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Label-Free Quantitation and Mapping of the ErbB2 Tumor Receptor by Multiple Protease Digestion with Data-Dependent (MS1) and Data-Independent (MS2) Acquisitions

DOI: 10.1155/2013/791985

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Abstract:

The receptor tyrosine kinase ErbB2 is a breast cancer biomarker whose posttranslational modifications (PTMs) are a key indicator of its activation. Quantifying the expression and PTMs of biomarkers such as ErbB2 by selected reaction monitoring (SRM) mass spectrometry has several limitations, including minimal coverage and extensive assay development time. Therefore, we assessed the utility of two high resolution, full scan mass spectrometry approaches, MS1 Filtering and SWATH MS2, for targeted ErbB2 proteomics. Endogenous ErbB2 immunoprecipitated from SK-BR-3 cells was in-gel digested with trypsin, chymotrypsin, Asp-N, or trypsin plus Asp-N in triplicate. Data-dependent acquisition with an AB SCIEX TripleTOF 5600 and MS1 Filtering data processing was used to assess peptide and PTM coverage as well as the reproducibility of enzyme digestion. Data-independent acquisition (SWATH) was also performed for MS2 quantitation. MS1 Filtering and SWATH MS2 allow quantitation of all detected analytes after acquisition, enabling the use of multiple proteases for quantitative assessment of target proteins. Combining high resolution proteomics with multiprotease digestion enabled quantitative mapping of ErbB2 with excellent reproducibility, improved amino acid sequence and PTM coverage, and decreased assay development time compared to typical SRM assays. These results demonstrate that high resolution quantitative proteomic approaches are an effective tool for targeted biomarker quantitation. 1. Introduction Large-scale efforts to understand biological processes, such as functional genomics, systems biology, and cancer mutation analysis, continue to uncover master regulators of cell signaling and potential biomarkers of human disease [1–3]. Understanding the regulation of these biomarkers and validating their role in disease processes, however, depends on measurement of their expression and regulatory status in response to different cellular conditions, drug treatments, or patient samples. The receptor tyrosine kinase ErbB2 (HER2) is an important biomarker that is overexpressed in ~25% of all breast cancers, is a key drug target, and is a member of a biologically important family of tyrosine kinases. ErbB2 is known to be heavily regulated by posttranslational modifications (PTMs) which can modulate its kinase activity and protein-protein interaction partners [4–6]. ErbB2 is also subject to membrane-associated proteolytic processing and has several poorly understood isoform variants [7]. Mass spectrometry-based proteomics combined with stable-isotope labeling or tagging

