Use of a 17-Gene Prognostic Assay in Contemporary Urologic Practice: Results of an Interim Analysis in an Observational Cohort

Genomics in active surveillance and post-prostatectomy patients: A review of when and how to use effectively

PURPOSE OF REVIEW

Prostate cancer (PCa) represents a significant health burden globally, ranking as the most diagnosed cancer among men and a leading cause of cancer-related mortality. Conventional treatment methods such as radiation therapy or radical prostatectomy have significant side effects which often impact quality of life. As our understanding of the natural history and progression of PCa has evolved, so has the evolution of management options.

RECENT FINDINGS

Active surveillance (AS) has become an increasingly favored approach to the management of very low, low, and properly selected favorable intermediate risk PCa. AS permits ongoing observation and postpones intervention until definitive treatment is required. There are, however, challenges with selecting patients for AS, which further emphasizes the need for more precise tools to better risk stratify patients and choose candidates more accurately. Tissue-based biomarkers, such as ProMark, Prolaris, GPS (formerly Oncotype DX), and Decipher, are valuable because they improve the accuracy of patient selection for AS and offer important information on the prognosis and severity of disease. By enabling patients to be categorized according to their risk profiles, these biomarkers help physicians and patients make better informed treatment choices and lower the possibility of overtreatment. Even with their potential, further standardization and validation of these biomarkers is required to guarantee their broad clinical utility. Active surveillance has emerged as a preferred strategy for managing low-risk prostate cancer, and tissue-based biomarkers play a crucial role in refining patient selection and risk stratification. Standardization and validation of these biomarkers are essential to ensure their widespread clinical use and optimize patient outcomes.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Tissue-Based Genomic Testing in Prostate Cancer: 10-Year Analysis of National Trends on the Use of Prolaris, Decipher, ProMark, and Oncotype DX

BACKGROUND

Prostate cancer (PCa) management is moving towards patient-tailored strategies. Advances in molecular and genetic profiling of tumor tissues, integrated with clinical risk assessments, provide deeper insights into disease aggressiveness. This study aims to offer a comprehensive overview of the pivotal genomic tests supporting PCa treatment decisions, analyzing-through real-world data-trends in their use and the growth of supporting literature evidence.

METHODS

A retrospective analysis was conducted using the extensive PearlDiver™ Mariner database, which contains de-identified patient records, in compliance with the Health Insurance Portability and Accountability Act (HIPAA). The International Classification of Diseases (ICD) and Current Procedural Terminology (CPT) codes were employed to identify patients diagnosed with PCa during the study period-2011 to 2021. We determined the utilization of primary tissue-based genetic tests (Oncocyte DX®, Prolaris®, Decipher®, and ProMark®) across all patients diagnosed with PCa. Subsequently, within the overall PCa cohort, patients who underwent radical prostatectomy (RP) and received genetic testing postoperatively were identified. The yearly distribution of these tests and the corresponding trends were illustrated with graphs.

RESULTS

During the study period, 1,561,203 patients with a PCa diagnosis were recorded. Of these, 20,748 underwent tissue-based genetic testing following diagnosis, representing 1.3% of the total cohort. An increasing trend was observed in the use of all genetic tests. Linear regression analysis showed a statistically significant increase over time in the use of individual tests (all p-values < 0.05). Among the patients who underwent RP, 3076 received genetic analysis following surgery, representing 1.27% of this group.

CONCLUSIONS

Our analysis indicates a growing trend in the utilization of tissue-based genomic testing for PCa. Nevertheless, they are utilized in less than 2% of PCa patients, whether at initial diagnosis or after surgical treatment. Although it is anticipated that their use may increase as more scientific evidence becomes available, their role requires further elucidation.

The Impact of Positron Emission Tomography Imaging and Tumor Molecular Profiling on Risk Stratification, Treatment Choice, and Oncological Outcomes of Patients with Primary or Relapsed Prostate Cancer: An International Collaborative Review of the Existing Literature

CONTEXT

The clinical introduction of next-generation imaging methods and molecular biomarkers ("radiogenomics") has revolutionized the field of prostate cancer (PCa). While the clinical validity of these tests has thoroughly been vetted, their clinical utility remains a matter of investigation.

OBJECTIVE

To systematically review the evidence to date on the impact of positron emission tomography (PET) imaging and tissue-based prognostic biomarkers, including Decipher, Prolaris, and Oncotype Dx, on the risk stratification, treatment choice, and oncological outcomes of men with newly diagnosed PCa or those with biochemical failure (BCF).

EVIDENCE ACQUISITION

We performed a quantitative systematic review of the literature using the MEDLINE, EMBASE, and Web of Science databases (2010-2022) following the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement guidelines. The validated Quality Assessment of Diagnostic Accuracy Studies 2 scoring system was used to assess the risk of bias.

