Unmasking the cryptic immunopeptidome of EZH2 mutated diffuse large B-cell lymphomas through combination drug treatment
Bibliographic record
Abstract
Abstract Exploring the repertoire of peptides presented on major histocompatibility complexes (MHC) has been utilized to identify targets for immunotherapy in many hematological malignancies. However, such data have not been described systematically for diffuse large B-cell lymphomas (DLBCL), which might be explained by the profound downregulation of MHC expression in many DLBCLs, and in particular in the EZH2-mutated subgroup. Epigenetic drug treatment, especially in the context of interferon gamma (IFNg), restored MHC expression in DLBCL. DLBCL MHC-presented peptides were identified via mass spectrometry following tazemetostat or decitabine treatments alone, or in combination with IFNg. Such treatment synergistically increased MHC class I surface protein expression up to 50-fold and class II expression up to 3-fold. Peptides presented on MHC complexes increased to a similar extent for MHC class I and remained constant for class II. Overall, these treatments restored the diversity of the immunopeptidome to levels described in healthy B cells and allowed the systematic search for new targets for immunotherapy. Consequently, we identified multiple MHC ligands from regulator of G protein signaling 13 (RGS13) and E2F transcription factor 8 (E2F8) on different MHC alleles, none of which have been described in healthy tissues and therefore represent tumor-specific MHC ligands, which are unmasked only after drug treatment. Overall, our results show that EZH2 inhibition in combination with decitabine and IFNg can expand the repertoire of MHC ligands presented on DLBCLs by revealing cryptic epitopes, thus allowing the systematic analysis and identification of new potential immunotherapy targets. Key points Combination therapy of interferon gamma with epigenetic regulators leads to large increases in the immunopeptidome of DLBCL. HLA ligands from proteins RGS13 and E2F8 may provide DLBCL-specific targets for immunotherapy.
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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) | 0.001 | 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".