A genomic classifier improves prediction of metastatic disease within 5 years after surgery in node-negative high-risk prostate cancer patients managed by radical prostatectomy without adjuvant therapy

Optimizing risk stratification for intermediate-risk prostate cancer - the prognostic value of baseline health-related quality of life

OBJECTIVE

To investigate the prognostic value of baseline health-related quality of life (HRQOL) for patients with intermediate-risk localized prostate cancer (IR-PCa) undergoing radical prostatectomy (RP).

METHODS

4780 patients with IR-PCa according to NCCN risk stratification were identified from a prospectively maintained database. All patients were treated with RP and had prospectively assessed baseline HRQOL. Main outcomes were oncologic endpoints metastasis-free survival (MFS); biochemical recurrence free survival (BRFS) and overall survival (OS). Multivariable Cox regression models assessed prognostic significance of baseline global health status (GHS) on survival outcomes. Harrell's discrimination C-index was applied to calculate the predictive accuracy of the model. Decision curve analysis (DCA) tested the clinical net benefit associated with adding the GHS domain to our multivariable model (p < 0.05).

RESULTS

Median follow-up was 51 months. Multivariable analysis confirmed baseline GHS as an independent predictor for increased MFS (HR 0.976, 95%CI 0.96-0.99; p < 0.001), increased BRFS (HR 0.993, 95%CI 0.99-1.00; p = 0.027) and increased OS (HR 0.969, 95%CI 0.95-0.99; p = 0.002), indicating a relative risk reduction of 2.4% for MFS, 0.7% for BRFS and 3.1% for OS per 1-point increase of baseline GHS. Baseline HRQOL improved discrimination in predicting MFS, BRFS and OS. DCA revealed a net benefit over all threshold probabilities.

CONCLUSIONS

We found baseline HRQOL to substantially improve risk stratification for the heterogeneous cohort of IR-PCa. Baseline HRQOL accurately predicts increased MFS, BRFS and OS. Our findings therefore support the role of preoperative HRQOL as an adjunct to established prognosticators for IR-PCa, potentially facilitating guidance of therapy.

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

BACKGROUND AND OBJECTIVE

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

METHODS

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

KEY FINDINGS AND LIMITATIONS

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

CONCLUSIONS AND CLINICAL IMPLICATIONS

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

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

BACKGROUND AND OBJECTIVE

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

METHODS

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

KEY FINDINGS AND LIMITATIONS

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

CONCLUSIONS AND CLINICAL IMPLICATIONS

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

PATIENT SUMMARY

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

Transcriptomic Profiling of Primary Prostate Cancers and Nonlocalized Disease on Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography: A Multicenter Retrospective Study

PURPOSE

To characterize the relationship between Decipher genomic classifier scores and prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT)-based metastatic spread.

MATERIALS AND METHODS

We identified patients from four institutions who underwent PSMA PET/CT scans pretreatment for primary staging or postradical prostatectomy (RP) for suspected recurrence and had Decipher transcriptomic data available from biopsy or RP specimens. PSMA PET/CT-based patterns of spread were classified as localized (miT + N0M0) or nonlocalized (miN1M0 or miM1a-c). We calculated the association between Decipher scores and the risk of nonlocalized disease on PSMA PET/CT using multivariable logistic regression for pretreatment patients and multivariable Cox regression for post-RP patients. We also compared select transcriptomic signatures between patients with localized and nonlocalized diseases.

RESULTS

Five hundred eighty-six patients were included (pretreatment: n = 329; post-RP: n = 257). Higher Decipher scores were associated with nonlocalized disease on PSMA PET/CT both pretreatment (odds ratio, 1.18 [95% CI, 1.03 to 1.36] per 0.1 increase in Decipher score, P = .02) and post-RP (hazard ratio, 1.15 [95% CI, 1.05 to 1.27] per 0.1 increase in Decipher score, P = .003). In the pretreatment setting, nonlocalized disease was associated with higher rates of TP53 mutations and lower rates of PAM50 luminal A subtype compared with localized disease. In the post-RP setting, overexpression of signatures related to metabolism, DNA repair, and androgen receptor signaling were associated with higher rates of nonlocalized disease.

CONCLUSION

Higher Decipher scores were associated with nonlocalized disease identified on PSMA PET/CT both pretreatment and post-RP. There were several transcriptomic differences between localized and nonlocalized diseases in both settings.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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.

