Mitra, Anirban P.

4565 Background: The efficient delivery of adjuvant therapy after radical prostatectomy (RP) in patients with prostate cancer is limited by the lack of biomarkers, beyond clinicopathologic factors, that are able to assess the risk of clinically significant disease progression. Previously, routine FFPE patient specimens from the Mayo Clinic Radical Prostatectomy Registry with long term follow-up were selected to develop a genomic classifier (GC) to predict clinical progression. Here, we present the validation of a GC in a cohort of patients at high risk of disease progression. Methods: A case-cohort study of high-risk RP patients from the Mayo Clinic (N=219) was used to validate the genomic classifier (GC) for predicting clinical progression (defined by positive bone or CT scan post-RP). Its performance was compared to a multivariable clinical classifier (CC) and a genomic-clinical classifier (GCC) which combines GC with established clinicopathologic variables. Concordance index, Cox modeling and decision curve analysis were used to compare the different models. Results: GC and GCC were predictive of clinical progression in the high-risk cohort with c-indices of 0.79 and 0.82, respectively, compared to the clinical classifier (0.70). Multivariable survival analysis showed that the majority of prognostic information of GCC came from the GC with a minor contribution from Gleason score. Decision curve analysis showed that GCC had a higher overall net benefit compared to CC over a wide range of ‘decision-to-treat’ thresholds for the risk of progression. Conclusions: In this high-risk cohort, GC and GCC classifiers showed improved performance over CC in prediction of clinical progression. GC is an independent prognostic factor in this cohort and captures the majority of prognostic information. GC and GCC’s prognostic performance and their usefulness in guiding decision-making in the adjuvant setting after RP need further testing in studies of additional prostate cancer risk groups.

36 Background: Following radical prostatectomy (RP), 30-40% of patients have adverse pathology and are deemed at high risk for metastatic progression. The objective of this study was to validate the ability of Decipher, a genomic classifier (GC), to improve prediction of metastatic disease progression compared with clinical variables in order to better identify candidates for therapy intensification. Methods: A previously developed 22-feature GC model was validated in a prospectively designed case-cohort study of a clinically high-risk population (i.e., with one or more adverse pathological features) of 1,010 RP patients treated at Mayo Clinic between 2000-2006. A random sample of 20% of the cohort was subjected to microarray analysis and GC scores were generated for 219 patients. The primary endpoint, the c-index for predicting metastatic disease progression (i.e., positive bone or CT scans) was evaluated in a blinded analysis. Cox modeling and decision curve analyses were used to compare the performance of GC to individual clinical variables and prediction models. Results: GC had a c-index 0.79 (95% CI 0.71-0.86) that was significantly better than any single clinical variable. Cumulative incidence curves in the cohort showed that 72% of patients had low GC scores with only 3% and 6% incidence of metastatic disease at 5 and 10 years post RP. In contrast, for the 28% of patients with high GC scores, the cumulative incidence was 17% and 25% at 5 and 10 years post RP. Decision curve analysis showed that the GC model had higher overall net benefit compared to clinical variables over a wide range of ‘decision-to-treat’ thresholds for risk of metastasis. In multivariable modeling with clinicopathologic variables, GC remained the only significant independent predictor of metastasis (HR=1.51, for each 0.1 unit increment, p<0.001). Conclusions: GC can better predict metastatic disease progression compared with clinical variables and may select among patients with adverse pathology a majority that is in fact at low risk for metastasis.