Abstract
Background
Tea and coffee are the most frequently consumed beverages in the world. Green tea in particular contains compounds with potential anti-cancer effects, but its association with survival after ovarian cancer is uncertain.
Methods
We investigated the associations between tea and coffee consumption before diagnosis and survival using data from 10 studies in the Ovarian Cancer Association Consortium. Data on tea (green, black, herbal), coffee and caffeine intake were available for up to 5724 women. We used Cox proportional hazards regression to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CI).
Results
Compared with women who did not drink any green tea, consumption of one or more cups/day was associated with better overall survival (aHR = 0.84, 95% CI 0.71–1.00, p-trend = 0.04). A similar association was seen for ovarian cancer-specific survival in five studies with this information (aHR = 0.81, 0.66–0.99, p-trend = 0.045). There was no consistent variation between subgroups defined by clinical or lifestyle characteristics and adjustment for other aspects of lifestyle did not appreciably alter the estimates. We found no evidence of an association between coffee, black or herbal tea, or caffeine intake and survival.
Conclusion
The observed association with green tea consumption before diagnosis raises the possibility that consumption after diagnosis might improve patient outcomes.
Subject terms: Ovarian cancer, Epidemiology
Background
Ovarian cancer is the seventh most common cause of cancer death among women worldwide and although 5-year survival has improved over time, it remains below 50% [1, 2]. Tea and coffee, two of the most commonly consumed beverages worldwide, contain compounds that have the potential to influence ovarian cancer risk and survival. Green tea, in particular, has an abundance of bioactive polyphenols including catechins. Epigallocatechin‐3‐gallate (EGCG) is the most biologically active catechin in green tea and in vitro studies in human cancer cell lines have suggested that in addition to functioning as an antioxidant, it may also inhibit angiogenesis and stimulate apoptosis by negatively regulating the cell cycle (reviewed in [3, 4]).
Meta-analyses suggest a possible inverse relationship between tea consumption, particularly green tea, and risk of ovarian cancer (including 5 case-control studies with 2994 cases for green tea) [5], but no strong association with coffee or caffeine consumption (15 cohort studies; 3927 cases) [6]. Fewer studies have explored the relationships between tea and coffee consumption and survival following diagnosis with ovarian cancer but the limited data indicate a possible benefit with greater green tea intake [7–9]. We used data from Ovarian Cancer Association Consortium to assess the associations between the consumption of these common beverages and survival following a diagnosis of ovarian cancer. Our primary hypothesis was that consumption of green tea, but not other types of tea or coffee/caffeine, would be associated with better survival.
Methods
We included primary data from one cohort, one case-only and eight case-control studies of ovarian cancer (Table 1) participating in the Ovarian Cancer Association Consortium (OCAC) that also provided dietary data through the Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG). Five studies were from the USA (Diseases of the Ovary and their Evaluation Study [DOV] [10], Hawaii Ovarian Cancer Study [HAW] [11], Los Angeles County Case Control Studies of Ovarian Cancer [LAC] [12], New England Case–Control Study of Ovarian Cancer [NEC] [13], New Jersey Ovarian Cancer Study [NJO] [14]), two from Europe (Danish Malignant Ovarian Tumour Study [MAL] [15], Polish Ovarian Cancer Study [POL] [16]), and three from Australia (Australian Ovarian Cancer Study [AUS] [17], Melbourne Collaborative Cohort Study [MCC] [18] and Ovarian Cancer Prognosis and Lifestyle Study [OPL] [19]). Women missing dietary (N = 2322) or follow-up information (N = 307), with more than 2 years between diagnosis and interview (N = 169) or implausible energy intake ( > 3 SD from the study mean, N = 58) were excluded. In primary analyses, we also excluded 240 women who died in the first year following diagnosis leaving an analysis cohort of 5724 women.
Table 1.