Integrated Clinical-Molecular Classification of Colorectal Liver Metastases: A Biomarker Analysis of the Phase 3 New EPOC Randomized Clinical Trial

Importance

Personalized treatment approaches for patients with oligometastatic colorectal liver metastases are critically needed. We previously defined 3 biologically distinct molecular subtypes of colorectal liver metastases: (1) canonical, (2) immune, and (3) stromal.

Objective

To independently validate these molecular subtypes in the phase 3 New EPOC randomized clinical trial.

Design, Setting, and Participants

This retrospective secondary analysis of the phase 3 New EPOC randomized clinical trial included a bi-institutional discovery cohort and multi-institutional validation cohort. The discovery cohort comprised patients who underwent hepatic resection for limited colorectal liver metastases (98% received perioperative chemotherapy) from May 31, 1994, to August 14, 2012. The validation cohort comprised patients who underwent hepatic resection for liver metastases with perioperative chemotherapy (fluorouracil, oxaliplatin, and irinotecan based) with or without cetuximab from February 26, 2007, to November 1, 2012. Data were analyzed from January 18 to December 10, 2021.

Interventions

Resected metastases underwent RNA sequencing and microRNA (miRNA) profiling in the discovery cohort and messenger RNA and miRNA profiling with microarray in the validation cohort.

Main Outcomes and Measures

A 31-feature (24 messenger RNAs and 7 miRNAs) neural network classifier was trained to predict molecular subtypes in the discovery cohort and applied to the validation cohort. Integrated clinical-molecular risk groups were designated based on molecular subtypes and the clinical risk score. The unique biological phenotype of each molecular subtype was validated using gene set enrichment analyses and immune deconvolution. The primary clinical end points were progression-free survival (PFS) and overall survival (OS).

Results

A total of 240 patients were included (mean [range] age, 63.0 [56.3-68.0] years; 151 [63%] male), with 93 in the discovery cohort and 147 in the validation cohort. In the validation cohort, 73 (50%), 28 (19%), and 46 (31%) patients were classified as having canonical, immune, and stromal metastases, respectively. The biological phenotype of each subtype was concordant with the discovery cohort. The immune subtype (best prognosis) demonstrated 5-year PFS of 43% (95% CI, 25%-60%; hazard ratio [HR], 0.37; 95% CI, 0.20-0.68) and OS of 63% (95% CI, 40%-79%; HR, 0.38; 95% CI, 0.17-0.86), which was statistically significantly higher than the canonical subtype (worst prognosis) at 14% (95% CI, 7%-23%) and 43% (95% CI, 32%-55%), respectively. Adding molecular subtypes to the clinical risk score improved prediction (the Gönen and Heller K for discrimination) from 0.55 (95% CI, 0.49-0.61) to 0.62 (95% CI, 0.57-0.67) for PFS and 0.59 (95% CI, 0.52-0.66) to 0.63 (95% CI, 0.56-0.70) for OS. The low-risk integrated group demonstrated 5-year PFS of 44% (95% CI, 20%-66%; HR, 0.38; 95% CI, 0.19-0.76) and OS of 78% (95% CI, 44%-93%; HR, 0.26; 95% CI, 0.08-0.84), superior to the high-risk group at 16% (95% CI, 10%-24%) and 43% (95% CI, 32%-52%), respectively.

Conclusions and Relevance

In this prognostic study, biologically derived colorectal liver metastasis molecular subtypes and integrated clinical-molecular risk groups were highly prognostic. This novel molecular classification warrants further study as a possible predictive biomarker for personalized systemic treatment for colorectal liver metastases.

Trial Registration

isrctn.org Identifier: ISRCTN22944367.

Racial disparity in the genomics of precision oncology of prostate cancer

BACKGROUND

Significant racial disparities in prostate cancer incidence and mortality have been reported between African American Men (AAM), who are at increased risk for prostate cancer, and European American Men (EAM). In most of the studies carried out on prostate cancer, this population is underrepresented. With the advancement of genome-wide association studies, several genetic predictor models of prostate cancer risk have been elaborated, as well as numerous studies that identify both germline and somatic mutations with clinical utility.

RECENT FINDINGS

Despite significant advances, the AAM population continues to be underrepresented in genomic studies, which can limit generalizability and potentially widen disparities. Here we outline racial disparities in currently available genomic applications that are used to estimate the risk of individuals developing prostate cancer and to identify personalized oncology treatment strategies. While the incidence and mortality of prostate cancer are different between AAM and EAM, samples from AAM remain to be unrepresented in different studies.

