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Enregistrement W2402088519 · doi:10.1158/1557-3125.advbc15-b16

Abstract B16: Response and resistance to BET bromodomain inhibitors in triple-negative breast cancer

2016· article· en· W2402088519 sur OpenAlexaff
Shaokun Shu, Charles Y. Lin, Housheng Hansen He, Robert M. Witwicki, Justin M. Roberts, Doris P. Tabassum, Yi Liang, Muhammad B. Ekram, Ernest Doherty, J. Jennifer R. Brown, Hisham Mohammed, Clive S. D’Santos, Michael R. McKeown, Christopher J. Ott, Jun Qi, Min Ni, Prakash K. Rao, Melissa Duarte, Shwu‐Yuan Wu, Cheng‐Ming Chiang, Richard A. Young, Jason S. Carroll, Henry W. Long, Myles Brown, Shirley X. Liu, Clifford A. Meyer, James E. Bradner, Kornélia Polyák

Notice bibliographique

RevueMolecular Cancer Research · 2016
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueProtein Degradation and Inhibitors
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésBromodomainTriple-negative breast cancerBRD4Cancer researchBET inhibitorEpigenomicsBiologyChromatinCell growthCancerTargeted therapyBreast cancerTranscription factorDownregulation and upregulationEnhancerCell cycleEpigeneticsGeneGene expressionDNA methylationGenetics

Résumé

récupéré en direct d'OpenAlex

Abstract Triple negative breast cancer (TNBC) is a heterogeneous and clinically aggressive disease for which there is no targeted therapy. Cancer genome sequencing studies focusing on TNBC failed to identify novel recurrently mutated cancer-driving genes, obviating immediate opportunities for targeted therapeutic development. Here we have identified BET bromodomain inhibitors (BBDIs) as promising novel therapeutic agents in TNBC. Specifically, we have found that TNBC cells are significantly and preferentially growth inhibited by BBDIs (e.g., JQ1) with IC50s in the low nM range compared to luminal breast cancer cells. The growth of established xenografts derived from TNBC cell lines and primary patient samples were also efficiently inhibited by BBDIs treatment. BRD4, a target of BBDIs, is highly expressed in TNBCs compared to luminal cell lines and its downregulation using shRNAs inhibits TNBC cell growth in cell culture and induces tumor regression in vivo. In line with the role of BRD4 in transcription restart after G2/M, BBDI treatment prevented cell cycle re-entry, arrested TNBC cells in early G1, apoptosis, and induced luminal epithelial cell differentiation. Using integrated epigenomic analysis, we have identified the direct transcriptional targets of BBDI in TNBC. BBDI was found to efficiently displace chromatin-bound BRD4 in sensitive TNBC cells. This effect was more pronounced at genomic regions exhibiting elevated Bio-JQ1 binding by Chem-seq, implying strong association between BRD4 and JQ1 genomic binding patterns. By genome-wide study with ChIP-seq and RNA-seq, we have also found that selective disruption of super-enhancers associated genes by JQ1, leading to deregulation of coordinated transcriptional pathways involved in cell proliferation, invasion, and survival. The sensitivity of TNBC and other tumor types to BET inhibition establishes a rationale for clinical investigation and a motivation to understand mechanisms of resistance. After selecting derivatives with acquired resistance to BET inhibition in previously sensitive TNBCs, we identified a unique mechanism of epigenomic resistance to this epigenetic therapy. Resistant cells remained dependent on BRD4. However, TNBC cells adapt to BET bromodomain inhibition by maintaining recruitment of unmutated BRD4 to super-enhancers in a bromodomain-independent manner. Differential super-enhancer analysis found a significant gain in the number of SEs in resistant cells, and a less pronounced loss of fewer SEs. The gain of Bio-JQ1 SEs was associated with gain of BRD4 binding to these genomic loci and also with increased transcription of the associated genes. Among the top gained super-enhancers in resistant cells was an upstream and intragenic region of H3K27ac enrichment at the BCL-xL locus. Notably, BCL-xL was among the few highly up-regulated genes in resistant cells by expression profiling. Proteomic studies of resistant TNBC identify strong association with MED1 and hyper-phosphorylation of BRD4 due to decreased activity of PP2A that we identified as a BRD4 phosphatase. We have defined that hyper-phosphorylation of BRD4 increases the recruitment of BRD4 to MED1 and contributes to BBDI resistance. To explore the translational relevance of our findings, we conducted synergy screens with JQ1 and molecules targeting BCL-XL (ABT737), a gained super-enhancer in resistant cells, and modulators of BRD4 phosphorylation, a CK2 inhibitor CX-4945, and PP2A activator perphenazine (PPZ). We observed significant synergy between JQ1 and ABT737, CX-4945, and also PPZ implying that these drug combinations will likely achieve even higher efficacy in TNBCs than BBDIs alone. Together, these studies provide a rationale for BET inhibition in TNBC and argue for combination strategies to anticipate clinical drug resistance. Citation Format: Shaokun Shu, Charles Lin, Housheng Hansen He, Robert Witwicki, Justin Roberts, Doris Tabassum, Yi Liang, Muhammad Ekram, Ernest Doherty, Jonathan Brown, Hisham Mohammed, Clive D'Santos, Michael McKeown, Christopher Ott, Jun Qi, Min Ni, Prakash Rao, Melissa Duarte, Shwu-Yuan Wu, Cheng-Ming Chiang, Richard Young, Jason Carroll, Henry Long, Myles Brown, Shirley X. Liu, Clifford Meyer, James Bradner, Kornelia Polyak. Response and resistance to BET bromodomain inhibitors in triple-negative breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research; Oct 17-20, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(2_Suppl):Abstract nr B16.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,053
Score d'incertitude au seuil0,642

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,022
Tête enseignante GPT0,346
Écart entre enseignants0,324 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2016
Routes d'admission1
Résumé présentoui

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