Daniel J Schaid1, Shannon K McDonnell2, Liesel M FitzGerald3, Lissa DeRycke4, Zachary Fogarty2, Graham G Giles5, Robert J MacInnis6, Melissa C Southey7, Tu Nguyen-Dumont8, Geraldine Cancel-Tassin9, Oliver Cussenot9, Alice S Whittemore10, Weiva Sieh11, Nilah Monnier Ioannidis12, Chih-Lin Hsieh13, Janet L Stanford14, Johanna Schleutker15, Cheryl D Cropp16, John Carpten17, Josef Hoegel18, Rosalind Eeles19, Zsofia Kote-Jarai19, Michael J Ackerman20, Christopher J Klein21, Diptasri Mandal22, Kathleen A Cooney23, Joan E Bailey-Wilson24, Brian Helfand25, William J Catalona26, Fredrick Wiklund27, Shaun Riska2, Saurabh Bahetti2, Melissa C Larson2, Lisa Cannon Albright28, Craig Teerlink28, Jianfeng Xu29, William Isaacs30, Elaine A Ostrander31, Stephen N Thibodeau32. 1. Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA. Electronic address: schaid@mayo.edu. 2. Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA. 3. Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia. 4. Specialized Services, National Marrow Donor Program, Minneapolis, MN, USA. 5. Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia; Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia; Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia. 6. Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia; Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia. 7. Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia; Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia. 8. Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia; Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia. 9. CeRePP, Tenon Hospital, Paris, France. 10. Department of Health Research and Policy, Stanford University, Stanford, CA, USA. 11. Population Health Science and Policy, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 12. Center for Computational Biology and Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA. 13. Department of Urology, University of Southern California, Los Angeles, CA, USA. 14. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. 15. Institute of Biomedicine, University of Turku, and Department of Medical Genetics, Genomics, Laboratory Division, Turku University Hospital, Turku, Finland. 16. Department of Pharmaceutical, Social and Administrative Sciences, McWhorter School of Pharmacy, Samford University, Birmingham, AL, USA. 17. Department of Translation Genomics, University of Southern California, Los Angeles, CA, USA. 18. Department of Human Genetics, University of Ulm, Ulm, Germany. 19. Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton Surrey, UK. 20. Division of Heart Rhythm Services, Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA; Division of Pediatric Cardiology, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, USA; Windland Smith Rice Sudden Death Genomics Laboratory, Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA. 21. Department of Neurology, Mayo Clinic, Rochester, MN, USA. 22. Department of Genetics, Louisiana State University Health Sciences Center, New Orleans, LA, USA. 23. Department of Medicine and Duke Cancer Institute, Duke University School of Medicine, Durham, NC, USA. 24. Computational and Statistical Genomics Branch, National Human Genome Research Institute, Baltimore, MD, USA. 25. Department of Surgery, North Shore University Health System/University of Chicago, Evanston, IL, USA. 26. Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 27. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. 28. Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA. 29. Northshore University Health System, Evanston, IL, USA. 30. Department of Urology, Johns Hopkins Hospital, Baltimore, MD, USA. 31. Cancer Genetics and Comparative Genomic Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. 32. Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
Abstract
BACKGROUND: Family history of prostate cancer (PCa) is a well-known risk factor, and both common and rare genetic variants are associated with the disease. OBJECTIVE: To detect new genetic variants associated with PCa, capitalizing on the role of family history and more aggressive PCa. DESIGN, SETTING, AND PARTICIPANTS: A two-stage design was used. In stage one, whole-exome sequencing was used to identify potential risk alleles among affected men with a strong family history of disease or with more aggressive disease (491 cases and 429 controls). Aggressive disease was based on a sum of scores for Gleason score, node status, metastasis, tumor stage, prostate-specific antigen at diagnosis, systemic recurrence, and time to PCa death. Genes identified in stage one were screened in stage two using a custom-capture design in an independent set of 2917 cases and 1899 controls. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Frequencies of genetic variants (singly or jointly in a gene) were compared between cases and controls. RESULTS AND LIMITATIONS: Eleven genes previously reported to be associated with PCa were detected (ATM, BRCA2, HOXB13, FAM111A, EMSY, HNF1B, KLK3, MSMB, PCAT1, PRSS3, and TERT), as well as an additional 10 novel genes (PABPC1, QK1, FAM114A1, MUC6, MYCBP2, RAPGEF4, RNASEH2B, ULK4, XPO7, and THAP3). Of these 10 novel genes, all but PABPC1 and ULK4 were primarily associated with the risk of aggressive PCa. CONCLUSIONS: Our approach demonstrates the advantage of gene sequencing in the search for genetic variants associated with PCa and the benefits of sampling patients with a strong family history of disease or an aggressive form of disease. PATIENT SUMMARY: Multiple genes are associated with prostate cancer (PCa) among men with a strong family history of this disease or among men with an aggressive form of PCa.
BACKGROUND: Family history of prostate cancer (PCa) is a well-known risk factor, and both common and rare genetic variants are associated with the disease. OBJECTIVE: To detect new genetic variants associated with PCa, capitalizing on the role of family history and more aggressive PCa. DESIGN, SETTING, AND PARTICIPANTS: A two-stage design was used. In stage one, whole-exome sequencing was used to identify potential risk alleles among affected men with a strong family history of disease or with more aggressive disease (491 cases and 429 controls). Aggressive disease was based on a sum of scores for Gleason score, node status, metastasis, tumor stage, prostate-specific antigen at diagnosis, systemic recurrence, and time to PCa death. Genes identified in stage one were screened in stage two using a custom-capture design in an independent set of 2917 cases and 1899 controls. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Frequencies of genetic variants (singly or jointly in a gene) were compared between cases and controls. RESULTS AND LIMITATIONS: Eleven genes previously reported to be associated with PCa were detected (ATM, BRCA2, HOXB13, FAM111A, EMSY, HNF1B, KLK3, MSMB, PCAT1, PRSS3, and TERT), as well as an additional 10 novel genes (PABPC1, QK1, FAM114A1, MUC6, MYCBP2, RAPGEF4, RNASEH2B, ULK4, XPO7, and THAP3). Of these 10 novel genes, all but PABPC1 and ULK4 were primarily associated with the risk of aggressive PCa. CONCLUSIONS: Our approach demonstrates the advantage of gene sequencing in the search for genetic variants associated with PCa and the benefits of sampling patients with a strong family history of disease or an aggressive form of disease. PATIENT SUMMARY: Multiple genes are associated with prostate cancer (PCa) among men with a strong family history of this disease or among men with an aggressive form of PCa.
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