Abstract 3288: Integration of GWAS, gene expression and protein interaction data identifies a HOX-centric gene network associated with serous ovarian cancer risk

Abstract

Listen

Translate article

Abstract GWAS have so far identified 12 loci associated with serous epithelial ovarian cancer (sEOC) risk at genome-wide significance. Network-based integration of other molecular data with GWAS is one way to elucidate the functional basis of these loci. We hypothesized that some of these loci may impact sEOC development via transcription factor (TF) genes encoded at the locus and target genes of these TFs may also harbor weaker association signals. Therefore, we began our multi-step network analysis by selecting all known TF genes within 1 Mb of the top SNP at each of the 12 loci. Since 19/29 TF genes selected belonged to either the HOXB or HOXD cluster, whose targets remain poorly characterized by other approaches, we used co-expression to identify potential TF target genes. Specifically, a mutual information algorithm with a strict threshold was used to infer networks of genes highly co-expressed with each risk locus TF gene in the 489-tumor TCGA high-grade sEOC unified expression microarray data set. All genes in the genome that were also profiled in TCGA data (11,864 genes) were then ranked according to their association with sEOC. The rank was derived from the minimum p value among all SNPs in each gene (adjusted for gene size, SNP density and intra- and intergenic linkage) after mapping ∼2.5 million SNPs to genes from a GWAS meta-analysis of sEOC risk (2,196 cases/4,396 controls). Gene set enrichment analysis (GSEA) showed that genes in co-expression networks centered on six TF genes, four at the 17q21.32 risk locus (HOXB2, HOXB5, HOXB6, HOXB7) and two at the 2q31.1 locus (HOXD1, HOXD3) were significantly over-represented (P<0.05 and FDR<0.05, 10,000 permutations) at the sEOC-associated end of the ranked list. To explore translational conservation of these transcriptional relationships, we pooled the genes driving the GSEA signal in each of the six significant co-expression networks to yield a single module of 49 genes and overlaid them on a database of 169,810 high confidence protein-protein interactions (PPIs). The single largest connected component after overlay had 29/49 genes and significantly more indirect connectivity than 10,000 random networks permuted with similar topology (P=0.01). Finally, we further demonstrated the context-specificity of the significant networks to sEOC in two separate analyses using microarray data from an additional 245 sEOC tumors (GSE9899) and non-cancer GWAS results. We will discuss the power of our approach by highlighting the identification of both novel and established sEOC genes in the final set of 49 obtained from the six seed HOX genes. Thus by combining the largest available sEOC GWAS and gene expression data sets and PPI data in a network paradigm, we adopted a method more context-specific than conventional GWAS pathway analysis. This identified a HOX-centric network contributing to sEOC susceptibility and prioritized risk locus genes for ongoing follow-up. Citation Format: Siddhartha Kar, Jonathan Tyrer, Thomas Sellers, Simon Gayther, Paul Pharoah, Ovarian Cancer Association Consortium. Integration of GWAS, gene expression and protein interaction data identifies a HOX-centric gene network associated with serous ovarian cancer risk. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3288. doi:10.1158/1538-7445.AM2014-3288