Modeling tumor cell heterogeneity and plasticity in adaptive therapy

TL;DR

This paper introduces a novel mathematical model for tumor heterogeneity and plasticity, enhancing adaptive therapy strategies by accounting for continuous phenotypic variation and probabilistic inheritance.

Contribution

It develops an integro-differential model that captures tumor cell heterogeneity and plasticity, extending traditional binary population models for better therapy design.

Findings

Continuous therapy accelerates resistant clone expansion.

Adaptive therapy maintains long-term tumor control.

High plasticity leads to earlier relapse.

Abstract

Adaptive therapy (AT) is designed to postpone the emergence of drug resistance by exploiting evolutionary competition among tumor subclones. Most mathematical models of AT assume a binary population structure of drug-sensitive and drug-resistant cells, which neglects the continuous nature of phenotypic plasticity. In this study, we propose a mathematical model that integrates a continuous drug susceptibility index with a probabilistic inheritance function to describe clonal dynamics under therapy. The resulting integro-differential system generalizes traditional two-type competition models and captures both heterogeneity and plasticity of tumor cells. Analytical and numerical studies show that (i) continuous therapy drives rapid expansion of resistant clones, (ii) adaptive therapy maintains long-term tumor control by dynamically regulating sensitive populations, and (iii) high phenotypic plasticity accelerates phenotype switching, leading to earlier tumor relapse following continuous therapy. These results identify critical parameter regimes where adaptive therapy outperforms fixed regimens and highlight the essential role of plasticity in shaping treatment outcomes. The proposed framework provides a more realistic mathematical foundation for the design of clinically relevant adaptive therapy strategies.