MétaCan
Menu
← Retour à la cohorte
Enregistrement W2979550461 · doi:10.1182/blood.v128.22.735.735

Crebbp Mutations Disrupt Dynamic Enhancer Acetylation in B-Cells, Enabling HDAC3 to Drive Lymphomagenesis

2016· article· en· W2979550461 sur OpenAlexaff
Hsia‐Yuan Ying, Yanwen Jiang, Ana Ortega-Molina, Huimin Geng, Dylan McNally, Ling Wang, Ashley S. Doane, Xabier Agirre, Haiying Ju, David Poloway, Matt Teater, Sara Parsa, Daisuke Ennishi, David W. Scott, Rita Shaknovich, Randy D. Gascoyne, Wayne Tam, Olivier Elemento, Hans‐Guido Wendel, Ari Melnick

Notice bibliographique

RevueBlood · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensBC Cancer Agency
Organismes subventionnairesnon disponible
Mots-clésEnhancerBiologyDiffuse large B-cell lymphomaCancer researchLymphomaHistone acetyltransferaseMolecular biologyGene knockdownHistoneGeneGene expressionGeneticsImmunology

Résumé

récupéré en direct d'OpenAlex

Abstract Somatic mutations in CREBBP occur frequently in germinal center derived lymphomas such as DLBCL and FL. However whether or how these mutations might contribute to lymphomagenesis is still largely unknown. Most CREBBP mutations are predicted to result in loss of function since they target the histone acetyltransferase (HAT) domain or give rise to premature stop codon prior to the HAT domain. Here, we show that Crebbp shRNA knockdown (KD) accelerated lymphomagenesis in VavP-Bcl2 transgenic mice, a model that recapitulates human GC-derived lymphomas. The median time to lymphoma onset in VavP-Bcl2/CrebbpKD mice was 114 days, significantly shorter than control VavP-Bcl2/GFP mice (193 days, p=0.04). Histopathology revealed that VavP-Bcl2/CrebbpKD lymphomas were more aggressive and widely disseminated than VavP-Bcl2/GFP lymphomas. CREBBP can regulate gene enhancer function through H3K27 acetylation. ChIP-seq in VavP-Bcl2/CrebbpKD lymphoma cells revealed significant reduction of H3K27ac peaks compared to control lymphoma cells (N = 1717, Kolmogorov-Smirnov test, p<2.2E-16). Loss of H3K27ac was markedly skewed towards enhancers. We observed similar loss of enhancer H3K27ac in human DLBCL cells after CREBBP shRNA KD. Enhancer H3K27ac loss was significantly associated with repression of nearby genes in both murine (FDR q=0.044) and human lymphoma cells (FDR q=0). RNA-seq performed in three independent FL or DLBCL patient cohorts revealed a characteristic CREBBP mutant gene expression signature featuring prominent transcriptional repression (p=1.32E-14, p=0.001, and p=0.0002 respectively). Notably, the human patient CREBBP mutant signature was highly enriched in murine and human cell line CREBBP shRNA profiles (FDR=0, GSEA), indicating that CREBBP KD signature was highly similar to CREBBP mutant signature in humans. Functional analysis of the CREBBP mutant/KD signature showed significant enrichment of GC exit pathways including genes induced by CD40, IRF4 and plasma cell differentiation; as well as immune response processes including antigen processing and presentation, such as MHC class II genes (BH-adjusted p<0.05). To better understand mechanism we performed an integrative analysis of CREBBP signatures against databases of B-cell transcription factor and epigenome profiles. This analysis yielded significant enrichment (BH-adjusted p<0.05) for i) enhancers bound by the BCL6 transcriptional repressor and its SMRT/HDAC3 corepressor complex, ii) enhancers that are normally deacetylated in GC B-cells, and iii) genes induced by BCL6 siRNA. This is notable because in normal GCs BCL6 represses enhancers by recruiting SMRT/HDAC3 complexes to deacetylate H3K27. Hence our data suggest that CREBBP is a counteracting HAT to BCL6/SMRT/HDAC3. Indeed, conditional knockout of Hdac3 in GC B-cells in mice resulted in impaired GC formation and a transcriptional signature featuring upregulation of the same genes that are repressed by CREBBP KD (GSEA FDR=0). Moreover, CREBBP KD in DLBCL cells resulted in H3K27ac loss at BCL6/SMRT/HDAC3 regulated enhancers, including those nearby CDKN1A, NFATC1, FOXP1, and MHC II genes, such as HLA-DQA1 and HLA-DRB5. CREBBP KD also resulted in silencing of these genes. Since we show HDAC3 is the opposing HDAC to CREBBP then we reasoned that CREBBP mutant DLBCLs might be especially dependent on HDAC3. Indeed we observed that HDAC3 shRNA resulted in profound suppression of CREBBP mutant DLBCL cells in vitro and in vivo (DLBCL xenografts in mice, p=0.005), whereas CREBBP WT cell lines were barely affected by HDAC3 KD. The opposing effects of BCL6/SMRT/HDAC3 and CREBBP on MHC class II could have implications for immune surveillance. Accordingly CREBBP KD induced significant loss of cell surface HLA-DR molecules (p<0.05), and these cells exhibited up to 90% less capability to stimulate T-cell response in allogeneic mixed lymphocyte reaction experiments. The loss of MHC class II molecules and T-cell response was rescued when CREBBP loss of function cells were exposed to a specific HDAC3 inhibitor. In summary, CREBBP mutations drive lymphomagenesis by enabling unopposed suppression of enhancers by BCL6/SMRT/HDAC3 complexes, resulting in a repressive transcriptional programming that disrupts GC exit and evades immune surveillance. HDAC3 targeted therapy may rescue these effects and serve as a precision approach for CREBBP mutant lymphomas. Disclosures Scott: Celgene: Consultancy; Roche: Honoraria; Janssen: Consultancy; BC Cancer Agency: Patents & Royalties: Inventor on a patent licensed to NanoString Technologies. Tam:Millennium Pharmaceuticals, Inc.: Consultancy. Melnick:Janssen: Research Funding.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut 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 enseignants0,004
Score d'incertitude au seuil0,015

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

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 Opus0,009
Tête enseignante GPT0,265
Écart entre enseignants0,256 · 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 source (Gemma direct ou Codex distillé), 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

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueBlood→Même sujetLymphoma Diagnosis and Treatment→Travaux en français237 207→