Cancer Causes & Control

, Volume 24, Issue 5, pp 989–1004

Cigarette smoking and risk of ovarian cancer: a pooled analysis of 21 case–control studies

Authors

  • Mette T. Faber
    • Unit of Virus, Lifestyle and GenesDanish Cancer Society Research Center
  • Susanne K. Kjær
    • Unit of Virus, Lifestyle and GenesDanish Cancer Society Research Center
    • Gynecologic ClinicCopenhagen University Hospital
  • Christian Dehlendorff
    • Unit of Statistics, Bioinformatics and RegistryDanish Cancer Society Research Center
  • Jenny Chang-Claude
    • Division of Cancer EpidemiologyGerman Cancer Research Center
  • Klaus K. Andersen
    • Unit of Statistics, Bioinformatics and RegistryDanish Cancer Society Research Center
  • Estrid Høgdall
    • Unit of Virus, Lifestyle and GenesDanish Cancer Society Research Center
    • Molecular Unit, Department of PathologyHerlev University Hospital, University of Copenhagen
  • Penelope M. Webb
    • Population Health DepartmentQueensland Institute of Medical Research
  • Susan J. Jordan
    • Population Health DepartmentQueensland Institute of Medical Research
  • The Australian Cancer Study (Ovarian Cancer)
    • Population Health DepartmentQueensland Institute of Medical Research
  • Australian Ovarian Cancer Study Group
    • Population Health DepartmentQueensland Institute of Medical Research
    • Peter MacCallum Cancer Centre
  • Mary Anne Rossing
    • Program in Epidemiology, Division of Public Health SciencesFred Hutchinson Cancer Research Center
    • Department of EpidemiologyUniversity of Washington
  • Jennifer A. Doherty
    • Program in Epidemiology, Division of Public Health SciencesFred Hutchinson Cancer Research Center
    • Department of Community and Family Medicine, Section of Biostatistics and EpidemiologyThe Geisel School of Medicine at Dartmouth
  • Galina Lurie
    • Epidemiology ProgramUniversity of Hawaii Cancer Center
  • Pamela J. Thompson
    • Epidemiology ProgramUniversity of Hawaii Cancer Center
  • Michael E. Carney
    • Epidemiology ProgramUniversity of Hawaii Cancer Center
  • Marc T. Goodman
    • Epidemiology ProgramUniversity of Hawaii Cancer Center
  • Roberta B. Ness
    • School of Public HealthUniversity of Texas
  • Francesmary Modugno
    • Division of Gyn/Onc, Department of Ob/Gyn/RS, School of Medicine and Department of Epidemiology, Graduate School of Public Health, Ovarian Cancer Center of ExcellenceWomen’s Cancer Program, Magee-Women’s Research Institute, University of Pittsburgh Cancer Institute, University of Pittsburgh
  • Robert P. Edwards
    • Division of Gyn/Onc, Department of Ob/Gyn/RS, and Ovarian Cancer Center of ExcellenceUniversity of Pittsburgh
  • Clareann H. Bunker
    • Department of Epidemiology, Graduate School of Public HealthUniversity of Pittsburgh
  • Ellen L. Goode
    • Division of Epidemiology, Department of Health Science ResearchMayo Clinic College of Medicine
  • Brooke L. Fridley
    • Division of Biomedical Statistics and Informatics, Department of Health Science ResearchMayo Clinic
  • Robert A. Vierkant
    • Division of Biomedical Statistics and Informatics, Department of Health Science ResearchMayo Clinic
  • Melissa C. Larson
    • Division of Biomedical Statistics and Informatics, Department of Health Science ResearchMayo Clinic
  • Joellen Schildkraut
    • Department of Community and Family MedicineDuke University Medical Center
    • Cancer Prevention, Detection & Control Research ProgramDuke Cancer Institute
  • Daniel W. Cramer
    • Obstetrics and Gynecology Epidemiology CenterBrigham and Women’s Hospital
  • Kathryn L. Terry
    • Robert Wood Johnson Medical SchoolThe Cancer Institute of New Jersey
  • Allison F. Vitonis
    • Obstetrics and Gynecology Epidemiology CenterBrigham and Women’s Hospital
  • Elisa V. Bandera
    • Robert Wood Johnson Medical SchoolThe Cancer Institute of New Jersey
  • Sara H. Olson
    • Department of Epidemiology and BiostatisticsMemorial Sloan-Kettering Cancer Center
  • Melony King
    • UMDNJ-School of Public Health
  • Urmila Chandran
    • Robert Wood Johnson Medical SchoolThe Cancer Institute of New Jersey
  • Lambertus A. Kiemeney
    • Department of Epidemiology, Biostatistics and HTARadboud University Nijmegen Medical Centre
    • Department of UrologyRadboud University Nijmegen Medical Centre
    • Comprehensive Cancer CenterRadboud University Nijmegen Medical Centre
  • Leon F. A. G. Massuger
    • Department of GynecologyRadboud University Nijmegen Medical Centre
  • Anne M. van Altena
    • Department of GynecologyRadboud University Nijmegen Medical Centre
  • Sita H. Vermeulen
    • Department of Epidemiology, Biostatistics and HTARadboud University Nijmegen Medical Centre
  • Louise Brinton
    • Division of Cancer Epidemiology and GeneticsNational Cancer Institute
  • Nicolas Wentzensen
    • Division of Cancer Epidemiology and GeneticsNational Cancer Institute
  • Jolanta Lissowska
    • Department of Cancer Epidemiology and PreventionM. Sklodowska-Curie Memorial Cancer Center and Institute of Oncology
  • Hannah P. Yang
    • Division of Cancer Epidemiology and GeneticsNational Cancer Institute
  • Kirsten B. Moysich
    • Department of Cancer Prevention and ControlRoswell Park Cancer Institute
  • Kunle Odunsi
    • Department of Gynecological OncologyRoswell Park Cancer Institute
  • Karin Kasza
    • Department of Cancer Prevention and ControlRoswell Park Cancer Institute
  • Oluwatosin Odunsi-Akanji
    • Department of Cancer Prevention and ControlRoswell Park Cancer Institute
  • Honglin Song
    • Strangeways Research Laboratory, Department of OncologyUniversity of Cambridge
  • Paul Pharaoh
    • Strangeways Research Laboratory, Department of OncologyUniversity of Cambridge
  • Mitul Shah
    • Strangeways Research Laboratory, Department of OncologyUniversity of Cambridge
  • Alice S. Whittemore
    • Department of Health Research and Policy, EpidemiologyStanford University School of Medicine
  • Valerie McGuire
    • Department of Health Research and Policy, EpidemiologyStanford University School of Medicine
  • Weiva Sieh
    • Department of Health Research and Policy, EpidemiologyStanford University School of Medicine
  • Rebecca Sutphen
    • Epidemiology Center, College of MedicineUniversity of South Florida
  • Usha Menon
    • Womens Cancer, EGA Institute for Women’s HealthUniversity College London
  • Simon A. Gayther
    • Department of Preventive Medicine, Keck School of MedicineUniversity of Southern California Norris Comprehensive Cancer Center
  • Susan J. Ramus
    • Department of Epidemiology and Public HealthYale University School of Public Health and School of Medicine
  • Aleksandra Gentry-Maharaj
    • Womens Cancer, EGA Institute for Women’s HealthUniversity College London
  • Celeste Leigh Pearce
    • Department of Preventive Medicine, Keck School of MedicineUniversity of Southern California Norris Comprehensive Cancer Center
  • Anna H. Wu
    • Department of Preventive Medicine, Keck School of MedicineUniversity of Southern California Norris Comprehensive Cancer Center
  • Malcolm C. Pike
    • Department of Epidemiology and BiostatisticsMemorial Sloan-Kettering Cancer Center
    • Department of Preventive Medicine, Keck School of MedicineUniversity of Southern California Norris Comprehensive Cancer Center
  • Harvey A. Risch
    • Department of Epidemiology and Public HealthYale University School of Public Health and School of Medicine
    • Unit of Virus, Lifestyle and GenesDanish Cancer Society Research Center
  • On behalf of the Ovarian Cancer Association Consortium
Original paper

