title: Collection, integration and analysis of cancer genomic profiles: from data to insight.
abstract: The recent deluge of cancer genomics data provides a tremendous opportunity for the discovery of detailed mechanisms of tumorigenesis and the development of therapeutics. However, identifying the functionally relevant genomic alterations ('drivers') among the many non-oncogenic events ('passengers') presents a major challenge. Several new methods have been developed over the past few years that identify recurrently altered genes. Mapping the recurrent genomic alterations, such as somatic mutations and focal DNA copy-number alterations, onto individual tumor samples as tumor-specific event calls facilitates the identification of altered processes and pathways. The resulting reduction in complexity makes cancer genomics data more easily interpretable by cancer researchers and is now driving the development of powerful yet intuitive web-based analysis tools.
authors.affiliation: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, Box 460, New York, NY 10065, USA.
authors.forename: Jianjiong
authors.initials: J
authors.lastname: Gao
authors.affiliation: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, Box 460, New York, NY 10065, USA.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Ciriello
authors.affiliation: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, Box 460, New York, NY 10065, USA.
authors.forename: Chris
authors.initials: C
authors.lastname: Sander
authors.affiliation: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, Box 460, New York, NY 10065, USA. Electronic address: schultz@cbio.mskcc.org.
authors.forename: Nikolaus
authors.initials: N
authors.lastname: Schultz
mesh_terms: D056915:DNA Copy Number Variations; D015894:Genome, Human; D023281:Genomics; D006801:Humans; D009154:Mutation; D009369:Neoplasms / Q000235:genetics*; D012008:Recurrence