Grant Number:	5U01CA097431-08 Interpret this number
Primary Investigator:	Kimmel, Marek
Organization:	Rice University
Project Title:	Lung Cancer Model: Risk, Progression and Intervention
Fiscal Year:	2009

DESCRIPTION (provided by applicant): In the course of previous CISNET funding period, we developed two complementary models of lung cancer: (i) Model of carcinogenesis, extended to include genetic susceptibility and impact of smoking pattern, (ii) Model of progression, detection and treatment, based on stochastic tumor growth and stochastic stage transitions. The main trust of the research planned will be focused on two Aims: Aim 1. To determine population impact of interventions such as: (a) Smoking cessation and prevention of initiation, (b) Early detection of lung cancer by periodic screening using helical CT, in a high-risk population, followed by therapy, (c) Lifestyle interventions (e.g., dietary), removal of exposures (ETS, asbestos, radon). Aim 2. Predict the population impact of novel interventions, not yet developed, such as genetic screening of heavy smokers and other high-risk groups, detection using new biomarkers, new treatment modalities and so forth..While the impact of smoking on lung cancer is generally well understood, there are certain aspects of this modeling which are still a major challenge, e.g., gaining a better understanding of the process of carcinogenesis for those who have quit smoking and understanding trends in lung cancer among nonsmokers. This implies our Aim 3. To model carcinogenesis and natural history of lung cancer in former smokers and never smokers. Modeling is the only method that allows extrapolation of results of controlled cancer intervention studies to estimates of US population and community effectiveness. Current models, as it is seen from the review above, do not address existing inter-individual variability in susceptibility, natural history, response to treatment, and so forth. The individual-based approach to modeling, which we are taking in this application, will allow addressing this variability. The individual-based approach is also suitable for modeling of interventions, which do not yet exist such as new treatments.





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