Simona Migliozzi, Young Taek Oh, Mohammad Hasanain, Luciano Garofano, Fulvio D’Angelo, Ryan D. Najac, Alberto Picca, Franck Bielle, Anna Luisa Di Stefano, Julie Lerond, Jann N. Sarkaria, Michele Ceccarelli, Marc Sanson, Anna Lasorella, Antonio Iavarone, Migliozzi, Simona, Oh, Young Taek, Hasanain, Mohammad, Garofano, Luciano, D'Angelo, Fulvio, Najac, Ryan D, Picca, Alberto, Bielle, Franck, Di Stefano, Anna Luisa, Lerond, Julie, Sarkaria, Jann N, Ceccarelli, Michele, Sanson, Marc, Lasorella, Anna, and Iavarone, Antonio
Despite producing a panoply of potential cancer-specific targets, the proteogenomic characterization of human tumors has yet to demonstrate value for precision cancer medicine. Integrative multi-omics using a machine-learning network identified master kinases responsible for effecting phenotypic hallmarks of functional glioblastoma subtypes. In subtype-matched patient-derived models, we validated PKCδ and DNA-PK as master kinases of glycolytic/plurimetabolic and proliferative/progenitor subtypes, respectively, and qualified the kinases as potent and actionable glioblastoma subtype-specific therapeutic targets. Glioblastoma subtypes were associated with clinical and radiomics features, orthogonally validated by proteomics, phospho-proteomics, metabolomics, lipidomics and acetylomics analyses, and recapitulated in pediatric glioma, breast and lung squamous cell carcinoma, including subtype specificity of PKCδ and DNA-PK activity. We developed a probabilistic classification tool that performs optimally with RNA from frozen and paraffin-embedded tissues, which can be used to evaluate the association of therapeutic response with glioblastoma subtypes and to inform patient selection in prospective clinical trials.