MicroRNA-based biomarkers of the radiation response in prostate cancer.

Abstract

163 Background: Intermediate and high-risk prostate cancer can be cured with radiation (RT) to the prostate and pelvic lymph nodes with androgen deprivation therapy (ADT), but both acute and late toxicity of the GU and GI systems are common. There are no biomarkers predicting radiation outcomes, limiting the opportunity to best personalize prostate radiation therapy. Methods: A prospectively enrolled single arm trial for locally advanced prostate cancer patients (T1-T4N0-N1M0) treated with definitive RT (74Gy IMRT) plus ADT was studied. Biologic samples were available in 108 of 138 patients. Toxicity was recorded using the RTOG morbidity grading system. We applied a panel of microRNA-based germline mutations shown to predict cancer therapy endpoints. Machine learning techniques were used to simultaneously identify prognostic features and perform classification of the biomarkers. Upsampling nested LOO-CV was used to assess performance and generality. Independent Fisher’s exact tests were performed to identify statistically significant marginal associations. Three classifiers were studied: logistic regression with elastic net regularization (EN-LR), classification trees (CT), and random forests (RF), with corresponding hyper-parameters of regularization weights (EN-LR), minimum split and bucket level sample size (CT), number of trees and mtry (RF). Normalized on the simplex, feature importance was defined as absolute value of regression weights for EN-LR, and cumulative decrease in Gini impurity for primary and surrogate splits at each node/splits for CT and RF. Results: Grade 2 or higher toxicity included acute GI (11%), acute GU (34%), late GI (3%) and late GU (16%). GI and GU toxicity and acute and late toxicity had unique predictive biomarkers. The top three marginal genetic associations for late GU toxicity were microRNA site variants in CD6 and CD274 (PDL1)(p.val < 0.01) and BRCA2 (p.val = 0.014). Using RF, CT and EN_LR we could predict late GU toxicity with up to 70% sensitivity, 96% specificity, and 90% accuracy. Conclusions: We have identified microRNA-based biomarkers that can predict late GU toxicity. Work incorporating patient reported outcomes and to identify biomarkers for additional endpoints is ongoing.