TY - JOUR
T1 - 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
AU - Jin, Jian Yue
AU - Wang, Weili
AU - Ten Haken, Randall K.
AU - Chen, Jie
AU - Bi, Nan
AU - Sadek, Ramses
AU - Zhang, Hong
AU - Lawrence, Theodore S.
AU - Kong, F. M.
N1 - Publisher Copyright:
© 2015 Elsevier Ireland Ltd. All rights reserved.
PY - 2015/10
Y1 - 2015/10
N2 - 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 D50 for 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. D50 values 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.
AB - 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 D50 for 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. D50 values 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.
KW - Biomarker
KW - Dose survival model
KW - ERCC1 and ERCC2
KW - Personalized radiotherapy
KW - Radiosensitivity
KW - Single-nucleotide-polymorphisms (SNPs)
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U2 - 10.1016/j.radonc.2015.07.024
DO - 10.1016/j.radonc.2015.07.024
M3 - Article
C2 - 26253951
AN - SCOPUS:84946482759
SN - 0167-8140
VL - 117
SP - 77
EP - 82
JO - Radiotherapy and Oncology
JF - Radiotherapy and Oncology
IS - 1
ER -