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.
Frontiers in oncology. 2020 Oct 15*** epublish ***
Michela Carlotta Massi, Francesca Gasperoni, Francesca Ieva, Anna Maria Paganoni, Paolo Zunino, Andrea Manzoni, Nicola Rares Franco, Liv Veldeman, Piet Ost, Valérie Fonteyne, Christopher J Talbot, Tim Rattay, Adam Webb, Paul R Symonds, Kerstie Johnson, Maarten Lambrecht, Karin Haustermans, Gert De Meerleer, Dirk de Ruysscher, Ben Vanneste, Evert Van Limbergen, Ananya Choudhury, Rebecca M Elliott, Elena Sperk, Carsten Herskind, Marlon R Veldwijk, Barbara Avuzzi, Tommaso Giandini, Riccardo Valdagni, Alessandro Cicchetti, David Azria, Marie-Pierre Farcy Jacquet, Barry S Rosenstein, Richard G Stock, Kayla Collado, Ana Vega, Miguel Elías Aguado-Barrera, Patricia Calvo, Alison M Dunning, Laura Fachal, Sarah L Kerns, Debbie Payne, Jenny Chang-Claude, Petra Seibold, Catharine M L West, Tiziana Rancati
Modelling and Scientific Computing Laboratory, Math Department, Politecnico di Milano, Milan, Italy., Medical Research Council-Biostatistic Unit, University of Cambridge, Cambridge, United Kingdom., Department of Human Structure and Repair, Ghent University, Ghent, Belgium., Leicester Cancer Research Centre, Department of Genetics and Genome Biology, University of Leicester, Leicester, United Kingdom., Department of Radiation Oncology, University Hospitals Leuven, Leuven, Belgium., Maastricht University Medical Center, Maastricht, Netherlands., Department of Radiation Oncology (Maastro), GROW Institute for Oncology and Developmental Biology, Maastricht, Netherlands., Translational Radiobiology Group, Division of Cancer Sciences, Manchester Academic Health Science Centre, Christie Hospital, University of Manchester, Manchester, United Kingdom., Department of Radiation Oncology, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany., Department of Radiation Oncology 1, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy., Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy., Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy., Department of Radiation Oncology, University Federation of Radiation Oncology, Montpellier Cancer Institute, Univ Montpellier MUSE, Grant INCa_Inserm_DGOS_12553, Inserm U1194, Montpellier, France., Department of Radiation Oncology, University Federation of Radiation Oncology, CHU Caremeau, Nîmes, France., Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, United States., Fundación Pública Galega de Medicina Xenómica, Grupo de Medicina Xenómica (USC), Santiago de Compostela, Spain., Instituto de Investigación Sanitaria de Santiago de Compostela, Santiago de Compostela, Spain., Strangeways Research Labs, Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom., Departments of Radiation Oncology and Surgery, University of Rochester Medical Center, Rochester, New York, NY, United States., Centre for Integrated Genomic Medical Research (CIGMR), University of Manchester, Manchester, United Kingdom., Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.