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.
The development of risk stratification approaches to identify individuals who would most benefit from primary prevention strategies has become increasingly important. Risk stratification models use the effect estimates for the risk/protective factors considered to be unequivocal in their association with the disease under study. Generally, the effect estimates come from analyses in which multiplicative relationships were assumed among risk and protective factors. Using invasive epithelial ovarian cancer (ovarian cancer), we offer a strategy for the initial steps needed to develop accurate risk stratification models, including a 4-criterion framework for assessing whether potential interactions should be included. Interaction analyses are notoriously underpowered, so using this framework ensures that important differences that may indicate departures from multiplicativity are not missed.
Criterion A (statistical approach): A likelihood ratio test comparing a logistic model with the interaction term vs the same model without the interaction term (a 2-sided P < .05 for interaction was considered statistically significant was used here, but other statistical approaches could be used);
Criterion B (qualitative approach): Comparing the consistency and magnitude of the odds ratios (ORs) of a factor across the levels of the other factor (visualization from stratified analysis);
Criterion C (biological approach): Considering biological plausibility; and
Criterion D (practical approach): Assessing the prevalence of the risk/protective factors to determine whether an interaction would have a meaningful impact on the risk stratification model.
Ovarian cancer is an ideal example for refining risk stratification approaches because primary prevention strategies are available for women at both average and high risk, including risk-reducing salpingo-oophorectomy, opportunistic salpingectomy, tubal ligation, and possibly hormonal contraceptives (1-4). Unequivocal ovarian cancer risk and protective factors include 14 non-genetic factors (4-14) and a 36-variant polygenic score for ovarian cancer (15) (15 factors are shown in Supplementary Table 1, available online). Importantly for ovarian and many other cancers affecting women, the effects of age and menopausal status on the risk/protective factors must first be disentangled to determine whether one, both, or neither modifies the associations (16).
We applied the framework to questionnaire data from 9 Ovarian Cancer Association Consortium case-control studies from Australia (17), Germany (18), and the United States (19-25). Institutional review board approval was obtained by the original studies, and all participants had provided written informed consent. To determine whether there was an age interaction, a menopausal status interaction, or both, the initial ovarian cancer and risk and protective factor analyses were conducted among participants in the following strata (Table 1):
Table 1.
Characteristics of participants with (cases) and without (controls) ovarian cancer included in the analysis, by age and menopausal status group
| Premenopausal women younger than 45 years |
Premenopausal women aged 45-54 years |
Postmenopausal women aged 45-54 years |
Postmenopausal women aged 55-64 years |
Postmenopausal women aged 65-84 years |
