For prognostic and diagnostic purposes, it is crucial to be able to separate the group of “driver” genes and their first-degree neighbours, (i.e. “core module”) from the general “disease module”. To facilitate this task, we developed a novel computational framework COMBINER: COre Module Biomarker Identification with Network ExploRation. We applied COMBINER to three benchmark breast cancer datasets for identifying prognostic biomarkers. We generated a list of “driver genes” by finding the common core modules between two sets of COMBINER markers identified with different module inference protocols. Overlaying the markers on the map of “the hallmarks of cancer” and constructing a weighted regulatory network with sensitivity analysis, we validated 29 driver genes. Our results show the COMBINER framework to be a promising approach for identifying and characterizing core modules and driver genes of many complex diseases.