Humanization and Pre-Clinical Validation of an Anti-HLA-A*02
Bibliographic record
Abstract
Introduction Achieving transplant tolerance with regulatory T cell (Treg) adoptive immunotherapy is currently under investigation as a therapy to reduce graft rejection and improve long-term outcomes. Traditional approaches involve the of polyclonal Tregs, which are known to be less potent than antigen-specific cells, or antigen-expanded Tregs, which have several technical limitations. We and others have developed an alternate approach to generate antigen-specific Tregs by expressing a chimeric antigen receptor specific for HLA-A*02:01 (A2-CAR). In our initial studies the antigen-binding region (scFV) of the A2-CAR was derived from the mouse BB7.2 hybridoma, which, due to the high degree of homology between HLA molecules, has been reported to bind to HLA-A alleles in addition to *02:01. Here we sought to systematically define the antigen-specificity of the A2-CAR as well as humanize the sequence to minimize the risk of immunogenicity. Methods We designed 20 humanized versions of the A2-CAR and systematically tested them to determine which were highly expressed on the surface of human Tregs and capable of mediating A2-stimulated activation, expansion, and suppression. We also developed a novel method to systematically, and comprehensively test HLA-allele specificity. Results Of the 20 humanized A2-CARs, 10 were expressed on Tregs and retained A*02:01 binding capacity. We used a series of functional screens to define which of these 10 A2-CARs most effectively stimulated Treg activation, proliferation and proliferation. We then took advantage of the Panel Reactive Antibody (PRA) assay (One Lambda) and created a new method to test CAR-expressing Tregs to bind to specific HLA-alleles. We found that the majority of the humanized A2-CARs had a significantly reduced reactivity to binding to alleles other than A*02:01. We also tested the biological relevance of HLA cross reactivity by stimulating A2-CAR expressing Tregs with cell lines expressing HLA alleles that were or were not found to be cross reactive using the PRA assay. Ultimately, six humanized anti-A2 CARs showed the desired properties, with an ability to activate Tregs, bind to HLA-A2 but not to a comprehensive panel of other common A or B alleles. The potent ability of one of these variants to suppress rejection was confirmed in a humanized model of xenogeneic graft-versus-host disease. Conclusion We successfully developed a series of humanized A2-CARs which were comprehensively screened for desirable properties to generate antigen-specific Tregs. This body of pre-clinical data will support the development of a first-in-human clinical trial of A2-CAR-engineered Tregs to prevent organ allograft rejection.
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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 | 0.003 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".