Abstract IA8: A new class of drugs active in T-ALL is revealed in a zebrafish screen
Bibliographic record
Abstract
Abstract T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive cancer frequently associated with activating NOTCH1 mutations and dysregulation of MYC. We performed two complementary screens to identify novel agents with activity against T-ALL: i) a zebrafish screen for small molecules toxic to MYC-overexpressing thymocytes, and ii) a human T-ALL cell line screen for small molecules that synergize with Notch inhibitors. “Hits” common to both screens included perphenazine, a phenothiazine antipsychotic that induced apoptosis of fish, mouse, and human T-ALL cells. Using ligand-affinity chromatography coupled to mass spectrometry, we identified protein phosphatase 2A (PP2A) as the critical perphenazine target. In line with this finding, T-ALL cell lines treated with perphenazine underwent apoptosis associated with rapid dephosphorylation of multiple PP2A substrates, indicating that perphenazine binds and activates the PP2A tumor supressor. Moreover, shRNA knockdown of the scaffolding or catalytic subunits of PP2A attenuated the activity of perphenazine, indicating that PP2A is required for its antileukemic activity. Finally, treatment of primary human T-ALLs with pherphenazine suppressed cell growth and caused dephosphorylated of PP2A targets in vitro and in vivo. Our findings provide a mechanistic explanation for the recurrent “rediscovery” of phenothiazines as a class of drugs with anti-cancer effects and highlight the therapeutic potential of pharmacologic PP2A activation in T-ALL and other cancers driven by hyperphosphorylated PP2A substrates. Citation Format: Alejandro Gutierrez, Li Pan, Richard Groen, Frederic Baleydier, Alex Kentsis, Jason Marineau, Ruta Grebliunaite, Elena Kozakewich, Casie Reed, Francoise Pflumio, Sandrine Poglio, Benjamin Uzan, Paul Clemons, Lynn Verplank, Frank An, Jason Burbank, Stephanie Norton, Nicola Tolliday, Hanno Steen, Andrew P. Weng, Huipin Yuan, James E. Bradner, Constantine Mitsiades, A. Thomas Look, Jon C. Aster. A new class of drugs active in T-ALL is revealed in a zebrafish screen. [abstract]. In: Proceedings of the AACR Special Conference: The Translational Impact of Model Organisms in Cancer; Nov 5-8, 2013; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2014;12(11 Suppl):Abstract nr IA8.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".