Combined EZH2 Inhibition and IKAROS Degradation Leads to Enhanced Antitumor Activity in Diffuse Large B-cell Lymphoma
Bibliographic record
Abstract
PURPOSE: The efficacy of EZH2 inhibition has been modest in the initial clinical exploration of diffuse large B-cell lymphoma (DLBCL), yet EZH2 inhibitors are well tolerated. Herein, we aimed to uncover genetic and pharmacologic opportunities to enhance the clinical efficacy of EZH2 inhibitors in DLBCL. EXPERIMENTAL DESIGN: We conducted a genome-wide sensitizing CRISPR/Cas9 screen with tazemetostat, a catalytic inhibitor of EZH2. The sensitizing effect of IKZF1 loss of function was then validated and leveraged for combination treatment with lenalidomide. RNA sequencing (RNA-seq) and chromatin immunoprecipitation sequencing analyses were performed to elucidate transcriptomic and epigenetic changes underlying synergy. RESULTS: We identified IKZF1 knockout as the top candidate for sensitizing DLBCL cells to tazemetostat. Treating cells with tazemetostat and lenalidomide, an immunomodulatory drug that selectively degrades IKAROS and AIOLOS, phenocopied the effects of the CRISPR/Cas9 screen. The combined drug treatment triggered either cell-cycle arrest or apoptosis in a broad range of DLBCL cell lines, regardless of EZH2 mutational status. Cell-line-based xenografts also showed slower tumor growth and prolonged survival in the combination treatment group. RNA-seq analysis revealed strong upregulation of interferon signaling and antiviral immune response signatures. Gene expression of key immune response factors such as IRF7 and DDX58 were induced in cells treated with lenalidomide and tazemetostat, with a concomitant increase of H3K27 acetylation at their promoters. Furthermore, transcriptome analysis demonstrated derepression of endogenous retroviruses after combination treatment. CONCLUSIONS: Our data underscore the synergistic interplay between IKAROS degradation and EZH2 inhibition on modulating epigenetic changes and ultimately enhancing antitumor effects in DLBCL.
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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 | 0.000 |
| 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".