Abstract 997: Potential targets for siRNA-mediated combinational therapy of breast cancer cells.
Notice bibliographique
Résumé
Abstract Chemotherapy is an effective approach to curb uncontrolled proliferation of malignant cells; however, even the most recent molecularly-targeted drugs lose their effect as a result of resistance development in a short period of time. Inherent plasticity of transformed cells and diverse resistance mechanisms enable cells to mount an effective resistance against the administered drugs. Molecular changes in drug resistant cells are beginning to emerge, including adjustments in expression level of proteins involved in cell survival and apoptosis. We are exploring the potential of siRNA silencing as a new approach to induce apoptosis and/or overcome drug resistance in breast cancer cells. This study was conducted to develop a global approach to identify crucial targets and deliver siRNAs to silence a combination of proteins in order to induce apoptosis (independent of chemotherapy) and/or sensitize the resistant cells to chemotherapeutic agents. Resistance was induced in two breast cancer cell lines by exposure to gradually increasing doses of doxorubicin, and resistance induction was confirmed by a significant increase in IC50 of DOX. An apoptosis microarray was used for analysis of mRNA levels of 84 apoptosis related proteins in wild type and resistant cells. While different members of caspase and TNFR family were among down-regulated proteins, over-expressed proteins in both resistant cell lines included Bcl2, survivin, NFB, and Mcl1. Human Apoptosis siRNA library (446 targets) and kinase siRNA library (719 targets) were screened using PEILA2.1 as delivery system. siRNAs were selected as hits which induced cell death in cancer cell lines without significant reduction in cell viability of skin fibroblast cells. The “hits” in apoptosis library included BIRC 7, NFB, and Mcl1. Eight kinases were selected (based on the same criteria and potential synergistic effect with Mcl-1). In vitro evaluations revealed that among the selected targets, Mitogen-activated protein kinase 3 (MAP2K3) was the most potent in decreasing cell viability, while Ribosomal protein S6 kinase (RPS6KA5) showed the most promising potential for simultaneous silencing with Mcl-1. The combination of Mcl-1 and RPS6KA5 was evaluated in vivo both as multiple intratumoral (1.5 μg for each siRNA or ∼75 μg/kg) and intraperitoneal (10 μg each siRNA or ∼500 μg/kg) treatments in a MDA-MB 435 WT xenograft model in NCR nu/nu nude mice. In the intratumoral treatment, a significant inhibition of tumor growth was observed for Mcl-1 single silencing compared to scrambled siRNA and Mcl-1/RPS6KA5 double silencing compared to Mcl-1 silencing. IP treatment showed a similar trend; however, only the combinational silencing of Mcl-1 and RPS6KA5 showed a significant inhibition of tumor growth. This study demonstrated the potential for kinase silencing as a therapeutic strategy in breast cancer therapy, as well as the promising synergistic effect for Mcl-1 and kinase silencing. Citation Format: Hamid Montazeri, Hasan Uludag, Parvin Mahdipoor. Potential targets for siRNA-mediated combinational therapy of breast cancer cells. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 997. doi:10.1158/1538-7445.AM2013-997
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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».