NLM DIR Seminar Schedule

Abstract:

As cancer expands from a tumor initiating cell, daughter cells accumulate mutations and epigenetic changes, evolving into subclones that exhibit distinct mutational profiles, gene expression, and phenotypes. The heterogeneous nature of these subclones can complicate disease progression and treatment through subclonal cooperation and competition with each other and the tumor microenvironment. Understanding how subclones interact with each other and the microenvironment, especially the immune system, is critical to identify effective strategies of cancer treatment and patient care.

In melanoma, it has been shown that subclones exhibit two major phenotypes, proliferative and invasive. The former is associated with rapid cell cycle, more melanocytic differentiation, and sensitivities to therapies, and the latter with static growth, more undifferentiated, and resistance to therapies. These two types of subclones often co-exist in melanomas. However, how the proliferative and invasive subclones interact with each other and microenvironment to determine the tumor growth outcome is not clear.

In order to further understand these dynamics, we designed a set of in vivo experiments together with a new computational model of melanoma tumor subclonal interactions. In particular, our full model considers a proliferative and invasive subclone, the immune system, and the interactions between each. To provide experimentally supported parameters and constraints for the computational model, we concurrently built an in vivo melanoma model that contained two subclones with these contrasting phenotypes. To ensure tractability and reliability of the parameter inference, experimental and computational models are built in parallel via two steps: (i) first we design models for clonal competition in silico and in mice deficient of adaptive immunity in vivo and (ii) then we extend the computational models to include immune response and the experimental model into immunocompetent mice.

Our mathematical model consists of a system of differential equations for each subclone and the immune system, represented by a population of T cells. The interactions between the two subclones are modeled using Lotka-Volterra equations, and we present a novel biologically feasible piecewise equation to model the dynamics of the T cell population. By tracing the cell populations in both experimental and computational models, their output can be cross-validated. The results demonstrated how the cell population dynamics impact therapeutic outcomes.