Prediction of Grade Reclassification of Prostate Cancer Patients on Active Surveillance through the Combination of a Three-miRNA Signature and Selected Clinical Variables

SIMPLE SUMMARY: Active surveillance (AS) has evolved as an alternative to radical treatment for potentially indolent prostate cancer. However, current selection criteria for entering AS are suboptimal, and a significant percentage of patients discontinue AS because of disease reclassification. Hence, there is an unmet need for novel biomarkers for the accurate identification of high-risk PCa and the unequivocal classification of indolent disease. Circulating biomarkers, including microRNAs identified through liquid biopsies, represent a valuable approach to improve on currently available clinicopathological risk-stratification tools. In an attempt to identify specific microRNA signatures as potential circulating biomarkers, the authors performed an unprecedented analysis of the global microRNA profile in plasma samples from AS patients and identified and validated a three-microRNA signature able to predict patient reclassification. The addition of the three-microRNA signature was able to improve the performance of currently available clinicopathological variables, thus showing potential for the refinement of AS patients’ selection. ABSTRACT: Active surveillance (AS) has evolved as a strategy alternative to radical treatments for very low risk and low-risk prostate cancer (PCa). However, current criteria for selecting AS patients are still suboptimal. Here, we performed an unprecedented analysis of the circulating miRNome to investigate whether specific miRNAs associated with disease reclassification can provide risk refinement to standard clinicopathological features for improving patient selection. The global miRNA expression profiles were assessed in plasma samples prospectively collected at baseline from 386 patients on AS included in three independent mono-institutional cohorts (training, testing and validation sets). A three-miRNA signature (miR-511-5p, miR-598-3p and miR-199a-5p) was found to predict reclassification in all patient cohorts (training set: AUC 0.74, 95% CI 0.60–0.87, testing set: AUC 0.65, 95% CI 0.51–0.80, validation set: AUC 0.68, 95% CI 0.56–0.80). Importantly, the addition of the three-miRNA signature improved the performance of the clinical model including clinicopathological variables only (AUC 0.70, 95% CI 0.61–0.78 vs. 0.76, 95% CI 0.68–0.84). Overall, we trained, tested and validated a three-miRNA signature which, combined with selected clinicopathological variables, may represent a promising biomarker to improve on currently available clinicopathological risk stratification tools for a better selection of truly indolent PCa patients suitable for AS.