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

Overview

Journal Cancers (Basel)

Publisher MDPI

Specialty Oncology

Date 2023 Jan 21

PMID 36672506

Authors

Paula Carrillo-Rodriguez

Frode Selheim

Maria Hernandez-Valladares

Affiliations

Soon will be listed here.

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.

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References

Kim M, Eetemadi A, Tagkopoulos I . DeepPep: Deep proteome inference from peptide profiles. PLoS Comput Biol. 2017; 13(9):e1005661. PMC: 5600403. DOI: 10.1371/journal.pcbi.1005661. View

Kong A, Leprevost F, Avtonomov D, Mellacheruvu D, Nesvizhskii A . MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat Methods. 2017; 14(5):513-520. PMC: 5409104. DOI: 10.1038/nmeth.4256. View

Manza L, Stamer S, Ham A, Codreanu S, Liebler D . Sample preparation and digestion for proteomic analyses using spin filters. Proteomics. 2005; 5(7):1742-5. DOI: 10.1002/pmic.200401063. View

Tyanova S, Albrechtsen R, Kronqvist P, Cox J, Mann M, Geiger T . Proteomic maps of breast cancer subtypes. Nat Commun. 2016; 7:10259. PMC: 4725767. DOI: 10.1038/ncomms10259. View

Panizza E, Branca R, Oliviusson P, Orre L, Lehtio J . Isoelectric point-based fractionation by HiRIEF coupled to LC-MS allows for in-depth quantitative analysis of the phosphoproteome. Sci Rep. 2017; 7(1):4513. PMC: 5495806. DOI: 10.1038/s41598-017-04798-z. View

Schessner J, Voytik E, Bludau I . A practical guide to interpreting and generating bottom-up proteomics data visualizations. Proteomics. 2022; 22(8):e2100103. DOI: 10.1002/pmic.202100103. View

Hayoun K, Gouveia D, Grenga L, Pible O, Armengaud J, Alpha-Bazin B . Evaluation of Sample Preparation Methods for Fast Proteotyping of Microorganisms by Tandem Mass Spectrometry. Front Microbiol. 2019; 10:1985. PMC: 6742703. DOI: 10.3389/fmicb.2019.01985. View

Ruther P, Husic I, Bangsgaard P, Gregersen K, Pantmann P, Carvalho M . SPIN enables high throughput species identification of archaeological bone by proteomics. Nat Commun. 2022; 13(1):2458. PMC: 9072323. DOI: 10.1038/s41467-022-30097-x. View

Sherman B, Hao M, Qiu J, Jiao X, Baseler M, Lane H . DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022; 50(W1):W216-W221. PMC: 9252805. DOI: 10.1093/nar/gkac194. View

10.

Aasebo E, Berven F, Bartaula-Brevik S, Stokowy T, Hovland R, Vaudel M . Proteome and Phosphoproteome Changes Associated with Prognosis in Acute Myeloid Leukemia. Cancers (Basel). 2020; 12(3). PMC: 7140113. DOI: 10.3390/cancers12030709. View

11.

Breuer K, Foroushani A, Laird M, Chen C, Sribnaia A, Lo R . InnateDB: systems biology of innate immunity and beyond--recent updates and continuing curation. Nucleic Acids Res. 2012; 41(Database issue):D1228-33. PMC: 3531080. DOI: 10.1093/nar/gks1147. View

12.

Rodrigues C, Ascher D, Pires D . Kinact: a computational approach for predicting activating missense mutations in protein kinases. Nucleic Acids Res. 2018; 46(W1):W127-W132. PMC: 6031004. DOI: 10.1093/nar/gky375. View

13.

Ping L, Kundinger S, Duong D, Yin L, Gearing M, Lah J . Global quantitative analysis of the human brain proteome and phosphoproteome in Alzheimer's disease. Sci Data. 2020; 7(1):315. PMC: 7522715. DOI: 10.1038/s41597-020-00650-8. View

14.

Deutsch E, Mendoza L, Shteynberg D, Slagel J, Sun Z, Moritz R . Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics. Proteomics Clin Appl. 2015; 9(7-8):745-54. PMC: 4506239. DOI: 10.1002/prca.201400164. View

15.

van der Pan K, Kassem S, Khatri I, de Ru A, Janssen G, Tjokrodirijo R . Quantitative proteomics of small numbers of closely-related cells: Selection of the optimal method for a clinical setting. Front Med (Lausanne). 2022; 9:997305. PMC: 9553008. DOI: 10.3389/fmed.2022.997305. View

16.

Bekker-Jensen D, Bernhardt O, Hogrebe A, Martinez-Val A, Verbeke L, Gandhi T . Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries. Nat Commun. 2020; 11(1):787. PMC: 7005859. DOI: 10.1038/s41467-020-14609-1. View

17.

Turei D, Valdeolivas A, Gul L, Palacio-Escat N, Klein M, Ivanova O . Integrated intra- and intercellular signaling knowledge for multicellular omics analysis. Mol Syst Biol. 2021; 17(3):e9923. PMC: 7983032. DOI: 10.15252/msb.20209923. View

18.

Toby T, Fornelli L, Srzentic K, DeHart C, Levitsky J, Friedewald J . A comprehensive pipeline for translational top-down proteomics from a single blood draw. Nat Protoc. 2018; 14(1):119-152. PMC: 6439476. DOI: 10.1038/s41596-018-0085-7. View

19.

Geiger T, Wisniewski J, Cox J, Zanivan S, Kruger M, Ishihama Y . Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics. Nat Protoc. 2011; 6(2):147-57. DOI: 10.1038/nprot.2010.192. View

20.

Wisniewski J, Zougman A, Mann M . Combination of FASP and StageTip-based fractionation allows in-depth analysis of the hippocampal membrane proteome. J Proteome Res. 2009; 8(12):5674-8. DOI: 10.1021/pr900748n. View