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A targeted proteomics–based pipeline for verification of biomarkers in plasma

Nature Biotechnology volume 29, pages 625–634 (2011)Cite this article

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Abstract

High-throughput technologies can now identify hundreds of candidate protein biomarkers for any disease with relative ease. However, because there are no assays for the majority of proteins and de novo immunoassay development is prohibitively expensive, few candidate biomarkers are tested in clinical studies. We tested whether the analytical performance of a biomarker identification pipeline based on targeted mass spectrometry would be sufficient for data-dependent prioritization of candidate biomarkers, de novo development of assays and multiplexed biomarker verification. We used a data-dependent triage process to prioritize a subset of putative plasma biomarkers from >1,000 candidates previously identified using a mouse model of breast cancer. Eighty-eight novel quantitative assays based on selected reaction monitoring mass spectrometry were developed, multiplexed and evaluated in 80 plasma samples. Thirty-six proteins were verified as being elevated in the plasma of tumor-bearing animals. The analytical performance of this pipeline suggests that it should support the use of an analogous approach with human samples.

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Figure 1: Multistage, targeted proteomic pipeline for triage and verification of biomarker candidates.
Figure 2: Concentrations of biomarker candidates in plasma of tumor-bearing animals compared to controls.
Figure 3: SRM-MS, ELISA and western blot analysis data confirm elevation of Lcn2 in plasma of tumor-bearing animals.
Figure 4: Immuno-SRM and ELISA data confirm elevation of Mfge8 in plasma of tumor-bearing animals.

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References

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

    Article  CAS  PubMed  Google Scholar 

  2. Anderson, N.L. et al. A human proteome detection and quantitation project. Mol. Cell. Proteomics 8, 883–886 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Bordeaux, J. et al. Antibody validation. Biotechniques 48, 197–209 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Haab, B.B. et al. A reagent resource to identify proteins and peptides of interest for the cancer community: a workshop report. Mol. Cell. Proteomics 5, 1996–2007 (2006).

    Article  CAS  PubMed  Google Scholar 

  5. Stoevesandt, O. & Taussig, M.J. Affinity reagent resources for human proteome detection: initiatives and perspectives. Proteomics 7, 2738–2750 (2007).

    Article  CAS  PubMed  Google Scholar 

  6. Jaffe, J.D. et al. Accurate inclusion mass screening: a bridge from unbiased discovery to targeted assay development for biomarker verification. Mol. Cell. Proteomics 7, 1952–1962 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kuhn, E. et al. Quantification of C-reactive protein in the serum of patients with rheumatoid arthritis using multiple reaction monitoring mass spectrometry and 13C-labeled peptide standards. Proteomics 4, 1175–1186 (2004).

    Article  CAS  PubMed  Google Scholar 

  8. Gerber, S.A., Rush, J., Stemman, O., Kirschner, M.W. & Gygi, S.P. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc. Natl. Acad. Sci. USA 100, 6940–6945 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Barr, J. et al. Isotope-dilution mass spectrometric quantification of specific proteins: model application with apolipoprotein A-1. Clin. Chem. 42, 1676–1682 (1996).

    Article  CAS  PubMed  Google Scholar 

  10. Wu, S. et al. Targeted proteomics of low-level proteins in human plasma by LC/MSn: using human growth hormone as a model system. J. Proteome Res. 1, 459–465 (2002).

    Article  CAS  PubMed  Google Scholar 

  11. Barnidge, D., Goodmanson, M., Klee, G. & Muddiman, D. Absolute quantification of the model biomarker prostate-specific antigen in serum by LC-MS/MS using protein cleavage and isotope dilution MS. J. Proteome Res. 3, 644–652 (2004).

    Article  CAS  PubMed  Google Scholar 

  12. Wang, P., Whiteaker, J.R. & Paulovich, A.G. The evolving role of mass spectrometry in cancer biomarker discovery. Cancer Biol. Ther. 8, 1083–1094 (2009).

    Article  CAS  PubMed  Google Scholar 

  13. Rifai, N., Gillette, M.A. & Carr, S.A. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat. Biotechnol. 24, 971–983 (2006).

    Article  CAS  PubMed  Google Scholar 

  14. Anderson, N.L. The roles of multiple proteomic platforms in a pipeline for new diagnostics. Mol. Cell. Proteomics 4, 1441–1444 (2005).

    Article  CAS  PubMed  Google Scholar 

  15. Makawita, S. & Diamandis, E.P. The bottleneck in the cancer biomarker pipeline and protein quantification through mass spectrometry-based approaches: current strategies for candidate verification. Clin. Chem. 56, 212–222 (2010).

