Immunohistochemical and nanoString-Based Subgrouping of Clinical Medulloblastoma Samples
Bibliographic record
Abstract
The diagnosis of medulloblastoma incorporates the histologic and molecular subclassification of clinical medulloblastoma samples into wingless (WNT)-activated, sonic hedgehog (SHH)-activated, group 3 and group 4 subgroups. Accurate medulloblastoma subclassification has important prognostic and treatment implications. Immunohistochemistry (IHC)-based and nanoString-based subgrouping methodologies have been independently described as options for medulloblastoma subgrouping, however have not previously been directly compared. We describe our experience with nanoString-based subgrouping in a clinical setting and compare this with our IHC-based results. Study materials included FFPE tissue from 160 medulloblastomas. Clinical data and tumor histology were reviewed. Immunohistochemical-based subgrouping using β-catenin, filamin A and p53 antibodies and nanoString-based gene expression profiling were performed. The sensitivity and specificity of IHC-based subgrouping of WNT and SHH-activated medulloblastomas was 91.5% and 99.54%, respectively. Filamin A immunopositivity highly correlated with SHH/WNT-activated subgroups (sensitivity 100%, specificity 92.7%, p < 0.001). Nuclear β-catenin immunopositivity had a sensitivity of 76.2% and specificity of 99.23% for detection of WNT-activated tumors. Approximately 23.8% of WNT cases would have been missed using an IHC-based subgrouping method alone. nanoString could confidently predict medulloblastoma subgroup in 93% of cases and could distinguish group 3/4 subgroups in 96.3% of cases. nanoString-based subgrouping allows for a more prognostically useful classification of clinical medulloblastoma samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".