Efficient Killing of High Risk Neuroblastoma Using Natural Killer Cells Activated by Plasmacytoid Dendritic Cells
Bibliographic record
Abstract
High-risk neuroblastoma (NB) remains a major therapeutic challenge despite the recent advent of disialoganglioside (GD2)-antibody treatment combined with interleukin (IL)-2 and granulocyte monocyte-colony stimulating factor (GM-CSF). Indeed, more than one third of the patients still die from this disease. Here, we developed a novel approach to improve the current anti-GD2 immunotherapy based on NK cell stimulation using toll-like receptor (TLR)-activated plasmacytoid dendritic cells (pDCs). We demonstrated that this strategy led to the efficient killing of NB cells. When the expression of GD2 was heterogeneous on NB cells, the combination of pDC-mediated NK-cell activation and anti-GD2 treatment significantly increased the cytotoxicity of NK cells against NB cells. Activation by pDCs led to a unique NK-cell phenotype characterized by increased surface expression of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), with increased expression of CD69 on CD56dim cytotoxic cells, and strong interferon-γ production. Additionally, NB-cell killing was mediated by the TRAIL death-receptor pathway, as well as by the release of cytolytic granules via the DNAX accessory molecule 1 pathway. NK-cell activation and lytic activity against NB was independent of cell contact, depended upon type I IFN produced by TLR-9-activated pDCs, but was not reproduced by IFN-α stimulation alone. Collectively, these results highlighted the therapeutic potential of activated pDCs for patients with high-risk NB.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".