Study acronym | Location and Type of study | Year of diagnosis | Number of cases (N = 5964)a | Deaths (%) | 5-year mortality (%) | Median Follow-up in years (range)b | Data availablec |
---|---|---|---|---|---|---|---|
AUS |
Australia Case-control |
2002–2005 | 1159 | 819 (71%) | 605 (52%) | 10.8 (9.1–13.5) | B/C/D/G/H |
DOV |
USA Case-control |
2002–2009 | 1040 | 624 (60%) | 440 (43%) | 11.7 (7.5–16.1) | B/C |
HAW |
USA Case-control |
1994–2008 | 378 | 218 (58%) | 149 (40%) | 10.2 (4.0–21.0) | B/C/D/G/H |
LAC |
USA Case-control |
1994–2004 | 639 | 439 (69%) | 281 (44%) | 15.9 (4.1–26.2) | B/C/D/G |
MAL |
Europe Case-control |
1994–1998 | 93 | 74 (80%) | 50 (55%) | 22.3 (20.2–23.4) | B/C |
MCC |
Australia Cohort |
1990–2008 | 99 | 74 (75%) | 56 (58%) | 19.5 (11.0–25.7) | B/C/H |
NEC |
USA Case-control |
1992–2008 | 1386 | 788 (57%) | 536 (39%) | 12.9 (6.9–22.8) | B/C/D |
NJO |
USA Case-control |
2005–2008 | 196 | 116 (59%) | 73 (38%) | 9.0 (5.9–11.2) | B/C/G |
OPL |
Australia Case-only |
2012–2015 | 718 | 373 (52%) | 316 (44%) | 6.6 (0.8–8.7) | B/C/D/G/H |
POL |
Europe Case-control |
2000–2003 | 256 | 140 (55%) | 129 (53%) | 5.3 (0.1–7.2) | B/C |
AUS Australian Ovarian Cancer Study, DOV Diseases of the Ovary and their Evaluation Study, HAW Hawaii Ovarian Cancer Study, LAC Los Angeles County Case–Control Studies of Ovarian Cancer, MAL Danish Malignant Ovarian Tumour Study, MCC Melbourne Collaborative Cohort Study, NEC New England Case–Control Study of Ovarian Cancer, NJO New Jersey Ovarian Cancer Study, OPL Ovarian Cancer Prognosis and Lifestyle Study, POL Polish Ovarian Cancer Study.
aWomen who completed the diet questionnaire > 2 years after diagnosis are excluded; deaths within the first year are included.
bAmong women who had not died.
cB = Black tea, C = Coffee – all, D = Decaffeinated vs. caffeinated coffee, G = Green tea, H = Herbal tea; All except MAL and POL also had total caffeine intake.
Researchers at QIMR Berghofer Medical Research Institute were responsible for pooling and harmonising the dietary data. All studies were approved by their relevant institutional review board and all participants provided informed consent.
Tea, coffee and caffeine intake
Tea and coffee consumption prior to diagnosis was assessed using food frequency questionnaires (FFQ) that asked about the year prior to diagnosis (or 5 years in POL). All studies provided information about total coffee and black tea consumption, five also asked about green tea (AUS, HAW, LAC, NJO, OPL), four about herbal teas (AUS, HAW, MCC, OPL) and five asked separately about caffeinated and decaffeinated coffee (AUS, HAW, LAC, NJO, OPL). All studies except MAL and POL provided information about total caffeine intake. Coffee and tea consumption was categorised as 0, <1, 1–2.49 and ≥ 2.5 cups/day; the top two groups were combined for decaffeinated coffee, green and herbal tea as few women drank more than 2.5 cups per day. Study-specific quartiles were created for caffeine intake.
Covariate information
Information regarding factors potentially associated with diet or survival was accessed from the central harmonised OCAC database. This included age at diagnosis (years); race/ethnicity (categorised as non-Hispanic white, Hispanic, Asian, Black; racial groups with ≤ 15 women in a study were combined as ‘other’); education (less than high school, completed high school, some post-high school education); body mass index (BMI) ( < 25, 25–29, ≥ 30 kg/m2) reported for the period one (AUS, LAC, NEC, NJO) or five years prior to diagnosis (DOV, HAW, MAL, OPL, POL) or at cohort entry (MCC); pre-diagnosis smoking status (never, former, current); pre-diagnosis physical activity (‘active’, ‘inactive’ [20]); and pre-diagnosis menopausal hormone therapy (MHT) use (any, none). Clinical information included tumour stage (local, regional, distant), histotype (high grade serous, low grade serous, mucinous, endometrioid, clear cell, other) and amount of residual disease remaining after surgery (none, any; AUS, HAW, MAL, NEC, OPL only). Missing indicators were assigned to non-dietary variables that were completely or partially missing for a study (see Table 1 for numbers missing).
