As our knowledge of the role of genetic and environmental factors in colorectal cancer grows, population screening and prevention efforts can be modified to incorporate this information. The goal of this study is to develop a framework for evaluating the clinical and economic tradeoffs that occur when considering gene-based strategies directed towards identifying persons at increased risk for colon cancer. We propose to extend an existing model of colorectal cancer progression, screening and diagnosis to incorporate genetic variation in the population, and the impact of common genetic polymorphisms and haplotypes on disease progression and incidence. The model will be used to project the cost-effectiveness of a variety of genetic testing and colorectal cancer screening strategies. We propose to inform the model using data from a unique and valuable resource - the Colorectal Cancer Family Registry - Seattle (Seattle CFR).
The specific aims are as follows: (1) (1a) Develop a model of genetic testing for common polymorphisms and haplotypes associated with CRC risk, by extending a state-of-the-art microsimulation model of colorectal cancer progression, screening and diagnosis (MISCAN-Colon). The extended model will include clinical and epidemiologic information related to genetic variants and will model their impact on colorectal cancer progression and incidence. (1b) Validate the model by comparing results generated from the model with polymorphism/haplotype frequencies among CRC cases and controls in the Seattle CFR. (2) Survey individuals enrolled in the Seattle CFR to estimate quality-of-life (QOL) effects related to polymorphism and haplotype screening, specifically considering individuals' personal and family history of colorectal cancer at the time of testing and how this influences the QOL impact of learning that one is a polymorphism carrier. Hypothesis: QOL for those with the polymorphism/haplotype state associated with increased CRC risk will be reduced, compared to those without the variant and those whose genetic status is unknown. (3) Using the model, simulate a variety of genetic testing and CRC screening approaches to estimate the cost effectiveness of alternative strategies of population testing for polymorphisms and haplotypes linked to colorectal cancer. The model will incorporate QOL information from Aim 2, along with family history, clinical, and screening information from the Seattle CFR.
