title: Incorporating single-nucleotide polymorphisms into the Lyman model to improve prediction of radiation pneumonitis.
abstract: OBJECTIVE
  To determine whether single-nucleotide polymorphisms (SNPs) in genes associated with DNA repair, cell cycle, transforming growth factor-β, tumor necrosis factor and receptor, folic acid metabolism, and angiogenesis can significantly improve the fit of the Lyman-Kutcher-Burman (LKB) normal-tissue complication probability (NTCP) model of radiation pneumonitis (RP) risk among patients with non-small cell lung cancer (NSCLC).
  
  
  METHODS
  Sixteen SNPs from 10 different genes (XRCC1, XRCC3, APEX1, MDM2, TGFβ, TNFα, TNFR, MTHFR, MTRR, and VEGF) were genotyped in 141 NSCLC patients treated with definitive radiation therapy, with or without chemotherapy. The LKB model was used to estimate the risk of severe (grade≥3) RP as a function of mean lung dose (MLD), with SNPs and patient smoking status incorporated into the model as dose-modifying factors. Multivariate analyses were performed by adding significant factors to the MLD model in a forward stepwise procedure, with significance assessed using the likelihood-ratio test. Bootstrap analyses were used to assess the reproducibility of results under variations in the data.
  
  
  RESULTS
  Five SNPs were selected for inclusion in the multivariate NTCP model based on MLD alone. SNPs associated with an increased risk of severe RP were in genes for TGFβ, VEGF, TNFα, XRCC1 and APEX1. With smoking status included in the multivariate model, the SNPs significantly associated with increased risk of RP were in genes for TGFβ, VEGF, and XRCC3. Bootstrap analyses selected a median of 4 SNPs per model fit, with the 6 genes listed above selected most often.
  
  
  CONCLUSIONS
  This study provides evidence that SNPs can significantly improve the predictive ability of the Lyman MLD model. With a small number of SNPs, it was possible to distinguish cohorts with >50% risk vs <10% risk of RP when they were exposed to high MLDs.
authors.affiliation: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77230-1402, USA. sltucker@mdanderson.org
authors.forename: Susan L
authors.initials: SL
authors.lastname: Tucker
authors.forename: Minghuan
authors.initials: M
authors.lastname: Li
authors.forename: Ting
authors.initials: T
authors.lastname: Xu
authors.forename: Daniel
authors.initials: D
authors.lastname: Gomez
authors.forename: Xianglin
authors.initials: X
authors.lastname: Yuan
authors.forename: Jinming
authors.initials: J
authors.lastname: Yu
authors.forename: Zhensheng
authors.initials: Z
authors.lastname: Liu
authors.forename: Ming
authors.initials: M
authors.lastname: Yin
authors.forename: Xiaoxiang
authors.initials: X
authors.lastname: Guan
authors.forename: Li-E
authors.initials: LE
authors.lastname: Wang
authors.forename: Qingyi
authors.initials: Q
authors.lastname: Wei
authors.forename: Radhe
authors.initials: R
authors.lastname: Mohan
authors.forename: Yevgeniy
authors.initials: Y
authors.lastname: Vinogradskiy
authors.forename: Mary
authors.initials: M
authors.lastname: Martel
authors.forename: Zhongxing
authors.initials: Z
authors.lastname: Liao
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002289:Carcinoma, Non-Small-Cell Lung / Q000235:genetics / Q000473:pathology / Q000532:radiotherapy*; D005260:Female; D005838:Genotype; D006801:Humans; D008168:Lung / Q000528:radiation effects; D008175:Lung Neoplasms / Q000235:genetics / Q000473:pathology / Q000532:radiotherapy*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D011237:Predictive Value of Tests; D017564:Radiation Pneumonitis / Q000209:etiology / Q000235:genetics*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012306:Risk; D018570:Risk Assessment