Lessons Learned from Mouse Models of Ovarian Cancer.
Notice bibliographique
Résumé
Epithelial ovarian cancer is thought to develop from the ovarian surface epithelium (OSE), although recent evidence suggests that some cases may arise from the fallopian tube. The cancer often goes undetected until after widespread dissemination and, as a result, the factors that contribute to its initiation and progression remain poorly understood. Numerous factors have been shown to affect risk, including rupture and repair of the OSE with each ovulation, genetic factors such as deleterious mutations in the BRCA1 tumor suppressor gene, and exogenous steroid hormones. Use of oral contraceptives decreases ovarian cancer risk, whereas women who use hormone replacement therapy are at increased risk, suggesting that the functionality of the ovary at the time of exposure to exogenous hormones can dramatically alter its susceptibility to becoming tumorigenic. To enable the study of the initiating events of ovarian cancer, several models have been generated. Mouse ovaries are structurally similar to human ovaries and research in this species is facilitated by the ease of genetic manipulation. However the lack of a promoter known to drive transgene expression uniquely in OSE cells is one of the major challenges in generating mouse models of human ovarian cancer. Consequently most mouse models are based on the conditional expression of loxP-flanked target genes, with intrabursal injection of adenovirus expressing Cre recombinase being used to efficiently inactivate a tumor suppressor gene (eg. Brca1) or activate an oncogene (eg. SV40 T-antigen) specifically in the OSE cells. Using such models, we have explored the hormonal and genetic factors that contribute to the transformation of OSE to ovarian cancer, with particular focus on: 1) the morphological changes associated with early disease; 2) the consequences of Brca1 deficiency on the behavior of OSE cells; 3) the impact of estradiol on the initiation and progression of ovarian cancer; and 4) the mechanisms by which estradiol accelerates tumor progression. Inactivation of Brca1 in OSE in vivo leads to the development of preneoplastic changes, such as hyperplasias, epithelial invaginations and inclusion cysts, which arise earlier and are more numerous than in control ovaries. Using a model in which ovarian cancer is caused by inducible expression of SV40 large T-antigen in the OSE, similar early morphological changes associated with tumor initiation have been observed. Mice with prolonged exposure to estradiol during ovarian tumorigenesis have a median survival less than half that of controls, with more differentiated epithelial tumor histology. Estradiol treatment causes a much earlier onset of disease and the sensitization of the OSE to transformation is associated with increased hyperplasia. In summary, the mouse models have taught us that loss of Brca1 function, exogenous estradiol, and oncogenic signals are each able to alter the morphology of OSE and increase the formation of preneoplastic lesions that resemble the structures found in women at high risk for ovarian cancer. Estrogen exposure not only alters the behavior of normal OSE, but can accelerate both onset and progression of ovarian cancer. These models provide unique opportunities to investigate the initiating events in ovarian cancer. This research was supported by grants from the Canadian Institutes of Health Research and the Ontario Institute of Cancer Research. (platform)
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 ».