ReviewThe Journal of experimental medicine2026
Follow the "DOTs": Vδ1+ γδ T cells as effectors of cancer immunotherapy.
Review in The Journal of experimental medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Cancer immunotherapy has mostly relied on conventional T cells to achieve success in a limited set of tumor types. A promising avenue to expand the repertoire of cancers effectively treated through immune intervention is to mobilize other anti-tumor effectors, such as γδ T cells. Among these, the Vδ1+ subset commonly predominates within peripheral tissues and within tumors, typically associating with good prognosis. In this Found in Translation, we discuss how to leverage the biological properties of Vδ1+ γδ T cells for cancer immunotherapy, with special focus on the delta one T (DOT) cell approach.
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Registered trials
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.