Toward understanding of the role of reversibility of phenotypic switching in the evolution of resistance to therapy

TL;DR

This paper models how reversible phenotypic switching in cell populations influences resistance to therapy, highlighting the importance of heterogeneity and reversibility in adapting to environmental changes, especially in cancer treatment.

Contribution

It introduces two models of cell population dynamics focusing on phenotypic reversibility and its role in drug resistance, providing insights into threshold behaviors and heterogeneity effects.

Findings

Reversibility thresholds are emergent in the models.

Cell heterogeneity impacts resistance development.

Reversibility influences adaptation to environmental variability.

Abstract

Reversibility of state transitions is intensively studied topic in many scientific disciplines over many years. In cell biology, it plays an important role in epigenetic variation of phenotypes, known as phenotypic plasticity. More interestingly, the cell state reversibility is probably crucial in the adaptation of population phenotypic heterogeneity to environmental fluctuations by evolving bet-hedging strategy, which might confer to cancer cells resistance to therapy. In this article, we propose a formalization of the evolution of highly reversible states in the environments of periodic variability. Two interrelated models of heterogeneous cell populations are proposed and their behavior is studied. The first model captures selection dynamics of the cell clones for the respective levels of phenotypic reversibility. The second model focuses on the interplay between reversibility and drug resistance in the particular case of cancer. Overall, our results show that the threshold dependencies are emergent features of the investigated model with eventual therapeutic relevance. Presented examples demonstrate importance of taking into account cell to cell heterogeneity within a system of clones with different reversibility quantified by appropriately chosen genetic and epigenetic entropy measures.