    Article  CAS  PubMed  Google Scholar 

  16. Lange, V., Picotti, P., Domon, B. & Aebersold, R. Selected reaction monitoring for quantitative proteomics: a tutorial. Mol. Syst. Biol. 4, 222 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Pan, S. et al. Mass spectrometry based targeted protein quantification: methods and applications. J. Proteome Res. 8, 787–797 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Carr, S.A. & Anderson, L. Protein quantitation through targeted mass spectrometry: the way out of biomarker purgatory? Clin. Chem. 54, 1749–1752 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Boja, E. et al. Restructuring proteomics through verification. Biomark Med 4, 799–803 (2010).

    Article  CAS  PubMed  Google Scholar 

  20. Paulovich, A.G., Whiteaker, J.R., Hoofnagle, A.N. & Wang, P. The interface between biomarker discovery and clinical validation: the tar pit of the protein biomarker pipeline. Proteomics Clin. Appl. 2, 1386–1402 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Whiteaker, J.R. et al. Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer. J. Proteome Res. 6, 3962–3975 (2007).

    Article  CAS  PubMed  Google Scholar 

  22. Schoenherr, R.M. et al. Proteome and transcriptome profiles of a Her2/Neu-driven mouse model of breast cancer. Proteomics Clin. Appl. 5, 179–188 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Moody, S.E. et al. Conditional activation of Neu in the mammary epithelium of transgenic mice results in reversible pulmonary metastasis. Cancer Cell 2, 451–461 (2002).

    Article  CAS  PubMed  Google Scholar 

  24. Omenn, G.S., Yocum, A.K. & Menon, R. Alternative splice variants, a new class of protein cancer biomarker candidates: findings in pancreatic cancer and breast cancer with systems biology implications. Dis. Markers 28, 241–251 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zhang, H. et al. Methods for peptide and protein quantitation by liquid chromatography-multiple reaction monitoring mass spectrometry. Mol. Cell. Proteomics 10, M110.006593 (2011).

    Article  CAS  Google Scholar 

  26. Anderson, N.L. et al. Mass spectrometric quantitation of peptides and proteins using Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA). J. Proteome Res. 3, 235–244 (2004).

    Article  CAS  PubMed  Google Scholar 

  27. Hoofnagle, A.N., Becker, J.O., Wener, M.H. & Heinecke, J.W. Quantification of thyroglobulin, a low-abundance serum protein, by immunoaffinity peptide enrichment and tandem mass spectrometry. Clin. Chem. 54, 1796–1804 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kuhn, E. et al. Developing multiplexed assays for troponin I and interleukin-33 in plasma by peptide immunoaffinity enrichment and targeted mass spectrometry. Clin. Chem. 55, 1108–1117 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Whiteaker, J.R., Zhao, L., Anderson, L. & Paulovich, A.G. An automated and multiplexed method for high throughput peptide immunoaffinity enrichment and multiple reaction monitoring mass spectrometry-based quantification of protein biomarkers. Mol. Cell. Proteomics 9, 184–196 (2010).

    Article  CAS  PubMed  Google Scholar 

  30. Want, E.J., Cravatt, B.F. & Siuzdak, G. The expanding role of mass spectrometry in metabolite profiling and characterization. ChemBioChem 6, 1941–1951 (2005).

    Article  CAS  PubMed  Google Scholar 

  31. Picotti, P. et al. High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nat. Methods 7, 43–46 (2010).

    Article  CAS  PubMed  Google Scholar 

  32. Kiyonami, R. et al. Increased selectivity, analytical precision, and throughput in targeted proteomics. Mol. Cell. Proteomics 10, M110.002931 (2011).

    Google Scholar 

  33. Whiteaker, J.R. et al. Evaluation of large scale quantitative proteomic assay development using peptide affinity-based mass spectrometry. Mol. Cell. Proteomics 10, M110.005645 (2011).

    Article  CAS  Google Scholar 

  34. Fraser, C.G. Biological Variation: from Principles to Practice (AACC Press, Washington, DC; 2001).

  35. Hoofnagle, A.N. & Wener, M.H. The fundamental flaws of immunoassays and potential solutions using tandem mass spectrometry. J. Immunol. Methods 347, 3–11 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Keshishian, H. et al. Quantification of cardiovascular biomarkers in patient plasma by targeted mass spectrometry and stable isotope dilution. Mol. Cell. Proteomics 8, 2339–2349 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Stahl-Zeng, J. et al. High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites. Mol. Cell. Proteomics 6, 1809–1817 (2007).

    Article  CAS  PubMed  Google Scholar 

  38. Keshishian, H., Addona, T., Burgess, M., Kuhn, E. & Carr, S.A. Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution. Mol. Cell. Proteomics 6, 2212–2229 (2007).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  40. Ekins, R. & Edwards, P. On the meaning of “sensitivity”. Clin. Chem. 43, 1824–1831 (1997).

