An ecological resilience perspective on cancer: insights from a toy model

TL;DR

This paper introduces an ecological resilience framework for understanding cancer dynamics through a simple model, proposing new indicators for treatment strategies and analyzing how resilience affects tumor growth and cure possibilities.

Contribution

It presents a novel resilience-based approach to cancer modeling, including a new method for calculating resilience indicators applicable to population dynamics models.

Findings

Two regimes of tumor growth identified: corruption of growth control and intrinsic repair failure.

A new, efficient method for calculating resilience indicators like latitude, precariousness, and resistance.

Simulations demonstrate how resilience analysis can inform personalized cancer treatment strategies.

Abstract

In this paper we propose an ecological resilience point of view on cancer. This view is based on the analysis of a simple ODE model for the interactions between cancer and normal cells. The model presents two regimes for tumor growth. In the first, cancer arises due to three reasons: a partial corruption of the functions that avoid the growth of mutated cells, an aggressive phenotype of tumor cells and exposure to external carcinogenic factors. In this case, treatments may be effective if they drive the system to the basin of attraction of the cancer cure state. In the second regime, cancer arises because the repair system is intrinsically corrupted. In this case, the complete cure is not possible since the cancer cure state is no more stable, but tumor recurrence may be delayed if treatment is prolongued. We review three indicators of the resilience of a stable equilibrium, related with size and shape of its basin of attraction: latitude, precariousness and resistance. A novel method to calculate these indicators is proposed. This method is simpler and more efficient than those currently used, and may be easily applied to other population dynamics models. We apply this method to the model and investigate how these indicators behave with parameters changes. Finally, we present some simulations to illustrate how the resilience analysis can be applied to validated models in order to obtain indicators for personalized cancer treatments. Keywords: Tumor growth; Chemotherapy; Basins of Attraction; Regime shifts; Critical transitions