Clinical applications of gene-based risk prediction for Lung Cancer and the central role of Chronic Obstructive Pulmonary Disease.

Abstract

Read online

Lung cancer is the leading cause of cancer death worldwide and nearly 90% of cases are attributable to smoking. Quitting smoking and early diagnosis of lung cancer, through computed tomographic screening, are the only ways to reduce mortality from lung cancer. Recent epidemiological studies show that risk prediction for lung cancer is optimized by using multivariate risk models that include age, smoking exposure, history of chronic obstructive pulmonary disease (COPD), family history of lung cancer and body mass index. Several recent epidemiological studies have shown that COPD predates lung cancer in 65-70% of cases and confers a 4-6 fold greater risk of lung cancer compared to smokers with normal lung function. In separate studies, genome-wide association studies have identified a number of genetic variants associated with COPD or lung cancer, several of which overlap. In a case control study, where smokers with normal lungs were compared to those who had spirometry-defined COPD and histology confirmed lung cancer, several of these overlapping variants were shown to confer the same susceptibility or protective effects on both COPD and lung cancer (independent of COPD status). In this perspective article, we demonstrate how combining clinical data with genetic variants can help identify heavy smokers at the greatest risk of lung cancer. Using this approach, we found that gene-based risk testing helped engage smokers in risk mitigating activities like quitting smoking and undertaking lung cancer screening. We suggest that such an approach could facilitate the targeted selection of smokers for cost-effective, life-saving interventions.

Keywords

• screening
• COPD
• lung cancer
• Genetic Susceptibility
• risk prediction