Targeting the MTF2-MDM2 Axis Sensitizes Refractory Acute Myeloid Leukemia to Chemotherapy
Bibliographic record
Abstract
Abstract Next generation sequencing of acute myeloid leukemia (AML) patient samples has enabled more granular risk stratification of patients; however, refractory AML patients can be found across all risk groups, suggesting that non-genetic lesions regulate chemoresponsiveness. Consistent with this hypothesis is the finding that many of the mutated AML driver genes are encode epigenetic modifiers. Thus, unraveling the epigenetic dysregulation in AML is critical to better understand disease initiation and progression, as well as develop targeted therapies. Metal Response Element Binding Transcription Factor 2/Polycomblike 2 (MTF2/PCL2) plays a fundamental role in recruiting the Polycomb repressive complex 2 (PRC2) to chromatin and we show that it is commonly silenced in primary AML patient cells at diagnosis. Furthermore, the loss of MTF2 in hematopoietic stem and progenitor cells (HSPCs) leads to an altered epigenetic state that underlies refractory AML. By implementing unbiased systems analyses, we identified the E3 ubiquitin ligase MDM2 that inhibits p53 as a direct target of MTF2-PRC2. MTF2 deficiency leads to over-expression of MDM2 and inhibition of p53-mediated cell cycle regulation and apoptosis, leading to chemoresistance and refractory AML. Targeting this dysregulated signaling pathway by MTF2 overexpression or MDM2 inhibitors sensitized refractory patient leukemic cells to induction chemotherapeutics and prevented relapse in AML patient-derived xenograft (PDX) mice. Therefore, we have uncovered a direct epigenetic mechanism by which MTF2 functions as a tumor suppressor required for AML chemotherapeutic sensitivity and identified a potential therapeutic strategy to treat refractory AML. Disclosures Sabloff: Celgene: Membership on an entity's Board of Directors or advisory committees.
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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 | 0.000 |
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".