Abstract
Objective:
Women with ovarian cancer who have a pathogenic germline variant in BRCA1 or BRCA2 (BRCA) have been shown to have better 5-year survival after diagnosis than women who are BRCA-wildtype (non-carriers). Modifiable lifestyle factors, including smoking, physical activity and body mass index (BMI) have previously been associated with ovarian cancer survival; however, it is unknown whether these associations differ by germline BRCA status.
Methods:
We investigated measures of lifestyle prior to diagnosis in two cohorts of Australian women with invasive epithelial ovarian cancer, using Cox proportional hazards regression to calculate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs).
Results:
In the combined studies (n=1,923), there was little association between physical activity, BMI or alcohol intake and survival, and no difference by BRCA status. However, the association between current smoking status before diagnosis and poorer survival was stronger for BRCA variant carriers (HR 1.98; 95% CI 1.20-3.27) than non-carriers (HR 1.18; 95% CI 0.96-1.46; p-interaction 0.02). We saw a similar differential association with smoking when we pooled results from two additional cohorts from the USA and UK (n=2,120). Combining the results from all four studies gave a pooled-HR of 1.94 (95% CI 1.28-2.94) for current smoking among BRCA variant carriers compared to 1.08 (0.90-1.29) for non-carriers.
Conclusions:
Our results suggest that the adverse effect of smoking on survival may be stronger for women with a BRCA variant than those without. Thus, while smoking cessation may improve outcomes for all women with ovarian cancer, it might provide a greater benefit for BRCA variant carriers.
Keywords: Ovarian cancer, BRCA1, BRCA2, lifestyle, smoking, survival
Introduction
Ovarian cancer is the eighth most commonly diagnosed cancer among women globally (1). The majority of patients have advanced disease at presentation and, as a result, the overall 5-year survival rate is less than 50% in high income countries (2).
Over 20% of ovarian cancers are due to hereditary causes, with the majority (65%-85%) of these associated with pathogenic germline mutations in the BRCA genes (3). However, despite their greatly increased risk of developing ovarian cancer, women with a germline pathogenic variant in BRCA1 or BRCA2 (BRCA) have better 5-year survival than women who are BRCA-wildtype (4). While this does not appear to lead to a long-term (10+ years) survival benefit (5, 6), it is possible this may change with the introduction of PARP inhibitors (7).
Other factors associated with ovarian cancer survival, include age, the histotype, stage and grade of the cancer at diagnosis, and the amount of residual disease left after surgery (8-10). However, recent research suggests potentially modifiable lifestyle factors may also play a role. Results from the Ovarian Cancer Association Consortium (OCAC) have shown that women who were physically inactive, smoked or obese before their ovarian cancer diagnosis had poorer survival than women who were physically active, non-smokers or not obese (11-13).
To date, no studies have evaluated whether the association between lifestyle and survival differs by BRCA germline variant status. It is possible that because of the important role the BRCA genes play in DNA repair, women harbouring pathogenic germline variants may be disproportionately affected by factors which can cause DNA damage or affect DNA repair, including modifiable factors such as smoking and alcohol consumption (14).
Our aim was to determine whether the association between lifestyle factors, including smoking, physical activity, body mass index (BMI) and alcohol consumption, and survival after ovarian cancer diagnosis differs between women with and without a pathogenic germline BRCA variant (here on in referred to as variant carriers and non-carriers, respectively).
Methods
The primary analysis included data from two Australian studies—the Ovarian cancer Prognosis And Lifestyle (OPAL) study and the Australian Ovarian Cancer Study (AOCS). Full details of the studies have been published previously (15) (16). In brief, both studies recruited Australian women aged 18-79 newly-diagnosed with invasive epithelial ovarian cancer (or primary peritoneal or fallopian tube cancer) through the major treatment centres across Australia and, for AOCS, the state-based cancer-registries. OPAL, a prospective cohort study, included 958 women diagnosed between January 2012 and May 2015 while AOCS, a population-based case-control study, included 2,009 women. Both studies were approved by the Human Research Ethics Committees at the QIMR Berghofer Medical Research Institute and all participating centres and all women gave signed informed consent to participate.