CONCLUSION

This disparity impacts the available genomic data on prostate cancer. As a result, the disparity can limit the predictive utility of the genomic applications and may lead to the widening of the existing disparities. More studies with substantially higher recruitment and engagement of African American patients are necessary to overcome this disparity.

Histology Specific Molecular Biomarkers: Ushering in a New Era of Precision Radiation Oncology

Histopathology and clinical staging have historically formed the backbone for allocation of treatment decisions in oncology. Although this has provided an extremely practical and fruitful approach for decades, it has long been evident that these data alone do not adequately capture the heterogeneity and breadth of disease trajectories experienced by patients. As efficient and affordable DNA and RNA sequencing have become available, the ability to provide precision therapy has become within grasp. This has been realized with systemic oncologic therapy, as targeted therapies have demonstrated immense promise for subsets of patients with oncogene-driver mutations. Further, several studies have evaluated predictive biomarkers for response to systemic therapy within a variety of malignancies. Within radiation oncology, the use of genomics/transcriptomics to guide the use, dose, and fractionation of radiation therapy is rapidly evolving but still in its infancy. The genomic adjusted radiation dose/radiation sensitivity index is one such early and exciting effort to provide genomically guided radiation dosing with a pan-cancer approach. In addition to this broad method, a histology specific approach to precision radiation therapy is also underway. Herein we review select literature surrounding the use of histology specific, molecular biomarkers to allow for precision radiotherapy with the greatest emphasis on commercially available and prospectively validated biomarkers.

Radiomics and Radiogenomics in Pelvic Oncology: Current Applications and Future Directions

Radiomics refers to the conversion of medical imaging into high-throughput, quantifiable data in order to analyse disease patterns, guide prognosis and aid decision making. Radiogenomics is an extension of radiomics that combines conventional radiomics techniques with molecular analysis in the form of genomic and transcriptomic data, serving as an alternative to costly, labour-intensive genetic testing. Data on radiomics and radiogenomics in the field of pelvic oncology remain novel concepts in the literature. We aim to perform an up-to-date analysis of current applications of radiomics and radiogenomics in the field of pelvic oncology, particularly focusing on the prediction of survival, recurrence and treatment response. Several studies have applied these concepts to colorectal, urological, gynaecological and sarcomatous diseases, with individual efficacy yet poor reproducibility. This article highlights the current applications of radiomics and radiogenomics in pelvic oncology, as well as the current limitations and future directions. Despite a rapid increase in publications investigating the use of radiomics and radiogenomics in pelvic oncology, the current evidence is limited by poor reproducibility and small datasets. In the era of personalised medicine, this novel field of research has significant potential, particularly for predicting prognosis and guiding therapeutic decisions. Future research may provide fundamental data on how we treat this cohort of patients, with the aim of reducing the exposure of high-risk patients to highly morbid procedures.

Are high-risk prostate cancer patients being treated equally? The impact of PSA

BACKGROUND

Patients with high-risk (HR) prostate cancer (PCa) represent a heterogeneous group, however, current treatment guidelines do not consider their specific features. The objective of this study was to evaluate treatment trends and outcomes in HR patients defined by PSA alone and otherwise low-risk features.

METHODS

Using the National Cancer Database, we identified patients diagnosed with HR PCa between 2010 and 2016. A study group of patients defined by PSA >20 ng/ml alone and otherwise low-risk features, was compared to a group of HR patients defined by Gleason score or stage. We compared treatment rates over time, the use of concomitant androgen deprivation therapy (ADT), and overall survival (OS). Examination of treatment trends was done using a Z-test analysis. A Kaplan-Meier survival analysis was used to determine 5-year OS with the Log-rank test for comparison. Statistical analyses were completed using R Version 3.5.2.

RESULTS

We identified 5,652 patients in the study group and 71,922 in the comparison group. Only 6.8% of the study group had disease ≥cT2, compared to 43.7% in the comparison group. In the study group, 12.5% (709), underwent active surveillance (AS), 36.4% (2,055) radiation therapy (EBRT) and 51.1% (2,888) radical prostatectomy (RP), while the rate of AS, EBRT, and RP in the comparison group were 0.3% (191), 43.0% (30,928), and 56.7% (40,803), respectively. Over the study period, adoption of AS increased from 6.2% in 2010 to 25.0% in 2016 in the study group (P< 0.001), but not in the comparison group. In patients undergoing EBRT, ADT treatment increased from 2010 to 2016 in both groups, though by 2016 only 45.3% of patients in the study group and 86.3% in the comparison group received ADT. The 5-year OS was 93.7% (95% CI 92.8-94.6) in the study group and 89.7% (95% CI 89.2-90.1) in the comparison group (P< 0.001).

