TY - JOUR TI - Integrative multi-omics networks identify PKCδ and DNA-PK as master kinases of glioblastoma subtypes and guide targeted cancer therapy AU - Migliozzi, Simona AU - Oh, Young Taek AU - Hasanain, Mohammad AU - Garofano, Luciano AU - D’Angelo, Fulvio AU - Najac, Ryan D. AU - Picca, Alberto AU - Bielle, Franck AU - Di Stefano, Anna Luisa AU - Lerond, Julie AU - Sarkaria, Jann N. AU - Ceccarelli, Michele AU - Sanson, Marc AU - Lasorella, Anna AU - Iavarone, Antonio T2 - Nature Cancer AB - Abstract 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. DA - 2023/02/02/ PY - 2023 DO - 10.1038/s43018-022-00510-x DP - DOI.org (Crossref) J2 - Nat Cancer LA - en SN - 2662-1347 UR - https://www.nature.com/articles/s43018-022-00510-x Y2 - 2023/02/04/14:06:08 ER -