Abstract B15: Optimization of an oncogenomics-based in vivo screen to validate candidate sarcoma genes
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".