Cell Fate Decisions in Malignant Hematopoiesis: Leukemia Phenotype Is Determined by Distinct Functional Domains of the MN1 Oncogene
Bibliographic record
Abstract
Extensive molecular profiling of leukemias and preleukemic diseases has revealed that distinct clinical entities, like acute myeloid (AML) and T-lymphoblastic leukemia (T-ALL), share similar pathogenetic mutations. It is not well understood how the cell of origin, accompanying mutations, extracellular signals or structural differences in a mutated gene determine the phenotypic identity of leukemias. We dissected the functional aspects of different protein regions of the MN1 oncogene and their effect on the leukemic phenotype, building on the ability of MN1 to induce leukemia without accompanying mutations. We found that the most C-terminal region of MN1 was required to block myeloid differentiation at an early stage, and deletion of an extended C-terminal region resulted in loss of myeloid identity and cell differentiation along the T-cell lineage in vivo. Megakaryocytic/erythroid lineage differentiation was blocked by the N-terminal region. In addition, the N-terminus was required for proliferation and leukemogenesis in vitro and in vivo through upregulation of HoxA9, HoxA10 and Meis2. Our results provide evidence that a single oncogene can modulate cellular identity of leukemic cells based on its active gene regions. It is therefore likely that different mutations in the same oncogene may impact cell fate decisions and phenotypic appearance of malignant diseases.
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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) | 0.001 | 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".