Abstract A045: EZH2 inhibition re-sensitizes drug resistant triple-negative breast cancer PDX models to Eribulin
Notice bibliographique
Résumé
Abstract Background: Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer predominantly affecting younger women. The mainstay of treatment is chemotherapy, to which most patients eventually stop responding. After demonstrating improved overall survival in the metastatic setting, eribulin, a microtubule inhibitor, was approved for the treatment of late-stage, heavily pretreated breast cancer. While frequently used in the clinic, most patients either do not respond initially or they stop responding to eribulin. Considering that there is a lack of oncology drugs including targeted therapies to treat TNBCs, patients are therefore left with few therapeutic options after eribulin. There is a paucity of data on mechanisms of acquired resistance to eribulin, and few potential uncovered targetable alterations. The goal of our study is to determine mechanisms of acquired resistance to Eribulin using isogenic acquired-resistant PDX models and to attempt to reverse this resistance with novel combinations. Methods: PDX models are grown as mammary fat pad engraftments of patient tumours in immuno-compromised (NGS) mice. To determine drug response, we treat tumour-bearing mice with weekly eribulin, measure the tumor volume once per week, then classify response based on mRECIST 1.1 criteria. Acquired resistance was generated in vivo in TNBC PDXs via the continuous or interrupted treatment of Eribulin in sensitive models that regressed on Eribulin. RNA-Seq analysis was performed on isogenic pairs of Eribulin sensitive and resistant PDX tumors and analyzed for differential gene expression and pathway enrichments. Drug combinations targeting candidate regulators of enriched pathways across our models were assessed in vivo in resistant PDXs. Drug trials were performed with n=5-7 mice per treatment arm until endpoint (tumour volume reaches 2000mm^3 or BW loss of 20%). Statistical analysis included t-tests for tumour volume and Wilcoxon test for survival. Results: 6 isogenic pairs of Eribulin acquired resistant TNBC PDX models were generated in vivo and molecular analysis was performed by comparing transcriptomic profiles of sensitive and resistant tumours. Pathway analysis revealed that, across 5 of 6 acquired resistant models, differentially expressed genes were significantly enriched for targets of SUZ12 (BH p-value<0.01). As SUZ12 makes up a core component of the PRC2 complex, we attempted to target EZH2, the catalytic component of PRC2, in vivo. Combining Tazemetostat, an FDA-approved EZH2 inhibitor, with Eribulin in one PDX model, resulted in tumor regression and improved overall survival (p<0.005) in one model. We will be testing this combination in more models, and performing differential chromatin accessibility studies to identify candidate driver genes in these regions. Conclusion: Our results suggest that combining an approved EZH2 inhibitor, Tazemetostat, with Eribulin can re-sensitize acquired-resistant TNBC PDXs to Eribulin. This approach could have significant clinical benefit in late stage hard to treat TNBCs. Citation Format: Kathryn Bozek, Cedric Darini, Cathy Lan, Marguerite Buchanan, Catherine Chabot, Josiane Lafleur, Juliet Guay, Eva Filosa, Adriana Aguilar-Mahecha, Mark Basik. EZH2 inhibition re-sensitizes drug resistant triple-negative breast cancer PDX models to Eribulin [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 A045.
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 | 0,000 | 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».