Molecular and Genetic Characterization of MHC Deficiency Identifies EZH2 as Therapeutic Target for Enhancing Immune Recognition
Bibliographic record
Abstract
Abstract We performed a genomic, transcriptomic, and immunophenotypic study of 347 patients with diffuse large B-cell lymphoma (DLBCL) to uncover the molecular basis underlying acquired deficiency of MHC expression. Low MHC-II expression defines tumors originating from the centroblast-rich dark zone of the germinal center (GC) that was associated with inferior prognosis. MHC-II–deficient tumors were characterized by somatically acquired gene mutations reducing MHC-II expression and a lower amount of tumor-infiltrating lymphocytes. In particular, we demonstrated a strong enrichment of EZH2 mutations in both MHC-I– and MHC-II–negative primary lymphomas, and observed reduced MHC expression and T-cell infiltrates in murine lymphoma models expressing mutant Ezh2Y641. Of clinical relevance, EZH2 inhibitors significantly restored MHC expression in EZH2-mutated human DLBCL cell lines. Hence, our findings suggest a tumor progression model of acquired immune escape in GC-derived lymphomas and pave the way for development of complementary therapeutic approaches combining immunotherapy with epigenetic reprogramming. Significance: We demonstrate how MHC-deficient lymphoid tumors evolve in a cell-of-origin–specific context. Specifically, EZH2 mutations were identified as a genetic mechanism underlying acquired MHC deficiency. The paradigmatic restoration of MHC expression by EZH2 inhibitors provides the rationale for synergistic therapies combining immunotherapies with epigenetic reprogramming to enhance tumor recognition and elimination. See related commentary by Velcheti et al., p. 472. This article is highlighted in the In This Issue feature, p. 453
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".