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Usage Information

γδ T cells and cancer
Lukas Bolini, Seth B. Coffelt, Bruno Silva-Santos, David L. Wiest, Lorenzo Galluzzi
Lukas Bolini, Seth B. Coffelt, Bruno Silva-Santos, David L. Wiest, Lorenzo Galluzzi
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Review

γδ T cells and cancer

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Abstract

γδ T cells are a subset of lymphoid cells that, unlike their αβ lineage counterparts, express a heterodimeric TCR that mostly operates in an MHC-independent manner. γδ T cells are abundant in barrier tissues, where they continuously monitor epithelial cells for signs of stress or damage. Thus, γδ T cells are among the first responders to pathophysiological conditions, including viral infection and oncogenesis. Human γδ T cells can be classified based on TCR γ and δ chain usage into three main subsets: (a) Vγ9+Vδ2+ cells, accounting for most circulating γδ T cells; (b) Vδ1+ cells, which are common in epithelial linings, and (c) Vδ3+ T cells, which are fairly rare but exhibit unique specificities. Moreover, both human and murine γδ T cells can assume a spectrum of states with divergent phenotypic and functional properties. Accumulating evidence demonstrates that γδ T cells can mediate robust anticancer effects or support tumor progression and resistance to therapy, depending on numerous variables, including functional state and tumor type. Here, we critically discuss the context-dependent interaction between γδ T cells and cancer, focusing on recent developments and the challenges facing current efforts to manipulate this versatile lymphocyte subset for therapeutic purposes.

Authors

Lukas Bolini, Seth B. Coffelt, Bruno Silva-Santos, David L. Wiest, Lorenzo Galluzzi

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Usage data is cumulative from September 2026 through November 2026.

Usage JCI PMC
Text version 2,634 54
PDF 695 24
Figure 646 0
Table 543 0
Citation downloads 267 0
Totals 4,785 78
Total Views 4,863

Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

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Copyright © 2026 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

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