EVIDENCE SYNTHESIS

A total of 148 studies (130 on PET and 18 on biomarkers) were included. In the primary PCa setting, prostate-specific membrane antigen (PSMA) PET imaging was not useful in improving T staging, moderately useful in improving N staging, but consistently useful in improving M staging in patients with National Comprehensive Cancer Network (NCCN) unfavorable intermediate- to very-high-risk PCa. Its use led to a management change in 20-30% of patients. However, the effect of these treatment changes on survival outcomes was not clear. Similarly, biomarkers in the pretherapy primary PCa setting increased and decreased the risk, respectively, in 7-30% and 32-36% of NCCN low-risk and 31-65% and 4-15% of NCCN favorable intermediate-risk patients being considered for active surveillance. A change in management was noted in up to 65% of patients, with the change being in line with the molecular risk-based reclassification, but again, the impact of these changes on survival outcomes remained unclear. Notably, in the postsurgical primary PCa setting, biomarker-guided adjuvant radiation therapy (RT) was associated with improved oncological control: Δ↓ 2-yr BCF by 22% (level 2b). In the BCF setting, the data were more mature. PSMA PET was consistently useful in improving disease localization-Δ↑ detection for T, N, and M staging was 13-32%, 19-58%, and 9-29%, respectively. Between 29% and 73% of patients had a change in management. Most importantly, these management changes were associated with improved survival outcomes in three trials: Δ↑ 4-yr disease-free survival by 24.3%, Δ↑ 6-mo metastasis-free survival (MFS) by 46.7%, and Δ↑ androgen deprivation therapy-free survival by 8 mo in patients who received PET-concordant RT (level 1b-2b). Biomarker testing in these patients also appeared to be helpful in risk stratifying and guiding the use of early salvage RT (sRT) and concomitant hormonal therapy. Patients with high-genomic-risk scores benefitted from treatment intensification: Δ↑ 8-yr MFS by 20% with the use of early sRT and Δ↑ 12-yr MFS by 11.2% with the use of hormonal therapy alongside early sRT, while low-genomic-risk score patients did equally well with initial conservative management (level 3).

CONCLUSIONS

Both PSMA PET imaging and tumor molecular profiling provide actionable information in the management of men with primary PCa and those with BCF. Emerging data suggest that radiogenomics-guided treatments translate into direct survival benefits for patients, however, additional prospective data are awaited.

PATIENT SUMMARY

In this review, we evaluated the utility of prostate-specific membrane antigen positron emission tomography and tumor molecular profiling in guiding the care of men with prostate cancer (PCa). We found that these tests augmented risk stratification, altered management, and improved cancer control in men with a new diagnosis of PCa or for those experiencing a relapse.

Association between urology practice use of multiparametric MRI and genomic testing and treatment of men with newly diagnosed prostate cancer

INTRODUCTION

Biomarkers for prostate cancer, such as multiparametric MRI (mpMRI) and tissue-based genomics, are increasingly used for treatment decision-making. Using biomarkers indiscriminately and thus ignoring competing risks of mortality may lead to treatment in some men who derive little clinical benefit. We assessed the relationship between urology practice use of biomarkers and subsequent treatment in men with newly diagnosed prostate cancer.

METHODS

We used a 20% random sample of national Medicare data to perform a retrospective cohort study of men with newly diagnosed prostate cancer diagnosed from 2015 through 2019. Urology practice-level use of biomarkers was characterized based on urology practice propensity to use either biomarker after diagnosis (never, below median, above the median). Noncancer mortality risk within 10 years of diagnosis was calculated for all men. Multilevel models were used to assess the relationship between practice-level biomarker use and treatment by noncancer mortality risk.

RESULTS

Between 2015 and 2019, 1,764 (65%) urology practices used mpMRI and 897 (33%) used genomic testing for prostate cancer. Compared with urology practices never using each biomarker, those using mpMRI above the median (56% vs. 47%, P = 0.003) and tissue-based genomics below the median (56% vs. 50%, P = 0.03) were more likely to treat men with >75% risk of noncancer mortality. Additionally, compared with urology practices never using either biomarker, use of mpMRI (72% vs. 69%, P = 0.07) or tissue-based genomics (71% vs. 70%, P = 0.65) did not impact treatment in the healthiest group (i.e., those with <25% risk of noncancer mortality).

CONCLUSIONS

Compared to practices that do not use each biomarker in men with newly diagnosed prostate cancer, urology practices using mpMRI, and tissue-based genomics to a lesser extent, are more likely to treat men at very high risk of dying from competing risks of mortality within 10 years of prostate cancer diagnosis.

Can We Predict Prostate Cancer Metastasis Based on Biomarkers? Where Are We Now?

The incidence of prostate cancer (PC) has risen annually. PC mortality is explained by the metastatic disease (mPC). There is an intermediate scenario in which patients have non-mPC but have initiated a metastatic cascade through epithelial-mesenchymal transition. There is indeed a need for more and better tools to predict which patients will progress in the future to non-localized clinical disease or already have micrometastatic disease and, therefore, will clinically progress after primary treatment. Biomarkers for the prediction of mPC are still under development; there are few studies and not much evidence of their usefulness. This review is focused on tissue-based genomic biomarkers (TBGB) for the prediction of metastatic disease. We develop four main research questions that we attempt to answer according to the current evidence. Why is it important to predict metastatic disease? Which tests are available to predict metastatic disease? What impact should there be on clinical guidelines and clinical practice in predicting metastatic disease? What are the current prostate cancer treatments? The importance of predicting metastasis is fundamental given that, once metastasis is diagnosed, quality of life (QoL) and survival drop dramatically. There is still a need and space for more cost-effective TBGB tests that predict mPC disease.

