Jian-Yue Jin1, Weili Wang2, Randall K Ten Haken2, Jie Chen3, Nan Bi2, Ramses Sadek3, Hong Zhang1, Theodore S Lawrence2, Feng-Ming Spring Kong4. 1. Department of Radiation Oncology, Georgia Regents University, Augusta, United States. 2. Department of Radiation Oncology, University of Michigan, Ann Arbor, United States. 3. Department of Biostatistics and Epidemiology, Georgia Regents University, Augusta, United States. 4. Department of Radiation Oncology, Georgia Regents University, Augusta, United States. Electronic address: Fkong@gru.edu.
Abstract
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 91Gy. 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.
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 NSCLCpatients with doses varying from 60 to 91Gy. 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.
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