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

• 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
• 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

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通过将现代和快速发展的“组学”技术的基因组数据整合到最先进的分子流行病学研究中，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。这个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，但需要进一步的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建模的建立，并获得更多的流行病学研究进行（研究对象筛选和登记）的经验。我组建了一个指导团队，其中包括一位主要导师Margaret Spitz博士，她是分子流行病学教授和知名领袖，因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；和四位共同导师：Farrah Kheradmand博士，肺癌（LC）和慢性阻塞性肺疾病（COPD）的教授和肺病学家；哈佛医学院副教授、COPD遗传流行病学家Edwin Silverman博士；达特茅斯-希区柯克诺里斯棉花癌症中心人口科学教授兼副主任Christopher I. Amos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Sanjay Shete博士。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；并通过将这些遗传变异添加到基线模型中，构建吸烟者早期发现LC的风险预测模型。具体目的是：(1)在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的抵抗吸烟者（重度吸烟史），重度COPD的易感吸烟者（轻度吸烟史），以及LC和重度COPD的易感吸烟者。(2)开展COPD和/或LC易感吸烟者的小队列研究，对候选罕见变异进行外部验证；(3)开发包含外显子组测序数据的LC风险预测模型。