DIPG-39. SIGNALING NETWORKS IN PEDIATRIC-TYPE DIFFUSE HIGH GRADE GLIOMAS

Pediatric-type diffuse high grade gliomas are aggressive brain cancers in children which to date lack effective treatment options and remain largely understudied. Even though genomic markers including histone H3 K27M and G34R mutations have been identified, their functional implications on signaling networks remain to be described. Phosphoproteomics might be able to uncover activated signaling pathways which could open up new paths for treatment. Here we analyzed formalin-fixed paraffin-embedded (FFPE) sections from K27M mutated, G34R mutated and histone H3 wild type patient tumors (n = 14) using phosphoproteomics. Our workflow not only enables the comprehensive characterization of phosphoserines and threonines but also of phosphotyrosines which, despite being rare modifications (0.1-1% of all phosphorylations), are known to play an important role in cancer. We found that signaling networks were distinct between the different tumor subtypes. For phosphotyrosines, tumor subtype accounted for the largest source of variation in the data (44% of variance) as determined by principle component analysis. The separation by subtype could also be seen from serine and threonine phosphoproteomics and proteomics, but was not as striking as for the phosphotyrosine data. Differential abundance analysis, partial least squares discriminant analysis and self-organizing maps revealed deregulated signaling networks between subtypes. The most downregulated pathway in K27M compared to wild type tumors was enriched for proteins involved in the epigenetic regulation of gene expression (FDR<5%), which is in accordance with the remodeling of the epigenome taking place in K27M mutated tumors. In addition, we could identify signaling networks (e.g. EGFR signaling) which were activated in a few individual patients independent of their subtype and might be targetable. Our study provides insights into the signaling landscape of pediatric-type diffuse high grade gliomas. Recognizing deregulated signaling networks across subtypes and in individual patients could offer new avenues for personalized therapy.