Cancer is a heterogeneous disease, originating in different tissues and involving a number of distinct molecular pathways. With the growing availability of high-throughput biological data, including but not limited to gene expression microarrays, integrative computational approaches are increasingly being applied to associate malignancies with specific underlying molecular mechanism(s). The goal of these approaches is to design more accurate diagnostics or “personalized” therapeutic regimens, and to generate a much more fine-grained knowledgebase of the progression of each cancer subtype. Many bioinformatic methods have shown limited success at identifying diagnostic and prognostic signatures for specific malignancies, such as breast or prostate cancer. In a similar context, these analyses have also been used to characterize pharmacological interventions. However, a key drawback thus far has been a focus on genes or proteins in relative isolation, without fully accounting for how their activity is mediated by an underlying network of interactions with other molecules in the cell. Furthermore, it is often difficult to separate the phenotypic cause versus its downstream effects, a problem referred to as the “driver” and “passenger” question. In this article, we present a critical synthesis of an emerging class of methods that use systems biology, or networks of gene interactions inside the cell, to help characterize cancer progression. We describe this emerging field, the types of high-throughput data used, and the various approaches investigators have taken. Lastly, we provide a discussion of the fields ability to more thoroughly capture the complex disease mechanisms at work and in doing so, work towards the promise of personalized medicine.