Cancer is a highly complex disease. This complexity hinders the identification of molecular pathways that are necessary for tumor growth, survival, and metastasis, thus impeding the development of effective cancer drug combinations. But recent technologic advances are providing remarkable new opportunities to embrace this complexity for therapeutic gain. Cutting-edge tools in genomics, proteomics, and metabolomics increasingly are being deployed worldwide in pre-clinical, translational, and clinical research to identify molecular pathways that drive a spectrum of cancer types. These technologies produce massive amounts of molecular data from both human tumors and model systems which, in turn, must be stored, analyzed, integrated, and displayed using quantitative methods to develop systems-level views of cancer. This "big data" approach is increasingly driving the discovery of clinically useful therapeutic targets and biomarkers. We will discuss the emergence of Computational Oncology as a new research discipline, review some exciting advances that have been made thus far, and envision the impact that this new approach will have on cancer research and medicine in the future.