Ruyang Zhang1, Sipeng Shen2, Yongyue Wei1, Ying Zhu3, Yi Li4, Jiajin Chen3, Jinxing Guan3, Zoucheng Pan3, Yuzhuo Wang5, Meng Zhu5, Junxing Xie6, Xiangjun Xiao7, Dakai Zhu7, Yafang Li7, Demetrios Albanes8, Maria Teresa Landi8, Neil E Caporaso8, Stephen Lam9, Adonina Tardon10, Chu Chen11, Stig E Bojesen12, Mattias Johansson13, Angela Risch14, Heike Bickeböller15, H-Erich Wichmann16, Gadi Rennert17, Susanne Arnold18, Paul Brennan13, James D McKay13, John K Field19, Sanjay S Shete20, Loic Le Marchand21, Geoffrey Liu22, Angeline S Andrew23, Lambertus A Kiemeney24, Shan Zienolddiny-Narui25, Annelie Behndig26, Mikael Johansson27, Angela Cox28, Philip Lazarus29, Matthew B Schabath30, Melinda C Aldrich31, Juncheng Dai5, Hongxia Ma5, Yang Zhao3, Zhibin Hu32, Rayjean J Hung33, Christopher I Amos7, Hongbing Shen32, Feng Chen34, David C Christiani35. 1. Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China; Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts; China International Cooperation Center (CICC) for Environment and Human Health, Nanjing Medical University, Nanjing, People's Republic of China. 2. Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China; Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts; China International Cooperation Center (CICC) for Environment and Human Health, Nanjing Medical University, Nanjing, People's Republic of China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, People's Republic of China. 3. Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China. 4. Department of Biostatistics, University of Michigan, Ann Arbor, Michigan. 5. Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Cancer Center, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, People's Republic of China. 6. Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China. 7. The Institute for Clinical and Translational Research, Department of Medicine, Baylor College of Medicine, Houston, Texas. 8. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland. 9. Department of Medicine, British Columbia Cancer Agency, University of British Columbia, Vancouver, Canada. 10. Faculty of Medicine, University of Oviedo and CIBERESP, Oviedo, Spain. 11. Department of Epidemiology, University of Washington School of Public Health, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington. 12. Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark. 13. Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France. 14. Department of Biosciences and Cancer Cluster Salzburg, University of Salzburg, Salzburg, Austria. 15. Department of Genetic Epidemiology, University Medical Center, Georg August University Göttingen, Göttingen, Germany. 16. Institute of Medical Informatics, Biometry and Epidemiology, Ludwig Maximilians University, Munich, Germany. 17. Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Carmel, Haifa, Israel. 18. Markey Cancer Center, University of Kentucky, Lexington, Kentucky. 19. Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom. 20. Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas. 21. Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii. 22. Princess Margaret Cancer Centre, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada. 23. Department of Epidemiology, Department of Community and Family Medicine, Dartmouth Geisel School of Medicine, Hanover, New Hampshire. 24. Department for Health Evidence, Department of Urology, Radboud University Medical Center, Nijmegen, The Netherlands. 25. National Institute of Occupational Health, Oslo, Norway. 26. Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden. 27. Department of Radiation Sciences, Umeå University, Umeå, Sweden. 28. Department of Oncology and Metabolism, The Medical School, University of Sheffield, Sheffield, United Kingdom. 29. Department of Pharmaceutical Sciences, College of Pharmacy, Washington State University, Spokane, Washington. 30. Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida. 31. Department of Thoracic Surgery and Division of Epidemiology, Vanderbilt University Medical Center, Nashville, Tennessee. 32. China International Cooperation Center (CICC) for Environment and Human Health, Nanjing Medical University, Nanjing, People's Republic of China; Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Cancer Center, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, People's Republic of China. 33. Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada. 34. Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China; China International Cooperation Center (CICC) for Environment and Human Health, Nanjing Medical University, Nanjing, People's Republic of China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Cancer Center, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, People's Republic of China. Electronic address: fengchen@njmu.edu.cn. 35. Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts; Pulmonary and Critical Care Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
