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And Diabetes-Associated Pancreatic Cancer: a GWAS Data Analysis

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And Diabetes-Associated Pancreatic Cancer: a GWAS Data Analysis Published OnlineFirst October 17, 2013; DOI: 10.1158/1055-9965.EPI-13-0437-T Cancer Epidemiology, Research Article Biomarkers & Prevention Genes–Environment Interactions in Obesity- and Diabetes-Associated Pancreatic Cancer: A GWAS Data Analysis Hongwei Tang1, Peng Wei3, Eric J. Duell4, Harvey A. Risch5, Sara H. Olson6, H. Bas Bueno-de-Mesquita7, Steven Gallinger9, Elizabeth A. Holly10, Gloria M. Petersen11, Paige M. Bracci10, Robert R. McWilliams11, Mazda Jenab12, Elio Riboli16, Anne Tjønneland18, Marie Christine Boutron-Ruault13,14,15, Rudolf Kaaks19, Dimitrios Trichopoulos20,21,22, Salvatore Panico23, Malin Sund24, Petra H.M. Peeters8,16, Kay-Tee Khaw17, Christopher I. Amos2, and Donghui Li1 Abstract Background: Obesity and diabetes are potentially alterable risk factors for pancreatic cancer. Genetic factors that modify the associations of obesity and diabetes with pancreatic cancer have previously not been examined at the genome-wide level. Methods: Using genome-wide association studies (GWAS) genotype and risk factor data from the Pancreatic Cancer Case Control Consortium, we conducted a discovery study of 2,028 cases and 2,109 controls to examine gene–obesity and gene–diabetes interactions in relation to pancreatic cancer risk by using the likelihood-ratio test nested in logistic regression models and Ingenuity Pathway Analysis (IPA). Results: After adjusting for multiple comparisons, a significant interaction of the chemokine signaling À pathway with obesity (P ¼ 3.29 Â 10 6) and a near significant interaction of calcium signaling pathway with À diabetes (P ¼ 1.57 Â 10 4) in modifying the risk of pancreatic cancer were observed. These findings were supported by results from IPA analysis of the top genes with nominal interactions. The major contributing genes to the two top pathways include GNGT2, RELA, TIAM1, and GNAS. None of the individual genes or single-nucleotide polymorphism (SNP) except one SNP remained significant after adjusting for multiple À testing. Notably, SNP rs10818684 of the PTGS1 gene showed an interaction with diabetes (P ¼ 7.91 Â 10 7)ata false discovery rate of 6%. Conclusions: Genetic variations in inflammatory response and insulin resistance may affect the risk of obesity- and diabetes-related pancreatic cancer. These observations should be replicated in additional large datasets. Impact: A gene–environment interaction analysis may provide new insights into the genetic susceptibility and molecular mechanisms of obesity- and diabetes-related pancreatic cancer. Cancer Epidemiol Biomarkers Prev; 23(1); 98–106. Ó2013 AACR. Authors' Affiliations: Departments of 1Gastrointestinal Medical Oncol- dom; 18Institute of Cancer Epidemiology, Danish Cancer Society, ogy and 2Epidemiology, The University of Texas MD Anderson Cancer Copenhagen, Denmark; 19Division of Cancer Epidemiology, German Center; 3Division of Biostatistics and Human Genetics Center, School of Cancer Research Center, Heidelberg, Germany; 20Department of Epi- Public Health, University of Texas Health Science Center, Houston, demiology, Harvard School of Public Health, Boston, Massachusetts; Texas; 4Catalan Institute of Oncology (ICO-IDIBELL), Barcelona, Spain; 21Bureau of Epidemiologic Research, Academy of Athens; 22Hellenic 5Yale University School of Public Health, New Haven, Connecticut; Health Foundation, Athens, Greece; 23Dipartimento di Medicina Clinica 6Department of Epidemiology and Biostatistics, Memorial Sloan-Ketter- e Chirurgia, Federico II University, Naples, Italy; and 24Department of ing Cancer Center, New York, New York; 7National Institute for Public Surgery, Umea University Hospital, Umea,Sweden Health and the Environment (RIVM), Bilthoven and Department of Gastroenterology and Hepatology, University Medical Center Utrecht, Note: Supplementary data for this article are available at Cancer Epide- Utrecht; 8Julius Center for Health Sciences and Primary Care, University miology, Biomarkers & Prevention Online (http://cebp.aacrjournals.org/). Medical Center Utrecht, Utrecht, the Netherlands; 9Samuel Lunenfeld Research Institute, Toronto General Hospital, University of Toronto, Current address for Christopher I. Amos: Department of Community and Toronto, Canada; 10Department of Epidemiology & Biostatistics, Uni- Family Medicine, Geisel School of Medicine, Dartmouth College, Lebanon, versity of California San Francisco, San Francisco, California; 11Depart- New Hampshire. ment of Health Sciences Research, Mayo Clinic, Rochester, Minnesota; 12International Agency for Research on Cancer, Lyon; 13Institut national Corresponding Author: Donghui Li, Department of Gastrointestinal Med- de la sante et de la recherche medicale (INSERM), Centre for research in ical Oncology, University of Texas MD Anderson Cancer Center, 1515 Epidemiology and Population Health (CESP), U1018, Nutrition, Hor- Holcombe Boulevard, Unit 426, Houston, TX 77030. Phone: 713-834- mones and Women's Health team; 14Univ. Paris Sud, UMRS 1018; 6690; Fax: 713-834-6153; E-mail: [email protected] 15 16 IGR, F-94805, Villejuif, France; Division of Epidemiology, Public doi: 10.1158/1055-9965.EPI-13-0437-T Health, and Primary Care, Imperial College London, London; 17School of Clinical Medicine, University of Cambridge, Cambridge, United King- Ó2013 American Association for Cancer Research. 