Translational Disruption of NRF2 By Zotatifin Enhances Sensitivity to Ferroptosis and CAR-T Cells in Diffuse Large B-Cell Lymphoma
Bibliographic record
Abstract
Background: Diffuse large B-cell lymphoma (DLBCL) poses a significant therapeutic challenge, with up to 40% of patients experiencing relapsed/refractory (R/R) disease and poor outcomes. While CAR-T cell therapy targeting CD19 (CAR-19) achieves durable responses in 30-40% of R/R DLBCL cases, high relapse rates highlight the need for further innovations. Emerging evidence suggests DLBCL is susceptible to ferroptosis, an iron-dependent cell death pathway driven by lipid peroxidation and enabled by expanded labile iron pools that are common in DLBCL cells. However, optimizing ferroptosis as a cancer treatment strategy remains challenging due to mechanisms that counteract lipid peroxidation, limiting efficacy in clinical settings. Notably, cells with low protein synthesis rates, such as hematopoietic stem cells, are inherently more susceptible to ferroptosis, but leveraging this vulnerability therapeutically remains minimally explored. Previous studies demonstrate DLBCL cells are sensitive to protein-synthesis inhibitors, including zotatifin (eFT226), a clinical-stage rocaglate disrupting cap-dependent translation via eIF4A1. Here we explored whether zotatifin plus inducers of ferroptosis would work as rational combinations, revealing dramatic synergy both in vitro and in vivo. Moreover, the enhanced sensitivity to ferroptosis carries over to the cytotoxic effects of IFN-γ released by CAR-T cells, significantly enhancing treatment responses. These effects are mediated by translational loss of the protective stress response transcription factor NRF2 downstream of zotatifin treatment. Methods: Using tandem mass tag (TMT) labeling and pulsed stable isotope labeling by amino acids (pSILAC), followed by mass spectrometry, we examined zotatifin's impact on overall translation in DLBCL cell lines. We assessed synergistic effects of zotatifin with ferroptosis inducers targeting the cystine/glutamate antiporter, measuring drug interactions, glutathione levels, lipid peroxidation and reactive oxygen species (ROS). We employed polysome profiling and a dual-luciferase reporter (DLR) to determine translational impact on NRF2 expression. In vivo, we utilized Imidazole Ketone Erastin (IKE), optimized for in vivo studies, for efficacy in lymphoma patient-derived xenografts (PDX) of the germinal center B-cell (GCB) subtype, to evaluate the combination with zotatifin. For CAR-T-zotatifin studies, BALB/c mice were injected with A20 cell lines to establish lymphoma models. Results: TMT-pSILAC analysis revealed that zotatifin modulates ferroptotic mechanisms by upregulating protective proteins such as SLC3A2, NFS1, and CBS. We observed strong synergy, however, between zotatifin and various ferroptosis inducers, including erastin, RSL3, dimethyl formamide (DMF), sulfasalazine (SASP), ML385, and the pharmacokinetically optimized compound imidazole ketone erastin (IKE), while the anti-ferroptotic antioxidant N-acetyl cysteine (NAC) was antagonistic. Despite minimal lipid peroxidation and ROS, zotatifin significantly increased glutathione (GSH), suggesting a compensatory response to ferroptotic stress from rocaglates. This effect was diminished when combined with ferroptosis inducers targeting the protective xc− antiporter, especially erastin. Further investigation showed NRF2 is rapidly depleted from cells during zotatifin exposure. Specifically, NRF2 mRNA is lost from translationally active polysomes, and a reporter system demonstrated its 5' UTR is eIF4A dependent, establishing NRF2 as a novel translational target of rocaglates. In vivo, zotatifin+IKE treatment led to complete regression of xenografted lymphomas, showing superior efficacy compared to single agents. Additionally, zotatifin combined with CAR-T cells extended overall survival in mice to over 40 days, compared to 21 days in the control group. Conclusions: Our findings indicate that zotatifin enhances sensitivity to ferroptosis induction and improves CAR-T cell efficacy, driven by loss of NRF2, offering promising new therapeutic combinations. These results advocate for clinical strategies to enhance investigational ferroptosis inducers and approved CAR-T cells. Further optimization of these regimens holds potential to enhance therapeutic efficacy and overcome resistance mechanisms in DLBCL treatment.
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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".