Tumor growth dynamics under adaptive therapy: a multi-scale computational approach.

Abstract

Cancer therapies often face the challenge of resistance, which arises from the selective pressures exerted by conventional treatment protocols such as the maximum tolerated dose (MTD). Adaptive therapy adjusts treatment intensity based on tumor response, which emerges as a promising alternative for managing tumor growth and delaying resistance. This study presents a multi-scale computational model that integrates cellular-level processes, tumor population dynamics, and adaptive therapy protocols to explore tumor growth under different therapeutic strategies. Through simulations, we compare the efficacy of MTD and adaptive therapy in controlling tumor size, managing resistance, and optimizing patient survival. Our findings highlight the potential of adaptive therapy to stabilize tumor size and delay resistance while maintaining a diverse population of tumor cells. Additionally, these findings suggest that adaptive therapy could be a promising alternative to MTD, offering improved tumor control and delayed resistance in clinical settings. Moreover, this study underscores the potential of adaptive therapy to provide a more sustainable approach to cancer treatment, offering a better quality of life for patients by delaying the development of resistance. By preserving tumor heterogeneity, adaptive therapy could optimize patient outcomes and offer a more effective long-term solution compared to conventional MTD treatments.