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.
Identification of commonalties in susceptibility variants for COPD and LC may lead to clues for early detection and to possible therapeutic targets. The genetic-based risk model enables us to distinguish smokers at low- and high- risk for both diseases. The high-risk individuals could subsequently be targeted for low-dose CT screening surveillance for LC, intense smoking cessation programs, and/or chemoprevention interventions trials.
