Inflammation Genes and Lung Cancer Risk

炎症基因和肺癌风险

基本信息

批准号：
7691628
负责人：
MARGARET R SPITZ
金额：
$ 39.98万
依托单位：
UNIVERSITY OF TX MD ANDERSON CAN CTR
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-01-01 至 2012-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7691628
关键词：
Adopted Affect Age Algorithms Anti-Inflammatory Agents Anti-inflammatory Antihistamines Apoptosis Area Asbestos Asthma Base Excision Repairs Bioinformatics Biological Assay Biology Caucasians Caucasoid Race Cell Cycle Cell Cycle Regulation Characteristics Chronic Chronic Obstructive Airway Disease Complex Computer Simulation Condition DNA DNA Repair DNA Repair Pathway Data Data Set Depth Development Dietary intake Disease Drug usage Dust Enrollment Environment Ethnic Origin Family Cancer History Frequencies Gender Genes Genetic Genetic Polymorphism Genetic Variation Genomics Genotype Glutathione S-Transferase Goals Growth Factor Haplotypes Hay fever Host Defense In Vitro Infection Inflammation Inflammatory Inflammatory Response Injury International Irritants Joints Letters Life Link Literature Logistics Lung Lymphocyte Machine Learning Malignant Neoplasms Malignant neoplasm of lung Maps Medical History Metabolism Modeling Molecular Molecular Biology Molecular Epidemiology Nature Newly Diagnosed Non-Small-Cell Lung Carcinoma Not Hispanic or Latino Nucleotide Excision Repair Occupational Exposure Outcome Oxidative Stress Participant Pathway interactions Phenotype Physicians Play Predisposition Probability Process Progress Review Group Proxy Public Health Publishing Pulmonary Emphysema Range Receiver Operating Characteristics Recommendation Recording of previous events Recruitment Activity Regression Analysis Research Resources Respiratory System Risk Risk Factors Role Score Smoking Smoking Behavior Smoking History Smoking Status Specimen Staging Structure of parenchyma of lung TP53 gene Testing Tissues Training Validation Variant aggregation pathway angiogenesis base cancer risk cancer site carcinogenesis case control cell growth chemical property cigarette smoking gene interaction genetic variant medical specialties neoplastic novel parent grant repository response tool tumorigenesis

项目摘要

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.

描述（由申请人提供）：积累的证据表明，呼吸道中的慢性损伤和炎症，例如吸烟引起的肺癌引起的肺癌。因此，我们建议使用测试和验证组的病例和对照组对炎症途径中的基因变异进行基于深度途径的分析。该提案建立在肺癌病例和控制危险因素研究（CA55769，Spitz，PI）的肺癌病例的良好标本库中。案件频率与对年龄，性别，种族和吸烟状态的对照组相匹配。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，灰尘）和以前的病史，包括慢性阻塞性气道疾病，哮喘和花粉症。可以使用基因组DNA和富候选基因型和表型数据。目的1：使用8.5至9K SNP的Illumina Iselect Infinium Chip，在1500例非小细胞肺癌和1500个匹配的对照组（所有高加索人）的测试组中，鉴定影响肺癌风险的新型遗传变异。 AIM 2：在使用Goldengate测定法的复制集中，我们将评估AIM1确定的前1500个SNP，以满足P <0.1标准，或者通过合理的优先级方法来选择，以结合已公开的结果，类型SNP，进化生物学，Physico-Physico-akemical Properties，Physico-agemical Properties，Physico-agemical Properties and Haplot型SNPS。目标3：在与AIM 2中选择的50个SNP的侧翼区域进行精细映射，将先验信息与用于预测功能的计算机方法相结合。对于这50个SNP中的每一个，我们将在所有2500例和2500个对照中选择平均每个基因区域的10个SNP进行再生。目标4。通过合并已建立的流行病学风险因素和基因变异数据来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。协变量将包括先前的肺气肿，哮喘，花粉症，灰尘和石棉暴露，吸烟特征，癌症家族史以及抗炎药物使用。国际肺癌财团将在要制定的提案中执行外部验证。我们在大型良好动力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着重要的病因学作用以及具有强烈炎症成分（如多余疾病）的各种其他癌症部位。有用的风险预测模式的公共卫生潜力是巨大的。公共卫生相关性陈述吸烟会导致呼吸道发炎，并且越来越多的证据表明慢性炎症过程易于肺癌。但是，该关联因果性质的基础机制尚不清楚。我们建议对炎症途径中的基因变异进行深入分析，作为肺癌的易感因素。该提案建立在现有带注释的标本存储库的基础上。我们将在肺癌病例的测试集中评估基因变体，并匹配对照，并验证独立数据集中的发现。最后，我们将这些发现纳入肺癌的扩展风险预测模型中。