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Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status
  1. Anika Hüsing1,
  2. Federico Canzian2,
  3. Lars Beckmann1,
  4. Montserrat Garcia-Closas3,
  5. W Ryan Diver4,
  6. Michael J Thun4,
  7. Christine D Berg3,
  8. Robert N Hoover3,
  9. Regina G Ziegler3,
  10. Jonine D Figueroa3,
  11. Claudine Isaacs5,
  12. Anja Olsen6,
  13. Vivian Viallon7,
  14. Heiner Boeing8,
  15. Giovanna Masala9,
  16. Dimitrios Trichopoulos10,
  17. Petra H M Peeters11,
  18. Eiliv Lund12,
  19. Eva Ardanaz13,
  20. Kay-Tee Khaw14,
  21. Per Lenner15,
  22. Laurence N Kolonel16,
  23. Daniel O Stram17,
  24. Loïc Le Marchand16,
  25. Catherine A McCarty18,
  26. Julie E Buring19,20,
  27. I-Min Lee19,
  28. Shumin Zhang20,
  29. Sara Lindström21,
  30. Susan E Hankinson20,
  31. Elio Riboli22,
  32. David J Hunter21,
  33. Brian E Henderson17,
  34. Stephen J Chanock3,
  35. Christopher A Haiman17,
  36. Peter Kraft21,
  37. Rudolf Kaaks1,
  38. on behalf of the BPC3
  1. 1Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
  2. 2Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
  3. 3Division of Genetics and Epidemiology, and Breakthrough Breast Cancer Centre, Institute of Cancer Research, London, UK
  4. 4Epidemiology Research Program, American Cancer Society, Atlanta, Georgia, USA
  5. 5Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
  6. 6Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen, Denmark
  7. 7INSERM U521, Institut Gustave Roussy, Villejuif, France
  8. 8Department of Epidemiology, Deutsches Institut für Ernährungsforschung, Potsdam-Rehbrücke, Germany
  9. 9Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute (ISPO), Florence, Italy
  10. 10Department of Epidemiology, Harvard School of Public Health, USA and Bureau of Epidemiologic Research, Academy of Athens, Athens, Greece
  11. 11Julius Center, University Medical Center, Utrecht, The Netherlands
  12. 12Institute of Community Medicine, University of Tromsø, Tromsø, Norway
  13. 13Navarre Public Health Institute, Pamplona, Spain and Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pública-CIBERESP), Spain
  14. 14School of Clinical Medicine, University of Cambridge, Cambridge, UK
  15. 15Department of Oncology, Umeå University, Umeå, Sweden
  16. 16Cancer Research Center, University of Hawaii, Honolulu, Hawaii, USA
  17. 17University of Southern California, Los Angeles, California, USA
  18. 18The Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA
  19. 19Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
  20. 20Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
  21. 21Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
  22. 22School of Public Health, Imperial College, London, UK
  1. Correspondence toDr Anika Hüsing, Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, Heidelberg 69120, Germany; a.huesing{at}


Objective There is increasing interest in adding common genetic variants identified through genome wide association studies (GWAS) to breast cancer risk prediction models. First results from such models showed modest benefits in terms of risk discrimination. Heterogeneity of breast cancer as defined by hormone-receptor status has not been considered in this context. In this study we investigated the predictive capacity of 32 GWAS-detected common variants for breast cancer risk, alone and in combination with classical risk factors, and for tumours with different hormone receptor status.

Material and methods Within the Breast and Prostate Cancer Cohort Consortium, we analysed 6009 invasive breast cancer cases and 7827 matched controls of European ancestry, with data on classical breast cancer risk factors and 32 common gene variants identified through GWAS. Discriminatory ability with respect to breast cancer of specific hormone receptor-status was assessed with the age adjusted and cohort-adjusted concordance statistic (AUROCa). Absolute risk scores were calculated with external reference data. Integrated discrimination improvement was used to measure improvements in risk prediction.

Results We found a small but steady increase in discriminatory ability with increasing numbers of genetic variants included in the model (difference in AUROCa going from 2.7% to 4%). Discriminatory ability for all models varied strongly by hormone receptor status.

Discussion and conclusions Adding information on common polymorphisms provides small but statistically significant improvements in the quality of breast cancer risk prediction models. We consistently observed better performance for receptor-positive cases, but the gain in discriminatory quality is not sufficient for clinical application.

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