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Published in final edited form as: Cancer Epidemiol Biomarkers Prev. 2022 Apr 1;31(4):909–913. doi: 10.1158/1055-9965.EPI-21-1091

Reproductive factors do not influence survival with ovarian cancer

Minh Tung Phung 1,*, Aliya Alimujiang 1,*, Andrew Berchuck 2, Hoda Anton-Culver 3, Joellen M Schildkraut 4, Elisa V Bandera 5, Jenny Chang-Claude 6,7, Anne Chase 8, Jennifer Anne Doherty 9, Bronwyn Grout 8, Marc T Goodman 10,11, Gillian E Hanley 12, Alice W Lee 13, Cindy McKinnon Deurloo 8, Usha Menon 14, Francesmary Modugno 15,16,17, Paul D P Pharoah 18,19, Malcolm C Pike 20,21, Jean Richardson 20, Harvey A Risch 22, Weiva Sieh 23,24, Kathryn L Terry 25,26; Multidisciplinary Ovarian Cancer Outcomes Group and the Ovarian Cancer Association Consortium, Penelope M Webb 27; Australian Ovarian Cancer Study Group, the Multidisciplinary Ovarian Cancer Outcomes Group and the Ovarian Cancer Association Consortium, Nicolas Wentzensen 28, Anna H Wu 20, Celeste Leigh Pearce 1; Multidisciplinary Ovarian Cancer Outcomes Group and the Ovarian Cancer Association Consortium
PMCID: PMC9444326  NIHMSID: NIHMS1775076  PMID: 35064059

Abstract

Background

Previous studies on the association between reproductive factors and ovarian cancer survival are equivocal, possibly due to small sample sizes.

Methods

Using data on 11,175 people diagnosed with primary invasive epithelial ovarian, fallopian tube, or primary peritoneal cancer (ovarian cancer) from 16 studies in the Ovarian Cancer Association Consortium (OCAC), we examined the associations between survival and age at menarche, combined oral contraceptive use, parity, breastfeeding, age at last pregnancy, and menopausal status using Cox proportional hazard models. The models were adjusted for age at diagnosis, race/ethnicity, education level, and OCAC study and stratified on stage and histotype.

Results

During the mean follow-up of 6.34 years (SD=4.80), 6,418 patients passed away (57.4%). There was no evidence of associations between the reproductive factors and survival among ovarian cancer patients overall or by histotype.

Conclusions

This study found no association between reproductive factors and survival after an ovarian cancer diagnosis.

Impact

Reproductive factors are well-established risk factors for ovarian cancer, but they are not associated with survival after a diagnosis of ovarian cancer.

Keywords: ovarian cancer, survival, reproductive, parity, oral contraceptive

Introduction

Invasive epithelial ovarian cancer (ovarian cancer) has a five-year survival rate of less than 50%. Cigarette smoking1 and higher body mass index2 prior to diagnosis are both associated with poor survival, whereas menopausal hormone therapy use is a positive prognostic indicator3. However, the literature surrounding the association between reproductive factors and ovarian cancer survival is equivocal even though many are associated with risk of the disease. Older age at menarche has been associated with both poor4 and longer survival5, but three other studies have found no relationship6–8. Similarly, some6, 7, but not all4, 5, 8 studies have reported that parity is associated with better survival. One study reported a decreased death rate among those who used combined oral contraceptives (COCs)8, but most studies did not observe an association4–6. A major concern with these studies is power; to our knowledge, the largest published study of reproductive factors and ovarian cancer survival included 1,698 patients4. Therefore, we have used data from 11,175 ovarian cancer patients in the Ovarian Cancer Association Consortium (OCAC) to clarify the associations between survival and age at menarche, COC use, parity, breastfeeding, age at last pregnancy, and menopausal status.

Materials and Methods

This analysis used self-reported data from 16 studies in the OCAC, including two studies from Australia, four from Europe, and ten from the United States (U.S.) (http://ocac.ccge.medschl.cam.ac.uk/; Table 1). All studies obtained institutional ethics committee approval and followed recognized ethnical guidelines, including the Declaration of Helsinki, the Belmont Report, and/or the U.S. Common Rule; and participants provided written informed consent. Participants who were diagnosed with primary invasive epithelial ovarian, fallopian tube, or primary peritoneal tumors (hereafter referred to as ovarian cancer) were included in the analysis. To be included, patients had to have been diagnosed with one of the five main histotypes (i.e., high-grade serous, endometrioid, clear cell, mucinous, and low-grade serous) and had follow-up time and vital status information available. Survival time was counted from date of diagnosis to either death or last follow-up. Follow-up is largely done via linkage with national death databases.

