Antipsychotic Chlorpromazine Suppresses STAT5 Signaling, Overcomes Resistance Mediated by the Gatekeeper Mutation FLT3-ITD/F691L, and Synergizes with Quizartinib in FLT3-ITD-Positive Cells
Bibliographic record
Abstract
Background: FLT3 mutations, including internal tandem duplication (ITD) and tyrosine kinase domain (TKD) point mutations, represent common genetic alterations in acute myeloid leukemia (AML), with FLT3-ITD associated with poor prognosis. Although FLT3 tyrosine kinase inhibitors (TKIs), such as quizartinib (Quiz) and gilteritinib, have improved clinical outcomes, secondary TKD mutations, particularly the gatekeeper mutation F691L, confer significant resistance. We previously demonstrated that chlorpromazine (CPZ), an antipsychotic drug, inhibits clathrin-mediated endocytosis and selectively suppresses the growth of cancer cells harboring mutant receptor tyrosine kinases. Methods: In this study, we examined the efficacy of CPZ in overcoming TKI resistance using Ba/F3 cells expressing FLT3-ITD or FLT3-ITD/F692L, the murine analog of F691L. Results: Quiz inhibited proliferation of FLT3-ITD cells but was ineffective against FLT3-ITD/F692L cells. CPZ suppressed growth in both cell types. Co-treatment with CPZ and Quiz exhibited synergistic effects in FLT3-ITD cells, but not in FLT3-ITD/F692L cells. CPZ reduced STAT5 phosphorylation and modulated downstream signaling in FLT3-ITD cells, while only partially affecting STAT5 in FLT3-ITD/F692L cells. Expression of constitutively active STAT5 partially rescued CPZ-induced growth inhibition. Conclusions: These findings suggest that STAT5 suppression is a key mechanism of CPZ’s antileukemic activity and support its potential as a therapeutic strategy for FLT3-ITD-positive AML.
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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".