A computational investigation of pathways for ex vivo differentiation and dysfunction of healthy and patient T cells
- 1Queensland University of Technology
Abstract
Adoptive T cell therapy is a promising field in cancer immunotherapy that leverages the natural ability of T cells to target and kill cancer cells. A crucial step in this therapy involves the ex vivo expansion of memory-like and effector-like T cells, but limiting their differentiation into exhaustion-associated phenotypes remains a challenge. Mathematical model-informed strategies to optimise T cell expansion may be able to exploit our understanding of T cell differentiation, but there is a large amount of uncertainty and disagreement in the biological literature regarding the ways in which T cells differentiate and become exhausted. We therefore conduct a large-scale mathematical investigation to help further our understanding of T cell differentiation during short-term ex vivo expansion. Using observations from biological literature, we first construct thousands of potential pathways connecting distinct T cell phenotypes and then formulate corresponding ordinary differential equations to test hypotheses regarding T cell differentiation and dysfunction. We fit each model to experimental data describing the ex vivo expansion of T cells originating from healthy individuals and cancer patients and accept those that most closely describe the data. From our findings, we hypothesise that cancer may alter T cell differentiation kinetics rather than the structure of the differentiation pathway, which has significant potential to inform therapeutic strategies. Our analysis proposes that T cells may not differentiate unidirectionally ex vivo; most accepted models suggest that the cells dynamically switch between functional phenotypes. We postulate that effector-like cells may be prone to upregulating inhibitory receptors that drive exhaustion, and find that modelling effector-like proliferation is unnecessary to capture observed population dynamics. Our hypothesis-testing framework generates recommendations for promising future research avenues and may pave the way to improve the production of patient-tailored T cell populations for adoptive cell therapy.
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BibTeX
@misc{lacy2026computational,
title = {{A computational investigation of pathways for ex vivo differentiation and dysfunction of healthy and patient T cells}},
author = {M. S. Lacy and Y. Binenbaum and P. R. Buenzli and A. L. Jenner},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.26.761778},
url = {https://www.biorxiv.org/content/10.64898/2026.10.26.761778v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - A computational investigation of pathways for ex vivo differentiation and dysfunction of healthy and patient T cells
AU - M. S. Lacy
AU - Y. Binenbaum
AU - P. R. Buenzli
AU - A. L. Jenner
PY - 2026
DA - 2026/10/26
PB - bioRxiv
DO - 10.64898/2026.10.26.761778
UR - https://www.biorxiv.org/content/10.64898/2026.10.26.761778v1
AB - Adoptive T cell therapy is a promising field in cancer immunotherapy that leverages the natural ability of T cells to target and kill cancer cells. A crucial step in this therapy involves the ex vivo expansion of memory-like and effector-like T cells, but limiting their differentiation into exhaustion-associated phenotypes remains a challenge. Mathematical model-informed strategies to optimise T cell expansion may be able to exploit our understanding of T cell differentiation, but there is a large amount of uncertainty and disagreement in the biological literature regarding the ways in which T cells differentiate and become exhausted. We therefore conduct a large-scale mathematical investigation to help further our understanding of T cell differentiation during short-term ex vivo expansion. Using observations from biological literature, we first construct thousands of potential pathways connecting distinct T cell phenotypes and then formulate corresponding ordinary differential equations to test hypotheses regarding T cell differentiation and dysfunction. We fit each model to experimental data describing the ex vivo expansion of T cells originating from healthy individuals and cancer patients and accept those that most closely describe the data. From our findings, we hypothesise that cancer may alter T cell differentiation kinetics rather than the structure of the differentiation pathway, which has significant potential to inform therapeutic strategies. Our analysis proposes that T cells may not differentiate unidirectionally ex vivo; most accepted models suggest that the cells dynamically switch between functional phenotypes. We postulate that effector-like cells may be prone to upregulating inhibitory receptors that drive exhaustion, and find that modelling effector-like proliferation is unnecessary to capture observed population dynamics. Our hypothesis-testing framework generates recommendations for promising future research avenues and may pave the way to improve the production of patient-tailored T cell populations for adoptive cell therapy.
ER -