Women with non-mucinous, grade 2 or 3 invasive cancers were eligible for inclusion in this analysis (OPAL 850; AOCS 1,450). Women with grade 1, borderline or mucinous cancers were not included as pathogenic germline BRCA variants are rarely detected among these women (17), and they are not eligible for genetic testing clinically.
Clinical data
Clinical information including FIGO (International Federation of Obstetricians and Gynecologists) stage, tumour grade and histotype was collected from diagnostic pathology reports and medical records. For this analysis, women missing information on FIGO stage (AOCS, 9) were assumed to have stage III cancers based on their survival characteristics and as these are the most common. If grade information was missing (OPAL 49; AOCS 72), serous cancers, carcinosarcomas and “other” cancers were assumed to be grade 3 and endometrioid cancers (AOCS, 3; OPAL, 2) were assumed to be grade 2 based on survival characteristics and the general distribution of grade by histotype. Information on clinical genetic testing and vital status was routinely updated during an annual review of medical records.
Genetic data
For OPAL, around 450 women had germline BRCA testing as part of their clinical care and the remainder were tested as part of the TRACEBACK project (18). For AOCS, blood samples for approximately 1,000 women had been tested for germline BRCA variants as part of a previous study (4) and most of the remainder were tested as part of clinical care, or through TRACEBACK (18). Women with a germline variant of uncertain significance (as determined by available datasets (19, 20) and literature (21)) (OPAL, 25; AOCS, 15) or who were missing information on their germline BRCA variant status because they did not provide a blood sample (OPAL, 91; AOCS, 163) were excluded from this analysis. Women in whom BRCA germline status was missing were less likely to have high-grade serous cancers and had worse survival than those who were tested.
Lifestyle data
In both studies, information was collected using a self-administered questionnaire at baseline. This included sociodemographic variables (ethnicity, education level) and information about comorbidities, which was used to derive the Charlson comorbidity index (22). While the OPAL study collected data for the period before and after diagnosis, AOCS only collected information about what women did before diagnosis so, for comparability, information about lifestyle pre-diagnosis was used for both.
Smoking status (never, former, current) was self-reported as at diagnosis (OPAL) or one year prior to diagnosis (AOCS). Self-reported weight and height were used to calculate body mass index (BMI) for 5 years prior to diagnosis for OPAL and 1 year before diagnosis (or at diagnosis if 1 year prior was unavailable, N=71) for AOCS. For five OPAL women who did not complete the baseline questionnaire, information was extracted from medical records where possible. The World Health Organisation classification was used to assign women as underweight (<18.5 kg/m2), normal (18.5-24.9 kg/m2), overweight (25.0-29.9 kg/m2), or obese (≥30.0 kg/m2) (23). As few women were underweight (OPAL, 3; AOCS, 26), they were combined with women with normal BMI for analyses.
For OPAL, pre-diagnosis physical activity (PA) was measured using the Active Australia Survey (24) and categorised into high, medium or low groups based on the duration and intensity of moderate and strenuous PA. Women recruited more than three months after diagnosis were not asked to report pre-diagnosis physical activity because of concerns regarding recall error. In the AOCS, pre-diagnosis PA was classified as high, medium, or low based on the frequency and intensity of moderate and strenuous PA (25).
Information about alcohol consumption pre-diagnosis was collected using a semi-quantitative food frequency questionnaire validated in an Australian population (26-28). Women were asked to report the frequency with which they consumed different types of alcohol and intake was categorised according to the number of standard drinks (10g ethanol) consumed per day (None, ≤1/day, >1/day). As for physical activity, OPAL women recruited more than three months after diagnosis were not asked to report pre-diagnosis alcohol consumption.
As smoking was considered a potential confounder of other lifestyle associations and the number of women missing smoking information was small (OPAL, 2; AOCS, 5) they were excluded. For OPAL, one additional woman missing comorbidity information was excluded, leaving a total of 731 women. For AOCS, one woman missing information on when she completed the baseline questionnaire was excluded, leaving a total of 1,192 women. Supplementary Figures 1A and 1B detail the exclusions applied to OPAL and the AOCS, including the extent of missing data.
Statistical analysis
Differences in pre-diagnosis lifestyle, socioeconomic and clinical variables by BRCA status were evaluated separately for OPAL and AOCS using ANOVA for continuous variables and χ2 tests for categorical variables. Kaplan-Meier plots were used to visualize survival characteristics for women by BRCA status.
