A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort.
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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DOI：
10.3389/fonc.2020.541281
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发表时间：
2020
影响因子：
4.7
通讯作者：
Rancati T
中科院分区：
文献类型：
--
作者：
Massi MC;Gasperoni F;Ieva F;Paganoni AM;Zunino P;Manzoni A;Franco NR;Veldeman L;Ost P;Fonteyne V;Talbot CJ;Rattay T;Webb A;Symonds PR;Johnson K;Lambrecht M;Haustermans K;De Meerleer G;de Ruysscher D;Vanneste B;Van Limbergen E;Choudhury A;Elliott RM;Sperk E;Herskind C;Veldwijk MR;Avuzzi B;Giandini T;Valdagni R;Cicchetti A;Azria D;Jacquet MF;Rosenstein BS;Stock RG;Collado K;Vega A;Aguado-Barrera ME;Calvo P;Dunning AM;Fachal L;Kerns SL;Payne D;Chang-Claude J;Seibold P;West CML;Rancati T
背景：REQUITE（验证放射治疗毒性的预测模型和生物标志物，以减少副作用并改善癌症幸存者的生活质量）是一项国际前瞻性队列研究。该项目的目的是使用深度学习算法分析REQUITE招募的一组患者，以识别与毒性发展相关的患者特异性特征，并通过尝试验证先前发表的遗传风险因素来测试该方法。方法：研究对象为接受外束放疗的REQUITE前列腺癌患者，随访2年。我们使用了5个单独的晚期毒性终点：≥1级晚期直肠出血，≥2级尿频，≥1级血尿，≥2级夜尿，≥1级尿流减少。文献中已报道的43个与毒性终点相关的单核苷酸多态性（snp）被纳入分析。在REQUITE队列之前没有研究过SNP。训练深度稀疏自动编码器（DSAE）识别识别无毒性患者的特征（snp），并在不同的独立混合人群中进行测试，包括无毒性和有毒性的患者。结果：纳入1401例患者，毒副反应率为：直肠出血11.7%，尿频4%，血尿5.5%，夜尿7.8%，尿流减少17.1%。与毒性终点相关的43个snp中有24个被验证为识别毒性患者。24个SNPs中有20个与文献报道的相同毒性终点相关：9个SNPs与泌尿系统症状相关，11个SNPs与总体毒性相关。其他4个snp与不同的终点相关。结论：深度学习算法可以验证与前列腺癌放疗后毒性相关的snp。该方法应进一步研究，以确定放射治疗毒性的多基因SNP风险特征。然后，这些特征可以被包括在正常组织并发症的综合概率模型中，并测试它们个性化放疗治疗计划的能力。
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.
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影响因子：
4.6
作者：
Oh JH;Kerns S;Ostrer H;Powell SN;Rosenstein B;Deasy JO
通讯作者：
Deasy JO
DOI：
10.1016/j.radonc.2016.06.017
发表时间：
2016-12
期刊：
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
影响因子：
--
作者：
Andreassen CN;Rosenstein BS;Kerns SL;Ostrer H;De Ruysscher D;Cesaretti JA;Barnett GC;Dunning AM;Dorling L;West CML;Burnet NG;Elliott R;Coles C;Hall E;Fachal L;Vega A;Gómez-Caamaño A;Talbot CJ;Symonds RP;De Ruyck K;Thierens H;Ost P;Chang-Claude J;Seibold P;Popanda O;Overgaard M;Dearnaley D;Sydes MR;Azria D;Koch CA;Parliament M;Blackshaw M;Sia M;Fuentes-Raspall MJ;Ramon Y Cajal T;Barnadas A;Vesprini D;Gutiérrez-Enríquez S;Mollà M;Díez O;Yarnold JR;Overgaard J;Bentzen SM;Alsner J;International Radiogenomics Consortium (RgC)
通讯作者：
International Radiogenomics Consortium (RgC)
影响因子：
4.8
作者：
Midgley, Adam C.;Rogers, Mathew;Steadman, Robert
通讯作者：
Steadman, Robert
DOI：
10.1016/j.ijrobp.2018.01.054
发表时间：
2018-05-01
期刊：
International journal of radiation oncology, biology, physics
影响因子：
--
作者：
Lee S;Kerns S;Ostrer H;Rosenstein B;Deasy JO;Oh JH
通讯作者：
Oh JH
DOI：
10.1158/1055-9965.epi-16-0106
发表时间：
2017-01
期刊：
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
影响因子：
--
作者：
Amos CI;Dennis J;Wang Z;Byun J;Schumacher FR;Gayther SA;Casey G;Hunter DJ;Sellers TA;Gruber SB;Dunning AM;Michailidou K;Fachal L;Doheny K;Spurdle AB;Li Y;Xiao X;Romm J;Pugh E;Coetzee GA;Hazelett DJ;Bojesen SE;Caga-Anan C;Haiman CA;Kamal A;Luccarini C;Tessier D;Vincent D;Bacot F;Van Den Berg DJ;Nelson S;Demetriades S;Goldgar DE;Couch FJ;Forman JL;Giles GG;Conti DV;Bickeböller H;Risch A;Waldenberger M;Brüske-Hohlfeld I;Hicks BD;Ling H;McGuffog L;Lee A;Kuchenbaecker K;Soucy P;Manz J;Cunningham JM;Butterbach K;Kote-Jarai Z;Kraft P;FitzGerald L;Lindström S;Adams M;McKay JD;Phelan CM;Benlloch S;Kelemen LE;Brennan P;Riggan M;O'Mara TA;Shen H;Shi Y;Thompson DJ;Goodman MT;Nielsen SF;Berchuck A;Laboissiere S;Schmit SL;Shelford T;Edlund CK;Taylor JA;Field JK;Park SK;Offit K;Thomassen M;Schmutzler R;Ottini L;Hung RJ;Marchini J;Amin Al Olama A;Peters U;Eeles RA;Seldin MF;Gillanders E;Seminara D;Antoniou AC;Pharoah PD;Chenevix-Trench G;Chanock SJ;Simard J;Easton DF
通讯作者：
Easton DF