CONCLUSIONS

Men with HR PCa defined by PSA with otherwise low risk features present at an earlier stage and receive less aggressive therapy than other HR patients. Despite increased rates of AS and decreased use of ADT, these patients appear to have improved survival when compared to other HR patients. These findings suggest that not all HR patients will benefit from aggressive definitive treatment.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

ADVANCES IN KNOWLEDGE

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

When to order genomic tests: development and external validation of a model to predict high-risk prostate cancer at the genotypic level

PURPOSE

The aim of this study was to develop a model to predict high-genomic-risk prostate cancer (PCa) according to Decipher score, a validated 22 gene prognostic panel. By doing so, one might select the individuals who are likely to benefit from genomic testing and improve pre-op counseling about the need for adjuvant treatments.

METHODS

We retrospectively reviewed IRB-approved databases at two institutions. All patients had preoperative magnetic resonance imaging (MRI) and Decipher prostate radical prostatectomy (RP), a validated 22 gene prognostic panel. We used binary logistic regression to estimate high-risk Decipher (Decipher score > 0.60) probability on RP specimen. Area under the curve (AUC) and calibration were used to assess the accuracy of the model in the development and validation cohort. Decision curve analysis (DCA) was performed to assess the clinical benefit of the model.

RESULTS

The development and validation cohort included 622 and 185 patients with 283 (35%) and 80 (43%) of those with high-risk Decipher. The multivariable model included PSA density, biopsy Gleason Grade Group, percentage of positive cores and MRI extracapsular extension. AUC was 0.73 after leave-one-out cross-validation. DCA showed a clinical benefit in a range of probabilities between 15 and 60%. In the external validation cohort, AUC was 0.70 and calibration showed that the model underestimates the actual probability of the outcome.

CONCLUSIONS

The proposed model to predict high-risk Decipher score at RP is helpful to improve risk stratification of patients with PCa and to assess the need for additional testing and treatments.

Using PSMA imaging for prognostication in localized and advanced prostate cancer

The use of prostate-specific membrane antigen (PSMA)-directed applications in modern prostate cancer management has evolved rapidly over the past few years, helping to establish new treatment pathways and provide further insights into prostate cancer biology. However, the prognostic implications of PSMA-PET have not been studied systematically, owing to rapid clinical implementation without long follow-up periods to determine intermediate-term and long-term oncological outcomes. Currently available data suggest that traditional prognostic factors and survival outcomes are associated with high PSMA expression (both according to immunohistochemistry and PET uptake) in men with localized and biochemically recurrent disease. Treatment with curative intent (primary and/or salvage) often fails when PSMA-positive metastases are present; however, the sensitivity of PSMA-PET in detecting all metastases is poor. Low PSMA-PET uptake in recurrent disease is a favourable prognostic factor; however, it can be associated with poor prognosis in conjunction with high ¹⁸F-fluorodeoxyglucose uptake in metastatic castration-resistant prostate cancer. Clinical trials embedding PSMA-PET for guiding management with reliable oncological outcomes are needed to support ongoing clinical use.

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.

Genomic biomarkers to guide precision radiotherapy in prostate cancer

Our ability to prognosticate the clinical course of patients with cancer has historically been limited to clinical, histopathological, and radiographic features. It has long been clear however, that these data alone do not adequately capture the heterogeneity and breadth of disease trajectories experienced by patients. The advent of efficient genomic sequencing has led to a revolution in cancer care as we try to understand and personalize treatment specific to patient clinico-genomic phenotypes. Within prostate cancer, emerging evidence suggests that tumor genomics (e.g., DNA, RNA, and epigenetics) can be utilized to inform clinical decision making. In addition to providing discriminatory information about prognosis, it is likely tumor genomics also hold a key in predicting response to oncologic therapies which could be used to further tailor treatment recommendations. Herein we review select literature surrounding the use of tumor genomics within the management of prostate cancer, specifically leaning toward analytically validated and clinically tested genomic biomarkers utilized in radiotherapy and/or adjunctive therapies given with radiotherapy.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

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

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

Radiogenomics influence on the future of prostate cancer risk stratification

In an era of powerful computing tools, radiogenomics provides a personalized, precise approach to the detection and diagnosis in patients with prostate cancer (PCa). Radiomics data are obtained through artificial intelligence (AI) and neural networks that analyze imaging, usually MRI, to assess statistical, geometrical, and textural features of images to provide quantitative data of shape, heterogeneity, and intensity of tumors. Genomics involves assessing the genomic markers that are present from tumor biopsies. In this article, we separately investigate the current landscape of radiomics and genomics within the realm of PCa and discuss the integration and validity of both into radiogenomics using the data from three papers on the topic. We also conducted a clinical trials search using the NIH’s database, where we found two relevant actively recruiting studies. Although there is more research needed to be done on radiogenomics to fully adopt it as a viable diagnosis tool, its potential by providing personalized data regarding each tumor cannot be overlooked as it may be the future of PCa risk-stratification techniques.

