Skip to main content

Identifying Clinically Relevant Proteins for Targeted Analysis in the Development of a Multiplexed Proteomic Biomarker Assay

  • Protocol
  • First Online:
Tissue Proteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1788))

  • 1084 Accesses

Abstract

In recent years, hundreds of candidate protein biomarkers have been identified using discovery-based proteomics. Despite the large number of candidate biomarkers, few proteins advance to clinical validation. Here, we describe a hypothesis driven approach to identify candidate biomarkers, previously characterized in the literature, with the highest probability of clinical applicability. A ranking method, the hypothesis directed biomarker ranking (HDBR) system, was developed to score candidate biomarkers based on seven criteria deemed important in the selection of clinically useful biomarkers. The HDBR system was initially applied to identify candidate biomarkers for the development of a diagnostic test for the early detection of colorectal cancer, but this system can be widely applied to identify biomarkers of relevance in different disease states, for diagnosis, prognostication, or any other specific purpose.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

References

  1. de Sousa Abreu R, Penalva LO, Marcotte EM, Vogel C (2009) Global signatures of protein and mRNA expression levels. Mol Biosyst 5:1512–1526

    PubMed  Google Scholar 

  2. Vogel C, Marcotte EM (2012) Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet 13:227–232

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Nilsson T, Mann M, Aebersold R, Yates JR et al (2010) Mass spectrometry in high-throughput proteomics: ready for the big time. Nat Methods 7:681–685

    Article  CAS  PubMed  Google Scholar 

  4. Anderson NL, Anderson NG (2002) The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics 1:845–867

    Article  CAS  Google Scholar 

  5. Anderson L, Hunter CL (2006) Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol Cell Proteomics 5:573–588

    Article  CAS  PubMed  Google Scholar 

  6. Mallick P, Kuster B (2010) Proteomics: a pragmatic perspective. Nat Biotechnol 28:695–709

    Article  CAS  PubMed  Google Scholar 

  7. Anderson NL (2010) The clinical plasma proteome: a survey of clinical assays for proteins in plasma and serum. Clin Chem 56:177–185

    Article  CAS  PubMed  Google Scholar 

  8. Harlan R, Zhang H (2014) Targeted proteomics: a bridge between discovery and validation. Expert Rev Proteomics (6):657–661

    Google Scholar 

  9. Rifai N, Gillette MA, Carr SA (2006) Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol 24:971–983

    Article  CAS  PubMed  Google Scholar 

  10. Paulovich AG, Whiteaker JR, Hoofnagle AN, Wang P (2008) The interface between biomarker discovery and clinical validation: the tar pit of the protein biomarker pipeline. Proteomics Clin Appl 2:1386–1402

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Mackay EM, Koppel J, Das P, Woo J, Schriemer DC, Bathe OF (2015) A hypothesis-directed approach to the targeted development of a multiplexed proteomic biomarker assay for cancer. Cancer Inform 14:65–70

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. http://www.agilent.com/labs/research/litsearch.html

  13. Gratzl S, Lex A, Gehlenborg N, Pfister H, Streit M (2013) LineUp: visual analysis of multi-attribute rankings—best paper award. IEEE Trans Vis Comput Graph (InfoVis '13) 19(12):2277–2286

    Article  Google Scholar 

  14. OCEBM Levels of Evidence Working Group. “The Oxford 2011 levels of evidence.” Oxford Centre for Evidence-Based Medicine. http://www.cebm.net/index.aspx?o=5653

  15. Pounds SB (2006) Estimation and control of multiple testing error rates for microarray studies. Brief Bioinform 7:25–36

    Article  CAS  PubMed  Google Scholar 

  16. Tinker AV, Boussioutas A, Bowtell DDL (2006) The challenges of gene expression microarrays for the study of human cancer. Cancer Cell 9:333–339

    Article  CAS  PubMed  Google Scholar 

  17. Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100:57–70

    Article  CAS  Google Scholar 

  18. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674

    Article  CAS  Google Scholar 

Download references

Acknowledgment

The authors have declared no conflict of interest.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Oliver F. Bathe .

Editor information

Editors and Affiliations

1 Electronic Supplementary Materials

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media New York

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Mackay, E.M., Bathe, O.F. (2017). Identifying Clinically Relevant Proteins for Targeted Analysis in the Development of a Multiplexed Proteomic Biomarker Assay. In: Sarwal, M., Sigdel, T. (eds) Tissue Proteomics. Methods in Molecular Biology, vol 1788. Humana Press, New York, NY. https://doi.org/10.1007/7651_2017_75

Download citation

  • DOI: https://doi.org/10.1007/7651_2017_75

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7852-6

  • Online ISBN: 978-1-4939-7854-0

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics