Metabolic Determinants of Ferroptosis in B-Cell Lymphoma
Notice bibliographique
Résumé
Background and Significance: Approaches for targeted elimination of B-cells as the root-cause of disease have been highly effective in the treatment of B-cell lymphoma (rituximab, ibrutinib). To expand the portfolio of B-cell-selective drugs, we developed an interactive computational tool (www.lymphoblasts.org) and identified ferroptosis previously unrecognized selective vulnerability in B-cell lymphomas. Ferroptosis, a non-apoptotic form of cell death driven by iron-dependent membrane lipid peroxidation, has shown therapeutic potential in highly therapy-resistant tumors. However, no potent ferroptosis inducers are available clinically, and genetic ablation of Gpx4, the main anti-ferroptotic defense, leads to lethal renal and neurological toxicities in mice. A better understanding of the factors regulating ferroptosis is therefore required to allow its therapeutic targeting and the development of clinical grade ferroptosis inducers. Results : Compound screening data (CTD, GDSC), together with gene-dependency scores from CRISPR and RNAi screens, were re-analyzed and integrated for B-ALL/mature B-cell lymphomas compared to myeloid leukemia and solid tumors. Most notably, among 4,518 compounds screened, we identified 5 top-ranking compounds that converge on targeting the ferroptosis pathway: Based on our computational approach (www.lymphoblasts.org), we identified ferroptosis inducers as particularly lethal to B-cell lymphoma compared to other tumor types. This was the case for multiple classes of ferroptosis inducers, including GPX4 inhibitors (RSL3, ML162, ML210), the cysteine-glutamate antiporter (system xc-, SLC7A11) inhibitor Erastin, and the iron oxidizer and GPX4 inactivator FINO2. Analysis of gene dependency in 89 B-cell lymphoma compared to 779 myeloid leukemia and solid tumor cell lines based on CRISPR and RNAi data (DepMap) revealed a selective dependence of B-cell lymphomas on anti-ferroptotic molecules. These genes notably include components of the glutathione synthesis machinery (GCLC, GCLM, GSS) and selenoprotein synthesis (SEPH2, LRP8, SELENOI). Interestingly, B-cell malignancies also showed marked dependence on molecules that sensitize to ferroptosis, including the transferrin receptor (TFRC; iron import) and ACSL4 (polyunsaturated fatty acid, PUFA). This suggests that B-cells might be addicted to iron- and PUFA-metabolism that make them intrinsically vulnerable to ferroptosis. Accordingly, iron chelation treatment showed a much higher toxicity in B-cell lymphoma compared to solid tumors. Based on transduction of mature splenic B-cells from Tfrc-fl/fl mice with MYC, BCL2 and dominant-negative p53 as B-cell lymphoma model, we confirmed that Cre-mediated ablation of Tfrc induced rapid cell death, selectively in mature B-lymphoma. In addition, analysis of clinical data from the DLBCL MMMLNP trial cohort revealed that greater than median expression of ACSL4 is associated with significantly worse survival (P=0.003). The opposite was seen with ACSL3 (P=0.0004), which counteracts ACSL4-dependent PUFA-metabolism. Combination of these two gene expression profiles strongly predicted prognosis, as individuals with the ACSL4high/ACSL3low gene expression signature had much higher mortality than those with the ACSL4low/ACSL3high signature (hazard ratio for mortality by log-rank test: 2.484, 95% confidence interval: 1.709 - 3.609). Whole-genome CRISPR screens in B-cell malignancies under the selective pressure of RSL3, Erastin, and FINO2 highlighted several known ferroptosis regulators and uncovered new ferroptosis-related genes and pathways including sphingolipid metabolism (KDSR), phosphatidylcholine synthesis (FLVCR1, PCYT1A), inositol metabolism (ITPK1, IPMK), and lipid membrane remodelling (ATP8B2). Conclusions: Integrating CRISPR dependency and drug sensitivity data revealed that B-cell lymphomas are uniquely sensitive to ferroptosis and highly depend on anti-ferroptotic defense mechanisms. This likely reflects the intrinsic dependency of B-cell lymphomas on iron import and PUFA-metabolism, at the cost of increased vulnerability to ferroptosis. Furthermore, our CRISPR screens performed with the ferroptosis inducers RSL3, Erastin, and FINO2 revealed phosphatidylcholine synthesis (FLVCR1, PCYT1A) and lipid membrane remodelling (ATP8B2) as central mechanistic targets in B-cell lymphomas.
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 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 | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,002 | 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 ».