||5U19CA148127-04 Interpret this number
||Transdisciplinary Research in Cancer of the Lung (TRICL)
Lung cancer is the leading cause of cancer-related mortality in the U.S. and the world. The genetic factors that commonly influence lung cancer susceptibility have not yet been investigated thoroughly, but eight genome-wide association studies have been performed. In area 1 of our response, we propose to integrate data from these eight studies that comprise over 13,000 lung cancer cases and 25,000 controls to increase
power to detect genetic factors influencing all types of lung cancer and to allow us to analyze specific subsets, such as cases with early onset, specific histological sets, gender-defined groups and never smokers and the extended sample size will allow us to study gene-environment interactions. Existing genome-wide association studies have not analyzed data from nonCaucasian ethnic backgrounds and we therefore propose characterizing and fine mapping genetic factoid In 1500 African-American and 1500 Asian case-control pairs along with an additional 3000 Caucasian case-control pairs. We will then replicate our findings in a broader collection of an additional 12,000 case-control pairs. In area 2, we will evaluate genes in specific loci including nicotinic acetycholine receptor subunits CHRNA5 and CHRNA3 for the effiects that
identified SNPs from area 1 have upon these genes functions. We will also study several other loci that have been identified through existing GWAS as strongly associated with lung cancer risk, including PSMA4, hTERT, CLPT1 ML, BAT3, and hMSHS. For each of these genes, we will study modulation of these genes
using cellular models relevant to their known functions. In area 3, we propose epidemiological characterization of genetic and environmental risk factors for lung cancer. We will characterize mechanisms by which variation in the loci identified in area 1 influence risk, in conjunction with smoking and other exposures. We will also evaluate the calibration of existing risk prediction models for lung cancer and then
develop new models based upon genetic and environmental data from this initiative. The overarching goal of our proposal is the identification of individuals at high risk for lung cancer development for whom screening and early detection would be most beneficial in reducing the burden of lung cancer.