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

JianYue Jin, Weili Wang, Randall K. Ten Haken, Jie Chen, Nan Bi, Ramses F Sadek, Hong Zhang, Theodore S. Lawrence, Feng Ming Kong

Research output: Contribution to journalArticle

6 Citations (Scopus)

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 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.

Original languageEnglish (US)
Pages (from-to)77-82
Number of pages6
JournalRadiotherapy and Oncology
Volume117
Issue number1
DOIs
StatePublished - Oct 1 2015

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Non-Small Cell Lung Carcinoma
DNA Repair
Single Nucleotide Polymorphism
Radiation
Survival
Genes
Genotype
Radiotherapy
Biomarkers
Radiation Tolerance
Prescriptions
Logistic Models
Regression Analysis
Mortality
Neoplasms
Therapeutics

Keywords

  • Biomarker
  • Dose survival model
  • ERCC1 and ERCC2
  • Personalized radiotherapy
  • Radiosensitivity
  • Single-nucleotide-polymorphisms (SNPs)

ASJC Scopus subject areas

  • Hematology
  • Oncology
  • Radiology Nuclear Medicine and imaging

Cite this

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. / Jin, JianYue; Wang, Weili; Ten Haken, Randall K.; Chen, Jie; Bi, Nan; Sadek, Ramses F; Zhang, Hong; Lawrence, Theodore S.; Kong, Feng Ming.

In: Radiotherapy and Oncology, Vol. 117, No. 1, 01.10.2015, p. 77-82.

Research output: Contribution to journalArticle

Jin, JianYue ; Wang, Weili ; Ten Haken, Randall K. ; Chen, Jie ; Bi, Nan ; Sadek, Ramses F ; Zhang, Hong ; Lawrence, Theodore S. ; Kong, Feng Ming. / 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. In: Radiotherapy and Oncology. 2015 ; Vol. 117, No. 1. pp. 77-82.
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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 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.",
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AU - Chen, Jie

AU - Bi, Nan

AU - Sadek, Ramses F

AU - Zhang, Hong

AU - Lawrence, Theodore S.

AU - Kong, Feng Ming

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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.

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KW - Single-nucleotide-polymorphisms (SNPs)

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