Towards Building a Quantitative Proteomics Toolbox in Precision Medicine: A Mini-Review

Alejandro Correa Rojo, Dries Heylen, Jan Aerts, Olivier Thas, Jef Hooyberghs, Gökhan Ertaylan, Dirk Valkenborg, Alejandro Correa Rojo, Dries Heylen, Jan Aerts, Olivier Thas, Jef Hooyberghs, Gökhan Ertaylan, Dirk Valkenborg

Abstract

Precision medicine as a framework for disease diagnosis, treatment, and prevention at the molecular level has entered clinical practice. From the start, genetics has been an indispensable tool to understand and stratify the biology of chronic and complex diseases in precision medicine. However, with the advances in biomedical and omics technologies, quantitative proteomics is emerging as a powerful technology complementing genetics. Quantitative proteomics provide insight about the dynamic behaviour of proteins as they represent intermediate phenotypes. They provide direct biological insights into physiological patterns, while genetics accounting for baseline characteristics. Additionally, it opens a wide range of applications in clinical diagnostics, treatment stratification, and drug discovery. In this mini-review, we discuss the current status of quantitative proteomics in precision medicine including the available technologies and common methods to analyze quantitative proteomics data. Furthermore, we highlight the current challenges to put quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data with genomics data for future applications in precision medicine.

Keywords: bioinformatics; biomarker discovery; clinical diagnostics; precision medicine; protein quantitative trait loci; quantitative proteomics; targeted techniques.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Correa Rojo, Heylen, Aerts, Thas, Hooyberghs, Ertaylan and Valkenborg.

Figures

Figure 1
Figure 1
General workflow for quantitative proteomics. The figure describes the different types of targeted technologies, and the common methodologies to analyse quantitative proteomics data. These analyses potentially provide clinical applications in biomarker and drug discovery and patient stratification. Image created with BioRender.

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