Table 1:

Description of the 16 OCAC studies included in the analysis.

Study Abbreviation Study full name Study Location Recruitment Period Data Collection Method Participants Number of deaths (%) Mean years of follow-up (standard deviation)
AUS Australian Ovarian Cancer Study Australia 2001–2006 Self-completed questionnaire 1,329 947 (71.3%) 5.02 (3.44)
OPL Ovarian Cancer Prognosis and Lifestyle Study Australia 2012–2015 Self-completed questionnaire 793 314 (39.6%) 3.52 (1.24)
GER German Ovarian Cancer Study Baden-Württemberg and Rhineland-Palatinate, Germany 1993–1998 Self-completed questionnaire 152 100 (65.8%) 7.61 (5.82)
POL Polish Ovarian Cancer Case-Control Study Poland 2000–2004 In-person interview 152 82 (53.9%) 3.72 (1.97)
SEA Study of Epidemiology and Risk Factors in Cancer Heredity East Anglia and West Midlands, United Kingdom 1993–2013 Self-completed questionnaire 1,242 550 (44.3%) 7.31 (5.53)
UKO United Kingdom Ovarian Cancer Population Study United Kingdom 2006–2009 Self-completed questionnaire 631 323 (51.2%) 6.73 (4.17)
CON Connecticut Ovary Study Connecticut, US 1999–2003 In-person interview 329 180 (54.7%) 5.86 (2.92)
DOV Diseases of the Ovary and their Evaluation Washington, US 2002–2009 In-person interview 886 519 (58.6%) 7.34 (4.45)
HAW Hawaii Ovarian Cancer Case-Control Study Hawai’i, US 1994–2008 In-person interview 359 203 (56.5%) 7.86 (5.18)
HOP Hormones and Ovarian Cancer Prediction Western Pennsylvania, Northeast Ohio, Western New York, US 2003–2009 In-person interview 615 372 (60.5%) 5.29 (3.19)
NCO North Carolina Ovarian Cancer Study North Carolina, US 1999–2008 In-person interview 814 534 (65.6%) 6.15 (3.97)
NEC New England Case Control Study New Hampshire and Eastern Massachusetts, US 1992–2008 In-person interview 1,373 774 (56.4%) 5.17 (4.59)
NJO New Jersey Ovarian Cancer Study New Jersey, US 2005–2009 Telephone interview 195 118 (60.5%) 6.08 (2.95)
STA Genetic Epidemiology of Ovarian Cancer Greater Bay Area, California, US 1997–2002 In-person interview 407 236 (58.0%) 6.55 (4.20)
UCI University California Irvine Ovarian Study Orange County and San Diego County, California, US 1994–2004 Self-completed questionnaire 363 168 (46.3%) 7.47 (3.58)
USC Study of Lifestyle and Women’s Health Los Angeles, California, US 1994–2010 In-person interview 1,535 998 (65.0%) 8.54 (6.84)
Overall 11,175 6,418 (57.4%) 6.34 (4.80)
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The six pre-diagnosis reproductive factors of interest were age at menarche, COC use, parity, breastfeeding, age at last pregnancy, and menopausal status. The covariates included age at diagnosis, race/ethnicity, education level, stage, histotype, and OCAC study. The percentage of patients missing data on any variables ranged from none for age to 5.9% for education level. Multiple imputation (mice package in R) was conducted to create 20 imputed datasets. All variables in the dataset with ≤70% missingness were included for imputation, including the six reproductive factors and those not used in the final models. Data were imputed separately by geographic region (i.e., Australia, Europe, and U.S.), and OCAC study was included as a predictor in all imputation models.

Cox proportional hazards models were fit for all-cause mortality among ovarian cancer patients overall and by histotype. All models included the six reproductive factors of interest (see above); were adjusted for age at diagnosis, race/ethnicity, education level, and OCAC study; and stratified on stage and histotype (see Table 2 for the coding schemes). Hazard ratios (HRs) and 95% confidence intervals (CIs) across the 20 imputed datasets were pooled using Rubin’s rule to obtain a single point estimate and pooled standard error for each reproductive factor. The pooled standard error is derived from within and between imputation variances. Adjusting for cigarette smoking, menopausal hormone therapy, body mass index, and aspirin use did not change the results. Including only patients with complete information (N=9,422) yielded similar results. No evidence of heterogeneity between the OCAC studies for each factor-survival association was found using standard meta-analytic techniques.

