Abstract GMM-030: MODELS AND ANALYTIC TECHNIQUES OF MULLERIAN TISSUE-DERIVED ORGANOIDS
Notice bibliographique
Résumé
Abstract INTRODUCTION: Ovarian cancer is the 5th deadliest cancer found in women and is the deadliest involving the gynecological tract. Most epithelial ovarian cancers have extra-ovarian origins and can be stratified into various histotypes: high and low-grade serous (HGS and LGS), endometrioid (ENOC), clear cell (CCOC), and mucinous – each of which are proposed to have distinct precursor lesions. We present organoids as a useful model to study precursor lesions and the process of tumorigenesis in epithelial ovarian carcinomas. Organoids recapitulate the in vivo growth microenvironment and are useful to study the mechanisms of tumorigenesis from healthy cells. We have previously proposed that ENOC arise from the secretory cell lineage, while CCOC originate from the ciliated cell lineage, and organoids are an ideal model to examine in greater depth the impact of mutation on specific cell populations, such as ciliated cells. METHODS: Surgical fallopian tube and endometrial tissues, removed for non-cancer reasons, were cultured in 2D followed by plating into Matrigel. Matrigel cultures were supplemented with media containing stem/progenitor differentiation factors promoting organoid growth. To study the effect of mutations often found in ovarian cancers on organoid growth and development, gene knockouts were produced using CRISPR lentiviruses on cells prior to Matrigel culture. Lentiviral transductions were optimized for organoid formation and for minimizing invasiveness accrued on cells. CRISPR gRNA constructs were validated by Western Blot and qPCR. Organoids containing knockouts of p53, BRCA1 and BRCA2 were used to model precursor lesions of HGS, whereas ARID1A knockouts and an inducible PIK3CA activating mutations were used to model CCOC. To gain further insight into ciliated cells of the endometrium, organoids were treated with the notch inhibitor-DBZ to drive differentiation of cells towards a ciliated cell lineage. We analyzed organoids by single-cell RNA sequencing (scRNA-seq), immunohistochemistry (IHC), and immunofluorescence staining (IF). Single cells were derived by purifying the organoids from Matrigel followed by a chemical and physical digestion. scRNA-seq was performed utilizing the 10X Genomics Platform and analyzed by in-house bioinformaticians. Bioinformatic analyses included stringent QC to remove low-quality and dead cells, before applying unsupervised learning algorithms like PCA and Gaussian mixture modeling as well as differential expression analysis to understand both how samples relate to each other and cell types discovered within each sample. RESULTS: We successfully recapitulated the histology observed in tissues by growing endometrial and fallopian tube organoids. The notch inhibitor, DBZ forced ciliated cell differentiation, as observed by IHC, IF and scRNA-seq. scRNA-seq clustering of DBZ-treated organoid cultures revealed a possible intermediary state between progenitor and ciliated cells. Initial IHC and IF analyses of CRISPR-mediated organoids reveal successful gene manipulation. CONCLUSIONS: Organoid cultures present as a powerful method for modelling precursor lesions; they can be readily manipulated genetically and with rapid turnaround compared to conventional mouse models. Organoids are also amenable to sequencing at single-cell resolution. The ability to model ovarian cancers with permanent knockouts in human tissue serves as a necessary link between animal models and human therapy. Citation Format: Germain C. Ho, Dawn R. Cochrane, Evan W. Gibbard, Kieran Campbell, Basile Tessier-Cloutier, Kendall Greening, Forouh Kalantari, Genny Trigo-Gonzalez, Yemin Wang, Jessica N. McAlpine, Sohrab P. Shah, David G. Huntsman. MODELS AND ANALYTIC TECHNIQUES OF MULLERIAN TISSUE-DERIVED ORGANOIDS [abstract]. In: Proceedings of the 12th Biennial Ovarian Cancer Research Symposium; Sep 13-15, 2018; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2019;25(22 Suppl):Abstract nr GMM-030.
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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,003 |
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 ».