Abstract 1381: Targeting histone acetyltransferases to reprogram high C-MYC expressing cancers
Bibliographic record
Abstract
Abstract In cancer, epigenetic modifications are strongly altered and are responsible for gene expression aberrations. In a drug screening initiative, we recently reported that proscillaridin, a cardiac glycoside (CG), exhibits unsuspected epigenetic and anticancer activities. To understand CG's epigenetic mechanisms of action, we performed RNA sequencing analysis, which showed proscillaridin effects on global gene expressions in acute lymphoblastic leukemia cells (MOLT-4). Genes associated with apoptosis and cell differentiation were upregulated whereas master transcription factors and oncogenic pathway genes were downregulated. Mechanistic studies revealed that proscillaridin decreased histone 3 acetylation, which correlated with histone acetyltransferase (KATs) downregulation (CBP, P300, TIP60, GCN5 and MOZ). Acetylome studies by mass spectrometry showed an acetylation loss in chromatin regulators, the oncogene C-MYC and its associated proteins. Proscillaridin induced C-MYC transcript and protein degradation. Moreover, in a panel of cancer cell lines, we measured that cancer cells sensitivity to proscillaridin treatment was positively correlated with C-MYC protein levels. Conversely, proscillaridin did not affect C-MYC protein level in low C-MYC expressing cancer cell lines. For the first time, we showed that CGs target histone acetyltransferases and C-MYC oncogene in high C-MYC expressing cancers. We propose that CGs can be repurposed as new epigenetic drugs in high C-MYC expressing cancers. Citation Format: Elodie M. Da Costa, Gregory Armaos, Annie Beaudry, Chantal Richer, Maxime Caron, Pascal St-Onge, Jeffrey Johnson, Nevan Krogan, Yuka Sai, Michael Downey, Daniel Sinnett, Serge McGraw, Noël J. Raynal. Targeting histone acetyltransferases to reprogram high C-MYC expressing cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1381.
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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 | 0.001 |
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".