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
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it