Extended exposure to low doses of azacitidine induces differentiation of leukemic stem cells through activation of myeloperoxidase
Bibliographic record
Abstract
Oral azacitidine (oral-Aza) treatment results in longer median overall survival (OS) (24.7 vs. 14.8 months in placebo) in patients with acute myeloid leukemia (AML) in remission after intensive chemotherapy. The dosing schedule of oral-Aza (14 days/28-day cycle) allows for low exposure of Aza for an extended duration thereby facilitating a sustained therapeutic effect. However, the underlying mechanisms supporting the clinical impact of oral-Aza in maintenance therapy remain to be fully understood. In this preclinical work, we explore the mechanistic basis of oral-Aza/extended exposure to Aza through in vitro and in vivo modeling. In cell lines, extended exposure to Aza results in sustained DNMT1 loss, leading to durable hypomethylation, and gene expression changes. In mouse models, extended exposure to Aza, preferentially targets immature leukemic cells. In leukemic stem cell (LSC) models, the extended dose of Aza induces differentiation and depletes CD34+CD38- LSC. Mechanistically, LSC differentiation is driven in part by increased myeloperoxidase (MPO) expression. Inhibition of MPO activity either by using an MPO-specific inhibitor or blocking oxidative stress, a known mechanism of MPO, partly reverses the differentiation of LSC. Overall, our preclinical work reveals novel mechanistic insights into oral-Aza and its ability to target LSC.
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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".