefit with apalutamide in patients with nonmetastatic castration-resistant prostate cancer (nmCRPC) and in metastatic hormone-sensitive prostate cancer (mHSPC).

AREAS COVERED

Herein, the authors discuss the development of apalutamide, from its discovery and early studies to phase III trials. They also examine new perspectives and biomarkers that may help oncologists to make decisions in patients taking apalutamide. Studies evaluating apalutamide in other settings and in combination with other therapies are also debated.

EXPERT OPINION

Apalutamide has become a relevant therapy for patients with nmCRPC and mHSPC for its benefit in delaying metastasis in addition to its improvement of overall survival, without compromising the quality of life. Apalutamide should be considered as a standard-of-care for patients with nmCRPC and patients with mHSPC.

Integrating the melanoma 31-gene expression profile test with surgical oncology practice within national guideline and staging recommendations

Aim: Define changes in clinical management resulting from the use of the prognostic 31-gene expression profile (31-GEP) test for cutaneous melanoma in a surgical oncology practice. Patients & methods: Management plans for 112 consecutively tested patients with stage I-III melanoma were evaluated for duration and number of clinical visits, blood work and imaging. Results: 31-GEP high-risk (class 2; n = 46) patients received increased management compared with low-risk (class 1; n = 66) patients. Test results were most closely associated with follow-up and imaging. Of class 1 patients, 65% received surveillance intensity within guidelines for stage I-IIA patients; 98% of class 2 patients received surveillance intensity equal to stage IIB-IV patients. Conclusion: We suggest clinical follow-up and metastatic screening be adjusted according to 31-GEP test results.

Identifying and treating ROBO1-ve /DOCK1+ve prostate cancer: An aggressive cancer subtype prevalent in African American patients

BACKGROUND

There is a need to develop novel therapies which could be beneficial to patients with prostate cancer (CaP) including those who are predisposed to poor outcome, such as African-Americans. This study investigates the role of ROBO1-pathway in predicting outcome and race-based disparity in patients with CaP.

METHODS AND RESULTS

Aided by RNA sequencing-based DECIPHER-testing and immunohistochemical (IHC) analysis of tumors we show that ROBO1 is lost during the progressive stages of CaP, a prevalent feature in African-Americans. We show that the loss of ROBO1 predicts high-risk of recurrence, metastasis and poor outcome of androgen-deprivation therapy in radical prostatectomy-treated patients. These data identified an aggressive ROBO1deficient /DOCK1+ve sub-class of CaP. Combined genetic and IHC data showed that ROBO1 loss is accompanied by DOCK1/Rac1 elevation in grade-III/IV primary-tumors and Mets. We observed that the hypermethylation of ROBO1-promoter contributes to loss of expression that is highly prevalent in African-Americans. Because of limitations in restoring ROBO1 function, we asked if targeting the DOCK1 could be an ideal strategy to inhibit progression or treat ROBO1deficient metastatic-CaP. We tested the pharmacological efficacy of CPYPP, a selective inhibitor of DOCK1 under in vitro and in vivo conditions. Using ROBO1-ve and ROBO1+ve CaP models, we determined the median effective concentration of CPYPP for growth. DOCK1-inhibitor treatment significantly decreased the (a) Rac1-GTP/β-catenin activity, (b) transmigration of ROBO1deficient cells across endothelial lining, and (c) metastatic spread of ROBO1deficient cells through the vasculature of transgenicfl Zebrafish model.

CONCLUSION

We suggest that ROBO1 status forms as predictive biomarker of outcome in high-risk populations such as African-Americans and DOCK1-targeting therapy has a clinical potential for treating metastatic-CaP.

Genetic alterations in the 3q26.31-32 locus confer an aggressive prostate cancer phenotype

Large-scale genetic aberrations that underpin prostate cancer development and progression, such as copy-number alterations (CNAs), have been described but the consequences of specific changes in many identified loci is limited. Germline SNPs in the 3q26.31 locus are associated with aggressive prostate cancer, and is the location of NAALADL2, a gene overexpressed in aggressive disease. The closest gene to NAALADL2 is TBL1XR1, which is implicated in tumour development and progression. Using publicly-available cancer genomic data we report that NAALADL2 and TBL1XR1 gains/amplifications are more prevalent in aggressive sub-types of prostate cancer when compared to primary cohorts. In primary disease, gains/amplifications occurred in 15.99% (95% CI: 13.02–18.95) and 14.96% (95% CI: 12.08–17.84%) for NAALADL2 and TBL1XR1 respectively, increasing in frequency in higher Gleason grade and stage tumours. Gains/amplifications result in transcriptional changes and the development of a pro-proliferative and aggressive phenotype. These results support a pivotal role for copy-number gains in this genetic region.

Benjamin Simpson et al. use publicly available cancer genomic data to investigate copy number changes at the 3q26.31–32 locus, which has been associated with aggressive prostate cancer based on single-nucleotide polymorphisms. They find that gains of NAALADL2 and TBL1XR1 in this locus are associated with more aggressive subtypes of prostate cancer and the transcription of pro-proliferative signalling processes.

Prostate Cancer, Version 2.2019, NCCN Clinical Practice Guidelines in Oncology

The NCCN Guidelines for Prostate Cancer include recommendations regarding diagnosis, risk stratification and workup, treatment options for localized disease, and management of recurrent and advanced disease for clinicians who treat patients with prostate cancer. The portions of the guidelines included herein focus on the roles of germline and somatic genetic testing, risk stratification with nomograms and tumor multigene molecular testing, androgen deprivation therapy, secondary hormonal therapy, chemotherapy, and immunotherapy in patients with prostate cancer.

Genomic and clinical characterization of stromal infiltration markers in prostate cancer

BACKGROUND

The progression of prostate cancer is a complex, multistep process that involves molecular alterations in cells of the tumor and the microenvironment, with associated interactions between the stroma and epithelium. Genomic expression analyses of stromal infiltration markers were performed to determine the significance thereof in prostate cancer.

METHODS

Genome-wide expression profiles of formalin-fixed, paraffin-embedded radical prostatectomy samples were evaluated from a prospective registry cohort (n = 5239) and 3 retrospective institutional cohorts (n = 1135). Two independent stromal gene expression signatures implied stromal infiltration. Cox proportional hazards regression defined the association between stromal infiltration expression and metastasis-free survival (MFS).

RESULTS

Stromal expression scores were correlated with stromal signature genes and with other key stromal markers (CAV1, VIM, and TAGLN), basal activity, and CD3 and CD4 immune biomarkers (r > 0.5 for all). The top decile of stromal expression was associated with high genomic risk scores (Decipher ≥ 0.6) , high Cancer of the Prostate Risk Assessment-Postsurgical scores, Gleason 9 to 10 disease, and a higher risk for metastasis (hazard ratio, 2.35; 95% CI, 1.37-4.02; P = .001). A higher stromal infiltration score was also associated with decreased expression of DNA repair genes and higher radiation sensitivity genomic scores. Postoperative radiation therapy (RT) was associated with an MFS benefit for patients with high stromal scores, but not for patients with low stromal scores (Pinteraction  = .02).

CONCLUSIONS

Expression of stromal infiltration markers is correlated with prostate cancer aggressiveness/progression and may be predictive of a response to RT. Stromal infiltration markers should be studied and considered for incorporation into clinical prognostication and decision making.

A novel stratification framework for predicting outcome in patients with prostate cancer

Background

Unsupervised learning methods, such as Hierarchical Cluster Analysis, are commonly used for the analysis of genomic platform data. Unfortunately, such approaches ignore the well-documented heterogeneous composition of prostate cancer samples. Our aim is to use more sophisticated analytical approaches to deconvolute the structure of prostate cancer transcriptome data, providing novel clinically actionable information for this disease.