Table 2:

Hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between each reproductive factor and survival among ovarian cancer patients overall and by histotype

All women High-grade serous Endometrioid Clear cell Mucinous Low-grade serous
11,175 cases 6,582 cases 1,898 cases 1,013 cases 923 cases 759 cases
Reproductive factors HR* 95% CI p-value HR** 95% CI p-value HR** 95% CI p-value HR** 95% CI p-value HR** 95% CI p-value HR** 95% CI p-value
Age at menarche (years)
 <12 1.00 0.94–1.07 0.99 1.02 0.95–1.10 0.57 0.91 0.74–1.12 0.39 0.98 0.75–1.29 0.89 0.80 0.56–1.15 0.22 1.09 0.82–1.46 0.54
 12–14 1.00 1.00 1.00 1.00 1.00 1.00
 15+ 1.02 0.94–1.10 0.65 1.02 0.93–1.11 0.72 0.93 0.72–1.21 0.60 1.14 0.80–1.63 0.47 0.92 0.62–1.36 0.68 1.40 0.99–1.97 0.05
p-trend= 0.63 p-trend=0.83 p-trend=0.72 p-trend=0.53 p-trend=0.50 p-trend=0.41
Combined oral contraceptive use duration (years)
 <1 1.00 1.00 1.00 1.00 1.00 1.00
 1–4.99 0.96 0.90–1.03 0.25 0.97 0.90–1.05 0.45 0.95 0.76–1.20 0.69 0.87 0.64–1.18 0.38 0.89 0.61–1.30 0.54 0.99 0.75–1.31 0.95
 5–9.99 0.98 0.90–1.06 0.61 1.01 0.92–1.10 0.88 1.04 0.80–1.36 0.74 0.88 0.62–1.26 0.49 0.70 0.44–1.09 0.12 0.97 0.67–1.40 0.86
 10+ 0.96 0.88–1.05 0.39 0.97 0.88–1.08 0.61 0.93 0.69–1.27 0.67 0.82 0.54–1.24 0.35 1.10 0.72–1.69 0.66 0.91 0.63–1.30 0.60
p-trend=0.29 p-trend=0.72 p-trend=0.84 p-trend=0.26 p-trend=0.76 p-trend=0.62
Parity
 0 1.00 1.00 1.00 1.00 1.00 1.00
 1 1.00 0.91–1.11 0.96 0.99 0.87–1.11 0.81 0.99 0.72–1.36 0.97 0.97 0.62–1.51 0.88 1.33 0.79–2.21 0.28 1.31 0.86–1.98 0.21
 2 1.00 0.91–1.10 1.00 0.98 0.88–1.10 0.74 1.02 0.77–1.37 0.88 1.01 0.65–1.57 0.96 0.72 0.43–1.19 0.20 1.30 0.85–1.99 0.22
 3+ 0.98 0.89–1.09 0.75 0.99 0.88–1.12 0.93 1.02 0.75–1.40 0.89 0.67 0.40–1.12 0.12 1.09 0.64–1.87 0.75 0.92 0.59–1.45 0.73
p-trend=0.83 p-trend=0.99 p-trend=0.84 p-trend=0.14 p-trend=0.90 p-trend=0.33
Breastfeeding
 Never breastfed 1.00 1.00 1.00 1.00 1.00 1.00
 <12 months 0.95 0.89–1.02 0.13 0.94 0.87–1.02 0.13 0.94 0.75–1.18 0.60 1.07 0.76–1.50 0.71 1.04 0.72–1.49 0.84 0.92 0.68–1.24 0.57
 12–23 months 1.00 0.91–1.09 0.95 0.96 0.87–1.07 0.50 1.00 0.72–1.39 0.99 1.09 0.68–1.73 0.73 1.22 0.72–2.05 0.46 1.23 0.80–1.88 0.34
 24+ months 1.11 0.99–1.24 0.07 1.12 0.99–1.27 0.08 0.77 0.49–1.19 0.24 1.13 0.65–1.99 0.66 1.44 0.83–2.48 0.19 1.05 0.66–1.67 0.84
p-trend=0.37 p-trend=0.41 p-trend=0.38 p-trend=0.60 p-trend=0.18 p-trend=0.60
Age at last pregnancy (years)
 <25 1.00 1.00 1.00 1.00 1.00 1.00
 25–29 0.99 0.91–1.07 0.76 0.99 0.90–1.08 0.78 1.13 0.88–1.45 0.34 1.02 0.70–1.50 0.91 0.98 0.65–1.48 0.92 0.92 0.65–1.30 0.63
 30–34 0.92 0.85–1.00 0.06 0.93 0.85–1.03 0.16 0.88 0.67–1.16 0.37 0.95 0.63–1.44 0.81 0.95 0.63–1.42 0.79 0.93 0.65–1.32 0.67
 35+ 0.94 0.86–1.03 0.17 0.97 0.88–1.08 0.61 0.82 0.60–1.10 0.19 0.88 0.56–1.40 0.59 0.65 0.40–1.05 0.08 0.92 0.63–1.35 0.68
p-trend=0.055 p-trend=0.36 p-trend=0.07 p-trend=0.54 p-trend=0.10 p-trend=0.72
Menopausal status
 Pre-menopausal 1.04 0.96–1.13 0.32 1.07 0.97–1.18 0.19 0.86 0.67–1.10 0.23 1.18 0.85–1.65 0.33 1.22 0.80–1.85 0.35 1.02 0.70–1.50 0.91
 Post-menopausal 1.00 1.00 1.00 1.00 1.00 1.00
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*

