Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications.

Autor: Piersma SR; OncoProteomics Laboratory Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands., Valles-Marti A; OncoProteomics Laboratory Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands., Rolfs F; OncoProteomics Laboratory Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands., Pham TV; OncoProteomics Laboratory Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands., Henneman AA; OncoProteomics Laboratory Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands., Jiménez CR; OncoProteomics Laboratory Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
Jazyk: angličtina
Zdroj: Mass spectrometry reviews [Mass Spectrom Rev] 2024 Jul-Aug; Vol. 43 (4), pp. 725-751. Date of Electronic Publication: 2022 Sep 26.
DOI: 10.1002/mas.21808
Abstrakt: Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.
(© 2022 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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