Mathematical modeling of tumor-immune interactions: methods, applications, and future perspectives

TL;DR

This paper reviews mathematical models of tumor-immune interactions, emphasizing their role in understanding cancer progression, treatment response, and guiding future research in cancer immunotherapy.

Contribution

It provides a comprehensive overview of existing models, their applications, limitations, and future directions in the mathematical modeling of tumor-immune dynamics.

Findings

Models help analyze immune escape mechanisms

Models evaluate treatment strategies

Insights into tumor-immune system interactions

Abstract

Mathematical oncology is a rapidly evolving interdisciplinary field that uses mathematical models to enhance our understanding of cancer dynamics, including tumor growth, metastasis, and treatment response. Tumor-immune interactions play a crucial role in cancer biology, influencing tumor progression and the effectiveness of immunotherapy and targeted treatments. However, studying tumor dynamics in isolation often fails to capture the complex interplay between cancer cells and the immune system, which is critical to disease progression and therapeutic efficacy. Mathematical models that incorporate tumor-immune interactions offer valuable insights into these processes, providing a framework for analyzing immune escape, treatment response, and resistance mechanisms. In this review, we provide an overview of mathematical models that describe tumor-immune dynamics, highlighting their applications in understanding tumor growth, evaluating treatment strategies, and predicting immune responses. We also discuss the strengths and limitations of current modeling approaches and propose future directions for the development of more comprehensive and predictive models of tumor-immune interactions. We aim to offer a comprehensive guide to the state of mathematical modeling in tumor immunology, emphasizing its potential to inform clinical decision-making and improve cancer therapies.