The 17-Gene Genomic Prostate Score Assay Is Prognostic for Biochemical Failure in Men With Localized Prostate Cancer After Radiation Therapy at a Community Cancer Center

Purpose

The objective of this study was to assess the association between the Oncotype DX Genomic Prostate Score (GPS) assay and long-term outcomes in men with localized prostate cancer (PCa) after radiation therapy (RT). We hypothesized that the GPS assay is prognostic for biochemical failure (BCF), along with distant metastasis (DM) and PCa-specific mortality in patients with PCa receiving RT.

Methods and Materials

We retrospectively studied men with localized PCa treated with definitive RT at Georgia Urology from 2010 to 2016. The primary objective was to assess the association between GPS results and time to BCF per the Phoenix criteria; we also assessed time to DM and PCa-specific mortality. We used Cox proportional hazards regression models for all analyses, with clinicopathologic covariates determined a priori for multivariable modeling.

Results

A total of 450 patients (median age, 65 years; 35% Black) met eligibility criteria. There was a strong univariable association between GPS result and time to BCF (hazard ratio [HR] per 20-unit increase = 3.08; 95% confidence interval [CI], 2.11-4.46; P < .001), which persisted after adjusting for clinicopathologic characteristics in multivariable analyses. We also observed this association for time to DM (HR = 5.19; 95% CI, 3.06-8.77; P < .001) and PCa-specific mortality (HR = 13.07; 95% CI, 4.42-49.39; P < .001). Race was not a predictor of time to BCF or DM, and the GPS assay was strongly prognostic for all endpoints in Black and White patients.

Conclusions

In a community-based cohort, the GPS assay was strongly prognostic for time to BCF as well as long-term outcomes in men treated with RT for localized PCa.

The 17-Gene Genomic Prostate Score Test Is Prognostic for Outcomes After Primary External Beam Radiation Therapy in Men With Clinically Localized Prostate Cancer

PURPOSE

The Oncotype DX Genomic Prostate Score (GPS) assay has been validated as a strong prognostic indicator of adverse pathology, biochemical recurrence, distant metastasis (DM), and prostate cancer (PCa)-related death (PCD) in men with localized PCa after radical prostatectomy. However, it has yet to be tested in men undergoing external beam radiation therapy (EBRT), for whom assessing PCa progression risk could inform decisions on treatment intensity. We analyzed whether GPS results are associated with time to biochemical failure (BCF), DM, and PCD after EBRT in men with localized PCa and whether the association is modified by race.

METHODS AND MATERIALS

We conducted a retrospective study of men with localized PCa treated with EBRT at the VA Health Care System in Durham, NC from 2000 to 2016. Study endpoints were time to BCF per the Phoenix criteria, DM, and PCD. The association of GPS results, per 20-unit increase or dichotomous variable (0-40 vs 41-100), was evaluated with each endpoint using univariable and multivariable Cox proportional hazards models. Results were then stratified by race.

RESULTS

A total of 238 patients (69% Black) met the eligibility criteria. Median follow-up for patients who did not experience BCF was 7.6 years. GPS results per 20-unit increase were significantly associated with BCF (hazard ratio [HR], 3.62; 95% confidence interval [CI], 2.59-5.02), DM (HR, 4.48; 95% CI, 2.75-7.38), and PCD (HR, 5.36; 95% CI, 3.06-9.76) in univariable analysis. GPS results remained significant in multivariable models adjusted for baseline clinical and pathological factors, with HRs being similar to the univariable analysis. There was no significant interaction between the GPS assay and race (P = .923). HRs for BCF were similar in Black men (HR, 3.88; 95% CI, 2.40-6.24) versus non-Black men (HR, 4.01; 95% CI, 2.42-6.45).

CONCLUSIONS

Among men treated with EBRT, the GPS assay is a strong, independent prognostic indicator of time to BCF, DM, and PCD, and performs similarly in Black and non-Black men.

The novel transcriptomic signature of angiogenesis predicts clinical outcome, tumor microenvironment and treatment response for prostate adenocarcinoma

