Functionalizing novel modulators of lung cancer

Abstract

Lung cancer is the leading cause of cancer mortality globally, with lung adenocarcinoma being the most prevalent subtype. The discovery of driver mutations has paved the way for the use of targeted therapy, notably EGFR inhibitors, in the management of advanced disease. However, the effectiveness of targeted therapy is limited firstly by the eventual development of treatment resistance, and secondly by the sizeable percentage of cases in which the driver mutations remain unknown. Furthermore, lung adenocarcinoma (LUAD) in Asians is known to exhibit a molecular profile distinct from that in Caucasians, yet the majority of existing cohort studies have been focused on the latter. To address this gap, we and our collaborators have spearheaded the largest Asian LUAD cohort study, comprising genomic and transcriptomic data from 305 patients. The MutSigCV and 20/20+ driver prediction algorithms were applied to whole exome sequencing data from our cohort and yielded novel candidate drivers that had not been previously implicated in LUAD. This thesis is focused on the functional validation and characterization of these candidates.