Phosphoproteomics: a valuable tool for uncovering molecular signaling in cancer cells. (3rd August 2021)

• Record Type: Journal Article
• Authors: Gerritsen, Jacqueline S.; White, Forest M.
• Is Part Of: Expert review of proteomics. Volume 18:Number 8(2021)
• Page Start: 661
• Page End: 674
• Publication Date: 2021-08-03
• Subjects: Cancer -- cellular networks -- kinase activity -- mass spectrometry -- molecular signaling -- phosphoproteomics -- phosphorylation; Proteins -- Biotechnology -- Periodicals; Proteomics -- Periodicals; 572.6
• DOI: 10.1080/14789450.2021.1976152
• Languages: English
• ISSNs: 1478-9450

Abstract

ABSTRACT: Introduction: Many pathologies, including cancer, have been associated with aberrant phosphorylation-mediated signaling networks that drive altered cell proliferation, migration, metabolic regulation, and can lead to systemic inflammation. Phosphoproteomics, the large-scale analysis of protein phosphorylation sites, has emerged as a powerful tool to define signaling network regulation and dysregulation in normal and pathological conditions. Areas Covered: We provide an overview of methodology for global phosphoproteomics as well as enrichment of specific subsets of the phosphoproteome, including phosphotyrosine and phospho-motif enrichment of kinase substrates. We review quantitative methods, advantages and limitations of different mass spectrometry acquisition formats, and computational approaches to extract biological insight from phosphoproteomics data. Throughout, we discuss various applications and their challenges in implementation. Expert opinion: Over the past 20 years the field of phosphoproteomics has advanced to enable deep biological and clinical insight through the quantitative analysis of signaling networks. Future areas of development include Clinical Laboratory Improvement Amendments (CLIA)-approved methods for analysis of clinical samples, continued improvements in sensitivity to enable analysis of small numbers of rare cells and tissue microarrays, and computational methods to integrate data resulting from multiple systems-level quantitative analytical methods.