Impacts of competition and phenotypic plasticity on the viability of adaptive therapy.

Cancer is heterogeneous and variability in drug sensitivity is widely documented across cancer types. Adaptive therapy is an emerging modality of cancer treatment that leverages this drug resistance heterogeneity to improve therapeutic outcomes. Current standard treatments typically eliminate a large fraction of drug-sensitive cells, leading to drug-resistant relapse due to competitive release. Adaptive therapy aims to retain some drug-sensitive cells, thereby limiting resistant cell growth by ecological competition. While early clinical trials of such a strategy have shown promise, optimisation of adaptive therapy is a subject of active study. Current methods largely assume cell phenotypes to remain constant, even though cell-state transitions could permit drug-sensitive and -resistant phenotypes to interchange and thus escape therapy. We address this gap using a deterministic model of population growth, in which sensitive and resistant cells grow under competition and undergo cell-state transitions. Based on the model's steady-state behaviour and temporal dynamics, we identify distinct balances of competition and phenotypic transitions that are suitable for effective adaptive versus constant dose therapy. Our data indicate that under adaptive therapy, models with cell-state transitions show a higher frequency of fluctuations than those without, suggesting that the balance between ecological competition and phenotypic transitions could determine population-level dynamical properties. Our analyses also identify key limitations of applying phenomenological models in clinical practice for therapy design and implementation, particularly when cell-state transitions are involved. These findings provide an overall perspective on the relevance of phenotypic plasticity for emerging cancer treatment strategies using population dynamics as an investigation framework.