Cox proportional hazards model including all reproductive factors, adjusted for age at diagnosis (continuous in years), race/ethnicity (non-Hispanic White, Hispanic White, Black, Asian, other), education level (less than high school, high school, some college, college graduate or above), OCAC study (n=16), stratified on stage at diagnosis (local, regional, distant) and histotype (high-grade serous, endometrioid, clear cell, mucinous, and low-grade serous).

**

Cox proportional hazards models including all reproductive factors, adjusted for age at diagnosis (continuous in years), race/ethnicity (non-Hispanic White, Hispanic White, Black, Asian, other), education level (less than high school, high school, some college, college graduate or above), OCAC study (n=16), stratified on stage at diagnosis (local, regional, distant).

Data availability

The data generated in this study are not publicly available due to 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.

Results

Of the 11,175 ovarian cancer patients included in the analysis, there were 6,418 deaths (57.4%) during an average follow-up of 6.34 years (SD=4.80) (Table 1). There were no statistically significant reproductive factor-survival associations among ovarian cancer patients overall or by histotype (Table 2). There were two borderline significant associations with survival: breastfeeding for 24+ months (HR=1.11, 95% CI 0.99–1.24) and age at last pregnancy 30–34 years (HR=0.92, 95% CI 0.85–1.00, Table 2). However, there were no trends across the categories of these exposures suggesting that the associations were likely due to chance. Similarly, there were several borderline significant associations within each histotype, but they were likely due to chance for the same reasons (Table 2).

Discussion

Our study was the largest to date to investigate reproductive factors and survival among ovarian cancer patients, and found no statistically significant associations. Our sample size of more than 11,000 patients afforded us sufficient statistical power to detect potential associations. It further enabled histotype-specific analyses, which had not been evaluated previously. Our cohort’s six-year survival of 43% is close to the Surveillance, Epidemiology, and End Results Program (SEER) five-year survival of 47%, suggesting that our cohort is well-representative of ovarian cancer patients. However, due to a large proportion of missing data for debulking status, treatment, and time to recurrence, we could not consider these factors in the analysis. Overall, our findings highlight that the pre-diagnosis reproductive factors included in this analysis have no significant impact on ovarian cancer survival regardless of their effects on the risk of developing ovarian cancer.

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. 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 of the women who participated in this research program; CON: The cooperation of the 32 Connecticut hospitals, including Stamford Hospital, in allowing patient access, is gratefully acknowledged. This study was approved by the State of Connecticut Department of Public Health Human Investigation Committee. Certain data used in this study were obtained from the Connecticut Tumor Registry in the Connecticut Department of Public Health. The authors assume full responsibility for analyses and interpretation of these data; GER: The German Ovarian Cancer Study (GER) thank Ursula Eilber for competent technical assistance; OPL: Members of the OPAL Study Group (http://opalstudy.qimrberghofer.edu.au/); SEA: SEARCH team, Craig Luccarini, Caroline Baynes, Don Conroy; UKO: We particularly thank I. Jacobs, M. Widschwendter, E. Wozniak, A. Ryan, J. Ford and N. Balogun for their contribution to the study. NJO: Drs. Sara Olson, Lisa Paddock, and Lorna Rodriguez, and research staff at the Rutgers Cancer Institute of New Jersey, Memorial Sloan-Kettering Cancer Center, and the New Jersey State Cancer Registry.

