Novel immunotherapy for AML using DNT cells (P4348)
Bibliographic record
Abstract
Abstract Acute myeloid leukemia (AML) is the most common form of adult acute leukemia with very poor survival due to high relapse rates after chemotherapy. New therapeutic approaches that can effectively prevent relapse are needed. Human CD56-CD3+CD4-CD8- double negative T cells (DNTs) are a small subset of peripheral T cells. We have developed a protocol enabling ex vivo expansion of DNTs from both healthy donors and AML patients and showed recently that these DNTs have potent anti-leukemia activity in vitro. Here we further characterized the anti-leukemia properties of DNTs in vivo using a murine xenograft model. Following infusion into NSG mice, human DNTs proliferated and persisted for at least 14 days, and were detectable in spleen and bone marrow. DNTs did not kill normal bone marrow cells in vitro or in vivo. Unlike infusion of CD4+ or CD8+ T cells, DNT infusion did not cause graft-vs-host disease. Pre-treatment of AML blasts with allogeneic DNTs in vitro led to a 4-fold reduction in subsequent engraftment of CD45+CD33+ AML blasts in NSG mice. Further, infusion of DNTs expanded from an AML patient resulted in a 75% reduction in autologous CD33+ and CD34+ leukemia cell engraftment in NSG mice. These studies demonstrate that expansion of DNTs to therapeutic quantities is possible and that DNTs may be used as a novel immunotherapy to reduce AML burden by targeting primary leukemic blasts and potentially leukemic stem cells.
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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 | 0.000 |
| 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".