Borrowing ecological theory to infer interactions between sensitive and resistant breast cancer cell populations

While some forms of breast cancer are highly responsive to treatment, endocrine therapy-resistant breast cancers are disproportionately lethal. There has been significant progress in understanding how endocrine therapy-resistant strains evolve from therapy-susceptible strains of cancer, but little is understood about the proliferation of resistance through cancer cell populations, or the interactions that occur between populations of resistant and sensitive cells. In this study, we characterize the nature of the ecological interaction between populations of resistant and susceptible breast cancer cells to reveal novel methods of controlling drug resistance. Using in-vitro data on fluorescent-tagged resistant and susceptible cells, we use an image processing algorithm to identify and count cell growth till equilibrium. We then borrow theory from population biology to infer the type of ecological interaction that occurs between populations of resistant and sensitive cells. In particular, we use a Bayesian approach to fit single culture cell populations to infer density-dependent growth parameters (growth rate, carrying capacity) and a Generalized Lotka-Volterra model to understand how susceptible and resistant co-culture populations may be depressing or supporting growth of the other. Our results identify a net mutualistic interaction between the susceptible and resistant cancer strains, demonstrating that there are ecological dynamics to cancer resistance. Our findings also suggest that ecological dynamics change in the presence of therapy, and that an adaptive treatment protocol can induce cycling behavior suggesting that heterogeneous ecological effects contribute to empirically observed adaptive-therapeutic dynamics.