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Article: Use a survival model to correlate single-nucleotide polymorphisms of DNA repair genes with radiation dose-response in patients with non-small cell lung cancer

TitleUse a survival model to correlate single-nucleotide polymorphisms of DNA repair genes with radiation dose-response in patients with non-small cell lung cancer
Authors
KeywordsRadiosensitivity
Dose survival model
Biomarker
Single-nucleotide-polymorphisms (SNPs)
Personalized radiotherapy
ERCC1 and ERCC2
Issue Date2015
Citation
Radiotherapy and Oncology, 2015, v. 117, n. 1, p. 77-82 How to Cite?
Abstract© 2015 Elsevier Ireland Ltd. All rights reserved. Purpose This study utilizes a survival model and clinical data with various radiation doses from prospective trials to determine radiation dose-response parameters, such as radiosensitivity, and identify single-nucleotide-polymorphism (SNP) biomarkers that can potentially predict the dose response and guide personalized radiotherapy. Methods The study included 92 consecutive stage-III NSCLC patients with doses varying from 60 to 91 Gy. Logistic regression analysis of survival varying with SNP genotype and radiation dose was used to screen candidates for dose-response analysis. The dose-response parameter, represented by D50, was derived by fitting survival data into a model that takes into account both tumor control and treatment mortality. A candidate would be considered as a predictor if the 90% confident intervals (90% CIs) of D50for the 2 groups stratified by the SNP genotype were separated. Results One SNP-signature (combining ERCC2:rs238406 and ERCC1:rs11615) was found to predict dose-response. D50values are 63.7 (90% CI: 53.5-66.3) Gy and 76.1 (90% CI: 71.3, 84.6) Gy for the 2 groups stratified by the genotypes. Using this biomarker-based model, a personalized dose prescription may be generated to improve 2-year survival from ∼50% to 85% and ∼3% to 73% for hypothetical sensitive and resistant patients, respectively. Conclusions We have developed a survival model that may be used to identify genomic markers, such as ERCC1/2 SNPs, to predict radiation dose-response and potentially guide personalized radiotherapy.
Persistent Identifierhttp://hdl.handle.net/10722/267015
ISSN
2023 Impact Factor: 4.9
2023 SCImago Journal Rankings: 1.702
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJin, Jian Yue-
dc.contributor.authorWang, Weili-
dc.contributor.authorTen Haken, Randall K.-
dc.contributor.authorChen, Jie-
dc.contributor.authorBi, Nan-
dc.contributor.authorSadek, Ramses-
dc.contributor.authorZhang, Hong-
dc.contributor.authorLawrence, Theodore S.-
dc.contributor.authorKong, F. M.-
dc.date.accessioned2019-01-31T07:20:15Z-
dc.date.available2019-01-31T07:20:15Z-
dc.date.issued2015-
dc.identifier.citationRadiotherapy and Oncology, 2015, v. 117, n. 1, p. 77-82-
dc.identifier.issn0167-8140-
dc.identifier.urihttp://hdl.handle.net/10722/267015-
dc.description.abstract© 2015 Elsevier Ireland Ltd. All rights reserved. Purpose This study utilizes a survival model and clinical data with various radiation doses from prospective trials to determine radiation dose-response parameters, such as radiosensitivity, and identify single-nucleotide-polymorphism (SNP) biomarkers that can potentially predict the dose response and guide personalized radiotherapy. Methods The study included 92 consecutive stage-III NSCLC patients with doses varying from 60 to 91 Gy. Logistic regression analysis of survival varying with SNP genotype and radiation dose was used to screen candidates for dose-response analysis. The dose-response parameter, represented by D50, was derived by fitting survival data into a model that takes into account both tumor control and treatment mortality. A candidate would be considered as a predictor if the 90% confident intervals (90% CIs) of D50for the 2 groups stratified by the SNP genotype were separated. Results One SNP-signature (combining ERCC2:rs238406 and ERCC1:rs11615) was found to predict dose-response. D50values are 63.7 (90% CI: 53.5-66.3) Gy and 76.1 (90% CI: 71.3, 84.6) Gy for the 2 groups stratified by the genotypes. Using this biomarker-based model, a personalized dose prescription may be generated to improve 2-year survival from ∼50% to 85% and ∼3% to 73% for hypothetical sensitive and resistant patients, respectively. Conclusions We have developed a survival model that may be used to identify genomic markers, such as ERCC1/2 SNPs, to predict radiation dose-response and potentially guide personalized radiotherapy.-
dc.languageeng-
dc.relation.ispartofRadiotherapy and Oncology-
dc.subjectRadiosensitivity-
dc.subjectDose survival model-
dc.subjectBiomarker-
dc.subjectSingle-nucleotide-polymorphisms (SNPs)-
dc.subjectPersonalized radiotherapy-
dc.subjectERCC1 and ERCC2-
dc.titleUse a survival model to correlate single-nucleotide polymorphisms of DNA repair genes with radiation dose-response in patients with non-small cell lung cancer-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.radonc.2015.07.024-
dc.identifier.pmid26253951-
dc.identifier.scopuseid_2-s2.0-84946482759-
dc.identifier.volume117-
dc.identifier.issue1-
dc.identifier.spage77-
dc.identifier.epage82-
dc.identifier.eissn1879-0887-
dc.identifier.isiWOS:000364247700013-
dc.identifier.issnl0167-8140-

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