01.18 Investigating the Development of Ulcerative Colitis-Associated Cancer through an Agent-Based Model

M. E. Stack1, C. Cockrell1, G. An1 1University Of Chicago,Department Of Surgery,Chicago, IL, USA

Introduction: Ulcerative colitis (UC) has an increased risk of colorectal cancer (CRC) due to the genomic instability caused by chronic inflammation, demonstrating a cumulative risk as high as 18% after 30 years of disease duration. Though sporadic CRC has a well described step-wise pattern of genetic and molecular alterations leading to malignancy, the dynamics of CRC arising in the setting of chronic inflammation is still not fully understood. In order to better understand the complex set of events leading to the development and progression of colitis-associated cancer, we will use a dynamic agent-based computational model (ABM) of colonic epithelium, termed the GI Cancer ABM (GICABM). The GICABM reproduces genetic events in colonic epithelium that lead to cancer, with the effect of inflammation manifested as increased rates of DNA damage. The GICABM is used to generate in silico patient populations, and is validated by matching epidemiological colon cancer rates seen in both sporadic and UC populations.

Methods: The GICABM is comprised of agents representing colonic epithelial cells and utilizes the DNA damage-repair functions from our prior oncogenesis ABMs. Rules for the GICABM were derived from existing literature concerning genetic factors associated with colon cancer. DNA damage was generated by a variable probability of mutation in a group of thirteen genes which have been implicated in the development of colon cancer: p53, telomerase, K-Ras, EGFR, TGF-beta, APC, Beta-catenin, PIK3CA, DCC, E-cadherin, SMAD4, BRAF, and C-src. The effect of chronic inflammation was modeled by increasing the rate of DNA damage. Sporadic colon cancer rates were retrieved from the SEER database, and a literature survey was used to generate a normalized risk progression for UC patients.

Results: The GICABM produced in silico populations (N = 1 million patients) over 60 years of simulated time that effectively reproduced the epidemiological data for both sporadic and UC CRC, with UC patients exhibiting a 2-5 fold increase in CRC risk when compared to sporadic CRC rates. Analysis of mutational patterns reproduced known patterns of mutational progression for sporadic CRC, and further suggested a conserved sequence for UC CRC involving impairment of DNA repair, followed by proliferation/immortality mutations.

Conclusion: The GICABM effectively reproduces existing data concerning sporadic CRC, and extrapolates the behavior of UC CRC. It allows visualization of the complex, lengthy processes involved in oncogenesis, and provides insight into mutational and evolutionary dynamics that lead to cancer under different conditions. This GICABM can potentially augment more traditional oncology research as both a hypothesis generating tool as well as a means for in silico hypothesis testing.