Lung Cancer Prediction Models Incorporating Exome Data from Extreme Phenotypes

纳入极端表型外显子组数据的肺癌预测模型

基本信息

批准号：
8766329
负责人：
Yanhong Liu
金额：
$ 12.77万
依托单位：
BAYLOR COLLEGE OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-01 至 2018-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8766329
关键词：
15q25 19q13 4q22 4q31 6p21.3 Adenocarcinoma Age Award Bioinformatics Biological Biometry Bronchodilator Agents Cancer Center Carcinoma Case-Control Studies Characteristics Chemoprevention Chest Chronic Obstructive Airway Disease Data Data Sources Development Disease Dose Early Diagnosis Enrollment Epidemiologic Studies Epidemiologist Epidemiology Family history of Funding Gender Genes Genetic Genetic Risk Genomics Genotype Goals Health Individual Inflammation Intervention Trial Large Cell Carcinoma Lead Malignant Neoplasms Malignant neoplasm of lung Mentors Methodology Modeling Molecular Epidemiology Non-Small-Cell Lung Carcinoma Norris Cotton Cancer Center Outcome Participant Performance Phenotype Population Sciences Predisposition Pulmonary Function Test/Forced Expiratory Volume 1 Research Research Design Research Personnel Research Proposals Resistance Risk Risk Factors Schools Science Smoker Smoking Smoking History Specimen Spirometry Squamous cell carcinoma Staging Study Subject Systems Biology Technology Tobacco Training Training and Education Undifferentiated United States National Institutes of Health Validation Variant Work X-Ray Computed Tomography Zein base cancer epidemiology cancer research center director cancer risk candidate validation career career development clinical research site cohort disease phenotype epidemiology study exome exome sequencing experience genetic variant genome wide association study high risk improved inclusion criteria lung cancer screening medical schools professor programs rare variant repository screening smoking cessation therapeutic target trait

项目摘要

DESCRIPTION (provided by applicant): My long-term career objective is to become an independent investigator in cancer epidemiology research, with an emphasis on early detection and risk prediction, by integrating genomic data from modern and rapidly evolving "omics" technologies into state-of-the-art molecular epidemiology studies. This goal builds upon my previous education and training in genetics and cancer epidemiology, but requires further training in statistical and bioinformatics methodologies to expedite high-dimensional genomic discovery and risk prediction modeling building, and gaining more experience in epidemiologic study conduct (study subject screening and enrollment). I have assembled a mentoring team comprised of a primary mentor, Dr. Margaret Spitz, Professor and renowned leader in molecular epidemiology and internationally noted for her work on tobacco-related cancers and the construction of lung cancer risk models; and four co-mentors: Dr. Farrah Kheradmand, Professor and pulmonologist specializing in lung cancer (LC) and chronic obstructive pulmonary disease (COPD); Dr. Edwin Silverman, Associate Professor and genetic epidemiologist of COPD at Harvard Medical School; Dr. Christopher I. Amos, Professor and Associate Director for Population Science at Dartmouth- Hitchcock Norris Cotton Cancer Center; and Dr. Sanjay Shete, Professor of Biostatistics and Epidemiology at MD Anderson Cancer Center (MDACC) and Director of Biostatistics, Bioinformatics, and Systems Biology at the Graduate School of Biomedical Sciences, Houston. My research proposal will focus on identifying shared rare genetic variants for risk of both COPD and LC from exome sequencing data, using an extreme-trait study design; and constructing risk prediction models for early detection of LC in smokers, by adding these genetic variants to the baseline models. The specific aims are: (1) Discovery and replication of shared genetic rare variants of COPD and LC in smokers with three extreme phenotypes -- resistant smokers without COPD (heavier smoking history), susceptible smokers with severe COPD (lighter smoking history), and susceptible smokers with both LC and severe COPD. (2) Launching a small cohort of susceptible smokers with COPD and or LC, external validation of the candidate rare variants, and (3) Development of risk prediction models for LC incorporating exome sequencing data.

描述（由申请人提供）：我的长期职业目标是成为癌症流行病学研究的独立研究者，重点是早期检测和风险预测，将现代和快速发展的“组学”技术的基因组数据整合到国家-最先进的分子流行病学研究。这一目标建立在我之前在遗传学和癌症流行病学方面的教育和培训的基础上，但需要进一步接受统计和生物信息学方法方面的培训，以加快高维基因组发现和风险预测模型的构建，并在流行病学研究行为（研究对象筛选和研究）方面获得更多经验。注册）。我组建了一个指导团队，由主要导师 Margaret Spitz 博士组成，她是教授和分子流行病学领域的著名领导者，因其在烟草相关癌症和肺癌风险模型构建方面的工作而享誉国际。以及四位共同导师：Farrah Kheradmand 博士，专攻肺癌 (LC) 和慢性阻塞性肺病 (COPD) 的教授和肺病学家； Edwin Silverman 博士，哈佛医学院副教授、COPD 遗传流行病学家； Christopher I. Amos 博士，达特茅斯-希区柯克·诺里斯·科顿癌症中心人口科学教授兼副主任； Sanjay Shete 博士，MD 安德森癌症中心 (MDACC) 生物统计学和流行病学教授，休斯顿生物医学科学研究生院生物统计学、生物信息学和系统生物学主任。我的研究计划将侧重于使用极端特征研究设计，从外显子组测序数据中识别 COPD 和 LC 风险的共享罕见遗传变异；通过将这些遗传变异添加到基线模型中，构建早期检测吸烟者 LC 的风险预测模型。具体目标是：（1）在具有三种极端表型的吸烟者中发现和复制 COPD 和 LC 的共享遗传罕见变异——无 COPD 的耐药吸烟者（较重的吸烟史）、患有严重 COPD 的易感吸烟者（较轻的吸烟史）和患有 LC 和严重 COPD 的易感吸烟者。 (2) 启动一小群患有 COPD 和/或 LC 的易感吸烟者，对候选罕见变异进行外部验证，以及 (3) 结合外显子组测序数据开发 LC 风险预测模型。