Towards the molecular characterization of disease: comparison of molecular and histological analysis of esophageal epithelia.

Reliable quantification of gene expression offers the possibility of more accurate and prognostically relevant characterization of tissues than potentially subjective interpretations of histopathologists. We measured the expression of 18 selected genes and compared them to histological features in a spectrum of esophageal disease to evaluate the feasibility of molecular characterization of normal and pathologic esophageal epithelia. Esophageal tissue biopsies from 82 patients with foregut symptoms were laser capture microdissected, and the expression levels of 18 selected genes were measured by quantitative real-time polymerase chain reaction. Linear discriminant analysis, which uses combinations of genes to distinguish between histological groups, was performed to compare gene expression and the following five histological groups: (1) normal squamous epithelium (n = 35), (2) reflux esophagitis (n = 13), (3) non-dysplastic Barrett's (n = 33), (4) dysplastic Barrett's (n = 16), (5) adenocarcinoma (n = 31). A panel of seven genes had 90-94% predictive power to distinguish non-dysplastic and dysplastic Barrett's esophagus. Clustering analysis revealed structure in gene expression values even in the absence of histology. Expression levels in 17 genes differed significantly across histological groups. Classification based on gene expression agreed with histopathological assessment in the following percentage of cases: normal squamous epithelium = 53%, reflux esophagitis = 31%, non-dysplastic Barrett's = 76%, dysplastic Barrett's = 40%, and adenocarcinoma = 59%. Interestingly, predictive power improved markedly when inflammatory and dysplastic tissues were removed (77-94%). Gene expression classification agrees well with histopathological examination. When differences occur, it is unclear whether this effect is due to intraobserver variability in pathological diagnosis or to a genuine difference between gene expression and histopathology.