Simplified Molecular Subtyping of Medulloblastoma for Reduced Cost and Improved Turnaround Time
Bibliographic record
Abstract
Molecular subtyping of medulloblastoma (MB) has become increasingly important for prognosis and management. Typically this involves detailed molecular genetic testing which may not be available in all centers. The purpose of the present study was to find a simplified approach to assign molecular subtypes of MB for routine use in centers with more limited resources. The molecular subtypes of MBs from 32 Thai patients, aged 0.5 to 35 years, were first determined by NanoString. These results were then compared with those obtained using a combination of limited immunohistochemistry (IHC) (β-catenin, GAB-1, YAP-1, p75-NGFR, OTX2) and CTNNTB exon 3 mutation analysis. By NanoString assay, there were 6 MBs (19%) in the wingless (WNT) group, 8 (25%) in the sonic hedgehog (SHH) group, 7 (22%) in group 3, and 11 (34%) in group 4. Although β-catenin immunostaining missed 4/6 WNT MBs, CTNNTB mutation analysis confirmed all WNT MB cases with amplifiable DNA. The IHC panel correctly assigned all the other molecular subtypes, except for 1 MB in group 4. Thus, our protocol was able to correctly categorized 31/32 cases or 97% of cases. Our study is the first to report molecular subtypes of MB in Southeast Asia. We found that molecular subgroups of MBs can be reliably assigned using a limited IHC panel of β-catenin, GAB-1, YAP-1, p75-NGFR, OTX2, together with CTNNTB exon 3 mutation analysis. This simplified approach incurs lower cost and faster turnaround time compared with more elaborate molecular methodologies and should be beneficial to centers with reduced laboratory resources.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".