Session: MP38: Prostate Cancer: Localized: Active Surveillance
MP38-02: The Impact of Genomic Biomarkers on a Validated Clinical Risk Prediction Model for Upgrading/Upstaging among Men with Low-risk Prostate Cancer
Introduction: The challenge of distinguishing indolent from aggressive tumors in clinically low-risk patients may complicate decision-making for men on active surveillance. Genomic classifiers (GCs) may improve clinical risk stratification by predicting the risk of upgrading or upstaging (UG/US) in this setting. We assessed the impact of GCs on UG/US risk prediction added to a rich clinical model to better guide disease management for low-risk patients. Methods: We used multivariate logistic regression with backward stepwise regression to eliminate non-predictive variables and compute AUC (area under the receiver operating characteristic curve) to develop a prediction model for UG/US in 864 men who were potential candidates for AS. Participants had low risk, low volume prostate cancer (cT1-2, PSA <15ng/ml, GG1 or low volume GG2 defined as <33% core involvement). The model was validated in an independent cohort of 2,267 men with similar risk characteristics. We then tested predictive ability for 9 additional models, each containing the base variables plus 1 GC (OncoType Dx Genomic Prostate Score, Decipher score, and 7 selected Decipher GRID scores) for the development cohort. Results: Among 864 patients in the development cohort, 450 (52%) experienced US/UG. The prediction model included five diagnostic variables that were significantly associated with risk of UG/US: diagnostic grade group (OR 5.83, 95% CI 3.73-9.10), prostate specific antigen (PSA) per 1 ng/ml (OR 1.10, 95% CI 1.01-1.20), percent positive cores (OR 1.01, 95% CI 1.01-1.02), TRUS prostate volume in ccs (OR 0.98, 95% CI 0.97-0.99), and age in years (OR 1.05, 95% CI 1.02-1.07). The pooled AUC was 0.72 for 10 iterations of the prediction model. When the addition of each GC was applied to this model, Genomic Prostate Score was the only one independently associated with risk of UG/US (OR 1.42, 95%CI 0.02-0.07, p=0.02). The pooled AUC for this model was 0.71, indicating comparable predictive performance to the risk prediction model. Conclusions: The addition of GCs to a validated rich model incorporating detailed clinical variables, when applied to men with low-risk PCa did not substantially improve prediction of UG/US. Our findings suggest that widespread use of biomarkers to guide management or intensity of follow up may not be supported in men with low-risk, low-volume disease pursuing AS. However, GCs may be valuable in those with higher risk disease. SOURCE OF Funding: DOD TIA #(W81XWH-13-2-0074)
