Inflammation Genes and Lung Cancer Risk

描述(申请人提供)：有越来越多的证据表明，呼吸道的慢性损伤和炎症，如吸烟引起的损伤和炎症，容易导致肺癌。因此，我们建议使用病例和对照的测试和验证集对炎症途径中的基因变异进行基于途径的深入分析。这项建议建立在一项正在进行的风险因素研究(CA55769，Spitz，PI)中登记的肺癌病例和对照的良好注释样本库。病例与从多专业医生诊所招募的年龄、性别、种族和吸烟状况的对照病例进行频率匹配。收集的数据包括吸烟史、饮食摄入量、癌症家族史、特定的职业暴露(例如石棉、粉尘)，以及以前的病史，包括慢性阻塞性呼吸道疾病、哮喘和花粉热。基因组DNA和丰富的候选基因型和表型数据是可用的。目的1：使用Illumina iSelect Infinium芯片和8.5到9K的SNP，在1500例非小细胞肺癌患者和1500名匹配的对照(全部是高加索人)的测试集中，识别影响肺癌风险的新的基因变异。目标2：在另外1000例病例和1000名对照的重复集中，使用金门试验，我们将评估从Aim1中鉴定为符合P&lt；0.1标准的前1500个SNP，或者通过结合已发表的结果、SNP类型、进化生物学、物理化学特性和单倍型标记SNP的合理优先方法来选择。目标3：通过与目标2相同的方法在50个SNP的侧翼区域进行精细定位，将先验信息与预测功能的电子方法相结合。对于这50个SNP中的每一个，我们将在所有2500例病例和2500名对照中，在每个基因区域平均选择10个额外的SNPs进行重新分型。目的4.通过纳入已建立的流行病学危险因素和基因变异数据，扩展我们的流行病学风险预测模型。我们将应用机器学习工具来识别基因-环境和基因-基因相互作用。协变量将包括既往肺气肿、哮喘、花粉热、粉尘和石棉暴露、吸烟特征、癌症家族史和抗炎药物的使用。国际肺癌联盟将在一份待开发的提案中进行外部验证。我们在一项功能强大的大型研究中综合评估候选途径中的变异的方法将适用于炎症发挥重要病因作用的各种其他癌症部位，以及具有强烈炎症成分的非肿瘤性疾病，如肺气肿。为公众健康提供了一种有用的肺癌风险预测模式的潜力是巨大的。公共卫生相关声明吸烟会导致呼吸道炎症，越来越多的证据表明，慢性炎症过程易患肺癌。然而，这种联系的因果性质背后的分子机制尚不清楚。我们建议对炎症途径中的基因变异作为肺癌的易感因素进行深入分析。这项建议建立在现有的注释良好的标本库的基础上。我们将评估肺癌病例和匹配对照的测试集的基因变异，并在独立的数据集中验证发现。最后，我们将把这些发现合并到肺癌的扩展风险预测模型中。

DESCRIPTION (provided by applicant): There is accumulating evidence that chronic injury and inflammation in the respiratory tract, such as that caused by cigarette smoking, predispose to lung cancer. We therefore propose to conduct an in depth pathway-based analysis of gene variants in the inflammatory pathway using test and validation sets of cases and controls. This proposal builds on a well-annotated specimen repository of lung cancer cases and controls enrolled in an ongoing risk factor study (CA55769, Spitz, PI). Cases are frequency-matched to controls on age, gender, ethnicity and smoking status recruited from a multi-specialty physician practice. Data collected include smoking history, dietary intake, cancer family history, specific occupational exposures (e.g., asbestos, dust), and previous medical history including chronic obstructive airway disease, asthma and hay fever. Genomic DNA and rich candidate genotype and phenotype data are available. Aim 1: To identify novel genetic variants influencing lung cancer risk in a test set of 1500 cases with non-small cell lung cancer and 1500 matched controls (all Caucasian), using the Illumina iSelect Infinium chip with 8.5 to 9K SNP's. Aim 2: In a replication set of an additional 1000 cases and 1000 controls, using a GoldenGate assay, we will evaluate the top 1500 SNPs identified from Aim1 as meeting the P<0.1 criterion, or selected by a rational prioritizing approach that incorporates published results, type of SNP, evolutionary biology, physico-chemical properties and haplotype tagging SNPs. Aim 3: To perform fine mapping in the flanking regions of 50 SNPs selected by the same approach as in Aim 2, combining prior information with in silico approaches for predicting functionality. For each of these 50 SNPs, we will select an average of 10 additional SNPs per gene region to regenotype in all 2500 cases and 2500 controls. Aim 4. To extend our epidemiologic risk prediction model by incorporating established epidemiologic risk factor and gene variant data. We will apply machine-learning tools to identify gene-environment and gene-gene interactions. Covariates will include prior emphysema, asthma, hay fever, dust and asbestos exposure, smoking characteristics, family history of cancer, and anti-inflammatory drug use. The International Lung Cancer Consortium will perform external validation in a proposal to be developed. Our approach to comprehensively evaluate variants in a candidate pathway in a large well- powered study will be applicable to a variety of other cancer sites where inflammation plays an important etiologic role, as well as in non-neoplastic diseases with a strong inflammatory component such as emphysema. The public health potential of a useful risk prediction modes for lung cancer is substantial. Public Health Relevance Statement Cigarette smoking results in inflammation in the respiratory tract and there is growing evidence that chronic inflammatory processes predispose to lung cancer. However, the molecular mechanisms underlying the causal nature of this association are unclear. We propose to conduct an in depth analysis of gene variants in the inflammatory pathway as susceptibility factors for lung cancer. This proposal builds upon an existing well annotated specimen repository. We will evaluate gene variants in a test set of lung cancer cases and matched controls and validate the findings in an independent dataset. Finally we will incorporate these findings into an extended risk prediction model for lung cancer.