Article abstract
Nature Genetics 39, 1338 - 1349 (2007)
Published online: 7 October 2007 | doi:10.1038/ng.2007.2
Network modeling links breast cancer susceptibility and centrosome dysfunction
Miguel Angel Pujana1,2,16,17, Jing-Dong J Han1,2,16,17, Lea M Starita3,16,17, Kristen N Stevens4,17, Muneesh Tewari1,2,16, Jin Sook Ahn1,2, Gad Rennert5, Víctor Moreno6,7, Tomas Kirchhoff8, Bert Gold9, Volker Assmann10, Wael M ElShamy2, Jean-François Rual1,2, Douglas Levine8, Laura S Rozek6, Rebecca S Gelman11, Kristin C Gunsalus12, Roger A Greenberg2, Bijan Sobhian2, Nicolas Bertin1,2, Kavitha Venkatesan1,2, Nono Ayivi-Guedehoussou1,2,16, Xavier Solé7, Pilar Hernández13, Conxi Lázaro13, Katherine L Nathanson14, Barbara L Weber14, Michael E Cusick1,2, David E Hill1,2, Kenneth Offit8, David M Livingston2, Stephen B Gruber4,6,15, Jeffrey D Parvin3,16 & Marc Vidal1,2
Abstract
Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network modeling strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer–associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network modeling strategy should be useful for the discovery of additional cancer-associated genes.
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute and Department of Genetics, Harvard Medical School, 44 Binney St., Boston, Massachusetts 02115, USA.
- Department of Cancer Biology, Dana-Farber Cancer Institute and Department of Genetics, Harvard Medical School, 44 Binney St., Boston, Massachusetts 02115, USA.
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, 77 Louis Pasteur Ave., Boston, Massachusetts 02115, USA.
- Department of Epidemiology, University of Michigan, 109 Zina Pitcher Pl., Ann Arbor, Michigan 48109, USA.
- CHS National Cancer Control Center, Department of Community Medicine and Epidemiology, Carmel Medical Center and Bruce Rappaport Faculty of Medicine, Technion, Haifa 34362, Israel.
- Department of Internal Medicine, University of Michigan, 109 Zina Pitcher Pl., Ann Arbor, Michigan 48109, USA.
- Department of Epidemiology and Cancer Registry, and Translational Research Laboratory, Catalan Institute of Oncology, IDIBELL, Gran Vía km 2.7, L'Hospitalet, Barcelona 08907, Spain.
- Clinical Genetics Service, Department of Medicine, Memorial Sloan-Kettering Cancer Center, 1275 York Ave., New York, New York 10021, USA.
- National Cancer Institute, Human Genetics Section, Laboratory of Genomic Diversity, Frederick, Maryland 21702, USA.
- Center for Experimental Medicine, Institute of Tumor Biology, University Hospital Hamburg–Eppendorf, Martinistrasse 52, Hamburg 20246, Germany.
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard School of Public Health, 44 Binney St., Boston, Massachusetts 02115, USA.
- Center for Comparative Functional Genomics, Department of Biology, New York University, 100 Washington Square East, New York, New York 10003, USA.
- Translational Research Laboratory, Catalan Institute of Oncology, IDIBELL, Gran Vía km 2.7, L'Hospitalet, Barcelona 08907, Spain.
- Abramson Family Cancer Research Institute, University of Pennsylvania School of Medicine, 421 Curie Blvd., Philadelphia, Pennsylvania 19104, USA.
- Department of Human Genetics, University of Michigan, 109 Zina Pitcher Pl., Ann Arbor, Michigan 48109, USA.
- Present addresses: Bioinformatics and Biostatistics Unit, Translational Research Laboratory, Catalan Institute of Oncology, IDIBELL, Gran Vía km 2.7, L'Hospitalet, Barcelona 08907, Spain (M.A.P.); Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Datun Rd., Beijing 100101, China (J.-D.J.H.); Department of Genome Sciences, University of Washington, 1705 NE Pacific St., Seattle, Washington 98195, USA (L.M.S.); Human Biology Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. North, Seattle, Washington 98109, USA (M.T.); Harvard School of Public Health, Boston, Massachusetts 02115, USA (N.A.-G.); Department of Biomedical Informatics, Ohio State University Medical Center, 460 West 12th Ave., Columbus, Ohio 43210, USA (J.D.P.).
- These authors contributed equally to this work.
Correspondence to: Marc Vidal1,2 e-mail: marc_vidal@dfci.harvard.edu
Correspondence to: Jeffrey D Parvin3,16 e-mail: jeffrey.parvin@osumc.edu
Correspondence to: Stephen B Gruber4,6,15 e-mail: sgruber@med.umich.edu
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