Author Manuscript Published OnlineFirst on May 21, 2020; DOI: 10.1158/1055-9965.EPI-20-0091 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Zhu et al. – Page 1 1 Associations between genetically predicted blood protein biomarkers and pancreatic 2 cancer risk 3 4 Jingjing Zhu 1, Xiang Shu 2, Xingyi Guo 2, Duo Liu 1,3, Jiandong Bao 2, Roger L Milne 4, 5, 6, 5 Graham G Giles 4, 5, 6, Chong Wu 7, Mengmeng Du 8, Emily White 9,10, Harvey A Risch11, Nuria 6 Malats 12, Eric J. Duell 13, Phyllis J. Goodman 14, Donghui Li 15, Paige Bracci 16, Verena Katzke 7 17, Rachel E Neale 18, Steven Gallinger 19, Stephen K Van Den Eeden 20, Alan A Arslan 21, 8 Federico Canzian 22, Charles Kooperberg 9, Laura E Beane-Freeman 23, Ghislaine Scelo 24, Kala 9 Visvanathan25, Christopher A. Haiman 26, Loïc Le Marchand 1, Herbert Yu 1, Gloria M Petersen 10 27, Rachael Stolzenberg-Solomon 23, Alison P Klein 25,28, Qiuyin Cai 2, Jirong Long 2, Xiao-Ou 11 Shu 2, Wei Zheng 2, Lang Wu 1 12 13 1. Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of 14 Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA 15 2. Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, 16 Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA 17 3. Department of Pharmacy, Harbin Medical University Cancer Hospital, Harbin, China 18 4. Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia 19 5. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global 20 Health, the University of Melbourne, VIC, Australia 21 6. Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, 22 Clayton, VIC, Australia 23 7. Department of Statistics, Florida State University, Tallahassee, FL, USA 24 8. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New 25 York, NY, USA 26 9. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 27 USA 28 10. Department of Epidemiology, University of Washington, Seattle, WA, USA 29 11. Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, 30 CT, USA 31 12. Spanish National Cancer Research Centre (CNIO) and CIBERONC, Madrid, Spain 32 13. Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of 33 Oncology (ICO-IDIBELL), L'Hospitalet de Llobregat, Spain 34 14. SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, USA 35 15. Department of Gastrointestinal Medical Oncology, University of Texas MD Anderson 36 Cancer Center, Houston, TX, USA 37 16. Department of Epidemiology and Biostatistics, University of California San Francisco, San 38 Francisco, CA, USA 39 17. Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, 40 Germany 41 18. Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 42 Australia 43 19. Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada 44 20. Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA 45 21. Department of Obstetrics and Gynecology, New York University School of Medicine, USA Downloaded from cebp.aacrjournals.org on October 1, 2021. © 2020 American Association for Cancer Research. Author Manuscript Published OnlineFirst on May 21, 2020; DOI: 10.1158/1055-9965.EPI-20-0091 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Zhu et al. – Page 2 46 22. Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, 47 Germany 48 23. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes 49 of Health, Rockville, MD, USA 50 24. Genetic Epidemiology Group, Section of Genetics, International Agency for Research on 51 Cancer, World Health Organization, Lyon, France 52 25. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, 53 MD, USA 54 26. Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of 55 Medicine, University of Southern California, Los Angeles, CA, 90033, USA 56 27. Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, 57 USA 58 28. Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins 59 School of Medicine, Baltimore, MD, USA 60 61 Running title: Genetically predicted protein biomarkers for pancreatic cancer 62 63 Abbreviations list: 64 Pancreatic ductal adenocarcinoma (PDAC) 65 protein quantitative trait loci (pQTL) 66 Mendelian randomization (MR) 67 Genome-wide association studies (GWAS) 68 the Pancreatic Cancer Cohort Consortium (PanScan) 69 the Pancreatic Cancer Case-Control Consortium (PanC4) 70 quality control (QC) 71 Hardy-Weinberg equilibrium (HWE) 72 inverse variance weights (IVW) 73 odds ratios (ORs) 74 confidence intervals (CIs) 75 false discovery rate (FDR) 76 Ingenuity Pathway Analysis (IPA) 77 78 Corresponding to: Lang Wu, Cancer Epidemiology Division, Population Sciences in the Pacific 79 Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 80 96813, USA. Email: [email protected]. Phone: (808)564-5965. 