Methods

We apply an unsupervised model called Latent Process Decomposition (LPD), which can handle heterogeneity within individual cancer samples, to genome-wide expression data from eight prostate cancer clinical series, including 1,785 malignant samples with the clinical endpoints of PSA failure and metastasis.

Results

We show that PSA failure is correlated with the level of an expression signature called DESNT (HR = 1.52, 95% CI = [1.36, 1.7], P = 9.0 × 10⁻¹⁴, Cox model), and that patients with a majority DESNT signature have an increased metastatic risk (X² test, P = 0.0017, and P = 0.0019). In addition, we develop a stratification framework that incorporates DESNT and identifies three novel molecular subtypes of prostate cancer.

Conclusions

These results highlight the importance of using more complex approaches for the analysis of genomic data, may assist drug targeting, and have allowed the construction of a nomogram combining DESNT with other clinical factors for use in clinical management.

Prostate cancer: more effective use of underutilized postoperative radiation therapy

Introduction: Adverse pathological features at radical prostatectomy such as extracapsular extension, seminal-vesicle involvement, positive surgical margins and/or lymph node invasion define a particular subgroup of patients that might benefit from additional treatment after surgery, in particular radiation therapy.Areas covered: Post-prostatectomy radiation is intended as adjuvant, early-salvage or salvage depending on the timing and PSA levels at the treatment. After providing the most used definitions, the high-level evidence supporting adjuvant radiation is reviewed together with the limitations affecting its utilization. In recent years early-salvage radiation was hypothesized to be a non-inferior alternative based on good-quality retrospective data. Recently, preliminary results of ongoing trials provide additional evidence. In light of the need to identify patients that will truly benefit from adjuvant radiation, clinically based and molecular tools available for this purpose are reviewed.Expert opinion: In order to tailor treatment for the patient after radical prostatectomy, there is a need for a tool that could both improve the oncological outcomes and be cost-effective. To date, genomic testing provides the most promising results that will be reasonably improved in the coming years.

Prognostic value of the SPOP mutant genomic subclass in prostate cancer

BACKGROUND

Speckle-type POZ protein (SPOP) mutation defines one of the dominant prostate cancer genomic subtypes, yet the impact of this mutation on clinical prognosis is unknown.

METHODS

We defined SPOP mutation status either by DNA sequencing or by transcriptional signature in a pooled retrospective multi-institutional cohort, the Decipher retrospective cohort, the Decipher Genomics Resource Information Database prospective cohort, and The Cancer Genome Atlas. Kaplan-Meier survival analysis and multivariable Cox models were used to assess the independent impact of SPOP mutation on survival, biochemical recurrence and time to metastasis. The Decipher retrospective cohort was also used to assess the impact of the addition of SPOP mutation status to a model predicting adverse pathology at prostatectomy which was then validated in the Decipher prospective cohort.

RESULTS

A fixed-effect model incorporating results from multivariable Cox regression including 5,811 subjects demonstrated that SPOP mutation was associated with a lower rate of adverse pathology at radical prostatectomy (odds ratios 0.57, 95% confidence interval 0.34-0.93), independent of preoperative prostate-specific antigen, age, and pathologic Gleason score. SPOP was not associated with biochemical recurrence, metastasis-free survival, or cancer-specific survival independent of pathologic information. The addition of SPOP status to prognostic models reclassified a large proportion of patients with the mutation (55%) into a favorable risk group when used to predict adverse pathology.

CONCLUSION

While the clinical utility of delineating any single molecular alteration in prostate cancer remains unclear, these results illustrates the importance of genomic subtypes in prostate cancer behavior and potential role in prognostic tools.

The Immune Landscape of Prostate Cancer and Nomination of PD-L2 as a Potential Therapeutic Target

BACKGROUND

Immunotherapy has been less successful in treating prostate cancer than other solid tumors. We sought to better understand the immune landscape in prostate cancer and identify immune-related biomarkers and potential therapeutic targets.

METHODS

We analyzed gene expression data from 7826 prospectively collected prostatectomy samples (2013-2016), and 1567 retrospective samples with long-term clinical outcomes, for a total of 9393 samples, all profiled on the same commercial clinical platform in a CLIA-certified lab. The primary outcome was distant metastasis-free survival (DMFS). Secondary outcomes included biochemical recurrence-free survival (bRFS), prostate cancer-specific survival (PCSS), and overall survival (OS). All statistical tests were two-sided.

RESULTS

Unsupervised hierarchical clustering of hallmark pathways demonstrated an immune-related tumor cluster. Increased estimated immune content scores based on immune-specific genes from the literature were associated with worse bRFS (hazard ratio [HR] = 1.26 [95% confidence interval [CI] = 1.12 to 1.42]; P < .001), DMFS (HR = 1.34 [95% CI = 1.13 to 1.58]; P < .001), PCSS (HR = 1.53 [95% CI = 1.21 to 1.92]; P < .001), and OS (HR = 1.27 [95% CI = 1.07 to 1.50]; P = .006). Deconvolution using Cibersort revealed that mast cells, natural killer cells, and dendritic cells conferred improved DMFS, whereas macrophages and T-cells conferred worse DMFS. Interestingly, while PD-L1 was not prognostic, consistent with its low expression in prostate cancer, PD-L2 was expressed at statistically significantly higher levels (P < .001) and was associated with worse bRFS (HR = 1.17 [95% CI = 1.03 to 1.33]; P = .01), DMFS (HR = 1.25 [95% CI = 1.05 to 1.49]; P = .01), and PCSS (HR = 1.45 [95% CI = 1.13 to 1.86]; P = .003). PD-L2 was strongly associated with immune-related pathways on gene set enrichment analysis suggesting that it is playing an important role in immune modulation in clinical prostate cancer samples. Furthermore, PD-L2 was correlated with radiation response pathways, and also predicted response to postoperative radiation therapy (PORT) on multivariable interaction analysis (P = .03).

CONCLUSION

In the largest study of its kind to date, these results illustrate the complex relationship between the tumor-immune interaction, prognosis, and response to radiotherapy, and nominate PD-L2 as a potential novel therapeutic target in prostate cancer, potentially in combination with radiotherapy.

Clinical-genomic Characterization Unveils More Aggressive Disease Features in Elderly Prostate Cancer Patients with Low-grade Disease

BACKGROUND

Over 20% of men diagnosed with prostate cancer (PC) are ≥75 yr old. More objective disease-specific indices for predicting outcomes beyond chronological age are necessary.

OBJECTIVE

To analyze age-related differences in clinical-genomic prognostic features of aggressiveness in localized PC.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective multicenter cross-sectional study reported the use of the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) guidelines. Clinical-genomic data of patients who underwent a prostate biopsy or radical prostatectomy (RP) were obtained from the Decipher Genomic Resource Information Database (NCT02609269).

INTERVENTION

Our analyses focused on the 22-gene Decipher genomic classifier (GC) and 50-gene (PAM50) models in the biopsy and RP cohorts stratified by age.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary endpoint was the impact of age on GC scores and PAM50 molecular subtypes. Prognostic indices including Decipher GC scores, PAM50 molecular subtypes, National Comprehensive Cancer Network risk categories, and ISUP grade groups (IGGs) were stratified by age using multivariable logistic regression analyses.

