Pathway approaches to ovarian cancer risk discovery

CN12-02

It is generally accepted that for every type of cancer, there are at least two forms, one which is “hereditary” and one which is not. With regard to epithelial ovarian cancer, there are several genes that when inherited in mutated form are known to significantly increase risk. These genes, including BRCA1 and BRCA2, plus genes involved with the Lynch Syndrome, are rare. The general conclusion is that there exist additional genetic susceptibility factors that account for non-Mendelian familial risks that remain to be discovered. The prevailing hypothesis is that these will turn out to be common polymorphisms, each associated with low levels of increased risk.
 Rapid advances in genotyping technology have greatly increased throughput capabilities at ever decreasing costs. In addition, collaborative efforts like the HapMap project have lead to the availability of large numbers of single nucleotide polymorphisms, some of which can serve as “tags” for haplotype blocks. Collectively, the ability to cover the genome is now feasible, either through genome wide association studies (GWAS) or more focused studies on particular pathways or networks. Both approaches are expected to increase our knowledge of the underlying biology of common cancers. We have formed a collaboration between several groups to apply both strategies in the study of epithelial ovarian cancer. This talk will focus on efforts considering specific pathways and networks.
 The foundation for this work is based upon two large, ongoing case-control studies in North America. Cases had incident epithelial ovarian cancer ascertained at the Mayo Clinic in Rochester Minnesota or a 48-county region in North Carolina (Duke University). Cancer-free controls were frequency matched to the cases on age, race, and residence. After an interview to obtain data on risk factors, a sample of blood was collected for DNA. Among Caucasians, a total of 837 incident cases (388 at Mayo and 449 at Duke) and 941 controls were included in these analyses.
 Genes and pathways were identified through a number of sources including peer-reviewed published literature and the Cancer Genome Anatomy Project (CGAP) Biokarta and Kegg pathway databases. For each gene, chromosome and protein attributes were selected and the data mined from the Ensembl database version 34 (Biomart) using the gene reference sequence identification number (RefSeq ID) and the approved gene symbol from HUGO or Entrez Gene. The chromosomal location on build 35 and strand (forward or reverse) were provided to Illumina (San Diego, CA). Illumina verified chromosomal coordinates. We requested all SNPs within each gene as well as up to 10kb in the 5’ and 3’ flanking regions and all non-synonymous SNPs with a MAF > 0.05 and Illumina Design Score > 0.6. The Illumina ADT (assay design tool) database includes all SNP data contained in the public domain, filtering out SNPs that are not suitable for the Illumina platform such as insertions/deletions, tri- and tetra-allelic SNPs, and SNPs that are not uniquely localized.
 We excluded subjects and individual SNPs with genotype call rates <95%. All SNP genotypes among controls were tested against Hardy-Weinberg equilibrium. Data were analyzed considering individual SNPs under dominant, log additive, and recessive genetic models. Haplotype analyses were also performed. Selected results will be presented on the association of genetic polymorphisms in the glycoslyation process and apoptosis with risk for ovarian cancer.