Medulloblastoma is heterogeneous, being characterized by molecular subgroups that demonstrate distinct gene expression profiles. Activation of the WNT or SHH signaling pathway characterizes two of these molecular subgroups, the former associated with low-risk disease and the latter potentially targeted by novel SHH pathway inhibitors. This manuscript reports the validation of a novel diagnostic immunohistochemical method to distinguish SHH, WNT, and non-SHH/WNT tumors and details their associations with clinical, pathological and cytogenetic variables. A cohort (n = 235) of medulloblastomas from patients aged 0.4–52 years was studied for expression of four immunohistochemical markers: GAB1, β-catenin, filamin A, and YAP1. Immunoreactivity (IR) for GAB1 characterizes only SHH tumors and nuclear IR for β-catenin only WNT tumors. IRs for filamin A and YAP1 identify SHH and WNT tumors. SHH, WNT, and non-SHH/WNT tumors contributed 31, 14, and 55% to the series. All desmoplastic/nodular (D/N) medulloblastomas were SHH tumors, while most WNT tumors (94%) had a classic phenotype. Monosomy 6 was strongly associated with WNT tumors, while PTCH1 loss occurred almost exclusively among SHH tumors. MYC or MYCN amplification and chromosome 17 imbalance occurred predominantly among non-SHH/WNT tumors. Among patients aged 3–16 years and entered onto the SIOP PNET3 trial, outcome was significantly better for children with WNT tumors, when compared to SHH or non-SHH/WNT tumors, which showed similar survival curves. However, high-risk factors (M+ disease, LC/A pathology, MYC amplification) significantly influenced survival in both SHH and non-SHH/WNT groups. We describe a robust method for detecting SHH, WNT, and non-SHH/WNT molecular subgroups in formalin-fixed medulloblastoma samples. In corroborating other studies that indicate the value of combining clinical, pathological, and molecular variables in therapeutic stratification schemes for medulloblastoma, we also provide the first outcome data based on a clinical trial cohort and novel data on how molecular subgroups are distributed across the range of disease.