Survival time was measured from the date of histological diagnosis (or the date a woman started neo-adjuvant chemotherapy if this occurred prior to the definitive histological diagnosis) until either the date of death or the date she was last known to be alive. The log-rank test was used to assess differences in survival between groups in univariable analyses.
Cox proportional hazards regression models were used to estimate hazard ratios and 95% confidence intervals for the association between the lifestyle factors and all-cause mortality, separately for women with and without a pathogenic BRCA variant. In the primary analyses, we did not differentiate between BRCA1 and BRCA2 variants due to small numbers. All-cause mortality was used rather than ovarian-cancer specific mortality as, among women who died within 5 years of diagnosis, most deaths were due to ovarian cancer (OPAL, 98%; AOCS, 97%). Models were run separately for OPAL and AOCS and on the studies combined.
Models were left-truncated to the date a woman completed the baseline questionnaire to avoid immortal time bias (because women had to survive until they completed the baseline questionnaire to be included). Primary models were right-censored at 5-years because this period is when the survival difference between BRCA-carriers and non-carriers is greatest. Additional analyses with up to 7.5 years of follow-up for OPAL and 10 years for AOCS were conducted to examine longer term trends. The proportional hazards assumption was tested for each model by examining Schoenfeld residuals. The only variable that did not satisfy the proportional hazards assumption was Charlson index in select AOCS models but, when we included an interaction with log time, the estimates of interest were essentially unchanged so the interaction was not included in the final models.
Potential confounders were determined by exploring the relationships between the exposure, survival and clinical and sociodemographic variables that might affect exposure and survival. All models were adjusted for age at diagnosis, comorbidity (0 or 1+ comorbid conditions) and stratified by stage of disease (I/II and III/IV) allowing the baseline hazard to vary by stage. Models for smoking, physical activity and alcohol were additionally adjusted for BMI, analyses of BMI were additionally adjusted for physical activity, and analyses of physical activity, BMI and alcohol were additionally adjusted for smoking. We did not further adjust for the amount of residual disease after surgery because this was not associated with smoking status. Combined analyses were also stratified by study.
To determine whether the associations varied by BRCA status, a model including all women, additionally stratified by BRCA status and including an interaction term between the exposure and BRCA status, was run. The Wald test was used to calculate p-values for the interaction term. All statistical analyses were performed using Stata statistical software: Release 15 (29).
Validation:
Data from two additional studies, from the Mayo Clinic in Minnesota, USA, and the SEARCH ovarian cancer study in the United Kingdom, were obtained through OCAC to validate the findings for smoking from OPAL and the AOCS. Full details of the MAYO and SEARCH studies have been reported previously (30-32). In brief, MAYO is a clinic-based study which recruited women aged 20 years or older diagnosed with ovarian cancer between 1999 and 2018. SEARCH is a population-based study which used cancer registries to recruit women diagnosed with ovarian cancer from 1998-2019 as well as those diagnosed from 1991 who were still alive in 1998.
After the same exclusions were applied, 1,304 women were included from MAYO and 816 from SEARCH. All lifestyle information was for the period pre-diagnosis. Information on pre-diagnosis smoking was collected in a similar way to AOCS and OPAL. These models were adjusted for age at diagnosis, BMI and stratified by stage. Models were additionally adjusted for year of diagnosis due to the longer recruitment periods for MAYO and SEARCH compared with AOCS and OPAL.
Comorbidity information was unavailable but, among AOCS and OPAL, this adjustment made little difference to the final estimates. Study-specific HRs from the two validation cohorts and from all four studies were combined using random effects meta-analysis. I2 and p-values for heterogeneity (from chi-square tests) were used to assess inter-study heterogeneity. Pairwise comparisons to assess differences in pooled estimates by BRCA status were made using Woolf’s test for homogeneity.
Data availability
Data analysed were obtained through OCAC and are not publicly available due to privacy and ethical restrictions.
Results
Clinical, socio-demographic, and lifestyle characteristics of the 731 OPAL and 1,192 AOCS women included in this analysis are presented in Table 1. Pathogenic germline BRCA variants were identified in 17% (125; 73 BRCA1, 51 BRCA2, 1 both) of women in OPAL, and in 15% (176; 109 BRCA1, 67 BRCA2) of women in AOCS.
