Development of Genetically Flexible Mouse Models of Sarcoma Using RCAS-TVA Mediated Gene Delivery
Bibliographic record
Abstract
Sarcomas are a heterogeneous group of mesenchymal malignancies and unfortunately there are limited functional genomics platforms to assess the molecular pathways contributing to sarcomagenesis. Thus, novel model systems are needed to validate which genes should be targeted for therapeutic intervention. We hypothesized that delivery of oncogenes into mouse skeletal muscle using a retroviral (RCAS-TVA) system would result in sarcomagenesis. We also sought to determine if the cell type transformed (mesenchymal progenitors vs. terminally differentiated tissues) would influence sarcoma biology. Cells transduced with RCAS vectors directing the expression of oncoproteins KrasG12D, c-Myc and/or Igf2 were injected into the hindlimbs of mice that expressed the retroviral TVA receptor in neural/mesenchymal progenitors, skeletal/cardiac muscle or ubiquitously (N-tva, AKE and BKE strains respectively). Disrupting the G1 checkpoint CDKN2 (p16/p19-/-) resulted in sarcoma in 30% of p16/p19-/- xN-tva mice with a median latency of 23 weeks (range 8-40 weeks). A similar incidence occurred in p16/p19-/- xBKE mice (32%), however, a shorter median latency (10.4 weeks) was observed. p16/p19-/- xAKE mice also developed sarcomas (24% incidence; median 9 weeks) yet 31% of mice also developed lung sarcomas. Gene-anchored PCR demonstrated retroviral DNA integration in 86% of N-tva, 93% of BKE and 88% of AKE tumors. KrasG12D was the most frequent oncogene isolated. Oncogene delivery by the RCAS-TVA system can generate sarcomas in mice with a defective cell cycle checkpoint. Sarcoma biology differed between the different RCAS models we created, likely due to the cell population being transformed. This genetically flexible system will be a valuable tool for sarcoma research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".