Systematic testing and specificity mapping of alloantigen-specific chimeric antigen receptors in regulatory T cells
Bibliographic record
Abstract
Chimeric antigen receptor (CAR) technology can be used to engineer the antigen specificity of regulatory T cells (Tregs) and improve their potency as an adoptive cell therapy in multiple disease models. As synthetic receptors, CARs carry the risk of immunogenicity, particularly when derived from nonhuman antibodies. Using an HLA-A*02:01-specific CAR (A2-CAR) encoding a single-chain variable fragment (Fv) derived from a mouse antibody, we developed a panel of 20 humanized A2-CARs (hA2-CARs). Systematic testing demonstrated variations in expression, and ability to bind HLA-A*02:01 and stimulate human Treg suppression in vitro. In addition, we developed a new method to comprehensively map the alloantigen specificity of CARs, revealing that humanization reduced HLA-A cross-reactivity. In vivo bioluminescence imaging showed rapid trafficking and persistence of hA2-CAR Tregs in A2-expressing allografts, with eventual migration to draining lymph nodes. Adoptive transfer of hA2-CAR Tregs suppressed HLA-A2+ cell-mediated xenogeneic graft-versus-host disease and diminished rejection of human HLA-A2+ skin allografts. These data provide a platform for systematic development and specificity testing of humanized alloantigen-specific CARs that can be used to engineer specificity and homing of therapeutic Tregs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".