Quantitative Gene Expression in Human Lung Epithelium

DESCRIPTION (provided by applicant): Only one in ten current or ex-smokers contracts lung cancer over a lifetime. Lung cancer prevention and early detection strategies all require the identification of a high risk subgroup of tobacco-smokers upon which to focus intensive smoking cessation, chemoprevention and early disease screening efforts. Identifying this high risk subgroup is the long-term objective of this laboratory. Tobacco smoke's composition includes polyaromatic hydrocarbons, nitrosamines, and aromatic amines. Therefore, plausible candidate susceptibility genes may be hypothesized to include those genes encoding enzymes involved in initial carcinogen bioactivation and inactivation, and those involved in quenching in-situ-generated reactive oxygen species, in the lung epithelial cells of first contact with tobacco smoke. Expression of these genes in defined, smoke-exposed lung epithelium has been understudied as it relates to lung cancer risk. Our general hypothesis is that carcinogen and antioxidant metabolizing enzyme expression levels in lung epithelium, by quantitative assays, will identify smokers at high risk for lung cancer. With this laboratory's development of RNA-specific real-time quantitative expression assays for phase I and II carcinogen and oxidant metabolism enzymes, applied to laser microdissected human lung epithelium, it is now feasible to quantify cancer-relevant gene induction in the target cells of interest. Therefore, the specific aims are to 1) Quantify gene expression and interindividual expression differences of selected carcinogen-metabolizing enzymes and antioxidant enzymes in laser capture microdissected, in situ-exposed human lung epithelium, 2) Compare the observed differences with sensitive biomarkers of tobacco smoke exposure (plasma nicotine and cotinine) and an intermediate biomarker (p53- mutation frequency, spectrum, and methylation) and 3) Correlate the gene expression and p53 data with lung cancer case versus control status in multivariate models. We therefore will quantitatively define carcinogen and oxidant metabolizing gene expression - related susceptibility to lung cancer, for future adaptation to broad population screening strategies.