98 Cancer Epidemiol Biomarkers Prev; 23(1) January 2014 Downloaded from cebp.aacrjournals.org on October 1, 2021. © 2014 American Association for Cancer Research. Published OnlineFirst October 17, 2013; DOI: 10.1158/1055-9965.EPI-13-0437-T G Â E Analysis of GWAS in Pancreatic Cancer Introduction interaction analysis of genetic factors that may modify the Pancreatic cancer is the fourth leading cause of cancer- associations of obesity and diabetes with pancreatic related deaths, accounting for more than 37,600 deaths cancer. each year in the United States (1). Epidemiologic studies Materials and Methods have identified cigarette smoking as the major modifiable risk factor for this disease. Obesity and long-term history Study population and dataset of diabetes mellitus may also affect risk and are also The study population was drawn from seven studies modifiable (2, 3). Genetic factors are known to play a role participating in the previously conducted GWAS of the in pancreatic cancer development. Although genome- Pancreatic Cancer Cohort Consortium (PanScan) and the wide association studies (GWAS) have identified a few PanC4 Consortium (4, 5), including six case–control stud- loci and chromosome regions that are significantly asso- ies conducted at the MD Anderson Cancer Center (Hous- ciated with the risk of pancreatic cancer (4, 5), these ton, TX), Mayo Clinic (Rochester, MN), Yale University findings explain only a portion of the heritability of this (New Haven, CT), University of California (San Francisco, disease. Because of the limitations of single marker anal- CA), Memorial Sloan-Kettering Cancer Center (New York, ysis on GWAS data, there have been increasing efforts NY), and University of Toronto (Toronto, ON, Canada), recently on GWAS pathway analysis, which uses prior and one nested case–control study from the European biologic knowledge of gene function and aims at combin- Prospective Investigation into Cancer and Nutrition ing moderate signals of single-nucleotide polymorphism (EPIC) cohort. Cases were defined as patients diagnosed (SNP) and obtaining biologically interpretable findings (6, with primary adenocarcinoma of the pancreas; in each 7). Despite its great promise in providing insights into study center, controls were matched to cases according to disease mechanisms, current GWAS pathway analysis birth year, sex, and self-reported race/ethnicity and were has some caveats including being limited to enrichment free of pancreatic cancer at the time of recruitment. of marginal genetic effects in biologic pathways without GWAS scanning was performed at the National Cancer considering possible interactions between pathways and Institute Core Genotyping Facility using the Illumina environmental factors (8). On the other hand, environ- HumanHap550 and HumanHap550-Duo SNP arrays and mental factors are likely to interact with multiple genes Illumina Human 610-Quad arrays (4, 5). Genotype data through various biologic pathways, contributing to the covering 562,000 and 621,000 SNPs from 4,195 study susceptibility of complex human diseases. Although cur- subjects (2,163 cases and 2,232 controls) were downloaded rent GWAS top hits account for only limited heritability, from the Database of Genotypes and Phenotypes website gene–environment interactions may account for some of (http://www.ncbi.nlm.nih.gov/gap) with the approval the missing heritability of pancreatic cancer (9). of MD Anderson Institutional Review Board (IRB). We We have previously conducted pathway analyses of the first conducted data cleaning and quality control by GWAS data in pancreatic cancer. Several novel pathways removing SNPs with minor allele frequency (MAF) less significantly associated with risk were identified (10, 11). than 5%, deviating from the Hardy–Weinberg equilibri- For example, the pancreas development pathway [Mature um (P < 0.001) or not genotyped in both SNP array plat- Onset Diabetes of the Young (MODY) pathway] was forms, resulting in a final dataset of 468,114 SNPs. Accord- identified as a top pathway in pancreatic cancer etiology. ing to the International HapMap Project genotype data One possible mechanism related to this pathway is (phase III release #3, NCBI build 36, dbSNP b126, 2010-05- through obesity
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