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Immunohistochemical and nanoString-Based Subgrouping of Clinical Medulloblastoma Samples
Author(s) -
Colleen D’Arcy,
Liaobre,
Anthony Arnaldo,
Vijay Ramaswamy,
Michael D. Taylor,
LiliNaz Hazrati,
Cynthia Hawkins
Publication year - 2020
Publication title -
journal of neuropathology and experimental neurology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.441
H-Index - 164
eISSN - 1554-6578
pISSN - 0022-3069
DOI - 10.1093/jnen/nlaa005
Subject(s) - medulloblastoma , immunohistochemistry , wnt signaling pathway , sonic hedgehog , medicine , pathology , oncology , biology , gene , biochemistry
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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