Three disease phenotypes, Barrett's esophagus (BE), high-grade dysplasia (HGD), esophageal adenocarcinoma (EAC), and a set of normal control (NC) serum samples are examined using a combination of ion mobility spectrometry (IMS), mass spectrometry (MS), and principal component analysis (PCA) techniques. Samples from a total of 136 individuals were examined, including 7 characterized as BE, 12 as HGD, 56 as EAC, and 61 as NC. In typical data sets, it was possible to assign ∼20 to 30 glycan ions based on MS measurements. Ion mobility distributions for these ions show multiple features. In some cases, such as the [S1H5N4 + 3Na]3+ and [S1F1H5N4 + 3Na]3+ glycan ions, the ratio of intensities of high-mobility features to low-mobility features vary significantly for different groups. The degree to which such variations in mobility profiles can be used to distinguish phenotypes is evaluated for 11 N-linked glycan ions. An outlier analysis on each sample class followed by an unsupervised PCA using a genetic algorithm for pattern recognition reveals that EAC samples are separated from NC samples based on 46 features originating from the 11-glycan composite IMS distribution.
- electrospray ionization mass spectrometry
- genetic algorithm
- ion mobility
- principal component analysis