A Network-based Approach to Enrich Gene Signatures for the Prediction of Breast Cancer Metastases

Despite the multiplicity of the gene expression analysis studies for the identification of genomics based origins of cancerous diseases, the presented gene signatures have generally little overlap. The genes do not function in isolation and therefore a more holistic approach that takes into account the interactions among them is needed. In this study we present a stepwise refinement methodology where starting from some initial set of biomarkers we expand and enrich this set taking into account existing biological information. In particular, we start with a 27 gene signature previously identified as indicative of the presence of circulating tumor cells (CTCs) in peripheral blood of breast cancer patients. We use the manually curated HINT database of protein-protein interactions as the background biological network to locate the network-based similarity of the input genes and how they connect to each other. The result is an enriched connected set of genes that is subsequently expanded to form an even bigger network based on the ability of the surrounding genes to strongly correlate with the phenotypes of a training set of breast cancer patient cases. The induced network is then used as a new gene signature for the classification of breast brain metastases in an independent dataset. The results are encouraging for the validity of this method.