Multiscale modeling of T cell exhaustion: A mathematical framework integrating continuous dynamics with spatial heterogeneity.

Authors

Chenghang Li, Yuhong Zhang, Xue Liu, Yipu Qu, Xiulan Lai, Jinzhi Lei

Published in

PLoS computational biology. Volume 22. Issue 8. Pages e1014690. Aug 26, 2026. Epub Aug 26, 2026.

Abstract

Continuous antigen exposure drives T cells into a progressive state of dysfunction known as exhaustion, enabling tumors to evade immune surveillance and promoting disease progression. Despite its importance, predictive modeling of T cell exhaustion remains a major challenge due to the complexity of its regulatory dynamics. To address this challenge, we developed a mathematical framework that characterizes the dynamic regulation of T cell exhaustion and its impact on tumor-immune interactions. Here, we integrate multi-source data, population dynamics modeling, and agent-based modeling to track the progressive stages of CD8+ T cell exhaustion. Our model demonstrates that immune checkpoint blockade significantly delays exhaustion and promotes the expansion of tumor-reactive T cells compared to untreated conditions. From a pseudo-potential energy perspective, we show that the core mechanism of immunotherapy lies in expanding the tumor-reactive T cell pool, which consequently reduces the overall state of exhaustion within the system. We find that T cell activation and exhaustion signals jointly govern tumor-immune dynamics. Enhancing activation alone without restricting exhaustion can inadvertently accelerate the loss of T cell function. In contrast, combining enhanced activation (via anti-CTLA-4) with suppressed exhaustion (via anti-PD-1) is essential for achieving a sustained antitumor response. Furthermore, spatial simulations confirm that a high-activation and low-exhaustion state effectively restricts tumor spread, maintaining substantially lower tumor densities compared to low-activation, high-exhaustion scenarios. Our framework provides quantitative insights into T cell exhaustion and a theoretical foundation for optimizing combination immunotherapies.

PMID:
42647568
Bibliographic data and abstract were imported from PubMed on 27 Aug 2026.

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