Abstract 2201: Pathway analysis of breast cancer genome wide association study highlights three pathways and one canonical signaling cascade

Genome-wide association studies (GWAS) focus on relatively few highly significant loci while less attention is given to other genotyped markers. Employing pathway analysis to existing GWAS data may shed light on relevant biological processes, and illuminate new candidate genes. We employed a pathway-based approach to the breast cancer GWAS data of the National Cancer Institute (NCI) Cancer Genetic Markers of Susceptibility (CGEMS) project that includes 1145 cases and 1142 controls. Pathways were retrieved from three databases: KEGG, BioCarta, and the NCI9s Protein Interaction Database (PID). Genes were represented by their most strongly associated SNP, and an enrichment score (ES) reflecting the overrepresentation of gene-based association signals in each pathway was calculated using a weighted Kolmogorov-Smirnov procedure. Finally, hierarchical clustering was used to identify pathways with overlapping genes, and clusters with excess of association signals were determined by the adaptive rank-truncated product (ARTP) method. A total of 421 pathways containing 3962 genes were included in our study. Of these, three pathways (‘Syndecan-1-mediated signaling ‘, ‘Signaling of Hepatocyte Growth Factor Receptor’ and ‘Growth Hormone Signaling’) were highly enriched with association signals (P ES ARTP = 0.0051, FDR = 0.07). These results suggest that genetic alterations associated with these three pathways and one canonical signaling cascade may contribute to breast cancer susceptibility. 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 2201.