Abstract 4961: Analysis of genomic alterations in breast cancer using an automated aCGH workstation

Abstract

Abstract The current sample preparation process for aCGH tests is tedious leading to errors that confound clinical results and increase re-test rates and costs. To overcome this problem, we have developed an aCGH workstation that automates the bench work from DNA labeling to array scanning. A robotic liquid handling and incubation system (ArrayPrep® Target Preparation System) automates the labeling and magnetic bead purification of 8-96 gDNA samples then loads them onto microarrays for hybridization using a rotating incubation system (Mai Tai® Hybridization System). Arrays are then post-processed using a robotic instrument (Little Dipper® Processor) for washing and drying. Previous studies have shown that the aCGH workstation generates high quality labeled gDNA and highly reproducible array data from batches of 8-96 samples. It requires less than 1hr hands-on technician time and can substantially enhance test reproducibility and lower cost (1). In this study, we examined the ability of the aCGH workstation to reproducibly detect genomic alterations in four well characterized breast cancer cell lines, MCF-7, SK-BR-3, BT-474 and MDA-MB-231 and in breast cancer tissue samples with known Her2/neu status. For each sample, replicate gDNA samples were processed using the aCGH workstation and Agilent 44K feature microarrays designed for genome-wide DNA copy number variation (CNV) profiling. The resulting array data were then analyzed for previously reported copy number alterations (CNAs) including loci within chromosomes 1q, 8q, 11q13, 17q and 20q13 (2, 3) and components of the epidermal growth factor receptor pathway affected by copy number changes (3). The results confirm that samples processed on the aCGH workstation reproducibly detect previously reported complex alterations involving multiple levels of change on chromosome arms 1p, 8q, 9p, 11q, 15q, 17q and 20q. Our data also confirmed that copy number alteration of multiple genetic loci involved in the EGF family of pathways is common in all four cell lines. The ERBB2 locus is highly amplified in the two known ERBB2-overexpressing cell lines (BT-474 and SK-BR-3). These two cell lines share amplifications at five additional gene loci, MAP2K6, CHN2, PRKCA, LIMK1 and cMYC, as previously reported (3). In summary, this initial study has shown the successful automation of the aCGH laboratory workflow for detection of genetic abnormalities at high resolution in breast cancer samples. This automated platform holds the potential to significantly improve test reliability and lower the cost of routine genetic analysis of clinical samples.