DOI: 10.1007/s10552-013-0174-4

Cite this article as:
Faber, M.T., Kjær, S.K., Dehlendorff, C. et al. Cancer Causes Control (2013) 24: 989. doi:10.1007/s10552-013-0174-4

Abstract

Purpose

The majority of previous studies have observed an increased risk of mucinous ovarian tumors associated with cigarette smoking, but the association with other histological types is unclear. In a large pooled analysis, we examined the risk of epithelial ovarian cancer associated with multiple measures of cigarette smoking with a focus on characterizing risks according to tumor behavior and histology.

Methods

We used data from 21 case–control studies of ovarian cancer (19,066 controls, 11,972 invasive and 2,752 borderline cases). Study-specific odds ratios (OR) and 95 % confidence intervals (CI) were obtained from logistic regression models and combined into a pooled odds ratio using a random effects model.

Results

Current cigarette smoking increased the risk of invasive mucinous (OR = 1.31; 95 % CI: 1.03–1.65) and borderline mucinous ovarian tumors (OR = 1.83; 95 % CI: 1.39–2.41), while former smoking increased the risk of borderline serous ovarian tumors (OR = 1.30; 95 % CI: 1.12–1.50). For these histological types, consistent dose–response associations were observed. No convincing associations between smoking and risk of invasive serous and endometrioid ovarian cancer were observed, while our results provided some evidence of a decreased risk of invasive clear cell ovarian cancer.

Conclusions

Our results revealed marked differences in the risk profiles of histological types of ovarian cancer with regard to cigarette smoking, although the magnitude of the observed associations was modest. Our findings, which may reflect different etiologies of the histological types, add to the fact that ovarian cancer is a heterogeneous disease.

Keywords

Case–control studiesHistological typeOvarian neoplasmsSmoking

Introduction

Among women in the Western world, ovarian cancer is the sixth most common cancer diagnosed and the sixth leading cause of cancer death [1]. Ovarian cancer is the most lethal gynecologic cancer with an overall 5-year survival of 30–40 % [1, 2]. Due to the poor prognosis, identification of potential factors for prevention of ovarian cancer may have important clinical and public health implications.

A growing body of studies has assessed cigarette smoking as a potential risk factor for ovarian cancer [3]. The strongest association appears to be with mucinous ovarian tumors [319], while the association with other histological types is less certain. A few studies have observed increased risk of serous ovarian tumors associated with smoking [15, 1921], but most studies found no association [36, 9, 11, 14, 1618]. The associations between smoking and endometrioid and clear cell ovarian cancer risk are also of interest; some studies have found an inverse association [4, 5, 18, 20], but not all [6, 16]. Concerning tumor behavior, some studies have suggested that the association with smoking is stronger for borderline mucinous tumors compared with invasive mucinous cancers [3, 8, 19, 20], although not all [10, 15]. The inconsistent results reported from previous studies may be due to small numbers of study subjects, which reduces the precision of the risk estimates, particularly in analyses of multiple measures of smoking and analyses of tumor behavior and histology. Recently, however, a large meta-analysis conducted by the Collaborative Group on Epidemiological Studies of Ovarian Cancer found that current smoking increased the risk of invasive and borderline mucinous ovarian tumors. Furthermore, a decreased risk of endometrioid and clear cell ovarian tumors was observed, while no association was found for serous ovarian tumors [22]. However, this study did not include analyses on dose–response associations between various measures of cigarette smoking and ovarian cancer risk.

To further assess the association between cigarette smoking and ovarian cancer risk, we have used data from 21 recent case–control studies associated with the Ovarian Cancer Association Consortium (OCAC) [23]. In a pooled analysis, we examined the risk of ovarian cancer in relation to multiple measures of cigarette smoking including dose and duration of smoking with a particular focus on characterizing risks among tumor subgroups according to histology and degree of invasiveness.

Methods

The Ovarian Cancer Association Consortium (OCAC) is an international collaboration of ovarian cancer studies formed in 2005 to investigate associations between genetic polymorphisms and ovarian cancer risk and to identify epidemiological risk factors associated with development of ovarian cancer.

In the present study, we obtained data from 21 case–control studies: 19 OCAC case–control studies [2443] and two case–control studies not included in OCAC (SON [44] and RPI [45]). Of these 21 studies, nine (AUS, GER, HAW, HOP, NEC, POL, RPI, SON, USC) [24, 27, 28, 32, 34, 40, 4245] were also included in the recent meta-analysis performed by the Collaborative Group on Epidemiological Studies of Ovarian Cancer [22], whereas 12 studies (CON, DOV, MAL, MAY, NCO, NJO, NTH, SEA, STA, TBO, TOR, UKO) [25, 26, 2931, 33, 3539, 41] were only included in the present analysis. Characteristics of all 21 studies included in our study are presented in Table 1. All data were checked for internal consistency and clarifications were provided by the original investigators. Women with missing data on smoking status and those with non-epithelial tumors were excluded from analyses. In total, 14,724 women with epithelial ovarian cancer (11,972 invasive ovarian cancers and 2,752 borderline ovarian tumors) and 19,066 controls were included for analyses. In the majority (12) of the included studies (CON, HAW, HOP, MAY, NCO, NEC, NJO, POL, RPI, TBO, TOR, UKO) [25, 28, 3034, 3740, 45], a pathological review was performed for all eligible cases. In three studies (MAL, SEA, STA) [29, 35, 36], a pathological review was performed for a subset of cases, while in six studies (AUS, DOV, GER, NTH, SON, USC) [24, 26, 27, 4144], there was no pathological review of cases. All analyses in the present paper were stratified by pathological review status, but the major results did not differ between the three groups and will not be presented further. All studies had institutional review board or ethics committee approvals.
Table 1

Characteristics of the case–control studies included in the pooled analysis of smoking and ovarian cancer

Region

Study

Site

Study period

Study type

Method of data collection

Age range

Matching

Australia

Australian Ovarian Cancer Study and Australian Cancer Study (Ovarian Cancer)

AUS

2002–2006

Population-based

Self-completed questionnaire

18–80

Age (5-year groups)

Europe

German Ovarian Cancer Study

GER

1993–1996

Population-based

Self-completed questionnaire

21–75

Age (±1 year)

The Danish Malignant Ovarian Tumor Study

MAL

1995–1999

Population-based

in-person interview

31–80

Age (5-year groups)

Nijmegen Ovarian Cancer Study

NTH

1989–2006

Hospital-based

Self-completed questionnaire

23–83

Age (5-year groups)

Polish Ovarian Cancer Study

POL

2000–2004

Population-based

in-person interview

24–74

Age (5-year groups)

Study of Epidemiology and Risk Factors in Cancer Heredity

SEA

1998-present

Population-baseda

Self-completed questionnaire

19–77

No matching

UK Ovarian Cancer Population Study

UKO

2006–2010

Hospital-based

Self-completed questionnaire

19–89

No matching

North America

Connecticut Ovary Study

CON

1998–2003

Population-based

in-person interview

35–79

Age (35–49, 50–64, 65–79)

