Epigenetic reprogramming of plasmacytoid dendritic cells drives type I interferon-dependent differentiation of acute myeloid leukemias for therapeutic benefit
Bibliographic record
Abstract
ABSTRACT Pharmacological inhibition of epigenetic enzymes can have therapeutic benefit, particularly against hematological malignancies. While these agents can affect tumor cell growth and proliferation, recent studies have demonstrated that pharmacological de-regulation of epigenetic modifiers may additionally mediate anti-tumor immune responses. Here we discovered a novel mechanism of immune regulation through the inhibition of histone deacetylases (HDACs). In a genetically engineered model of t(8;21) AML, leukemia cell differentiation and therapeutic benefit mediated by the HDAC inhibitor panobinostat required activation of the type I interferon (IFN) signaling pathway. Plasmacytoid dendritic cells (pDCs) were identified as the cells producing type I IFN in response to panobinostat, through transcriptional activation of IFN genes concomitant with increased H3K27 acetylation at these loci. Depletion of pDCs abrogated panobinostat-mediated activation of type I IFN signaling in leukemia cells and impaired therapeutic efficacy, while combined treatment of panobinostat and recombinant IFNα improved therapeutic outcomes. These discoveries offer a new therapeutic approach for t(8;21) AML and demonstrate that epigenetic rewiring of pDCs enhances anti-tumor immunity, opening the possibility of exploiting this cell type as a new target for immunotherapy.
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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) | 0.002 | 0.001 |
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".