Data from Acquired Resistance to EZH2 Inhibitor GSK343 Promotes the Differentiation of Human DLBCL Cell Lines toward an ABC-Like Phenotype
Bibliographic record
Abstract
Abstract Diffuse large B-cell lymphoma (DLBCL) accounts for 40% of non-Hodgkin lymphoma, and 30% to 40% of patients will succumb to relapsed/refractory disease (rrDLBCL). Patients with rrDLBCL generally have low long-term survival rates due to a lack of efficient salvage therapies. Small-molecule inhibitors targeting the histone methyltransferase EZH2 represent an emerging group of novel therapeutics that show promising clinical efficacy in patients with rrDLBCL. The mechanisms that control acquired resistance to this class of targeted therapies, however, remain poorly understood. Here, we develop a model of resistance to the EZH2 inhibitor (EZH2i) GSK343 and use RNA-seq data and in vitro investigation to show that GCB (germinal center B-cell)-DLBCL cell lines with acquired drug resistance differentiate toward an ABC (activated B-cell)-DLBCL phenotype. We further observe that the development of resistance to GSK343 is sufficient to induce cross-resistance to other EZH2i. Notably, we identify the immune receptor SLAMF7 as upregulated in EZH2i-resistant cells, using chromatin immunoprecipitation profiling to uncover the changes in chromatin landscape remodeling that permit this altered gene expression. Collectively, our data reveal a previously unreported response to the development of EZH2i resistance in DLBCL, while providing strong rationale for pursuing investigation of dual-targeting of EZH2 and SLAMF7 in rrDLBCL.
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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 | 0.001 |
| 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) | 0.005 | 0.001 |
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".