Inflammation Genes and Lung Cancer Risk

描述（由申请人提供）：越来越多的证据表明，呼吸道的慢性损伤和炎症，如吸烟引起的慢性损伤和炎症，易患肺癌。因此，我们建议使用测试和验证病例和对照组对炎症通路中的基因变异进行深入的基于通路的分析。该提案建立在一项正在进行的风险因素研究（CA 55769，Spitz，PI）中登记的肺癌病例和对照的注释良好的标本库基础上。病例与从多专业医师实践中招募的年龄、性别、种族和吸烟状况的对照频率匹配。收集的数据包括吸烟史、饮食摄入、癌症家族史、特定职业暴露（例如，石棉、粉尘）和既往病史，包括慢性阻塞性气道疾病、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。目标1：在1500例非小细胞肺癌患者和1500例匹配对照（均为白种人）的测试集中，使用具有8.5至9 K SNP的Illumina iSelect Infinium芯片鉴定影响肺癌风险的新型遗传变异。目标二：在另外1000例病例和1000例对照的复制组中，使用GoldenGate测定，我们将评估从Aim 1中鉴定的前1500个SNP是否符合P<0.1标准，或者通过合理的优先排序方法选择，该方法结合了已发表的结果、SNP类型、进化生物学、理化性质和单倍型标记SNP。目标3：在通过与目标2相同的方法选择的50个SNP的侧翼区域中进行精细定位，将先验信息与计算机模拟方法相结合以预测功能性。对于这50个SNPs中的每一个，我们将在所有2500例病例和2500例对照中每个基因区域平均选择10个额外的SNPs进行重新基因分型。目标4。通过整合已建立的流行病学风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使用。国际肺癌联盟将在一份待制定的提案中进行外部验证。我们在一项大型的把握度良好的研究中全面评估候选途径中的变体的方法将适用于炎症起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。肺癌风险预测模型的公共卫生潜力是巨大的。公共卫生相关性声明吸烟导致呼吸道炎症，越来越多的证据表明慢性炎症过程易患肺癌。然而，这种关联的因果关系的分子机制尚不清楚。我们建议对炎症通路中作为肺癌易感因素的基因变异进行深入分析。这一建议建立在现有的注释良好的标本库。我们将评估肺癌病例和匹配对照的测试集中的基因变异，并在独立数据集中验证发现。最后，我们将把这些发现纳入一个扩展的肺癌风险预测模型。

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.