Abstract 2902: The effectiveness of autophagy inhibition in sensitizing triple-negative breast cancer cells to chemotherapy
Notice bibliographique
Résumé
Abstract Introduction: High recurrence rates, drug resistance after initial response to chemotherapy, and overall poor prognosis along with the limited treatment options make triple-negative breast cancers (TNBCs) a major clinical challenge. Autophagy, an evolutionary conserved degradation and recycling process, has been shown to function as an adaptive survival response to chemotherapy. Previous studies have indicated higher expression of autophagy markers in TNBCs compared to other breast cancer subtypes, as well as their dependence on autophagy for survival. Our laboratory has also shown in xenograft models an enhanced effectiveness of chemotherapy for the treatment of TNBC when given in combination with autophagy inhibition (AI). These results support TNBCs as a good candidate for AI to improve efficacy of existing therapeutic regimens. However, currently available agents for AI in cancer patients have limited effectiveness, and development of more potent autophagy inhibitors (AIs) is underway. Objective: Develop and test new tools for more potent AI in vivo. Experimental Design: We are employing in vitro models using TNBC lines MDA-MB-231 and SUM159PT, as well as their derivatives R8 and R75, resistant to Epirubicin (EPI) and other anthracyclines. We are evaluating effects of various AIs, including lysosomotropic agents HCQ and lys05, siRNAs and shRNAs targeting autophagy-related (Atg) proteins, and small molecule inhibitors (under development) of ATG4B protein. In vivo xenograft mouse models of MDA-MB-231 and R8 are being used to evaluate the effects of combinatorial therapy with EPI and AI. Methods: For the assessment of autophagy levels before and after AI we used autophagy flux (degradative completion of autophagy) assays. We evaluated the effects of chemotherapy alone and in combination with AIs on parent and resistant sub-lines by assessing their proliferation. For in vivo studies, TNBC cells are injected subcutaneously in Rag2M mice. Treatment with EPI, AI, or their combination is administered after tumor formation. Treatment efficacy is evaluated by tumor volume measurements; tumors are also assessed for the expression of autophagy markers. Results: Our in vitro experiments showed enhanced cytotoxicity of lys05 compared to HCQ either alone or in combination with EPI. However, the use of lys05 in vivo gives contradictory results and requires further evaluation. AI targeting ATG4B, using shRNA-inducible monoclonal cell lines derived from MDA-MB-231 cells and novel small molecule inhibitors of ATG4B, significantly affected cancer cell proliferation in vitro, and is currently being investigated in vivo. Conclusion: Novel approaches to AI may serve as useful tools to assess the effects of AI in vitro and in vivo. Our preliminary results suggest that more potent AIs may improve the effectiveness of treatment of TNBC. Supported by CIHR GPG102167 and CIHR/AVON OBC127216. Citation Format: Svetlana Bortnik, Suganthi Chittaranjan, Jing Xu, Wieslawa H. Dragowska, Jianghong An, Adrienne Kyle, Nancy E. Go, Lubomir Vezenkov, Courtney Choutka, Amy Leung, Suzana Kovacic, Damien Bosc, Karen Gelmon, Marcel Bally, Steven Jones, Robert Young, Sharon Gorski. The effectiveness of autophagy inhibition in sensitizing triple-negative breast cancer cells to chemotherapy. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2902. doi:10.1158/1538-7445.AM2015-2902
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 ».