An integrative genomics approach to biomarker discovery in breast cancer

Abstract

Genome-wide association studies (GWAS) have successfully identified genetic variants associated with risk for breast cancer. However, the molecular mechanisms through which the identified variants confer risk or influence phenotypic expression remains poorly understood. Here, we present a novel integrative genomics approach that combines GWAS information with gene expression data to assess the combined contribution of multiple genetic variants acting within genes and putative biological pathways, and to identify novel genes and biological pathways that could not be identified using traditional GWAS. The results show that genes containing SNPs associated with risk for breast cancer are functionally related and interact with each other in biological pathways relevant to breast cancer. Additionally, we identified novel genes that are co-expressed and interact with genes containing SNPs associated with breast cancer. Integrative analysis combining GWAS information with gene expression data provides functional bridges between GWAS findings and biological pathways involved in breast cancer.

Keywords: genome-wide association studies gene expression pathway.

Figure A1.

Patterns of gene expression profiles for 52 candidate genes only in the Caucasian population. The roles represent genes, columns represent breast cancer patients and controls. The red and blue colors indicate up and down regulation, respectively.

Figure A2.

Patterns of gene expression profiles for 9 candidate genes only in the Asian population. The roles represent genes, columns represent breast cancer patients and controls. The red and blue colors indicate up and down regulation, respectively.

Figure A3.

Patterns of gene expression profiles for 23 candidate genes only in the ER⁺ and ER⁻ population. The roles represent genes, columns represent breast cancer patients and controls. The red and blue colors indicate up and down regulation, respectively.

Figure 1.

Patterns of gene expression profiles for candidate and novel genes in the Caucasian population. The rows represent genes, columns represent 143 cancer-free controls and 42 breast cancer patients. The red and blue colors indicate up and down regulation, respectively.

Figure 2.

Patterns of gene expression profiles for candidate and novel genes in the Asian population. The rows represent genes, columns represent 43 (N) controls and 43 (C) breast cancer patients. The red and blue colors indicate up and down regulation, respectively.

Figure 3.

Patterns of gene expression profiles for candidate and novel genes in the ER⁺ and ER⁻Caucasian population. The rows represent genes, columns represent 209 ER⁺ and 77 ER⁻ breast cancer patients, respectively. The red and blue colors indicate up and down regulation, respectively.

Figure 4.

Color code indicating the biological process in which the genes in predicted biological pathways and the regulatory networks are involved.

Figure 5.

Gene interaction networks for genes containing SNPs (Red) and novel genes (Blue) identified using a threshold (P < 10⁻⁶) and other functionally related genes (in black) correlated with candidate genes and novel genes in the Caucasian population only. The size of the nodes: Large indicate SNP-containing and Novel genes identified through differential expression analysis, whereas small nodes indicate genes experimentally confirmed in the literature and through co-expression analysis that are functionally related and interact with SNP-containing and novel genes.

Figure 6.

Gene interaction networks for genes containing SNPs (Red) and novel genes (Blue) identified using a threshold (P < 10⁻⁶) and other functionally related genes (in black) correlated with candidate genes and novel genes in the Asian population only.

Figure 7.

Gene interaction networks for genes containing SNPs (Red) and novel genes (Blue) identified using a threshold (P < 10⁻⁶) and other functionally related genes (in black) correlated with candidate genes and novel genes in the ER⁺ and ER⁻ breast cancer patients only.