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.

Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization

Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.

Molecular features of exceptional response to neoadjuvant anti-androgen therapy in high-risk localized prostate cancer

High-risk localized prostate cancer (HRLPC) is associated with a substantial risk of recurrence and disease mortality. Recent clinical trials have shown that intensifying anti-androgen therapies administered before prostatectomy can induce pathologic complete responses or minimal residual disease, called exceptional response, although the molecular determinants of these clinical outcomes are largely unknown. Here, we perform whole-exome and transcriptome sequencing on pre-treatment multi-regional tumor biopsies from exceptional responders (ERs) and non-responders (NRs, pathologic T3 or lymph node-positive disease) to intensive neoadjuvant anti-androgen therapies. Clonal SPOP mutation and SPOPL copy-number loss are exclusively observed in ERs, while clonal TP53 mutation and PTEN copy-number loss are exclusively observed in NRs. Transcriptional programs involving androgen signaling and TGF-β signaling are enriched in ERs and NRs, respectively. These findings may guide prospective validation studies of these molecular features in large HRLPC clinical cohorts treated with neoadjuvant anti-androgens to improve patient stratification.

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

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

Radiogenomics in prostate cancer evaluation

PURPOSE OF REVIEW

Radiogenomics, fusion between radiomics and genomics, represents a new field of research to improve cancer comprehension and evaluation. In this review, we give an overview of radiogenomics and its most recent and relevant applications in prostate cancer management.

RECENT FINDINGS

Literature about radiogenomics in prostate cancer emerged last 5 years but remains scarce. Radiogenomics in prostate cancer mainly rely on MRI-based features. Several imaging biomarkers, mostly based on the identification of radiomic features from deep learning studies, have been studied for the prediction of genomic profiles, such as PTEN Decipher Oncotype DX or Prolaris expression. However, despite promising results, several limitations still preclude any integration of radiogenomics in daily practice.

SUMMARY

In the future, the emergence of artificial intelligence in urology, with an increasing use of radiomics and genomics data, may enable radiogenomics to assume a growing role in the evaluation of prostate cancer, with a noninvasive and personal approach in the field of personalized medicine. Further efforts are necessary for integration of this promising approach in prostate cancer decision-making.

A polymorphism in the promoter of FRAS1 is a candidate SNP associated with metastatic prostate cancer

BACKGROUND

Inflammation and one of its mediators, NF-kappa B (NFκB), have been implicated in prostate cancer carcinogenesis. We assessed whether germline polymorphisms associated with NFκB are associated with the risk of developing lethal disease (metastases or death from prostate cancer).

METHODS

Using a Bayesian approach leveraging NFκB biology with integration of publicly available datasets we used a previously defined genome-wide functional association network specific to NFκB and lethal prostate cancer. A dense-module-searching method identified modules enriched with significant genes from a genome-wide association study (GWAS) study in a discovery data set, Physicians' Health Study and Health Professionals Follow-up Study (PHS/HPFS). The top 48 candidate single nucleotide polymorphisms (SNPs) from the dense-module-searching method were then assessed in an independent prostate cancer cohort and the one SNP reproducibly associated with lethality was tested in a third cohort. Logistic regression models evaluated the association between each SNP and lethal prostate cancer. The candidate SNP was assessed for association with lethal prostate cancer in 6 of 28 studies in the prostate cancer association group to investigate cancer associated alterations in the genome (PRACTICAL) Consortium where there was some medical record review for death ascertainment which also had SNP data from the ONCOARRAY platform. All men self-identified as Caucasian.

RESULTS

The rs1910301 SNP which was reproducibly associated with lethal disease was nominally associated with lethal disease (odds ratio [OR] = 1.40; p = .02) in the discovery cohort and the minor allele was also associated with lethal disease in two independent cohorts (OR = 1.35; p = .04 and OR = 1.35; p = .07). Fixed effects meta-analysis of all three cohorts found an association: OR = 1.37 (95% confidence interval [CI]: 1.15-1.62, p = .0003). This SNP is in the promoter region of FRAS1, a gene involved in epidermal-basement membrane adhesion and is present at a higher frequency in men with African ancestry. No association was found in the subset of studies from the PRACTICAL consortium studies which had a total of 106 deaths out total of 3263 patients and a median follow-up of 4.4 years.

