Targeting MYC Overexpressing Leukemia with Cardiac Glycoside Proscillaridin Through Downregulation of Histone Acetyltransferases
Bibliographic record
Abstract
ABSTRACT Targeting MYC oncogene remains a major therapeutic goal in cancer chemotherapy. Here, we demonstrate that proscillaridin, a cardiac glycoside approved for heart failure treatment exhibit anticancer selectivity towards high MYC expressing leukemic cell lines and leukemia stem cells. At a clinically relevant concentration, proscillaridin induced a rapid downregulation of MYC protein level, due to a significant decrease in MYC protein half-life. Proscillaridin treatment induced a downregulation of gene sets involved in MYC pathway, and a concomitant upregulation of genes involved in hematopoietic differentiation. Proscillaridin induced a significant loss of lysine acetylation in histone H3 (K9, K14, K18 and K27) and in non-histone proteins such as MYC, MYC target proteins, and a series of histone acetylation regulators. Loss of lysine acetylation correlated with a rapid downregulation of histone acetyltransferase protein levels, involved in histone and MYC acetylation (CBP, P300, GCN5, TIP60, and MOZ), preferentially in MYC overexpressing leukemia as compared to other cancer cells. These results support the repurposing of proscillaridin in MYC overexpressing leukemia and propose a novel strategy to target MYC in cancer.
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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".