Faculty Bibliography

Molecular studies have identified distinct genomic drivers providing insights in biology of brain tumors. Advances in genetic and epigenetic analysis, as well as development of mutation-specific antibodies enable more accurate classification of histologically indistinguishable tumors. Compared with histopathologic grading, molecular biomarkers are also superior in predicting natural behavior of tumors and therapeutic response. Diffuse gliomas can be separated in astrocytoma and oligodendroglioma based on IDH1/2, ATRX, and TP53 mutational status. Pediatric gliomas are molecularly distinct from adult tumors and molecular drivers include histone H3 genes and fusions involving the MAPK pathway. Using genetic and epigenetic profiling, ependymal tumors, medulloblastomas, and atypical teratoid/rhabdoid tumors can be separated in biologically and clinically distinct entities. Identification of novel gene fusions and matched DNA methylation signatures enable accurate diagnosis of primitive neuroectodermal tumors, which were previously misdiagnosed. Genomic classification of central nervous system tumors is being readily translated into the clinical practice and will enable molecularly based patient management and clinical trials.

Medulloblastoma (MB) is the most common childhood malignant brain tumor and is a leading cause of cancer-related death in children. DNA methylation profiling has rapidly advanced our understanding of MB pathogenesis at the molecular level, but assessments in Saudi Arabian (SA)-MB cases are sparse. MBs can be sub-grouped according to methylation patterns from FPPE samples into Wingless (WNT-MB), Sonic Hedgehog (SHH-MB), Group 3 (G3), and Group 4 (G4) tumors. The WNT-MB and SHH-MB subgroups are characterized by gain-of function mutations that activate oncogenic cell signaling, whilst G3/G4 tumors show recurrent chromosomal alterations. Given that each subgroup has distinct clinical outcomes, the ability to subgroup SA-FPPE samples holds significant prognostic and therapeutic value. Here, we performed the first assessment of MB-DNA methylation patterns in an SA cohort using archival biopsy material (FPPE n = 49). Of the 41 materials available for methylation assessments, 39 could be classified into the major DNA methylation subgroups (SHH, WNT, G3, and G4). Furthermore, methylation analysis was able to reclassify tumors that could not be sub-grouped through next-generation sequencing, highlighting its superior accuracy for MB molecular classifications. Independent assessments demonstrated known clinical relationships of the subgroups, exemplified by the high survival rates observed for WNT tumors. Surprisingly, the G4 subgroup did not conform to previously identified phenotypes, with a high prevalence in females, high metastatic rates, and a large number of tumor-associated deaths. Taking our results together, we demonstrate that DNA methylation profiling enables the robust sub-classification of four disease sub-groups in archival FFPE biopsy material from SA-MB patients. Moreover, we show that the incorporation of DNA methylation biomarkers can significantly improve current disease-risk stratification schemes, particularly concerning the identification of aggressive G4 tumors. These findings have important implications for future clinical disease management in MB cases across the Arab world.