Angiogenesis plays the critical roles in promoting tumor progression, aggressiveness, and metastasis. Although few studies have revealed some angiogenesis-related genes (ARGs) could serve as prognosis-related biomarkers for the prostate cancer (PCa), the integrated role of ARGs has not been systematically studied. The RNA-sequencing data and clinical information of prostate adenocarcinoma (PRAD) were downloaded from The Cancer Genome Atlas (TCGA) as discovery dataset. Twenty-three ARGs in total were identified to be correlated with prognosis of PRAD by the univariate Cox regression analysis, and a 19-ARG signature was further developed with significant correlation with the disease-free survival (DFS) of PRAD by the least absolute shrinkage and selection operator (LASSO) Cox regression with tenfold cross-validation. The signature stratified PRAD patients into high- and low-ARGs signature score groups, and those with high ARGs signature score were associated with significantly poorer outcomes (median DFS: 62.71 months vs unreached, p < 0.0001). The predicting ability of ARGs signature was subsequently validated in two independent cohorts of GSE40272 & PRAD_MSKCC. Notably, the 19-ARG signature outperformed the typical clinical features or each involved ARG in predicting the DFS of PRAD. Furthermore, a prognostic nomogram was constructed with three independent prognostic factors, including the ARGs signature, T stage and Gleason score. The predicted results from the nomogram (C-index = 0.799, 95%CI = 0.744-0.854) matched well with the observed outcomes, which was verified by the calibration curves. The values of area under receiver operating characteristic curve (AUC) for DFS at 1-, 3-, 5-year for the nomogram were 0.82, 0.83, and 0.83, respectively, indicating the performance of nomogram model is of reasonably high accuracy and robustness. Moreover, functional enrichment analysis demonstrated the potential targets of E2F targets, G2M checkpoint pathways, and cell cycle pathways to suppress the PRAD progression. Of note, the high-risk PRAD patients were more sensitive to immune therapies, but Treg might hinder benefits from immunotherapies. Additionally, this established tool also could predict response to neoadjuvant androgen deprivation therapy (ADT) and some chemotherapy drugs, such as cisplatin, paclitaxel, and docetaxel, etc. The novel ARGs signature, with prognostic significance, can further promote the application of targeted therapies in different stratifications of PCa patients.

Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networks

Motivation

Molecular phenotyping by gene expression profiling is central in contemporary cancer research and in molecular diagnostics but remains resource intense to implement. Changes in gene expression occurring in tumours cause morphological changes in tissue, which can be observed on the microscopic level. The relationship between morphological patterns and some of the molecular phenotypes can be exploited to predict molecular phenotypes from routine haematoxylin and eosin-stained whole slide images (WSIs) using convolutional neural networks (CNNs). In this study, we propose a new, computationally efficient approach to model relationships between morphology and gene expression.

Results

We conducted the first transcriptome-wide analysis in prostate cancer, using CNNs to predict bulk RNA-sequencing estimates from WSIs for 370 patients from the TCGA PRAD study. Out of 15 586 protein coding transcripts, 6618 had predicted expression significantly associated with RNA-seq estimates (FDR-adjusted P-value <1×10⁻⁴) in a cross-validation and 5419 (81.9%) of these associations were subsequently validated in a held-out test set. We furthermore predicted the prognostic cell-cycle progression score directly from WSIs. These findings suggest that contemporary computer vision models offer an inexpensive and scalable solution for prediction of gene expression phenotypes directly from WSIs, providing opportunity for cost-effective large-scale research studies and molecular diagnostics.

Availability and implementation

A self-contained example is available from http://github.com/phiwei/prostate_coexpression. Model predictions and metrics are available from doi.org/10.5281/zenodo.4739097.

Supplementary information

Supplementary data are available at Bioinformatics online.

Optimal Use of Tumor-Based Molecular Assays for Localized Prostate Cancer

PURPOSEOF REVIEW

The use of genomic testing for prostate cancer continues to grow; however, utilization remains institutionally dependent. Herein, we review current tissue-based markers and comment on current use with active surveillance and prostate MRI.

RECENT FINDINGS

While data continues to emerge, several studies have shown a role for genomic testing for treatment selection. Novel testing options include ConfirmMDx, ProMark, Prolaris, and Decipher, which have shown utility in select patients. The current body of literature on this specific topic remains very limited; prospective trials with long-term follow-up are needed to improve our understanding on how these genomic tests fit when combined with our current clinical tools. As the literature matures, it is likely that newer risk calculators that combine our classic clinical variables with genomic and imaging data will be developed to bring about standard protocols for prostate cancer decision-making.

Biomarkers for prostate cancer detection and risk stratification

Although prostate cancer (PCa) is the most commonly diagnosed cancer in men, most patients do not die from the disease. Prostate specific antigen (PSA), the most widely used oncologic biomarker, has revolutionized screening and early detection, resulting in reduced proportion of patients presenting with advanced disease. However, given the inherent limitations of PSA, additional diagnostic and prognostic tools are needed to facilitate early detection and accurate risk stratification of disease. Serum, urine, and tissue-based biomarkers are increasingly being incorporated into the clinical care paradigm, but there is still a limited understanding of how to use them most effectively. In the current article, we review test characteristics and clinical performance data for both serum [4 K score, prostate health index (phi)] and urine [SelectMDx, ExoDx Prostate Intelliscore, MyProstateScore (MPS), and PCa antigen 3 (PCA3)] biomarkers to aid decisions regarding initial or repeat biopsies as well as tissue-based biomarkers (Confirm MDx, Decipher, Oncotype Dx, and Polaris) aimed at risk stratifying patients and identifying those patients most likely to benefit from treatment versus surveillance or monotherapy versus multi-modal therapy.

NCCN Risk Reclassification in Black Men with Low and Intermediate Risk Prostate Cancer After Genomic Testing

OBJECTIVES

To assess the utility of genomic testing in risk-stratifying Black patients with low and intermediate risk prostate cancer.

