Abstract PR12: A genome-wide shRNA screen reveals that inhibiting kinases potentiates the anti-breast cancer activity of fluvastatin
Notice bibliographique
Résumé
Abstract Background: Statins are widely used to manage hypercholesterolemia, and have also been shown to possess anti-tumor effects. Breast cancer clinical trials have demonstrated that statins are effective in some but not all patients. We aim to identify combination treatments that can expand the anti-tumor benefit of statins to a larger subset of breast cancer patients. We hypothesize that a genome-wide shRNA screen will identify novel genomic targets to inhibit in co-therapies with fluvastatin. Methods: We used a pooled shRNA dropout screen to determine if knocking down specific genomic targets increases the anti-proliferative effects of fluvastatin. Cells transduced with the 80K TRC1 shRNA library were treated with either sublethal doses of fluvastatin or vehicle control over 12 days. Genomic DNA was collected from these cells every three days for hybridization to custom Affymetrix Gene Modulation Array Platform (GMAP) arrays. Candidate shRNA dropout hits were validated using shRNAs, siRNAs, and pharmacological inhibitors. Results: Our shRNA screen identified several kinase targets as dropouts, suggesting that knocking down specific kinases can potentiate the anti-proliferative effects of fluvastatin. We validated two candidate hits, PI4KB (phosphatidylinositol 4-kinase beta) and CSNK2B (casein kinase 2, beta polypeptide) in two breast cancer cell lines: MDA-MB-231 cells, which are highly sensitive to fluvastatin, and MCF-7 cells, which are less sensitive to fluvastatin. We used shRNAs and siRNAs targeting PI4KB or CSNK2B to confirm that knockdown of these kinases potentiates the anti-proliferative effects of fluvastatin. We also used pharmacological inhibitors of PI4KB and CSNK2B, which also increased the anti-proliferative activity of fluvastatin. Conclusions: Statins show promising anti-tumor effects, but co-treatments will be required to increase both their efficacy and the number of patients who will respond. We show here that kinases are a class of targets that can potentiate fluvastatin efficacy in breast cancer cell lines. We are now performing a small-molecule kinase inhibitor library screen that is designed to identify FDA-approved kinase inhibitors to combine with fluvastatin. The screen readout involves high-content confocal imaging and is currently underway. This work may lead to the discovery of effective and novel co-treatments for breast cancer that will better impact patient care. This abstract is also presented as Poster A16. Citation Format: Janice Pong, Aleksandra Pandyra, Carolyn Goard, Elke Ericson, Kevin Brown, Jarkko Ylanko, David Andrews, Corey Nislow, Jason Moffat, Linda Penn. A genome-wide shRNA screen reveals that inhibiting kinases potentiates the anti-breast cancer activity of fluvastatin. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr PR12.
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 ».