Ligand-dependent hedgehog signaling maintains an undifferentiated, malignant osteosarcoma phenotype
Bibliographic record
Abstract
TP53 and RB1 loss-of-function mutations are common in osteosarcoma. During development, combined loss of TP53 and RB1 function leads to downregulation of autophagy and the aberrant formation of primary cilia, cellular organelles essential for the transmission of canonical Hedgehog (Hh) signaling. Excess cilia formation then leads to hypersensitivity to Hedgehog (Hh) ligand signaling. In mouse and human models, we now show that osteosarcomas with mutations in TP53 and RB1 exhibit enhanced ligand-dependent Hh pathway activation through Smoothened (SMO), a transmembrane signaling molecule required for activation of the canonical Hh pathway. This dependence is mediated by hypersensitivity to Hh ligand and is accompanied by impaired autophagy and increased primary cilia formation and expression of Hh ligand in vivo. Using a conditional genetic mouse model of Trp53 and Rb1 inactivation in osteoblast progenitors, we further show that deletion of Smo converts the highly malignant osteosarcoma phenotype to benign, well differentiated bone tumors. Conversely, conditional overexpression of SHH ligand, or a gain-of-function SMO mutant in committed osteoblast progenitors during development blocks terminal bone differentiation. Finally, we demonstrate that the SMO antagonist sonidegib (LDE225) induces growth arrest and terminal differentiation in vivo in osteosarcomas that express primary cilia and Hh ligand combined with mutations in TP53. These results provide a mechanistic framework for aberrant Hh signaling in osteosarcoma based on defining mutations in the tumor suppressor, TP53.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".