Clinical and survival data
Each study reported vital status and survival time, calculated from date of diagnosis to date of death from any cause or date of last follow-up for those still alive. Cause of death information was available for five studies (AUS, DOV, HAW, MAL, OPL) but, in these studies, the vast majority of deaths were from ovarian cancer (92% overall, 95% in the first five years).
Statistical analyses
Data from the studies were pooled. We combined women missing stage information (3%) with advanced cancers, and combined the small group of low-grade serous cancers (N = 179) with mucinous cancers, as in both cases the groups had very similar adjusted survival outcomes. Because the vast majority of deaths were from ovarian cancer, we used death from any cause as the primary outcome to maximise power and conducted a secondary analysis looking at death from ovarian cancer in the subset of studies with this information. In our primary analyses, we excluded women who died in the first year; we therefore left-truncated survival to one year after diagnosis or, in case-control studies, the date of questionnaire completion if this occurred more than one year after diagnosis. This was to avoid immortal time bias and reduce the potential of survivorship bias arising from the exclusion of eligible women who died before recruitment.
We used Cox proportional hazards regression to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the associations with survival. All models were adjusted for age (in years) and stratified by study, tumour stage and histotype as the baseline hazard varied greatly by these factors. Final models were also adjusted for race, education, BMI, smoking status and physical activity; additional adjustment for total energy intake, intake of dairy foods or sugar (which might preferentially be added to some types of hot beverage), year of diagnosis, interval between diagnosis and recruitment, and MHT use did not alter the estimates so these variables were not included. We assessed linear trends by assigning each group a value from 0 (lowest) to 2 or 3 (highest). The only variables to violate the proportional hazards assumption were age at diagnosis and stage; log-time interactions were not included in the final models as their inclusion did not alter the estimates of interest.
Finally, we also investigated whether associations differed according to study, stage of disease (local/regional vs. distant), histotype (high-grade serous cancer [HGSC] vs. other), race, residual disease, menopausal status, BMI, smoking or physical inactivity. Analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA) and STATA version 13 (College Station, TX, USA). Code can be accessed from the investigators if required.
Results
Table 2 shows the characteristics of the combined study sample. The average age at diagnosis was 57 years and most women were diagnosed with advanced HGSC. As expected, older age, more advanced disease at diagnosis, HGSC, presence of residual disease after surgery, smoking and obesity (BMI > 30 kg/m2) were associated with worse survival.
Table 2.