    Article  CAS  PubMed  Google Scholar 

  41. Ekins, R. & Edwards, P. On the meaning of “sensitivity”: a rejoinder. Clin. Chem. 44, 1773–1778 (1998).

    Article  CAS  PubMed  Google Scholar 

  42. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Schoenherr, R.M. et al. Automated screening of monoclonal antibodies for SISCAPA assays using a magnetic bead processor and liquid chromatography-selected reaction monitoring-mass spectrometry. J. Immunol. Methods 353, 49–61 (2010).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was funded by a grant from The Paul G. Allen Family Foundation and the National Institutes of Health grant U24 CA126476 from the National Cancer Institute Clinical Proteomic Technology Assessment Center (CPTAC). We thank our Biomarker Project advisory board members for advice and input: R. Aebersold, L. Anderson, E. Diamandis, R. Smith, F. Appelbaum, D. Gottschling, M. Groudine, J. Roberts, V. Vasioukhin and N. Urban. We thank all members of the Biomarker Project team for input: L. Hartwell, S.M. Hanash, S.J. Pitteri, H. Wong, K.E. Gurley, D. Liggitt, D.B. Martin, T. Whitmore, A. Peterson, R. Prueitt, M. Fitzgibbon, J.K. Eng, D. May, T. Holzman, Y. Zhang, A. Stimmel, S.L. Zriny, R. Dumpit, I. Coleman and T.D. Lorentzen.

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Author notes

  1. Jeffrey R Whiteaker, Chenwei Lin, Jacob Kennedy, Liming Hou, Mary Trute and Izabela Sokal: These authors contributed equally to this work.

Authors and Affiliations

  1. Fred Hutchinson Cancer Research Center, Seattle, Washington, USA

    Jeffrey R Whiteaker, Chenwei Lin, Jacob Kennedy, Liming Hou, Mary Trute, Izabela Sokal, Ping Yan, Regine M Schoenherr, Lei Zhao, Uliana J Voytovich, Karen S Kelly-Spratt, Alexei Krasnoselsky, Philip R Gafken, Jason M Hogan, Lisa A Jones, Pei Wang, Lynn Amon, Peter S Nelson, Martin W McIntosh, Christopher J Kemp & Amanda G Paulovich

  2. Department of Cancer Biology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA

    Lewis A Chodosh

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Contributions

J.R.W. oversaw all proteomic experiments, guided the data analysis and assisted in writing the manuscript. C.L. performed or oversaw all data analysis and assisted in writing the manuscript. J.K. performed all verification studies using the 57-plex SRM assay. L.H. performed all SQ-SRM studies, assisted with the AIMS studies and assisted with configuration of the multiplex SRM assays. M.T. performed plasma sample processing throughout the project. I.S. led all verification studies using the 31-plex immuno-SRM assay and performed all ELISA analyses. P.Y. performed analysis of all response curve SRM data. R.M.S. assisted with the 31-plex immuno-SRM assay characterization and verification studies, identification of candidate biomarkers, depletion of plasma samples for verification studies and editing of the manuscript. L.Z. assisted with the 31-plex immuno-SRM assay characterization and verification studies. U.J.V. performed all western blot analyses. L.A.C. provided the Her2 mouse model. K.S.K.-S. and C.J.K. helped design the mouse experiments, performed all mouse husbandry and provided the required plasma samples. A.K. assisted in selection of candidate biomarkers, assembly of the inclusion lists for the AIMS analyses and analysis of the AIMS data. P.R.G., J.M.H. and L.A.J. performed the AIMS analyses. P.S.N. managed the overall consortium, contributed data for candidate selection, made intellectual contributions to the design of the study and assisted with editing the manuscript. P.W., M.W.M. and L.A. provided statistical input for the design of the experiments and the analysis of the data and assisted with editing the manuscript. A.G.P. designed experiments, coordinated execution of the project, coordinated analysis/interpretation of the data and assumed primary responsibility for writing the manuscript.

Corresponding author

Correspondence to Amanda G Paulovich.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Results Sections 1–6, Supplementary Appendices A–C and Supplementary Methods (PDF 7024 kb)

Supplementary Worksheets 1–6 (XLS 2589 kb)

Supplementary Data 1

Skyline data file name: “mouseQSRM_targets.sky” (XML 122 kb)

Supplementary Data 2

Skyline data file name: “mouseImmunoSRM_targets.sky” (XML 66 kb)

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Whiteaker, J., Lin, C., Kennedy, J. et al. A targeted proteomics–based pipeline for verification of biomarkers in plasma. Nat Biotechnol 29, 625–634 (2011). https://doi.org/10.1038/nbt.1900

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