Abstract 2801: Identification of oncogenic/metastatic driver genes that cooperate with p53 or p53/Rb-loss to induce triple-negative breast cancer
Notice bibliographique
Résumé
Abstract Triple-negative breast cancer (TNBCs) is an aggressive subtype with poor prognosis; drug-resistant metastases are common and highly lethal for which identification of therapeutic targets is of major medical interest. Previously we demonstrated that deletion of p53 and Rb in mouse mammary epithelium induced TNBC-like tumors but with limited capacity to metastasize. Therefore, we decided to employ sleeping beauty (SB) mutagenesis system that can identify oncogenic networks responsible to drive primary and metastatic tumors. In order to do this, we generated mice in which p53 (p53f/f) or p53 plus Rb (p53f/f:Rbf/f ) are deleted and the SB transposon is mobilized from chromosome 9 as well as controls in which both p53 and Rb are wild type. Forty primary tumors and forty metastases from each cohort will be harvested to identify common integration sites followed by deep sequencing. Then, the top 5-10 candidate oncogenes/tumor suppressors will be validated to determine whether their loss/activation would accelerate tumorigenesis/metastasis. H&E staining of mammary tumors collected so far from different genotypes revealed histologically diverse tumor types which includes but not limited to poorly differentiated tumors with areas of mesenchymal/spindle-like cells, acinar adenocarcinomas, and well differentiated tumors. All mice analyzed developed multiple mammary tumors in more than one mammary gland with average latency of 108, 158 and 170 days for p53/Rb deleted (N=7), p53 deleted (N=12), and wild type mice (N=2), respectively (p < 0.05). However, we did not always observe macro metastases in mice with primary mammary tumors and we decided to perform primary tumor survival surgery when tumors are oversized and let the mice live longer to develop metastases. With this technique, 3 out of 4 mice that underwent tumor removal surgery developed lung metastases when sacrificed. In total, lung metastases were observed in 5 out of 28 mice analyzed so far with/without surgery (18%) which histologically resemble their primary tumors. Since removing tumors from multiple glands is challenging, we decided to take a transplantation approach of the primary mammary epithelial cells of mid-pregnancy (13-16 dpc) p53f/f:Rbf/f:SB+ females and injecting them into the mammary glands of 3-4 week old NSG mice. So far, four NSG mice developed mammary tumors with average latency of 176 days. These tumors resemble spindle-shape histology. SB mutagenesis can promote metastasis in mouse models with limited dissemination potential such as the model proposed herein. The proposed experiments will identify novel oncogenic events that cooperate with p53-loss or combined p53/Rb-loss to induce primary and metastatic TNBCs which may serve as therapeutic targets for this aggressive disease. I have generated 3 sets of cohort of SB mutagenesis to identify these oncogenic events which are currently being analyzed in details. Citation Format: Ronak Ghanbari Azarnier, Agatha Zuchelkowski, Philip.E Chung, Zhe Jiang, Eldad Zacksenhaus. Identification of oncogenic/metastatic driver genes that cooperate with p53 or p53/Rb-loss to induce triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2801. doi:10.1158/1538-7445.AM2017-2801
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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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».