METHODS

We retrospectively identified 63 Black men deemed eligible for active surveillance based on National Comprehensive Cancer Network (NCCN) guidelines, who underwent OncotypeDx Genomic Prostate Score testing between April 2016 and July 2020. Nonparametric statistical testing was used to compare relevant features between patients reclassified to a higher NCCN risk after genomic testing and those who were not reclassified.

RESULTS

The median age was 66 years and median pre-biopsy PSA was 7.3. Initial risk classifications were: very low risk: 7 (11.1%), low risk: 24(38.1%), favorable intermediate risk: 31(49.2%), and unfavorable intermediate risk: 1 (1.6%). Overall, NCCN risk classifications after Genomic Prostate Score testing were significantly higher than initial classifications (P=.003, Wilcoxon signed-rank). Among patients with discordant risk designations, 28(28/40, 70%) were reclassified to a higher NCCN risk after genomic testing. A pre-biopsy prostate specific antigen of greater than 10 did not have significantly higher odds of HBR (OR:2.16 [95% CI: 0.64,7.59, P=.2). Of favorable intermediate risk patients, 20(64.5%) were reclassified to a higher NCCN risk. Ultimately, 18 patients underwent definitive treatment.

CONCLUSIONS

Incorporation of genomic testing in risk stratifying Black men with low and intermediate-risk prostate cancer resulted in overall higher NCCN risk classifications. Our findings suggest a role for increased utilization of genomic testing in refining risk-stratification within this patient population. These tests may better inform treatment decisions on an individualized basis.

Active Surveillance in Prostate Cancer: Role of Available Biomarkers in Daily Practice

Prostate cancer (PCa) is the most commonly diagnosed cancer in men. The diagnosis is currently based on PSA levels, which are associated with overdiagnosis and overtreatment. Moreover, most PCas are localized tumours; hence, many patients with low-/very low-risk PCa could benefit from active surveillance (AS) programs instead of more aggressive, active treatments. Heterogeneity within inclusion criteria and follow-up strategies are the main controversial issues that AS presently faces. Many biomarkers are currently under investigation in this setting; however, none has yet demonstrated enough diagnostic ability as an independent predictor of pathological or clinical progression. This work aims to review the currently available literature on tissue, blood and urine biomarkers validated in clinical practice for the management of AS patients.

Impact of a Genomic Test on Treatment Decision in a Predominantly African American Population With Favorable-Risk Prostate Cancer: A Randomized Trial

PURPOSE

The Genomic Prostate Score (GPS), performed on biopsy tissue, predicts adverse outcome in prostate cancer (PCa) and has shown promise for improving patient selection for active surveillance (AS). However, its impact on treatment choice in high-risk populations of African Americans is largely unknown and, in general, the effect of the GPS on this difficult decision has not been evaluated in randomized trials.

METHODS

Two hundred men with National Comprehensive Cancer Network very low to low-intermediate PCa from three Chicago hospitals (70% Black, 16% college graduates) were randomly assigned at diagnosis to standard counseling with or without a 12-gene GPS assay. The primary end point was treatment choice at a second postdiagnosis visit. The proportion of patients choosing AS was compared, and multivariable modeling was used to estimate the effects of various factors on AS acceptance.

RESULTS

AS acceptance was high overall, although marginally lower in the intervention group (77% v 88%; P = .067), and lower still when men with inadequate specimens were excluded (P = .029). Men with lower health literacy who received a GPS were seven-fold less likely to choose AS compared with controls, whereas no difference was seen in men with higher health literacy (Pinteraction = .022). Among men with low-intermediate risk, 69% had GPS values consistent with unfavorable intermediate or high-risk cancer. AS choice was also independently associated with a family history of PCa and having health insurance.

CONCLUSION

In contrast to other studies, the net effect of the GPS was to move patients away from AS, primarily among men with low health literacy. These findings have implications for our understanding of how prognostic molecular assays that generate probabilities of poor outcome can affect treatment decisions in diverse clinical populations.

A 17-Gene Panel Genomic Prostate Score Has Similar Predictive Accuracy for Adverse Pathology at Radical Prostatectomy in African American and European American Men

OBJECTIVE

To validate the 17-gene Oncotype DX Genomic Prostate Score (GPS) as a predictor of adverse pathology (AP) in African American (AA) men and to assess the distribution of GPS in AA and European American (EA) men with localized prostate cancer.

METHODS

The study populations were derived from 2 multi-institutional observational studies. Between February 2009 and September 2014, AA and EA men who elected immediate radical prostatectomy after a ≥10-core transrectal ultrasound biopsy were included in the study. Logistic regressions, area under the receiver operating characteristics curves (AUC), calibration curves, and predictive values were used to compare the accuracy of GPS. AP was defined as primary Gleason grade 4, presence of any Gleason pattern 5, and/or non-organ-confined disease (≥pT3aN0M0) at radical prostatectomy.

RESULTS

Overall, 96 AA and 76 EA men were selected and 46 (26.7%) had AP. GPS result was a significant predictor of AP (odds ratio per 20 GPS units [OR/20 units] in AA: 4.58; 95% confidence interval (CI) 1.8-11.5, P = .001; and EA: 4.88; 95% CI 1.8-13.5, P = .002). On multivariate analysis, there was no significant interaction between GPS and race (P >.10). GPS remained significant in models adjusted for either National Comprehensive Cancer Network (NCCN) risk group or Cancer of the Prostate Risk Assessment (CAPRA) score. In race-stratified models, area under the receiver operating characteristics curves for GPS/20 units was 0.69 for AAs vs 0.74 for EAs (P = .79). The GPS distributions were not statistically different by race (all P >.05).

CONCLUSION

In this clinical validation study, the Oncotype DX GPS is an independent predictor of AP at prostatectomy in AA and EA men with similar predictive accuracy and distributions.

Molecular Biomarkers in Localized Prostate Cancer: ASCO Guideline

PURPOSE

This guideline provides recommendations for available tissue-based prostate cancer biomarkers geared toward patient selection for active surveillance, identification of clinically significant disease, choice of postprostatectomy adjuvant versus salvage radiotherapy, and to address emerging questions such as the relative value of tissue biomarkers compared with magnetic resonance imaging.

METHODS

An ASCO multidisciplinary Expert Panel, with representatives from the European Association of Urology, American Urological Association, and the College of American Pathologists, conducted a systematic literature review of localized prostate cancer biomarker studies between January 2013 and January 2019. Numerous tissue-based molecular biomarkers were evaluated for their prognostic capabilities and potential for improving management decisions. Here, the Panel makes recommendations regarding the clinical use and indications of these biomarkers.

RESULTS

Of 555 studies identified, 77 were selected for inclusion plus 32 additional references selected by the Expert Panel. Few biomarkers had rigorous testing involving multiple cohorts and only 5 of these tests are commercially available currently: Oncotype Dx Prostate, Prolaris, Decipher, Decipher PORTOS, and ProMark. With various degrees of value and validation, multiple biomarkers have been shown to refine risk stratification and can be considered for select men to improve management decisions. There is a paucity of prospective studies assessing short- and long-term outcomes of patients when these markers are integrated into clinical decision making.

RECOMMENDATIONS

Tissue-based molecular biomarkers (evaluating the sample with the highest volume of the highest Gleason pattern) may improve risk stratification when added to standard clinical parameters, but the Expert Panel endorses their use only in situations in which the assay results, when considered as a whole with routine clinical factors, are likely to affect a clinical decision. These assays are not recommended for routine use as they have not been prospectively tested or shown to improve long-term outcomes-for example, quality of life, need for treatment, or survival. Additional information is available at www.asco.org/genitourinary-cancer-guidelines.

Genomics-Enabled Precision Medicine for Cancer

Genomic information is increasingly being incorporated into clinical cancer care. Large-scale sequencing efforts have deepened our understanding of the genomic landscape of cancer and contributed to the expanding catalog of alterations being leveraged to aid in cancer diagnosis, prognosis, and treatment. Genomic profiling can provide clinically relevant information regarding somatic point mutations, copy number alterations, translocations, and gene fusions. Genomic features, such as mutational burden, can also be measured by more comprehensive sequencing strategies and have shown value in informing potential treatment options. Ongoing clinical trials are evaluating the use of molecularly targeted agents in genomically defined subsets of cancers within and across tumor histologies. Continued advancements in clinical genomics promise to further expand the application of genomics-enabled medicine to a broader spectrum of oncology patients.

Should pretest genetic counselling be required for patients pursuing genomic sequencing? Results from a survey of participants in a large genomic implementation study

PURPOSE

We assessed the decision-making of individuals pursuing genomic sequencing without a requirement for pretest genetic counselling. We sought to describe the extent to which individuals who decline genetic counselling reported decisional conflict or struggled to make a decision to pursue genomic testing.

METHODS

We administered a 100-item survey to 3037 individuals who consented to the Return of Actionable Variants Empirical study, a genomic medicine implementation study supported by the National Institutes of Health (USA) eMERGE consortium. The primary outcomes of interest were self-reported decisional conflict about the decision to participate in the study and time required to reach a decision.

RESULTS

We received 2895 completed surveys (response rate=95.3%), and of these respondents 97.8% completed the decisional conflict scale in its entirety. A majority of individuals (63%) had minimal or no decisional conflict about the pursuit of genomic sequencing and were able to reach a decision quickly (78%). Multivariable logistic regression analyses identified several characteristics associated with decisional conflict, including lower education, lower health literacy, lower self-efficacy in coping, lack of prior experience with genetic testing, not discussing study participation with a family member or friend, and being male.

CONCLUSION

As genomic sequencing is used more widely, genetic counselling resources may not be sufficient to meet demand. Our results challenge the notion that all individuals need genetic counselling in order to make an informed decision about genomic sequencing.

Clinical Utility of Gene Expression Classifiers in Men With Newly Diagnosed Prostate Cancer

PURPOSE

Tissue-based gene expression classifiers (GECs) may assist with management decisions in patients with newly diagnosed prostate cancer. We sought to assess the current use of GEC tests and determine how the test results are associated with primary disease management.

METHODS

In this observational study, patients diagnosed with localized prostate cancer were tracked through the Michigan Urological Surgery Improvement Collaborative registry. The utilization and results of three GECs (Decipher Prostate Biopsy, Oncotype DX Prostate, and Prolaris) were prospectively collected. Practice patterns, predictors of GEC use, and effect of GEC results on disease management were investigated.