Diseases of the Ovary and their Evaluation Study

DOV

2002–2005

Population-based

in-person interview

35–74

Age (5-year groups)

Hawaii Ovarian Cancer Study

HAW

1993–2008

Population-based

in-person interview

18–93

Age (±2.5 year groups) and race/ethnicity

Hormones and Ovarian Cancer Prediction

HOP

2003–2009

Population-based

in-person interview

24–93

Age (5-year groups)

Mayo Clinic Ovarian Cancer Case Control Study

MAY

2000–2009

Hospital-based

Self-completed questionnaire

20–93

Age (5-year groups)

North Carolina Ovarian Cancer Study

NCO

1999–2008

Population-based

Self-completed questionnaire

20–75

Age (5-year groups) and race/ethnicity

New England-based Case–Control Study of Ovarian Cancer

NEC

1992–2003

Population-based

in-person interview

18–78

Age (5-year groups)

New Jersey Ovarian Cancer Study

NJO

2002–2008

Population-based

in-person interview

23–88

No matching

Roswell Park Ovarian Cancer Case–Control Study

RPI

1982–1998

Hospital-based

Self-completed questionnaire

14–96

Age (5-year groups)

Southern Ontario Study

SON

1990–1993

Population-based

in-person interview

35–79

Age (35–49, 50–64, 65–79)

Family Registry for Ovarian Cancer and Genetic Epidemiology of Ovarian Cancer

STA

1997–2002

Population-based

in-person interview

19–66

Age (5-year groups) and race/ethnicity

Tampa Bay Ovarian Cancer Study

TBO

2000-present

Population-based

Self-completed questionnaire

26–93

Age (5-year groups)

Familial Ovarian Tumor Study

TOR

1995–2003

Population-baseda

in-person interview

21–99

Age (5-year groups)

Los Angeles County Case–Control Studies of Ovarian Cancer

USC

1993–2005

Population-based

in-person interview

19–86

Age (5-year groups) and race/ethnicity

aNon-population-based controls

Assessment of cigarette smoking

The present study addressed associations between cigarette smoking and risk of ovarian cancer, as data about use of other tobacco products were limited to a few studies. Data on cigarette smoking were collected either through self-administered questionnaires or in-person interviews (Table 1). We obtained information on the following variables: smoking status (current, former, or never smoker), cigarette consumption (average number of cigarettes/day), total duration of smoking (years), age at smoking initiation, and time since smoking cessation (years). Ever smokers were defined differently by the participating studies either as those who had smoked at least 100 cigarettes in their lifetime (AUS, CON, DOV, MAY, NCO, NEC, TBO, and TOR), those who had smoked daily for a period of 3, 6, or 12 months (GER, HAW, HOP, NJO, POL, RPI, SEA, STA, UKO, and USC), or those who had ever smoked without any further specification (MAL, NTH, and SON). Current smokers were generally defined as women who were smoking within 12 months of the date of diagnosis (cases) or date of interview (controls). Information on smoking status and age at smoking initiation was available for all 21 studies. All studies but two (SEA and TBO) had information on average number of cigarettes smoked per day and duration of smoking. Information about time since smoking cessation was available from all but one study (TBO).

Statistical analysis

Associations between the various smoking exposures and risk of ovarian cancer were analyzed using a two-stage approach [46]. First, study-specific odds ratios (ORs) were obtained from logistic regression models adjusted for the selected confounding variables. The study-specific estimates were then combined into a pooled odds ratio (pOR) with corresponding 95 % confidence intervals (CIs). The pooled estimate was computed by weighting each estimate by the inverse of the sum of its variance and the across-studies variance using a random effects model [47]. Statistical heterogeneity among studies was evaluated using the Cochran Q test and I2 statistics. Where heterogeneity was evident, we examined the data for potential sources of heterogeneity, including type of study (population-based vs. hospital-based case–control study) and method of data collection (self-administered questionnaire vs. in-person interview). For analyses, the variables ‘cigarette consumption,’ ‘duration of smoking,’ ‘age at smoking initiation,’ and ‘time since smoking cessation’ were parameterized both as categorical and continuous variables. Each categorical variable was categorized into ordinal groups with never smokers as the reference group. Dose–response associations between the continuous variables ‘cigarette consumption,’ ‘duration of smoking,’ and ‘age at smoking initiation’ and ovarian cancer were evaluated among ever smokers (current and former smokers combined) only, while the dose–response association between ‘time since smoking cessation’ and ovarian cancer was evaluated among former smokers only. In order to model these dose–response associations, smoking status was included as a categorical indicator variable together with the continuous variable for the smoking variable in question as suggested by Leffondré et al. [48].

We also examined the associations between cigarette consumption, duration of smoking, age at smoking initiation, and ovarian cancer among current smokers only. However, this did not alter the results substantially, and no changes in the direction of the associations were observed (data not shown). Therefore, all analyses for cigarette consumption, duration of smoking, and age at smoking initiation presented in the present paper are for former and current smokers combined. To consider the possibility that early cancer symptoms might have induced smoking cessation, we performed analyses using smoking status 1 year before diagnosis for cases and 1 year before interview for controls. However, as these analyses did not change the results substantially, we used smoking status within 12 months of the date of diagnosis/interview in our final analyses.

All models were adjusted for age, parity (ever/never having given birth and number of births as a continuous variable), oral contraceptive use (ever/never use and duration of use as a continuous variable), total months of breastfeeding (continuous variable), family history of breast or ovarian cancer in first-degree relatives (yes/no), and education (high school or less vs. more than high school). In studies that employed matching on age and/or race/ethnicity (Table 1), we adjusted for these variables by means of conditional logistic regression. For studies without matching (Table 1), we divided age into 5-year age groups and used unconditional logistic regression. Other potential confounding variables considered but not included in the final models were: age at first and last birth, hormone replacement therapy use, history of endometriosis, hysterectomy or tubal ligation, body mass index (BMI), menopausal status, and age at menarche. A covariate was included in the final models of all studies only if it altered the log of the pooled effect estimate for overall ovarian cancer risk by 10 % or more, that is, all studies were adjusted for the same confounders if information on the respective confounders was available.

Interactions between smoking status and menopausal status (pre-/perimenopausal vs. postmenopausal), parity (ever vs. never having given birth), oral contraceptive use (ever vs. never use), family history of breast or ovarian cancer in first-degree relatives (yes vs. no), and education (high school or less vs. more than high school), respectively, were tested. Linearity for all quantitative variables was tested by comparison with restricted cubic splines (5 knots placed at equidistant quintiles between 0.05 and 0.95), but no statistically significant deviations from linearity were observed. The significance of the interactions and nonlinear effects was computed by testing the interaction/nonlinearity in each study separately by likelihood ratio tests and then comparing the distribution of these study-specific p values with a uniform distribution by a Kolmogorov–Smirnov test [49].

Subgroup analyses were conducted by tumor behavior (invasive or borderline) and histological type. For the most general variable ‘smoking status,’ heterogeneity across the different histological types was evaluated by pairwise comparisons of the risk estimates for each of the histological types. Invasive cancers were categorized as serous, mucinous, endometrioid, clear cell, or ‘other’ tumors (including mixed cell, undifferentiated tumors, and tumors with unknown histology). Invasive serous tumors were additionally categorized as low- (grade 1) or high- (grade 2+) grade [50]. Borderline ovarian tumors were categorized as either serous or mucinous. Borderline endometrioid and clear cell tumors are uncommon and therefore these tumor types were only included in the analyses for overall borderline ovarian tumors. All analyses were conducted using the statistical software package R, version 2.14.0 [51]. All p values were two-sided, and a significance level of 5 % was used.

