17-Gene Genomic Prostate Score Test Results in the Canary Prostate Active Surveillance Study (PASS) Cohort.
Abstract
The 17-gene Onco<i>type</i> DX Genomic Prostate Score (GPS) test predicts adverse pathology (AP) in patients with low-risk prostate cancer treated with immediate surgery. We evaluated the GPS test as a predictor of outcomes in a multicenter active surveillance cohort.
Diagnostic biopsy tissue was obtained from men enrolled at 8 sites in the Canary Prostate Active Surveillance Study. The primary endpoint was AP (Gleason Grade Group [GG] ≥ 3, ≥ pT3a) in men who underwent radical prostatectomy (RP) after initial surveillance. Multivariable regression models for interval-censored data were used to evaluate the association between AP and GPS. Inverse probability of censoring weighting was applied to adjust for informative censoring. Predictiveness curves were used to evaluate how models stratified risk of AP. Association between GPS and time to upgrade on surveillance biopsy was evaluated using Cox proportional hazards models.
GPS results were obtained for 432 men (median follow-up, 4.6 years); 101 underwent RP after a median 2.1 years of surveillance, and 52 had AP. A total of 167 men (39%) upgraded at a subsequent biopsy. GPS was significantly associated with AP when adjusted for diagnostic GG (hazards ratio [HR]/5 GPS units, 1.18; 95% CI, 1.04 to 1.44; <i>P</i> = .030), but not when also adjusted for prostate-specific antigen density (PSAD; HR, 1.85; 95% CI, 0.99 to 4.19; <i>P</i> = .066). Models containing PSAD and GG, or PSAD, GG, and GPS may stratify risk better than a model with GPS and GG. No association was observed between GPS and subsequent biopsy upgrade (<i>P</i> = .48).
In our study, the independent association of GPS with AP after initial active surveillance was not statistically significant, and there was no association with upgrading in surveillance biopsy. Adding GPS to a model containing PSAD and diagnostic GG did not significantly improve stratification of risk for AP over the clinical variables alone.
Authors
- Boyer H
- Brooks JD
- Brown MD
- Crager M
- Dash A
- Fabrizio MD
- Gleave ME
- Kolb S
- Lin DW
- Liss M
- Lu R
- McKenney JK
- Morgan TM
- Nelson PS
- Newcomb LF
- Pingitore A
- Thompson IM
- Tretiakova M
- Tsiatis A
- Wagner AA
- Zheng Y