Propagation of weakly advantageous mutations in cancer cell population

TL;DR

This paper develops mathematical models to study how weakly advantageous mutations influence cancer cell population growth, combining deterministic differential equations and stochastic Gillespie simulations to better understand cancer evolution.

Contribution

It introduces models incorporating weakly advantageous mutations into cancer evolution, extending previous models that only considered neutral or strongly advantageous mutations.

Findings

Models predict that weakly advantageous mutations can significantly impact tumor growth.

Stochastic simulations align with deterministic model predictions.

Comparison with observational data supports the models' relevance.

Abstract

Research into somatic mutations in cancer cell DNA and their role in tumour growth and progression between successive stages is crucial for improving our understanding of cancer evolution. Mathematical and computer modelling can provide valuable insights into the scenarios of cancer growth, the roles of somatic mutations, and the types and strengths of evolutionary forces they introduce. Previous studies have developed mathematical models of cancer evolution, incorporating driver and passenger somatic mutations. Driver mutations were assumed to have a strong advantageous effect on the growth of the cancer cell population, while passenger mutations were considered fully neutral or mildly deleterious. However, according to several studies, passenger mutations may have a weakly advantageous effect on tumour growth. In this paper, we develop models of cancer evolution with somatic mutations that introduce a weakly advantageous force to the evolution of cancer cells. The models used in this study can be classified into two categories: deterministic and stochastic. Deterministic models are based on systems of differential equations that balance the average number of cells and mutations during evolution. To verify the results of our deterministic modelling, we use a stochastic model based on the Gillespie algorithm. We compare the predictions of our modelling with some observational data on cancer evolution.