<i>Sleeping Beauty</i> mutagenesis in a mouse medulloblastoma model defines networks that discriminate between human molecular subgroups
Why this work is in the frame
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Bibliographic record
Abstract
The Sleeping Beauty (SB) transposon mutagenesis screen is a powerful tool to facilitate the discovery of cancer genes that drive tumorigenesis in mouse models. In this study, we sought to identify genes that functionally cooperate with sonic hedgehog signaling to initiate medulloblastoma (MB), a tumor of the cerebellum. By combining SB mutagenesis with Patched1 heterozygous mice (Ptch1(lacZ/+)), we observed an increased frequency of MB and decreased tumor-free survival compared with Ptch1(lacZ/+) controls. From an analysis of 85 tumors, we identified 77 common insertion sites that map to 56 genes potentially driving increased tumorigenesis. The common insertion site genes identified in the mutagenesis screen were mapped to human orthologs, which were used to select probes and corresponding expression data from an independent set of previously described human MB samples, and surprisingly were capable of accurately clustering known molecular subgroups of MB, thereby defining common regulatory networks underlying all forms of MB irrespective of subgroup. We performed a network analysis to discover the likely mechanisms of action of subnetworks and used an in vivo model to confirm a role for a highly ranked candidate gene, Nfia, in promoting MB formation. Our analysis implicates candidate cancer genes in the deregulation of apoptosis and translational elongation, and reveals a strong signature of transcriptional regulation that will have broad impact on expression programs in MB. These networks provide functional insights into the complex biology of human MB and identify potential avenues for intervention common to all clinical subgroups.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it