||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Case participants | Control participants | Case participants | Control participants | Case participants | Control participants | Case participants | Control participantss | Case participants | Control participants | |||
| (n = 965) | (n = 2111) | (n = 1269) | (n = 2109) | (n = 903) | (n = 1214) | (n = 2493) | (n = 3502) | (n = 2226) | (n = 3148) | |||
| Ovarian Cancer Association Consortium study, No. (%) | ||||||||||||
| AUS | 2001-2005 | Australia | 114 (11.8) | 266 (12.6) | 183 (14.4) | 226 (10.7) | 134 (14.8) | 146 (12.0) | 479 (19.2) | 454 (13.0) | 435 (19.5) | 390 (12.4) |
| DOV | 2002-2009 | Washington, USA | 116 (12.0) | 182 (8.6) | 209 (16.5) | 311 (14.7) | 99 (11.0) | 122 (10.0) | 425 (17.0) | 660 (18.8) | 224 (10.1) | 414 (13.2) |
| GER | 1993-1998 | Germany | 26 (2.7) | 90 (4.3) | 25 (2.0) | 66 (3.1) | 15 (1.7) | 53 (4.4) | 72 (2.9) | 175 (5.0) | 42 (1.9) | 125 (4.0) |
| HAW | 1993-2008 | Hawaii, USA | 105 (10.9) | 246 (11.7) | 89 (7.0) | 174 (8.3) | 111 (12.3) | 127 (10.5) | 175 (7.0) | 240 (6.9) | 203 (9.1) | 290 (9.2) |
| HOP | 2003-2009 | Western Pennsylvania, northeast Ohio, western New York, USA | 64 (6.6) | 176 (8.3) | 120 (9.5) | 354 (16.8) | 34 (3.8) | 125 (10.3) | 208 (8.3) | 489 (14.0) | 252 (11.3) | 534 (17.0) |
| NEC | 1992-2008 | New Hampshire, eastern Massachusetts, USA | 235 (24.4) | 496 (23.5) | 249 (19.6) | 354 (16.8) | 187 (20.7) | 214 (17.6) | 422 (16.9) | 542 (15.5) | 347 (15.6) | 452 (14.4) |
| NJO | 2002-2009 | New Jersey, USA | 22 (2.3) | 19 (0.9) | 50 (3.9) | 43 (2.0) | 20 (2.2) | 21 (1.7) | 77 (3.1) | 154 (4.4) | 45 (2.0) | 205 (6.5) |
| UCI | 1994-2005 | Southern California, USA | 41 (4.2) | 132 (6.3) | 74 (5.8) | 99 (4.7) | 33 (3.7) | 74 (6.1) | 101 (4.1) | 150 (4.3) | 117 (5.3) | 140 (4.4) |
| USC | 1993-2010 | Los Angeles, California, USA | 242 (25.1) | 504 (23.9) | 270 (21.3) | 482 (22.9) | 270 (29.9) | 332 (27.3) | 534 (21.4) | 638 (18.2) | 561 (25.2) | 598 (19.0) |
| Age at diagnosis for cases/reference age for controls, year | ||||||||||||
| Mean (SD) | 38.3 (5.28) | 36.9 (5.92) | 48.9 (2.63) | 48.7 (2.63) | 51.4 (2.31) | 51.4 (2.34) | 59.6 (2.77) | 59.5 (2.78) | 70.8 (4.46) | 70.9 (4.49) | ||
| Median (Min, Max) | 40.0 (20.0, 44.0) | 38.0 (18.0, 44.0) | 49.0 (45.0, 54.0) | 49.0 (45.0, 54.0) | 52.0 (45.0, 54.0) | 52.0 (45.0, 54.0) | 60.0 (55.0, 64.0) | 59.0 (55.0, 64.0) | 70.0 (65.0, 84.0) | 70.0 (65.0, 84.0) | ||
| Race/ethnicity, No. (%) | ||||||||||||
| Asian | 111 (11.5) | 148 (7.0) | 108 (8.5) | 121 (5.7) | 69 (7.6) | 61 (5.0) | 100 (4.0) | 99 (2.8) | 130 (5.8) | 158 (5.0) | ||
| Black | 30 (3.1) | 54 (2.6) | 24 (1.9) | 45 (2.1) | 28 (3.1) | 24 (2.0) | 57 (2.3) | 53 (1.5) | 35 (1.6) | 52 (1.7) | ||
| Hispanic White | 67 (6.9) | 135 (6.4) | 49 (3.9) | 92 (4.4) | 59 (6.5) | 67 (5.5) | 120 (4.8) | 110 (3.1) | 70 (3.1) | 56 (1.8) | ||
| Non-Hispanic White | 691 (71.6) | 1603 (75.9) | 1044 (82.3) | 1749 (82.9) | 688 (76.2) | 1005 (82.8) | 2121 (85.1) | 3116 (89.0) | 1927 (86.6) | 2778 (88.2) | ||
| Othera | 62 (6.4) | 157 (7.4) | 41 (3.2) | 97 (4.6) | 54 (6.0) | 55 (4.5) | 84 (3.4) | 119 (3.4) | 58 (2.6) | 99 (3.1) | ||
| Missing | 4 (0.4) | 14 (0.7) | 3 (0.2) | 5 (0.2) | 5 (0.6) | 2 (0.2) | 11 (0.4) | 5 (0.1) | 6 (0.3) | 5 (0.2) | ||
| Education level, No. (%) | ||||||||||||
