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

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.

Fig. 1.

The autocorrelation series (starting with lag 7) of a typical spectrum pre-baseline correction (left) and post-baseline correction (right). See Section 2.1.

Fig. 2.

Graphs of raw data and baseline-corrected, shifted-log-transformed data (the ‘final data’) for a typical spectrum.

Fig. 3.

A typical peak in the final data is approximately parabolic. See Section 2.3.

Fig. 4.

The dashed line is the line y =x. Each point represents a peak that was declared statistically significant based on Benjamini–Hochberg with FDR 0.1 using the ANCOVA P-value (see Table 2). The five points in the lower right (four of which represent different isotope peaks of the same compound) were removed before calculating the regression line and correlation described in the text. See Section 3.