Integrative multi-omics networks identify PKCdelta
Menée à partir de données multi-omiques portant sur des glioblastomes et menée à l'aide d'un algorithme, cette étude met en évidence une relation entre l'activité de deux kinases (PKCdelta et DNA-PK) et des sous-types tumoraux agressifs puis examine la performance d'un outil de classification probabiliste pour évaluer l'association entre la réponse thérapeutique et le sous-type tumoral
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 PKCdelta
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.
Nature Cancer , article en libre accès, 2023