A computational investigation of pathways for ex vivo differentiation and dysfunction of healthy and patient T cells

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.