A combined comparative genomic hybridization and expression microarray analysis of gastric cancer reveals novel molecular subtypes.

Abstract

Comparative genomic hybridization (CGH), microsatellite instability (MSI) assays, and expression microarrays were used to molecularly subclassify a common set of gastric tumor samples. We identified a number of novel genomic aberrations associated with gastric cancer and discovered that gastric tumors could be grouped by their expression profiles into three broad classes: "tumorigenic," "reactive," and "gastric-like." Patients with gastric-like tumors exhibited a significantly better overall survival than patients belonging to the other two classes (P < 0.05). A novel supervised learning methodology for multiclass prediction was used to identify optimal predictor gene sets that accurately predicted the class of an unknown tumor sample. These predictor sets may prove useful in the development of new diagnostic applications for gastric cancer staging and prognostication.