Validation and Derivation of MicroRNA-based Germline Signatures Predicting Radiation Toxicity in Prostate Cancer

Abstract

Purpose: While radiation therapy (RT) is one of the primary treatment modalities used in the treatment of cancer, patients often experience toxicity during or after treatment. RT-induced genitourinary (GU) toxicity is a significant survivorship challenge for patients with prostate cancer (PCa), but identifying those at risk has been challenging. Herein, we attempt (i) to validate a previously identified biomarker of late RT-induced GU toxicity, PROSTOX, consisting primarily of microRNA-based germline biomarkers (mirSNPs), and (ii) to investigate the possibility of temporally and genetically defining other forms of RT-associated GU toxicity. Experimental Design: We included 148 patients enrolled in MIRAGE (NCT 04384770), a trial comparing MRI versus CT-guided prostate stereotactic body radiotherapy (SBRT). Linear regression was used to evaluate the association between PROSTOX score and late GU grade toxicity. Machine learning approaches were used to develop predictive models for acute and chronic GU toxicity and the accuracy of all models was assessed using area under the curve (AUC) metrics. A comparative gene ontology (GO) analysis was performed. Results: PROSTOX accurately predicts late GU toxicity, achieving an AUC of 0.76, and demonstrates strong correlation with GU toxicity grade (p-1.2E-9). mirSNP-based signatures can distinguish acute and chronic RT toxicity (AUCs of 0.770 and 0.763). Finally, GO analysis identifies unique pathways involved in each form of GU toxicity -- acute, chronic and late. Conclusions: These findings provide strong evidence for the continued application of mirSNPs to predict toxicity to RT, and to act as a path for the continued personalization of RT with improved patient outcomes.