Abstract
Medulloblastoma, the most common primary pediatric malignant brain tumor is a molecularly heterogeneous disease with different developmental origins, distinct phenotypes, diverse biological behaviour, and contrasting clinical outcomes. The current clinico-radiological risk-classification fails to take account of this heterogeneity and existent prognostic variability. It is widely accepted that dysregulation of normal developmental processes constitute a key mechanism of tumorigenesis in at least a subset of medulloblastomas. Several attempts at biological classification have successfully identified distinct subgroups with subgroup-specific gene signatures, demographics, histologic subtypes, and rates of metastases. Several research groups have classified medulloblastoma into molecular subgroups using a variety of different genomic approaches and platforms such as gene expression profiling, microRNA profiling and methylation arrays. Recently, a consensus has emerged that classifies medulloblastoma into four distinct molecular subgroups named as wingless (WNT), sonic hedgehog (SHH), Group 3 and Group 4 respectively. However, such integrative approaches have limited applicability in the clinic due to the need of fresh-frozen tissues and elaborate molecular biology tools. In parallel, some groups have proposed and validated traditional antibody-based approaches using immunohistochemistry on archival specimen for rapid and reliable molecular subgrouping to be applied in any basic neuropathology laboratory. Heterogeneity within each of these four consensus subgroups has also been demonstrated that needs to be considered in the design of future clinical trials. There is a compelling need to integrate molecular biomarkers with clinico-pathologic outcome indicators to refine risk-stratification as well as develop novel molecularly targeted agents for optimizing therapeutic index and personalizing therapy.
Keywords: Expression profiling, genomics, medulloblastoma, molecular biology, subgrouping, targeted therapy.
Graphical Abstract
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