Allogeneic DNT cell therapy synergizes with T cells to promote anti-leukemic activities while suppressing GvHD
Bibliographic record
Abstract
Abstract Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a second-line treatment with curative potential for leukemia patients. However, the prognosis of allo-HSCT patients with disease relapse or graft-versus-host disease (GvHD) is poor. CD4+ or CD8+ conventional T (Tconv) cells are critically involved in mediating anti-leukemic immune responses to prevent relapse and detrimental GvHD. Hence, treatment for one increases the risk of the other. Thus, therapeutic strategies that can address relapse and GvHD are considered the Holy Grail of allo-HSCT. CD3+CD4−CD8− double-negative T cells (DNTs) are unconventional mature T cells with potent anti-leukemia effects with “off-the-shelf” potential. A phase I clinical trial demonstrated the feasibility, safety, and potential efficacy of allogeneic DNT therapy for patients with relapsing acute myeloid leukemia (AML) post-allo-HSCT. Here, we studied the impact of DNTs on the anti-leukemic and GvHD-inducing activities of Tconv cells. DNTs synergized with Tconv cells to mediate superior anti-leukemic activity. Mechanistically, DNTs released soluble factors which activated and evoked potent anti-leukemic activities of Tconv cells. In contrast, DNTs suppressed GvHD-inducing activities of Tconv cells in a CD18-dependent manner by mediating cytotoxicity against proliferative Tconv cells. The seemingly opposite immunological activities of DNTs were dictated by the presence or absence of AML cells. Collectively, these results support the potential of DNTs as an adjuvant to allo-HSCT to address both disease relapse and GvHD.
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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".