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  • Open Access

Enabling proteomics-based identification of human cancer variations

  • 1,
  • 1,
  • 2,
  • 1, 3,
  • 1, 2, 3 and
  • 1Email author
BMC Bioinformatics201011 (Suppl 4) :P29

  • Published:


  • Mutation Information
  • Protein Identification
  • HCT116 Cell
  • Colorectal Cancer Cell
  • Colorectal Cancer Cell Line


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.


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


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.



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

Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
Jim Ayers Institute for Precancer Detection and Diagnosis, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA


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© Zhang et al; licensee BioMed Central Ltd. 2010

This article is published under license to BioMed Central Ltd.