Combined Value of Validated Clinical and Genomic Risk Stratification Tools for Predicting Prostate Cancer Mortality in a High-risk Prostatectomy Cohort

doi:10.1016/j.eururo.2014.05.039

European Urology

Volume 67, Issue 2, February 2015, Pages 326-333

https://doi.org/10.1016/j.eururo.2014.05.039 Get rights and content

Abstract

Background

Risk prediction models that incorporate biomarkers and clinicopathologic variables may be used to improve decision making after radical prostatectomy (RP). We compared two previously validated post-RP classifiers—the Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) and the Decipher genomic classifier (GC)—to predict prostate cancer–specific mortality (CSM) in a contemporary cohort of RP patients.

Objective

To evaluate the combined prognostic ability of CAPRA-S and GC to predict CSM.

Design, setting, and participants

A cohort of 1010 patients at high risk of recurrence after RP were treated at the Mayo Clinic between 2000 and 2006. High risk was defined by any of the following: preoperative prostate-specific antigen >20 ng/ml, pathologic Gleason score ≥8, or stage pT3b. A case-cohort random sample identified 225 patients (with cases defined as patients who experienced CSM), among whom CAPRA-S and GC could be determined for 185 patients.

Outcome measurements and statistical analysis

The scores were evaluated individually and in combination using concordance index (c-index), decision curve analysis, reclassification, cumulative incidence, and Cox regression for the prediction of CSM.

Results and limitations

Among 185 men, 28 experienced CSM. The c-indices for CAPRA-S and GC were 0.75 (95% confidence interval [CI], 0.55–0.84) and 0.78 (95% CI, 0.68–0.87), respectively. GC showed higher net benefit on decision curve analysis, but a score combining CAPRA-S and GC did not improve the area under the receiver-operating characteristic curve after optimism-adjusted bootstrapping. In 82 patients stratified to high risk based on CAPRA-S score ≥6, GC scores were likewise high risk for 33 patients, among whom 17 had CSM events. GC reclassified the remaining 49 men as low to intermediate risk; among these men, three CSM events were observed. In multivariable analysis, GC and CAPRA-S as continuous variables were independently prognostic of CSM, with hazard ratios (HRs) of 1.81 (p < 0.001 per 0.1-unit change in score) and 1.36 (p = 0.01 per 1-unit change in score). When categorized into risk groups, the multivariable HR for high CAPRA-S scores (≥6) was 2.36 (p = 0.04) and was 11.26 (p < 0.001) for high GC scores (≥0.6). For patients with both high GC and high CAPRA-S scores, the cumulative incidence of CSM was 45% at 10 yr. The study is limited by its retrospective design.

Conclusions

Both GC and CAPRA-S were significant independent predictors of CSM. GC was shown to reclassify many men stratified to high risk based on CAPRA-S ≥6 alone. Patients with both high GC and high CAPRA-S risk scores were at markedly elevated post-RP risk for lethal prostate cancer. If validated prospectively, these findings suggest that integration of a genomic-clinical classifier may enable better identification of those post-RP patients who should be considered for more aggressive secondary therapies and clinical trials.

Patient summary

The Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) and the Decipher genomic classifier (GC) were significant independent predictors of prostate cancer–specific mortality. These findings suggest that integration of a genomic-clinical classifier may enable better identification of those post–radical prostatectomy patients who should be considered for more aggressive secondary therapies and clinical trials.

Introduction

Accurate risk stratification of prostate cancer (PCa), both at time of diagnosis and at other decision points, is essential to identify those patients at high risk of PCa-specific mortality (CSM). These patients are most likely to benefit from aggressive multimodal therapy, and it is important to distinguish them from the larger majority of patients who are cured by surgery or are otherwise at low risk of CSM, who may be spared the potential impact of additive treatments. The Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) score was developed in a multi-institutional, community-based cohort to predict biochemical recurrence (BCR) and CSM following radical prostatectomy (RP) by incorporating preoperative prostate-specific antigen (PSA) levels and pathologic information into a straightforward, easy-to-use calculation of postoperative patient risk [1]. CAPRA-S has also been validated in another multi-institutional, sociodemographically and clinically diverse cohort, which confirmed its ability to predict both recurrence and CSM [2].

