Cancer Informatics

Genome-wide association studies (GWAS) have identified genetic variants associated with an increased risk of developing breast cancer. However, the association of genetic variants and their associated genes with the most aggressive subset of breast cancer, the triple-negative breast cancer (TNBC), remains a central puzzle in molecular epidemiology. The objective of this study was to determine whether genes containing single nucleotide polymorphisms (SNPs) associated with an increased risk of developing breast cancer are connected to and could stratify different subtypes of TNBC. Additionally, we sought to identify molecular pathways and networks involved in TNBC. We performed integrative genomics analysis, combining information from GWAS studies involving over 400,000 cases and over 400,000 controls, with gene expression data derived from 124 breast cancer patients classified as TNBC (at the time of diagnosis) and 142 cancer-free controls. Analysis of GWAS reports produced 500 SNPs mapped to 188 genes. We identified a signature of 159 functionally related SNP-containing genes which were significantly (P , 10-5) associated with and stratified TNBC. Additionally, we identified 97 genes which were functionally related to, and had similar patterns of expression profiles, SNP-containing genes. Network modeling and pathway prediction revealed multi-gene pathways including p53, NFkB, BRCA, apoptosis, DNA repair, DNA mismatch, and excision repair pathways enriched for SNPs mapped to genes significantly associated with TNBC. The results provide convincing evidence that integrating GWAS information with gene expression data provides a unified and powerful approach for biomarker discovery in TNBC.