The Cancer Cell Map Initiative: Defining the Hallmark Networks of Cancer

• Published in: Molecular cell Vol. 58; no. 4; pp. 690 - 698
• Main Authors: Krogan, Nevan J., Lippman, Scott, Agard, David A., Ashworth, Alan, Ideker, Trey
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 21.05.2015
• Subjects: Gene Regulatory Networks; Genome, Human - genetics; Genome-Wide Association Study; Humans; Models, Genetic; Mutation; Neoplasms - genetics; Neoplasms - therapy; Precision Medicine; Sequence Analysis, DNA; Signal Transduction - genetics
• ISSN: 1097-2765; 1097-4164
• DOI: 10.1016/j.molcel.2015.05.008

Progress in DNA sequencing has revealed the startling complexity of cancer genomes, which typically carry thousands of somatic mutations. However, it remains unclear which are the key driver mutations or dependencies in a given cancer and how these influence pathogenesis and response to therapy. Although tumors of similar types and clinical outcomes can have patterns of mutations that are strikingly different, it is becoming apparent that these mutations recurrently hijack the same hallmark molecular pathways and networks. For this reason, it is likely that successful interpretation of cancer genomes will require comprehensive knowledge of the molecular networks under selective pressure in oncogenesis. Here we announce the creation of a new effort, The Cancer Cell Map Initiative (CCMI), aimed at systematically detailing these complex interactions among cancer genes and how they differ between diseased and healthy states. We discuss recent progress that enables creation of these cancer cell maps across a range of tumor types and how they can be used to target networks disrupted in individual patients, significantly accelerating the development of precision medicine. Krogan et al. discuss the establishment of the Cancer Cell Map Initiative (CCMI), a resource that will use experimental and computational approaches to systematically generate hallmark cancer networks, an effort that will be essential in interpreting cancer genomic data.