Skip to main content

Multiplexed Liquid Chromatography-Multiple Reaction Monitoring Mass Spectrometry Quantification of Cancer Signaling Proteins

  • Protocol
  • First Online:
Proteomics for Drug Discovery

Abstract

Quantitative evaluation of protein expression across multiple cancer-related signaling pathways (e.g., Wnt/β-catenin, TGF-β, receptor tyrosine kinases (RTK), MAP kinases, NF-κB, and apoptosis) in tumor tissues may enable the development of a molecular profile for each individual tumor that can aid in the selection of appropriate targeted cancer therapies. Here, we describe the development of a broadly applicable protocol to develop and implement quantitative mass spectrometry assays using cell line models and frozen tissue specimens from colon cancer patients. Cell lines are used to develop peptide-based assays for protein quantification, which are incorporated into a method based on SDS-PAGE protein fractionation, in-gel digestion, and liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM/MS). This analytical platform is then applied to frozen tumor tissues. This protocol can be broadly applied to the study of human disease using multiplexed LC-MRM assays.

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

Access this chapter

Institutional subscriptions

References

  1. Siegel RL, Miller KD, Jemal A (2016) Cancer statistics. CA Cancer J Clin 66:7–30

    Article  PubMed  Google Scholar 

  2. Taylor IW, Wrana JL (2012) Protein interaction networks in medicine and disease. Proteomics 12(10):1706–1716

    Article  CAS  PubMed  Google Scholar 

  3. Koomen JM, Haura EB, Bepler G et al (2008) Proteomic contributions to personalized cancer care. Mol Cell Proteomics 7:1780–1794

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Myers MV, Manning HC, Coffey RJ, Liebler DC (2012) Protein expression signatures for inhibition of epidermal growth factor receptor-mediated signaling. Mol Cell Proteomics 11:M111.015222

    Article  PubMed  Google Scholar 

  5. Jendrossek V (2012) The intrinsic apoptosis pathways as a target in anticancer therapy. Curr Pharm Biotechnol 13:1426–1438

    Article  CAS  PubMed  Google Scholar 

  6. Network CGA (2012) Comprehensive molecular characterization of human colon and rectal cancer. Nature 487:330–337

    Article  Google Scholar 

  7. De Sousa E, Melo F, Wang X et al (2013) Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions. Nat Med 19:614–618

    Article  Google Scholar 

  8. Sadanandam A, Lyssiotis CA, Homicsko K et al (2013) A colorectal cancer classification system that associates cellular phenotype and responses to therapy. Nat Med 19:619–625

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ren Z, Wang W, Li J (2016) Identifying molecular subtypes in human colon cancer using gene expression and DNA methylation microarray data. Int J Oncol 48:690–702

    Article  CAS  PubMed  Google Scholar 

  10. Zhang B, Wang J, Wang X et al (2014) Proteogenomic characterization of human colon and rectal cancer. Nature 513:382–387

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Turtoi A, Musmeci D, Wang Y et al (2011) Identification of novel accessible proteins bearing diagnostic and therapeutic potential in human pancreatic ductal adenocarcinoma. J Proteome Res 10:4302–4313

    Article  CAS  PubMed  Google Scholar 

  12. Whiteaker JR, Lin C, Kennedy J et al (2011) A targeted proteomics-based pipeline for verification of biomarkers in plasma. Nat Biotechnol 29:625–634

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Agard NJ, Mahrus S, Trinidad JC et al (2012) Global kinetic analysis of proteolysis via quantitative targeted proteomics. Proc Natl Acad Sci U S A 109:1913–1918

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Shi T, Su D, Liu T et al (2012) Advancing the sensitivity of selected reaction monitoring-based targeted quantitative proteomics. Proteomics 12:1074–1092

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Collins BC, Miller CA, Sposny A et al (2012) Development of a pharmaceutical hepatotoxicity biomarker panel using a discovery to targeted proteomics approach. Mol Cell Proteomics 11:394–410

    Article  PubMed  PubMed Central  Google Scholar 

  16. Boja ES, Rodriguez H (2012) Mass spectrometry-based targeted quantitative proteomics: achieving sensitive and reproducible detection of proteins. Proteomics 12:1093–1110

    Article  CAS  PubMed  Google Scholar 

  17. Hoofnagle AN, Becker JO, Oda MN et al (2012) Multiple-reaction monitoring-mass spectrometric assays can accurately measure the relative protein abundance in complex mixtures. Clin Chem 58:777–781

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Sprung RW, Martinez MA, Carpenter KL et al (2012) Precision of multiple reaction monitoring mass spectrometry analysis of formalin-fixed, paraffin-embedded tissue. J Proteome Res 11:3498–3505

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kuhn E, Whiteaker JR, Mani DR et al (2012) Inter-laboratory evaluation of automated, multiplexed peptide immunoaffinity enrichment coupled to multiple reaction monitoring mass spectrometry for quantifying proteins in plasma. Mol Cell Proteomics 11:M111.013854

    Article  PubMed  Google Scholar 

  20. Lam MP, Scruggs SB, Kim TY et al (2012) An MRM-based workflow for quantifying cardiac mitochondrial protein phosphorylation in murine and human tissue. J Proteome 75(15):4602–4609

    Article  CAS  Google Scholar 

  21. Fonseca-Sánchez MA, Rodríguez Cuevas S, Mendoza-Hernández G et al (2012) Breast cancer proteomics reveals a positive correlation between glyoxalase 1 expression and high tumor grade. Int J Oncol 41:670–680

    Article  PubMed  Google Scholar 

  22. Tang A, Li N, Li X et al (2012) Dynamic activation of the key pathways: linking colitis to colorectal cancer in a mouse model. Carcinogenesis 33:1375–1383

