A robust biomarker discovery pipeline for high-performance mass spectrometry data

Wayne G. Fisher, Kevin P. Rosenblatt, David A. Fishman, Gordon R. Whitteley, Alvydas Mikulskis, Scott A. Kuzdzal, Mary F. Lopez, Niclas Chiang Tan, Dwight C. German, Harold R. Garner

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


A high-throughput software pipeline for analyzing high-performance mass spectral data sets has been developed to facilitate rapid and accurate biomarker determination. The software exploits the mass precision and resolution of high-performance instrumentation, bypasses peak-finding steps, and instead uses discrete m/z data points to identify putative biomarkers. The technique is insensitive to peak shape, and works on overlapping and non-Gaussian peaks which can confound peak-finding algorithms. Methods are presented to assess data set quality and the suitability of groups of m/z values that map to peaks as potential biomarkers. The algorithm is demonstrated with serum mass spectra from patients with and without ovarian cancer. Biomarker candidates are identified and ranked by their ability to discriminate between cancer and noncancer conditions. Their discriminating power is tested by classifying unknowns using a simple distance calculation, and a sensitivity of 95.6% and a specificity of 97.1% are obtained. In contrast, the sensitivity of the ovarian cancer blood marker CA125 is ∼50% for stage I/II and ∼80% for stage III/IV cancers. While the generalizability of these markers is currently unknown, we have demonstrated the ability of our analytical package to extract biomarker candidates from high-performance mass spectral data.

Original languageEnglish (US)
Pages (from-to)1023-1045
Number of pages23
JournalJournal of Bioinformatics and Computational Biology
Issue number5
StatePublished - Oct 2007


  • Biomarkers
  • Mass spectra
  • Ovarian cancer

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications


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