RESULTS AND LIMITATIONS

Within histological low-risk IGGs, there were a higher proportion of patients with high-risk Decipher biopsy scores with age (age <60 yr: 10.1% IGG 1 and 29.9% IGG 2 vs age ≥80 yr: 22% IGG 1 and 37.7% IGG 2). The prevalence of the adverse phenotype luminal B (PAM50-defined) increased with age (age <60 yr: 22.7% and 40.2% vs age ≥80 yr: 29.7% and 49.1%, in patients with IGG 1 and IGG 2, respectively). In IGGs 3-5, no age differences were observed. Multivariable models demonstrated that each age decile entailed a 19% (odds ratio [OR] 1.19, 95% confidence interval [CI] 1.10-1.29, p < 0.001) and a 10% (OR 1.1, 95% CI 1.05-1.16) increased probability for a high-risk Decipher biopsy and RP score, respectively. Aside from an obvious selection bias, data on race, family history, prostate volume, and long-term follow-up outcomes were unavailable.

CONCLUSIONS

These data demonstrated that elderly men with favorable pathology (IGG 1-2), might harbor more aggressive disease than younger patients based on validated GC scores.

PATIENT SUMMARY

The presented clinical-genomic data demonstrate that elderly patients with low-risk prostate cancer might harbor more aggressive disease than their younger counterparts. This suggests that standard well-accepted paradigm of elderly prostate cancer patients not being aggressively treated, based solely on their chronological age, might need to be reconsidered.

Benefits and Risks of Primary Treatments for High-risk Localized and Locally Advanced Prostate Cancer: An International Multidisciplinary Systematic Review

CONTEXT

The optimal treatment for men with high-risk localized or locally advanced prostate cancer (PCa) remains unknown.

OBJECTIVE

To perform a systematic review of the existing literature on the effectiveness of the different primary treatment modalities for high-risk localized and locally advanced PCa. The primary oncological outcome is the development of distant metastases at ≥5 yr of follow-up. Secondary oncological outcomes are PCa-specific mortality, overall mortality, biochemical recurrence, and need for salvage treatment with ≥5 yr of follow-up. Nononcological outcomes are quality of life (QoL), functional outcomes, and treatment-related side effects reported.

EVIDENCE ACQUISITION

Medline, Medline In-Process, Embase, and the Cochrane Central Register of Randomized Controlled Trials were searched. All comparative (randomized and nonrandomized) studies published between January 2000 and May 2019 with at least 50 participants in each arm were included. Studies reporting on high-risk localized PCa (International Society of Urologic Pathologists [ISUP] grade 4-5 [Gleason score {GS} 8-10] or prostate-specific antigen [PSA] >20 ng/ml or ≥ cT2c) and/or locally advanced PCa (any PSA, cT3-4 or cN+, any ISUP grade/GS) or where subanalyses were performed on either group were included. The following primary local treatments were mandated: radical prostatectomy (RP), external beam radiotherapy (EBRT) (≥64 Gy), brachytherapy (BT), or multimodality treatment combining any of the local treatments above (±any systemic treatment). Risk of bias (RoB) and confounding factors were assessed for each study. A narrative synthesis was performed.

EVIDENCE SYNTHESIS

Overall, 90 studies met the inclusion criteria. RoB and confounding factors revealed high RoB for selection, performance, and detection bias, and low RoB for correction of initial PSA and biopsy GS. When comparing RP with EBRT, retrospective series suggested an advantage for RP, although with a low level of evidence. Both RT and RP should be seen as part of a multimodal treatment plan with possible addition of (postoperative) RT and/or androgen deprivation therapy (ADT), respectively. High levels of evidence exist for EBRT treatment, with several randomized clinical trials showing superior outcome for adding long-term ADT or BT to EBRT. No clear cutoff can be proposed for RT dose, but higher RT doses by means of dose escalation schemes result in an improved biochemical control. Twenty studies reported data on QoL, with RP resulting mainly in genitourinary toxicity and sexual dysfunction, and EBRT in bowel problems.

CONCLUSIONS

Based on the results of this systematic review, both RP as part of multimodal treatment and EBRT + long-term ADT can be recommended as primary treatment in high-risk and locally advanced PCa. For high-risk PCa, EBRT + BT can also be offered despite more grade 3 toxicity. Interestingly, for selected patients, for example, those with higher comorbidity, a shorter duration of ADT might be an option. For locally advanced PCa, EBRT + BT shows promising result but still needs further validation. In this setting, it is important that patients are aware that the offered therapy will most likely be in the context a multimodality treatment plan. In particular, if radiation is used, the combination of local with systemic treatment provides the best outcome, provided the patient is fit enough to receive both. Until the results of the SPCG15 trial are known, the optimal local treatment remains a matter of debate. Patients should at all times be fully informed about all available options, and the likelihood of a multimodal approach including the potential side effects of both local and systemic treatment.

PATIENT SUMMARY

We reviewed the literature to see whether the evidence from clinical studies would tell us the best way of curing men with aggressive prostate cancer that had not spread to other parts of the body such as lymph glands or bones. Based on the results of this systematic review, there is good evidence that both surgery and radiation therapy are good treatment options, in terms of prolonging life and preserving quality of life, provided they are combined with other treatments. In the case of surgery this means including radiotherapy (RT), and in the case of RT this means either hormonal therapy or combined RT and brachytherapy.

A piece in prostate cancer puzzle: Future perspective of novel molecular signatures

Prostate cancer (PCa) has a variable biological potential. It constitutes the second most common cancer amongst men worldwide and the fifth most common cancer in Saudi Arabia. Identifying men at higher risk of developing PCa, differentiating indolent from aggressive disease and predicting the likelihood of progression will improve decision-making and selection for active surveillance protocols. Biomarkers have been utilized for PCa screening and predicting cancer behavior and response to treatment. The prostate specific antigen (PSA) screening helps detect PCa in early stages, while implementing a plan for management and outcome. However, PSA screening is still controversial, due to the risks of over diagnosis and treatment, and its inability to detect a good proportion of advanced tumors. Alternatively, a new era of PCa biomarkers has emerged with higher PCa specificity than PSA and its isoforms hopefully improving screening methods, such as Prostate Health Index (PHI) score, Progensa Prostate Cancer Antigen 3 (PCA3), Mi-Prostate Score (MiPS), Prostate Stem Cell Antigen (PSCA), 4Kscore test, and Urokinase Plasminogen Activation (uPA and uPAR). Few novel biomarkers have shown promise in preliminary results. This review will display promising biomarkers including some important FDA approved ones, highlighting their clinical implication and future place in the PCa puzzle, along with addressing their current limitations.

A new era: artificial intelligence and machine learning in prostate cancer

Artificial intelligence (AI) - the ability of a machine to perform cognitive tasks to achieve a particular goal based on provided data - is revolutionizing and reshaping our health-care systems. The current availability of ever-increasing computational power, highly developed pattern recognition algorithms and advanced image processing software working at very high speeds has led to the emergence of computer-based systems that are trained to perform complex tasks in bioinformatics, medical imaging and medical robotics. Accessibility to 'big data' enables the 'cognitive' computer to scan billions of bits of unstructured information, extract the relevant information and recognize complex patterns with increasing confidence. Computer-based decision-support systems based on machine learning (ML) have the potential to revolutionize medicine by performing complex tasks that are currently assigned to specialists to improve diagnostic accuracy, increase efficiency of throughputs, improve clinical workflow, decrease human resource costs and improve treatment choices. These characteristics could be especially helpful in the management of prostate cancer, with growing applications in diagnostic imaging, surgical interventions, skills training and assessment, digital pathology and genomics. Medicine must adapt to this changing world, and urologists, oncologists, radiologists and pathologists, as high-volume users of imaging and pathology, need to understand this burgeoning science and acknowledge that the development of highly accurate AI-based decision-support applications of ML will require collaboration between data scientists, computer researchers and engineers.

