Enhanced Ferroptosis Induction Susceptibility in Diffuse Large B-Cell Lymphoma through Rocaglate-Mediated Translation Inhibition
Bibliographic record
Abstract
Introduction: Diffuse Large B-cell Lymphoma (DLBCL) is a genomically complex hematologic malignancy characterized by heterogeneous pathogenesis across multiple molecular subtypes. Despite recent advances, standard frontline treatment options leave a third of patients with relapsed or refractory disease due to tumor populations resistant to therapy by a wide variety of mechanisms. Ferroptosis, an iron-dependent non-programmed cell death mechanism driven by lipid peroxidation, has emerged as a promising therapeutic opportunity in DLBCL. DLBCL demonstrates distinct sensitivity to ferroptosis activators compared to other cancer types, yet the implications in DLBCL prognosis, immune infiltration, and drug resistance remain elusive. Notably, the loss of histone deubiquitinase MYSM1 in hematopoietic stem cells significantly reduces protein synthesis, leading to reduced ferroptosis suppressors, ultimately rendering the cells more susceptible to ferroptosis. Moreover, NRF2, encoded by NFE2L, a known modulator of ferroptosis, has been linked to resistance to rocaglates (cap-dependent translation inhibitors). Intriguingly, our preliminary data indicate that rocaglates induce translation of key proteins involved in oxidative stress (i.e. SDHB) and transcription factors related to the oxidative stress response (NRF1, ATF2, NRF2). Additionally, our published findings in glioblastoma suggest the upregulation of the ferroptosis suppressor 4F2 ( SLC3A2) under translation inhibition conditions. Furthermore, our investigations uncover another potential mechanism, wherein rocaglates induce NF-κB, which was previously shown to be suppressed by dimethyl fumarate (DMF) upon ferroptosis induction in DLBCL. However, the intricate interplay between ferroptosis and translation inhibition in DLBCL remains uninvestigated, prompting our hypothesis that protein synthesis inhibitors might enhance therapeutic ferroptosis induction. Method: To test our hypothesis, we conducted a comprehensive analysis, employing TMT-pSILAC to assess the global transcriptome response to rocaglates treatment, and subsequently verified significant findings via western blot analysis. Additionally, we quantified reduced glutathione (GSH) levels, reactive oxygen species (ROS) levels, and synergy with ferroptosis inducers (e.g., erastin, RSL3) to elucidate the mechanism of rocaglates in enhancing susceptibility to the ferroptosis pathway. Moreover, we explored NF-κB, previously identified as a ferroptosis regulator in our RNAseq analysis, using ELISA and NF-κB reporter assays. Results: Our TMT-pSILAC analysis unveiled upregulation of key ferroptosis suppressors, specifically NFS1, CBS, and 4F2. Concurrent treatment with rocaglates resulted in a notable increase in GSH levels, which was attenuated when administered in combination with ferroptosis inducers (p-value < 0.005) concomitant with the reduction of Glutathione Peroxidase 4 (GPX4), modulator of lipid peroxidation. Although rocaglates alone did not elicit significant levels of reactive oxygen species (ROS), their combination with ferroptosis inducers resulted in dramatic elevation of ROS levels (p-value < 0.0005). Correspondingly, there was strong synergy between the clinical rocaglate zotatifin and different ferroptosis inducers (Bliss δ synergy score > 10) and antagonism with ferroptosis inhibitors (Bliss δ synergy score < -10). Furthermore, our investigation revealed a noteworthy upregulation of NFKB1 at the translatome level, accompanied by a significant increase in TNF-α expression at 48 hours. The NF-κB-EGFP reporter assay confirmed the enhanced expression of NF-κB in response to the treatment. Conclusion: In conclusion, rocaglate-induced stress upregulates multiple ferroptosis suppressors, suggesting that cap-dependent translation disruption triggers a protective response against ferroptosis. Strikingly, these factors did not protect DLBCL tumors from ferroptosis inducers, which synergized remarkably with rocaglates, yielding promising new combination drug strategies. The proteomic basis for ferroptosis sensitization by rocaglate therapy remains under active investigation. Understanding the intricate interplay between these pathways may unveil potential key mechanisms for DLBCL treatment, potentially offering novel therapeutic targets to improve patient outcomes.
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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".