References

[1]  E. Hodis, I R. Watson, G V. Kryukov, et al., “A landscape of driver mutations in melanoma,” Cell, vol. 150, no. 2, pp. 251–263, 2012.
[2]  M. R. Stratton, P. J. Campbell, and P. A. Futreal, “The cancer genome,” Nature, vol. 458, no. 7239, pp. 719–724, 2009.
[3]  Y. Liang, H. Wu, R. Lei et al., “Transcriptional network analysis identifies BACH1 as a master regulator of breast cancer bone metastasis,” The Journal of Biological Chemistry, vol. 287, no. 40, pp. 33533–33544, 2012.
[4]  C. Marx, J. M. Held, B. W. Gibson, and C. C. Benz, “ErbB2 trafficking and degradation associated with K48 and K63 polyubiquitination,” Cancer Research, vol. 70, no. 9, pp. 3709–3717, 2010.
[5]  C. M. Warren and R. Landgraf, “Signaling through ERBB receptors: multiple layers of diversity and control,” Cellular Signalling, vol. 18, no. 7, pp. 923–933, 2006.
[6]  J. Park, R. Neve, J. Szollosi, and C. Benz, “Unraveling the biologic and clinical complexities of HER2,” Clinical Breast Cancer, vol. 8, no. 5, pp. 392–401, 2008.
[7]  T. M. Ward, E. Iorns, X. Liu et al., “Truncated p110 ERBB2 induces mammary epithelial cell migration, invasion and orthotopic xenograft formation, and is associated with loss of phosphorylated STAT5,” Oncogene, 2012.
[8]  S. E. Ong, B. Blagoev, I. Kratchmarova et al., “Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics,” Molecular and Cellular Proteomics, vol. 1, no. 5, pp. 376–386, 2002.
[9]  P. L. Ross, Y. N. Huang, J. N. Marchese et al., “Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents,” Molecular and Cellular Proteomics, vol. 3, no. 12, pp. 1154–1169, 2004.
[10]  T. A. Addona, S. E. Abbatiello, B. Schilling et al., et al., “Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma,” Nature Biotechnology, vol. 27, no. 7, pp. 633–641, 2009.
[11]  G. Aad, B. Abbott, J. Abdallah et al., “Search for new particles in two-jet final states in 7?TeV proton-proton collisions with the ATLAS detector at the LHC,” Physical Review Letters, vol. 105, no. 16, Article ID 161801, 19 pages, 2010.
[12]  J. M. Held, D. J. Britton, G. K. Scott et al., “Ligand binding promotes CDK-dependent phosphorylation of ER-alpha on hinge serine 294 but inhibits ligand-independent phosphorylation of serine 305,” Molecular Cancer Research, vol. 10, no. 8, pp. 1120–1132, 2012.
[13]  Y. Levin, E. Hradetzky, and S. Bahn, “Quantification of proteins using data-independent analysis (MSE) in simple andcomplex samples: a systematic evaluation,” Proteomics, vol. 11, no. 16, pp. 3273–3287, 2011.
[14]  K. A. Neilson, N. A. Ali, S. Muralidharan et al., “Less label, more free: approaches in label-free quantitative mass spectrometry,” Proteomics, vol. 11, no. 4, pp. 535–553, 2011.
[15]  J. Cox and M. Mann, “MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification,” Nature Biotechnology, vol. 26, no. 12, pp. 1367–1372, 2008.
[16]  B. Schilling, M. J. Rardin, B. X. MacLean et al., “Platform-independent and label-free quantitation of proteomic data using MS1 extracted ion chromatograms in Skyline: application to protein acetylation and phosphorylation,” Molecular and Cellular Proteomics, vol. 11, no. 5, pp. 202–214, 2012.
[17]  L. C. Gillet, P. Navarro, S. Tate et al., “Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis,” Molecular and Cellular Proteomics, vol. 11, no. 6, Article ID O111.016717, 2012.
[18]  J. D. Venable, M. Q. Dong, J. Wohlschlegel, A. Dillin, and J. R. Yates, “Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra,” Nature Methods, vol. 1, no. 1, pp. 39–45, 2004.
[19]  J. M. Asara, X. Zhang, B. Zheng, H. H. Christofk, N. Wu, and L. C. Cantley, “In-gel stable-isotope labeling (ISIL): a strategy for mass spectrometry-based relative quantification,” Journal of Proteome Research, vol. 5, no. 1, pp. 155–163, 2006.
[20]  C. Atsriku, D. J. Britton, J. M. Held et al., “Systematic mapping of posttranslational modifications in human estrogen receptor-α with emphasis on novel phosphorylation sites,” Molecular and Cellular Proteomics, vol. 8, no. 3, pp. 467–480, 2009.
[21]  R. Sturm, G. Sheynkman, C. Booth, L. M. Smith, J. A. Pedersen, and L. Li, “Absolute quantification of prion protein (90–231) using stable isotope-labeled chymotryptic peptide standards in a LC-MRM AQUA workflow,” Journal of the American Society for Mass Spectrometry, vol. 23, no. 9, pp. 1522–1533, 2012.
[22]  D. J. C. Pappin, P. Hojrup, and A. J. Bleasby, “Rapid identification of proteins by peptide-mass fingerprinting,” Current Biology, vol. 3, no. 6, pp. 327–332, 1993.
[23]  I. V. Shilov, S. L. Seymourt, A. A. Patel et al., “The paragon algorithm, a next generation search engine that uses sequence temperature values sequence temperature values and feature probabilities to identify peptides from tandem mass spectra,” Molecular and Cellular Proteomics, vol. 6, no. 9, pp. 1638–1655, 2007.
[24]  S. R. Danielson, J. M. Held, B. Schilling, M. Oo, B. W. Gibson, and J. K. Andersen, “Preferentially increased nitration of α-synuclein at tyrosine-39 in a cellular oxidative model of Parkinson's disease,” Analytical Chemistry, vol. 81, no. 18, pp. 7823–7828, 2009.
[25]  B. MacLean, D. M. Tomazela, N. Shulman et al., “Skyline: an open source document editor for creating and analyzing targeted proteomics experiments,” Bioinformatics, vol. 26, no. 7, Article ID btq054, pp. 966–968, 2010.
[26]  B. Frewen and M. J. MacCoss, “UNIT 13.7 using BiblioSpec for creating and searching tandem MS peptide libraries,” Current Protocols in Bioinformatics, 2007.
[27]  J. A. Mead, L. Bianco, V. Ottone et al., “MRMaid, the web-based tool for designing multiple reaction monitoring (MRM) transitions,” Molecular and Cellular Proteomics, vol. 8, no. 4, pp. 696–705, 2009.
[28]  S. Makawita and E. P. Diamandis, “The bottleneck in the cancer biomarker pipeline and protein quantification through mass spectrometry—based approaches: current strategies for candidate verification,” Clinical Chemistry, vol. 56, no. 2, pp. 212–222, 2010.
[29]  M. Li, W. Gray, H. Zhang et al., “Comparative shotgun proteomics using spectral count data and quasi-likelihood modeling,” Journal of Proteome Research, vol. 9, no. 8, pp. 4295–4305, 2010.

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