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Bioinformatics Advance Access originally published online on December 9, 2008
Bioinformatics 2009 25(2):251-257; doi:10.1093/bioinformatics/btn610
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Detecting glycan cancer biomarkers in serum samples using MALDI FT-ICR mass spectrometry data

Donald A. Barkauskas 1,*, Hyun Joo An 2, Scott R. Kronewitter 2, Maria Lorna de Leoz 2, Helen K. Chew 3, Ralph W. de Vere White 4, Gary S. Leiserowitz 5, Suzanne Miyamoto 3, Carlito B. Lebrilla 2 and David M. Rocke 6

1Graduate Group in Biostatistics with a Designated Emphasis in Biotechnology, 2Department of Chemistry, 3Cancer Center, Division of Hematology/Oncology, 4Cancer Center, Division of Urology, 5Cancer Center, Division of Gynecologic Oncology and 6Division of Biostatistics, School of Medicine, University of California, Davis, CA 95616, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: The development of better tests to detect cancer in its earliest stages is one of the most sought-after goals in medicine. Especially important are minimally invasive tests that require only blood or urine samples. By profiling oligosaccharides cleaved from glycosylated proteins shed by tumor cells into the blood stream, we hope to determine glycan profiles that will help identify cancer patients using a simple blood test. The data in this article were generated using matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry (MALDI FT-ICR MS). We have developed novel methods for analyzing this type of mass spectrometry data and applied it to eight datasets from three different types of cancer (breast, ovarian and prostate).

Results: The techniques we have developed appear to be effective in the analysis of MALDI FT-ICR MS data. We found significant differences between control and cancer groups in all eight datasets, including two structurally related compounds that were found to be significantly different between control and cancer groups in all three types of cancer studied.

Availability: The software used to perform the analysis described in this article is available in the form of an R package called FTICRMS, version 0.6, either from the Comprehensive R Archive Network (http://www.r-project.org/) or from the first author.

Contact: barkda{at}wald.ucdavis.edu

Associate Editor: Jonathan Wren

Received on May 26, 2008; revised on October 25, 2008; accepted on November 20, 2008

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