Abstract A65: Unraveling the rhabdomyosarcoma genome using mouse mosaicism
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".