Niquelle Brown Wadé1,2, Cindy M Chang3, David Conti1,4, Joshua Millstein1,4, Christine Skibola5, Alexandra Nieters6, Sophia S Wang7, Silvia De Sanjose8,9, Eleanor Kane10, John J Spinelli11,12, Paige Bracci13, Yawei Zhang14, Susan Slager15, Jun Wang1,4, Henrik Hjalgrim16,17, Karin Ekstrom Smedby18, Elizabeth E Brown19, Ruth F Jarrett20, Wendy Cozen21,22,23. 1. Department of Preventive Medicine, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. 2. Cigna Health and Life Insurance Company (Cigna), Bloomfield, CT, USA. 3. Division of Population Health Sciences, Center for Tobacco Products, Food and Drug Administration, Bethesda, MD, USA. 4. USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. 5. Department of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, GA, USA. 6. Center for Chronic Immunodeficiency (CCI), University Medical Center Freiburg, University of Freiburg, Freiburg, Germany. 7. Department of Computational and Quantitative Medicine, City of Hope Comprehensive Cancer Center, Duarte, CA, USA. 8. Sexual and Reproductive Health, PATH, Seattle, WA, USA. 9. Centro de Investigación Biomédica en Red: Epidemiología y Salud Pública (CIBERESP), Madrid, Spain. 10. Department of Health Sciences, University of York, York, YO10 5DD, UK. 11. Population Oncology, BC Cancer Agency, Vancouver, Canada. 12. School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada. 13. Department of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, CA, USA. 14. Department of Surgery, Yale School of Medicine and Yale School of Public Health, New Haven, CT, USA. 15. Department of Epidemiology, Mayo Clinic, Rochester, MN, USA. 16. Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark. 17. Department of Haematology, Rigshospitalet, Copenhagen, Denmark. 18. Karolinska Institutet, Sweden University Hospital, Karolinska University, Stockholm, Sweden. 19. Department of Pathology, O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA. 20. MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland. 21. Department of Preventive Medicine, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. wcozen@med.usc.edu. 22. USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. wcozen@med.usc.edu. 23. Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA. wcozen@med.usc.edu.
Abstract
PURPOSE: We explored the interaction between non-Hodgkin lymphoma (NHL), infectious mononucleosis (IM) history, and immune-related genotypes in a pooled case-control analysis. METHODS: A total of 7,926 NHL patients and 10,018 controls from 12 case-control studies were included. Studies were conducted during various time periods between 1988 and 2008, and participants were 17-96 years of age at the time of ascertainment/recruitment. Self-reported IM history and immune response genotypes were provided by the InterLymph Data Coordinating Center at Mayo Clinic. Odds ratios (OR) were estimated using multivariate logistic regression, and interactions were estimated using the empirical Bayes method. PACT was used to account for multiple comparisons. RESULTS: There was evidence of an interaction effect between IM history and two variants on T-cell lymphoma (TCL) risk: rs1143627 in interleukin-1B (IL1B) (pinteraction = 0.04, ORinteraction = 0.09, 95% confidence interval [CI] 0.01, 0.87) and rs1800797 in interleukin-6 (IL6) (pinteraction = 0.03, ORinteraction = 0.08, 95% CI 0.01, 0.80). Neither interaction effect withstood adjustment for multiple comparisons. There were no statistically significant interactions between immune response genotypes and IM on other NHL subtypes. CONCLUSIONS: Genetic risk variants in IL1B and IL6 may affect the association between IM and TCL, possibly by influencing T-cell activation, growth, and differentiation in the presence of IM, thereby decreasing risk of immune cell proliferation.
PURPOSE: We explored the interaction between non-Hodgkin lymphoma (NHL), infectious mononucleosis (IM) history, and immune-related genotypes in a pooled case-control analysis. METHODS: A total of 7,926 NHL patients and 10,018 controls from 12 case-control studies were included. Studies were conducted during various time periods between 1988 and 2008, and participants were 17-96 years of age at the time of ascertainment/recruitment. Self-reported IM history and immune response genotypes were provided by the InterLymph Data Coordinating Center at Mayo Clinic. Odds ratios (OR) were estimated using multivariate logistic regression, and interactions were estimated using the empirical Bayes method. PACT was used to account for multiple comparisons. RESULTS: There was evidence of an interaction effect between IM history and two variants on T-cell lymphoma (TCL) risk: rs1143627 in interleukin-1B (IL1B) (pinteraction = 0.04, ORinteraction = 0.09, 95% confidence interval [CI] 0.01, 0.87) and rs1800797 in interleukin-6 (IL6) (pinteraction = 0.03, ORinteraction = 0.08, 95% CI 0.01, 0.80). Neither interaction effect withstood adjustment for multiple comparisons. There were no statistically significant interactions between immune response genotypes and IM on other NHL subtypes. CONCLUSIONS: Genetic risk variants in IL1B and IL6 may affect the association between IM and TCL, possibly by influencing T-cell activation, growth, and differentiation in the presence of IM, thereby decreasing risk of immune cell proliferation.
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