Results

The distribution of histological types among invasive and borderline cases is presented in Table 2. Among the 11,972 women with invasive ovarian cancer, never smokers comprised 54.7 %, whereas former and current smokers constituted 31.8 and 13.6 %, respectively. For the 2,752 women with borderline ovarian tumors, 48.2 % were never smokers, 29.9 % were former, and 21.9 % current smokers. Among the 19,066 control women, 52.8 % were never smokers, 31.0 % were former, and 16.3 % current smokers.
Table 2

Number of observations among cases and controls by study site and histological type

Region

Study

Site

Controls

Invasive cases

Borderline cases

All

Serous (%)a

Mucinous (%)a

Endometrioid (%)a

Clear cell (%)a

Other (%)a

All

Serous (%)b

Mucinous (%)b

Other (%)b, c

Australia

Australian Ovarian Cancer Study and Australian Cancer Study (Ovarian Cancer)

AUS

1,448

1,035

635 (61.4)

39 (3.77)

126 (12.2)

79 (7.63)

156 (15.1)

284

135 (47.5)

137 (48.2)

12 (4.23)

Europe

German Ovarian Cancer Study

GER

527

224

113 (50.4)

25 (11.2)

26 (11.6)

6 (2.68)

54 (24.1)

28

16 (57.1)

9 (32.1)

3 (10.7)

The Danish Malignant Ovarian Tumor Study

MAL

1,551

541

333 (61.6)

50 (9.24)

75 (13.9)

41 (7.58)

42 (7.76)

198

101 (51.0)

87 (43.9)

10 (5.05)

Nijmegen Ovarian Cancer Study

NTH

567

206

92 (44.7)

29 (14.1)

49 (23.8)

14 (6.80)

22 (10.7)

    

Polish Ovarian Cancer Study

POL

516

209

94 (45.0)

15 (7.18)

29 (13.9)

7 (3.35)

64 (30.6)

20

16 (80.0)

3 (15.0)

1 (5.0)

Study of Epidemiology and Risk Factors in Cancer Heredity

SEA

1,077

1,246

531 (42.6)

131 (10.5)

201 (16.1)

135 (10.8)

248 (19.9)

283

65 (23.0)

150 (53.0)

68 (24.0)

UK Ovarian Cancer Population Study

UKO

800

467

246 (52.7)

44 (9.42)

75 (16.1)

43 (9.21)

59 (12.6)

    

North America

Connecticut Ovary Study

CON

420

298

178 (59.7)

17 (5.70)

58 (19.5)

26 (8.72)

19 (6.38)

85

51 (60.0)

30 (35.3)

4 (4.71)

Diseases of the Ovary and their Evaluation Study

DOV

1,309

593

335 (56.5)

23 (3.88)

97 (16.4)

35 (5.90)

103 (17.4)

217

117 (53.9)

89 (41.0)

11 (5.07)

Hawaii Ovarian Cancer Study

HAW

1,104

709

315 (44.4)

71 (10.0)

117 (16.5)

82 (11.6)

124 (17.5)

187

89 (47.6)

91 (48.7)

7 (3.74)

Hormones and Ovarian Cancer Prediction

HOP

1,802

677

366 (54.1)

36 (5.32)

97 (14.3)

52 (7.68)

126 (18.6)

97

58 (59.8)

29 (29.9)

10 (10.3)

Mayo Clinic Ovarian Cancer Case Control Study

MAY

639

483

321 (66.5)

16 (3.31)

80 (16.6)

31 (6.42)

35 (7.25)

75

47 (62.7)

17 (22.7)

11 (14.7)

North Carolina Ovarian Cancer Study

NCO

1,048

830

451 (54.3)

41 (4.94)

134 (16.1)

88 (10.6)

116 (14.0)

217

151 (69.6)

60 (27.6)

6 (2.76)

New England-based Case–Control Study of Ovarian Cancer

NEC

1,242

829

460 (55.5)

55 (6.63)

167 (20.1)

112 (13.5)

35 (4.22)

293

172 (58.7)

99 (33.8)

22 (7.51)

New Jersey Ovarian Cancer Study

NJO

444

224

129 (57.6)

11 (4.91)

31 (13.8)

31 (13.8)

22 (9.82)

    

Roswell Park Ovarian Cancer Case–control Study

RPI

952

444

201 (45.3)

38 (8.56)

45 (10.1)

23 (5.18)

137 (30.9)

    

Southern Ontario Study

SON

551

363

212 (58.4)

39 (10.7)

69 (19.0)

29 (7.99)

14 (3.86)

84

42 (50.0)

40 (47.6)

2 (2.38)

Family Registry for Ovarian Cancer and Genetic Epidemiology of Ovarian Cancer

STA

620

499

276 (55.3)

43 (8.62)

65 (13.0)

50 (10.0)

65 (13.0)

171

113 (66.1)

46 (26.9)

12 (7.02)

Tampa Bay Ovarian Cancer Study

TBO

96

184

117 (63.6)

10 (5.43)

25 (13.6)

11 (5.98)

21 (11.4)

    

Familial Ovarian Tumor Study

TOR

542

590

385 (65.3)

54 (9.15)

112 (19.0)

29 (4.92)

10 (1.69)

104

34 (32.7)

66 (63.5)

4 (3.85)

Los Angeles County Case–Control Studies of Ovarian Cancer

USC

1,811

1,321

829 (62.8)

111 (8.40)

183 (13.9)

87 (6.59)

111 (8.40)

409

242 (59.2)

162 (39.6)

5 (1.22)

Total

  

19,066

11,972

6,619 (55.3)

898 (7.50)

1,861 (15.5)

1,011 (8.44)

1,583 (13.2)

2,752

1,449 (52.7)

1,115 (40.5)

188 (6.83)

aProportion of all invasive cases

bProportion of all borderline cases

cIncludes endometrioid, clear cell, and other histologies

Invasive ovarian cancer

Results of the pooled analysis for overall invasive ovarian cancer and stratified by histological type are presented in Table 3 and Fig. 1a–d. We found no association between cigarette smoking and overall invasive ovarian cancer risk, either for current (OR = 0.89; 95 % CI: 0.76, 1.04) or for former smokers (OR = 1.01; 95 % CI: 0.96, 1.07). No associations between the other smoking variables and risk of overall invasive ovarian cancer were observed, except for duration of smoking where each extra 5 years of smoking among women who were ever smokers was associated with a 4 % increased risk (95 % CI: 1.02, 1.06).
Table 3

Adjusted pooled odds ratios and 95 % CI for the association between smoking and invasive ovarian cancer, overall and by histological type

 

Studies (n)

Controls (n)

Overalla

Serousa

Mucinousa

Endometrioida

Clear cella

  

Cases

pORb

95 % CI

Cases

pORb

95 % CI

Cases

pORb

95 % CI

Cases

pORb

95 % CI

Cases

pORb

95 % CI

Smoking status

21

                

 Never smokers

 

10,060

6,544

1.00

Ref.

3,530

1.00

Ref.

452

1.00

Ref.

1,049

1.00

Ref.

605

1.00

Ref.

