Evolutionary dynamics of imatinib-treated leukemic cells by stochastic approach

TL;DR

This study models the stochastic evolutionary dynamics of leukemic cells under imatinib therapy using Monte Carlo simulations, revealing how mutation rates influence therapy resistance and aligning with clinical observations.

Contribution

It introduces a stochastic Monte Carlo approach to analyze the impact of targeted therapy on leukemia cell populations and resistance development.

Findings

High mutation rates increase resistance likelihood.

Therapy efficacy varies with mutation dynamics.

Results align with clinical resistance patterns.

Abstract

The evolutionary dynamics of a system of cancerous cells in a model of chronic myeloid leukemia (CML) is investigated by a statistical approach. Cancer progression is explored by applying a Monte Carlo method to simulate the stochastic behavior of cell reproduction and death in a population of blood cells which can experience genetic mutations. In CML front line therapy is represented by the tyrosine kinase inhibitor imatinib which strongly affects the reproduction of leukemic cells only. In this work, we analyze the effects of a targeted therapy on the evolutionary dynamics of normal, first-mutant and cancerous cell populations. Several scenarios of the evolutionary dynamics of imatinib-treated leukemic cells are described as a consequence of the efficacy of the different modeled therapies. We show how the patient response to the therapy changes when an high value of the mutation rate from healthy to cancerous cells is present. Our results are in agreement with clinical observations. Unfortunately, development of resistance to imatinib is observed in a proportion of patients, whose blood cells are characterized by an increasing number of genetic alterations. We find that the occurrence of resistance to the therapy can be related to a progressive increase of deleterious mutations.