Abstract
Cancer is heterogeneous and variability in drug sensitivity is widely documented across cancer types. Adaptive therapy is an emerging treatment strategy that leverages this heterogeneity to improve therapeutic outcomes. Current standard treatments eliminate a majority of drug-sensitive cells, leading to relapse by competitive release. Adaptive therapy retains some drug-sensitive cells, limiting resistant cell growth by ecological competition. This strategy has shown some early promise, but 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. The model's steady-state behaviour and temporal dynamics identify optimal balances of competition and transitions suitable for effective adaptive versus constant dose therapy. Furthermore, under adaptive therapy, models with cell-state transitions show slower oscillations than those without, suggesting that the competition-transitions balance could impinge on population-level dynamical properties. Our analyses also identify key limitations of phenomenological models in therapy design and implementation, particularly with cell-state transitions. These findings elucidate the relevance of phenotypic plasticity for emerging cancer treatment strategies using population dynamics as an investigation framework.