Glioblastoma is a highly heterogeneous tumor with a very poor prognosis. Surgical resection is often incomplete, because of the diffuse infiltrating nature of this brain tumor, leaving the necessity for adjuvant therapy. In this project, we will develop a workflow based on phosphopeptide profiling and bioinformatics analysis to characterize glioblastoma tumor tissue with the purpose of better predict the outcome of the treatment regime and guidance to select individualized therapies (see proposed workflow below).
Physiological changes in cells and tissues are driven by changes in protein levels and post-translational modifications (PTMs). These crucial events are not detected by gene expression profiling and mutational profiling. This is why phosphoproteomic profiling is a better representation of cellular phenotypes and as a consequence a better representation of what the “real life” situation.
Coupling the phosphoproteomic profiling output from patients using Mass Spectrometry with a dedicated bioinformatics tool (Coremine data-mining and data analysis platform from PubGene to unravel the activated signaling pathways and therapeutic targets is an essential objective of the GlioPhos workflow. Also, using the GlioPhos quantitative phosphoproteomics downstream effectors of mutant kinases will be revealed, thereby providing an opportunity to understand the molecular mechanisms that lead to oncogenesis. Furthermore, identification of cellular targets of kinase inhibitors in cancers and off-target effects of kinase inhibitors are some of the additional main exciting applications from the GlioPhos workflow.