Genetically engineered mouse models and human osteosarcoma
Bibliographic record
Abstract
Osteosarcoma is the most common form of bone cancer. Pivotal insight into the genes involved in human osteosarcoma has been provided by the study of rare familial cancer predisposition syndromes. Three kindreds stand out as predisposing to the development of osteosarcoma: Li-Fraumeni syndrome, familial retinoblastoma and RecQ helicase disorders, which include Rothmund-Thomson Syndrome in particular. These disorders have highlighted the important roles of P53 and RB respectively, in the development of osteosarcoma. The association of OS with RECQL4 mutations is apparent but the relevance of this to OS is uncertain as mutations in RECQL4 are not found in sporadic OS. Application of the knowledge or mutations of P53 and RB in familial and sporadic OS has enabled the development of tractable, highly penetrant murine models of OS. These models share many of the cardinal features associated with human osteosarcoma including, importantly, a high incidence of spontaneous metastasis. The recent development of these models has been a significant advance for efforts to improve our understanding of the genetics of human OS and, more critically, to provide a high-throughput genetically modifiable platform for preclinical evaluation of new therapeutics.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".