A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort
Creators: name: Massi, Michela Carlotta
name: Gasperoni, Francesca
name: Ieva, Francesca
name: Paganoni, Anna Maria
name: Zunino, Paolo
name: Manzoni, Andrea
name: Franco, Nicola Rares
name: Veldeman, Liv
name: Ost, Piet
name: Fonteyne, Valérie
name: Talbot, Christopher J.
name: Rattay, Tim
name: Webb, Adam
name: Symonds, Paul R.
name: Johnson, Kerstie
name: Lambrecht, Maarten
name: Haustermans, Karin
name: De Meerleer, Gert
name: De Ruysscher, Dirk
name: Vanneste, Ben
name: Van Limbergen, Evert
name: Choudhury, Ananya
name: Elliott, Rebecca M.
name: Sperk, Elena
name: Herskind, Carsten
name: Veldwijk, Marlon R.
name: Avuzzi, Barbara
name: Giandini, Tommaso
name: Valdagni, Riccardo
name: Cicchetti, Alessandro
name: Azria, David
name: Jacquet, Marie-Pierre Farcy
name: Rosenstein, Barry S.
name: Stock, Richard G.
name: Collado, Kayla
name: Vega, Ana
name: Aguado-Barrera, Miguel Elías
name: Calvo, Patricia
name: Dunning, Alison M.
name: Fachal, Laura
name: Kerns, Sarah L.
name: Payne, Debbie
name: Chang-Claude, Jenny
name: Seibold, Petra
name: West, Catharine M. L.
name: Rancati, Tiziana
Publisher: name: Apollo - University of Cambridge Repository
Year: 2020
Subjects: Oncology
prostate cancer
late toxicity
snps
deep learning
autoencoder
validation

description: 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.