Multiscale modeling of spatiotemporal evolution in Barrett's esophagus

The practical goal of this project is to obtain high-resolution genetic and epigenetic maps that reveal the evolutionary relationships and dynamics over space and time in Barrett’s esophagus (BE). Barrett’s is the precursor to esophageal adenocarcinoma (EAC) therefore patients undergo surveillance exams to detect early cancers. Our study will provide an unprecedented level of molecular detail that has not been achieved in any previous study of pre-cancer evolution in BE. Importantly, the proposed experiments and analyses will define a BE patient’s “tissue phylogeography”, including significant features of clonal expansions that are predictive of BE progressing to future EAC. To this end, we will leverage a rich set of serially collected tissue samples and genomic data from patients in the Seattle BE natural history cohort that includes cancer outcome patients and an age-matched group of patients with non-cancer outcomes, sampled at multiple time points. The unique design of this case-control study enables us to identify (epi)genetic markers prognostic of progression using data from advanced multi-omic platforms. Computational modeling and phylogenetics will be used to extract the elusive but essential information on when BE arises in a patient, how fast particular clones spread in BE, and how dispersive these clones are within the tissue. Ultimately, we will use these evolutionary quantities to forecast outcomes of cancer versus non-cancer in a well-documented prospective patient population. The long-term goal of the project is to assess the feasibility and performance of data-driven predictive models that can be translated to improved clinical care. Notably, this project will quantify the utility of robust molecular markers for EAC risk to improve the current practice of relying solely on histopathologic features that are difficult to assess and interpret. To facilitate this goal, we will parameterize the inferred space-time dynamics in phylogeographic reconstructions of this pre-cancer, and embed these measurements in a multiscale model framework for progression from BE to EAC in a population. This multiscale approach explicitly models the stochastic clonal expansions at the cellular level over a patient’s lifetime, within the spatial constraints of the esophagus. The three specific aims for our project are: 1) Measure how new clones arise and spread within Barrett’s glands; 2) Measure how glands move and grow through the Barrett’s lesion by quantifying epigenetic drift to estimate Barrett’s tissue age and constructing phylogeographies to infer how Barrett’s clones spatially evolve; and 3) Integrate spatiotemporal measurements from multi-region Barrett’s samples into a multiscale model of EAC development. The proposed project is innovative because we will infer evolutionary parameters, such as rates of stem cell replacement and TP53 two-hit inactivation in BE, from (epi)genomic data for the first time. This research is significant because it is expected to provide predictive models that incorporate dynamic biomarkers of EAC progression in BE patients to potentially offer new strategies of risk-based surveillance.