Genetic Ablation of HLA Class I, Class II, and the T-cell Receptor Enables Allogeneic T Cells to Be Used for Adoptive T-cell Therapy
Bibliographic record
Abstract
Abstract Adoptive immunotherapy can induce sustained therapeutic effects in some cancers. Antitumor T-cell grafts are often individually prepared in vitro from autologous T cells, which requires an intensive workload and increased costs. The quality of the generated T cells can also be variable, which affects the therapy's antitumor efficacy and toxicity. Standardized production of antitumor T-cell grafts from third-party donors will enable widespread use of this modality if allogeneic T-cell responses are effectively controlled. Here, we generated HLA class I, HLA class II, and T-cell receptor (TCR) triple-knockout (tKO) T cells by simultaneous knockout of the B2M, CIITA, and TRAC genes through Cas9/sgRNA ribonucleoprotein electroporation. Although HLA-deficient T cells were targeted by natural killer cells, they persisted better than HLA-sufficient T cells in the presence of allogeneic peripheral blood mononuclear cells (PBMC) in immunodeficient mice. When transduced with a CD19 chimeric antigen receptor (CAR) and stimulated by tumor cells, tKO CAR-T cells persisted better when cultured with allogeneic PBMCs compared with TRAC and B2M double-knockout T cells. The CD19 tKO CAR-T cells did not induce graft-versus-host disease but retained antitumor responses. These results demonstrated the benefit of HLA class I, HLA class II, and TCR deletion in enabling allogeneic-sourced T cells to be used for off-the-shelf adoptive immunotherapy.
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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) | 0.001 | 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".