Abstract A65: Unraveling the rhabdomyosarcoma genome using mouse mosaicism
Notice bibliographique
Résumé
Abstract Introduction: Rhabdomyosarcoma (RMS) represents the most common pediatric soft tissue sarcoma. Despite advances in multimodality therapy, outcomes in intermediate and high-risk RMS groups have plateaued. To develop more efficacious therapies, improved biologic models of RMS are required to better characterize the molecular pathogenesis of RMS. Results: We have developed a mouse model of RMS that targets maturing myoblasts, the likely cell-of-origin for RMS. In this study, myoblasts were isolated from neonatal skeletal muscle harvested from p53-/- mice (Trp53tm1Tyj), as loss of function in the p53 pathway is a common mutation in RMS. Lentiviral particles encoding a bicistronic construct of a known RMS oncogene (Kras) and a green fluorescent protein (GFP) reporter (KrasG12DIRES-Emerald) were used to transduce two different p53-/- primary myoblast cell lines. GFP+ cells were purified using fluorescence activated cell sorting (FACS) and expanded. In parallel, myoblasts expressing empty vector (IRES-Emerald) were also selected using FACS. Kras overexpression was confirmed by immunoblot and resulted in a striking transformation of p53-/- myoblasts, demonstrated by increased colony formation in anchorage-independent growth assays when compared to parental cell lines and empty vector controls. Additionally, a concomitant increase in proliferation and decrease in differentiation potential was observed in myoblasts expressing oncogenic Kras compared to controls. Intramuscular injection of KrasG12D-overexpressing myoblasts into the hind limbs of neonatal p53+/- hosts resulted in rapid tumor formation with a median latency of 4.2 weeks (range 2.7 to 5.9 weeks) and a penetrance of 85%. Live mouse imaging demonstrated that the hind limb tumors expressed GFP. Empty vector control constructs did not form when host animals were aged for at least 35 weeks. All tumors (n=21) were examined using histological staining and immunohistochemistry with a panel of sarcoma specific markers. Histopathologic analysis revealed that the tumors represented high-grade sarcomas with myogenic differentiation based on their expression of multiple muscle markers, including desmin, MyoD1 and myogenin. Metastases were not identified in any mice. Transcriptome analysis using gene expression arrays will assist in further characterization of the tumors and determine whether this murine model of RMS recapitulates aberrant gene expression described in human RMS. Conclusions: Our novel mosaic mouse model supports previous studies describing the development of RMS following transformation of maturing myoblasts. Moreover, we observed that dysregulation of p53 and RAS signalling are synergistic events in the molecular pathogenesis of RMS. Using a lentiviral system to deliver genes and transform key cell populations, followed by engraftment into syngeneic hosts, represents a powerful method to experimentally generate RMS and, potentially, other solid tumors. This flexible functional genomics platform is highly amenable to the study of candidate drivers of RMS, and as a preclinical tumor model to test novel therapeutic agents. Citation Format: Timothy McKinnon, Rosemarie Venier, Manon Alkema, Leah Kabaroff, Javed Khan, Brendan Dickson, Rebecca Gladdy. Unraveling the rhabdomyosarcoma genome using mouse mosaicism. [abstract]. In: Proceedings of the AACR Special Conference on Pediatric Cancer at the Crossroads: Translating Discovery into Improved Outcomes; Nov 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2013;74(20 Suppl):Abstract nr A65.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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) | 0,005 | 0,002 |
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 ».