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

doi:10.1093/hmg/ddv440

. 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

David C Qian¹, Jinyoung Byun¹, Younghun Han¹, Casey S Greene², John K Field³, Rayjean J Hung⁴, Yonathan Brhane⁴, John R Mclaughlin⁵, Gordon Fehringer⁴, Maria Teresa Landi⁶, Albert Rosenberger⁷, Heike Bickeböller⁷, Jyoti Malhotra⁸, Angela Risch⁹, Joachim Heinrich¹⁰, David J Hunter¹¹, Brian E Henderson¹², Christopher A Haiman¹², Fredrick R Schumacher¹², Rosalind A Eeles¹³, Douglas F Easton¹⁴, Daniela Seminara⁶, Christopher I Amos¹⁵

Affiliations

¹ Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.
² Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA.
³ Department of Molecular and Clinical Cancer Medicine, University of Liverpool Cancer Research Centre, Liverpool L69 3GA, UK.
⁴ Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada.
⁵ Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada.
⁶ National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
⁷ Department of Genetic Epidemiology, University Medical Centre Göttingen, 37099 Göttingen, Germany.
⁸ Division of Hematology and Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
⁹ Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center, 69120 Heidelberg, Germany.
¹⁰ Institute of Epidemiology I, German Research Center for Environmental Health, 85764 Neuherberg, Germany.
¹¹ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
¹² Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
¹³ Department of Cancer Genetics, Institute of Cancer Research, London SW7 3RP, UK and.
¹⁴ Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK.
¹⁵ Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA, christopher.i.amos@dartmouth.edu.

PMID: 26483192
PMCID: PMC4664175
DOI: 10.1093/hmg/ddv440

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

David C Qian et al. Hum Mol Genet. 2015.

. 2015 Dec 20;24(25):7406-20.

doi: 10.1093/hmg/ddv440. Epub 2015 Oct 19.

Authors

Affiliations

¹ Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.
² Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA.
³ Department of Molecular and Clinical Cancer Medicine, University of Liverpool Cancer Research Centre, Liverpool L69 3GA, UK.
⁴ Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada.
⁵ Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada.
⁶ National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
⁷ Department of Genetic Epidemiology, University Medical Centre Göttingen, 37099 Göttingen, Germany.
⁸ Division of Hematology and Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
⁹ Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center, 69120 Heidelberg, Germany.
¹⁰ Institute of Epidemiology I, German Research Center for Environmental Health, 85764 Neuherberg, Germany.
¹¹ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
¹² Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
¹³ Department of Cancer Genetics, Institute of Cancer Research, London SW7 3RP, UK and.
¹⁴ Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK.
¹⁵ Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA, christopher.i.amos@dartmouth.edu.

PMID: 26483192
PMCID: PMC4664175
DOI: 10.1093/hmg/ddv440

Abstract

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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Figures

**Figure 1.**
Schematic overview of study design. Lung cancer is used as an illustrative example. (A) The lung cancer susceptibility network was constructed from the lung-expressed products of genes that are located within 10 kb of independent lung cancer-associated SNPs (‘key proteins’) along with mutual tissue-specific interaction partners (‘linking proteins’). (B) This cartoon depicts protein participants in a pathway from the Reactome database. Key proteins for lung cancer are bubbled in yellow. (C) Key proteins for both lung cancer and breast cancer involved in the same pathway are bubbled in orange, the intersection of yellow and red. Shared and unique key proteins between other cancer pairs are portrayed analogously. (D) Formula for the hypergeometric pathway enrichment P-value.

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References

1. International Agency for Research on Cancer (2012) GLOBOCAN 2012: Estimated Cancer Incidence, Mortality and Prevalence Worldwide in 2012.
1. American Cancer Society (2014) Cancer Facts & Figures 2014.
1. Figueiredo J.C., Stram D.O., Haiman C.A. (2014) The impact of GWAS findings on cancer etiology and prevention. Curr. Epidemiol. Rep., 1, 130–137.
1. Chung C.C., Magalhaes W.C., Gonzalez-Bosquet J., Chanock S.J. (2010) Genome-wide association studies in cancer—current and future directions. Carcinogenesis, 31, 111–120. - PMC - PubMed
1. Wang K., Li M., Hakonarson H. (2010) Analysing biological pathways in genome-wide association studies. Nat. Rev. Genet., 11, 843–854. - PubMed

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[1] International Agency for Research on Cancer (2012) GLOBOCAN 2012: Estimated Cancer Incidence, Mortality and Prevalence Worldwide in 2012.

[2] International Agency for Research on Cancer (2012) GLOBOCAN 2012: Estimated Cancer Incidence, Mortality and Prevalence Worldwide in 2012.

[3] American Cancer Society (2014) Cancer Facts & Figures 2014.

[4] American Cancer Society (2014) Cancer Facts & Figures 2014.

[5] Figueiredo J.C., Stram D.O., Haiman C.A. (2014) The impact of GWAS findings on cancer etiology and prevention. Curr. Epidemiol. Rep., 1, 130–137.

[6] Figueiredo J.C., Stram D.O., Haiman C.A. (2014) The impact of GWAS findings on cancer etiology and prevention. Curr. Epidemiol. Rep., 1, 130–137.

[7] Chung C.C., Magalhaes W.C., Gonzalez-Bosquet J., Chanock S.J. (2010) Genome-wide association studies in cancer—current and future directions. Carcinogenesis, 31, 111–120. - PMC - PubMed

[8] Chung C.C., Magalhaes W.C., Gonzalez-Bosquet J., Chanock S.J. (2010) Genome-wide association studies in cancer—current and future directions. Carcinogenesis, 31, 111–120. - PMC - PubMed

[9] Wang K., Li M., Hakonarson H. (2010) Analysing biological pathways in genome-wide association studies. Nat. Rev. Genet., 11, 843–854. - PubMed

[10] Wang K., Li M., Hakonarson H. (2010) Analysing biological pathways in genome-wide association studies. Nat. Rev. Genet., 11, 843–854. - PubMed

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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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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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