Differential gene expression associated with estrogen receptor status of breast cancer identified by microarray meta-analysis.
Authors: Holko M , Scholtens D , Khan SA 
Published: December 2010

Abstract #2041

Introduction: Estrogen receptor (ER) positive breast cancer can be prevented by Tamoxifen. Currently, there are no markers for breast cancer risk that discriminate between the risk of ER positive versus ER negative disease. To identify patients likely to benefit from endocrine chemoprevention, a robust marker predicting ER status is needed. Microarray technology has identified genomic classifiers for breast cancer prognosis and prediction. We therefore conducted a meta-analysis of microarray data to identify individual genes associated with ER status of breast cancer.&#x2028; Methods: Publicly available microarray gene expression data sets were mined using Gene Expression Omnibus (GEO), Oncomine, ArrayExpress, and Stanford Microarray Database (SMD). Study inclusion criteria are no prior drug treatment, known ER status, and raw data files available for at least 50 arrays. All analyses were conducted using Bioconductor Project's (http://www.bioconductor.org) open-source software in R statistical programming language. Packages used include limma, ArrayQualityMetrics, MetaArray, and GOstats. Data sets were individually normalized using rma, and differential gene expression was based on ER status. Meta-analytic test statistic was computed, multiple test correction was performed, and genes above the threshold of |Zscore| = 19.5 were included in further analysis.&#x2028; Results: The search identified 23 publicly available gene expression data sets of primary breast tumors with known ER status, ten of which met inclusion criteria. Four of the ten contained cDNA array data files from SMD and were combined since they contained redundant files. To minimize platform and methodology discrepancies, we chose to focus our analysis on the six data sets which utilized the Affymetrix U133A array. The six data sets each contain 99-245 arrays, and represent a total of 644 ER+ and 268 ER- breast tumors. Bioinformatic analysis of the 86 differentially expressed genes associated with ER status using the criteria described above was then performed. To enrich for gene expression changes in pre-neoplastic lesions, genes known to be involved in progression and metastasis were eliminated from further study. The remaining genes were searched in Oncomine for differential expression according to ER status, in PubMed for previous association with breast cancer, and with our unpublished data for Gene Ontology annotations associated with ER status in breast cancer. We further narrowed the resulting list of 22 to 15 (ESR1, TFF1, AGR2, SCUBE2, CYP21A2, PIP, DACH1, BCL2, PROM1, RARES1, TMSL8, SRD5A1, SLC39A6, SCGB2A2, and FOXA1) based on functional redundancy.&#x2028; Conclusion: Over the next 4 months, this gene set (along with 5 house-keeping genes) will be tested for differential expression in samples that we have collected from the contralateral unaffected breasts of women with ER positive and ER negative breast cancer. These studies are likely to identify candidate genes that may serve as markers of ER-specific risk.

Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2041.