Armored human CAR T <sub>reg</sub> cells with PD1 promoter-driven IL-10 have enhanced suppressive function
Bibliographic record
Abstract
Regulatory T cell (T reg cell) therapy has been transformed through the use of chimeric antigen receptors (CARs). We previously found that human T reg cells minimally produce IL-10 and have a limited capacity to control innate immunity compared to type 1 regulatory T cells (T r 1 cells). To create “hybrid” CAR T reg cells with T r 1 cell-like properties, we examined whether the PDCD1 locus could be exploited to endow T reg cells with CAR-regulated IL-10 expression. CRISPR-mediated PD1 deletion increased CAR T reg cell activation, and knock-in of IL10 under control of the PD1 promoter resulted in CAR-induced IL-10 secretion. IL10 knock-in improved CAR T reg cell function, as determined by increased suppression of dendritic cells and alloantigen- and islet autoantigen–specific T cells. In vivo, IL10 knock-in CAR T reg cells were stable, safe, and suppressed dendritic cells and xenogeneic graft-versus-host disease. CRISPR-mediated engineering to simultaneously remove an inhibitory signal and enhance a suppressive mechanism is a previously unexplored approach to improve CAR T reg cell potency.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".