Volume 11 Supplement 4

UT-ORNL-KBRIN Bioinformatics Summit 2010

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Enabling proteomics-based identification of human cancer variations

  • Jing Li1,
  • Zeqiang Ma1,
  • Robbert JC Slebos2,
  • David L Tabb1, 3,
  • Daniel C Liebler1, 2, 3 and
  • Bing Zhang1Email author
BMC Bioinformatics201011(Suppl 4):P29

DOI: 10.1186/1471-2105-11-S4-P29

Published: 23 July 2010

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.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2105-11-S4-P29/MediaObjects/12859_2010_Article_4072_Fig1_HTML.jpg
Figure 1

Architecture for identifying mutant peptides from cancer shotgun proteome data

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.

Declarations

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.

Authors’ Affiliations

(1)
Department of Biomedical Informatics, Vanderbilt University School of Medicine
(2)
Jim Ayers Institute for Precancer Detection and Diagnosis, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine
(3)
Department of Biochemistry, Vanderbilt University School of Medicine

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Copyright

© Zhang et al; licensee BioMed Central Ltd. 2010

This article is published under license to BioMed Central Ltd.

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