| Less than high school | 55 (5.7) | 95 (4.5) | 87 (6.9) | 105 (5.0) | 86 (9.5) | 89 (7.3) | 352 (14.1) | 348 (9.9) | 467 (21.0) | 439 (13.9) | ||
| High school | 205 (21.2) | 389 (18.4) | 261 (20.6) | 374 (17.7) | 172 (19.0) | 251 (20.7) | 585 (23.5) | 805 (23.0) | 634 (28.5) | 932 (29.6) | ||
| Some college | 297 (30.8) | 605 (28.7) | 376 (29.6) | 644 (30.5) | 275 (30.5) | 374 (30.8) | 742 (29.8) | 1021 (29.2) | 593 (26.6) | 869 (27.6) | ||
| College graduate or above | 396 (41.0) | 959 (45.4) | 529 (41.7) | 935 (44.3) | 346 (38.3) | 461 (38.0) | 742 (29.8) | 1237 (35.3) | 441 (19.8) | 810 (25.7) | ||
| Missing | 12 (1.2) | 63 (3.0) | 16 (1.3) | 51 (2.4) | 24 (2.7) | 39 (3.2) | 72 (2.9) | 91 (2.6) | 91 (4.1) | 98 (3.1) | ||
Other includes mixed race and those that do not belong in one of the specified racial/ethnic groups. AUS = Australian Ovarian Cancer Study; DOV = Diseases of the Ovary and their Evaluation; GER = German Ovarian Cancer Study; HAW = Hawaii Ovarian Cancer Case-Control Study; HOP = Hormones and Ovarian Cancer Prediction; NEC = New England Case Control Study; NJO = New Jersey Ovarian Cancer Study; SD = Standard deviation; USA = United States of America; UCI = University California Irvine Ovarian Study; USC = Study of Lifestyle and Women’s Health.
Stratum 1: Younger than 45 years of age and premenopausal
Stratum 2: Aged 45 to 54 years and premenopausal
Stratum 3: Aged 45 to 54 years and postmenopausal
Stratum 4: Aged 55 to 64 years and postmenopausal
Stratum 5: Aged 65 to 84 years and postmenopausal
We found differences in the associations between the risk/protective factors for ovarian cancer by menopausal status but not by age (particularly informed by comparing results between strata 2 and 3; Supplementary Table 2, A-D, available online) based on the 4-criterion interaction evaluation framework described earlier.
Menopausal status appeared to modify the associations between ovarian cancer risk and endometriosis, first-degree family history of ovarian cancer, breastfeeding, and depot-medroxyprogesterone acetate use (Table 2). For example, a self-reported history of endometriosis was associated with a greater increase in risk of ovarian cancer among premenopausal women than among postmenopausal participants (P = .04 for interaction; criterion A). Moreover, although no standardized definitions exist on how different the 2 stratum-specific associations should be for a factor to be an effect modifier, it is widely accepted that an OR less than 1.5 is considered a small effect size, while an OR between 1.5-2.0 is considered medium (26). Thus, the magnitude of the difference in the endometriosis association between premenopausal (OR = 1.94) and postmenopausal (OR = 1.33) women is qualitatively meaningful (criterion B). Further, the endometriosis-menopausal status interaction is biologically plausible (criterion C) because during the premenopausal period, endometriosis is active (ovulatory proinflammatory and proliferative processes) (27-30), whereas endometriosis is generally quiescent in the postmenopausal period (31). Finally, endometriosis is estimated to have a prevalence of up to 10% in the general population (32); thus, it is sufficiently common to warrant fitting separate risk stratification models for pre- and postmenopausal women to be able to incorporate different effect estimates for endometriosis (criterion D).
