Allogeneic Human Double Negative T Cells as a Novel Immunotherapy for Acute Myeloid Leukemia and Its Underlying Mechanisms
Bibliographic record
Abstract
Abstract Purpose: To explore the potential of ex vivo expanded healthy donor–derived allogeneic CD4 and CD8 double-negative cells (DNT) as a novel cellular immunotherapy for leukemia patients. Experimental Design: Clinical-grade DNTs from peripheral blood of healthy donors were expanded and their antileukemic activity and safety were examined using flow cytometry–based in vitro killing assays and xenograft models against AML patient blasts and healthy donor–derived hematopoietic cells. Mechanism of action was investigated using antibody-mediated blocking assays and recombinant protein treatment assays. Results: Expanded DNTs from healthy donors target a majority (36/46) of primary AML cells, including 9 chemotherapy-resistant patient samples in vitro, and significantly reduce the leukemia load in patient-derived xenograft models in a DNT donor–unrestricted manner. Importantly, allogeneic DNTs do not attack normal hematopoietic cells or affect hematopoietic stem/progenitor cell engraftment and differentiation, or cause xenogeneic GVHD in recipients. Mechanistically, DNTs express high levels of NKG2D and DNAM-1 that bind to cognate ligands preferentially expressed on AML cells. Upon recognition of AML cells, DNTs rapidly release IFNγ, which further increases NKG2D and DNAM-1 ligands’ expression on AML cells. IFNγ pretreatment enhances the susceptibility of AML cells to DNT-mediated cytotoxicity, including primary AML samples that are otherwise resistant to DNTs, and the effect of IFNγ treatment is abrogated by NKG2D and DNAM-1–blocking antibodies. Conclusions: This study supports healthy donor–derived allogeneic DNTs as a therapy to treat patients with chemotherapy-resistant AML and also reveals interrelated roles of NKG2D, DNAM-1, and IFNγ in selective targeting of AML by DNTs. Clin Cancer Res; 24(2); 370–82. ©2017 AACR.
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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".