Array CGH characterization of esophago-gastric adenocarcinoma

The incidence of esophageal and junctional adenocarcinoma has increased 6-fold in the west in the last 30 years and 5 year survival remains <14%. We aimed to characterize genome-wide aberrations in esophageal adenocarcinoma to further understand disease pathogenesis and ultimately identify groups with differential survivals with implications for clinical management. Oligo-array-based high-resolution analysis of copy number changes in 89 fresh frozen esophageal adenocarcinoma resection sections with long-term clinical follow-up data was performed. Good quality array comparative genomic hybridization profiles were obtained from 56/89 of the samples (63%). Our analyses confirmed known genomic aberrations in esophageal adenocarcinoma and identified 3 novel genomic regions with copy number changes. A modified T test with adjusted Bonferroni correction identified 17 genes with significantly different log2 ratios (p<4x10-7), including 6 novel genes, that characterize a group with a poorer survival. Total RNA isolated from human tissue sections was used to make fluorescently labeled cRNA that was hybridized to DNA oligonucleotide. Briefly, 4 µg of total RNA was used to synthesize dsDNA through reverse transcription. cRNA was produced by in vitro transcription and labeled postsynthetically with Cy3 or Cy5. Two populations of labeled cRNA, a reference population and an experimental population, were compared with each other by competitive hybridization to microarrays. Two hybridizations were done with each cRNA sample pair using a fluorescent dye reversal strategy. Human microarrays contained oligonucleotide probes corresponding to approximately 21,000 genes. All oligonucleotide probes on the microarrays were synthesized in situ with inkjet technology (Agilent Technologies, Palo Alto, CA). After hybridization, arrays were scanned and fluorescence intensities for each probe were recorded. Ratios of transcript abundance (experimental to control) were obtained following normalization and correction of the array intensity data. Gene expression data analysis was done with the Rosetta Resolver gene expression analysis software (version 7.0, Rosetta Biosoftware, Seattle, WA).