Table 2.
Associations between risk/protective factors and ovarian cancer that differed by menopausal status among women aged 45-54 years
| Risk/protective factor | All women aged 45-54 years (pre- and postmenopausal combined) |
Premenopausal women aged 45-54 years |
Postmenopausal women aged 45-54 years |
||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Case participants,a No. | Control participants,a No. | ORb (95% CI) | Case participants,a No. | Control participants,a No. | ORb(95% CI) | Case participants,a No. | Control participants,a No. | ORb (95% CI) | P- interaction c | Interaction criteria metd | |
| Breastfeeding | |||||||||||
| Never | 1212 | 1278 | 1.0 | 693 | 763 | 1.0 | 519 | 515 | 1.0 | ||
| <12 months | 551 | 978 | 0.76 (0.64 to 0.89) | 327 | 595 | 0.78 (0.62 to 0.98) | 224 | 383 | 0.71 (0.55 to 0.92) | ||
| ≥12 months | 397 | 998 | 0.59 (0.49 to 0.72) | 240 | 706 | 0.53 (0.42 to 0.68) | 157 | 292 | 0.69 (0.51 to 0.94) | .064 | (b), (c), (d) |
| Depot-medroxyprogesterone acetate use | |||||||||||
| No | 1886 | 2793 | 1.0 | 1109 | 1793 | 1.0 | 777 | 1000 | 1.0 | ||
| Yes | 21 | 76 | 0.61 (0.36 to 1.02) | 10 | 52 | 0.51 (0.26 to 1.00) | 11 | 24 | 0.80 (0.38 to 1.69) | .35 | (b), (c), (d) |
| First-degree family history of ovarian cancer | |||||||||||
| No | 1610 | 2548 | 1.0 | 943 | 1633 | 1.0 | 667 | 915 | 1.0 | ||
| Yes | 119 | 87 | 2.15 (1.56 to 2.97) | 69 | 48 | 2.43 (1.58 to 3.73) | 50 | 39 | 1.83 (1.15 to 2.91) | .39 | (b), (c), (d) |
| Endometriosis | |||||||||||
| No | 1889 | 3058 | 1.0 | 1128 | 1981 | 1.0 | 761 | 1077 | 1.0 | ||
| Yes | 269 | 255 | 1.60 (1.32 to 1.95) | 135 | 123 | 1.94 (1.47 to 2.57) | 134 | 132 | 1.33 (1.00 to 1.76) | .041 | (a), (b), (c), (d) |
Numbers may not sum to total because of missing values. CI = confidence interval; OR = odds ratio.
Pooled estimates from logistic regression models in the 50 imputed datasets, adjusted for age at diagnosis for cases/reference age for controls (45-49 years vs 50-54 years), race/ethnicity, education level, and Ovarian Cancer Association Consortium study.
P value for interaction between risk or protective factor and menopausal status using the likelihood ratio test.
Criteria to assess interactions: (a) P < .05 for interaction; (b) odds ratios of a factor across the levels of the other factor are consistent, and the differences in magnitude are large; (c) the interaction is biologically plausible; and (d) the prevalence of the risk factors is large enough so that the interaction would have a meaningful impact on the risk stratification model.
