MB-87INTEGRATED GENOMICS REVEALS NOVEL SUBTYPES OF MEDULLOBLASTOMA SUBGROUPS
Bibliographic record
Abstract
BACKGROUND: Medulloblastoma is now accepted to comprise four distinct molecular variants, and current clinical trials are stratifying patients using a combined biological and clinical risk stratification. Despite the identification of the 4 core subgroups, there appears to exist tremendous clinical heterogeneity within the four subgroups suggesting additional substructure. METHODS: Integrative clustering of 763 primary medulloblastoma samples with gene expression and genome wide methylation data was performed with the Similarity Network fusion method (SNF), and correlated with clinical features and copy-number aberrations. RESULTS: Integrative clustering faithfully recapitulated the four principal subgroups of medulloblastoma, with a boundary between Group 3 and 4, and intra-subgroup biological heterogeneity more clearly apparent than either expression or methylation alone. The WNT subgroup consists of two subtypes, one defined by monosomy 6 and a second of older patients without monosomy 6. We found the highest evidence for four subtypes of SHH, 1) two infants subgroups with clear prognostic differences, pathway aberrations and copy number profiles, 2) childhood group with a poor prognosis and 3) an adult group. MYC amplifications enrich in a distinct cluster of Group 3 with a significantly poor prognosis. Copy number profiles and driver pathways define three subtypes of Group 4. CONCLUSIONS: Integrative clustering provides profound insights into the biological heterogeneity within each of the principle medulloblastoma subgroups. As current therapies result in significant long-term sequelae, the identification of substructure within the four subgroups allows for more refinement in biological risk stratification as well as identification of novel agents for future rationale targeted therapies.
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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 | 0.000 |
| 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 | 0.000 | 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".