Data from A Novel Somatic Mouse Model to Survey Tumorigenic Potential Applied to the Hedgehog Pathway
Bibliographic record
Abstract
Abstract We report a novel mouse model for the generation of sporadic tumors and show the efficiency of this approach by surveying Hedgehog (Hh)–related tumors. Up-regulation of the Hh pathway is achieved by conditionally regulated expression of an activated allele of Smoothened (R26-SmoM2) using either sporadic leakage or global postnatal induction of a ubiquitously expressed inducible Cre transgene (CAGGS-CreER). Following postnatal tamoxifen induction, CAGGS-CreER; R26-SmoM2 mice developed tumors with short latency and high penetrance. All mice exhibited rhabdomyosarcoma and basal cell carcinoma; 40% also developed medulloblastoma. In addition, mice showed a novel pancreatic lesion resembling low-grade mucinous cystic neoplasms in humans. In contrast, widespread activation of SmoM2 in the postnatal prostate epithelium results in no detectable morphologic outcome in 12-month-old mice. Comparison of gene expression profiles among diverse tumors identified several signature genes, including components of platelet-derived growth factor and insulin-like growth factor pathways, which may provide a common mechanistic link to the Hh-related malignancies. This experimental model provides a robust tool for exploring the process of Hh-dependent tumorigenesis and the treatment of such tumors. More generally, this approach provides a genetic platform for identifying tumorigenic potential in putative oncogenes and tumor suppressors and for more effective modeling of sporadic cancers in mice. (Cancer Res 2006; 66(20): 10171-7)
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".