Given that 4 risk and protective factors suggest an interaction with menopausal status based on our framework, including one that met all 4 criteria, we further evaluated pairwise interactions between the risk and protective factors separately for pre- and postmenopausal women. Ultimately, our application of the framework led to the decision that there were no meaningful interactions among the 14 environmental factors or the polygenic score within the pre- or postmenopausal groups. As an example, among premenopausal women, the pairwise interaction between family history and parity was statistically significant (P = .022 for interaction; criterion A; Supplementary Table 2, O, available online). Parity also appeared to be more protective among women with a family history of ovarian cancer (OR = 0.25 for 3+ parity compared with nulliparity) vs women without a family history (OR = 0.52) (criterion B); this interaction may also be biologically plausible (criterion C). Elevated progesterone levels during pregnancy may clear genetically abnormal cells in the Fallopian tube fimbriae (33), which may preferentially benefit genetically driven ovarian cancers (34). Although this potential pairwise interaction may be useful for individual-level precision prevention, it would have minor impacts on ovarian cancer risk stratification because of the low proportion of people with a positive family history of ovarian cancer [approximately 2% (35)] as well as the low absolute risk of ovarian cancer among premenopausal women (36) (criterion D). Thus, we concluded that it is not necessary to include an interaction term for family history and parity in a risk stratification model.
Our proposed framework has some level of subjectivity. The risk associations for 3 of the 4 risk factors that drove our conclusion that associations differ by menopausal status were not statistically significantly different in the 2 strata (criterion A) but met the other 3 criteria used for evaluation. Some investigators, however, may want to prioritize statistical significance (either using the interaction test presented here or using the Bayes false-positive probability) over the other 3 criteria and only use criteria B through D to decide against there being an interaction. Operationally, we decided that criterion A or B must be met before criteria C and D are considered. When criteria conflict with each other, however, we considered all criteria to inform our decision-making process (see the examples earlier). Another example is the age-parity interaction among postmenopausal women. The interaction was statistically significant (P = .009 for interaction; criterion A) and the prevalence of ever having given birth [85% (37)] is sufficient for this potential interaction to have a meaningful impact on risk stratification (criterion D). There was no pattern in the odds ratios for parity across the age groups (Supplementary Table 2, C, available online; criterion B), suggesting that this is a chance finding. We therefore determined, based on applying our framework, that this was not an interaction that should be incorporated into a risk stratification model.
In conclusion, the application of our 4-criterion interaction evaluation framework (Supplementary Tables 2, A-F, available online) demonstrates that menopausal status modifies the association of at least one ovarian cancer risk/protective factor and the disease risk, supporting the use of separate models by menopausal status in risk stratification. The menopausal status–risk factors interactions are likely not influenced by histotype because the distributions are similar between pre- and postmenopausal women aged 45 to 54 years (Supplementary Table 3, available online). The finding of no age–risk factor interactions could in part be due to the differences in histotype distributions across age groups. Interaction analyses stratified by histotype, however, would not be meaningful because of the small sample size of the rare histotypes. Additional research in prospective cohorts is needed to estimate absolute risk incorporating interactions to assess their impact on risk stratification.
To develop meaningful risk stratification models, it is critical first to comprehensively assess interactions using statistical, qualitative, biological, and practical approaches (criteria A-D). Many published cancer risk stratification models either do not consider interactions or are based solely on P values (criterion A) to assess interactions (38-44). This approach has limitations because P values vary according to sample size, and there are issues related to multiple comparison. As such, we propose a framework that co-emphasizes the statistical (criterion A) and qualitative (criterion B) approaches and also includes the biological approach (criterion C) and practical approach (criterion D). Comprehensive interaction analysis for risk stratification can most effectively be done within consortia with large sample sizes. Continued collaboration in the field is necessary, and using the data fully must be a priority to move closer to realizing the goals of precision cancer prevention.
