Incorporating Single-nucleotide Polymorphisms Into the Lyman Model to Improve Prediction of Radiation Pneumonitis

被引:65
作者
Tucker, Susan L. [1 ]
Li, Minghuan [5 ]
Xu, Ting [2 ]
Gomez, Daniel [2 ]
Yuan, Xianglin [6 ]
Yu, Jinming [5 ]
Liu, Zhensheng [3 ]
Yin, Ming [3 ]
Guan, Xiaoxiang [3 ]
Wang, Li-E [3 ]
Wei, Qingyi [3 ]
Mohan, Radhe [4 ]
Vinogradskiy, Yevgeniy [7 ]
Martel, Mary [4 ]
Liao, Zhongxing [2 ]
机构
[1] Univ Texas MD Anderson Canc Ctr, Dept Bioinformat & Computat Biol, Houston, TX 77230 USA
[2] Univ Texas MD Anderson Canc Ctr, Dept Radiat Oncol, Houston, TX 77230 USA
[3] Univ Texas MD Anderson Canc Ctr, Dept Epidemiol, Houston, TX 77230 USA
[4] Univ Texas MD Anderson Canc Ctr, Dept Radiat Phys, Houston, TX 77230 USA
[5] Shandong Canc Hosp, Dept Radiat Oncol, Jinan, Shandong, Peoples R China
[6] Huazhong Univ Sci & Technol, Tongji Med Coll, Tongji Hosp, Dept Oncol, Wuhan 430074, Peoples R China
[7] Univ Colorado, Sch Med, Aurora, CO USA
来源
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS | 2013年 / 85卷 / 01期
关键词
COMPLICATION PROBABILITY; LUNG; CHEMORADIOTHERAPY; RADIOTHERAPY; SURVIVAL; RISK; GENE;
D O I
10.1016/j.ijrobp.2012.02.021
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Purpose: To determine whether single-nucleotide polymorphisms (SNPs) in genes associated with DNA repair, cell cycle, transforming growth factor-beta, 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, TGF beta, TNF alpha, 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. Boot-strap 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 TGF beta, VEGF, TNF alpha, XRCC1 and APEX1. With smoking status included in the multivariate model, the SNPs significantly associated with increased risk of RP were in genes for TGF beta, 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. (C) 2013 Elsevier Inc.
引用
收藏
页码:251 / 257
页数:7
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