A genomic classifier predicting metastatic disease progression in men with biochemical recurrence after prostatectomy

Abstract

BACKGROUND Due to their varied outcomes, men with biochemical recurrence (BCR) following radical prostatectomy (RP) present a management dilemma. Here, we evaluate Decipher, a genomic classifier (GC), for its ability to predict metastasis following BCR. METHODS The study population included 85 clinically high-risk patients who developed BCR after RP. Time-dependent receiver operating characteristic (ROC) curves, weighted Cox proportional hazard models and decision curves were used to compare GC scores to Gleason score (GS), PSA doubling time (PSAdT), time to BCR (ttBCR), the Stephenson nomogram and CAPRA-S for predicting metastatic disease progression. All tests were two-sided with a type I error probability of 5%. RESULTS GC scores stratified men with BCR into those who would or would not develop metastasis (8% of patients with low versus 40% with high scores developed metastasis, P<0.001). The area under the curve for predicting metastasis after BCR was 0.82 (95% CI, 0.76–0.86) for GC, compared to GS 0.64 (0.58–0.70), PSAdT 0.69 (0.61–0.77) and ttBCR 0.52 (0.46–0.59). Decision curve analysis showed that GC scores had a higher overall net benefit compared to models based solely on clinicopathologic features. In multivariable modeling with clinicopathologic variables, GC score was the only significant predictor of metastasis (P = 0.003). CONCLUSIONS When compared to clinicopathologic variables, GC better predicted metastatic progression among this cohort of men with BCR following RP. While confirmatory studies are needed, these results suggest that use of GC may allow for better selection of men requiring earlier initiation of treatment at the time of BCR.

Genomic Predictors of Outcome in Prostate Cancer

“…The prognostic accuracy was highest when the genomic classifier and clinical models (CAPRA-S) were combined [61]. In another study, including 85 highrisk RP patients, the 22-gene panel was the only variable associated with metastatic progression in a multivariable model and had a favorable net benefit compared with clinical models (CAPRA-S and Stephenson postoperative nomogram) [68,74] in decision-curve analysis [71,72]. The test also improved prediction of BCR and metastatic progression risk in a cohort of 139 men undergoing EBRT after RP [73].…”

“…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].…”

Commercialized biomarkers: new horizons in prostate cancer diagnostics

“…The GC was then validated on an independent cohort (n=186), outperforming clinical variables and other gene signatures [52]. The GC was clinically validated in two studies on 1095 patients who were clinically high-risk and developed BCR after RP [53,54]. Decipher could independently forecast which patients developed metastasis.…”

The Landscape of Prognostic Outlier Genes in High-Risk Prostate Cancer

“…Prognostic outlier score was defined as the number of prognostic outlier genes with outlier status. Scores were also generated using existing prognostic instruments Decipher, CAPRA-S, and cell-cycle progression (CCP) signature (18,(25)(26)(27)(28). As the CCP signature was ported to our microarray platform, we will refer to the microarray…”