RESULTS

Of 3,966 newly diagnosed patients, 747 (18.8%) underwent GEC testing. The rate of GEC use in individual practices ranged from 0% to 93%, and patients undergoing GEC testing were more likely to have a lower prostate-specific antigen level, lower Gleason score, lower clinical T stage, and fewer positive cores (all P < .05). Among patients with clinical favorable risk of cancer, the rate of active surveillance (AS) differed significantly among patients with a GEC result above the threshold (46.2%), those with a GEC result below the threshold (75.9%), and those who did not undergo GEC (57.9%; P < .001 for comparison of the three groups). This results in an estimate that, for every nine men with favorable risk of cancer who undergo GEC testing, one additional patient may have their disease initially managed with AS. On multivariable analysis, patients with favorable-risk prostate cancer who were classified as GEC low risk were more likely to be managed on AS than those without testing (odds ratio, 1.84; P = .006).

CONCLUSION

There is large variability in practice-level use and GEC tests ordered in patients with newly diagnosed, localized prostate cancer. In patients with clinical favorable risk of cancer, GEC testing significantly increased the use of AS. Additional follow-up will help determine whether incorporation of GEC testing into initial patient care favorably affects clinical outcomes.

Tissue-based multigene expression tests for pretreatment prostate cancer risk assessment: current status and future perspectives

Prostate cancer is a highly prevalent disease with ample spectrum of aggressiveness and treatment options. Low-risk disease can be safely managed by nonintervention strategies, such as active surveillance; however, accurate risk assessment is warranted. Molecular tests have been developed and validated to complement standard clinicopathological parameters and help to improve risk stratification in prostate cancer. Herein, we review selected tissue-based assays, including genomic prostate score, cell cycle progression score and genomic classifier, with particular emphasis on their role in patient risk assessment in a pretreatment setting, in view of their current or potential utilization in active surveillance.

Molecular correlates of intermediate- and high-risk localized prostate cancer

Clinicopathologic parameters, including Gleason score, remain the most validated prognostic factors for patients diagnosed with localized prostate cancer (PCa). However, patients of the same risk groups have exhibited heterogeneity of disease outcomes. To improve risk classification, multiple molecular risk classifiers have been developed, which were designed to inform beyond existing clinicopathologic classifiers. Alterations affecting tumor suppressors and oncogenes, such as PTEN, MYC, BRCA2, and TP53, which have been long associated with aggressive PCa, demonstrated grade-dependent frequency of alterations in localized PCas. In addition to these genetic hallmarks, several RNA-based commercial tests have been recently developed to help identify men who would benefit from earlier interventions. Large genomic studies also correlate germline genetic alterations and epigenetic features with adverse outcomes, further strengthening the link between the risk of metastasis and a stepwise accumulation of driver molecular lesions.

Biomarkers in active surveillance

The use of active surveillance (AS) is increasing for favorable-risk prostate cancer. However, there remain challenges in patient selection for AS, due to the limitations of current clinical staging. In addition, monitoring protocols relying on serial biopsies is invasive and presents risks such as infection. For these reasons, there is substantial interest in identifying markers that can be used to improve AS selection and monitoring. In this article, we review the evidence on serum, urine and tissue markers in AS.

Balancing Confounding and Generalizability Using Observational, Real-world Data: 17-gene Genomic Prostate Score Assay Effect on Active Surveillance

Randomized, controlled trials can provide high-quality, unbiased evidence for therapeutic interventions but are not always a practical or viable study design for certain healthcare decisions, such as those involving prognostic or predictive testing. Studies using large, real-world databases may be more appropriate and more generalizable to the intended target population of physicians and patients to answer these questions but carry potential for hidden bias. We illustrate several emerging methods of analyzing observational studies using propensity score matching (PSM) and coarsened exact matching (CEM). These advanced statistical methods are intended to reveal a "hidden experiment" within an observational database, and so refute or confirm a potential causal effect of assignment to an intervention and study outcome. We applied these methods to the Optum™ Research Database (ORD; Eden Prairie, MN) of electronic health records and administrative claims data to assess the effect of the 17-gene Genomic Prostate Score® (GPS™; Genomic Health, Redwood City, CA) assay on use of active surveillance (AS). In a traditional multivariable logistic regression, the GPS assay increased the use of AS by 29% (95% CI, 24%-33%). Upon applying the matching methods, the effect of the GPS assay on AS use varied between 27% and 80% and the matched data were significant among all algorithms. All matching algorithms performed well in identifying matched data that improved the imbalance in baseline covariates. By using different matching methods to assess causal inference in an observational database, we provide further confidence that the effect of the GPS assay on AS use is statistically significant and unlikely to be a result of confounding due to differences in baseline characteristics of the patients or the settings in which they were seen.

Genomic Markers in Prostate Cancer Decision Making

CONTEXT

Although the widespread use of prostate-specific antigen (PSA) has led to an early detection of prostate cancer (PCa) and a reduction of metastatic disease at diagnosis, PSA remains one of the most controversial biomarkers due to its limited specificity. As part of emerging efforts to improve both detection and management decision making, a number of new genomic tools have recently been developed.

