|Defining high-risk individuals in a population-based molecular-epidemiological study of lung cancer|
Authors: Adrian Cassidy, Jonathan P. Myles, Triantafillos Liloglou, Stephen W. Duffy, John K. Field
Roy Castle Lung Cancer Research Programme, University of Liverpool Cancer Research Centre, University of Liverpool, Liverpool L3 9TA, UK
Within the framework of the Liverpool Lung Project (LLP), population-based case-control and prospective cohort studies are in progress to identify molecular and epidemiological risk factors and define populations and individuals most at risk of developing lung cancer. This report describes a strategy for selection of a high-risk population and further provides support for the inclusion of occupational and genetic risk factors in future models. Data from the case-control study (256 incident cases and 314 population controls) were analysed to define a high-risk population. Detailed lifestyle and occupational information were collected during structured interviews. Models were constructed using conditional logistic regression and included terms for age, tobacco consumption and previous respiratory disease. Smoking duration was chosen as the most important predictor of lung cancer risk [>50 years (OR 15.65, 95% CI 6.10-40.15)]. However, such a model would preclude younger individuals. Several combinations of previous respiratory disease were also considered, of which a history of bronchitis, emphysema or pneumonia (BEP) was the most significant (OR 1.86, 95% CI 1.28-2.69). A high-risk subset (based on combinations of smoking duration and BEP) was identified, which have a 4.5-fold greater risk of developing lung cancer (OR 4.5, 95% CI 2.33-8.68). Future refinement of the risk model to include individuals occupationally exposed to asbestos and with the p21 genotypes is discussed. There is real potential for environmental and genetic factors to improve on risk prediction and targeting of susceptible individuals beyond the traditional models based only on smoking and age. The development of a molecular-epidemiological model will inform the development of effective surveillance, early detection and chemoprevention strategies.