title: A robust biomarker discovery pipeline for high-performance mass spectrometry data.
abstract: 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 approximately 50% for stage I/II and approximately 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.
authors.affiliation: Division of Translational Research, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA. Wayne.Fisher@utsouthwestern.edu
authors.forename: Wayne G
authors.initials: WG
authors.lastname: Fisher
authors.forename: Kevin P
authors.initials: KP
authors.lastname: Rosenblatt
authors.forename: David A
authors.initials: DA
authors.lastname: Fishman
authors.forename: Gordon R
authors.initials: GR
authors.lastname: Whiteley
authors.forename: Alvydas
authors.initials: A
authors.lastname: Mikulskis
authors.forename: Scott A
authors.initials: SA
authors.lastname: Kuzdzal
authors.forename: Mary F
authors.initials: MF
authors.lastname: Lopez
authors.forename: Niclas Chiang
authors.initials: NC
authors.lastname: Tan
authors.forename: Dwight C
authors.initials: DC
authors.lastname: German
authors.forename: Harold R
authors.initials: HR
authors.lastname: Garner
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000032:analysis*; D014408:Biomarkers, Tumor / Q000097:blood; D018394:CA-125 Antigen / Q000097:blood; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D013058:Mass Spectrometry / Q000706:statistics & numerical data*; D010051:Ovarian Neoplasms / Q000097:blood; D020543:Proteome; D012984:Software; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000706:statistics & numerical data
chemical_list: D015415:Biomarkers; D014408:Biomarkers, Tumor; D018394:CA-125 Antigen; D020543:Proteome