CONCLUSIONS

Through its connection with the NFκB pathway, a candidate SNP with a higher frequency in men of African ancestry without cancer was found to be associated with lethal prostate cancer across three well-annotated independent cohorts of Caucasian men.

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.

Reference-free transcriptome signatures for prostate cancer prognosis

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

Question

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

Findings

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

Meaning

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

Importance

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

Objective

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

Design, Setting, and Participants

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

Intervention

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

Main Outcomes and Measures

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

Results

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

Conclusions and Relevance

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

Trial Registration

ClinicalTrials.gov identifier: NCT00002874

Does post prostatectomy decipher score predict biochemical recurrence and impact care?

PURPOSE

To examine the ability of the Decipher test to predict early biochemical recurrence after radical prostatectomy and to impact clinical decisions in advance of metastasis and death.

METHODS

We identified Decipher tests ordered after radical prostatectomy for adverse pathology in men treated for prostate cancer between 1/1/14 and 8/31/18. Biochemical recurrence was defined as prostate-specific antigen > 0.02 ng/mL. Decipher score is reported as lower risk (< 0.6) and higher risk ≥ 0.60). Kaplan-Meier analysis was used to examine the relationship between Decipher score and time to biochemical recurrence (months). Cox regression was used to analyze the relationship between Decipher score and time to biochemical recurrence while controlling for a number of clinical characteristics. Secondary analyses focused on a subset of men with prostate-specific antigen > 0.02 and < 0.20 ng/mL to determine if high-risk Decipher scores were associated with receipt of salvage treatment.

RESULTS

A total of 203 cases were analyzed: 37.9% and 62.1% had lower and higher risk Decipher scores respectively, and 56.2% had a biochemical recurrence. Median (inter-quartile range) follow-up was 20 (13.5, 25.3) months. Decipher score was significantly associated with time to biochemical recurrence (p = 0.027) while in the secondary analyses, high-risk Decipher scores (≥ 0.60) were associated with salvage treatment (p = 0.018). Stage category and Decipher score were significant predictors of time from elevated PSA to salvage treatment in the secondary analyses.

CONCLUSION

While it might not contribute statistically, Decipher score can be clinically useful in helping patients reach treatment decisions.

GPS Assay Association With Long-Term Cancer Outcomes: Twenty-Year Risk of Distant Metastasis and Prostate Cancer-Specific Mortality

PURPOSE

To assess the association between the Oncotype DX Genomic Prostate Score (GPS) result and long-term oncological outcomes following radical prostatectomy (RP).

METHODS

We evaluated the association of the GPS result assayed from the index lesion from RP tissue with the risk of distant metastases (DM) and prostate cancer–specific mortality (PCSM) over the 20 years following RP in a stratified cohort sample of 428 patients from 2,641 treated between 1987 and 2004. Cox regression of cause-specific hazards was used to estimate the absolute risk of both end points, with death from other causes treated as a competing risk. A correction for regression to the mean (RM) was applied since the GPS test was developed using this cohort. Exploratory analysis using presurgical parameters and the GPS test as prognostic variables was performed to assess the additional value of the GPS test on 20-year risk of DM and PCSM. Model discrimination was measured using the area under the receiver operating characteristic curve.

RESULTS

The GPS test appears to be independently associated with both 20-year risk of DM and PCSM with a low false discovery rate. Per 20-unit increase in GPS, multivariable analysis with RM correction estimated hazard ratios of 2.24 (95% CI, 1.49 to 3.53) and 2.30 (95% CI, 1.45 to 4.36) for DM and PCSM, respectively. Accuracy of models including clinical risk factors alone appeared to improve when including the GPS test in assessing risk of both end points.

CONCLUSION

The results suggest that the GPS test provides information on the risk for the meaningful long-term outcomes of DM and PCSM.

A review on the role of tissue-based molecular biomarkers for active surveillance

PURPOSE

Over the last decade, we have seen the emergence of tissue-based genomic prognostic markers that can be used for decision-making regarding the need for treatment. This review provides an up-to-date summary of the relevant literature surrounding these markers with a discussion of the relevant strength and limitations.

