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Principal Investigator: Marek Kimmel
Institution: Rice University
Grant Number: 2U01CA097431-04
Awarded under CA-05-018
Originally
funded under CA-02-010 (view abstract)
Abstract: 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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Awarded under CA-02-010
Title: Modeling Lung Cancer: Risks, Progression, and Screening
Abstract: We propose to construct a realistic statistical model of lung
cancer risk and progression that will make it possible to relate current trends in lung cancer
incidence and mortality to past trends in smoking in the US population. We depart from existing
approaches by having the model include genetic and behavioral determinants of susceptibility,
progression of the disease from precursor lesions through early localized tumors to disseminated
disease, detection by various modalities, and medical intervention. Using model estimates
as a foundation, we intend to predict mortality reduction caused by primary prevention, and
early-detection and intervention programs, under different scenarios. This includes utilization
of genetic indicators of susceptibility to lung cancer to define the highest-risk subgroups
of the high-risk behavior population (smokers). To allow for uncertainty in the various sources
of data we will develop parameter estimation techniques using simulation and Bayesian hierarchical
modeling approaches. Along with developing new methodology, we will apply our techniques
to a variety of data sets available to us, which will allow calibration and validation of
the model. To investigate and develop lung cancer susceptibility, we will use tobacco impact
estimates developed at the University of California at San Diego, as well as case-control
genetic data on lung cancer maintained by the Epidemiology Department at MD Anderson Cancer
Center. To investigate incidence of lung cancer we will use public registry data of the SEER
type. For disease progression, early detection and intervention, we will use data from the
NCI lung cancer chest X-ray screening studies, and the recent ELCAP CT-scan screening study
developed at Weill Medical College of Cornell University. The team assembled for the proposed
work includes researchers at Rice University, MD Anderson Cancer Center, Weill Medical College
of Cornell University and University of California at San Diego, whose documented expertise
spans population studies, modeling of natural history of cancer, impact of screening, Bayesian
techniques, genetic epidemiology, statistical genetics and risks analysis of smoking. Data
used and generated by the project, as well as software, will be made available to CISNET
members.
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