Characteristics at diagnosis | Subgroup | N | % | Age- and stage-adjusted HR (95% CI) |
---|---|---|---|---|
Age (mean & range) | 5724 | 57.1 (20–87) | ||
Vital status | Alive | 2295 | 40.1 | |
Deceased | 3429 | 59.9 | ||
Year of diagnosis | 1992-9 | 974 | 17.0 | 1.00 |
2000-3 | 1109 | 19.4 | 0.77 (0.67–0.89) | |
2003-6 | 2083 | 36.4 | 0.76 (0.66–0.87) | |
2006-9 | 761 | 13.3 | 0.75 (0.64–0.88) | |
2012-5 | 797 | 13.9 | 0.68 (0.50–0.93) | |
Age group | < 40 | 346 | 6.0 | 0.58 (0.48–0.70) |
40–49 | 1058 | 18.5 | 0.79 (0.71–0.88) | |
50–59 | 1858 | 32.5 | 1.00 (Ref) | |
60–69 | 1676 | 29.3 | 1.15 (1.06–1.25) | |
70+ | 786 | 13.7 | 1.42 (1.28–1.57) | |
Tumour stage | Local | 1122 | 19.6 | 1.00 |
Regional | 939 | 16.4 | 1.64 (1.39–1.94) | |
Distant | 3488 | 60.9 | 6.05 (5.31–6.90) | |
Missing | 175 | 3.1 | ||
Histotype | High grade serous | 3436 | 60.0 | 1.00 |
Low grade serous | 179 | 3.1 | 0.66 (0.54–0.80) | |
Mucinous | 313 | 5.5 | 0.61 (0.49–0.76) | |
Endometrioid | 844 | 14.7 | 0.54 (0.47–0.62) | |
Clear cell | 377 | 6.6 | 0.74 (0.62–0.89) | |
Mixed/Other | 575 | 10.0 | 0.88 (0.78–0.99) | |
Residual disease after surgerya | Nil | 1088 | 19.0 | 1.00 |
Any | 878 | 15.3 | 1.94 (1.70–2.21) | |
Missing | 3758 | 65.7 | ||
Menopausal statusb | Pre- and perimenopause | 1543 | 27.0 | 1.00 |
Post menopause | 4045 | 70.7 | 1.09 (0.97–1.23) | |
Missing | 136 | 2.4 | ||
Menopausal hormone | No | 3760 | 65.7 | 1.00 |
use | Yes | 1856 | 32.4 | 0.92 (0.85–0.99) |
Missing | 108 | 1.9 | ||
Race/Ethnicity | Non-Hispanic White | 4852 | 84.8 | 1.00 |
Hispanic | 141 | 2.5 | 0.92 (0.72–1.18) | |
Black | 64 | 1.1 | 1.11 (0.76–1.63) | |
Asian | 393 | 6.9 | 0.97 (0.83–1.14) | |
Other | 274 | 4.8 | 1.06 (0.90–1.26) | |
Education | Less than high school | 1055 | 18.4 | 1.00 |
High school | 2836 | 49.6 | 1.01 (0.92–1.11) | |
College or University | 1784 | 31.2 | 0.94 (0.85–1.05) | |
Missing | 49 | 0.9 | ||
Smoking status | Never smoker | 3097 | 54.1 | 1.00 |
Former smoker | 1842 | 32.2 | 1.13 (1.05–1.22) | |
Current smoker | 743 | 13.0 | 1.25 (1.13–1.39) | |
Missing | 42 | 0.7 | ||
Body Mass Index | <25.0 | 2762 | 48.3 | 1.00 |
(kg/m2) | ≥ 25 to < 30 | 1668 | 29.1 | 0.93 (0.86–1.01) |
≥ 30 | 1243 | 21.7 | 1.12 (1.03–1.22) | |
Missing | 51 | 0.9 | ||
Physical activityc | Inactive | 1255 | 21.9 | 1.00 |
Active | 3986 | 69.6 | 0.93 (0.85–1.01) | |
Missing | 483 | 8.4 |
CI confidence interval, HR hazard ratio, adjusted for age and stratified by site.
aResidual disease information was not available for DOV, LAC, MCC, NJO or POL.
bMenopausal status data were not available for MCC.
cPhysical inactivity data were not available for MCC or POL.
We found no evidence of an association between consumption of coffee (any, caffeinated or decaffeinated), black tea, herbal tea or caffeine before diagnosis and overall survival (Table 3). Consumption of one or more cups of green tea per day was, however, associated with significantly better survival in both the minimal and fully-adjusted models (fully-adjusted HR 0.84, 95% CI 0.71–1.00, p-trend = 0.04). There was no evidence of heterogeneity among the five studies that contributed to the green tea analysis (AUS, HAW, LAC, NJO, OPL; I2 = 0.17, p = 0.3) and the effect estimate was below 1.0 in four of the studies, with a non-significant positive association in LAC (Supplementary Figure). When we omitted studies individually the pooled estimates ranged from 0.79 (0.66–0.95) omitting LAC to 0.87 (0.70–1.07) omitting OPL.
Table 3.
