Validation and Derivation of miRNA-Based Germline Signatures Predicting Radiation Toxicity in Prostate Cancer

Although radiotherapy (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, 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 miRNA-based germline biomarkers (mirSNPs), and (ii) investigate the possibility of temporally and genetically defining other forms of RT-associated GU toxicity.

We included 148 patients enrolled in Magnetic Resonance Imaging-Guided Stereotactic Body Radiotherapy for Prostate Cancer (MIRAGE; NCT04384770), a trial comparing MRI- versus CT-guided prostate stereotactic body RT. 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 toxicity and chronic GU toxicity, and the accuracy of all models was assessed using AUC metrics. A comparative Gene Ontology analysis was performed.

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 RT-associated GU toxicity and chronic RT-associated GU toxicity (AUCs of 0.770 and 0.763, respectively). Finally, Gene Ontology analysis identifies unique pathways involved in each form of GU toxicity: acute, chronic, and late.

These findings provide strong evidence for the continued application of mirSNPs to predict toxicity to RT and act as a path for the continued personalization of RT with improved patient outcomes.