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Abstract Phosphorylation is a crucial post-translational modification involved in virtually all biological processes; its dysregulation is a critical factor in the development and progression of pancreatic ductal adenocarcinoma (PDAC). A large family of kinases drives the phosphorylation of specific substrates, and understanding kinase substrate specificities is central to deciphering kinase activities in PDAC. Recent experimental approaches using positional scanning peptide arrays (PSPA) have determined position-specific scoring matrices (PSSMs) for many human kinases. Inherent limitations and the resource-intensive nature of this empirical method prompted development of a bottom-up computational data-driven method (CDDM) to generate PSSMs of 289 human kinases from large kinase-substrate datasets. Our results align with previous experimental findings and proved effective in identifying kinase-substrate interactions and predicting kinase functions. Moreover, we applied the data-driven PSSMs in analyzing the phosphoproteomics data of PDAC from Clinical Proteomic Tumor Analysis Consortium (CPTAC). We generated kinase activity profiles for each patient and identified subtypes of PDAC with different kinase activities. These clusters were further correlated with key genomic alterations and hallmark cancer processes. Together, we anticipate that such comprehensive knowledge of the kinome specificities will enhance our interpretation of phosphoproteomics data from clinical samples, elucidate signaling pathways under various conditions, and identify key kinases implicated in PDAC. Citation Format: Li Cai, Jared L Johnson, Chen Peng, Tomer M Yaron, Lewis C Cantley, Ronald A DePinho. Decoding human kinome specificities through a computational data-driven approach and unveiling kinase activity in pancreatic ductal adenocarcinoma abstract. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84 (17 Suppl₂): Abstract nr A072.
Cai et al. (2024) studied this question.