? Overall Lung Cancer (LC) has been the most common cancer since 1985, resulting in over 1.8 million deaths worldwide per year. LC survival is dismal with a 5 year mortality of about 90%, largely because of its usual late stage diagnosis. Low dose CT (LDCT) reduces overall and lung cancer specific mortality among highly exposes smokers but yields excess positive findings and currently fails to identify many cases. This program project pursues a comprehensive and complementary set of research agendas to understand predictors of smoking and lung cancer. By integrating a comprehensive set of studies in the domains of smoking, genomics and biomarkers in the ILCCO, TRICL and LC3 consortia, and evaluating them together in the context of NLST and 5 other lung cancer screening trials, substantial progress can be made in early detection of lung cancer. Project 1: Genomic Predictors of Smoking Lung Cancer Risk studies large samples to identify uncommon variants, variants with low risk, and variants that affect risk through gene-environment interactions or through perturbations of pathways and further uses this information for studying environmental exposures. Project 2: Biomarkers of Lung Cancer Risk evaluates a wide range of risk biomarkers that have been implicated as promising lung cancer risk biomarkers, including miRNAs, metabolic, immune, protein, and epigenetic markers, using pre-diagnostic biospecimen from the Lung Cancer Cohort Consortium (LC3), and will identify a panel of validated risk biomarkers for use in risk prediction models Project 3: Translating Molecular and Clinical Data to Population Lung Cancer Risk Assessment establishes an integrated risk prediction model-based on lung cancer CT screening populations in US, Canada and Europe, combining personal health and exposure history, targeted molecular and genomic profiles and lung function data, and establishes comprehensive nodule assessment models for individuals with LDCT-detected non-calcified pulmonary nodules based on both diameter-based and volume-based probability models . These research studies are supported by integrating Administrative and Biostatistics cores. Efficiency is created from the scientific synergies among the projects and the use of shared samples and data. We believe that this level of integration across the P01 with three complementary projects working towards a unifying goal will yield novel observations about lung cancer development and provide unique translational opportunities to refine screening eligibility criteria and ultimately help improve screening efficiency and further reduce lung cancer mortality.
Overall Lung Cancer (LC) has been the most common cancer since 1985, resulting in over 1.8 million deaths worldwide per year and has low survival due to its usual late-stage diagnosis. The purpose of this program project is to pursue an integrated set of aims focusing on a better understanding of the individual characteristics that lead to lung cancer development along with improved approaches for its early detection. Biomarkers are studied and combined with genetic, epidemiological and environmental factors to develop improved approaches for early detection of lung cancer.
|Qian, David C; Molfese, David L; Jin, Jennifer L et al. (2017) Genome-wide imaging association study implicates functional activity and glial homeostasis of the caudate in smoking addiction. BMC Genomics 18:740|
|McAllister, Kimberly; Mechanic, Leah E; Amos, Christopher et al. (2017) Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. Am J Epidemiol 186:753-761|
|Barfield, Richard; Shen, Jincheng; Just, Allan C et al. (2017) Testing for the indirect effect under the null for genome-wide mediation analyses. Genet Epidemiol 41:824-833|
|Patel, Chirag J; Kerr, Jacqueline; Thomas, Duncan C et al. (2017) Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies. Cancer Epidemiol Biomarkers Prev 26:1370-1380|
|Gauderman, W James; Mukherjee, Bhramar; Aschard, Hugues et al. (2017) Update on the State of the Science for Analytical Methods for Gene-Environment Interactions. Am J Epidemiol 186:762-770|