Journal Article

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

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

in Bioinformatics

Volume 25, issue 2, pages 251-257
Published in print January 2009 | ISSN: 1367-4803
Published online December 2008 | e-ISSN: 1460-2059 | DOI:
Detecting glycan cancer biomarkers in serum samples using MALDI FT-ICR mass spectrometry data

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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 ( or from the first author.


Journal Article.  5055 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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