Title: The Cancer Cell Map Initiative: Defining the Hallmark Networks of Cancer
Abstract: 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. 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. Ever since the first draft of the human genome was published (Lander et al., 2001Lander E.S. Linton L.M. Birren B. Nusbaum C. Zody M.C. Baldwin J. Devon K. Dewar K. Doyle M. FitzHugh W. et al.International Human Genome Sequencing ConsortiumInitial sequencing and analysis of the human genome.Nature. 2001; 409: 860-921Crossref PubMed Scopus (11914) Google Scholar, Venter et al., 2001Venter J.C. Adams M.D. Myers E.W. Li P.W. Mural R.J. Sutton G.G. Smith H.O. Yandell M. Evans C.A. 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Is further sequencing of larger cohorts the answer (Lawrence et al., 2014Lawrence M.S. Stojanov P. Mermel C.H. Robinson J.T. Garraway L.A. Golub T.R. Meyerson M. Gabriel S.B. Lander E.S. Getz G. Discovery and saturation analysis of cancer genes across 21 tumour types.Nature. 2014; 505: 495-501Crossref PubMed Scopus (185) Google Scholar)? Or should we more efficiently extract biological insight from existing genomic data using orthogonal tools? While not precluding the former, we argue strongly here that the latter approach will be essential in interpreting mutations that cause cancer. We 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. Over the last few years, a number of global consortia, such as The Cancer Genome Atlas (TCGA) (Cancer Genome Atlas Research Network, 2008Cancer Genome Atlas Research NetworkComprehensive genomic characterization defines human glioblastoma genes and core pathways.Nature. 2008; 455: 1061-1068Crossref PubMed Scopus (2461) Google Scholar, Cancer Genome Atlas Research Network, 2011Cancer Genome Atlas Research NetworkIntegrated genomic analyses of ovarian carcinoma.Nature. 2011; 474: 609-615Crossref PubMed Scopus (1368) Google Scholar, Cancer Genome Atlas Research Network, 2012aCancer Genome Atlas Research NetworkComprehensive molecular portraits of human breast tumours.Nature. 2012; 490: 61-70Crossref PubMed Scopus (1309) Google Scholar, Cancer Genome Atlas Research Network, 2012bCancer Genome Atlas Research NetworkComprehensive genomic characterization of squamous cell lung cancers.Nature. 2012; 489: 519-525Crossref PubMed Scopus (554) Google Scholar, Cancer Genome Atlas Research Network, 2012cCancer Genome Atlas Research NetworkComprehensive molecular characterization of human colon and rectal cancer.Nature. 2012; 487: 330-337Crossref PubMed Scopus (939) Google Scholar, Cancer Genome Atlas Research Network, 2013aCancer Genome Atlas Research NetworkGenomic and epigenomic landscapes of adult de novo acute myeloid leukemia.N. 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Such "somatic" events are examined statistically over a population of cancer patients to identify genes that are altered more often than expected by chance, resulting in a list of approximately 250 frequently mutated genes thus far (Lawrence et al., 2013Lawrence M.S. Stojanov P. Polak P. Kryukov G.V. Cibulskis K. Sivachenko A. Carter S.L. Stewart C. Mermel C.H. Roberts S.A. et al.Mutational heterogeneity in cancer and the search for new cancer-associated genes.Nature. 2013; 499: 214-218Crossref PubMed Scopus (315) Google Scholar, Lawrence et al., 2014Lawrence M.S. Stojanov P. Mermel C.H. Robinson J.T. Garraway L.A. Golub T.R. Meyerson M. Gabriel S.B. Lander E.S. Getz G. Discovery and saturation analysis of cancer genes across 21 tumour types.Nature. 2014; 505: 495-501Crossref PubMed Scopus (185) Google Scholar). 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