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Inflammation Genes and Lung Cancer Risk

炎症基因和肺癌风险

基本信息

批准号：
7371751
负责人：
MARGARET R SPITZ
金额：
$ 54.97万
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/7371751
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）：越来越多的证据表明，呼吸道的慢性损伤和炎症（例如吸烟引起的损伤和炎症）容易患肺癌。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深入的基于途径的分析。该提案建立在一个注释明确的肺癌病例和对照样本库的基础上，该样本库参与了一项正在进行的危险因素研究（CA55769，Spitz，PI）。病例与从多专业医师诊所招募的年龄、性别、种族和吸烟状况的对照进行频率匹配。收集的数据包括吸烟史、饮食摄入量、癌症家族史、特定职业暴露（例如石棉、灰尘）以及既往病史，包括慢性阻塞性气道疾病、哮喘和花粉热。可以获得基因组 DNA 和丰富的候选基因型和表型数据。目标 1：使用具有 8.5 至 9K SNP 的 Illumina iSelect Infinium 芯片，在由 1500 例非小细胞肺癌病例和 1500 例匹配对照（均为白种人）组成的测试组中识别影响肺癌风险的新遗传变异。目标 2：在另外 1000 个病例和 1000 个对照的复制集中，使用 GoldenGate 测定，我们将评估从 Aim1 中确定的前 1500 个 SNP，以满足 P<0.1 标准，或者通过合理的优先顺序方法（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）进行选择。目标 3：通过与目标 2 中相同的方法选择的 50 个 SNP 的侧翼区域进行精细定位，将先验信息与计算机方法相结合以预测功能。对于这 50 个 SNP 中的每一个，我们将在每个基因区域平均选择 10 个额外的 SNP，以在所有 2500 个病例和 2500 个对照中进行重新基因分型。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。协变量包括既往肺气肿、哮喘、花粉热、灰尘和石棉暴露、吸烟特征、癌症家族史和抗炎药物的使用。国际肺癌联盟将在即将制定的提案中进行外部验证。我们在大型有力研究中全面评估候选途径中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，以及具有强炎症成分（如肺气肿）的非肿瘤性疾病。有用的肺癌风险预测模式对公共卫生的潜力是巨大的。公共健康相关性声明吸烟会导致呼吸道炎症，并且越来越多的证据表明慢性炎症过程容易引发肺癌。然而，这种关联的因果性质背后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。该提案建立在现有注释良好的样本库的基础上。我们将评估肺癌病例测试组和匹配对照中的基因变异，并在独立数据集中验证结果。最后，我们将把这些发现纳入肺癌的扩展风险预测模型中。