Integrated Molecular, Cellular, and Imaging Characterization of NLST detected lung cancer

The landmark NLST demonstrated a 20% mortality reduction in lung cancer in individuals who underwent low dose computed tomography (LDCT) screening relative to plain chest radiography. The results of the NLST have resulted in a sea change in US health policy, such that third party payers and Medicare now provide LDCT screening as a preventive service benefit in eligible, high risk smokers. Understanding the factors underlying tumor indolence or aggression that result in heterogeneous clinical outcomes, may facilitate clinical decision making in the context of lung cancer screening and thereby greatly increase its effectiveness. We hypothesize that the mutational landscape of screen-detected lung cancers is an important contributor to their indolence or aggressiveness. To address this hypothesis we will take advantage of the comprehensively annotated NLST biorepository. Whole exome sequencing (WES) of the previously collected NLST samples will be performed to determine the genomic features that distinguish between screen-detected indolent and aggressive lung tumors that result in heterogeneous clinical outcomes. Samples from 110 patients with aggressive cancers and 494 with indolent cancers along with matching reference tissues will be assessed. By utilizing WES, we will be able to identify the impact of genes not previously associated with lung cancer as well as novel alleles of genes with known roles in lung cancer. This will be the first comprehensive genomic characterization of aggressive and indolent lung cancers diagnosed in the course of a lung cancer screening trial. The detailed genomic and imaging-based characterization of screen-detected tumors will ultimately impact clinical management of those cancers. Through separate funding, we will also integrate the genomic data obtained through this research with the tumor immune microenvironment (as characterized by multiplex immunofluorescence analysis of the same specimens) and CT imaging features to build integrated, multiparametric models of tumor biology that can be used to predict the biological behavior of lung cancers in the screening setting.