 Former smokers

 

5,907

3,804

1.01

0.96, 1.07

2,195

1.05

0.97, 1.14

243

0.99

0.83, 1.17

578

0.98

0.86, 1.13

264

0.77

0.66, 0.91

 Current smokers

 

3,099

1,624

0.89

0.76, 1.04*

894

0.92

0.76, 1.10*

182

1.31

1.03, 1.65

234

0.84

0.69, 1.02

104

0.74

0.56, 0.98

Cigarette consumption (per day)

19

                

 Never smokers

 

9,403

5,727

1.00

Ref.

3,151

1.00

Ref.

393

1.00

Ref.

915

1.00

Ref.

530

1.00

Ref.

 >0–≤10

 

3,533

1,979

0.98

0.92, 1.05

1,173

1.04

0.96, 1.13

131

0.99

0.80, 1.23

299

0.94

0.80, 1.11

123

0.72

0.58, 0.89

 >10–≤20

 

3,358

1,875

0.99

0.92, 1.07

1,080

1.02

0.92, 1.13

148

1.16

0.94, 1.44

268

0.92

0.76, 1.12

126

0.75

0.60, 0.93

 >20

 

1,442

854

0.99

0.90, 1.09

511

1.05

0.94, 1.19

70

1.41

1.06, 1.89

130

0.99

0.80, 1.22

53

0.71

0.52, 0.97

 Per 5 cigarettes/dayc

   

1.00

0.99, 1.02

 

1.00

0.98, 1.02

 

1.04

0.99, 1.10

 

1.00

0.97, 1.04

 

0.99

0.94, 1.05

Duration of smoking (years)

19

                

 Never smokers

 

9,403

5,727

1.00

Ref.

3,151

1.00

Ref.

393

1.00

Ref.

915

1.00

Ref.

530

1.00

Ref.

 >0–≤10

 

2,047

1,034

0.91

0.83, 1.00

601

1.00

0.90, 1.12

68

0.82

0.61, 1.09

179

0.97

0.79, 1.19

66

0.71

0.54, 0.94

 >10–≤20

 

1,822

1,013

0.98

0.87, 1.10

592

1.05

0.91, 1.21

76

1.03

0.78, 1.36

156

0.90

0.69, 1.17*

66

0.72

0.55, 0.96

 >20–≤30

 

1,794

1,049

1.03

0.94, 1.13

598

1.07

0.96, 1.20

80

1.28

0.98, 1.68

144

0.91

0.74, 1.13

70

0.78

0.59, 1.02

 >30

 

2,617

1,611

1.03

0.93, 1.15*

971

1.05

0.95, 1.16

124

1.62

1.22, 2.16

221

1.04

0.86, 1.26

100

0.76

0.58, 1.00

 Per 5-year periodc

   

1.04

1.02, 1.06

 

1.03

1.00, 1.06*

 

1.12

1.05, 1.19

 

1.03

1.00, 1.07

 

1.04

0.99, 1.10

Age at smoking initiation (years)

21

                

 Never smokers

 

10,060

6,544

1.00

Ref.

3,530

1.00

Ref.

452

1.00

Ref.

1,049

1.00

Ref.

605

1.00

Ref.

 <16

 

3,039

1,748

0.96

0.89, 1.04

1,005

1.03

0.94, 1.13

158

1.15

0.88, 1.51

242

0.83

0.68, 1.02

119

0.77

0.62, 0.97

 16–19

 

3,099

1,808

0.96

0.89, 1.03

1,009

0.97

0.89, 1.07

137

1.10

0.89, 1.37

286

0.96

0.83, 1.12

133

0.79

0.64, 0.98

 >19

 

2,763

1,781

1.02

0.94, 1.09

1,026

1.06

0.97, 1.15

126

1.17

0.94, 1.46

265

1.00

0.84, 1.18

112

0.73

0.58, 0.91

 Per 1 yearc

   

1.00

1.00, 1.01

 

1.00

0.99, 1.01

 

1.01

0.99, 1.03

 

1.00

0.99, 1.02

 

1.00

0.98, 1.03

Time since smoking cessation (years)

20

                

 Never smokers

 

10,011

6,446

1.00

Ref.

3,466

1.00

Ref.

452

1.00

Ref.

1,036

1.00

Ref.

605

1.00

Ref.

 0–≤10

 

1,660

1,118

1.16

1.05, 1.27

636

1.21

1.05, 1.41*

86

1.20

0.93, 1.56

167

1.13

0.93–1.36

68

0.89

0.67, 1.18

 >10–≤20

 

1,555

925

0.96

0.86, 1.07

505

0.97

0.87, 1.09

66

1.19

0.89, 1.59

148

0.96

0.79–1.17

63

0.71

0.53, 0.95

 >20

 

2,444

1,478

0.95

0.84,1.07*

888

1.00

0.88, 1.14*

74

0.85

0.65, 1.12

225

0.99

0.79–1.24*

113

0.80

0.64, 1.00

 Per 5-year periodd

   

0.99

0.97, 1.01*

 

0.99

0.97, 1.01*

 

0.98

0.94, 1.01

 

1.00

0.97–1.03*

 

0.96

0.92, 0.99

pOR pooled odds ratio, CI confidence interval

p value for heterogeneity < 0.05

aNumbers may not sum up to total because of missing data

bAdjusted for parity (never/ever and continuous), breastfeeding (continuous), oral contraceptive use (yes/no and continuous), family history of breast and/or ovarian cancer (yes/no), and education (high school or less/more than high school)

cAmong women who were ever smokers

dAmong women who were former smokers

The risk of serous ovarian cancer was not associated with any of the smoking variables, except for a tendency toward an increased risk associated with duration of smoking (OR = 1.03; 95 % CI: 1.00, 1.06, p = 0.07, per 5 years of smoking) and a statistically significantly increased risk among women who stopped smoking less than 10 years ago (OR = 1.21; 95 % CI: 1.05, 1.41) (Fig. 1a, b; Table 3). Additional analyses revealed no evidence of a statistically significant association between smoking and risk of low-grade (OR = 1.13; 95 % CI: 0.83, 1.53 for current smokers; OR = 1.08; 95 % CI: 0.84, 1.38 for former smokers) or high-grade (OR = 0.91; 95 % CI: 0.75, 1.10 for current smokers; OR = 1.05; 95 % CI: 0.96, 1.13 for former smokers) serous ovarian cancers (data not shown).
https://static-content.springer.com/image/art%3A10.1007%2Fs10552-013-0174-4/MediaObjects/10552_2013_174_Fig1_HTML.gif
Fig. 1

Risk of invasive ovarian cancer associated with cigarette smoking status, by study site and overall. OR and 95 % CI were estimated using logistic regression models. a Serous ovarian cancer, current versus never smokers, b serous ovarian cancer, former versus never smokers, c mucinous ovarian cancer, current versus never smokers, and d mucinous ovarian cancer former versus never smokers. Each square and line in the figures represents the odds ratio and 95% confidence intervals from each study and the diamond at the bottom of the plot represents the pooled odds ratio. The size of the squares indicates the size of each study

Women who currently smoked had a statistically significant increased risk of mucinous ovarian cancer of 1.31 (95 % CI: 1.03, 1.65, Fig. 1c; Table 3), whereas former smokers had no increased risk (Fig. 1d; Table 3). In addition, the risk of mucinous ovarian cancer increased with increasing numbers of cigarettes smoked per day and duration of smoking (OR = 1.12: 95 % CI: 1.05, 1.19, per 5 years of smoking). In contrast, age at smoking initiation and time since smoking cessation were not convincingly associated with mucinous ovarian cancer risk (Table 3).