Correlation between cribriform/intraductal prostatic adenocarcinoma and percent Gleason pattern 4 to a 22-gene genomic classifier

BACKGROUND

The Decipher test measures expression of 22 RNA biomarkers associated with aggressive prostate cancer used to improve risk stratification of patients to help guide management. To date, Decipher's genomic classification has not been extensively correlated with specific histologic growth patterns in prostatic adenocarcinoma. With a growing understanding of the clinical aggressiveness associated with cribriform growth pattern (CF), intraductal carcinoma (IDC), and percent Gleason pattern 4 (G4%), we sought to determine if their presence was associated with an increased genomic risk as measured by the Decipher assay.

DESIGN

Clinical use of the Decipher assay was performed on the highest Gleason score (GS) tumor nodule of prostatectomy specimens from a prospective cohort of 48 patients, with GS varying from 7 through 9 to help guide clinical risk stratification. The tumors were reviewed for CF, IDC, and G4%, which were then compared to the Decipher score (0-1) and risk stratification (high vs not high).

RESULTS

The presence of CF/IDC was significantly associated with Decipher risk score (P = .007), with a high-risk Decipher score in 22% vs 56% of patients without or with CF/IDC. On binary logistic regression analysis, G4% (odds ratio [OR] 1.04 per percent increase [95% confidence interval [CI], 1.02-1.06]; P = .0004) and CF predominant (OR, 9.60 [95%CI, 1.48-62.16]; P = .02) were significantly associated with a high-risk GC score. IDC did not reach significance (OR, 1.92 [95%CI, 0.65-5.67]; P = .24).

CONCLUSIONS

Our findings add to an expanding knowledge base that supports G4% and CF/IDC as molecularly unique and clinically relevant features in prostatic adenocarcinoma. These histologic features should be standardly reported as they are associated with more aggressive prostate cancer. Future work should determine the independent information of these histologic findings that are relative to genomic assessment on long-term outcomes.

The Genomic and Molecular Pathology of Prostate Cancer: Clinical Implications for Diagnosis, Prognosis, and Therapy

Prostate cancer (PCa) is the most common noncutaneous malignancy affecting American men and the second most common cause of cancer death. The traditional risk classification schemes for PCa are limited due to the vast clinical and molecular heterogeneity of the disease. Fortunately, recent advancements in sequencing technologies have provided us with valuable insight into the genomics of PCa. To date, a wide array of recurrent genomic alterations in PCa have been identified. Incorporating these distinct molecular subtypes of PCa into prediction models provides opportunities for improved risk stratification and ultimately better patient outcomes. In this review, we summarize the key molecular subtypes of PCa and focus on those genomic alterations that have clinical implications for diagnosis, prognosis, and therapeutic response.

Validation of a genomic classifier for prediction of metastasis and prostate cancer-specific mortality in African-American men following radical prostatectomy in an equal access healthcare setting

BACKGROUND

The Decipher 22-gene genomic classifier (GC) may help in post-radical prostatectomy (RP) decision making given its superior prognostic performance over clinicopathologic variables alone. However, most studies evaluating the GC have had a modest representation of African-American men (AAM). We evaluated the GC within a large Veteran Affairs cohort and compared its performance to CAPRA-S for predicting outcomes in AAM and non-AAM after RP.

METHODS

GC scores were generated for 548 prostate cancer (PC) patients, who underwent RP at the Durham Veteran Affairs Medical Center between 1989 and 2016. This was a clinically high-risk cohort and was selected to have either pT3a, positive margins, seminal vesicle invasion, or received post-RP radiotherapy. Multivariable Cox models and survival C-indices were used to compare the performance of GC and CAPRA-S for predicting the risk of metastasis and PC-specific mortality (PCSM).

RESULTS

Median follow-up was 9 years, during which 37 developed metastasis and 20 died from PC. Overall, 55% (n = 301) of patients were AAM. In multivariable analyses, GC (high vs. intermediate and intermediate vs. low) was a significant predictor of metastasis in all men (all p < 0.001). Consistent with prior studies, relative to CAPRA-S, GC had a higher C-index for 5-year metastasis (0.78 vs. 0.72) and 10-year PCSM (0.85 vs. 0.81). There was a suggestion GC was a stronger predictor in AAM than non-AAM. Specifically, the 5-year metastasis risk C-index was 0.86 in AAM vs. 0.69 in non-AAM and the 10-year PCSM risk C-index was 0.91 in AAM vs. 0.78 in non-AAM. However, the test for interaction of race and the performance of the GC in the Cox model was not significant for either metastasis or PCSM (both p ≥ 0.3).

CONCLUSIONS

GC was a very strong predictor of poor outcome and performed well in both AAM and non-AAM. Our data support the use of GC for risk stratification in AAM post-RP. While our data suggest that GC may actually work better in AAM, given the limited number of events, further validation is needed.

Assessment of biochemical recurrence of prostate cancer (Review)

The assessment of the risk of biochemical recurrence (BCR) is critical in the management of males with prostate cancer (PC). Over the past decades, a comprehensive effort has been focusing on improving risk stratification; a variety of models have been constructed using PC-associated pathological features and molecular alterations occurring at the genome, protein and RNA level. Alterations in RNA expression (lncRNA, miRNA and mRNA) constitute the largest proportion of the biomarkers of BCR. In this article, we systemically review RNA-based BCR biomarkers reported in PubMed according to the PRISMA guidelines. Individual miRNAs, mRNAs, lncRNAs and multi-gene panels, including the commercially available signatures, Oncotype DX and Prolaris, will be discussed; details related to cohort size, hazard ratio and 95% confidence intervals will be provided. Mechanistically, these individual biomarkers affect multiple pathways critical to tumorigenesis and progression, including epithelial-mesenchymal transition (EMT), phosphatase and tensin homolog (PTEN), Wnt, growth factor receptor, cell proliferation, immune checkpoints and others. This variety in the mechanisms involved not only validates their associations with BCR, but also highlights the need for the coverage of multiple pathways in order to effectively stratify the risk of BCR. Updates of novel biomarkers and their mechanistic insights are considered, which suggests new avenues to pursue in the prediction of BCR. Additionally, the management of patients with BCR and the potential utility of the stratification of the risk of BCR in salvage treatment decision making for these patients are briefly covered. Limitations will also be discussed.

A four-gene transcript score to predict metastatic-lethal progression in men treated for localized prostate cancer: Development and validation studies

BACKGROUND

Molecular studies have tried to address the unmet need for prognostic biomarkers in prostate cancer (PCa). Some gene expression tests improve upon clinical factors for prediction of outcomes, but additional tools for accurate prediction of tumor aggressiveness are needed.

METHODS

Based on a previously published panel of 23 gene transcripts that distinguished patients with metastatic progression, we constructed a prediction model using independent training and testing datasets. Using the validated messenger RNAs and Gleason score (GS), we performed model selection in the training set to define a final locked model to classify patients who developed metastatic-lethal events from those who remained recurrence-free. In an independent testing dataset, we compared our locked model to established clinical prognostic factors and utilized Kaplan-Meier curves and receiver operating characteristic analyses to evaluate the model's performance.

RESULTS

Thirteen of 23 previously identified gene transcripts that stratified patients with aggressive PCa were validated in the training dataset. These biomarkers plus GS were used to develop a four-gene (CST2, FBLN1, TNFRSF19, and ZNF704) transcript (4GT) score that was significantly higher in patients who progressed to metastatic-lethal events compared to those without recurrence in the testing dataset (P = 5.7 × 10^-11 ). The 4GT score provided higher prediction accuracy (area under the ROC curve [AUC] = 0.76; 95% confidence interval [CI] = 0.69-0.83; partial area under the ROC curve [pAUC] = 0.008) than GS alone (AUC = 0.63; 95% CI = 0.56-0.70; pAUC = 0.002), and it improved risk stratification in subgroups defined by a combination of clinicopathological features (ie, Cancer of the Prostate Risk Assessment-Surgery).

