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

Abstract

Background Risk prediction models that incorporate biomarkers and clinicopathologic variables may be used to improve decision-making post radical prostatectomy (RP). We compared two previously validated post-RP classifiers—the Cancer of the Prostate Risk Assessment post-Surgical (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 1,010 patients at high risk of recurrence post-RP was treated at Mayo Clinic between 2000–06. High-risk was defined by any of: pre-operative PSA >20ng/mL, pathological Gleason score ≥8 or stage pT3b. A case-cohort random sample identified 225 patients (cases defined as patients who experienced CSM), among whom CAPRA-S and GC could be determined for 185. Outcome Measurements and Statistical Analysis The scores were evaluated individually and in combination using concordance (c)-index, decision curve analysis, re-classification, cumulative incidence, and Cox regression for prediction of CSM. Results and Limitations Among 185 men, 28 experienced CSM. The c-index for CAPRA-S and GC were 0.75 (95% CI 0.65–0.84) and 0.78 (95% CI 0.68–0.88), respectively. GC showed higher net-benefit on decision curve analysis but a score combining CAPRA-S and GC did not improve AUC 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, among whom 17 had CSM events. GC reclassified the remaining 49 men as low to intermediate-risk; among these men 3 CSM events were observed. In multivariable analysis, GC and CAPRA-S as continuous variables were independently prognostic of CSM, with hazard ratios of 1.81 (p<0.001, per 0.1 unit change in score) and 1.36 (p=0.05, per one 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 11.26 (p<0.001) for high GC scores (≥0.6). For patients with both high GC and CAPRA-S scores, cumulative incidence of CSM was 45% at 10 years. The study is limited by its retrospective design. Conclusions Both GC and CAPRA-S were significant independent predictors of CSM. GC was shown to re-stratify many men classified as high-risk based on CAPRA-S ≥6 alone. Patients with both high GC and 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.

Genomic Predictors of Outcome in Prostate Cancer

“…This panel has also been externally validated in multiple cohorts and is commercially available as the Decipher genetic test (GenomeDX Biosciences, Vancouver, BC, Canada). Four studies reported the utilization of this gene panel to predict BCR, metastatic progression, or DSS after RP plus or minus EBRT [61,[71][72][73]. The prognostic accuracy was highest when the genomic classifier and clinical models (CAPRA-S) were combined [61].…”

“…The biomarkers to be included in clinical decision making have to provide additional independent prognostic information or additive value together with established clinical and pathologic variables in a multivariate setting like the Memorial Sloan Kettering Cancer Center or Cleveland Clinic nomograms or Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) risk stratification for PCa. [54,60,61]. A few studies investigated TURP tissue to predict the outcome of men undergoing conservative treatment [51,62,63].…”

“…These approaches included traditional statistical methods (survival analyses, multivariable models with other clinicopathologic variables, and receiver operating characteristic analysis) [42,[51][52][53]59,60,[62][63][64]. In some studies, results from expression panels were combined with other variables or a nomogram to determine if genomic data added prognostic information above the baseline models [54,57,58,61,65].…”

Clinical Utility of Biomarkers in Localized Prostate Cancer

“…Refinement of the high-density micro-arrays from roughly 1.4 million features was performed using machine learning algorithms to select a panel of markers highly associated with clinical outcomes. The Decipher score (reported on a 0-1 scale) predicting early metastatic progression following RP was examined in a cohort of men with adverse pathological features, where increases in the GC score were significantly associated with metastatic progression and PCSM, even after adjustment for the CAPRA-S score or other models incorporating postprostatectomy data [42,43] The Decipher genomic classifier was evaluated for the ability to selectively identify men who may benefit from adjuvant radiation therapy following RP [44]. Den et al reported on 188 men with pathological T3 or marginpositive PCa at surgery who were treated with adjuvant radiation therapy at two institutions between 1990 and 2009.…”

Clinical and Translational Assessment of VEGFR1 as a Mediator of the Premetastatic Niche in High-Risk Localized Prostate Cancer

“…Recently, various genomic predictors have allowed for refinement of prognosis across all risk strata of localized prostate cancer (5,6). These tools harness tumor characteristics to define recurrence risk -ostensibly, features of the tumor microenvironment may also predict clinical outcome.…”