Leukaemic transformation by CALM–AF10 involves upregulation of Hoxa5 by hDOT1L
Bibliographic record
Abstract
Chromosomal translocation is a common cause of leukaemia1 and the most common chromosome translocations found in leukaemia patients involve the mixed lineage leukaemia (MLL) gene2,3. AF10 is one of more than 30 MLL fusion partners in leukaemia4. We have recently demonstrated that the H3K79 methyltransferase hDOT1L contributes to MLL–AF10-mediated leukaemogenesis through its interaction with AF10 (ref. 5). In addition to MLL, AF10 has also been reported to fuse to CALM (clathrin-assembly protein-like lymphoid–myeloid) in patients with T-cell acute lymphoblastic leukaemia (T-ALL) and acute myeloid leukaemia (AML)6,7. Here, we analysed the molecular mechanism of leukaemogenesis by CALM–AF10. We demonstrate that CALM–AF10 fusion is both necessary and sufficient for leukaemic transformation. Additionally, we provide evidence that hDOT1L has an important role in the transformation process. hDOT1L contributes to CALM–AF10-mediated leukaemic transformation by preventing nuclear export of CALM–AF10 and by upregulating the Hoxa5 gene through H3K79 methylation. Thus, our study establishes CALM–AF10 fusion as a cause of leukaemia and reveals that mistargeting of hDOT1L and upregulation of Hoxa5 through H3K79 methylation is the underlying mechanism behind leukaemia caused by CALM–AF10 fusion.
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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 | 0.001 |
| 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".