CONCLUSION

Our validated 4GT score has prognostic value for metastatic-lethal progression in men treated for localized PCa and warrants further evaluation for its clinical utility.

Prediction of biochemical recurrence in prostate cancer patients who underwent prostatectomy using routine clinical prostate multiparametric MRI and decipher genomic score

BACKGROUND

Biochemical recurrence (BCR) affects a significant proportion of patients who undergo robotic-assisted laparoscopic prostatectomy (RALP).

PURPOSE

To evaluate the performance of a routine clinical prostate multiparametric magnetic resonance imaging (mpMRI) and Decipher genomic classifier score for prediction of biochemical recurrence in patients who underwent RALP.

STUDY TYPE

Retrospective cohort study.

SUBJECTS

Ninety-one patients who underwent RALP performed by a single surgeon, had mpMRI before RALP, Decipher taken from RALP samples, and prostate specific antigen (PSA) follow-up for >3 years or BCR within 3 years, defined as PSA >0.2 mg/ml. FIELD STRENGTH/SEQUENCE: mpMRI was performed at 27 different institutions using 1.5T (n = 10) or 3T scanners and included T₂ w, diffusion-weighted imaging (DWI), or dynamic contrast-enhanced (DCE) MRI.

ASSESSMENT

All mpMRI studies were reported by one reader using Prostate Imaging Reporting and Data System v. 2.1 (PI-RADsv2.1) without knowledge of other findings. Eighteen (20%) randomly selected cases were re-reported by reader B to evaluate interreader variability.

STATISTICAL TESTS

Univariate and multivariate analysis using greedy feature selection and tournament leave-pair-out cross-validation (TLPOCV) were used to evaluate the performance of various variables for prediction of BCR, which included clinical (three), systematic biopsy (three), surgical (six: RALP Gleason Grade Group [GGG], extracapsular extension, seminal vesicle invasion, intraoperative surgical margins [PSM], final PSM, pTNM), Decipher (two: Decipher score, Decipher risk category), and mpMRI (eight: prostate volume, PSA density, PI-RADv2.1 score, MRI largest lesion size, summed MRI lesions' volume and relative volume [MRI-lesion-percentage], mpMRI ECE, mpMRI seminal vesicle invasion [SVI]) variables. The evaluation metric was the area under the curve (AUC).

RESULTS

Forty-eight (53%) patients developed BCR. The best-performing individual features with TLPOCV AUC of 0.73 (95% confidence interval [CI] 0.64-0.82) were RALP GGG, MRI-lesion-percentage followed by biopsy GGG (0.72, 0.62-0.82), and Decipher score (0.71, 0.60-0.82). The best performance was achieved by feature selection of Decipher+Surgery and MRI + Surgery variables with TLPOCV AUC of 0.82 and 0.81, respectively DATA CONCLUSION: Relative lesion volume measured on a routine clinical mpMRI failed to outperform Decipher score in BCR prediction.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1075-1085.

High-fat diet fuels prostate cancer progression by rewiring the metabolome and amplifying the MYC program

Systemic metabolic alterations associated with increased consumption of saturated fat and obesity are linked with increased risk of prostate cancer progression and mortality, but the molecular underpinnings of this association are poorly understood. Here, we demonstrate in a murine prostate cancer model, that high-fat diet (HFD) enhances the MYC transcriptional program through metabolic alterations that favour histone H4K20 hypomethylation at the promoter regions of MYC regulated genes, leading to increased cellular proliferation and tumour burden. Saturated fat intake (SFI) is also associated with an enhanced MYC transcriptional signature in prostate cancer patients. The SFI-induced MYC signature independently predicts prostate cancer progression and death. Finally, switching from a high-fat to a low-fat diet, attenuates the MYC transcriptional program in mice. Our findings suggest that in primary prostate cancer, dietary SFI contributes to tumour progression by mimicking MYC over expression, setting the stage for therapeutic approaches involving changes to the diet.

Prostate cancer pathology: What has changed in the last 5 years

Prostate cancer is the most frequent non-cutaneous malignancy in men in the United States. In the last few years, many recommendations have been made available from the 2014 International Society of Urologic Pathology consensus conference, 2016 World Health Organization blue book and 2018 8th edition of American Joint Committee on Cancer Staging System. Here, we focus on four topics which are considered relevant on the basis of their common appearance in routine practice, clinical importance and 'need to improve communication between pathology reports and clinicians': prostate cancer classification, prostate cancer grading, prostate cancer staging, and current definition of clinically significant prostate cancer. Tissue biomarkers that can predict significant disease and/or upgrading and tissue-based genomics for the purpose of diagnosis and prognosis are mentioned briefly.

Cellular and Molecular Mechanisms Underlying Prostate Cancer Development: Therapeutic Implications

Prostate cancer is the most frequent nonskin cancer and second most common cause of cancer-related deaths in man. Prostate cancer is a clinically heterogeneous disease with many patients exhibiting an aggressive disease with progression, metastasis, and other patients showing an indolent disease with low tendency to progression. Three stages of development of human prostate tumors have been identified: intraepithelial neoplasia, adenocarcinoma androgen-dependent, and adenocarcinoma androgen-independent or castration-resistant. Advances in molecular technologies have provided a very rapid progress in our understanding of the genomic events responsible for the initial development and progression of prostate cancer. These studies have shown that prostate cancer genome displays a relatively low mutation rate compared with other cancers and few chromosomal loss or gains. The ensemble of these molecular studies has led to suggest the existence of two main molecular groups of prostate cancers: one characterized by the presence of ERG rearrangements (~50% of prostate cancers harbor recurrent gene fusions involving ETS transcription factors, fusing the 5' untranslated region of the androgen-regulated gene TMPRSS2 to nearly the coding sequence of the ETS family transcription factor ERG) and features of chemoplexy (complex gene rearrangements developing from a coordinated and simultaneous molecular event), and a second one characterized by the absence of ERG rearrangements and by the frequent mutations in the E3 ubiquitin ligase adapter SPOP and/or deletion of CDH1, a chromatin remodeling factor, and interchromosomal rearrangements and SPOP mutations are early events during prostate cancer development. During disease progression, genomic and epigenomic abnormalities accrued and converged on prostate cancer pathways, leading to a highly heterogeneous transcriptomic landscape, characterized by a hyperactive androgen receptor signaling axis.

FOXA1 mutations alter pioneering activity, differentiation and prostate cancer phenotypes

Mutations in the FOXA1 transcription factor define a unique subset of prostate cancers but the functional consequences of these mutations and whether they confer gain or loss of function is unknown^1-9. By annotating the FOXA1 mutation landscape from 3086 human prostate cancers, we define two hotspots in the forkhead domain: Wing2 (~50% of all mutations) and R219 (~5%), a highly conserved DNA contact residue. Clinically, Wing2 mutations are seen in adenocarcinomas at all stages, whereas R219 mutations are enriched in metastatic tumors with neuroendocrine histology. Interrogation of the biologic properties of FOXA1^WT and 14 FOXA1 mutants revealed gain-of-function in mouse prostate organoid proliferation assays. 12 of these mutants, as well as FOXA1^WT, promoted an exaggerated pro-luminal differentiation program whereas two different R219 mutants blocked luminal differentiation and activate a mesenchymal and neuroendocrine transcriptional program. ATAC-seq of FOXA1^WT and representative Wing2 and R219 mutants revealed dramatic, mutant-specific changes in open chromatin at thousands of genomic loci, together with novel sites of FOXA1 binding and associated increases in gene expression. Of note, peaks in R219 mutant expressing cells lack the canonical core FOXA1 binding motifs (GTAAAC/T) but are enriched for a related, non-canonical motif (GTAAAG/A), which is preferentially activated by R219 mutant FOXA1 in reporter assays. Thus, FOXA1 mutations alter its normal pioneering function through perturbation of normal luminal epithelial differentiation programs, providing further support to the role of lineage plasticity in cancer progression.