Supplementary Material
Acknowledgements
We are grateful to the family and friends of Kathryn Sladek Smith for their generous support of the Ovarian Cancer Association Consortium through their donations to the Ovarian Cancer Research Fund. The OncoArray and COGS genotyping projects would not have been possible without the contributions of the following: Per Hall (COGS); Douglas F. Easton, Kyriaki Michailidou, Manjeet K. Bolla, Qin Wang (BCAC); Marjorie J. Riggan (OCAC); Rosalind A. Eeles, Douglas F. Easton, Ali Amin Al Olama, Zsofia Kote-Jarai, and Sara Benlloch (PRACTICAL); Georgia Chenevix-Trench, Antonis Antoniou, Lesley McGuffog, Fergus Couch, and Ken Offit (CIMBA); Joe Dennis, Jonathan P. Tyrer, Siddhartha Kar, Alison M. Dunning, Andrew Lee, and Ed Dicks; Craig Luccarini and the staff of the Centre for Genetic Epidemiology Laboratory; Javier Benitez, Anna Gonzalez-Neira, and the staff of the CNIO genotyping unit; Jacques Simard and Daniel C. Tessier; Francois Bacot, Daniel Vincent, Sylvie LaBoissière, Frederic Robidoux, and the staff of McGill University and Génome Québec Innovation Centre; Stig E. Bojesen, Sune F. Nielsen, Borge G. Nordestgaard, and the staff of the Copenhagen DNA laboratory; and Julie M. Cunningham, Sharon A. Windebank, Christopher A. Hilker, Jeffrey Meyer, and the staff of Mayo Clinic Genotyping Core Facility. We pay special tribute to the contribution of Brian Henderson to the GAME-ON consortium until he sadly passed away on June 20, 2015; to Olga M. Sinilnikova for her contribution to CIMBA and for her part in the initiation and coordination of GEMO until she sadly passed away on June 30, 2014, and to Catherine M. Phelan for her contribution to OCAC and coordination of the OncoArray until she passed away on September 22, 2017. We thank the study participants, doctors, nurses, clinical and scientific collaborators, health care providers, and health information sources who have contributed to the many studies contributing to this manuscript.
Acknowledgements for individual studies: AUS: The AOCS also acknowledges the cooperation of the participating institutions in Australia and the contribution of the study nurses, research assistants, and all clinical and scientific collaborators. The complete AOCS Study Group can be found at www.aocstudy.org. We would like to thank all the women who participated in this research program; GER: The German Ovarian Cancer Study (GER) thank Ursula Eilber for competent technical assistance; NJO: We thank Drs Sara Olson, Lisa Paddock, Lorna Rodriguez, and all participants and research staff at the Rutgers Cancer Institute of New Jersey, the New Jersey State Cancer Registry, and Memorial Sloan Kettering Cancer Center.
The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Earlier results were presented at the American Association for Cancer Research annual meeting in June 2020 as an e-poster.
Contributor Information
Minh Tung Phung, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA.
Alice W Lee, Department of Public Health, California State University, Fullerton, Fullerton, CA, USA.
Karen McLean, Department of Gynecologic Oncology and Department of Pharmacology & Therapeutics, Elm & Carlton Streets, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
Hoda Anton-Culver, Department of Medicine, University of California, Irvine, Irvine, CA, USA.
Elisa V Bandera, Cancer Epidemiology and Health Outcomes, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA.
Michael E Carney, Department of Obstetrics and Gynecology, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, USA.
Jenny Chang-Claude, Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Daniel W Cramer, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Obstetrics and Gynecology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA.
Jennifer Anne Doherty, Huntsman Cancer Institute, Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA.
Renee T Fortner, Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Research, Cancer Registry of Norway, Oslo, Norway.
Marc T Goodman, Samuel Oschin Comprehensive Cancer Institute, Cancer Prevention and Genetics Program, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Community and Population Health Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Holly R Harris, Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA.
Allan Jensen, Department of Lifestyle, Reproduction and Cancer, Danish Cancer Society Research Center, Copenhagen, Denmark.
Francesmary Modugno, Women’s Cancer Research Center, Magee-Women’s Research Institute and Hillman Cancer Center, Pittsburgh, PA, USA; Division of Gynecologic Oncology, Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburg, PA, USA.
Kirsten B Moysich, Division of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
Paul D P Pharoah, Department of Computational Biomedicine, Cedars-Sinai Medical Centre, Los Angeles, CA, USA.
Bo Qin, Cancer Epidemiology and Health Outcomes, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA.