We found no convincing association between smoking and risk of endometrioid ovarian cancer. In contrast, both former (OR = 0.77; 95 % CI: 0.66, 0.91) and current smokers (OR = 0.74; 95 % CI: 0.56, 0.98) had a statistically significant decreased risk of clear cell ovarian cancer. There was no dose–response association with duration of smoking or increasing number of cigarettes smoked per day, but clear cell ovarian cancer risk decreased slightly with increasing time since smoking cessation (OR = 0.96; 95 % CI: 0.92, 0.99) (Table 3). As BMI is a potential risk factor for endometrioid [52] and clear cell ovarian cancer, all analyses for these histological types were additionally adjusted for BMI. However, this did not change the risk estimates considerably (data not shown) and the final analysis did not include BMI as a confounder (Table 3).

Pairwise comparisons of the risk estimates for each of the histological types of ovarian cancer revealed that former smoking was associated with a reduced risk of clear cell invasive ovarian cancer that differed statistically significantly from the risk of both serous (p < 0.001), mucinous (p < 0.05), and endometrioid (p < 0.05) invasive ovarian cancer, while current smoking was associated with an increased risk of mucinous invasive ovarian cancer that differed statistically significantly from the risk of both serous (p < 0.05), endometrioid (p < 0.01), and clear cell (p < 0.01) invasive ovarian cancer (data not shown).

In a subanalysis, we analyzed the associations between smoking status and risk of invasive ovarian cancer (overall and according to histological type) among the 12 case–control studies included only in the present study and not in the previous meta-analysis from the Collaborative Group on Epidemiological Studies of Ovarian Cancer [22]. The results were virtually unchanged; the direction of the associations was not altered, but due to less statistical power the confidence intervals were wider and some estimates did not reach statistical significance (data not shown).

Lastly, for each histological type of invasive ovarian cancer, we also investigated interactions between smoking status and menopausal status, parity, oral contraceptive use, family history of breast or ovarian cancer, and education. Our results showed no interaction with any of these potential effect modifiers (all p values >0.05) (data not shown).

Borderline ovarian tumors

Results of the pooled analysis for borderline ovarian tumors are based on data from 16 studies (Table 2). Both current (OR = 1.36; 95 % CI: 1.13, 1.64) and former smoking (OR = 1.18; 95 % CI: 1.01, 1.36) were associated with an increase in the risk of borderline ovarian tumors. Furthermore, we observed statistically significant increased risks for number of cigarettes per day and duration of smoking (Table 4).
Table 4

Adjusted pooled odds ratios and 95 % CI for the association between smoking and borderline ovarian tumors, overall and by histological type

 

Studies (n)

Controls (n)

Overalla

Serousa

Mucinousa

Cases

pORb

95 % CI

Cases

pORb

95 % CI

Cases

pORb

95 % CI

Smoking status

16

          

 Never smokers

 

8,610

1,327

1.00

Ref.

703

1.00

Ref.

521

1.00

Ref.

 Former smokers

 

4,853

822

1.18

1.01, 1.36*

472

1.30

1.12, 1.50

286

1.03

0.85, 1.25

 Current smokers

 

2,744

603

1.36

1.13, 1.64*

274

1.16

0.98, 1.37

296

1.83

1.39, 2.41*

Cigarette consumption (per day)

15

          

 Never smokers

 

8,002

1,174

1.00

Ref.

667

1.00

Ref.

446

1.00

Ref.

 >0–≤10

 

2,988

529

1.14

0.94, 1.39*

319

1.21

0.97, 1.51*

184

1.13

0.87, 1.47

 >10–≤20

 

2,835

496

1.27

1.07, 1.51*

256

1.16

0.98, 1.37

210

1.50

1.19, 1.89

 >20

 

1,203

248

1.57

1.28, 1.92

132

1.51

1.22, 1.87

104

1.74

1.27, 2.39

 Per 5 cigarettes/dayc

   

1.04

1.01, 1.08

 

1.04

1.00, 1.08

 

1.05

1.01, 1.09

Duration of smoking (years)

15

          

 Never smokers

 

8,002

1,174

1.00

Ref.

667

1.00

Ref.

446

1.00

Ref.

 >0–≤10

 

1,737

324

1.03

0.83, 1.27*

210

1.17

0.88, 1.54*

97

0.87

0.67, 1.14

 >10–≤20

 

1,521

283

1.12

0.96, 1.31

165

1.15

0.95, 1.40

105

1.16

0.92, 1.48

 >20–≤30

 

1,505

344

1.59

1.32, 1.91

162

1.35

1.11, 1.64

162

2.05

1.54, 2.72

 >30

 

2,196

323

1.54

1.26, 1.88

170

1.44

1.18, 1.75

134

1.72

1.22, 2.43*

 Per 5-year periodc

   

1.08

1.04, 1.11

 

1.06

1.02, 1.11

 

1.08

1.03, 1.13

Age at smoking initiation (years)

16

          

 Never smokers

 

8,610

1,327

1.00

Ref.

703

1.00

Ref.

521

1.00

Ref.

 <16

 

2,537

584

1.31

1.10, 1.55*

304

1.26

1.08, 1.47

238

1.38

1.07, 1.80*

 16–19

 

2,637

441

1.13

0.96, 1.33

236

1.17

0.98, 1.40

176

1.18

0.91, 1.53

 >19

 

2,345

379

1.31

1.11, 1.54

195

1.31

1.08, 1.59

160

1.51

1.24, 1.85

 Per 1 yearc

   

1.00

0.98, 1.01

 

1.00

0.98, 1.02

 

1.00

0.98, 1.02

Time since smoking cessation (years)

16

          

 Never smokers

 

8,610

1,327

1.00

Ref.

703

1.00

Ref.

521

1.00

Ref.

 0–≤10

 

1,354

316

1.44

1.21, 1.70

190

1.52

1.20, 1.91

106

1.28

1.01, 1.62

 >10–≤20

 

1,320

202

1.03

0.82, 1.28

109

1.10

0.87, 1.38

82

1.17

0.90, 1.52

 >20

 

2,031

243

1.01

0.84, 1.21

152

1.26

1.03, 1.54

67

0.77

0.58, 1.02

 Per 5-year periodd

   

0.99

0.96, 1.03

 

1.03

1.00, 1.06

 

0.95

0.90, 1.01

pOR pooled odds ratio, CI confidence interval

p value for heterogeneity < 0.05

aNumbers may not sum up to total because of missing data

bAdjusted for parity (never/ever and continuous), breastfeeding (continuous), oral contraceptive use (yes/no and continuous), family history of breast and/or ovarian cancer (yes/no), and education (high school or less/more than high school)

cAmong women who were ever smokers

dAmong women who were former smokers

For serous borderline ovarian tumors, risk was statistically significantly increased among former smokers (OR = 1.30; 95 % CI: 1.12, 1.50, Fig. 2b; Table 4), but not among current smokers (Fig. 2a; Table 4). There was also a statistically significant increased risk of serous borderline ovarian tumors associated with duration of smoking and a non-significant increased risk associated with number of cigarettes smoked per day (OR = 1.04; 95 % CI: 1.00, 1.08, p = 0.07, per 5 cigarettes per day). Furthermore, women who quit smoking less than 10 years ago had an increased risk of 1.52 (95 % CI: 1.20, 1.91). No statistically significant relationship was observed for age at smoking initiation and risk of serous borderline ovarian tumors (Table 4).
https://static-content.springer.com/image/art%3A10.1007%2Fs10552-013-0174-4/MediaObjects/10552_2013_174_Fig2_HTML.gif
Fig. 2

Risk of borderline ovarian tumors associated with cigarette smoking status, by study site and overall. OR and 95 % CI were estimated using logistic regression models. a Serous borderline ovarian tumors, current versus never smokers, b serous borderline ovarian tumors, former versus never smokers, c mucinous borderline ovarian tumors, current versus never smokers, and d mucinous borderline ovarian tumors, former versus never smokers. Each square and line in the figures represents the odds ratio and 95% confidence intervals from each study and the diamond at the bottom of the plot represents the pooled odds ratio. The size of the squares indicates the size of each study

Current smoking increased the risk of mucinous borderline ovarian tumors (OR = 1.83; 95 % CI: 1.39, 2.41, Fig. 2c; Table 4), but we observed no increased risk among former smokers (Fig. 2d; Table 4). The risk of mucinous borderline ovarian tumors was also associated with all other smoking variables, except age at smoking initiation (Table 4).