None | <1 cup/day | 1–2.49 cups/day | ≥ 2.5 cups/day | ||||
---|---|---|---|---|---|---|---|
Modela | Nb | Reference | HR (95% CI) | HR (95% CI) | HR (95% CI) | P-trendc | |
Coffee | 1 | 5713 | 1.0 | 0.93 (0.83–1.04) | 0.97 (0.88–1.08) | 0.99 (0.89–1.10) | 0.8 |
2 | 5688 | 1.0 | 0.93 (0.83–1.04) | 0.97 (0.88–1.07) | 0.97 (0.87–1.08) | 0.8 | |
Caffeinated | 1 | 4108 | 1.0 | 0.93 (0.83–1.05) | 0.94 (0.84–1.05) | 1.00 (0.89–1.12) | 0.97 |
2 | 4096 | 1.0 | 0.94 (0.83–1.05) | 0.93 (0.83–1.05) | 0.99 (0.88–1.10) | 0.8 | |
Decaffeinatedd | 1 | 4108 | 1.0 | 1.05 (0.94–1.17) | 0.97 (0.85–1.10) | – | 0.9 |
2 | 4096 | 1.0 | 1.07 (0.96–1.20) | 0.97 (0.85–1.11) | – | 0.9 | |
Black tea | 1 | 5707 | 1.0 | 0.95 (0.87–1.04) | 0.93 (0.83–1.04) | 1.00 (0.89–1.11) | 0.8 |
2 | 5682 | 1.0 | 0.96 (0.88–1.05) | 0.95 (0.85–1.06) | 1.02 (0.91–1.14) | 0.8 | |
Green tead | 1 | 2938 | 1.0 | 0.92 (0.83–1.03) | 0.84 (0.71–0.99) | – | 0.02 |
2 | 2926 | 1.0 | 0.94 (0.84–1.06) | 0.84 (0.71–1.00) | – | 0.04 | |
Herbal tead | 1 | 2206 | 1.0 | 0.98 (0.86–1.11) | 0.99 (0.81–1.21) | – | 0.8 |
2 | 2201 | 1.0 | 1.03 (0.90–1.17) | 1.02 (0.83–1.25) | – | 0.7 | |
Total caffeinee | 1 | 5408 | 1.0 | 0.99 (0.90–1.09) | 0.94 (0.85–1.04) | 1.05 (0.95–1.16) | 0.5 |
2 | 5388 | 1.0 | 0.99 (0.90–1.09) | 0.94 (0.85–1.04) | 1.03 (0.93–1.14) | 0.8 |
aModel 1: Hazard ratios (HR) and 95% confidence intervals (CI) adjusted for age and stratified by study, stage and histotype; Model 2 additionally adjusted for race, education, smoking, BMI and physical inactivity.
bSee Table 1 for studies contributing to each model.
cAssessed by assigning each level a number from 0 (Q1) to 3 (Q4) and modelling this as continuous variable.
dThe top two groups were combined ( ≥ 1 cup/day) as few individuals drank more than 2.5 cups/day.
eModelled in quartiles from lowest to highest.
In stratified analyses there was no significant heterogeneity by stage of disease (Supplementary Table 1). Similarly, there was little variation by BMI, smoking status, physical inactivity, menopausal status, histotype, residual disease after surgery or study site (data not shown). The only statistically significant heterogeneity was for black tea where increasing consumption was associated with worse survival among pre-menopausal women only (HR = 1.09, 95% CI 1.01–1.17), and herbal tea where higher consumption was associated with worse survival among former smokers (HR = 1.24; 95% CI 1.07–1.44), pre-menopausal women (HR = 1.24, 95% CI 1.00–1.53) and in HAW and MCC (HR for ≥ 1 cup/day vs. none: 4.69, 95% CI 2.22–9.94 and 2.82, 1.08–7.34, respectively); however, these estimates are based on small numbers, particularly for herbal tea, so are likely due to chance.
The results were essentially the same in sensitivity analyses that included women who died in the first year and when we truncated survival at five years after diagnosis (Supplementary Table 2). They were also similar when we considered death from ovarian cancer as the outcome in the subgroup of studies that provided this information (Supplementary Table 3; HR for ≥ 1 vs. 0 cups green tea per day = 0.81, 95% CI 0.66–0.99, p-trend = 0.045).
