Hematopoietic Stem Cells Are Intrinsically Protected against MLL-ENL-Mediated Transformation
Bibliographic record
Abstract
Studies of developmental pathways of hematopoietic stem cells (HSCs) have defined lineage relationships throughout the blood system. This is relevant to acute myeloid leukemia (AML), where aggressiveness and therapeutic responsiveness can be influenced by the initial stage of transformation. To address this, we generated a mouse model in which the mixed-lineage leukemia/eleven-nineteen-leukemia (MLL-ENL) transcription factor can be conditionally activated in any cell type. We show that AML can originate from multiple hematopoietic progenitor subsets with granulocytic and monocytic potential, and that the normal developmental position of leukemia-initiating cells influences leukemic development. However, disease failed to arise from HSCs. Although it maintained or upregulated the expression of target genes associated with leukemic development, MLL-ENL dysregulated the proliferative and repopulating capacity of HSCs. Therefore, the permissiveness for development of AML may be associated with a narrower window of differentiation than was previously appreciated, and hijacking the self-renewal capacity of HSCs by a potent oncogene is insufficient for leukemic development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".