Abstract 5515: <i>In vivo</i> efficacy of the PARG inhibitor Gallotannin against triple negative breast cancer.
Notice bibliographique
Résumé
Abstract Breast cancer patients whose tumors fall into the triple negative category have the poorest clinical outcomes. These patients are at high risk for metastatic recurrence and have poor overall survival. Clearly, new therapeutic strategies are needed to combat triple negative tumors both at the time of onset and if necessary, at recurrence. Toward this goal, we initially tested the anti-proliferative properties of Gallotannin (GLTN) against a variety of breast cancer cell lines in vitro. Gallotannin was chosen due to its potent chemotherapeutic effects against cholangiocarcinoma in mice, and the lack of data concerning its potential use as an anti-cancer agent in breast cancer models. We observed potent anti-proliferative properties of GLTN in the low micromolar range in most breast cancer cell lines tested, but not in corresponding non-transformed mammary epithelial cells. Notably, triple negative breast cancer cells displayed the highest level of sensitivity to GLTN. The loss of proliferative capacity in these cells is due to cell cycle arrest in S phase. This corresponded with increased Chk1 phosphorylation, decreased Cyclin D1 protein levels, and changes to growth related genes as determined by Nanostring technology. GLTN may have several cellular targets that could potentially mediate growth arrest. The most thoroughly described target of GLTN is PARG, which is responsible for removing poly(ADP)ribose moieties from target proteins. GLTN potently inhibits PARG activity both in vitro and in vivo. Surprisingly, we found that PARG knockdown results in a dramatic growth inhibition of breast cancer cells, similar to what is observed in cells exposed to GLTN. To our knowledge, this is the first report indicating PARG inhibition may be exploited therapeutically to impair breast cancer growth. Importantly, PARG knockdown cells showed less sensitivity to GLTN, indicating PARG is a primary effector of GLTN-mediated growth inhibition. To test the capacity of GLTN to diminish tumor outgrowth in vivo, we utilized a mouse xenograft model where MDA-MB-468 triple negative breast cancer cells were injected into the mammary fat pad and allowed to grow to a palpable size before drug treatment. GLTN was administered ad libitum orally at 0.05% in drinking water or via intraperitoneal (IP) injections dosed at 10mg/kg daily. Both orally and IP administered GLTN greatly reduced tumor outgrowth by approximately 3 fold within a 25 day period without significant signs of weight loss, morbidity or liver toxicity. Beyond 25 days, orally ingested GLTN continued to prevent tumor growth by 3-fold, whereas tumors exposed to GLTN given by IP exhibited decreased sensitivity. In conclusion, these data strongly suggest that orally administered GLTN represents a novel approach to treat triple negative breast carcinomas and that PARG may represent a new therapeutic target for anti-cancer compounds. Citation Format: Tiejun Zhao, Qiang Sun, Amanda Lovato, Sonia del Rincon, Michael Witcher. In vivo efficacy of the PARG inhibitor Gallotannin against triple negative breast cancer. [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 5515. doi:10.1158/1538-7445.AM2013-5515
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) | 0,001 | 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) | 0,002 | 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 ».