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Hum Mol Genet. 2015 Dec 20;24(25):7406-20. doi: 10.1093/hmg/ddv440. Epub 2015 Oct 19.

Identification of shared and unique susceptibility pathways among cancers of the lung, breast, and prostate from genome-wide association studies and tissue-specific protein interactions.

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Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.
Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA.
Department of Molecular and Clinical Cancer Medicine, University of Liverpool Cancer Research Centre, Liverpool L69 3GA, UK.
Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada.
Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada.
National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Department of Genetic Epidemiology, University Medical Centre Göttingen, 37099 Göttingen, Germany.
Division of Hematology and Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center, 69120 Heidelberg, Germany.
Institute of Epidemiology I, German Research Center for Environmental Health, 85764 Neuherberg, Germany.
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Department of Cancer Genetics, Institute of Cancer Research, London SW7 3RP, UK and.
Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK.
Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA,


Results from genome-wide association studies (GWAS) have indicated that strong single-gene effects are the exception, not the rule, for most diseases. We assessed the joint effects of germline genetic variations through a pathway-based approach that considers the tissue-specific contexts of GWAS findings. From GWAS meta-analyses of lung cancer (12 160 cases/16 838 controls), breast cancer (15 748 cases/18 084 controls) and prostate cancer (14 160 cases/12 724 controls) in individuals of European ancestry, we determined the tissue-specific interaction networks of proteins expressed from genes that are likely to be affected by disease-associated variants. Reactome pathways exhibiting enrichment of proteins from each network were compared across the cancers. Our results show that pathways associated with all three cancers tend to be broad cellular processes required for growth and survival. Significant examples include the nerve growth factor (P = 7.86 × 10(-33)), epidermal growth factor (P = 1.18 × 10(-31)) and fibroblast growth factor (P = 2.47 × 10(-31)) signaling pathways. However, within these shared pathways, the genes that influence risk largely differ by cancer. Pathways found to be unique for a single cancer focus on more specific cellular functions, such as interleukin signaling in lung cancer (P = 1.69 × 10(-15)), apoptosis initiation by Bad in breast cancer (P = 3.14 × 10(-9)) and cellular responses to hypoxia in prostate cancer (P = 2.14 × 10(-9)). We present the largest comparative cross-cancer pathway analysis of GWAS to date. Our approach can also be applied to the study of inherited mechanisms underlying risk across multiple diseases in general.

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