Epigenetic Activation of Plasmacytoid DCs Drives IFNAR-Dependent Therapeutic Differentiation of AML
Bibliographic record
Abstract
Pharmacologic inhibition of epigenetic enzymes can have therapeutic benefit against hematologic malignancies. In addition to affecting tumor cell growth and proliferation, these epigenetic agents may induce antitumor immunity. Here, we discovered a novel immunoregulatory mechanism through inhibition of histone deacetylases (HDAC). In models of acute myeloid leukemia (AML), leukemia cell differentiation and therapeutic benefit mediated by the HDAC inhibitor (HDACi) panobinostat required activation of the type I interferon (IFN) pathway. Plasmacytoid dendritic cells (pDC) produced type I IFN after panobinostat treatment, through transcriptional activation of IFN genes concomitant with increased H3K27 acetylation at these loci. Depletion of pDCs abrogated panobinostat-mediated induction of type I IFN signaling in leukemia cells and impaired therapeutic efficacy, whereas combined treatment with panobinostat and IFNα improved outcomes in preclinical models. These discoveries offer a new therapeutic approach for AML and demonstrate that epigenetic rewiring of pDCs enhances antitumor immunity, opening the possibility of exploiting this approach for immunotherapies. SIGNIFICANCE: We demonstrate that HDACis induce terminal differentiation of AML through epigenetic remodeling of pDCs, resulting in production of type I IFN that is important for the therapeutic effects of HDACis. The study demonstrates the important functional interplay between the immune system and leukemias in response to HDAC inhibition. This article is highlighted in the In This Issue feature, p. 1397.
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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) | 0.003 | 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".