METHODS

We performed a literature search of tissue-based genomic prognostic markers and selected those that were currently available for clinical use. We selected the following markers for further review: Decipher (Decipher Bioscience), Polaris (Myriad), Genome Prostate Score (Oncotype Dx), and Promark. We selected the initial validation study for each marker along with other validation studies in independent cohorts. Furthermore, we selected available clinical utility studies or studies combining multi parametric MRI.

RESULTS

In this article, we provide an in-depth review of four commercially available biomarkers and discuss the current literature surrounding these markers, including the benefits and limitations of their use. We found that each of these markers has evidence supporting their role as an independent predictor of relevant prostate cancer endpoints, which can be helpful for clinical decision-making. However, issues related to heterogeneity and a lack of prospective randomized studies supporting their utility are limitations. Evidence appears to suggest that MRI and genomic risk assessment maybe complementary.

CONCLUSION

Although these markers can help in improved risk stratification of patients eligible for AS, more prospective studies with head to head comparison between markers are needed to elucidate the true potential of these markers in AS.

Commercialized Blood-, Urinary- and Tissue-Based Biomarker Tests for Prostate Cancer Diagnosis and Prognosis

In the diagnosis and prognosis of prostate cancer (PCa), the serum prostate-specific antigen test is widely used but is associated with low specificity. Therefore, blood-, urinary- and tissue-based biomarker tests have been developed, intended to be used in the diagnostic and prognostic setting of PCa. This review provides an overview of commercially available biomarker tests developed to be used in several clinical stages of PCa management. In the diagnostic setting, the following tests can help selecting the right patients for initial and/or repeat biopsy: PHI, 4K, MiPS, SelectMDx, ExoDx, Proclarix, ConfirmMDx, PCA3 and PCMT. In the prognostic setting, the Prolaris, OncotypeDx and Decipher test can help in risk-stratification of patients regarding treatment decisions. Following, an overview is provided of the studies available comparing the performance of biomarker tests. However, only a small number of recently published head-to-head comparison studies are available. In contrast, recent research has focused on the use of biomarker tests in relation to the (complementary) use of multiparametric magnetic resonance imaging in PCa diagnosis.

Utilization of multimodality therapy with primary radical prostatectomy versus radiation therapy for Gleason 8-10 prostate cancer

PURPOSE

The role of multimodality therapy (MMT) in the treatment of Gleason 8-10 prostate cancer remains controversial. We sought to evaluate factors associated with MMT utilization for primary radical prostatectomy (RP) and primary radiation therapy (RT).

METHODS AND MATERIALS

From the National Cancer Database, we conducted a retrospective review of 81,528 men with National Cancer Center Network Gleason 8-10 prostate cancer diagnosed between 2004 and 2015, who underwent (1) primary RP with or without early postoperative external beam RT (EBRT) or (2) primary RT (androgen deprivation therapy + EBRT) with or without brachytherapy (BT) boost. Using multivariable logistic regression models, we evaluated factors associated with the utilization of MMT, defined as early postoperative EBRT for primary RP or BT boost for primary RT.

RESULTS

For primary RP, the percentages of men who underwent MMT for Gleason 8 and 9-10 disease were 12.2% and 24.1%, respectively. On multivariable logistic regression, men with Gleason 9-10 were more likely to undergo MMT (odds ratio 1.03 [1.02, 1.04]), although adverse pathologic features such as T3b-4 (1.24 [1.23, 1.25]) disease demonstrated the strongest associations. For primary RT, the percentages of men who underwent BT boost for Gleason 8 and 9-10 disease were 11.8% and 9.8%, respectively. On multivariable logistic regression, men with Gleason 9-10 disease were less likely to receive BT boost (0.99 [0.98, 0.99]).

CONCLUSIONS

Men with more aggressive Gleason 9 disease were more likely to undergo MMT if they underwent primary RP but not primary RT. Further blood-based or imaging biomarkers may aid in identifying optimal candidates for MMT, especially for primary RT.

