Work

Personalized cancer treatment strategies incorporating irreversible and reversible drug resistance mechanisms

Year: 2025

Type: article

Abstract: Despite advances in targeted cancer therapy, the promise of precision medicine has been limited by resistance to these treatments. In this study, we propose a mathematical modelling framework incorporating cellular heterogeneity, genetic evolutionary dynamics, and non-genetic plasticity, accounting for both irreversible and reversible drug resistance. Previously we proposed Dynamic Precision Medicine (DPM), a personalized treatment strategy that designed individualized treatment sequences by simulations of irreversible genetic evolutionary dynamics in a heterogeneous tumor. Here we apply DPM to the joint model of reversible and irreversible drug resistance mechanisms, analyze the simulation results and compare the efficacy of various treatment strategies. The results indicate that this enhanced version of DPM significantly outperforms current personalized medicine treatment approaches. Our results provide insights into cancer treatment strategies for heterogeneous tumors with genetic evolutionary dynamics and non-genetic cellular plasticity, potentially leading to improvements in survival time for cancer patients.

Source: npj Systems Biology and Applications

DOI: 10.1038/s41540-025-00547-5

Authors: Wei He, Matthew D. McCoy, Rebecca B. Riggins, Robert A. Beckman, Chen-Hsiang Yeang

Institutions: Georgetown University Medical Center, Georgetown Lombardi Comprehensive Cancer Center, Institute of Statistical Science, Academia Sinica, Academia Sinica

Language: English

Topic: Mathematical Biology Tumor Growth

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,490