OBJECTIVE

This review summarizes the ability of genomic biomarkers to recognize men at high risk of developing PCa, discriminate clinically insignificant and aggressive tumors, and facilitate the selection of therapies in patients with advanced disease.

EVIDENCE ACQUISITION

A PubMed-based literature search was conducted up to May 2017. We selected the most recent and relevant original articles and clinical trials that have provided indispensable information to guide treatment decisions.

EVIDENCE SYNTHESIS

Genome-wide association studies have identified several genetic polymorphisms and inherited variants associated with PCa susceptibility. Moreover, the urine-based assays SelectMDx, Mi-Prostate Score, and ExoDx have provided new insights into the identification of patients who may benefit from prostate biopsy. In men with previous negative pathological findings, Prostate Cancer Antigen 3 and ConfirmMDx predicted the outcome of subsequent biopsy. Commercially available tools (Decipher, Oncotype DX, and Prolaris) improved PCa risk stratification, identifying men at the highest risk of adverse outcome. Furthermore, other biomarkers could assist in treatment selection in castration-resistant PCa. AR-V7 expression predicts resistance to abiraterone/enzalutamide, while poly(ADP-ribose) polymerase-1 inhibitor and platinum-based chemotherapy could be indicated in metastatic patients who are carriers of mutations in DNA mismatch repair genes.

CONCLUSIONS

Introduction of genomic biomarkers has dramatically improved the detection, prognosis, and risk evaluation of PCa. Despite the progress made in discovering suitable biomarker candidates, few have been used in a clinical setting. Large-scale and multi-institutional studies are required to validate the efficacy and cost utility of these new technologies.

PATIENT SUMMARY

Prostate cancer is a heterogeneous disease with a wide variability. Genomic biomarkers in combination with clinical and pathological variables are useful tools to reduce the number of unnecessary biopsies, stratify low-risk from high-risk tumors, and guide personalized treatment decisions.

Prostate Cancer Imaging and Biomarkers Guiding Safe Selection of Active Surveillance

Background

Active surveillance (AS) is a widely adopted strategy to monitor men with low-risk, localized prostate cancer (PCa). Current AS inclusion criteria may misclassify as many as one in four patients. The advent of multiparametric magnetic resonance imaging (mpMRI) and novel PCa biomarkers may offer improved risk stratification. We performed a review of recently published literature to characterize emerging evidence in support of these novel modalities.

Methods

An English literature search was conducted on PubMed for available original investigations on localized PCa, AS, imaging, and biomarkers published within the past 3 years. Our Boolean criteria included the following terms: PCa, AS, imaging, biomarker, genetic, genomic, prospective, retrospective, and comparative. The bibliographies and diagnostic modalities of the identified studies were used to expand our search.

Results

Our review identified 222 original studies. Our expanded search yielded 244 studies. Among these, 70 met our inclusion criteria. Evidence suggests mpMRI offers improved detection of clinically significant PCa, and MRI-fusion technology enhances the sensitivity of surveillance biopsies. Multiple studies demonstrate the promise of commercially available screening assays for prediction of AS failure, and several novel biomarkers show promise in this setting.

Conclusion

In the era of AS for men with low-risk PCa, improved strategies for proper stratification are needed. mpMRI has dramatically enhanced the detection of clinically significant PCa. The advent of novel biomarkers for prediction of aggressive disease and AS failure has shown some initial promise, but further validation is warranted.

Active Surveillance Use Among a Low-risk Prostate Cancer Population in a Large US Payer System: 17-Gene Genomic Prostate Score Versus Other Risk Stratification Methods

Many men with low-risk prostate cancer (PCa) receive definitive treatment despite recommendations that have been informed by two large, randomized trials encouraging active surveillance (AS). We conducted a retrospective cohort study using the Optum™ Research Database (Eden Prairie, MN) of electronic health records and administrative claims data to assess AS use for patients tested with a 17-gene Genomic Prostate Score™ (GPS; Genomic Health, Redwood City, CA) assay and/or prostate magnetic resonance imaging (MRI). De-identified records were extracted on health plan members enrolled from June 2013 to June 2016 who had ≥1 record of PCa (n 5 291,876). Inclusion criteria included age ≥18 years, new diagnosis, American Urological Association low-risk PCa (stage T1-T2a, prostate-specific antigen ≤10 ng/mL, Gleason score 5 6), and clinical activity for at least 12 months before and after diagnosis. Data included baseline characteristics, use of GPS testing and/or MRI, and definitive procedures. GPS or MRI testing was performed in 17% of men (GPS, n 5 375, 4%; MRI, n 5 1174, 13%). AS use varied from a low of 43% for men who only underwent MRI to 89% for GPStested men who did not undergo MRI (P <.001). At 6-month follow-up, AS use was 31.0% higher (95% CI, 27.6%-34.5%; P <.001) for men receiving the GPS test only versus men who did not undergo GPS testing or MRI; the difference was 30.5% at 12-month follow-up. In a large US payer system, the GPS assay was associated with significantly higher AS use at 6 and 12 months compared with men who had MRI only, or no GPS or MRI testing.