    Article  CAS  PubMed  Google Scholar 

  23. Casadonte R, Caprioli RM (2011) Proteomic analysis of formalin-fixed paraffin-embedded tissue by MALDI imaging mass spectrometry. Nat Protoc 6:1695–1709

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Clemis EJ, Smith DS, Camenzind AG et al (2012) Quantitation of spatially-localized proteins in tissue samples using MALDI-MRM imaging. Anal Chem 84:3514–3522

    Article  CAS  PubMed  Google Scholar 

  25. Tang HY, Beer LA, Barnhart KT, Speicher DW (2011) Rapid verification of candidate serological biomarkers using gel-based, label-free multiple reaction monitoring. J Proteome Res 10:4005–4017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wang M, Heo GY, Omarova S et al (2012) Sample pre-fractionation for mass spectrometry quantification of low-abundance membrane proteins. Anal Chem 84:5186–5191

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Martin DB, Holzman T, May D et al (2008) MRMer, an interactive open source and cross-platform system for data extraction and visualization of multiple reaction monitoring experiments. Mol Cell Proteomics 7:2270–2278

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Mead JA, Bianco L, Ottone V et al (2009) MRMaid, the web-based tool for designing multiple reaction monitoring (MRM) transitions. Mol Cell Proteomics 8:696–705

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Picotti P, Lam H, Campbell D et al (2008) Database of mass spectrometric assays for the yeast proteome. Nat Methods 5:913–914

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Sharma V, Eckels J, Taylor GK et al (2014) Panorama: a targeted proteomics knowledge base. J Proteome Res 13:4205–4210

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Cham JA, Bianco L, Barton C, Bessant C (2010) MRMaid-DB: a repository of published SRM transitions. J Proteome Res 9:620–625

    Article  CAS  PubMed  Google Scholar 

  33. Remily-Wood ER, Liu RZ, Xiang Y et al (2011) A database of reaction monitoring mass spectrometry assays for elucidating therapeutic response in cancer. Proteomics Clin Appl 5:383–396

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Reiter L, Rinner O, Picotti P et al (2011) mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat Methods 8:430–435

    Article  CAS  PubMed  Google Scholar 

  35. Picotti P, Bodenmiller B, Mueller LN et al (2009) Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 138:795–806

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Bluemlein K, Ralser M (2011) Monitoring protein expression in whole-cell extracts by targeted label- and standard-free LC-MS/MS. Nat Protoc 6:859–869

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  38. Wu R, Haas W, Dephoure N et al (2011) A large-scale method to measure absolute protein phosphorylation stoichiometries. Nat Methods 8:677–683

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Jovanovic M, Reiter L, Picotti P et al (2010) A quantitative targeted proteomics approach to validate predicted microRNA targets in C. elegans. Nat Methods 7:837–842

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kettenbach AN, Rush J, Gerber SA (2011) Absolute quantification of protein and post-translational modification abundance with stable isotope-labeled synthetic peptides. Nat Protoc 6:175–186

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kim YJ, Zaidi-Ainouch Z, Gallien S, Domon B (2012) Mass spectrometry-based detection and quantification of plasma glycoproteins using selective reaction monitoring. Nat Protoc 7:859–871

    Article  CAS  PubMed  Google Scholar 

  42. Addona TA, Abbatiello SE, Schilling B et al (2009) Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat Biotechnol 27:633–641

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Prakash A, Rezai T, Krastins B et al (2010) Platform for establishing interlaboratory reproducibility of selected reaction monitoring-based mass spectrometry peptide assays. J Proteome Res 9:6678–6688

    Article  CAS  PubMed  Google Scholar 

  44. Desiere F, Deutsch EW, King NL et al (2006) The PeptideAtlas project. Nucleic Acids Res 34:D655–D658

    Article  CAS  PubMed  Google Scholar 

  45. Vizcaíno JA, Deutsch EW, Wang R et al (2014) ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat Biotechnol 32:223–226

    Article  PubMed  PubMed Central  Google Scholar 

  46. Vizcaíno JA, Côté R, Reisinger F et al (2009) A guide to the proteomics identifications database proteomics data repository. Proteomics 9:4276–4283

    Article  PubMed  PubMed Central  Google Scholar 

  47. Prakash A, Tomazela DM, Frewen B et al (2009) Expediting the development of targeted SRM assays: using data from shotgun proteomics to automate method development. J Proteome Res 8:2733–2739

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Saldanha AJ (2004) Java Treeview – extensible visualization of microarray data. Bioinformatics 20:3246–3248

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the following core facilities at Moffitt Cancer Center: Tissue Core, Analytic Microscopy, Proteomics, and Biostatistics; funding provided in part by the Cancer Center Support Grant, P30-CA076292, from the National Cancer Institute. Proteomics instruments are supported by funding from the Moffitt Foundation and shared instrument grants from the Bankhead-Coley Cancer Research program of the Florida Department of Health (06BS-02-9614 and 09BN-14). Project funding was received as subcontract from the Moffitt National Functional Genomics Center funded by the US Army Medical Research and Materiel Command under award DAMD17-02-2-0051. Amino acid analysis was performed by the Protein Chemistry Laboratory at Texas A&M University by Virginia Johnson, MS, and Larry Dangott, PhD.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to John M. Koomen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this protocol

Cite this protocol

Chen, Y. et al. (2017). Multiplexed Liquid Chromatography-Multiple Reaction Monitoring Mass Spectrometry Quantification of Cancer Signaling Proteins. In: Lazar, I., Kontoyianni, M., Lazar, A. (eds) Proteomics for Drug Discovery. Methods in Molecular Biology, vol 1647. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7201-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7201-2_2

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7200-5

  • Online ISBN: 978-1-4939-7201-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics