Abstract – 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.
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