Expression of a dominant T-cell receptor can reduce toxicity and enhance tumor protection of allogeneic T-cell therapy
Bibliographic record
Abstract
Due to the lack of specificity for tumor antigens, allogeneic T-cell therapy is associated with graft-versus-host disease. Enhancing the anti-tumor specificity while reducing the graft-versus-host disease risk of allogeneic T cells has remained a research focus. In this study, we demonstrate that the introduction of 'dominant' T-cell receptors into primary murine T cells can suppress the expression of endogenous T-cell receptors in a large proportion of the gene-modified T cells. Adoptive transfer of allogeneic T cells expressing a 'dominant' T-cell receptor significantly reduced the graft-versus-host toxicity in recipient mice. Using two bone marrow transplant models, enhanced anti-tumor activity was observed in the presence of reduced graft-versus-host disease. However, although transfer of T-cell receptor gene-modified allogeneic T cells resulted in the elimination of antigen-positive tumor cells and improved the survival of treated mice, it was associated with accumulation of T cells expressing endogenous T-cell receptors and the development of delayed graft-versus-host disease. The in-vivo deletion of the engineered T cells, mediated by endogenous mouse mammary tumor virus MTV8 and MTV9, abolished graft-versus-host disease while retaining significant anti-tumor activity of adoptively transferred T cells. Together, this study shows that the in-vitro selection of allogeneic T cells expressing high levels of a 'dominant' T-cell receptor can lower acute graft-versus-host disease and enhance anti-tumor activity of adoptive cell therapy, while the in-vivo outgrowth of T cells expressing endogenous T-cell receptors remains a risk factor for the delayed onset of graft-versus-host disease.
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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 | 0.000 |
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".