Skip to main content
Download PDF

Volume 11 Supplement 4

UT-ORNL-KBRIN Bioinformatics Summit 2010

  • Poster presentation
  • Open access
  • Published:

Enabling proteomics-based identification of human cancer variations

BMC Bioinformatics volume 11, Article number: P29 (2010) Cite this article

Background

Shotgun proteomics is a powerful technology for protein identification in complex samples with remarkable applications in elucidating cellular and subcellular proteomes [1, 2], and discovering disease biomarkers [3, 4]. Shotgun proteomics data analysis usually relies on database search. Commonly used protein sequence databases in shotgun proteomics data analysis do not contain mutation information. This becomes a problem in cancer studies in which the detection of disease-related mutated peptides/proteins is crucial for understanding cancer biology [5]. Including protein mutation information into sequence databases can help alleviate this problem.

Results

Based on the human Cancer Proteome Variation Database developed by us recently [6], which comprises 41,541 nonsynonymous SNPs in 30,322 proteins from the dbSNP database and around 9000 cancer-related variations in 2,921 proteins, we created a variation-containing protein sequence database and a data analysis workflow for mutant protein identification in shotgun proteomics (Figure 1). Applying this workflow on colorectal cancer cell lines identified many peptides that contain either non-cancer-specific or very important cancer-related variations, such as a known somatic mutation in K-Ras in HCT116 cell line. Our workflow for mutant peptide identification has been tested for compatibility with various popular database search engines including Sequest, Mascot, X!Tandom as well as MyriMatch.

Figure 1
figure 1

Architecture for identifying mutant peptides from cancer shotgun proteome data

Full size image

Conclusion

Owing to its protein-centric nature, the approach we proposed can serve as a bridge between genomic variation data and proteomics studies in human cancer.

References

  1. Foster LJ, de Hoog CL, Zhang Y, Zhang Y, Xie X, Mootha VK, Mann M: A mammalian organelle map by protein correlation profiling. Cell 2006, 125: 187–199. 10.1016/j.cell.2006.03.022

    Article  CAS  PubMed  Google Scholar 

  2. Kislinger T, Cox B, Kannan A, Chung C, Hu P, Ignatchenko A, Scott MS, Gramolini AO, Morris Q, Hallet MT, Rossant J, Hughes TR, Frey B, Emili A: Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling. Cell 2006, 125: 173–186. 10.1016/j.cell.2006.01.044

    Article  CAS  PubMed  Google Scholar 

  3. Decramer S, Wittke S, Mischak H, Zürbig P, Walden M, Bouissou F, Bascands JL, Schanstra P: Predicting the clinical outcome of congenital unilateral ureteropelvic junction obstruction in newborn by urinary proteome analysis. Nat Med 2006, 12: 398–400. 10.1038/nm1384

    Article  CAS  PubMed  Google Scholar 

  4. Whiteaker JR, Zhang H, Zhao L, Wang P, Kelly-Spratt KS, Ivey RG, Piening BD, Feng LC, Kasarda E, Gurley KE, Eng JK, Chodosh LA, Kemp CJ, McIntosh MW, Paulovich AG: Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer. J Proteome Res 2007, 6: 3962–3975. 10.1021/pr070202v

    Article  CAS  PubMed  Google Scholar 

  5. Wang Z, Moult J: SNPs, protein structure, and disease. Hum mutat. 2001, 17: 263–270. 10.1002/humu.22

    Article  PubMed  Google Scholar 

  6. Li J, Duncan DT, Zhang B: CanProVar: a human cancer proteome variation database. Hum Mutat 2010, 31(3):219–28. 10.1002/humu.21176

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Institutes of Health (NIH)/ National Cancer Institute (NCI) through grant R01 CA126218 and the NIH/National Institute of General Medical Sciences through grant R01 GM88822.

Author information

Authors and Affiliations

  1. Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA

    Jing Li, Zeqiang Ma, David L Tabb, Daniel C Liebler & Bing Zhang

  2. Jim Ayers Institute for Precancer Detection and Diagnosis, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA

    Robbert JC Slebos & Daniel C Liebler

  3. Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA

    David L Tabb & Daniel C Liebler

Authors
  1. Jing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zeqiang Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Robbert JC Slebos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David L Tabb
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daniel C Liebler
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bing Zhang.

Rights and permissions

Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Li, J., Ma, Z., Slebos, R.J. et al. Enabling proteomics-based identification of human cancer variations. BMC Bioinformatics 11 (Suppl 4), P29 (2010). https://doi.org/10.1186/1471-2105-11-S4-P29

Download citation

  • Published:

  • DOI: https://doi.org/10.1186/1471-2105-11-S4-P29

Keywords

BMC Bioinformatics

ISSN: 1471-2105

Contact us