Background:
The WHO classification of central nervous system neoplasms (2016) recognized 4 histologic variants and genetically defined molecular subgroups within medulloblastoma (MB). Further, in the 2021 classification, new subtypes have been provisionally added within the existing subgroups reflecting the biological diversity. YAP1, GAB1, and β-catenin were conventionally accepted as surrogate markers to identify these genetic subgroups.
Objectives:
We aimed to stratify MB into molecular subgroups using 3 immunohistochemical markers. TP53 mutation was also assessed in Wingless (WNT), and Sonic Hedgehog (SHH) subgroups. Demographic profiles, imaging details, and survival outcomes were compared within these molecular subgroups.
Patients and methods:
Our cohort included 164 MB cases diagnosed over the last 10 years. The histologic variants were identified on histology, and tumors were molecularly stratified using YAP1, GAB1, and β-catenin. Further, TP53 mutation was assessed using immunohistochemical in WNT and SHH subgroups. The clinical details and survival outcomes were retrieved from the records, and the mentioned correlates were evaluated statistically.
Results:
The age ranged from 1 to 52 years with M:F ratio of 2:1. Group 3/group 4 constituted the majority (48.4%), followed by SHH (45.9%) and WNT subgroups (5.7%). Desmoplastic/nodular and MB with extensive nodularity had the best survival, whereas large cell/anaplastic had the worst. The follow-up period ranged from 1 to 129 months. The best outcome was observed for the WNT subgroup, followed by the SHH subgroup; group 3/group 4 had the worst. Among the SHH subgroup, TP53 mutant tumors had a significantly poorer outcome compared with SHH-TP53 wildtype.
Conclusions:
Molecular stratification significantly contributes to prognostication, and a panel of 3 antibodies is helpful in stratifying MB into its subgroups in centers where access to advanced molecular testing is limited. Our study reinforces the efficacy of incorporating this cost-effective, minimal panel into routine practice for stratification. Further, we propose a 3-risk stratification grouping, incorporating morphology and molecular markers.