In vitro differentiated plasmacytoid dendritic cells as a tool to induce anti-leukemia activity of natural killer cells
Bibliographic record
Abstract
Acute lymphoblastic leukemia (ALL) is believed to be resistant to NK cell-mediated killing. To overcome this resistance, we developed an innovative approach based on NK cell stimulation with Toll-like receptor (TLR)-activated plasmacytoid dendritic cells (pDC). The translation of this approach into the clinic requires the production of high numbers of human pDC. Herein, we show that in vitro differentiation of cord blood CD34 + progenitors in the presence of aryl hydrocarbon receptor antagonists gives rise to clinically relevant numbers of pDC, as about 10 8 pDC can be produced from a typical cord blood unit. Blocking the aryl hydrocarbon receptor (AHR) pathway significantly increased the yield of pDC. When compared to pDC isolated from peripheral blood, in vitro differentiated pDC (ivD-pDC) exhibited an increased capacity to induce NK cell-mediated killing of ALL. Although ivD-pDC produced lower amounts of IFN-α than peripheral blood pDC upon TLR activation, they produced more IFN-λ2, known to play a critical role in the induction of anti-tumoral NK cell functions. Both TLR-9 and TLR-7 ligands triggered pDC-induced NK cell activation, offering the possibility to use any clinical-grade TLR-7 or TLR-9 ligands in future clinical trials. Finally, adoptive transfer of ivD-pDC cultured in the presence of an AHR antagonist cured humanized mice with minimal ALL disease. Collectively, our results pave the way to clinical-grade production of sufficient numbers of human pDC for innate immunotherapy against ALL and other refractory malignancies.
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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".