Abstract P1-03-11: In vivo rapid discovery of tumor suppressors in breast cancer
Notice bibliographique
Résumé
Abstract In breast cancer, genome sequencing of patient have identified hundreds of genetic mutations associated with cancer, but which of these mutations drive tumor formation and which are simply bystanders is largely unknown. This is especially true for mutations which individually occur in a small percentage of patients, but when considered together these less common mutations make a significant proportion of the mutational landscape of breast cancer. To functionally test these genes, we performed a CRISPR screen in vivo to identify which of these genes function as tumor suppressors in a mouse model of breast cancer. To do this, we created a library of sgRNAs targeting 215 putative tumor suppressor genes that are found to be mutated in human patients but had not been previously validated as tumor suppressors. We delivered this library via lentivirus to the adult mammary glands of tumor susceptible mice so each lentiviral infected cell has a knockout of one of the genes in the library as well as an oncogenic PIK3CA mutation. Importantly, each infected cell is surrounded by unmodified normal cells, mimicking the initiation of human breast tumors. As expected, mice injected with the putative tumor suppressor library formed tumor significantly faster and reached endpoint sooner than control library injected mice. Tumors from tumor suppressor library injected mice were deep sequenced which revealed known tumor suppressors such as APC and NF1, as well as an enrichment in multiple epigenetic regulators previously unidentified as tumor suppressors in breast cancer. Further validation using independent individual sgRNAs targeting each of the 4 most commonly targeted epigenetic regulators confirmed their role as tumor suppressors. To test the role of these genes in a human context, we used CRISPR to knockout these epigenetic regulators in normal human mammary epithelial MCF10A cells carrying the same PIK3CA mutation. MCF10A lines with epigenetic regulator knockout formed abnormal and protrusive spheres when seeded in matrigel and were capable of forming tumors when engrafted into immunocompromised mice unlike the parent MCF10A line, indicative of transformation into cancerous cells. To identify the mechanism of action of these epigenetic regulators as tumor suppressors, RNA-seq and ATAC-seq were performed on tumors with knockout of epigenetic genes. When compared to control tumors without epigenetic knockout, pathway analysis revealed a role for these epigenetic regulators in EMT, differentiation and cell metabolism. Analysis of ATAC-seq data is currently underway to identify enhancers associated with these transcriptional changes. Additionally, primary cell lines were isolated from knockout tumors and used to perform a drug screen to find drug sensitivities specific to these regulators. This screen identified several compound classes, including CBP/p300 inhibitors which are more effective at killing epigenetic knockout tumor cell lines than control tumor cell lines or normal mouse mammary epithelial (NMUMG) cells. Overall, these data are the first to identify several epigenetic regulators as tumor suppressors in breast cancer and to test them in a parallel and syngeneic background to establish their mechanism and drug susceptibility. By studying these genes, we seek to not only find treatments that target tumors in which these genes are mutated but also to understand the greater role of epigenetic regulators in tumor suppression. Citation Format: Ellen R Langille, Sampath Loganathan, Daniel Schramek. In vivo rapid discovery of tumor suppressors in breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-03-11.
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 | 0,001 | 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 | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 ».