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    A deep learning approach validates genetic risk factors for late toxicity after prostate cancer radiotherapy in a REQUITE multi-national cohort

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    Authors
    Massi, M. C.
    Gasperoni, F.
    Ieva, F.
    Paganoni, A. M.
    Zunino, P.
    Manzoni, A.
    Franco, N. R.
    Veldeman, L.
    Ost, P.
    Fonteyne, V.
    Talbot, C. J.
    Rattay, T.
    Webb, A.
    Symonds, P. R.
    Johnson, K.
    Lambrecht, M.
    Haustermans, K.
    De Meerleer, G.
    De Ruysscher, D
    Vanneste, B.
    Van Limbergen, E.
    Choudhury, Ananya
    Elliott, R. M.
    Sperk, E.
    Herskind, C.
    Veldwijk, M. R.
    Avuzzi, B.
    Giandini, T.
    Valdagni, R.
    Cicchetti, A.
    Azria, D.
    Jacquet, M. F.
    Rosenstein, B. S.
    Stock, R. G.
    Collado, K.
    Vega, A.
    Aguado-Barrera, M. E.
    Calvo, P.
    Dunning, A. M.
    Fachal, L.
    Kerns, S. L.
    Payne, D.
    Chang-Claude, J.
    Seibold, P.
    West, Catharine M L
    Rancati, T.
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    Affiliation
    Modelling and Scientific Computing Laboratory, Math Department, Politecnico di Milano, Milan, Italy.
    Issue Date
    2020
    
    Metadata
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    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.
    Citation
    Massi 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.
    Journal
    Frontiers in Oncology
    URI
    http://hdl.handle.net/10541/623527
    DOI
    10.3389/fonc.2020.541281
    PubMed ID
    33178576
    Additional Links
    https://dx.doi.org/10.3389/fonc.2020.541281
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.3389/fonc.2020.541281
    Scopus Count
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