Turing pattern induced by cross-diffusion in cancer immunotherapy model

TL;DR

This paper explores how cross-diffusion effects in a mathematical cancer immunotherapy model can lead to Turing patterns, revealing insights into spatial immune response structures in different treatment scenarios.

Contribution

It extends a previous cancer immunotherapy model by incorporating cross-diffusion, analyzing pattern formation and stability in both treated and untreated cases.

Findings

Cross-diffusion induces Turing patterns in the model.

Stability and bifurcation analysis reveal conditions for pattern emergence.

Numerical simulations confirm theoretical predictions.

Abstract

In this paper, we investigate a mathematical model describing the interactions between effector cells (E), cancer cells (T), and the IL-2 compound (IL). The model considered here is a generalization, taking into account some cross-diffusion effects, of a spatial cancer immunotherapy model proposed by S. Suddin et al in 2021. These modifications allow us to describe two biologically relevant scenarios: a patient treated with Adoptive Cell Immunotherapy (ACI) and a patient not receiving any treatment/therapy. Cross-diffusion effects are particularly relevant in the interactions between tumor cells and the immune system, in fact they play a key role in immune response dynamics and cannot be neglected. We analyze the equilibrium points of the homogeneous system, along with their stability and bifurcation mechanisms. Furthermore, adopting the Turing approach for reaction-diffusion systems, we investigate the diffusion-driven instability and the emergence of spatial regular structures (stationary in time), i.e. the patterns. Finally, numerical simulations based on the Finite Difference Method (FDM) are presented for the two previously mentioned scenarios.