A framework for assessing interactions for risk stratification models: the example of ovarian cancer

Author:

Phung Minh Tung1ORCID,Lee Alice W2,McLean Karen3,Anton-Culver Hoda4ORCID,Bandera Elisa V5,Carney Michael E6,Chang-Claude Jenny78,Cramer Daniel W910,Doherty Jennifer Anne11,Fortner Renee T712,Goodman Marc T1314,Harris Holly R1516,Jensen Allan17ORCID,Modugno Francesmary181920,Moysich Kirsten B21,Pharoah Paul D P22,Qin Bo5,Terry Kathryn L910,Titus Linda J23,Webb Penelope M24,Wu Anna H25,Zeinomar Nur5ORCID,Ziogas Argyrios4ORCID,Berchuck Andrew26,Cho Kathleen R27,Hanley Gillian E28,Meza Rafael129,Mukherjee Bhramar130ORCID,Pike Malcolm C2531,Pearce Celeste Leigh1,Trabert Britton3233

Affiliation:

1. Department of Epidemiology, University of Michigan School of Public Health , Ann Arbor, MI, USA

2. Department of Public Health, California State University , Fullerton, Fullerton, CA, USA

3. Department of Gynecologic Oncology and Department of Pharmacology & Therapeutics, Elm & Carlton Streets, Roswell Park Comprehensive Cancer Center , Buffalo, NY, USA

4. Department of Medicine, University of California, Irvine , Irvine, CA, USA

5. Cancer Epidemiology and Health Outcomes, Rutgers Cancer Institute of New Jersey , New Brunswick, NJ, USA

6. Department of Obstetrics and Gynecology, John A. Burns School of Medicine, University of Hawaii , Honolulu, HI, USA

7. Division of Cancer Epidemiology, German Cancer Research Center (DKFZ) , Heidelberg, Germany

8. Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf , Hamburg, Germany

9. Department of Epidemiology, Harvard T.H. Chan School of Public Health , Boston, MA, USA

10. Department of Obstetrics and Gynecology, Brigham and Women’s Hospital and Harvard Medical School , Boston, MA, USA

11. Huntsman Cancer Institute, Department of Population Health Sciences, University of Utah , Salt Lake City, UT, USA

12. Department of Research, Cancer Registry of Norway , Oslo, Norway

13. Samuel Oschin Comprehensive Cancer Institute, Cancer Prevention and Genetics Program, Cedars-Sinai Medical Center , Los Angeles, CA, USA

14. Community and Population Health Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center , Los Angeles, CA, USA

15. Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center , Seattle, WA, USA

16. Department of Epidemiology, University of Washington School of Public Health , Seattle, WA, USA

17. Department of Lifestyle, Reproduction and Cancer, Danish Cancer Society Research Center , Copenhagen, Denmark

18. Women’s Cancer Research Center, Magee-Women’s Research Institute and Hillman Cancer Center , Pittsburgh, PA, USA

19. Division of Gynecologic Oncology, Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine , Pittsburgh, PA, USA

20. Department of Epidemiology, University of Pittsburgh Graduate School of Public Health , Pittsburg, PA, USA

21. Division of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center , Buffalo, NY, USA

22. Department of Computational Biomedicine, Cedars-Sinai Medical Centre , Los Angeles, CA, USA

23. Public Health, Muskie School of Public Service, University of Southern Maine , Portland, ME, USA

24. Population Health Program, QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia

25. Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California , Los Angeles, CA, USA

26. Division of Gynecologic Oncology, Duke University School of Medicine , Durham, NC, USA

27. Department of Pathology, University of Michigan Medical School , Ann Arbor, MI, USA

28. Department of Obstetrics & Gynecology, University of British Columbia Faculty of Medicine , Vancouver, BC, Canada

29. Department of Integrative Oncology, BC Cancer Research Institute , Vancouver, BC, Canada

30. Department of Biostatistics, University of Michigan School of Public Health , Ann Arbor, MI, USA

31. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center , New York, NY, USA

32. Department of Obstetrics and Gynecology, University of Utah , Salt Lake City, UT, USA

33. Cancer Control and Populations Sciences Program, Huntsman Cancer Institute at the University of Utah , Salt Lake City, UT, USA

Abstract

Abstract Generally, risk stratification models for cancer use effect estimates from risk/protective factor analyses that have not assessed potential interactions between these exposures. We have developed a 4-criterion framework for assessing interactions that includes statistical, qualitative, biological, and practical approaches. We present the application of this framework in an ovarian cancer setting because this is an important step in developing more accurate risk stratification models. Using data from 9 case-control studies in the Ovarian Cancer Association Consortium, we conducted a comprehensive analysis of interactions among 15 unequivocal risk and protective factors for ovarian cancer (including 14 non-genetic factors and a 36-variant polygenic score) with age and menopausal status. Pairwise interactions between the risk/protective factors were also assessed. We found that menopausal status modifies the association among endometriosis, first-degree family history of ovarian cancer, breastfeeding, and depot-medroxyprogesterone acetate use and disease risk, highlighting the importance of understanding multiplicative interactions when developing risk prediction models.

Funder

Ovarian Cancer Association Consortium

Ovarian Cancer Research Fund

National Cancer Institute

Wellcome Trust

National Human Genome Research Institute

National Institutes of Health

Canadian Institutes of Health Research

Army Medical Research and Materiel Command

National Health & Medical Research Council of Australia

Cancer Councils of New South Wales

Cancer Foundation of Western Australia

AstraZeneca

Publisher

Oxford University Press (OUP)

Subject

Cancer Research,Oncology

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