A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort

Abstract

Background: REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) is an international prospective cohort study. The purpose of this project was to analyse a cohort of patients recruited into REQUITE using a deep learning algorithm to identify patient-specific features associated with the development of toxicity, and test the approach by attempting to validate previously published genetic risk factors. Methods: The study involved REQUITE prostate cancer patients treated with external beam radiotherapy who had complete 2-year follow-up. We used five separate late toxicity endpoints: ≥grade 1 late rectal bleeding, ≥grade 2 urinary frequency, ≥grade 1 haematuria, ≥ grade 2 nocturia, ≥ grade 1 decreased urinary stream. Forty-three single nucleotide polymorphisms (SNPs) already reported in the literature to be associated with the toxicity endpoints were included in the analysis. No SNP had been studied before in the REQUITE cohort. Deep Sparse AutoEncoders (DSAE) were trained to recognize features (SNPs) identifying patients with no toxicity and tested on a different independent mixed population including patients without and with toxicity. Results: One thousand, four hundred and one patients were included, and toxicity rates were: rectal bleeding 11.7%, urinary frequency 4%, haematuria 5.5%, nocturia 7.8%, decreased urinary stream 17.1%. Twenty-four of the 43 SNPs that were associated with the toxicity endpoints were validated as identifying patients with toxicity. Twenty of the 24 SNPs were associated with the same toxicity endpoint as reported in the literature: 9 SNPs for urinary symptoms and 11 SNPs for overall toxicity. The other 4 SNPs were associated with a different endpoint. Conclusion: Deep learning algorithms can validate SNPs associated with toxicity after radiotherapy for prostate cancer. The method should be studied further to identify polygenic SNP risk signatures for radiotherapy toxicity. The signatures could then be included in integrated normal tissue complication probability models and tested for their ability to personalize radiotherapy treatment planning.

Development of a Management Algorithm for Acute and Chronic Radiation Urethritis and Cystitis

“…Genetic variants and biomarkers are gaining importance in the prediction of the development of adverse effects following RT [8]. Recently, replicated genetic associations for adverse reactions to RT were reported [11]. Genetic biomarkers could help to identify high-risk on toxicity patients and that could allow radiotherapists to personalize treatments for patients for which extra care should be taken to decrease the dose to the bladder or urethra.…”

Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity

“…Here we aim to identify the combined effect of several SNPs on late radiotherapy toxicity and propose a novel scoring method to summarise genetic information that incorporates epistatic effects. Our work builds on the results of a previous study where SNPs identified as affecting risk of late radio-induced toxicity were confirmed by external independent validation [8]. There, the authors considered a pool of 43 SNPs associated with late radiotherapy toxicity from the literature.…”

“…SNPs selected for SNP-allele sets learning for each toxicity endpoint. The SNPs in the table are those identified as relevant in[8] to separate between patients with/without late toxicity symptoms. .. Urinary Frequency grade !…”

A Ten-year-long Update on Radiation Proctitis Among Prostate Cancer Patients Treated With Curative External Beam Radiotherapy

“…Similarly, the REQUITE project is a trial to validate predictive models and biomarkers of radiotherapy toxicity to reduce side-effects and improve quality of life in cancer survivors (70). In the latter cohort of patients, Massi et al (71) isolated, by means of a deep learning approach, eight SNP signatures that could identify patients most likely to experience adverse effects after radiotherapy for prostate cancer. The genetic susceptibility to develop RP could also have a cancerspecific prognostic value.…”