There now exists a vast amount of sequence data from tumors associated with many different cancer types, and efforts are ongoing to extract mechanistic insight from this information. Given all of this progress, what is now needed is an integrated computational and experimental strategy that will help place these alterations into context of the higher order biological mechanisms in cancer cells. This is the goal of the Cancer Cell Map Initiative, which will create a resource that can be used for cancer genome interpretation. This will allow us to identify key complexes and pathways to be studied in greater mechanistic detail to get a deeper understanding about the biology underlying different cancer states. Genomic data derived from tumor sequencing studies identifies key genes implicated in different cancer cells. Integrated physical and genetic networks based on these factors will help put the mutations into biological context, enabling the discovery of new disease genes as interacting partners become apparent. Ultimately, all of this knowledge will translate into improved ability to stratify and treat patients based on the particular networks that are altered.