Bibliographic record
Abstract
Sonic hedgehog medulloblastoma (Shh-MB) encompasses a clinically and molecularly diverse group of cancers of the developing central nervous system. It initiates within the cerebellum and, in 20% of cases, disseminates throughout the brain and spinal cord. Current therapy consists of maximal safe resection, radiotherapy in patients over 36 months, and cytotoxic chemotherapy. Unbiased sequencing of the transcriptome across a large cohort of 250 primary tumors reveals differences between molecular subtypes of the disease, with a previously unappreciated importance of non-coding RNA transcripts. Analysis of a large cohort of a single molecular type of cancer allows for identification of novel genes with single nucleotide variants (MYCN, GNAS, IKBKAP, and KDM6A) as well as gene fusions, some of which are secondary to rearrangement of the genome (ZBTB20, NCOR1), while others appear to arise through trans-splicing (RALGAPA2, and GNAS). Integration of genetic and transcriptomic data allows further differentiation of driver from passenger genes. Molecular convergence on a core of specific genes by nucleotide variants, copy number aberrations, and gene fusion further emphasize the key role of specific pathways in the pathogenesis of primary Shh-MB. Little is known about genes driving metastatic progression since matching human primary and metastatic samples are rare. The Shh-MB Sleeping Beauty (SB) mouse model uses random integration of transposons to initiate tumorigenesis and drive the metastatic cascade providing valuable insight onto the human disease. Common insertion site analysis using 549 metastatic tumors from 131 mice reveal networks of recurrent metastatic drivers (n = 336) and demonstrate extensive heterogeneity between metastasis. A subset of drivers, such as loss-of-function events in Crebbp and Ctnna3, arise independently between metastasis and are under the pressure of convergent evolution. Recurrent gain-of-function insertions in Lgalg3 suggest an oncogenic role in metastatic progression which was validated using Lglas3 knockout experiments in multiple models. Mice missing copies of Lgals3 had no change in metastatic burden in the brain but showed significantly less metastasis along the spinal cord suggesting a site-specific role as a metastasis driver gene. These findings enhance our understanding of the genomic complexity and heterogeneity underlying Shh-MB pathogenesis and highlight several targets for therapeutic development.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".