From Oncogenesis to Immunotherapy: Mathematical Modeling of Heterogeneous Cancers

Abstract

We utilize mathematical modeling to study two types of cancer with substantial mutational heterogeneity: chronic lymphocytic leukemia (CLL) and mismatch-repair deficient colorectal cancer (MMR-D CRC). First, to study the progression of CLL into an aggressive lymphoma, Richter’s Syndrome (RS), we analyze data from a recent mouse model and utilize a Bayesian modeling approach to show that growth patterns present in human disease are recapitulated in murine CLL/RS. Next, we use a stochastic branching process model to simulate the acquisition of tumor-specific neoantigens in MMR-D CRC. By using these in-silico tumors as initial conditions in a dynamical systems model of tumor-immune interactions, parameterized using clinical trial data, we characterize features associated with a durable response to immune checkpoint inhibitor (ICI) immunotherapy in MMR-D CRC.