Alloantigen-specific regulatory T cells generated with a chimeric antigen receptor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it