Alloantigen-specific regulatory T cells generated with a chimeric antigen receptor
Bibliographic record
Abstract
Adoptive immunotherapy with regulatory T cells (Tregs) is a promising treatment for allograft rejection and graft-versus-host disease (GVHD). Emerging data indicate that, compared with polyclonal Tregs, disease-relevant antigen-specific Tregs may have numerous advantages, such as a need for fewer cells and reduced risk of nonspecific immune suppression. Current methods to generate alloantigen-specific Tregs rely on expansion with allogeneic antigen-presenting cells, which requires access to donor and recipient cells and multiple MHC mismatches. The successful use of chimeric antigen receptors (CARs) for the generation of antigen-specific effector T cells suggests that a similar approach could be used to generate alloantigen-specific Tregs. Here, we have described the creation of an HLA-A2-specific CAR (A2-CAR) and its application in the generation of alloantigen-specific human Tregs. In vitro, A2-CAR-expressing Tregs maintained their expected phenotype and suppressive function before, during, and after A2-CAR-mediated stimulation. In mouse models, human A2-CAR-expressing Tregs were superior to Tregs expressing an irrelevant CAR at preventing xenogeneic GVHD caused by HLA-A2+ T cells. Together, our results demonstrate that use of CAR technology to generate potent, functional, and stable alloantigen-specific human Tregs markedly enhances their therapeutic potential in transplantation and sets the stage for using this approach for making antigen-specific Tregs for therapy of multiple diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".