A JAK/STAT-mediated inflammatory signaling cascade drives oncogenesis in AF10-rearranged AML
Bibliographic record
Abstract
Leukemias bearing fusions of the AF10/MLLT10 gene are associated with poor prognosis, and therapies targeting these fusion proteins (FPs) are lacking. To understand mechanisms underlying AF10 fusion-mediated leukemogenesis, we generated inducible mouse models of acute myeloid leukemia (AML) driven by the most common AF10 FPs, PICALM/CALM-AF10 and KMT2A/MLL-AF10, and performed comprehensive characterization of the disease using transcriptomic, epigenomic, proteomic, and functional genomic approaches. Our studies provide a detailed map of gene networks and protein interactors associated with key AF10 fusions involved in leukemia. Specifically, we report that AF10 fusions activate a cascade of JAK/STAT-mediated inflammatory signaling through direct recruitment of JAK1 kinase. Inhibition of the JAK/STAT signaling by genetic Jak1 deletion or through pharmacological JAK/STAT inhibition elicited potent antioncogenic effects in mouse and human models of AF10 fusion AML. Collectively, our study identifies JAK1 as a tractable therapeutic target in AF10-rearranged leukemias.
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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) | 0.001 | 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".