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Record 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 on 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

Bibliographic record

VenueBlood · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsCanada's Michael Smith Genome Sciences CentreSimon Fraser UniversityPrincess Margaret Cancer CentreUniversity of TorontoUniversity of British ColumbiaSpinal Cord Injury BC
Fundersnot available
KeywordsBiologyMajor histocompatibility complexDiffuse large B-cell lymphomaCancer researchMHC class IImmune systemLymphomaImmunology

Abstract

fetched live from 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.

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 score0.002

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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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".

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Citations3
Published2018
Admission routes2
Has abstractyes

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