Pathway-based stratification of glioblastoma by multi-omics informs subtype-specific master kinases-phosphosite substrates

Abstract Tumor heterogeneity and broad therapeutic resistance are major challenges when treating patients with glioblastoma (GBM). Moreover, different from other cancer types, the absence of clinically useful classifiers for glioblastoma has hampered the translation of genomic and proteomic information to improve diagnosis and design precision therapeutics for patients. We analyzed a large dataset of human GBM to determine the multi-omics features characterizing the four GBM subtypes that we have recently identified through single-cell RNA sequencing and validated in bulk tumor analysis. Each subtype exhibits activation of unique functional traits that confer distinct therapeutic vulnerabilities. The examination of proteomics, phosphoproteomics, metabolomics, lipidomics and acetylomics data revealed that each GBM subtype has a coherent molecular structure driving the dominant function traceable in each analytical platform. Functional classes are not a specific attribute of GBM as we identified the same subtypes in breast and lung cancer. To test the translational impact of proteomic data in GBM, we developed an unbiased protein kinase signaling network approach for the selection of master kinases (MKs) aberrantly activated in each GBM subtype. We identified therapeutically actionable MKs and novel phosphorylation substrates that we experimentally validated. To provide rapid translation of the functional classifier for precision medicine in GBM, we developed a probabilistic classification tool which determines the probability that a patient’s GBM belongs to one of the four subtypes, exhibiting optimal performance when using RNA extracted from either frozen and paraffin-embedded tissues. The algorithm is publicly accessible and can be used to evaluate the association of therapeutic response with GBM subtypes and as tool for selection criteria in prospective clinical trials.