OCAC 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 to A. Berchuck). The scientific development and funding for this project were in part supported by the US National Cancer Institute GAME-ON Post-GWAS Initiative (U19-CA148112 to C.L. Pearce and J.M. Schildkraut).

Funding for individual studies:

AUS: The Australian Ovarian Cancer Study (AOCS) was supported by the U.S. Army Medical Research and Materiel Command (DAMD17-01-1-0729 to P.M. Webb), National Health & Medical Research Council of Australia (199600, 400413 and 400281 to P.M. Webb), 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 to P.M. Webb). AOCS gratefully acknowledges additional support from Ovarian Cancer Australia and the Peter MacCallum Foundation; Dr. Webb is supported by NHMRC Investigator Grant APP1173346; OPL: National Health and Medical Research Council (NHMRC) of Australia (APP1025142, APP1120431 to P.M. Webb) and Brisbane Women’s Club (to P.M. Webb); GER: German Federal Ministry of Education and Research, Program of Clinical Biomedical Research (01 GB 9401 to J. Chang-Claude) and the German Cancer Research Center (DKFZ, to J. Chang-Claude); POL: Intramural Research Program of the National Cancer Institute (to N. Wentzensen); SEA: The SEARCH study was supported by Cancer Research UK (C490/A8339, C490/A10119, C490/A10124 and C490/A16561 to P.D.P. Pharoah) and UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge (to P.D.P. Pharoah); UKO: The UKOPS study was funded by The Eve Appeal (The Oak Foundation to U. Menon) with investigators supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre and MRC Core Funding (MR_UU_12023 to U. Menon); STA: NIH grants U01 CA71966 and U01 CA69417 (to W. Sieh); CON: National Institutes of Health (R01-CA063678, R01-CA074850; R01-CA080742 to H.A. Risch); DOV: National Institutes of Health R01-CA112523 and R01-CA87538 (to J.A. Doherty); HAW: U.S. National Institutes of Health (R01-CA58598, N01-CN-55424 and N01-PC-67001 to M.T. Goodman); HOP: Department of Defense (DAMD17-02-1-0669 to F. Modugno) and NCI (K07-CA080668, R01-CA95023, P50-CA159981, MO1-RR000056, R01-CA126841 to F. Modugno); NCO: National Institutes of Health (R01-CA76016 to A. Berchuck and J.M. Schildkraut) and the Department of Defense (DAMD17-02-1-0666 to A. Berchuck); NEC: National Institutes of Health R01-CA54419 and P50-CA105009 (to K.L. Terry) and Department of Defense W81XWH-10-1-02802 (to K.L. Terry); NJO: National Cancer Institute (NIH-K07 CA095666, R01-CA83918, NIH-K22-CA138563, and P30-CA072720 to E.V. Bandera) and the Rutgers Cancer Institute of New Jersey (to E.V. Bandera); UCI: NIH (R01-CA058860 to H. Anton-Culver) and the Lon V Smith Foundation (grant LVS-39420 to H. Anton-Culver); USC: National Institutes of Health (P01CA17054, N01PC67010, N01CN025403, to A.H. Wu, M.C. Pike and C.L. Pearce; P30CA14089 to A.H. Wu and M.C. Pike; R01CA61132 to M.C. Pike; R03CA113148 and R03CA115195 to C.L. Pearce); and California Cancer Research Program (00-01389V-20170 to M.C. Pike and C.L. Pearce; 2II0200 to A.H. Wu); Dr Pike is partially supported by the NIH/NCI Support Grant P30 CA008748 to Memorial Sloan Kettering Cancer Center.

LIST OF ABBREVIATIONS

AUS

Australian Ovarian Cancer Study

CI

Confidence interval

COC

Combined oral contraceptive

CON

Connecticut Ovary 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

HR

Hazard ratio

NCO

North Carolina Ovarian Cancer Study

NEC

New England Case Control Study

NJO

New Jersey Ovarian Cancer Study

OCAC

Ovarian Cancer Association Consortium

OPL

Ovarian Cancer Prognosis and Lifestyle Study

POL

Polish Ovarian Cancer Case-Control Study

SEA

Study of Epidemiology and Risk Factors in Cancer Heredity

STA

Genetic Epidemiology of Ovarian Cancer

U.S.

United States

UCI

University California Irvine Ovarian Study

UKO

United Kingdom Ovarian Cancer Population Study

USC

Study of Lifestyle and Women’s Health

Footnotes

Conflict of interest disclosure statement

No conflicts of interest were reported beyond grant funding to carry out research as described in the Funding section.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data generated in this study are not publicly available due to 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.

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