Predicting the germline dependence of hematuria risk in prostate cancer radiotherapy patients

•A machine learning-based genetic model was developed to predict the risk of radiation-induced hematuria using genome-wide single nucleotide polymorphisms (SNPs) data.•The predictive model distinguished the high-risk group from the low-risk group in the validation data with the odds ratio of 2.87.•Post-hoc bioinformatics analyses identified key biological correlates that have been previously reported to be associated with the bladder and urinary tract. Late radiation-induced hematuria can develop in prostate cancer patients undergoing radiotherapy and can negatively impact the quality-of-life of survivors. If a genetic component of risk could be modeled, this could potentially be the basis for modifying treatment for high-risk patients. We therefore investigated whether a previously developed machine learning-based modeling method using genome-wide common single nucleotide polymorphisms (SNPs) can stratify patients in terms of the risk of radiation-induced hematuria. We applied a two-step machine learning algorithm that we previously developed for genome-wide association studies called pre-conditioned random forest regression (PRFR). PRFR includes a pre-conditioning step, producing adjusted outcomes, followed by random forest regression modeling. Data was from germline genome-wide SNPs for 668 prostate cancer patients treated with radiotherapy. The cohort was stratified only once, at the outset of the modeling process, into two groups: a training set (2/3 of samples) for modeling and a validation set (1/3 of samples). Post-modeling bioinformatics analysis was conducted to identify biological correlates plausibly associated with the risk of hematuria. The PRFR method achieved significantly better predictive performance compared to other alternative methods (all p