Combinatorial targeting of nuclear export and translation of RNA inhibits aggressive B-cell lymphomas
Bibliographic record
Abstract
Aggressive double- and triple-hit (DH/TH) diffuse large B-cell lymphomas (DLBCLs) feature activation of Hsp90 stress pathways. Herein, we show that Hsp90 controls posttranscriptional dynamics of key messenger RNA (mRNA) species including those encoding BCL6, MYC, and BCL2. Using a proteomics approach, we found that Hsp90 binds to and maintains activity of eIF4E. eIF4E drives nuclear export and translation of BCL6, MYC, and BCL2 mRNA. eIF4E RNA-immunoprecipitation sequencing in DLBCL suggests that nuclear eIF4E controls an extended program that includes B-cell receptor signaling, cellular metabolism, and epigenetic regulation. Accordingly, eIF4E was required for survival of DLBCL including the most aggressive subtypes, DH/TH lymphomas. Indeed, eIF4E inhibition induces tumor regression in cell line and patient-derived tumorgrafts of TH-DLBCL, even in the presence of elevated Hsp90 activity. Targeting Hsp90 is typically limited by counterregulatory elevation of Hsp70B, which induces resistance to Hsp90 inhibitors. Surprisingly, we identify Hsp70 mRNA as an eIF4E target. In this way, eIF4E inhibition can overcome drug resistance to Hsp90 inhibitors. Accordingly, rational combinatorial inhibition of eIF4E and Hsp90 inhibitors resulted in cooperative antilymphoma activity in DH/TH DLBCL in vitro and in vivo.
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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".