The Evolution of Our Understanding of the Biology of Cancer Is the Key to Avoiding Overdiagnosis and Overtreatment

There has been a tremendous evolution in our thinking about cancer since the 1880s. Breast cancer is a particularly good example to evaluate the progress that has been made and the new challenges that have arisen due to screening that inadvertently identifies indolent lesions. The degree to which overdiagnosis is a problem depends on the reservoir of indolent disease, the disease heterogeneity, and the fraction of the tumors that have aggressive biology. Cancers span the spectrum of biological behavior, and population-wide screening increases the detection of tumors that may not cause harm within the patient's lifetime or may never metastasize or result in death. Our approach to early detection will be vastly improved if we understand, address, and adjust to tumor heterogeneity. In this article, we use breast cancer as a case study to demonstrate how the approach to biological characterization, diagnostics, and therapeutics can inform our approach to screening, early detection, and prevention. Overdiagnosis can be mitigated by developing diagnostics to identify indolent disease, incorporating biology and risk assessment in screening strategies, changing the pathology rules for tumor classification, and refining the way we classify precancerous lesions. The more the patterns of cancers can be seen across other cancers, the more it is clear that our approach should transcend organ of origin. This will be particularly helpful in advancing the field by changing both our terminology for what is cancer and also by helping us to learn how best to mitigate the risk of the most aggressive cancers.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

The role of radiomics in prostate cancer radiotherapy

"Radiomics," as it refers to the extraction and analysis of a large number of advanced quantitative radiological features from medical images using high-throughput methods, is perfectly suited as an engine for effectively sifting through the multiple series of prostate images from before, during, and after radiotherapy (RT). Multiparametric (mp)MRI, planning CT, and cone beam CT (CBCT) routinely acquired throughout RT and the radiomics pipeline are developed for extraction of thousands of variables. Radiomics data are in a format that is appropriate for building descriptive and predictive models relating image features to diagnostic, prognostic, or predictive information. Prediction of Gleason score, the histopathologic cancer grade, has been the mainstay of the radiomic efforts in prostate cancer. While Gleason score (GS) is still the best predictor of treatment outcome, there are other novel applications of quantitative imaging that are tailored to RT. In this review, we summarize the radiomics efforts and discuss several promising concepts such as delta-radiomics and radiogenomics for utilizing image features for assessment of the aggressiveness of prostate cancer and its outcome. We also discuss opportunities for quantitative imaging with the advance of instrumentation in MRI-guided therapies.

Omics Derived Biomarkers and Novel Drug Targets for Improved Intervention in Advanced Prostate Cancer

Prostate cancer (PCa) is one of the most frequently diagnosed malignancies, and the fifth leading cause of cancer related mortality in men. For advanced PCa, radical prostatectomy, radiotherapy, and/or long-term androgen deprivation therapy are the recommended treatment options. However, subsequent progression to metastatic disease after initial therapy results in low 5-year survival rates (29%). Omics technologies enable the acquisition of high-resolution large datasets that can provide insights into molecular mechanisms underlying PCa pathology. For the purpose of this article, a systematic literature search was conducted through the Web of Science Database to critically evaluate recent omics-driven studies that were performed towards: (a) Biomarker development and (b) characterization of novel molecular-based therapeutic targets. The results indicate that multiple omics-based biomarkers with prognostic and predictive value have been validated in the context of PCa, with several of those being also available for commercial use. At the same time, omics-driven potential drug targets have been investigated in pre-clinical settings and even in clinical trials, holding the promise for improved clinical management of advanced PCa, as part of personalized medicine pipelines.

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.

SEER and Gene Expression Data Analysis Deciphers Racial Disparity Patterns in Prostate Cancer Mortality and the Public Health Implication

A major racial disparity in prostate cancer (PCa) is that African American (AA) patients have a higher mortality rate than European American (EA) patients. We filtered the SEER 2009-2011 records and divided them into four groups regarding patient races and cancer grades. On such a partition, we performed a series of statistical analyses to further clarify the aforementioned disparity. Molecular evidence for a primary result of the epidemiological analysis was obtained from gene expression data. The results include: (1) Based on the registry-specific measures, a significant linear regression of total mortality rate (as well as PCa specific mortality rate) on the percentage of (Gleason pattern-based) high-grade cancers (PHG) is demonstrated in EAs (p < 0.01) but not in AAs; (2) PHG and its racial disparity are differentiated across ages and the groups defined by patient outcomes; (3) For patients with cancers in the same grade category, i.e. the high or low grade, the survival stratification between races is not significant in most geographical areas; and (4) The genes differentially expressed between AAs' and EAs' tumors of the same grade category are relatively rare. The perception that prostate tumors are more lethal in AAs than in EAs is reasonable regarding AAs' higher PHG, while high grade alone could not imply aggressiveness. However, this perception is questionable when the comparison is focused on cases within the same grade category. Supporting observations for this conclusion hold a remarkable implication for erasing racial disparity in PCa. That is, "Equal grade, equal outcomes" is not only a verifiable hypothesis but also an achievable public health goal.

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.

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.