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Record W2159753430 · doi:10.1186/s13148-015-0118-9

Dual EZH2 and EHMT2 histone methyltransferase inhibition increases biological efficacy in breast cancer cells

2015· article· en· W2159753430 on OpenAlexafffund
Edward Curry, Ian Green, Nadine Chapman‐Rothe, Elham Shamsaei, Sarah Kandil, Fanny Cherblanc, Luke Payne, Emma Bell, Thota Ganesh, Nitipol Srimongkolpithak, Joachim Caron, Fengling Li, Anthony G. Uren, James P. Snyder, Masoud Vedadi, Matthew J. Fuchter, Robert Brown

Bibliographic record

VenueClinical Epigenetics · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsStructural Genomics ConsortiumUniversity of Toronto
FundersMinistero dello Sviluppo EconomicoCancer Research UKOntario Ministry of Economic Development and InnovationOntario Genomics InstituteOntario GenomicsGenome CanadaWellcome TrustGlaxoSmithKlineEuropean CommissionPfizerOvarian Cancer ActionEngineering and Physical Sciences Research CouncilEli Lilly and Company
KeywordsHistone methyltransferaseGene silencingEZH2BiologyHistoneHistone methylationChromatinChromatin immunoprecipitationGene knockdownMethyltransferaseCancer researchRNA interferenceGene expressionMethylationCell biologyDNA methylationGeneBiochemistryPromoterRNA

Abstract

fetched live from OpenAlex

BACKGROUND: Many cancers show aberrant silencing of gene expression and overexpression of histone methyltransferases. The histone methyltransferases (HKMT) EZH2 and EHMT2 maintain the repressive chromatin histone methylation marks H3K27me and H3K9me, respectively, which are associated with transcriptional silencing. Although selective HKMT inhibitors reduce levels of individual repressive marks, removal of H3K27me3 by specific EZH2 inhibitors, for instance, may not be sufficient for inducing the expression of genes with multiple repressive marks. RESULTS: We report that gene expression and inhibition of triple negative breast cancer cell growth (MDA-MB-231) are markedly increased when targeting both EZH2 and EHMT2, either by siRNA knockdown or pharmacological inhibition, rather than either enzyme independently. Indeed, expression of certain genes is only induced upon dual inhibition. We sought to identify compounds which showed evidence of dual EZH2 and EHMT2 inhibition. Using a cell-based assay, based on the substrate competitive EHMT2 inhibitor BIX01294, we have identified proof-of-concept compounds that induce re-expression of a subset of genes consistent with dual HKMT inhibition. Chromatin immunoprecipitation verified a decrease in silencing marks and an increase in permissive marks at the promoter and transcription start site of re-expressed genes, while Western analysis showed reduction in global levels of H3K27me3 and H3K9me3. The compounds inhibit growth in a panel of breast cancer and lymphoma cell lines with low to sub-micromolar IC50s. Biochemically, the compounds are substrate competitive inhibitors against both EZH2 and EHMT1/2. CONCLUSIONS: We have demonstrated that dual inhibition of EZH2 and EHMT2 is more effective at eliciting biological responses of gene transcription and cancer cell growth inhibition compared to inhibition of single HKMTs, and we report the first dual EZH2-EHMT1/2 substrate competitive inhibitors that are functional in cells.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.070
GPT teacher head0.381
Teacher spread0.311 · 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

Citations81
Published2015
Admission routes2
Has abstractyes

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