Pairwise comparisons of the risk estimates for the histological types of borderline ovarian tumors revealed that current smoking was associated with an increased risk of mucinous borderline ovarian tumors that differed statistically significantly from the risk of serous borderline ovarian tumors (p < 0.01). In contrast, for former smoking, the different associations between smoking and risk of serous and mucinous borderline ovarian tumors did not reach statistical significance (p = 0.07) (data not shown).

When we analyzed the associations between smoking status and risk of borderline ovarian tumors (overall and according to histological type) among the 12 studies included only in the present study and not in the previous meta-analysis from the Collaborative Group on Epidemiological Studies of Ovarian Cancer [22], there was no change in the direction of the associations. However, due to less statistical power, the confidence intervals were wider and some estimates did not reach statistical significance (data not shown).

Finally, no effect modification between smoking status and any of the potential risk factors for serous or mucinous borderline ovarian tumors was observed (all p values >0.05) (data not shown).

Analysis of heterogeneity across studies

For most analyses, no statistically significant heterogeneity across studies was observed. There were some exceptions, however, most notably for the analyses of smoking status associated with risk for overall and serous invasive ovarian cancer and for overall and mucinous borderline ovarian tumors. To examine whether study type or method of data collection could explain the observed heterogeneity, we conducted these analyses for population-based studies only and for studies that conducted in-person interviews only. However, these subanalyses did not show increased consistency among studies of the same type, as heterogeneity remained (data not shown).

Discussion

In this large pooled analysis, we examined the association between cigarette smoking and ovarian cancer risk according to tumor histology and behavior. Our results show that associations with cigarette smoking differ across histological types of ovarian cancer, although the strength of the associations observed was moderate. We found increased risks of both invasive and borderline mucinous tumors as well as of serous borderline ovarian tumors associated with cigarette smoking. For these histological types, consistent dose–response associations with multiple measures of smoking were observed, suggesting that the associations are likely to be causal. In contrast, no convincing associations between smoking and serous or endometrioid invasive ovarian cancer risk were observed. Lastly, we found some evidence of a decreased risk of clear cell invasive ovarian cancer associated with smoking. The present pooled analysis succeeds a meta-analysis on smoking and ovarian cancer risk from the Collaborative Group on Epidemiological Studies of Ovarian Cancer [22]. The majority of the case–control studies included in our paper (12 out of 21 studies) was not included in the previous meta-analysis. While the meta-analysis from the Collaborative Group on Epidemiological Studies of Ovarian Cancer focused on associations between overall smoking status (never, former, and current smoking) and ovarian cancer, our study also reported dose–response associations with ovarian cancer for different measures of cigarette smoking (cigarette consumption, duration of smoking, age at smoking initiation, and time since smoking cessation).

Our results showed a 31 % increased risk of invasive mucinous ovarian cancer and an 83 % increased risk of borderline mucinous ovarian tumors among current smokers. This is consistent with results from a recent meta-analysis from the Collaborative Group on Epidemiological Studies of Ovarian Cancer [22], where current smoking was associated with increased risks of mucinous invasive and borderline ovarian tumors of 49 % and 125 %, respectively. Furthermore, in line with other studies [58, 1015, 19, 20], we found consistent dose–response associations between smoking duration and number of cigarettes smoked per day and risk of mucinous ovarian tumors, providing further evidence of the association.

As the number of female smokers is increasing globally [53] and invasive serous ovarian cancer constitutes the most common and lethal histological type of ovarian cancer, even a small increase in smoking-related risk of invasive serous ovarian cancer could have important implications for the worldwide incidence of ovarian cancer. However, in agreement with most previous studies [36, 9, 11, 14, 1618, 22], but not all [45, 54], our results revealed no convincing evidence of an association between smoking and this histological type. Our study is the first to analyze the association between cigarette smoking and risk of low- and high-grade serous ovarian cancer separately, but our results did not reveal any significant risk differences for these subgroups. In contrast, we found evidence of a dose–response association between smoking and risk of serous borderline ovarian tumors, which is in line with a few previous studies [15, 19, 20], but not all [9, 14]. Our study showed a statistically significantly increased risk of serous borderline ovarian tumors among former but not current smokers. This finding may have been explained if former smokers were heavier smokers than current smokers, but this was not the case in our study material. Hence, this result is not easily explainable and may be a chance finding.

Some studies have suggested a protective effect of smoking against endometrioid ovarian cancer [4, 5, 18, 20, 22], while in agreement with our results others found no convincing association [3, 6, 16, 45, 54, 55]. In accordance with our results, two meta-analyses [3, 22] and one case–control study [55] found decreased risks for clear cell invasive ovarian cancer associated with smoking, while four other studies reported inverse, but statistically non-significant, associations [18, 20, 45, 54].

Experimental studies support the potential for an increased risk of ovarian cancer associated with cigarette smoking. Cigarette smoke contains numerous carcinogenic chemicals, including benzo[a]pyrene, which has been shown to initiate development of ovarian tumors in mice [56]. Benzo[a]pyrene DNA adducts have also been found in ovarian follicular cells among women exposed to cigarette smoke and the presence of these adducts may increase the risk of DNA damage [57].

The observed risk differences between smoking and histological types of ovarian cancer may reflect their different etiologies [58]. Mucinous ovarian tumors are characterized by cells that resemble those of the cervix or intestines [59]. As cigarette smoking has been found to increase the risk of cervical and colon cancer [60, 61], it is reasonable to assume that smoking may also increase the risk of mucinous ovarian cancer. Smoking is known to reduce the risk of endometrial cancer [62], presumably because of its anti-estrogenic metabolic effects [62]. As clear cell ovarian tumors are histologically similar to those of the endometrium [59], the decreased risk of clear cell invasive ovarian cancer associated with smoking may reflect similar biological mechanisms. Furthermore, the differing risks of histological types of ovarian cancer associated with smoking may reflect that effects of smoking act on different stages in the development of the various histological ovarian tumors. Mucinous ovarian tumors seem to develop along a continuum of benign to borderline to invasive disease, whereas high-grade serous cancers appear to develop from microscopic precursors and evolve more rapidly [63]. It has been suggested that the major carcinogenic effect of smoking occurs in the early stages of a progression from benign to malignant disease [6, 8], which may explain why mucinous ovarian tumor cells are more vulnerable to cigarette smoking than serous ovarian tumor cells. Lastly, it is possible that genetic variation may explain some of the differences in the observed risk patterns. It has been proposed that smoking may induce mutations in the KRAS gene or exert a stronger carcinogenic effect on cells without a functional KRAS gene [18]. KRAS mutations are common in both invasive and borderline mucinous ovarian tumors, occur often in borderline and low-grade serous ovarian tumors, but are rarely observed in high-grade serous invasive ovarian cancer [63]. This difference in the distribution of KRAS mutations might partly explain the increased risk of borderline serous but not invasive serous ovarian tumors observed in our study. Consideration of gene-environment interactions in the future ovarian cancer studies may help to further clarify the association between cigarette smoking and different histological types of ovarian cancer.

