Approaches to identify kinase dependencies in cancer signalling networks

Abstract

Edited by Wilhelm JustCells integrate extracellular signals into appropriate responses through a complex network of biochemical reactions driven by the activity of protein and lipid kinases, among other proteins. In order to understand this complexity, new approaches, both experimental and computational, have recently been developed with the aim to identify regulatory kinases and infer their activation status in the context of their signalling network. Here, we review such approaches with particular focus on those based on phosphoproteomics. Integration of kinase activity measurements inferred from phosphoproteomics data with other 'omics' datasets is starting to be used to identify regulatory nodes in biochemical networks. These methodologies may in the future be used to identify patient-specific targets and thus advance personalised cancer medicine.

Integrated phosphoproteomics and transcriptional classifiers reveal hidden RAS signaling dynamics in multiple myeloma

“…9,10 Alternatively, as an indirect measure of cellular signaling, downstream transcriptional signatures may reveal specific functional readouts of upstream kinase activity. 11 In MM, it is thought that aberrant signaling is strongly driven by mutations in the RAS family of proto-oncogenes. These mutations are proposed to activate oncogenic signaling primarily via the MAPK and phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) pathways.…”

KRSA: Network-based Prediction of Differential Kinase Activity from Kinome Array Data

“…Alterations in kinase activity have been implicated in many diseases, both in and out of the nervous system, including cancers and neurological disorders (Dermit et al, 2017;McGuire et al, 2014;Zheng et al, 2012). The core KRSA algorithm, prior to transformation into a publicly available web-application, has been used to answer many research questions and generate new hypothesis by our research team and collaborators (Bentea et al, 2019;Dorsett et al, 2017;Flaherty et al, 2019;Schrode et al, 2019).…”

Computational Analysis of Cholangiocarcinoma Phosphoproteomes Identifies Patient-Specific Drug Targets

“…Liquid chromatographytandem MS (LC-MS/MS) phosphoproteomics is now a routine technique in most proteomics laboratories with these methods enabling quantification of more than 10,000 phosphorylation sites per experiment [33,34]. The recent emergence of big data platforms, with the development of computational approaches that can infer responses to drug therapy such as kinase activities from phosphoproteomic data, is starting to present a means by which therapies can be recommended [35][36][37][38].…”