Identification of Protein Kinase Drug Targets Using Activity Estimation in Clinical Phosphoproteomics.

This chapter describes protein kinase (will be termed here kinase) activity estimation methods and their application to clinical cancer phosphoproteomics datasets, proposing a novel approach for identification of protein kinases as therapeutic targets. Despite significant advances in genomics-based target identification, clinical proteomics and phosphoproteomics remain underutilized. We highlight the growing availability of proteomics data from projects like Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Proteomics Identifications Database (PRIDE), and review key kinase activity estimation algorithms, including PTM-SEA, KSEA, Rokai, KStar, and Kinome Atlas. Applying these methods on clinical phosphoproteomic data, we demonstrate the identification of hyperactivated kinases in specific cancer indications and highlight HER2 and EGFR as benchmarks. Our description underscores the potential of integrating kinase activity estimation with clinical phosphoproteomics to uncover new therapeutic targets and develop precision oncology therapies.