Evidence map›Paper›PMID 40227601›Full record

ReviewCancers2025

γδ T Cells: Game Changers in Immune Cell Therapy for Cancer.

Nabil Subhi-Issa, Daniel Tovar Manzano, Alejandro Pereiro Rodriguez, Silvia Sanchez Ramon, Pedro Perez Segura, Alberto Ocaña

Abstract readReview
In one paragraph

Review in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Nabil Subhi-IssaDepartment of Immunology, Hospital Clínico San Carlos, IdISSC, 28040 Madrid, Spain.
Daniel Tovar ManzanoDepartment of Immunology, Hospital Clínico San Carlos, IdISSC, 28040 Madrid, Spain.ORCID 0009-0007-1797-5321
Alejandro Pereiro RodriguezDepartment of Immunology, Hospital Clínico San Carlos, IdISSC, 28040 Madrid, Spain.
Silvia Sanchez RamonDepartment of Immunology, Hospital Clínico San Carlos, IdISSC, 28040 Madrid, Spain.ORCID 0000-0001-9585-6167
Pedro Perez SeguraDepartment of Oncology, Hospital Clínico San Carlos, IdISSC, 28040 Madrid, Spain.
Alberto OcañaDepartment of Oncology, Hospital Clínico San Carlos, IdISSC, 28040 Madrid, Spain.ORCID 0000-0002-1067-9630

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gamma delta (γδ) T cells are a unique subset of T lymphocytes with distinctive features that make them highly promising candidates for cancer therapy. Their MHC-independent recognition of tumor antigens, ability to mediate direct cytotoxicity, and role in modulating the tumor microenvironment position them as versatile agents in cancer immunotherapy. This review integrates and synthesizes the existing data on γδ T cells, with an emphasis on the development and optimization of in vitro expansion protocols. Critical aspects are detailed such as activation strategies, co-culture systems, cytokine use, and other parameters to ensure robust cell proliferation and functionality, which may be valuable for those developing or optimizing clinical practices. Finally, we discuss current advancements in γδ T cell research, clinical experience, and highlight areas needing further exploration. Considering these data, we hypothesize and propose potential new applications such as engineering γδ T cells for enhanced resistance to immune checkpoint pathways or for localized cytokine delivery within the tumor microenvironment, which could broaden their therapeutic impact in the treatment of cancer and beyond.

Indexed as

adoptive cell therapycancer immunotherapycellular expansion protocolstumor microenvironmentγδ T cells

Identifiers

PMID40227601
PMCPMC11987767

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.