Phenothiazines induce PP2A-mediated apoptosis in T cell acute lymphoblastic leukemia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
T cell acute lymphoblastic leukemia (T-ALL) is an aggressive cancer that is frequently associated with activating mutations in NOTCH1 and dysregulation of MYC. Here, we performed 2 complementary screens to identify FDA-approved drugs and drug-like small molecules with activity against T-ALL. We developed a zebrafish system to screen small molecules for toxic activity toward MYC-overexpressing thymocytes and used a human T-ALL cell line to screen for small molecules that synergize with Notch inhibitors. We identified the antipsychotic drug perphenazine in both screens due to its ability to induce apoptosis in fish, mouse, and human T-ALL cells. Using ligand-affinity chromatography coupled with mass spectrometry, we identified protein phosphatase 2A (PP2A) as a perphenazine target. T-ALL cell lines treated with perphenazine exhibited rapid dephosphorylation of multiple PP2A substrates and subsequent apoptosis. Moreover, shRNA knockdown of specific PP2A subunits attenuated perphenazine activity, indicating that PP2A mediates the drug's antileukemic activity. Finally, human T-ALLs treated with perphenazine exhibited suppressed cell growth and dephosphorylation of PP2A targets in vitro and in vivo. Our findings provide a mechanistic explanation for the recurring identification of phenothiazines as a class of drugs with anticancer effects. Furthermore, these data suggest that pharmacologic PP2A activation in T-ALL and other cancers driven by hyperphosphorylated PP2A substrates has therapeutic potential.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| 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) | 0.000 | 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 it