Abstract 2153: Copy number variants in histologically normal breast epithelium from reduction mammoplasty controls and breast cancer cases

Whole genome arrays have been used to study DNA aberrations in a wide variety of samples. The genetics of breast cancer has been intensely studied using multiple techniques including whole genome SNP genotyping. Research using genotyping data to study copy number variants (CNVs) has been limited by suboptimal algorithms for segmentation. Recently, a new algorithm has been developed that is easy to use and highly adaptive to individual data sets. Since genomic changes are now appreciated prior to the appearance of malignancy - even in histologically normal epithelium - it was our aim to apply this algorithm and test whether CNVs differ between histologically normal ducts from reduction mammoplasty controls (RM) and histologically normal ducts from breast cancer cases (HN). Using data imported from Illumina9s Human 610-quad beadchip, CNV segmentation and copy number status was calculated using the OmicSoft algorithm. This algorithm utilizes three types of information, the Log2Ratio, B-Allele Frequency and the genotype calls. Segmentation criteria were optimized and then validated on 4 well-characterized breast cancer cell lines (MCF7, MDA-MB-231, T47D, BT474) and these data were compared to publically available Affymetrix SNP6 chip data. The validated segmentation method was then applied to data obtained with the Illumina beadchip from 19 snap-frozen human breast epithelial samples. Ducts from 8 RM (mean age 47y) and 11 HN (mean age 54y) samples from untreated ER+ cancers were identified by a pathologist and captured via laser microdissection. Samples were anonymous and obtained through an IRB approved protocol. Data from the cell lines were analyzed using OmicSoft9s Array Studio. There was good concordance with the publically available data, although the Illumina-generated data had fewer segments. This may be because there are more SNP/CNV markers on the Affy SNP6.0 chip and thus higher resolution. Using the optimized segmentation criteria, small CNVs were detected in all RM and HN samples. In the 8 RM cases, there was an average of 39 segments and 5-12 CNVs per case. The 11 HN cases had an average of 37 segments and 4-12 CNVs per case. The CNVs were small sized (35-400KB) and were more often losses than gains, with approximately 75% and 85% of the CNVs representing losses in RMs and HNs respectively. The gains were low level amplifications with copy number of 3N. The most common loss was on chromosome 3q (97KB), found in 2/8 (25%) RMs and 6/11 (55%) HNs (p=0.18) and had no association with self-identified ethnicity. OmicSoft9s algorithm for segmentation was easy to use and is ideal for CNV analysis because it can be modified to provide optimal segmentation for specific data sets. Small CNVs are detected commonly in histologically normal breast epithelium, but substantial differences in CNVs are not present between RM and HN samples. These changes must occur later in tumor development. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2153.