Our long-term goals are to understand the evolutionary dynamics of neoplastic progression and to develop effective interventions that can prevent or delay cancer. Neoplasms progress to malignancy through a process of clonal evolution. However, the dynamics of that evolution are poorly understood. We propose to develop an agent-based computational model of neoplastic progression in Barrett's esophagus as a tool to study the dynamics of neoplastic progression and to integrate the genetic, pathological, clinical, and epidemiological data on this disease. We will represent the cells of the Barrett's epithelium as the agents of the model so that we can capture the genetic diversity and evolutionary dynamics that drive neoplastic progression. Barrett's esophagus is a human, pre-malignant condition in which the squamous lining of the esophagus is replaced by a crypt structured intestinal metaplasia. Barrett's esophagus is the only known precursor to esophageal adenocarcinoma, the incidence of which is increasing faster than any other cancer in the Western world. However, most people with Barrett's esophagus never develop cancer, so there is an urgent need for methods to predict risk of progression and intervene in patients at high risk. We will carry out a sensitivity analysis of our model to identify the model parameters that are likely to make the best biomarkers for cancer risk prediction and targets for cancer prevention interventions. Key aspects of neoplastic progression in Barrett's esophagus are unknown and will have to be measured to develop a comprehensive, predictive model of the disease. We have previously shown that the genetic diversity of clones of cells, at a single time point, within Barrett's epithelium is predictive of future progression to cancer. This is either because genetic diversity increases during progression or because high-risk patients have high, constant levels of genetic diversity compared to low-risk patients. We have shown that we can use a cell lineage assay based on detecting mutations in a panel of 244 highly mutable microsatellites in single cells, to measure genetic diversity among cells in Barrett's esophagus. We will determine how genetic diversity changes over time, in 60 well-characterized patients with Barrett's esophagus, and fit the parameters of the model to those results using approximate Bayesian computation. We will test whether or not non-steroidal anti-inflammatory drug (NSAID) use, which is associated with a dramatic reduction in cancer risk in Barrett's esophagus, is associated with a decrease in genetic diversity among cells. We will also measure the density of crypts in a cohort of 243 patients with Barrett's esophagus at two time points to 1) determine the number of crypts that should be simulated in the model in order to represent the tissue, 2) determine if crypt density changes over time, 3) test if the number or density of crypts predicts progression to cancer and 4) test for an association between NSAID use and crypt density. This project will result in an improved understanding of neoplastic progression in Barrett's esophagus and a model that can act as a predictive tool for identifying promising targets for intervention and biomarker development.
Once a tumor has invaded other organs, it is very difficult to cure. Thus, we are now focusing on preventing cancer before it becomes incurable. In particular, we study Barrett's esophagus, a pre-malignant condition that can develop into esophageal cancer. This is an important disease because the incidence of esophageal cancer is increasing faster than any other cancer in the United States. However, most patients with Barrett's esophagus will never develop cancer, so there is a need to understand the process by which Barrett's cells evolve malignancy and identify patients at high risk, so that we can focus our medical resources, and the inherent risks of any interventions, on them. We are proposing to develop computational models of the evolution of malignancy in Barrett's esophagus and to measure the dynamics of that process in biopsies from patients with Barrett's esophagus. These models will help to identify good biomarkers for measuring cancer risk in patients with Barrett's esophagus as well as targets for cancer prevention. Our methods should be generalizable to other pre-malignant conditions.
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