MN1 overexpression induces acute myeloid leukemia in mice and predicts ATRA resistance in patients with AML
Bibliographic record
Abstract
Overexpression of wild-type MN1 is a negative prognostic factor in patients with acute myeloid leukemia (AML) with normal cytogenetics. We evaluated whether MN1 plays a functional role in leukemogenesis. We demonstrate using retroviral gene transfer and bone marrow (BM) transplantation that MN1 overexpression rapidly induces lethal AML in mice. Insertional mutagenesis and chromosomal instability were ruled out as secondary aberrations. MN1 increased resistance to all-trans retinoic acid (ATRA)-induced cell-cycle arrest and differentiation by more than 3000-fold in vitro. The differentiation block could be released by fusion of a transcriptional activator (VP16) to MN1 without affecting the ability to immortalize BM cells, suggesting that MN1 blocks differentiation by transcriptional repression. We then evaluated whether MN1 expression levels in patients with AML (excluding M3-AML) correlated with resistance to ATRA treatment in elderly patients uniformly treated within treatment protocol AMLHD98-B. Strikingly, patients with low MN1 expression who received ATRA had a significantly prolonged event-free (P = .008) and overall (P = .04) survival compared with patients with either low MN1 expression and no ATRA, or high MN1 expression with or without ATRA. MN1 is a unique oncogene in hematopoiesis that both promotes proliferation/self-renewal and blocks differentiation, and may become useful as a predictive marker in AML treatment.
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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 | 0.001 | 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) | 0.001 | 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".