Abstract C051: Alteration of protein translation by eIF4A1 inhibition exquisitely primes lymphoma cells to induction of ferroptosis
Notice bibliographique
Résumé
Abstract Background: Ferroptosis induction as a cancer therapy faces challenges establishing clinical therapeutic windows. Cells employ diverse mechanisms to prevent iron-mediated oxidation of membrane polyunsaturated fatty acids that causes ferroptosis, but populations with low rates of protein synthesis such as hematopoietic stem cells are more susceptible. Therefore, we hypothesized protein synthesis inhibitors might enhance therapeutic ferroptosis induction. Rocaglates, inhibitors targeting eIF4A1, have entered clinical trials with promising potential against diffuse large B-cell lymphoma (DLBCL), which also exhibits pre-clinical susceptibility to ferroptosis induction. Up to 40% of patients with DLBCL, the most common lymphoma type, have poor outcomes requiring new approaches to address these persistent unmet clinical needs. Our results reveal striking synergy of rocaglates, including the clinical compound zotatifin (eFT226), with ferroptosis inducers. TMT-pSILAC and RNA-seq analyses revealed profound deregulation of ferroptotic protection resulting from rocalate effects on protein synthesis. Methods: We used RNA-seq and TMT-pSILAC followed by mass spectrometry to analyze transcriptome and protein output in SU-DHL-10 DLBCL cells treated with zotatifin or DMSO for 24h. Enrichment analyses (Enrichr platform, ChIP-X Enrichment Analysis 3, GSEA) identified differentially expressed genes, proteins and transcription factors. We explored synergy between zotatifin and ferroptosis modulators in DLBCL cell lines and measured a wide variety of associated cellular endpoints including lipid peroxidation, glutathione levels, and glutathione peroxidase 4 (GPX4) expression. Results: TMT-pSILAC translatome assessment during rocaglate exposure revealed expression alteration(log2FC ≥ 1.5) of numerous key proteins involved in oxidative stress (i.e. SDHB, SDHA, TRAP1, SCO1, UQCRH, ETFA, COX7C) and enrichment of factors indicating induction of an antioxidant response (CD98HC, NRF1, ATF2, SP2, and NFE2L). These changes led to high synergy of zotatifin in combination with a wide variety of ferroptosis inducers (Bliss δ synergy score > 10) and antagonism with ferroptosis inhibitors (Bliss δ synergy score < - 10). These results were consistent across multiple DLBCL mouse and human experimental systems, and preliminarily appear present also in models of T-cell lymphomas. Lipid peroxidation levels were significantly enhanced when zotatifin was combined with erastin (p-value < 0.0005). Moreover, zotatifin treatment resulted in elevated levels of reduced glutathione (GSH), an effect overcome by addition of ferroptosis inducers (p-value < 0.005). Consistently, GPX4 expression was also impaired. Conclusions: Rocaglates induce novel alterations in oxidative stress factors and associated response pathways, sensitizing B and T lymphomas to therapeutic ferroptosis induction. These findings provide valuable insights for using rocaglates in combination with ferroptosis induction to overcome heterogeneous resistance pathways in high-risk lymphoma patients. Citation Format: Paola Manara, Alexa Marie Barroso, Abdessamad Youssfi Alaoui, Olivia Barbara Lightfuss, Tyler Andrew Cunningham, Kyle Hoffman, Caroline Alice Coughlin, Jonathan Harry Schatz. Alteration of protein translation by eIF4A1 inhibition exquisitely primes lymphoma cells to induction of ferroptosis [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C051.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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) | 0,000 | 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 tête enseignante, 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 ».