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Record W2953145956 · doi:10.1186/s13046-019-1242-8

Heart failure drug proscillaridin A targets MYC overexpressing leukemia through global loss of lysine acetylation

2019· article· en· W2953145956 on OpenAlexafffund
Elodie M. Da Costa, Gregory Armaos, Gabrielle McInnes, Annie Beaudry, Gaël Moquin‐Beaudry, Virginie Bertrand-Lehouillier, Maxime Caron, Chantal Richer, Pascal St-Onge, Jeffrey R. Johnson, Nevan J. Krogan, Yuka Sai, Michael Downey, Moutih Rafei, Meaghan Boileau, Kolja Eppert, Ema Flores-Díaz, André Haman, Trang Hoang, Daniel Sinnett, Christian Beauséjour, Serge McGraw, Noël J.‐M. Raynal

Bibliographic record

VenueJournal of Experimental & Clinical Cancer Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHistone Deacetylase Inhibitors Research
Canadian institutionsInstitute for Research in Immunology and CancerMcGill UniversityUniversity of OttawaUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersFonds de Recherche du Québec - SantéCancer Research Society
KeywordsAcetylationLysineDrugCancer researchChemistryApoptosisHeart failureLeukemiaPharmacologyInternal medicineMedicineBiochemistryGeneAmino acid

Abstract

fetched live from OpenAlex

BACKGROUND: pump inhibitors. Their repurposing in oncology is currently investigated in preclinical and clinical studies. However, the identification of a specific cancer type defined by a molecular signature to design targeted clinical trials with cardiac glycosides remains to be characterized. Here, we demonstrate that cardiac glycoside proscillaridin A specifically targets MYC overexpressing leukemia cells and leukemia stem cells by causing MYC degradation, epigenetic reprogramming and leukemia differentiation through loss of lysine acetylation. METHODS: Proscillaridin A anticancer activity was investigated against a panel of human leukemia and solid tumor cell lines with different MYC expression levels, overexpression in vitro systems and leukemia stem cells. RNA-sequencing and differentiation studies were used to characterize transcriptional and phenotypic changes. Drug-induced epigenetic changes were studied by chromatin post-translational modification analysis, expression of chromatin regulators, chromatin immunoprecipitation, and mass-spectrometry. RESULTS: At a clinically relevant dose, proscillaridin A rapidly altered MYC protein half-life causing MYC degradation and growth inhibition. Transcriptomic profile of leukemic cells after treatment showed a downregulation of genes involved in MYC pathways, cell replication and an upregulation of hematopoietic differentiation genes. Functional studies confirmed cell cycle inhibition and the onset of leukemia differentiation even after drug removal. Proscillaridin A induced a significant loss of lysine acetylation in histone H3 (at lysine 9, 14, 18 and 27) and in non-histone proteins such as MYC itself, MYC target proteins, and a series of histone acetylation regulators. Global loss of acetylation correlated with the rapid downregulation of histone acetyltransferases. Importantly, proscillaridin A demonstrated anticancer activity against lymphoid and myeloid stem cell populations characterized by MYC overexpression. CONCLUSION: Overall, these results strongly support the repurposing of proscillaridin A in MYC overexpressing leukemia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.055
GPT teacher head0.477
Teacher spread0.421 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations45
Published2019
Admission routes2
Has abstractyes

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