The strengths of our work include the pooling of data from 21 individual studies, which increased the statistical power and enabled us to examine associations between multiple smoking variables, including dose and duration of smoking, and the major histological types of ovarian cancer. Moreover, the majority of the studies were population based with information about smoking from in-person interviews. In addition, the participating studies were not selected from published studies, that is, they were included even though separate analyses of individual studies did not demonstrate associations. Therefore, our analyses have not been affected by publication bias. The analyses relied on individual data combined into a single dataset following careful central data harmonization of smoking variables. Using a two-stage method, we were able to take into account differences in design and data collection across studies and to control for multiple confounders.

Our study also had some limitations. An important one is the possibility of recall bias, as all of the studies relied on retrospective reports of smoking behaviors. In addition, smokers may be more difficult to enroll, and therefore, controls who agreed to participate in the included studies may have had a lower prevalence of smoking than the general population, which would have lead to an overestimation of the risk estimates. For example, a study by Pandeya et al. [64] found that non-participation of otherwise eligible controls falsely identified a significant association between smoking and serous ovarian cancer. However, if such response differences are present in our study, it is unlikely that they would entirely explain our findings of consistent and relatively strong associations between smoking and risk of mucinous tumors, but they may to some extent explain the weak associations observed between smoking and risk of serous tumors. In contrast, these potential response differences may have caused an underestimation of the inverse association between smoking and risk of clear cell tumors. Another potential limitation is that not all tumors from ovarian cancer cases have undergone a systematic histopathological review and thus some extent of misclassification of the histological types of ovarian cancer cannot be excluded. In particular, the diagnosis of mucinous ovarian cancer is complicated, and it is possible that a small proportion of tumors classified as invasive mucinous was really of gastrointestinal or cervical origin [59]. Moreover, some serous tumors may have been falsely identified as endometrioid tumors and vice versa [59]. However, all studies contributing to the analysis were conducted in the past two decades and histological misclassification is less likely to have been of concern in these studies compared with studies conducted in the more distant past. Lastly, heterogeneity was identified in some analyses. Restricting our analyses to population-based studies only or to in-person interview studies only did not eliminate this heterogeneity, and thus other unknown factors might be responsible. However, due to the large number of studies, the large number of statistical analyses performed, and study differences concerning factors that we were not able to address, it is unlikely that the homogeneity assumption would be satisfied in all analyses [65]. By using a random effects model, we accounted for heterogeneity, and for analyses with little or no heterogeneity, the random effects model is virtually identical to the fixed effects model.

In conclusion, in this large pooled analysis, we observed moderate increases in risk of invasive and borderline mucinous tumors and borderline serous tumors associated with cigarette smoking. For each of these histological types, the risk increased with increased daily cigarette consumption and duration of smoking. This dose–response relationship supports a causal association between smoking and ovarian cancer. In contrast, our results suggest that smoking is not likely to importantly increase the risk of invasive serous ovarian cancer. There was a decreased risk of invasive clear cell ovarian cancer in relation to smoking. Thus, our results indicate that differences in risk profiles with regard to cigarette smoking are not only present between mucinous and non-mucinous ovarian tumors but across the major histological types of invasive ovarian cancer. These findings further underscore the importance of histological subtype analyses in epidemiological, genetic, and clinical investigations of ovarian cancer, due to the vast heterogeneity in this disease.

Acknowledgments

The work was supported by the European Commission’s Seventh Framework Programme grant agreement no. 223175 (HEALTH-F2-2009-223175). It was also supported by the National Institutes of Health (R01 CA074850, and R01 CA080742 [CON], R01 CA112523, and R01 CA87538 [DOV], R01 CA58598, N01 CN55424, and N01 PC67001 [HAW], R01 CA95023 [HOP], R01 CA61107 [MAL], R01 CA122443, and P50 CA136393 [MAY], R01 CA76016 [NCO], R01 CA54419, and P50 CA105009 [NEC], K07 CA095666, R01 CA83918, and K22 CA138563 [NJO], U01 CA71966, R01 CA16056, K07 CA143047, and U01 CA69417 [STA], R01 CA106414 [TBO], R01 CA063682, R01 CA063678, and R01 CA080978 [TOR], CA 8860, CA92044, and PSA 042205 [UCI], CA17054, CA14089, CA61132, and N01-PC-67010 [USC]); Danish Cancer Society (94 222 52 [MAL]); Mermaid 1 (MAL); German Federal Ministry of Education and Research, Programme of Clinical Biomedical Research (01 GB9401 (GER); German Cancer Research Center (GER); U.S. Army Medical Research and Materiel Command (DAMD17-01-1-0729 [AUS]); National Health & Medical Research Council of Australia (AUS); Cancer Councils of New South Wales, Victoria, Queensland, South Australia, and Tasmania (AUS); Cancer Foundation of Western Australia (AUS); National Health and Medical Research Council of Australia (199600 [AUS]); Department of Defense (DAMD17-02-1-0669 [HOP], DAMD17-02-1-0666 [NCO], W81XWH-10-1-02802 [NEC], and DAMD17-98-1-8659 [TBO]); The Cancer Institute of New Jersey (NJO); Radboud University Nijmegen Medical Centre (NTH); Intramural Research Program of the National Cancer Institute (POL); Roswell Park Alliance Foundation (RPI); Cancer Research UK (C490/A10119, and C490/A10124 [SEA]); National Health Research and Development Program of Health and Welfare Canada (6613-1415-53 [SON]); American Cancer Society (CRTG-00-196-01-CCE [TBO]); Celma Mastery Ovarian Cancer Foundation (TBO); Lon V Smith Foundation (LVS-39420 [UCI]); Cancer Research UK (UKO, SEA); Eve Appeal (UKO); OAK Foundation (UKO); California Cancer Research Program (00-01389V-20170, R03 CA113148, R03 CA115195, and N01 CN25403 [USC]); California Cancer Research Program (2II0200 [USC]); and National Cancer Institute (P01 CA17054 [USC]). A portion of this work was done at UCLH/UCL within the ‘Women’s Health Theme’ of the NIHR UCLH/UCL Comprehensive Biomedical Research Centre supported by the Department of Health (UKO). The German group thanks Ursula Eilber and Tanja Koehler for competent technical assistance (GER). The Australian group thanks all the clinical and scientific collaborators and the women for their contribution (AUS). The cooperation of the 32 Connecticut hospitals, including Stamford Hospital, in allowing patient access, is gratefully acknowledged (CON). Some data used in the CON study were obtained from the Connecticut Tumor Registry, Connecticut Department of Public Health. The CON study assumes full responsibility for analyses and interpretation of these data. The MALOVA group is grateful to Nick Martinussen for data management assistance (MAL). The NJO group thanks Lorna Rodriguez, Lisa Paddock, and the staff at the New Jersey State Cancer Registry and Thanusha Puvananayagam for their contribution to the study (NJO). The SEARCH group thanks the SEARCH team, Craig Luccarini, Caroline Baynes, and Don Conroy (SEA). The UKOPS group thanks Ian Jacobs, Eva Wozniak, Andy Ryan, Jeremy Ford, and Nyaladzi Balogun for their contribution to the study (UKO).

Conflict of interest

The authors declare that they have no conflict of interest.

Copyright information

© Springer Science+Business Media Dordrecht 2013