A Novel Pathway-Based Approach Improves Lung Cancer Risk Prediction Using Germline Genetic Variations.
A Novel Pathway-Based Approach Improves Lung Cancer Risk Prediction Using Germline Genetic Variations.
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DOI：
10.1158/1055-9965.epi-15-1318
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发表时间：
2016-08
期刊：
影响因子：
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通讯作者：
Amos CI
中科院分区：
文献类型：
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作者：
Qian DC;Han Y;Byun J;Shin HR;Hung RJ;McLaughlin JR;Landi MT;Seminara D;Amos CI
尽管全基因组关联研究（GWAS）已经确定了许多与肺癌密切相关的遗传变异，但这些变异的遗传率较低，并且在个体中作为肺癌的不良预测因子。我们试图通过在生物学途径的背景下考虑其累积效应来增加种系变异的预测价值。对于肺癌病因学研究中的环境和遗传学个体（1，815例病例/1，971例对照），我们计算了路径水平的易感性效应，即相关单核苷酸多态性（SNP）变异等位基因的总和，通过来自单独的肺癌GWAS荟萃分析（7，766例病例/37，482例对照）的对数加性效应进行加权。基于年龄，性别，吸烟，遗传变异和途径效应和途径-吸烟相互作用的主成分的逻辑回归模型在交叉验证中进行了训练和优化，并在独立数据集（556例/830对照）上进行了进一步测试。我们使用受试者工作特征曲线下面积（AUC）评估预测性能。与具有流行病学预测因子（AUC = 0.607）以及顶级GWAS变体（AUC = 0.617）的典型二项式预测模型相比，我们的基于路径的吸烟交互多项式模型在外部验证中显著提高了预测性能（AUC = 0.656，P < 0.0001）。我们的生物学方法表明，相对于先前的方法，结合变异体，非遗传对应模型的AUC增加更大。该模型是第一个使用组织成通路并与吸烟相互作用的亚型分层遗传效应来评估肺癌预测的模型。我们提出了一个潜在的强大的新贡献者风险推断的途径暴露的相互作用。
Although genome-wide association studies (GWAS) have identified many genetic variants that are strongly associated with lung cancer, these variants have low penetrance and serve as poor predictors of lung cancer in individuals. We sought to increase the predictive value of germline variants by considering their cumulative effects in the context of biologic pathways. For individuals in the Environment and Genetics in Lung Cancer Etiology study (1,815 cases/1,971 controls), we computed pathway-level susceptibility effects as the sum of relevant single-nucleotide polymorphism (SNP) variant alleles weighted by their log-additive effects from a separate lung cancer GWAS meta-analysis (7,766 cases/37,482 controls). Logistic regression models based on age, sex, smoking, genetic variants, and principal components of pathway effects and pathway-smoking interactions were trained and optimized in cross-validation, and further tested on an independent dataset (556 cases/830 controls). We assessed prediction performance using area under the receiver operating characteristic curve (AUC). Compared to typical binomial prediction models which have epidemiologic predictors (AUC = 0.607) in addition to top GWAS variants (AUC = 0.617), our pathway-based smoking-interactive multinomial model significantly improved prediction performance in external validation (AUC = 0.656, P < 0.0001). Our biologically informed approach demonstrated a larger increase in AUC over non-genetic counterpart models relative to previous approaches that incorporate variants. This model is the first of its kind to evaluate lung cancer prediction using subtype-stratified genetic effects organized into pathways and interacted with smoking. We propose pathway-exposure interactions as a potentially powerful new contributor to risk inference.