Medulloblastoma in the age of molecular subgroups: a review
Bibliographic record
Abstract
Medulloblastoma is the most common pediatric malignant brain tumor. Advances in molecular profiling have uncovered significant heterogeneity among medulloblastomas and led to the identification of four distinct subgroups (wingless [WNT], sonic hedgehog [SHH], group 3, and group 4) that represent distinct disease entities in both underlying biology and clinical characteristics. The rapidly expanding repertoire of tools to study developmental and cancer biology is providing a wealth of knowledge about these embryonal tumors and is continuously refining the understanding of this complex cancer. In this review, the history of discovery in medulloblastoma is discussed, setting a foundation to outline the current state of understanding of the molecular underpinnings of this disease, with a focus on genomic events that define the aforementioned subgroups and evolving areas of focus, such as the cell of origin of medulloblastoma and medulloblastoma subtypes. With these recent discoveries in mind, the current state of medulloblastoma treatment and clinical trials is reviewed, including a novel risk stratification system that accounts for the molecular biomarkers of patients with a high risk for refractory disease. Lastly, critical areas of focus for future basic science and clinical research on this disease are discussed, such as the complexities of medulloblastoma metastases and recurrence as well as the priorities and strategies to implement in future clinical trials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".