Engineering antigen specific T regulatory cells for transplant tolerance (P2148)
Bibliographic record
Abstract
Abstract T regulatory cells (Tregs) are critical for self-tolerance and are being pursued as a potential cell therapy in transplantation and autoimmunity. While polyclonal Tregs can prevent transplant rejection in mice, allo-antigen specific Tregs are more potent. A chimeric antigen receptor (CAR) may be an effective way to make ag-specific Tregs in suitable numbers for the clinic. CARs are created by combining antibody variable domains with T cell receptor signaling domains. However, CARs for Treg cell therapy must a) function within the distinct signaling network of Tregs and b) recognize a transplant-relevant antigen. CAR function in Tregs was assessed by transducing sorted cells with a highly-expressed, humanized HER2 CAR. Cells were labeled with proliferation dye and assessed for their ability to respond to target antigen. Two CARs specific for HLA-A2 were generated by cloning the variable portions of the anti-A2 antibody from a hybridoma, creating a single chain antibody and ligating it in frame in the humanized CAR. These CARs were assessed for expression and activity by flow cytometry, and cytotoxicity and proliferation assays. Preliminary data suggests that CARs can induce strong, ag-specific proliferation in human Tregs and that an ag-specific HLA-A2 CAR has been generated. Further work includes determining the effect of CAR stimulation on Treg phenotype via cytokine staining and suppression assays, and optimizing the surface expression of the HLA-A2 CAR via phage display.
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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) | 0.003 | 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".