Identification of a Breast Cancer Associated Regulatory Network

We are developing a new framework for discovery of genes involved in the breast carcinogenesis process. Among families that have a predisposition to breast cancer, approximately 25% have inherited mutations in either breast cancer associated (ldquoBRCArdquo) genes BRCA1 or BRCA2, but the predisposing mutated genes in the majority of the families are unknown. BRCA1 and BRCA2 gene products both regulate cellular pathways that involve DNA repair and centrosome duplication, and their expression is correlated in microarray analyses in many cell types. We hypothesize that other unidentified BRCA genes may be involved in the same pathways that BRCA1 and BRCA2 regulate, and thus may be discovered by identifying genes whose expression also is correlated with that of BRCA1 and BRCA2. We interrogate public-domain gene expression databases using newly developed computational tools that include combinatorial and algebraic clustering methods to identify genes whose expression correlates with these tumor suppressors. Identified genes are then tested in the laboratory. RNA interference is used to disrupt the expression of the candidate BRCA gene products in two different cell-based assays that are dependent on BRCA1 and BRCA2 expression. The first assay models the regulation of homology-directed recombination repair of double-strand DNA breaks, and the second assay tests the control of duplication of the centrosome. We have selected nine genes that tightly cluster with BRCA1 and BRCA2 expression in multiple datasets, and these nine genes have never before been linked with the two reference genes. When tested in the lab using RNA interference to deplete the specific protein, six of these genes were found to affect homologous recombination and four affected the regulation of centrosome number. If the informatics analysis is considered a screening tool to find genes/proteins involved in breast carcinogenesis, then this approach has an extremely high success rate in finding proteins that impact phenotypes regulated by BRCA1 and BRCA2. In summary, we employ a novel experimental framework that develops new bioinformatic tools for identifying candidate genes whose regulation suggests the potential for involvement in breast carcinogenesis, and we validate the gene in the lab. This experimental framework may also be applicable to the identification of networks of genes involved in common pathways in other disease processes.