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Incorporating single-nucleotide polymorphisms into the Lyman model to improve prediction of radiation pneumonitis.

Publication ,  Journal Article
Tucker, SL; Li, M; Xu, T; Gomez, D; Yuan, X; Yu, J; Liu, Z; Yin, M; Guan, X; Wang, L-E; Wei, Q; Mohan, R; Vinogradskiy, Y; Martel, M; Liao, Z
Published in: Int J Radiat Oncol Biol Phys
January 1, 2013

PURPOSE: 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 AND MATERIALS: 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.

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Published In

Int J Radiat Oncol Biol Phys

DOI

EISSN

1879-355X

Publication Date

January 1, 2013

Volume

85

Issue

1

Start / End Page

251 / 257

Location

United States

Related Subject Headings

  • Risk Assessment
  • Risk
  • Retrospective Studies
  • Reproducibility of Results
  • Radiation Pneumonitis
  • Predictive Value of Tests
  • Polymorphism, Single Nucleotide
  • Oncology & Carcinogenesis
  • Models, Statistical
  • Middle Aged
 

Citation

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Tucker, S. L., Li, M., Xu, T., Gomez, D., Yuan, X., Yu, J., … Liao, Z. (2013). Incorporating single-nucleotide polymorphisms into the Lyman model to improve prediction of radiation pneumonitis. Int J Radiat Oncol Biol Phys, 85(1), 251–257. https://doi.org/10.1016/j.ijrobp.2012.02.021
Tucker, Susan L., Minghuan Li, Ting Xu, Daniel Gomez, Xianglin Yuan, Jinming Yu, Zhensheng Liu, et al. “Incorporating single-nucleotide polymorphisms into the Lyman model to improve prediction of radiation pneumonitis.Int J Radiat Oncol Biol Phys 85, no. 1 (January 1, 2013): 251–57. https://doi.org/10.1016/j.ijrobp.2012.02.021.
Tucker SL, Li M, Xu T, Gomez D, Yuan X, Yu J, et al. Incorporating single-nucleotide polymorphisms into the Lyman model to improve prediction of radiation pneumonitis. Int J Radiat Oncol Biol Phys. 2013 Jan 1;85(1):251–7.
Tucker, Susan L., et al. “Incorporating single-nucleotide polymorphisms into the Lyman model to improve prediction of radiation pneumonitis.Int J Radiat Oncol Biol Phys, vol. 85, no. 1, Jan. 2013, pp. 251–57. Pubmed, doi:10.1016/j.ijrobp.2012.02.021.
Tucker SL, Li M, Xu T, Gomez D, Yuan X, Yu J, Liu Z, Yin M, Guan X, Wang L-E, Wei Q, Mohan R, Vinogradskiy Y, Martel M, Liao Z. Incorporating single-nucleotide polymorphisms into the Lyman model to improve prediction of radiation pneumonitis. Int J Radiat Oncol Biol Phys. 2013 Jan 1;85(1):251–257.
Journal cover image

Published In

Int J Radiat Oncol Biol Phys

DOI

EISSN

1879-355X

Publication Date

January 1, 2013

Volume

85

Issue

1

Start / End Page

251 / 257

Location

United States

Related Subject Headings

  • Risk Assessment
  • Risk
  • Retrospective Studies
  • Reproducibility of Results
  • Radiation Pneumonitis
  • Predictive Value of Tests
  • Polymorphism, Single Nucleotide
  • Oncology & Carcinogenesis
  • Models, Statistical
  • Middle Aged