Meningioma 1 is indispensable for mixed lineage leukemia-rearranged acute myeloid leukemia
Bibliographic record
Abstract
Mixed lineage leukemia (MLL/KMT2A) rearrangements (MLL-r) are one of the most frequent chromosomal aberrations in acute myeloid leukemia. We evaluated the function of Meningioma 1 (MN1), a co-factor of HOXA9 and MEIS1, in human and murine MLL-rearranged leukemia by CRISPR-Cas9 mediated deletion of MN1. MN1 was required for in vivo leukemogenicity of MLL positive murine and human leukemia cells. Loss of MN1 inhibited cell cycle and proliferation, promoted apoptosis and induced differentiation of MLL-rearranged cells. Expression analysis and chromatin immunoprecipitation with sequencing from previously reported data sets demonstrated that MN1 primarily maintains active transcription of HOXA9 and HOXA10, which are critical downstream genes of MLL, and their target genes like BCL2, MCL1 and Survivin. Treatment of MLL-rearranged primary leukemia cells with anti-MN1 siRNA significantly reduced their clonogenic potential in contrast to normal CD34+ hematopoietic progenitor cells, suggesting a therapeutic window for MN1 targeting. In summary, our findings demonstrate that MN1 plays an essential role in MLL fusion leukemias and serve as a therapeutic target in MLL-rearranged acute myeloid leukemia.
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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".