Evidence map›Paper›PMID 41246323›Full record

ReviewFrontiers in immunology2025

Regulatory T cell therapies: from patient data to biological insights.

Kameron B Rodrigues, Peter J Eggenhuizen, Rosa Bacchetta, Zinaida Good

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
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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

4 authors.

Kameron B RodriguesDivision of Immunology and Rheumatology, Department of Medicine, Stanford University, Stanford, CA, United States.
Peter J EggenhuizenCenter for Inflammatory Diseases, Department of Medicine, School of Clinical Sciences, Monash University, Clayton, VIC, Australia.
Rosa BacchettaDivision of Hematology, Oncology, Stem Cell Transplantation and Regenerative Medicine, Department of Pediatrics, Stanford University, Stanford, CA, United States.
Zinaida GoodDivision of Immunology and Rheumatology, Department of Medicine, Stanford University, Stanford, CA, United States.

Funding

Multimodal AI modeling of T cell therapies to predict patient response and nominate advanced cell design strategiesOT2OD038101 · OD · STANFORD UNIVERSITY · PI Olivier Gevaert, Zinaida Good · 2025 to 2026
$4.2M
Learning features of optimal CAR T cells for LBCL from patient dataR00CA293149 · NCI · STANFORD UNIVERSITY · PI Zinaida Good · 2025 to 2026
$418k
Learning features of optimal CAR T cells for LBCL from patient dataK99CA293149 · NCI · STANFORD UNIVERSITY · PI GOOD, ZINAIDA · 2024 to 2024
$171k
NCI NIH HHS K99 CA293149NCI NIH HHS R00 CA293149NIH HHS OT2 OD038101
6 · The paper itself

Abstract

Regulatory T cell (Treg) therapies are emerging as powerful tools for treating autoimmune and inflammatory diseases, preventing graft-versus-host disease (GvHD), and promoting organ transplant tolerance. Building on the identification of chimeric antigen receptor (CAR)-expressing Tregs as a correlate of poor patient outcomes in CD19-CAR T cell therapy, this review examines strategies for learning from clinical samples and data to improve Treg therapies. We highlight current and next-generation Treg modalities, including polyclonal, antigen-specific, converted, TCR-engineered, and CAR-engineered Tregs, provide a comprehensive overview of Treg clinical trials, and evaluate the evolving toolkit for

Indexed as

Immunotherapy, AdoptiveT-Lymphocytes, RegulatoryAnimalsGraft vs Host DiseaseHumansReceptors, Chimeric AntigenReceptors, Chimeric AntigenautoimmunityGvHDimmune toleranceimmunomonitoringregulatory T cellT cell therapytransplantationTreg

Identifiers

PMID41246323
PMCPMC12615433

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.