Murine models of osteosarcoma: A piece of the translational puzzle
Bibliographic record
Abstract
Osteosarcoma (OS) is the most common cancer of bone in children and young adults. Despite extensive research efforts, there has been no significant improvement in patient outcome for many years. An improved understanding of the biology of this cancer and how genes frequently mutated contribute to OS may help improve outcomes for patients. While our knowledge of the mutational burden of OS is approaching saturation, our understanding of how these mutations contribute to OS initiation and maintenance is less clear. Murine models of OS have now been demonstrated to be highly valid recapitulations of human OS. These models were originally based on the frequent disruption of p53 and Rb in familial OS syndromes, which are also common mutations in sporadic OS. They have been applied to significantly improve our understanding about the functions of recurrently mutated genes in disease. The murine models can be used as a platform for preclinical testing and identifying new therapeutic targets, in addition to testing the role of additional mutations in vivo. Most recently these models have begun to be used for discovery based approaches and screens, which hold significant promise in furthering our understanding of the genetic and therapeutic sensitivities of OS. In this review, we discuss the mouse models of OS that have been reported in the last 3-5 years and newly identified pathways from these studies. Finally, we discuss the preclinical utilization of the mouse models of OS for identifying and validating actionable targets to improve patient outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".