Targeted Apc;Twist Double-Mutant Mice: A New Model of Spontaneous Osteosarcoma That Mimics the Human Disease
Bibliographic record
Abstract
TWIST and adenomatosis polyposis coli (APC) are critical signaling factors in normal bone development. In previous studies examining a homogeneously treated cohort of pediatric osteosarcoma patients, we reported the frequent and concurrent loss of both TWIST and APC genes. On these bases, we created a related animal model to further explore the oncogenic cooperation between these two genes. We performed intercrosses between twist-null/+ and Apc1638N/+ mice and studied their progeny. The Apc1638N/+;twistnull/+ mice developed bone abnormalities observed by macroscopic skeletal analyses and in vivo imaging. Complementary histologic, cellular, and molecular analyses were used to characterize the identified bone tumors, including cell culture and immunofluorescence of bone differentiation markers. Spontaneous localized malignant bone tumors were frequently identified in Apc1638N/+;twist-null/+ mice by in vivo imaging evaluation and histologic analyses. These tumors possessed several features similar to those observed in human localized osteosarcomas. In particular, the murine tumors presented with fibroblastic, chondroblastic, and osteoblastic osteosarcoma histologies, as well as mixtures of these subtypes. In addition, cellular analyses and bone differentiation markers detected by immunofluorescence on tumor sections reproduced most murine and human osteosarcoma characteristics. For example, the early bone differentiation marker Runx2, interacting physically with hypophosphorylated pRb, was undetectable in these murine osteosarcomas, whereas phosphorylated retinoblastoma was abundant in the osteoblastic and chondroblastic tumor subtypes. These characteristics, similar to those observed in human osteosarcomas, indicated that our animal model may be a powerful tool to further understand the development of localized osteosarcoma.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".