Competition Models of Hormone-sensitive Cancers

Abstract

Tumors are ecologically dynamic systems composed of heterogeneous cell populations in competition for space and resources. Adaptive therapy---a novel therapy regimen with potential use for hormone sensitive cancers---leverages this competition to control therapy-resistant tumors. However, its success relies on understanding the composition of the tumor to better model the interpopulation competition. This thesis combines time-lapse imaging of competing prostate cancer cells with physics-inspired analysis (mean-squared displacement, correlation maps, clustering) to characterize the competitive dynamics between phenotypically distinct prostate cancer cell populations. Notably, we find that cancer cells exhibit intrapopulation anisotropic ordering. This suggests that cells preferentially align head-to-tail rather than side-by-side, creating a bias in mechanical interactions that can affect tumorigenesis. We also show that competitor abundances dynamically affect carrying capacities and drive preferential cluster growth. Together, these quantitative insights provide a framework for optimizing adaptive therapy based on tumor composition and spatial organization.