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Review
. 2019 May 21;20(3):767-777.
doi: 10.1093/bib/bbx141.

Proteomics and phosphoproteomics in precision medicine: applications and challenges

Girolamo Giudice  1 , Evangelia Petsalaki  1
Affiliations
Review

Proteomics and phosphoproteomics in precision medicine: applications and challenges

Girolamo Giudice et al. Brief Bioinform. .

Abstract

Recent advances in proteomics allow the accurate measurement of abundances for thousands of proteins and phosphoproteins from multiple samples in parallel. Therefore, for the first time, we have the opportunity to measure the proteomic profiles of thousands of patient samples or disease model cell lines in a systematic way, to identify the precise underlying molecular mechanism and discover personalized biomarkers, networks and treatments. Here, we review examples of successful use of proteomics and phosphoproteomics data sets in as well as their integration other omics data sets with the aim of precision medicine. We will discuss the bioinformatics challenges posed by the generation, analysis and integration of such large data sets and present potential reasons why proteomics profiling and biomarkers are not currently widely used in the clinical setting. We will finally discuss ways to contribute to the better use of proteomics data in precision medicine and the clinical setting.

Keywords: data integration; phosphoproteomics; precision medicine; proteomics.

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Figures

Figure 1
Figure 1
Example workflow for precision medicine. Multi-omics data are initially collected from patients and integrated to create their individual molecular profiles. These profiles are then matched to previously defined disease profiles that can guide the selection of treatment. This is achieved either through a match to known biomarkers, omics signatures or network/pathway signatures. The appropriate drug is then selected based on this match, to improve the chance of successful treatment and reduce the probability of side effects.
Figure 2
Figure 2
Different methods used in biomarker discovery. (A) Differentially expressed method, (B) machine learning method, (C) network-based method DE, differentially expressed; NN, neural network; RF, random forest; DT, decision tree; GA, genetic algorithm; NBS, network-based stratification; RW, random walk.
See this image and copyright information in PMC

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