Wnt/ -catenin Pathway Modulates the Sensitivity of the Mutant FLT3 Receptor Kinase Inhibitors in a GSK-3 Dependent Manner
Bibliographic record
Abstract
The FLT3 tyrosine kinase receptor is involved in both hematopoiesis and hematological malignancies. The Wnt/β-catenin pathway has been shown to participate in the self-renewal of hematopoietic stem cells and to cooperate with the mutant FLT3 receptors in leukemic transformation. However, the detailed biological impact of such a constitutively activated Wnt pathway remains to be further explored. Here, the authors report that activating mutations of FLT3 constitutively activate β-catenin by inhibition of GSK-3β in a PI3 kinase pathway-dependent manner. Ectopic expression of a dominant negative form of GSK-3β in FLT3-ITD-expressing cells activated β-catenin and blocked the downregulation of the TCF/β-catenin transcriptional activity induced by inhibition of FLT3 kinase. Furthermore, inhibition of cell proliferation and colony formation induced by such suppression of FLT3 kinase activity could be partially reversed by knockdown of GSK-3β and restored by knockdown of either TCF4 or β-catenin. Moreover, exogenous activation of the Wnt pathway also attenuated such inhibitory effect. These findings indicate that the potencies of the inhibitors of FLT3 kinase activity could be modulated by the activity of the Wnt/β-catenin pathway in the cells harboring FLT3-ITD mutations, and FLT3-ITDs signal through GSK-3β to activate β-catenin that this is likely to directly contribute to the leukemic phenotype.
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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) | 0.002 | 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".