Over the last decade, many studies have tried to address the unmet clinical need for predicting aggressive PCa using genomic information [3], [4], [5], [6], [7]. The Decipher PCa genomic classifier (GC) risk prediction model was developed by investigators at the Mayo Clinic and GenomeDx Biosciences to predict, with high specificity, early metastasis after RP [4]. Using oligonucleotide-microarray expression profiling of approximately 1.4 million markers in 545 tumors, machine learning algorithms were used to discover and validate a 22-marker gene expression signature of metastasis. The GC model measures the activity of genes implicated in proliferation, cell migration and adhesion, tumor motility, androgen-signaling, and immune system evasion [8]. In blinded validation studies in prospectively accrued cohorts [9], the GC model demonstrated improved performance over any individual clinicopathologic variable or clinical prediction model for clinical metastasis (confirmed by radiographic bone and computed tomography [CT] imaging) in post-RP [10] and post-BCR [11] patient cohorts.

In this study, we further examined the relationship between the CAPRA-S and GC scores for predicting CSM from the time of RP. We aimed to determine whether integrated genomic and clinical risk prediction models may further improve risk prediction compared with either model alone.

Section snippets

Patient population

Subjects were identified from a population of 1010 men prospectively enrolled in the Mayo Clinic Department of Urology RP registry for PCa from 2000 to 2006. This population was clinically high risk, as defined by preoperative PSA level >20 ng/ml, pathologic Gleason score ≥8, or stage pT3b. Patients who received neoadjuvant therapy or who were diagnosed with metastatic disease or failed to achieve PSA nadir after surgery were excluded. Clinical staging for patients with D’Amico high-risk

Performance of genomic and clinicopathologic risk models for predicting prostate cancer–specific mortality

GC scores were available for 187 patients (28 cases; median follow-up: 6.4 yr). Complete clinical data required to calculate CAPRA-S scores were available for 185 patients (Table 1). Patients in this high-risk cohort experienced CSM a median 4.8 yr after RP (interquartile range: 3.2–6.6). Medians and ranges for CAPRA-S and GC were 5 (2–12) and 0.37 (0.01–0.99), respectively.

The AUCs of CAPRA-S and GC as prediction models for CSM in comparison with individual clinicopathologic variables were

Discussion

The integration of tumor genomics into clinical practice for use in individualized patient risk prediction models holds great promise to improve management of high-risk PCa. This investigation follows previous reports on the validation of CAPRA-S [15] and GC [10], with results in this paper demonstrating that an integrated risk model combining GC and CAPRA-S provides improved risk prediction over either model alone. We have shown in this study that both GC and CAPRA-S can accurately predict

Conclusions

For patients with adverse pathology after RP, outcomes vary greatly. Patients with CAPRA-S > 5 and GC > 0.6 were associated with a significantly increased risk of CSM in this cohort. As such, these men may benefit from additional secondary therapies, preferably in a clinical trial setting. Conversely, patients with both low CAPRA-S and low GC scores had excellent CSM-free survival even after adjusting for use of adjuvant therapy in this cohort. Future studies— ideally, randomized controlled

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^†: These authors made equal contributions.

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Prostate CancerCombined Value of Validated Clinical and Genomic Risk Stratification Tools for Predicting Prostate Cancer Mortality in a High-risk Prostatectomy Cohort

Abstract

Background

Objective

Design, setting, and participants

Outcome measurements and statistical analysis

Results and limitations

Conclusions

Patient summary

Introduction

Section snippets

Patient population

Performance of genomic and clinicopathologic risk models for predicting prostate cancer–specific mortality

Discussion

Conclusions

Eur Urol

Lancet Oncol

J Urol

J Clin Epidemiol

J Urol

Eur Urol

The CAPRA-S score: a straightforward tool for improved prediction of outcomes after radical prostatectomy

Cancer

Biomarkers in prostate cancer surveillance and screening: past, present, and future

Ther Adv Urol

A tissue biomarker panel predicting systemic progression after PSA recurrence post-definitive prostate cancer therapy

PLoS ONE

Analytical validation of the oncotype DX prostate cancer assay—a clinical RT-PCR assay optimized for prostate needle biopsies

BMC Genomics

Validation of a cell-cycle progression gene panel to improve risk stratification in a contemporary prostatectomy cohort

J Clin Oncol

Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy

PLoS ONE

Prostate Cancer
Combined Value of Validated Clinical and Genomic Risk Stratification Tools for Predicting Prostate Cancer Mortality in a High-risk Prostatectomy Cohort