The identification of target combinationswith synergistic effects on cancer is at the leading edge of modern cancer research, especially for the development of combined anticancer therapies. However, at present, the basis for selection of beneficial targetcombinations commonly relies on expert opinion without any systematic rationale. The development of high-throughput technologies has led to the availability of large-scale clinical gene expression data sets.2–4Mining of these data sets for identification of gene combinations with synergetic effects on survival outcome in cancer could provide a systematic rationale for the identification of target combinations with potential therapeutic synergy. .