Discussion
This large international study provides support for the hypothesis that higher consumption of green tea is associated with better survival among women with ovarian cancer. We found no consistent associations between consumption of other types of tea, coffee or caffeine intake and survival overall. The minor variations we observed between sub-groups of the population are likely due to chance.
Published data regarding the relationship between tea, coffee and caffeine and ovarian cancer survival are limited. An Australian study (N = 609) reported no evidence of association with pre-diagnosis tea consumption but did not consider coffee or green tea [7]. In a second Australian study (N = 811, AUS included in this analysis), there was no significant association between higher pre-diagnosis intake of coffee, black or green tea and survival overall. However, the overall estimate for green tea was comparable to that observed here (HR for ≥ 1 vs. 0 cups/day = 0.83, 95% CI 0.60–1.15) and there was a significant dose-response with higher green tea consumption (p = 0.02). Excluding AUS from the current analysis did not appreciably change the magnitude of the estimate for green tea (HR for ≥ 1 vs. 0 cups per day = 0.86, 95% CI 0.68–1.08). The only study to date with information about tea consumption after diagnosis, conducted among 244 women in China, reported an inverse association between tea (predominantly green tea) and ovarian cancer-specific survival (HR = 0.43, 95% CI 0.20–0.92 for 1+ cup/day, p-trend <0.05) [9]. Data for other cancer types are also limited. A recent meta-analysis reported a 44% reduction in risk of recurrence for women with breast cancer (stage I or II) who drank green tea before diagnosis [21], although this was based on only two Japanese studies conducted more than 20 years ago.
The potential benefits of green tea have been attributed to the EGCG it contains. In vitro studies have shown that EGCG affects a number of signalling pathways linked to tumorigenesis, including the MAP kinase pathway which is involved in cell proliferation, differentiation and death (reviewed in [22, 23]). It also modulates inflammation and immunity, two processes that are commonly dysregulated in cancer [24]. EGCG has also been shown to inhibit tumour growth and progression in ovarian cancer cell lines [25, 26]. Although the antioxidant activity of theaflavins found in black tea is similar to that of EGCG, their total antioxidant capacity is much lower [27], potentially explaining the lack of association between black tea consumption and survival in our analysis. In contrast, a 2015 meta-analysis reported similar associations between both black tea and green tea consumption and all-cause mortality (based on 12 and 5 studies, respectively), but only black tea was associated with lower cancer-specific mortality (based on 4 and 6 studies) [28]. However, the results of that analysis are hard to interpret as the focus was on mortality not cancer survival and, of only two studies that reported all-cause mortality results for both types of tea, one reported a benefit only for black tea and the other a benefit only for green tea.
To our knowledge, this is the largest study to assess the association between tea and coffee consumption and ovarian cancer survival. We have included studies with long follow-up periods and detailed information on other covariates, including clinical and lifestyle factors. The limitations are that we only had self-reported data about pre-diagnosis consumption of tea and coffee and did not have information about the types of green tea that women drank or how it was prepared but levels of EGCG vary between brands [29] and preparation methods [30]. However, a woman’s lifestyle before diagnosis is likely to be highly correlated with her lifestyle after diagnosis and treatment [8, 31, 32]. Furthermore, any recall error or misclassification due to variation in the EGCG content of different teas is probably non-differential and so would likely have attenuated estimates for the highest vs. lowest levels of intake. Adjustment for potential confounders including level of education, smoking, BMI and physical inactivity had little impact on our estimates suggesting residual or unmeasured confounding by these factors is an unlikely explanation for the observed association with green tea. Although we cannot rule out the possibility that other factors such as variation in access to healthcare could explain the observed association, the inverse association was also seen in the Australian studies where all residents are entitled to free healthcare.
In summary, our results provide support for the hypothesis that higher consumption of green tea is associated with better survival among women with ovarian cancer. Further adequately powered studies, preferably with information about consumption after diagnosis, are needed to confirm the possible beneficial effects of green tea on ovarian cancer survival.
