The impact of genomic biomarkers on a validated clinical risk prediction model for upgrading/upstaging among men with low-risk prostate cancer.

Abstract

262 Background: While active surveillance (AS) is the preferred management strategy for men diagnosed with low-risk prostate cancer (PCa), the inability to distinguish indolent from aggressive tumors in clinically low-risk patients complicates decision-making. Genomic classifiers (GCs) were introduced to improve risk stratification, based on their ability to predict 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 management decision-making for low-risk patients. Methods: We used multivariate logistic regression and receiver operating characteristic (ROC) curves to develop a prediction model for UG/US in men with low-risk, low-volume intermediate-risk PCa who were potential candidates for AS. The model was developed among 864 men and validated in an independent cohort of 2,267 men with similar risk profiles. After computing areas under the ROC curve (AUC) from these probabilities, we tested the model’s predictive ability with the addition of a series of GCs (OncoType Dx Genomic Prostate Score, Decipher score, and selected Decipher GRID scores) using the logistic model constructed for the development cohort to estimate the predicted probability of UG/US. Results: The prediction model for the development cohort included five diagnostic variables that were significantly associated with risk of UG/US: diagnostic grade (OR 5.83, 95% CI 3.73-9.10), PSA (OR 1.10, 95% CI 1.01-1.20), percent positive cores (OR 1.01, 95% CI 1.01-1.02), TRUS prostate volume (OR 0.98, 95% CI 0.97-0.99), and age (OR 1.05, 95% CI 1.02-1.07). The pooled AUC was 0.72 for 10 iterations of the model. When the addition of GCs was applied to this model, Genomic Prostate Score was independently associated with risk of UG/US (OR 1.42, 95%CI 0.016-0.066, p=0.017). However, the pooled AUC 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 favorable-risk PCa patients, did not substantially improve prediction of UG/US. Our findings suggest that widespread use of biomarkers to guide management or the intensity of follow up may not be supported in men with low-risk, low-volume disease pursuing AS.