Kathryn L Terry, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Obstetrics and Gynecology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA.
Linda J Titus, Public Health, Muskie School of Public Service, University of Southern Maine, Portland, ME, USA.
Penelope M Webb, Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
Anna H Wu, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Nur Zeinomar, Cancer Epidemiology and Health Outcomes, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA.
Argyrios Ziogas, Department of Medicine, University of California, Irvine, Irvine, CA, USA.
Andrew Berchuck, Division of Gynecologic Oncology, Duke University School of Medicine, Durham, NC, USA.
Kathleen R Cho, Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA.
Gillian E Hanley, Department of Obstetrics & Gynecology, University of British Columbia Faculty of Medicine, Vancouver, BC, Canada.
Rafael Meza, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada.
Bhramar Mukherjee, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA; Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
Malcolm C Pike, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Celeste Leigh Pearce, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA.
Britton Trabert, Department of Obstetrics and Gynecology, University of Utah, Salt Lake City, UT, USA; Cancer Control and Populations Sciences Program, Huntsman Cancer Institute at the University of Utah, Salt Lake City, UT, USA.
Data availability
The data generated in this study are not publicly available because of limitations imposed by the original studies in which these data were collected. The corresponding author will facilitate access through existing data request processes for the Ovarian Cancer Association Consortium.
Author contributions
Minh Tung Phung, PhD, MPH (Conceptualization; Formal analysis; Methodology; Writing—original draft; Writing—review & editing), Malcolm C. Pike, PhD (Funding acquisition; Writing—review & editing), Bhramar Mukherjee, PhD (Writing—review & editing), Rafael Meza, PhD (Writing—review & editing), Gillian E. Hanley, PhD (Writing—review & editing), Kathleen R. Cho, MD (Writing—review & editing), Andrew Berchuck, MD (Funding acquisition; Writing—review & editing), Argyrios Ziogas, PhD (Funding acquisition; Writing—review & editing), Nur Zeinomar, PhD (Funding acquisition; Writing—review & editing), Anna H. Wu, PhD (Funding acquisition; Writing—review & editing), Penelope M. Webb, PhD (Funding acquisition; Writing—review & editing), Linda J. Titus, PhD (Funding acquisition; Writing—review & editing), Kathryn L. Terry, ScD (Funding acquisition; Writing—review & editing), Bo Qin, PhD (Funding acquisition; Writing—review & editing), Celeste Leigh Pearce, PhD, MPH (Conceptualization; Funding acquisition; Methodology; Resources; Supervision; Writing—original draft; Writing—review & editing), Paul D. P. Pharoah, PhD (Funding acquisition; Writing—review & editing), Francesmary Modugno, PhD, MPH (Funding acquisition; Writing—review & editing), Allan Jensen, PhD (Funding acquisition; Writing—review & editing), Holly R. Harris, ScD, MPH (Funding acquisition; Writing—review & editing), Marc T. Goodman, PhD (Funding acquisition; Writing—review & editing), Renee T. Fortner, PhD (Funding acquisition; Writing—review & editing), Jennifer Anne Doherty, MS, PhD (Funding acquisition; Writing—review & editing), Daniel W. Cramer, MD, ScD (Funding acquisition; Writing—review & editing), Jenny Chang-Claude, PhD (Funding acquisition; Writing—review & editing), Michael E. Carney, MD (Funding acquisition; Writing—review & editing), Elisa V. Bandera, MD, PhD (Funding acquisition; Writing—review & editing), Hoda Anton-Culver, PhD (Funding acquisition; Writing—review & editing), Karen McLean, MD, PhD (Writing—review & editing), Alice W. Lee, PhD, MPH (Writing—review & editing), Kirsten B. Moysich, MS, PhD (Funding acquisition; Writing—review & editing), Britton Trabert, PhD (Conceptualization; Funding acquisition; Methodology; Supervision; Writing—original draft; Writing—review & editing).
