Unmasking the suppressed immunopeptidome of EZH2-mutated diffuse large B-cell lymphomas through combination drug treatment
Bibliographic record
Abstract
Exploring the repertoire of peptides presented on major histocompatibility complexes (MHCs) helps identify targets for immunotherapy in many hematologic malignancies. However, there is a paucity of such data for diffuse large B-cell lymphomas (DLBCLs), which might be explained by the profound downregulation of MHC expression in many DLBCLs, and in particular in the enhancer of zeste homolog 2 (EZH2)-mutated subgroup. Epigenetic drug treatment, especially in the context of interferon-γ (IFN-γ), restored MHC expression in DLBCL. In DLBCL, peptides presented on MHCs were identified via mass spectrometry after treatment with tazemetostat or decitabine alone or in combination with IFN-γ. Such treatment synergistically increased the expression of MHC class I surface proteins up to 50-fold and the expression of class II surface proteins up to threefold. Peptides presented on MHCs increased to a similar extent for both class I and class II MHCs. Overall, these treatments restored the diversity of the immunopeptidome to levels described in healthy B cells for 2 of 3 cell lines and allowed the systematic search for new targets for immunotherapy. Consequently, we identified multiple MHC ligands from the 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 that are unmasked only after drug treatment. Overall, our results show that EZH2 inhibition in combination with decitabine and IFN-γ can expand the repertoire of MHC ligands presented on DLBCLs by revealing suppressed epitopes, thus allowing the systematic analysis and identification of new potential immunotherapy targets.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".