Development of a predictive model for stromal content in prostate cancer samples to improve signature performance

Prostate cancer is heterogeneous in both cellular composition and patient outcome, and development of biomarker signatures to distinguish indolent from aggressive tumours is a high priority. Stroma plays an important role during prostate cancer progression and undergoes histological and transcriptional changes associated with disease. However, identification and validation of stromal markers is limited by a lack of datasets with defined stromal/tumour ratio. We have developed a prostate‐selective signature to estimate the stromal content in cancer samples of mixed cellular composition. We identified stromal‐specific markers from transcriptomic datasets of developmental prostate mesenchyme and prostate cancer stroma. These were experimentally validated in cell lines, datasets of known stromal content, and by immunohistochemistry in tissue samples to verify stromal‐specific expression. Linear models based upon six transcripts were able to infer the stromal content and estimate stromal composition in mixed tissues. The best model had a coefficient of determination R² of 0.67. Application of our stromal content estimation model in various prostate cancer datasets led to improved performance of stromal predictive signatures for disease progression and metastasis. The stromal content of prostate tumours varies considerably; consequently, deconvolution of stromal proportion may yield better results than tumour cell deconvolution. We suggest that adjusting expression data for cell composition will improve stromal signature performance and lead to better prognosis and stratification of men with prostate cancer. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Biologic Significance of Magnetic Resonance Imaging Invisibility in Localized Prostate Cancer

PURPOSE

Multiparametric magnetic resonance imaging (mpMRI) is used widely for prostate cancer (PCa) evaluation. Approximately 35% of aggressive tumors, however, are not visible on mpMRI. We sought to identify the molecular alterations associated with mpMRI-invisible tumors and determine whether mpMRI visibility is associated with PCa prognosis.

METHODS

Discovery and validation cohorts included patients who underwent mpMRI before radical prostatectomy and were found to harbor both mpMRI-visible (Prostate Imaging and Reporting Data System 3 to 5) and -invisible (Prostate Imaging and Reporting Data System 1 or 2) foci on surgical pathology. Next-generation sequencing was performed to determine differential gene expression between mpMRI-visible and -invisible foci. A genetic signature for tumor mpMRI visibility was derived in the discovery cohort and assessed in an independent validation cohort. Its association with long-term oncologic outcomes was evaluated in a separate testing cohort.

RESULTS

The discovery cohort included 10 patients with 26 distinct PCa foci on surgical pathology, of which 12 (46%) were visible and 14 (54%) were invisible on preoperative mpMRI. Next-generation sequencing detected prioritized genetic mutations in 14 (54%) tumor foci (n = 8 mpMRI visible, n = 6 mpMRI invisible). A nine-gene signature (composed largely of cell organization/structure genes) associated with mpMRI visibility was derived (area under the curve = 0.89), and the signature predicted MRI visibility with 75% sensitivity and 100% specificity (area under the curve = 0.88) in the validation cohort. In the testing cohort (n = 375, median follow-up 8 years) there was no significant difference in biochemical recurrence, distant metastasis, or cancer-specific mortality in patients with predicted mpMRI-visible versus -invisible tumors (all P > .05).

CONCLUSION

Compared with mpMRI-invisible disease, mpMRI-visible tumors are associated with underexpression of cellular organization genes. mpMRI visibility does not seem to be predictive of long-term cancer outcomes, highlighting the need for biopsy strategies that detect mpMRI-invisible tumors.

A novel combined miRNA and methylation marker panel (miMe) for prediction of prostate cancer outcome after radical prostatectomy

Improved prognostic biomarkers are needed to guide personalized prostate cancer (PC) treatment decisions. Due to the prominent molecular heterogeneity of PC, multimarker panels may be more robust. Here, 25 selected top-candidate miRNA and methylation markers for PC were profiled by qPCR in malignant radical prostatectomy (RP) tissue specimens from 198 PC patients (Cohort 1, training). Using GLMnet, we trained a novel multimarker model (miMe) comprising nine miRNAs and three methylation markers that predicted postoperative biochemical recurrence (BCR) independently of the established clinicopathological CAPRA-S nomogram in Cox multivariate regression analysis in Cohort 1 (HR [95% CI]: 1.53 [1.26-1.84], p < 0.001). This result was successfully validated in two independent RP cohorts (Cohort 2, n = 159: HR [95% CI]: 1.35 [1.06-1.73], p = 0.015. TCGA, n = 350: HR [95% CI]: 1.34 [1.01-1.77], p = 0.04). Notably, in CAPRA-S low-risk patients, a high miMe score was associated with >6 times higher risk of BCR, suggesting that miMe may help identify PC patients at high risk of progression despite favorable clinicopathological factors postsurgery. Finally, miMe was a significant predictor of cancer-specific survival (p = 0.019, log-rank test) in a merged analysis of 357 RP patients. In conclusion, we trained, tested and validated a novel 12-marker panel (miMe) that showed significant independent prognostic value in three RP cohorts. In the future, combining miMe score with existing clinical nomograms may improve PC risk stratification and thus help guide treatment decisions.

Genetic polymorphism and carbonic anhydrase 9 expression can predict nodal metastatic prostate cancer risk in patients with prostate-specific antigen levels ≤10 ng/ml at initial biopsy

OBJECTIVES

Active surveillance is a common management method for low-risk prostate cancer (CaP). However, devising a method to prevent disease progression is crucial. Carbon anhydrase 9 (CA9) plays a vital role in cell adhesion and intercellular communication correlated to tumor metastasis. Our study explored the impact of CA9 genetic polymorphism on the clinicopathological features and prognosis of CaP.

MATERIALS AND METHODS

In total, 579 patients with CaP who underwent robot-assisted radical prostatectomy were enrolled, 270 of whom had an initial prostate-specific antigen (PSA) level ≤10 ng/ml and 309 had initial one >10 ng/ml. Three single-nucleotide polymorphisms of CA9 gene were examined using real-time polymerase chain reaction assay.

RESULTS

After adjusting the confounding factors, participants carrying at least one G allele at CA9 rs3829078 had a 2.241-fold change in PSA compared with the wild-type carrier (AA), leading to an initial PSA level of ≤10 ng/ml. Furthermore, patients with CaP with an initial PSA level ≤10 ng/ml who carried at least one G allele at CA9 rs3829078 had a 4.532-fold and 3.484-fold risk of lymph node metastasis and lymphovascular invasion, respectively. Moreover, The Cancer Genome Atlas database showed that the CA9 mRNA expression significantly increased N1 disease risk and worsened overall survival trends.

CONCLUSION

The rs3829078 polymorphic genotype of CA9 can predict the risk of lymph node metastasis of CaP with an initial PSA level ≤10 ng/ml. This is the first study to report a correlation between CA9 gene polymorphisms/CA9 mRNA expression and early detection of CaP.

Associations of Luminal and Basal Subtyping of Prostate Cancer With Prognosis and Response to Androgen Deprivation Therapy

Importance

There is a clear need for a molecular subtyping approach in prostate cancer to identify clinically distinct subgroups that benefit from specific therapies.

Objectives

To identify prostate cancer subtypes based on luminal and basal lineage and to determine associations with clinical outcomes and response to treatment.

Design, Setting, and Participants

The PAM50 classifier was used to subtype 1567 retrospectively collected (median follow-up, 10 years) and 2215 prospectively collected prostate cancer samples into luminal- and basal-like subtypes.

Main Outcomes and Measures

Metastasis, biochemical recurrence, overall survival, prostate cancer–specific survival, associations with biological pathways, and clinicopathologic variables were the main outcomes.

