Lynch syndrome patients inherit a germline mutation in one of several DNA mismatch repair (MMR) genes, leading to a significantly earlier onset and a higher penetrance of colorectal cancer than in sporadic cases due to the occurrence of microsatellite instability (MSI). Long term administration of aspirin has been shown to reduce the incidence of colon cancer in Lynch syndrome patients, as documented by epidemiological data. The mechanisms underlying this effect are not fully understood. It is thought that protection is achieved through COX-dependent and independent activities. Basic kinetic parameters, such as the division rate, death rate, and mutation rate of cells have been shown to be affected. In order to better understand how protection is achieved, it is important to find out how changes in cellular parameters determine the degree of protection observed on the population level. The hypothesis is explored that the degree of protection observed on the population level can be predicted from data that quantify how aspirin changes parameters related to the evolutionary dynamics of cells. This hypothesis will be tested with an inter-disciplinary approach that combines experiments with mathematical models and that links data on cellular kinetics with those on incidence in the population. The firs two aims will quantify the extent to which aspirin changes a set of key parameters that are related to the growth and evolution of cells. These include the rate of cell division, the rate of ell death, the rate at which genetic changes are incurred, the probability of repair, the mean duration of repair, etc. This will be done with different cell lines exposed to varying levels of inflammation. The investigation starts first in an in vitro setting and is then extended to human tumor xenografts in mice, which represent a more complex growth environment for the cells. Subsequently, the data on cellular kinetics will inform a mathematical model of in vivo carcinogenesis in Lynch Syndrome patients. The model will be used to generate theoretical age-incidence curves for colon cancer in Lynch syndrome patients in the presence and absence of aspirin treatment. It will test whether the model can successfully predict the observed age-incidence curves through model application to epidemiological data. The model will allow us to determine which cellular parameter(s) contribute the most to the protection observed in the population data, and to explore possible avenues to enhance this level of protection. Implications of our results for understanding aspirin-mediated protection against sporadic colorectal cancer will be explored by analyzing appropriate epidemiological data.

Public Health Relevance

In order to better understand how aspirin treatment reduces the incidence of colorectal cancer in Lynch Syndrome patients, it is important to determine how treatment-induced changes in cellular parameters determine the degree of protection observed at the population level. Thus, we quantify the extent to which aspirin changes a set of key parameters that are related to the growth and evolution of cells, using different cell lines that ae exposed to varying levels of inflammation and varying doses of aspirin. By comparing observed age-incidence curves for Lynch Syndrome patients in the presence and absence of aspirin with those predicted by a mathematical model that is informed by the experimental parameter estimates, we will determine which cellular parameter(s) contribute the most to the protection observed in the population data, which allows us to explore possible avenues to enhance the level of protection.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
1U01CA187956-01
Application #
8759524
Study Section
Special Emphasis Panel (ZCA1-SRLB-B (M1))
Program Officer
Couch, Jennifer A
Project Start
2014-09-23
Project End
2019-06-30
Budget Start
2014-09-23
Budget End
2015-08-31
Support Year
1
Fiscal Year
2014
Total Cost
$595,990
Indirect Cost
$131,036
Name
Baylor Research Institute
Department
Type
DUNS #
145745022
City
Dallas
State
TX
Country
United States
Zip Code
75204