Abstract
INTRODUCTION: Although genome-wide association studies have been conducted to investigate genetic variation of lung tumorigenesis, little is known about gene-gene (G × G) interactions that may influence the risk of non-small cell lung cancer (NSCLC). METHODS: Leveraging a total of 445,221 European-descent participants from the International Lung Cancer Consortium OncoArray project, Transdisciplinary Research in Cancer of the Lung and UK Biobank, we performed a large-scale genome-wide G × G interaction study on European NSCLC risk by a series of analyses. First, we used BiForce to evaluate and rank more than 58 billion G × G interactions from 340,958 single-nucleotide polymorphisms (SNPs). Then, the top interactions were further tested by demographically adjusted logistic regression models. Finally, we used the selected interactions to build lung cancer screening models of NSCLC, separately, for never and ever smokers. RESULTS: With the Bonferroni correction, we identified eight statistically significant pairs of SNPs, which predominantly appeared in the 6p21.32 and 5p15.33 regions (e.g., rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.17, p = 6.57 × 10-13; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.17, p = 2.43 × 10-13; rs2858859HLA-DQA1 and rs9275572HLA-DQA2, ORinteraction = 1.15, p = 2.84 × 10-13; rs2853668TERT and rs62329694CLPTM1L, ORinteraction = 0.73, p = 2.70 × 10-13). Notably, even with much genetic heterogeneity across ethnicities, three pairs of SNPs in the 6p21.32 region identified from the European-ancestry population remained significant among an Asian population from the Nanjing Medical University Global Screening Array project (rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.13, p = 0.008; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.11, p = 5.23 × 10-4; rs3135369BTNL2 and rs9271300HLA-DQA1, ORinteraction = 0.89, p = 0.006). The interaction-empowered polygenetic risk score that integrated classical polygenetic risk score and G × G information score was remarkable in lung cancer risk stratification. CONCLUSIONS: Important G × G interactions were identified and enriched in the 5p15.33 and 6p21.32 regions, which may enhance lung cancer screening models.
INTRODUCTION: Although genome-wide association studies have been conducted to investigate genetic variation of lung tumorigenesis, little is known about gene-gene (G × G) interactions that may influence the risk of non-small cell lung cancer (NSCLC). METHODS: Leveraging a total of 445,221 European-descent participants from the International Lung Cancer Consortium OncoArray project, Transdisciplinary Research in Cancer of the Lung and UK Biobank, we performed a large-scale genome-wide G × G interaction study on European NSCLC risk by a series of analyses. First, we used BiForce to evaluate and rank more than 58 billion G × G interactions from 340,958 single-nucleotide polymorphisms (SNPs). Then, the top interactions were further tested by demographically adjusted logistic regression models. Finally, we used the selected interactions to build lung cancer screening models of NSCLC, separately, for never and ever smokers. RESULTS: With the Bonferroni correction, we identified eight statistically significant pairs of SNPs, which predominantly appeared in the 6p21.32 and 5p15.33 regions (e.g., rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.17, p = 6.57 × 10-13; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.17, p = 2.43 × 10-13; rs2858859HLA-DQA1 and rs9275572HLA-DQA2, ORinteraction = 1.15, p = 2.84 × 10-13; rs2853668TERT and rs62329694CLPTM1L, ORinteraction = 0.73, p = 2.70 × 10-13). Notably, even with much genetic heterogeneity across ethnicities, three pairs of SNPs in the 6p21.32 region identified from the European-ancestry population remained significant among an Asian population from the Nanjing Medical University Global Screening Array project (rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.13, p = 0.008; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.11, p = 5.23 × 10-4; rs3135369BTNL2 and rs9271300HLA-DQA1, ORinteraction = 0.89, p = 0.006). The interaction-empowered polygenetic risk score that integrated classical polygenetic risk score and G × G information score was remarkable in lung cancer risk stratification. CONCLUSIONS: Important G × G interactions were identified and enriched in the 5p15.33 and 6p21.32 regions, which may enhance lung cancer screening models.