{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Queensland University of Technology"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "M. S. Lacy"
    },
    {
      "name": "Y. Binenbaum"
    },
    {
      "name": "P. R. Buenzli"
    },
    {
      "affs": [
        0
      ],
      "name": "A. L. Jenner"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6q30f90383r8a.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.26.761778",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.26.761778v1.full",
  "id": "6q30f90383r8a",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.26.761778v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "immunology"
    ],
    [
      "openalex",
      "2403|T-cell and B-cell Immunology"
    ]
  ],
  "openalex": "W7224086278",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.26.761778v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6q30f90383r8a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:08Z",
      "native_id": "10.64898/2026.10.26.761778",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.26.761778v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:immunology",
      "subfield": "immunology"
    }
  ],
  "title": "A computational investigation of pathways for ex vivo differentiation and dysfunction of healthy and patient T cells",
  "title_text": "A computational investigation of pathways for ex vivo differentiation and dysfunction of healthy and patient T cells",
  "type": "preprint",
  "updated_at": "2026-10-31T00:20:43Z",
  "url": "https://research.draconitic.com/paper/6q30f90383r8a",
  "venue": "bioRxiv",
  "versions": []
}