Funding
The Ovarian Cancer Association Consortium is supported by a grant from the Ovarian Cancer Research Fund thanks to donations by the family and friends of Kathryn Sladek Smith (PPD/RPCI.07). The scientific development and funding for this project were in part supported by the US National Cancer Institute GAME-ON (Genetic Associations and Mechanisms in Oncology) Post-GWAS Initiative (U19-CA148112). This study used data generated by the Wellcome Trust Case Control consortium, which was funded by the Wellcome Trust under award No. 076113. The results published here are in part based on data generated by The Cancer Genome Atlas Pilot Project established by the National Cancer Institute and National Human Genome Research Institute (dbGap accession No. phs000178.v8.p7).
The Ovarian Cancer Association Consortium OncoArray genotyping project was funded through grants from the US National Institutes of Health (CA1X01HG007491-01, U19-CA148112, R01-CA149429, and R01-CA058598; Canadian Institutes of Health Research (MOP-86727 and the Ovarian Cancer Research Fund (A.B.). The COGS project was funded through a European Commission’s Seventh Framework Programme grant (agreement No. 223175 - HEALTH-F2-2009-223175).
Funding for individual studies: AUS: The Australian Ovarian Cancer Study was supported by the US Army Medical Research and Materiel Command (DAMD17-01-1-0729); National Health & Medical Research Council of Australia (199600, 400413, and 400281); Cancer Councils of New South Wales, Victoria, Queensland, South Australia, and Tasmania; and Cancer Foundation of Western Australia (Multi-State Applications 191, 211, and 182). The Australian Ovarian Cancer Study gratefully acknowledges additional support from Ovarian Cancer Australia and the Peter MacCallum Foundation; DOV: National Institutes of Health R01-CA112523 and R01-CA87538; GER: German Federal Ministry of Education and Research, Programme of Clinical Biomedical Research (01 GB 9401), and the German Cancer Research Center (DKFZ); HAW: US National Institutes of Health (R01-CA58598, N01-CN-55424, and N01-PC-67001); HOP: University of Pittsburgh School of Medicine Dean’s Faculty Advancement Award (F. Modugno), US Department of Defense (DAMD17-02-1-0669), and National Cancer Institute (K07-CA080668, R01-CA95023, P50-CA159981, MO1-RR000056, R01-CA126841); NEC: R01-CA54419 and P50-CA105009 and US Department of Defense W81XWH-10-1-02802; NJO: National Cancer Institute (NIH-K07 CA095666, R01-CA83918, NIH-K22-CA138563, and P30-CA072720) and the Rutgers Cancer Institute of New Jersey; UCI: Australian Ovarian Cancer Study R01-CA058860 and the Lon V. Smith Foundation grant LVS-39420; USC: P01CA17054, P30CA14089, R01CA61132, N01PC67010, R03CA113148, R03CA115195, N01CN025403, and California Cancer Research Program (00-01389 V-20170, 2II0200).
Funding for individuals: B.T.: Cancer Center Support Grant, National Cancer Institute, P30CA040214; P.M.W.: National Health & Medical Research Council of Australia GNT1173346; C.L.P.: the National Institutes of Health/National Cancer Institute Support Grant P30 CA046592. M.C.P. was supported in part through the National Institutes of Health/National Cancer Institute Support Grant P30 CA008748 (P.I. S.M. Vickers) to Memorial Sloan Kettering Cancer Center.
Conflicts of interest
P.M.W. has received a speaker’s fee and funding from AstraZeneca (2017-2020) for an unrelated study of ovarian cancer. E.V.B. served in Pfizer’s Advisory Board to enhance participation of people from minority groups in clinical trials. Other authors do not have any conflicts of interest.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data generated in this study are not publicly available because of limitations imposed by the original studies in which these data were collected. The corresponding author will facilitate access through existing data request processes for the Ovarian Cancer Association Consortium.