Developmental Basis of SHH Medulloblastoma Heterogeneity
Bibliographic record
Abstract
Abstract Medulloblastoma (MB) is one of the most common malignant pediatric brain tumors. The sonic hedgehog (SHH) subtype accounts for 30% of MB cases and likely arises from mutations in granule cell precursors (GCPs), neuronal progenitors of the cerebellar cortex that differentiate into granule neurons. SHH MB is extremely heterogeneous, but it is unknown whether this heterogeneity relates to the tumors’ developmental origins. To investigate this question, we performed single-nucleus RNA-Sequencing on seven highly differentiated SHH MB with extensively nodular histology and observed malignant cells resembling each stage of granule neuron development. Using novel computational approaches, we connected these results to published datasets and found that established molecular subtypes of SHH MB are enriched for specific developmental cell types. Additionally, some genomic copy number variations are associated with certain developmental stages, and we observed distinct metabolic and histological profiles for tumors containing cells resembling late-stage granule neurons. This work details computational and experimental approaches that can be repurposed for analysis of tumor cell differentiation in other cancers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".