Core module biomarker identification with network exploration for breast cancer metastasis

We have developed COMBINER: COre Module Biomarker Identification with Network ExploRation. COMBINER is a novel pathway-based approach for selecting highly reproducible discriminative biomarkers. We applied COMBINER to three benchmark breast cancer datasets for identifying prognostic biomarkers. COMBINER-derived biomarkers exhibited 10-fold higher reproducibility than other methods, with up to 30-fold greater enrichment for known cancer-related genes, and 4-fold enrichment for known breast cancer susceptible genes. More than 50% and 40% of the resulting biomarkers were cancer and breast cancer specific, respectively. The identified modules were overlaid onto a map of intracellular pathways that comprehensively highlighted the hallmarks of cancer. Furthermore, we constructed a global regulatory network intertwining several functional clusters and uncovered 13 confident "driver" genes of breast cancer metastasis.COMBINER can efficiently and robustly identify disease core module genes and construct their associated regulatory network. In the same way, it is potentially applicable in the characterization of any disease that can be probed with microarrays.In recent years, gene expression signatures based on DNA microarray technology have proven useful for predicting the risk of breast cancer. Agendia's MammaPrint has become the first FDA-cleared breast cancer prognosis marker chip containing 70 gene signatures [1]. Many other microarray-based biomarkers, such as 76 gene signatures [2] have been derived using independent data sources. However, there are only three overlaps between MammaPrint's 70-gene and Wang's 76-gene signatures. Furthermore, many of these markers are functionally unrelated to breast cancer. In order to identify robust, functionally relevant disease biomarkers, it is crucial to find gene signatures that are consistent in various data sources.A complex disease such as breast cancer results in many differentially expressed genes (DEGs), which together can