Results

Among the 3782 samples, the PAM50 classifier consistently segregated prostate cancer into 3 subtypes in both the retrospective and prospective cohorts: luminal A (retrospective, 538 [34.3%]; prospective, 737 [33.3%]), luminal B (retrospective, 447 [28.5%]; prospective, 723 [32.6%]), and basal (retrospective, 582 [37.1%]; prospective, 755 [34.1%]). Known luminal lineage markers, such as NKX3.1 and KRT18, were enriched in luminal-like cancers, and the basal lineage CD49f signature was enriched in basal-like cancers, demonstrating the connection between these subtypes and established prostate cancer biology. In the retrospective cohort, luminal B prostate cancers exhibited the poorest clinical prognoses on both univariable and multivariable analyses accounting for standard clinicopathologic prognostic factors (10-year biochemical recurrence-free survival [bRFS], 29%; distant metastasis-free survival [DMFS], 53%; prostate cancer-specific survival [PCSS], 78%; overall survival [OS], 69%), followed by basal prostate cancers (10-year bRFS, 39%; DMFS, 73%; PCSS, 86%; OS, 80%) and luminal A prostate cancers (10-year bRFS, 41%; DMFS, 73%; PCSS, 89%; OS, 82%). Although both luminal-like subtypes were associated with increased androgen receptor expression and signaling, only luminal B prostate cancers were significantly associated with postoperative response to androgen deprivation therapy (ADT) in a subset analysis in our retrospective cohorts (n = 315) matching patients based on clinicopathologic variables (luminal B 10-year metastasis: treated, 33% vs untreated, 55%; nonluminal B 10-year metastasis: treated, 37% vs untreated, 21%; P = .006 for interaction).

Conclusions and Relevance

Luminal- and basal-like prostate cancers demonstrate divergent clinical behavior, and patients with luminal B tumors respond better to postoperative ADT than do patients with non–luminal B tumors. These findings contribute novel insight into prostate cancer biology, providing a potential clinical tool to personalize ADT treatment for prostate cancer by predicting which men may benefit from ADT after surgery.

A Rich Array of Prostate Cancer Molecular Biomarkers: Opportunities and Challenges

Prostate cancer is the most prevalent non-skin cancer in men and is the leading cause of cancer-related death. Early detection of prostate cancer is largely determined by a widely used prostate specific antigen (PSA) blood test and biopsy is performed for definitive diagnosis. Prostate cancer is asymptomatic in the early stage of the disease, comprises of diverse clinico-pathologic and progression features, and is characterized by a large subset of the indolent cancer type. Therefore, it is critical to develop an individualized approach for early detection, disease stratification (indolent vs. aggressive), and prediction of treatment response for prostate cancer. There has been remarkable progress in prostate cancer biomarker discovery, largely through advancements in genomic technologies. A rich array of prostate cancer diagnostic and prognostic tests has emerged for serum (4K, phi), urine (Progensa, T2-ERG, ExoDx, SelectMDx), and tumor tissue (ConfirmMDx, Prolaris, Oncoytype DX, Decipher). The development of these assays has created new opportunities for improving prostate cancer diagnosis, prognosis, and treatment decisions. While opening exciting opportunities, these developments also pose unique challenges in terms of selecting and incorporating these assays into the continuum of prostate cancer patient care.

ARv7 Represses Tumor-Suppressor Genes in Castration-Resistant Prostate Cancer

Androgen deprivation therapy for prostate cancer (PCa) benefits patients with early disease, but becomes ineffective as PCa progresses to a castration-resistant state (CRPC). Initially CRPC remains dependent on androgen receptor (AR) signaling, often through increased expression of full-length AR (ARfl) or expression of dominantly active splice variants such as ARv7. We show in ARv7-dependent CRPC models that ARv7 binds together with ARfl to repress transcription of a set of growth-suppressive genes. Expression of the ARv7-repressed targets and ARv7 protein expression are negatively correlated and predicts for outcome in PCa patients. Our results provide insights into the role of ARv7 in CRPC and define a set of potential biomarkers for tumors dependent on ARv7.

Correlation between MRI phenotypes and a genomic classifier of prostate cancer: preliminary findings

OBJECTIVES

We sought to evaluate the correlation between MRI phenotypes of prostate cancer as defined by PI-RADS v2 and the Decipher Genomic Classifier (used to estimate the risk of early metastases).

METHODS

This single-center, retrospective study included 72 nonconsecutive men with prostate cancer who underwent MRI before radical prostatectomy performed between April 2014 and August 2017 and whose MRI registered lesions were microdissected from radical prostatectomy specimens and then profiled using Decipher (89 lesions; 23 MRI invisible [PI-RADS v2 scores ≤ 2] and 66 MRI visible [PI-RADS v2 scores ≥ 3]). Linear regression analysis was used to assess clinicopathologic and MRI predictors of Decipher results; correlation coefficients (r) were used to quantify these associations. AUC was used to determine whether PI-RADS v2 could accurately distinguish between low-risk (Decipher score < 0.45) and intermediate-/high-risk (Decipher score ≥ 0.45) lesions.

RESULTS

MRI-visible lesions had higher Decipher scores than MRI-invisible lesions (mean difference 0.22; 95% CI 0.13, 0.32; p < 0.0001); most MRI-invisible lesions (82.6%) were low risk. PI-RADS v2 had moderate correlation with Decipher (r = 0.54) and had higher accuracy (AUC 0.863) than prostate cancer grade groups (AUC 0.780) in peripheral zone lesions (95% CI for difference 0.01, 0.15; p = 0.018).

CONCLUSIONS

MRI phenotypes of prostate cancer are positively correlated with Decipher risk groups. Although PI-RADS v2 can accurately distinguish between lesions classified by Decipher as low or intermediate/high risk, some lesions classified as intermediate/high risk by Decipher are invisible on MRI.

KEY POINTS

• MRI phenotypes of prostate cancer as defined by PI-RADS v2 positively correlated with a genomic classifier that estimates the risk of early metastases. • Most but not all MRI-invisible lesions had a low risk for early metastases according to the genomic classifier. • MRI could be used in conjunction with genomic assays to identify lesions that may carry biological potential for early metastases.

A positive role of c-Myc in regulating androgen receptor and its splice variants in prostate cancer

Increased expression of the full-length androgen receptor (AR-FL) and AR splice variants (AR-Vs) drives the progression of castration-resistant prostate cancer (CRPC). The levels of AR-FL and AR-V transcripts are often tightly correlated in individual CRPC samples, yet our understanding of how their expression is co-regulated is limited. Here, we report a role of c-Myc in accounting for coordinated AR-FL and AR-V expression. Analysis of gene expression data from 159 metastatic CRPC samples and 2142 primary prostate tumors showed that the level of c-Myc is positively correlated with that of individual AR isoforms. A striking positive correlation also exists between the activity of the c-Myc pathway and the level of individual AR isoforms, between the level of c-Myc and the activity of the AR pathway, and between the activities of the two pathways. Moreover, the c-Myc signature is highly enriched in tumors expressing high levels of AR, as is the AR signature in c-Myc-high-expressing tumors. Using shRNA knockdown, we confirmed c-Myc regulation of expression and activity of AR-FL and AR-Vs in cell models and a patient-derived xenograft model. Mechanistically, c-Myc promotes the transcription of the AR gene and enhances the stability of the AR-FL and AR-V proteins without altering AR RNA splicing. Importantly, inhibiting c-Myc sensitizes enzalutamide-resistant cells to growth inhibition by enzalutamide. Overall, this study highlights a critical role of c-Myc in regulating the coordinated expression of AR-FL and AR-Vs that is commonly observed in CRPC and suggests the utility of targeting c-Myc as an adjuvant to AR-directed therapy.

