The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Targeted proteomic strategy for clinical biomarker discovery
Open accessAbstract
The high complexity and large dynamic range of blood plasma proteins currently prohibit the sensitive and high‐throughput profiling of disease and control plasma proteome sample sets large enough to reliably detect disease indicating differences. To circumvent these technological limitations we describe here a new two‐stage strategy for the mass spectrometry (MS) assisted discovery, verification and validation of disease biomarkers. In an initial discovery phase N‐linked glycoproteins with distinguishable expression patterns in primary normal and diseased tissue are detected and identified. In the second step the proteins identified in the initial phase are subjected to targeted MS analysis in plasma samples, using the highly sensitive and specific selected reaction monitoring (SRM) technology. Since glycosylated proteins, such as those secreted or shed from the cell surface are likely to reside and persist in blood, the two‐stage strategy is focused on the quantification of tissue derived glycoproteins in plasma. The focus on the N‐glycoproteome not only reduces the complexity of the analytes, but also targets an information‐rich subproteome which is relevant for remote sensing of diseases in the plasma. The N‐glycoprotein based biomarker discovery and validation workflow reviewed here allows for the robust identification of protein candidate panels that can finally be selectively monitored in the blood plasma at high sensitivity in a reliable, non‐invasive and quantitative fashion.
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Citations
Cites
Gene expression profiling predicts clinical outcome of breast cancer
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536
Quantitative analysis of complex protein mixtures using isotope-coded affinity tags
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“The introduction of stable isotopes through stable isotope labeling into protein samples has the advantage that after the labeling process the samples can be processed in parallel and thus the variability among the samples can be limited (Gygi et al., 1999).”
BackgroundAnderson, N. L., & Anderson, N. G. (2002) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r200007-mcp200 · Cited by 4,414
Use of proteomic patterns in serum to identify ovarian cancer
Petricoin, E. F., Ardekani, A. M., Hitt, B. A., Levine, P. J., Fusaro, V. A., Steinberg, S. M., Mills, G. B., Simone, C., Fishman, D. A., Kohn, E. C., et al. (2002) · The Lancet · doi:10.1016/s0140-6736(02)07746-2 · Cited by 3,236
Protein biomarker discovery and validation: the long and uncertain path to clinical utility
Rifai, N., Gillette, M. A., & Carr, S. A. (2006) · Nature Biotechnology · doi:10.1038/nbt1235 · Cited by 1,867
Systems Biology and New Technologies Enable Predictive and Preventative Medicine
Hood, L., Heath, J. R., Phelps, M. E., & Lin, B. (2004) · Science · doi:10.1126/science.1104635 · Cited by 1,108 · cited 2×
“Recent technological advancements along with the information generated by the human genome project offer great hope for making the early detection of diseases a reality within the next few years in many disease settings (Goncalves et al., 2004; Hood et al., 2004; Jain, 2004).”
BackgroundWhy mammalian cell surface proteins are glycoproteins
Gahmberg, C. G., & Tolvanen, M. (1996) · Trends in Biochemical Sciences · doi:10.1016/s0968-0004(96)10034-7 · Cited by 177
Gravett, M. G., Thomas, A., Schneider, K. A., Reddy, A. P., Dasari, S., Jacob, T., Lu, X., Rodland, M., Pereira, L., Sadowsky, D. W., et al. (2007) · Journal of Proteome Research · doi:10.1021/pr060149v · Cited by 81
Cited by
Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present
Bantscheff, M., Lemeer, S., Savitski, M. M., & Kuster, B. (2012) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-012-6203-4 · Cited by 801
Quantitative measurements of N‐linked glycoproteins in human plasma by SWATH‐MS
Liu, Y., Hüttenhain, R., Surinova, S., Gillet, L. C., Mouritsen, J., Brunner, R., Navarro, P., & Aebersold, R. (2013) · PROTEOMICS · doi:10.1002/pmic.201200417 · Cited by 204
Self-citationAn automated pipeline for high-throughput label-free quantitative proteomics
Weisser, H., Nahnsen, S., Grossmann, J., Nilse, L., Quandt, A., Brauer, H., Sturm, M., Kenar, E., Kohlbacher, O., Aebersold, R., et al. (2013) · Journal of Proteome Research · doi:10.1021/pr300992u · Cited by 160
Self-citationPanchaud, A., Affolter, M., & Kussmann, M. (2012) · Journal of Proteomics · doi:10.1016/j.jprot.2011.12.022 · Cited by 159
Glycoprotein biomarker panel for pancreatic cancer discovered by quantitative proteomics analysis
Nie, S., Lo, A., Wu, J., Zhu, J., Tan, Z., Simeone, D. M., Anderson, M. A., Shedden, K. A., Ruffin, M. T., & Lubman, D. M. (2014) · Journal of Proteome Research · doi:10.1021/pr400967x · Cited by 128
Metadata
- Authors
- Schiess, R., Wollscheid, B., & Aebersold, R.
- Venue
- Molecular Oncology
- Publisher
- Wiley
- Published
- 2008-12-11
- Volume
- 3
- Issue
- 1
- Pages
- 33-44
- ISSN
- 1574-7891 (ISSN-L)
- 1878-0261
- OpenAlex
- W2167874610
- Cited by
- 373
- References
- 8
- FWCI
- 11.00
- Type
- journal article
- Language
- en
- Open access
- bronze
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