Abstract 4247: Impact of 70% ethanol intraductal injections in MNU rat models for breast cancer prevention
Notice bibliographique
Résumé
Abstract Breast cancer (BC) is the leading cancer diagnosis in women. While many BC treatment options exist, only two methods are approved for prevention. Prophylactic mastectomy is an invasive surgery that removes the breast, thereby removing the targeted epithelial ductal tree cells, where most BC arises. Despite a 90% risk reduction, recovery time, risk of infection, pain management, and mental health toll on women deter them from this surgery. Hormonal preventatives, tamoxifen and raloxifene, reduce BC risk by up to 50%. However, increased risk of uterine cancer and intolerable sides effect deter women from this option. Therefore, new preventative methods are needed to maintain BC risk reduction while minimizing current BC prevention deterrents. Intraductal (ID) injection is a technique that can circumvent systemic toxicity by inserting a needle directly into the ductal tree opening. We study BC prevention using 70% ethanol (EtOH) as a cell killing, ablative solution for ID injections and have shown its effectiveness of ablation in an aggressive genetically engineered mouse model with minimum collateral tissue damage. EtOH is a safe, inexpensive reagent clinically used as a sclerosing and ablative agent. We introduced ethyl cellulose (EC), a gelling agent which further minimized damage outside the ductal tree. To confirm successful ductal tree filling without animal sacrifice, we introduced Tantalum Oxide (TaOx), a nanoparticle-based contrast agent for real time and long-term visualization of ductal tree filling. TaOx showed remarkable local retention, without impeding 70% EtOH ablative effects or EC compared to other contrast agents. Despite our refined ablative solution, ID injection is a difficult technique to perform with limited ability to confirm success of a fully filled ductal tree. To elucidate any impact of partial ductal tree filling, we utilized a Methyl-N-Nitrosourea (MNU) rat model. Rat models improve scalability toward clinical trials and increase ductal tree filling volume and control to study the impact of partial ductal tree filling. Our MNU rat model provides a time frame between carcinogen exposure and tumor formation optimal for prevention studies. Two weeks after intraperitoneal MNU injections, rats received full or half volume (partial) injections, fat pad (partial), or no injection into individual mammary glands. Rats were monitored for tumor formation from 2 weeks post injection until reaching euthanasia criteria. Fully injected glands had the highest tumor latency increase by 1 month (130 days, p < 0.0001) compared to non-injected controls (95 days). Partial and fat pad tumor latency also improved compared to control (122 days, p <0.01 and 110 days, p <0.05 respectively). No iatrogenic effects of EtOH were observed. Here, we show that ID injection of 70% EtOH is a safe, effective, and scalable technique for BC prevention, which can be enhanced with TaOx and EC for visualization and retention. Citation Format: Erin K. Zaluzec, Mohamed Ashry, Elizabeth Kenyon, Elizabeth G. Phelps, Legend Kenney, Katherine Powell, Maximilian Volk, Shatadru Chakravarty, Jeremy M.L. Hix, Matti Kiupel, Erik Shapiro, Lorenzo F. Sempere. Impact of 70% ethanol intraductal injections in MNU rat models for breast cancer prevention. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4247.
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 | 0,001 | 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 ».