Validation of the Decipher Test for predicting adverse pathology in candidates for prostate cancer active surveillance

Background

Many men diagnosed with prostate cancer are active surveillance (AS) candidates. However, AS may be associated with increased risk of disease progression and metastasis due to delayed therapy. Genomic classifiers, e.g., Decipher, may allow better risk-stratify newly diagnosed prostate cancers for AS.

Methods

Decipher was initially assessed in a prospective cohort of prostatectomies to explore the correlation with clinically meaningful biologic characteristics and then assessed in diagnostic biopsies from a retrospective multicenter cohort of 266 men with National Comprehensive Cancer Network (NCCN) very low/low and favorable-intermediate risk prostate cancer. Decipher and Cancer of the Prostate Risk Assessment (CAPRA) were compared as predictors of adverse pathology (AP) for which there is universal agreement that patients with long life-expectancy are not suitable candidates for AS (primary pattern 4 or 5, advanced local stage [pT3b or greater] or lymph node involvement).

Results

Decipher from prostatectomies was significantly associated with adverse pathologic features (p-values < 0.001). Decipher from the 266 diagnostic biopsies (64.7% NCCN-very-low/low and 35.3% favorable-intermediate) was an independent predictor of AP (odds ratio 1.29 per 10% increase, 95% confidence interval [CI] 1.03–1.61, p-value 0.025) when adjusting for CAPRA. CAPRA area under curve (AUC) was 0.57, (95% CI 0.47–0.68). Adding Decipher to CAPRA increased the AUC to 0.65 (95% CI 0.58–0.70). NPV, which determines the degree of confidence in the absence of AP for patients, was 91% (95% CI 87–94%) and 96% (95% CI 90–99%) for Decipher thresholds of 0.45 and 0.2, respectively. Using a threshold of 0.2, Decipher was a significant predictor of AP when adjusting for CAPRA (p-value 0.016).

Conclusion

Decipher can be applied to prostate biopsies from NCCN-very-low/low and favorable-intermediate risk patients to predict absence of adverse pathologic features. These patients are predicted to be good candidates for active surveillance.

Exploring the transcriptome of hormone-naive multifocal prostate cancer and matched lymph node metastases

Background

The current inability to predict whether a primary prostate cancer (PC) will progress to metastatic disease leads to overtreatment of indolent PCs as well as undertreatment of aggressive PCs. Here, we explored the transcriptional changes associated with metastatic progression of multifocal hormone-naive PC.

Methods

Using total RNA-sequencing, we analysed laser micro-dissected primary PC foci (n = 23), adjacent normal prostate tissue samples (n = 23) and lymph node metastases (n = 9) from ten hormone-naive PC patients. Genes important for PC progression were identified using differential gene expression and clustering analysis. From these, two multi-gene-based expression signatures (models) were developed, and their prognostic potential was evaluated using Cox-regression and Kaplan–Meier analyses in three independent radical prostatectomy (RP) cohorts (>650 patients).

Results

We identified several novel PC-associated transcripts deregulated during PC progression, and these transcripts were used to develop two novel gene-expression-based prognostic models. The models showed independent prognostic potential in three RP cohorts (n = 405, n = 107 and n = 91), using biochemical recurrence after RP as the primary clinical endpoint.

Conclusions

We identified several transcripts deregulated during PC progression and developed two new prognostic models for PC risk stratification, each of which showed independent prognostic value beyond routine clinicopathological factors in three independent RP cohorts.

The long noncoding RNA landscape of neuroendocrine prostate cancer and its clinical implications

Background

Treatment-induced neuroendocrine prostate cancer (tNEPC) is an aggressive variant of late-stage metastatic castrate-resistant prostate cancer that commonly arises through neuroendocrine transdifferentiation (NEtD). Treatment options are limited, ineffective, and, for most patients, result in death in less than a year. We previously developed a first-in-field patient-derived xenograft (PDX) model of NEtD. Longitudinal deep transcriptome profiling of this model enabled monitoring of dynamic transcriptional changes during NEtD and in the context of androgen deprivation. Long non-coding RNA (lncRNA) are implicated in cancer where they can control gene regulation. Until now, the expression of lncRNAs during NEtD and their clinical associations were unexplored.

Results

We implemented a next-generation sequence analysis pipeline that can detect transcripts at low expression levels and built a genome-wide catalogue (n = 37,749) of lncRNAs. We applied this pipeline to 927 clinical samples and our high-fidelity NEtD model LTL331 and identified 821 lncRNAs in NEPC. Among these are 122 lncRNAs that robustly distinguish NEPC from prostate adenocarcinoma (AD) patient tumours. The highest expressed lncRNAs within this signature are H19, LINC00617, and SSTR5-AS1. Another 742 are associated with the NEtD process and fall into four distinct patterns of expression (NEtD lncRNA Class I, II, III, and IV) in our PDX model and clinical samples. Each class has significant (z-scores >2) and unique enrichment for transcription factor binding site (TFBS) motifs in their sequences. Enriched TFBS include (1) TP53 and BRN1 in Class I, (2) ELF5, SPIC, and HOXD1 in Class II, (3) SPDEF in Class III, (4) HSF1 and FOXA1 in Class IV, and (5) TWIST1 when merging Class III with IV. Common TFBS in all NEtD lncRNA were also identified and include E2F, REST, PAX5, PAX9, and STAF. Interrogation of the top deregulated candidates (n = 100) in radical prostatectomy adenocarcinoma samples with long-term follow-up (median 18 years) revealed significant clinicopathological associations. Specifically, we identified 25 that are associated with rapid metastasis following androgen deprivation therapy (ADT). Two of these lncRNAs (SSTR5-AS1 and LINC00514) stratified patients undergoing ADT based on patient outcome.

Discussion

To date, a comprehensive characterization of the dynamic landscape of lncRNAs during the NEtD process has not been performed. A temporal analysis of the PDX-based NEtD model has for the first time provided this dynamic landscape. TFBS analysis identified NEPC-related TF motifs present within the NEtD lncRNA sequences, suggesting functional roles for these lncRNAs in NEPC pathogenesis. Furthermore, select NEtD lncRNAs appear to be associated with metastasis and patients receiving ADT. Treatment-related metastasis is a clinical consequence of NEPC tumours. Top candidate lncRNAs FENDRR, H19, LINC00514, LINC00617, and SSTR5-AS1 identified in this study are implicated in the development of NEPC. We present here for the first time a genome-wide catalogue of NEtD lncRNAs that characterize the transdifferentiation process and a robust NEPC lncRNA patient expression signature. To accomplish this, we carried out the largest integrative study that applied a PDX NEtD model to clinical samples. These NEtD and NEPC lncRNAs are strong candidates for clinical biomarkers and therapeutic targets and warrant further investigation.

Molecular Biomarkers in the Clinical Management of Prostate Cancer

Prostate cancer, one of the most common noncutaneous malignancies in men, is a heterogeneous disease with variable clinical outcome. Although the majority of patients harbor indolent tumors that are essentially cured by local therapy, subsets of patients present with aggressive disease or recur/progress after primary treatment. With this in mind, modern clinical approaches to prostate cancer emphasize the need to reduce overdiagnosis and overtreatment via personalized medicine. Advances in our understanding of prostate cancer pathogenesis, coupled with recent technologic innovations, have facilitated the development and validation of numerous molecular biomarkers, representing a range of macromolecules assayed from a variety of patient sample types, to help guide the clinical management of prostate cancer, including early detection, diagnosis, prognostication, and targeted therapeutic selection. Herein, we review the current state of the art regarding prostate cancer molecular biomarkers, emphasizing those with demonstrated utility in clinical practice.