81 82 Competing financial interests 83 The authors declare no competing financial interests. 84 85 86 87 88 89 Downloaded from cebp.aacrjournals.org on October 1, 2021. © 2020 American Association for Cancer Research. Author Manuscript Published OnlineFirst on May 21, 2020; DOI: 10.1158/1055-9965.EPI-20-0091 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Zhu et al. – Page 3 90 Abstract 91 Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies 92 with few known risk factors and biomarkers. Several blood protein biomarkers have been linked 93 to PDAC in previous studies, but these studies have assessed only a limited number of 94 biomarkers usually in small samples. In this study, we evaluated associations of circulating 95 protein levels and PDAC risk using genetic instruments. 96 Methods: To identify novel circulating protein biomarkers of PDAC, we studied 8,280 cases and 97 6,728 controls of European descent from the Pancreatic Cancer Cohort Consortium and the 98 Pancreatic Cancer Case-Control Consortium, using genetic instruments of protein quantitative 99 trait loci (pQTL). 100 Results: We observed associations between predicted concentrations of 38 proteins and PDAC 101 risk at a false discovery rate of < 0.05, including 23 of those proteins that showed an association 102 even after Bonferroni correction. These include the protein encoded by ABO, which has been 103 implicated as a potential target gene of PDAC risk variant. Eight of the identified proteins 104 (LMA2L, TM11D, IP-10, ADH1B, STOM, TENC1, DOCK9, and CRBB2) were associated with 105 PDAC risk after adjusting for previously reported PDAC risk variants (odds ratio ranged from 106 0.79 to 1.52). Pathway enrichment analysis showed that the encoding genes for implicated 107 proteins were significantly enriched in cancer-related pathways, such as STAT3 and IL-15 108 production. 109 Conclusions: We identified 38 candidates of protein biomarkers for PDAC risk. 110 Impact: This study identifies novel protein biomarker candidates for PDAC, which if validated 111 by additional studies, may contribute to the etiological understanding of PDAC development. 112 Key words: Biomarkers, epidemiology, genetics, pancreatic cancer, risk 113 Downloaded from cebp.aacrjournals.org on October 1, 2021. © 2020 American Association for Cancer Research. Author Manuscript Published OnlineFirst on May 21, 2020; DOI: 10.1158/1055-9965.EPI-20-0091 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Zhu et al. – Page 4 114 Introduction 115 Pancreatic cancer, 95% of which is pancreatic ductal adenocarcinoma (PDAC), is the 116 second most commonly diagnosed gastrointestinal malignancy and the third leading cause of 117 cancer-related death in the United States (US) (1). With a five-year survival of 8%, the 118 incidence of pancreatic cancer keeps increasing in the US (2). Because pancreatic cancer is 119 typically asymptomatic in early stages, most patients are diagnosed at an advanced stage, which 120 precludes the possible application of curative surgery. Therefore, identifying biomarkers that 121 would contribute to screening or early diagnosis in high-risk populations may improve pancreatic 122 cancer outcomes. Serum CA 19-9 is currently the only biomarker for pancreatic cancer used in 123 clinical settings. However, it is mainly used for diagnosing symptomatic patients, and monitoring 124 disease prognosis and response to treatment (3). Besides CA 19-9, several other blood circulating 125 proteins have been reported to be potentially associated with pancreatic cancer risk, such as 126 CA242, PIVKA-II, PAM4, S100A6, OPN, RBM6, EphA2 and OPG (4–7), but the results in 127 those studies are inconsistent. For example, those studies often only involved a small sample size 128 and evaluated a few candidate proteins, and were often limited by a lack of external validation. 129 Additionally, due to the observational study design, they were potentially subject to selection 130 bias and residual and unmeasured confounding. 131 Mendelian randomization (MR) analysis is a widely applied