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Enregistrement W2910882807 · doi:10.1182/blood-2018-99-112010

Molecular and Genetic Characterization of MHC Deficiency Identifies EZH2 As a Therapeutic Target for Restoring MHC Expression in Diffuse Large B-Cell Lymphoma

2018· article· en· W2910882807 sur OpenAlexaffabout
Daisuke Ennishi, Katsuyoshi Takata, Wendy Béguelin, Gerben Duns, Anja Mottok, Pedro Farinha, Ali Bashashati, David W. Scott, Merrill Boyle, Barbara Meissner, Susana Ben‐Neriah, Bruce W. Woolcock, Adèle Telenius, Daniel Lai, Matthew Teater, Robert Kridel, Kerry J. Savage, Laurie H. Sehn, Ryan D. Morin, Marco A. Marra, Sohrab P. Shah, Joseph M. Connors, Randy D. Gascoyne, Ari Melnick, Christian Steidl

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineImmunology and Microbiology
ThématiqueImmune Cell Function and Interaction
Établissements canadiensCanada's Michael Smith Genome Sciences CentreSimon Fraser UniversityPrincess Margaret Cancer CentreUniversity of TorontoUniversity of British ColumbiaSpinal Cord Injury BC
Organismes subventionnairesnon disponible
Mots-clésBiologyMajor histocompatibility complexDiffuse large B-cell lymphomaCancer researchMHC class IImmune systemLymphomaImmunology

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Among the tumor immune escape mechanisms described to date, alterations in the expression of major histocompatibility complex (MHC) molecules play a crucial role in the development of diffuse large B-cell lymphoma (DLBCL). Although the frequency of loss of MHC expression differs between ABC- and GCB-DLBCL cell of origin (COO) subtypes, distinct genetic alterations and molecular features that affect MHC expression and the composition of immune cells in the tumor microenvironment remain ill-defined. Here, we aimed to uncover the biologic and genomic basis underlying acquired loss of MHC expression. Method: We analyzed biopsies from 347 patients newly diagnosed with de novo DLBCL and uniformly treated with R-CHOP in British Columbia. We performed targeted resequencing, SNP6.0 array and RNAseq for genetic analyses. Immunohistochemical (IHC) staining of MHC-I and -II was performed on tissue microarrays (n=332). COO was assigned by the Lymph2Cx assay in 323 cases (183 GCB, 104 ABC and 36 unclassifiable). Immune cell composition was assessed by IHC, flow cytometry and gene expression profiling (GEP)-based deconvolution of cellular signatures. To experimentally confirm decreased MHC expression induced by EZH2 mutation, we measured surface MHC-I and -II expression on tumor B cells using EZH2Y641/BCL2 mouse model which was previously established (Beguelin et al, Cancer Cell 2013). We also treated human DLBCL cells harboring EZH2 mutation and wild type using EZH2 inhibitor (EPZ-6438), and evaluated their surface MHC-I and -II expression. Results: Loss of MHC-I and -II expression was observed in 43% and 28% of DLBCL cases, respectively. MHC-II loss of expression was significantly associated with the reduction of tumor-infiltrating lymphocytes (TILs), especially CD4 positive T-cells (FOXP3+ cells, PD-1+ cells, and CD4+ naïve and memory T-cells), and cytolytic activity (GZMB and PRF1 mRNA expression) in GCB-DLBCL (all; p<0.001), but not in ABC-DLBCL. MHC-II-negativity was associated with unfavorable prognosis only in GCB-DLBCL (5-year time-to-progression; 59% vs 79%, p=0.007), whereas there was no prognostic impact of MHC-I expression in either subtype, suggesting a link between loss of MHC-II expression and reduced immune surveillance leading to poor prognosis, specifically in GCB-DLBCL. We next performed GEP using RNAseq separately in each COO subtype. Interestingly, only four genes (HLA-DMA, DRA, DPA1 and CD74) were differentially expressed according to MHC-II expression (FDR<0.001) in ABC-DLBCL. By contrast, a total of 641 genes were differentially expressed in GCB-DLBCL. Of importance, a dark zone (DZ) B-cell signature was strongly enriched in MHC-II-negative GCB-DLBCL cases (FDR<0.001), suggesting that MHC-II deficiency defines the tumor originated from DZ of the germinal center. Correlative genetic analysis revealed that, as expected, mutations of CIITA and RFXAP were detected more frequently in MHC-II-negative GCB-DLBCL (p=0.01 and 0.003, respectively). Strikingly, CD83 mutations, which elevate and stabilize MHC-II expression in centrocytes of the light zone (LZ), were significantly enriched in MHC-II positive GCB-DLBCL (p= 0.008), suggesting that these mutations affecting the antigen presentation machinery are selectively acquired in GCB-DLBCL tumors to further reduce and increase the surface MHC-II expression. Genetic analysis also highlighted that EZH2 mutations were most significantly enriched in MHC-II-negative as well as MHC-I-negative GCB-DLBCL cases (both, p<0.001). Indeed, 77% of EZH2 mutated cases demonstrated loss of either MHC-I and/or MHC-II expression on the tumor cells. Notably, we found significantly lower MHC-I and MHC-II expression in high-grade lymphomas of EZH2 mutant Vav-BCL2 transgenic mice compared to EZH2 wildtype control tumors. Furthermore, of potential clinical relevance, in-vitro EZH2 inhibition significantly restored MHC-I and MHC-II gene expression as well as protein expression in EZH2-mutated human DLBCL cells, but not EZH2 wild type tumor cells. Conclusion: Our findings provide important implications for understanding the cancer biology underlying acquired loss of MHC expression. The restoration of MHC expression by EZH2 inhibitors suggests a novel approach of epigenetically enhancing tumor recognition and eradication in combination with immune therapies. Disclosures Sehn: Abbvie: Consultancy, Honoraria; Roche/Genentech: Consultancy, Honoraria; Morphosys: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Lundbeck: Consultancy, Honoraria; TG Therapeutics: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Merck: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria. Connors:Roche Canada: Research Funding; Takeda: Research Funding; Merck: Research Funding; F Hoffmann-La Roche: Research Funding; Cephalon: Research Funding; Seattle Genetics: Honoraria, Research Funding; Amgen: Research Funding; Bayer Healthcare: Research Funding; Bristol Myers-Squibb: Research Funding; Lilly: Research Funding; NanoString Technologies: Patents & Royalties: Named Inventor on a patent licensed to NanoString Technologies, Research Funding; Janssen: Research Funding; Genentech: Research Funding. Gascoyne:NanoString: Patents & Royalties: Named Inventor on a patent licensed to NanoString Technologies. Scott:Roche: Research Funding; Janssen: Research Funding; NanoString: Patents & Royalties: Named Inventor on a patent licensed to NanoString Technologies, Research Funding; Celgene: Consultancy, Honoraria. Steidl:Juno Therapeutics: Consultancy; Roche: Consultancy; Seattle Genetics: Consultancy; Nanostring: Patents & Royalties: patent holding; Bristol-Myers Squibb: Research Funding; Tioma: 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 enseignants0,001
Score d'incertitude au seuil0,002

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 recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,008
Tête enseignante GPT0,224
Écart entre enseignants0,216 · 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

Citations3
Publié2018
Routes d'admission2
Résumé présentoui

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