Abstract B15: Optimization of an oncogenomics-based in vivo screen to validate candidate sarcoma genes
Notice bibliographique
Résumé
Abstract Animal models are a powerful means to dissect the molecular events regulating carcinogenesis and also serve as pre-clinical models for drug development. Rhabdomyosarcoma (RMS) is amongst the most common of pediatric sarcomas whose dismal outcome in high-risk groups has unfortunately not improved in the past decade. Current animal models of RMS include zebrafish - an elegant genetic system that has many advantages such as rapid generation of mutants and ease of live animal imaging, but may not faithfully recapitulate sarcomagenesis in mammals. Genetically-engineered mice are a widely accepted mammalian model; however, this system can be limited by the number of mutations that can be introduced in a single strain. Here, we describe optimisation of a novel murine model of RMS using mosaicism, whereby the cell type from which these sarcomas arise is isolated, lentivirally transduced with genes of interest, and then injected into host mice to assess for sarcoma formation. Since pools of genes can be introduced into the cell of origin, mosaicism has the capacity to test the interaction of several candidate genes simultaneously. Maturing myoblasts from p53-deficient mice (Trp53tm1Tyj) were employed in our models as p53 mutation is common in RMS. p53-/- myoblasts expressed muscle-specific mRNA and maintained the capacity to differentiate into functional muscle fibers in vitro. p53-/- myoblast cell lines (Myo25, Myo26) were transduced with lentiviral particles encoding a known RMS oncogene and a fluorescent reporter (KrasG12DIRES-Emerald) or empty vector (IRES-Emerald). To assess the effect of gene dosage, myoblasts were infected with high (250) or low (2) multiplicities of infection. GFP+ cells were then selected using fluorescence activated cell sorting and expanded, resulting in 4 transgenic myoblast cell lines: M25KrasG12D High, M26KrasG12D Low, M25Empty High and M26Empty Low. Proviral integration into the genome was assessed using real-time PCR. Ras and GFP protein expression was confirmed by immunoblot. Transformed cells were subsequently injected into the hind limbs of neonatal p53+/- host mice to assess tumor forming capacity and the effect of transgene copy number. Although differences in latency were observed between KrasG12D High and Low groups (median latency 3.6 (range 3-6) and 4.9 (range 4-6) weeks, respectively), penetrance was similar in both groups, at 85% and 83%, respectively. We next sought to determine if the developmental age of host mice affected tumor formation by comparing neonatal injections with injections of adolescent p53+/- mice (21 days old) with M25KrasG12D High myoblasts. In these experiments, an increase in latency was noted in the adolescent-injected group (median 5.1 weeks). Finally, to examine the role of microenvironment in sarcoma formation, immunocompromised (NSG) mice were injected with M25KrasG12D High and M25Empty High myoblasts. Median latency decreased in this cohort to 2.7 (range 2-5) weeks with 100% of mice affected. Transgene-specific PCR demonstrated all tumors contained the KrasG12DIRES-Emerald construct. Pathologic analysis of tumors demonstrated that our mosaic mouse models generated high-grade sarcomas with myogenic differentiation based on their immunohistochemical profiles. Gene expression profiles of the sarcomas will be used to compare our murine models to human RMS, to gain insight into the effects of gene dosage and tumor microenvironment. In conclusion, we have developed a highly flexible, functional genomics platform that will allow us to determine the impact of gene dosage, developmental age of host and the role of the immune system on the formation of RMS. Using this system, we will screen candidate oncogenes and tumor suppressors to determine which have contributing roles in RMS biology and thus may be important targets for future therapies. Citation Format: Timothy McKinnon, Rosemarie Venier, Manon Alkema, Leah Kabaroff, Brendan Dickson, Rebecca Gladdy. Optimization of an oncogenomics-based in vivo screen to validate candidate sarcoma genes. [abstract]. In: Proceedings of the AACR Special Conference: The Translational Impact of Model Organisms in Cancer; Nov 5-8, 2013; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2014;12(11 Suppl):Abstract nr B15.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 ».