Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps

SIMPLE SUMMARY: Liquid chromatography–mass spectrometry (LC-MS)-based proteomics is a powerful technology for discovering new cancer biomarkers. In addition to last generation instrumentation, it uses experimental designs of different complexity that describe key steps from sample selection to data analysis and interpretation. All aspects must be optimized to obtain the most satisfactory results. However, planning proteomics procedures can be challenging unless their advantages and drawbacks are known. This review aims to highlight the methodological features that cancer researchers must consider before executing an LC-MS-based proteomics project. Based on these features, we suggest straightforward and complex workflows whereby researchers can discover new molecules or therapeutic pathways to defeat or significantly decrease the impact of oncological diseases. ABSTRACT: The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography–mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.