A deep learning approach validates genetic risk factors for late toxicity after prostate cancer radiotherapy in a REQUITE multi-national cohort
AuthorsMassi, M. C.
Paganoni, A. M.
Franco, N. R.
Talbot, C. J.
Symonds, P. R.
De Meerleer, G.
De Ruysscher, D
Van Limbergen, E.
Elliott, R. M.
Veldwijk, M. R.
Jacquet, M. F.
Rosenstein, B. S.
Stock, R. G.
Aguado-Barrera, M. E.
Dunning, A. M.
Kerns, S. L.
West, Catharine M L
AffiliationModelling and Scientific Computing Laboratory, Math Department, Politecnico di Milano, Milan, Italy.
MetadataShow full item record
AbstractBackground: 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.
CitationMassi MC, Gasperoni F, Ieva F, Paganoni AM, Zunino P, Manzoni A, et al. A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort. Front Oncol. 2020;10:541281.
JournalFrontiers in Oncology
- Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity.
- Authors: Franco NR, Massi MC, Ieva F, Manzoni A, Paganoni AM, Zunino P, Veldeman L, Ost P, Fonteyne V, Talbot CJ, Rattay T, Webb A, Johnson K, Lambrecht M, Haustermans K, De Meerleer G, de Ruysscher D, Vanneste B, Van Limbergen E, Choudhury A, Elliott RM, Sperk E, Veldwijk MR, Herskind C, Avuzzi B, Noris Chiorda B, Valdagni R, Azria D, Farcy-Jacquet MP, Brengues M, Rosenstein BS, Stock RG, Vega A, Aguado-Barrera ME, Sosa-Fajardo P, Dunning AM, Fachal L, Kerns SL, Payne D, Chang-Claude J, Seibold P, West CML, Rancati T, REQUITE Consortium Collaborators.
- Issue date: 2021 Jun
- External Validation of a Predictive Model for Acute Skin Radiation Toxicity in the REQUITE Breast Cohort.
- Authors: Rattay T, Seibold P, Aguado-Barrera ME, Altabas M, Azria D, Barnett GC, Bultijnck R, Chang-Claude J, Choudhury A, Coles CE, Dunning AM, Elliott RM, Farcy Jacquet MP, Gutiérrez-Enríquez S, Johnson K, Müller A, Post G, Rancati T, Reyes V, Rosenstein BS, De Ruysscher D, de Santis MC, Sperk E, Stobart H, Symonds RP, Taboada-Valladares B, Vega A, Veldeman L, Webb AJ, West CM, Valdagni R, Talbot CJ, REQUITE consortium.
- Issue date: 2020
- REQUITE: A prospective multicentre cohort study of patients undergoing radiotherapy for breast, lung or prostate cancer.
- Authors: Seibold P, Webb A, Aguado-Barrera ME, Azria D, Bourgier C, Brengues M, Briers E, Bultijnck R, Calvo-Crespo P, Carballo A, Choudhury A, Cicchetti A, Claßen J, Delmastro E, Dunning AM, Elliott RM, Fachal L, Farcy-Jacquet MP, Gabriele P, Garibaldi E, Gómez-Caamaño A, Gutiérrez-Enríquez S, Higginson DS, Johnson K, Lobato-Busto R, Mollà M, Müller A, Payne D, Peleteiro P, Post G, Rancati T, Rattay T, Reyes V, Rosenstein BS, De Ruysscher D, De Santis MC, Schäfer J, Schnabel T, Sperk E, Symonds RP, Stobart H, Taboada-Valladares B, Talbot CJ, Valdagni R, Vega A, Veldeman L, Ward T, Weißenberger C, West CML, Chang-Claude J, REQUITE consortium.
- Issue date: 2019 Sep
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- Authors: Kerns SL, Dorling L, Fachal L, Bentzen S, Pharoah PD, Barnes DR, Gómez-Caamaño A, Carballo AM, Dearnaley DP, Peleteiro P, Gulliford SL, Hall E, Michailidou K, Carracedo Á, Sia M, Stock R, Stone NN, Sydes MR, Tyrer JP, Ahmed S, Parliament M, Ostrer H, Rosenstein BS, Vega A, Burnet NG, Dunning AM, Barnett GC, West CM, Radiogenomics Consortium.
- Issue date: 2016 Aug
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