Supplementary information
Acknowledgements
We thank all the study participants who contributed to this study and all the researchers, clinicians, technical and administrative staff who have made this work possible including all of the members of the OCAC and MOCOG consortia. AUS: The AOCS also acknowledges the cooperation of the participating institutions in Australia, including the Australian Institute of Health and Welfare who coordinated linkage to the National Death Index, and the contribution of the study nurses, research assistants and all clinical and scientific collaborators. The complete AOCS Study Group and AOCS Management Group can be found at http://www.aocstudy.org/. We would like to thank all of the women who participated in this research programme; MCC: Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database; NJO: We thank Drs. Sara Olson, Lisa Paddock and 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 Centre; OPL: We thank the members of the OPAL Study Group (http://opalstudy.qimrberghofer.edu.au/).
Author contributions
Conceptualisation: PMW, CLP, KLT, GEH, JR and MP; Funding acquisition: CLP, MP and AB; Resources: EVB, DC, JAD, GGG, MTG, HRH, AJ, SKK, AWL, RLM, BQ, NS, WS, KLT, LT, BT, NW, AHW and PMW contributed data; Data curation: TII harmonized the data across studies; Formal analysis: TII and PMW; Writing – original draft: CMN and PMW; Writing – review and editing: all authors.
Funding
The Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG) was supported by the Department of Defense Congressionally Directed Medical Research Programmes (grant number W81XSH-16-2-0010). The Ovarian Cancer Association Consortium is currently funded by the generous contributions of its research investigators and through philanthropic donations; previous funding came from a grant from the Ovarian Cancer Research Fund (OCRF). Individual studies were supported as follows: AUS: The Australian Ovarian Cancer Study (AOCS) was supported by the U.S. 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). AOCS gratefully acknowledges additional support from Ovarian Cancer Australia and the Peter MacCallum Foundation; DOV: National Institutes of Health R01-CA112523 and R01-CA87538; HAW: U.S. National Institutes of Health (R01-CA58598, N01-CN-55424 and N01-PC-67001). LAC: National Institutes of Health P01-CA17054, P30-CA14089, R01-CA61132, N01-PC67010, R03-CA113148, R03-CA115195, N01-CN025403, and California Cancer Research Programme (00-01389V-20170, 2II0200). MAL: Funding for this study was provided by research grant R01-CA61107 from the National Cancer Institute, Bethesda, MD, research grant 94 222 52 from the Danish Cancer Society, Copenhagen, Denmark; and the Mermaid I and Mermaid III projects; MCC: Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further augmented by Australian National Health and Medical Research Council grants 209057, 396414 and 1074383 and by infrastructure provided by Cancer Council Victoria; NEC: National Institutes of Health R01-CA54419 and P50-CA105009 and Department of Defense W81XWH-10-1-02802; NJO: National Cancer Institute (NIH-K07-CA095666, R01-CA83918, NIH-K22-CA138563, and P30-CA072720) and the Cancer Institute of New Jersey; OPL: National Health and Medical Research Council (NHMRC) of Australia (GNT1025142, GNT1120431); POL: Intramural Research Programme of the National Cancer Institute; Authors: PW was funded by the National Health and Medical Research Council of Australia (GNT1173346).
Data availability
Data cannot be made publicly available due to privacy and ethical reasons. Contact the corresponding author to discuss access to data through existing data request processes for OCAC.
Competing interests
PMW has received funding from AstraZeneca for an unrelated study of ovarian cancer. EVB has served on an Advisory Board for Pfizer to enhance minoritized and underrepresented populations in clinical trials, unrelated to this study.
Ethics
All studies were approved by their relevant institutional review board and all participants provided informed consent.
Footnotes
Patient advocate: Jean Richardson.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
The online version contains supplementary material available at 10.1038/s41416-024-02792-7.
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Data Availability Statement
Data cannot be made publicly available due to privacy and ethical reasons. Contact the corresponding author to discuss access to data through existing data request processes for OCAC.