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Record W4416085782 · doi:10.1093/neuonc/noaf201.1072

PTHP-15. Enhancing Medulloblastoma Classification: Integrating IHC and DNA Methylation Data from the SJMB12 Clinical Trial

2025· article· en· W4416085782 on OpenAlexaff
Sandeep Kumar Dhanda, Alexander Breuer, Quynh T. Tran, Kyle Smith, Douglas Strother, Michael J. Fisher, Anne Bendel, Eugene I. Hwang, Sibo Zhao, Tim Hassall, Jennifer Elster, Sébastien Perreault, Vijay Ramaswamy, Sonia Partap, Stephen Laughton, Richard J. Cohn, Murali Chintagumpala, Geoffrey McCowage, Michael Sullivan, Elias Sayour, Daniel C. Bowers, Asher Marks, Avery Wright, Arzu Onar-Thomas, David W. Ellison, Paul A. Northcott, Amar Gajjar, Giles Robinson, Brent A. Orr

Bibliographic record

VenueNeuro-Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsHospital for Sick ChildrenCentre Hospitalier Universitaire Sainte-JustineAlberta Children's Hospital
Fundersnot available
KeywordsMedulloblastomaImmunohistochemistryConcordanceWnt signaling pathwayDNA methylationMethylation

Abstract

fetched live from OpenAlex

Abstract The SJMB12 trial used immunohistochemistry (IHC)-based molecular grouping to separate medulloblastoma (MB) into WNT, SHH, and non-WNT/non-SHH (NWNS) groups for risk and treatment stratification. IHC was selected due to its rapid turnaround, cost-effectiveness, and low-tech nature, which allowed its immediate implementation in a prospective clinical trial. Nevertheless, the accuracy and reproducibility of IHC on a prospective cohort were unknown. Here, we compared IHC-based MB molecular group assignment to DNA methylation profiling for 634 evaluable patients from the SJMB12 trial. Robust concordance was observed. All 88 IHC-defined WNT MBs were classified as WNT by methylation. Of 107 IHC-defined SHH MBs, 106 were identified as SHH. Furthermore, 390 (98%) of 398 IHC-defined NWNS cases were identified as Group 3 or Group 4 MBs. Discordant cases were minimal: one IHC-defined SHH tumor was reclassified as glioblastoma by methylation, and 8 IHC-defined NWNS cases were reclassified (SHH (n=4), Pineoblastoma (n=2), WNT (n=1), or unclassified (n=1)). The performance metrics for IHC across MB groups were robust. For WNT, sensitivity was 93.6%, specificity 100%, PPV 100%, and NPV 98.9%. For SHH, sensitivity was 89.1%, specificity 99.8%, PPV 99.1%, and NPV 97.5%. For NWNS (proxy for Group 3/4), sensitivity was 94.9%, specificity 96.4%, PPV 98.0%, and NPV 91.1%. Notably, 41 (6%) tumors could not be grouped by IHC and were placed into an “indeterminate” category. These were assigned by methylation profiling to WNT (n=5), SHH (n=9), Group 3 (n=20), Group 4 (n=1), medullomyoblastoma (n=5), and GBM (n=1). Intriguingly, these cases were not random; they were enriched in MBs belonging to Group 3 subgroup 2, displaying myogenic and/or melanotic differentiation, and TP53-mutated SHH tumors. In conclusion, these findings demonstrate that IHC is a valuable tool for molecular grouping in medulloblastoma. Despite some limitations, it provides accurate results and, even when indeterminate, can reveal intriguing disease characteristics.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.128
GPT teacher head0.426
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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