The Flt3 receptor tyrosine kinase collaborates with NUP98-HOX fusions in acute myeloid leukemia
Bibliographic record
Abstract
In leukemogenesis, several genetic changes conferring a proliferative and/or survival advantage to hematopoietic progenitor cells in addition to a block in differentiation are required. Here, we demonstrate that overexpression of the wild-type (wt) Flt3 receptor tyrosine kinase collaborates with NUP98-HOX fusions (NUP98-HOXA10 and NUP98-HOXD13) to induce aggressive acute myeloid leukemia (AML). We used a mouse transplantation model to show their synergism in cotransduced bone marrow cells as well as in a cellular model of leukemic progression. Furthermore, our data support the finding that Meis1 overexpression leads to marked elevation in Flt3 transcription and extend it to the context of NUP98-HOX-induced leukemia. Together, these results support a multistep model where the synergism between NUP98-HOX and wt-Flt3 is the result of the ability of Flt3 to increase proliferation of myeloid progenitors blocked in differentiation by NUP98-HOX fusions and reveal a direct role for wt-Flt3 in the pathobiology of AML. Given the similarities in the leukemogenic role of native HOX and NUP98-fused HOX genes, our results underscore the clinical significance of the recurrent co-overexpression of wt-FLT3 and HOX in human leukemia and suggest that specific FLT3 inhibitors could be useful in treatment of HOX-induced AML or acute lymphoblastic leukemia (ALL).
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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".