Translational Disruption of NRF2 By Zotatifin Enhances Sensitivity to Ferroptosis and CAR-T Cells in Diffuse Large B-Cell Lymphoma
Notice bibliographique
Résumé
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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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».