Table 1:
Clinical, sociodemographic and lifestyle factors by germline BRCA status, OPAL and AOCS
| OPAL | AOCS | |||||||
|---|---|---|---|---|---|---|---|---|
| Variable | Total n (%) n=731 |
BRCA variant status | p- valuea |
Total n (%) n=1,192 |
BRCA variant status | p-valuea | ||
| Carrier n=125 |
Non- carrier n=606 |
Carrier n=176 |
Non-carrier n=1,606 |
|||||
| Country | Australia | Australia | ||||||
| Study period | 2012-2015 | 2002-2006 | ||||||
| Age range | 18-79 | 18-79 | ||||||
| Days from diagnosis to recruitment (median) | 48 | 61 | ||||||
| Age (years), mean (SD) | 60.7 (10.4) | 57.2 (10.6) | 61.4 (10.3) | <0.001 | 60.3 (10.3) | 56.3 (9.9) | 61.0 (10.2) | <0.001 |
| FIGO stage | 0.1 | 0.04 | ||||||
| I | 95 (13.0) | 8 (6.4) | 87 (14.4) | 162 (13.6) | 12 (6.8) | 150 (14.8) | ||
| II | 64 (8.8) | 14 (11.2) | 50 (8.3) | 106 (8.9) | 16 (9.1) | 90 (8.9) | ||
| III | 467 (63.9) | 84 (67.2) | 383 (63.2) | 789 (66.2) | 126 (71.6) | 663 (65.3) | ||
| IV | 105 (14.4) | 19 (15.2) | 86 (14.2) | 135 (11.3) | 22 (12.5) | 113 (11.1) | ||
| Histology | 0.003 | <0.001 | ||||||
| High-grade serous | 608 (83.2) | 119 (95.2) | 489 (80.7) | 944 (79.2) | 157 (89.2) | 787 (77.5) | ||
| Endometrioid | 41 (5.6) | 2 (1.6) | 39 (6.4) | 86 (7.2) | 7 (4.0) | 79 (7.8) | ||
| Clear cell | 44 (6.0) | 2 (1.6) | 42 (6.9) | 82 (6.9) | 1 (0.6) | 81 (8.0) | ||
| Carcinosarcoma | 27 (3.7) | 1 (0.8) | 26 (4.3) | 44 (3.7) | 3 (1.7) | 41 (4.0) | ||
| Other | 11 (1.5) | 1 (0.8) | 10 (1.7) | 36 (3.0) | 8 (4.5) | 28 (2.8) | ||
| Grade | 0.2 | 0.4 | ||||||
| 2 | 76 (10.4) | 9 (7.2) | 67 (11.1) | 234 (19.6) | 30 (17.1) | 204 (20.1) | ||
| 3 | 655 (89.6) | 116 (92.8) | 539 (88.9) | 958 (80.4) | 146 (82.9) | 812 (79.9) | ||
| Residual disease | 0.3 | 0.04 | ||||||
| None | 406 (58.8) | 78 (63.4) | 328 (57.8) | 447 (41.2) | 54 (33.8) | 393 (42.4) | ||
| Any | 284 (41.2) | 45 (36.6) | 239 (42.2) | 639 (58.8) | 106 (66.3) | 533 (57.6) | ||
| No surgery/missing | 41 | 2 | 39 | 106 | 16 | 90 | ||
| Number of comorbidities | 0.3 | <0.001 | ||||||
| 0 | 550 (75.2) | 87 (69.6) | 463 (76.4) | 733 (70.7) | 93 (61.6) | 640 (72.2) | ||
| 1 | 114 (15.6) | 24 (19.2) | 90 (14.8) | 149 (14.4) | 16 (10.6) | 133 (15.0) | ||
| 2+ | 67 (9.2) | 14 (11.2) | 53 (8.8) | 155 (14.9) | 42 (27.8) | 113 (12.8) | ||
| Missing | 0 | 0 | 0 | 155 | 25 | 130 | ||
| Previous breast cancer | <0.001 | <0.001 | ||||||
| No | 676 (92.5) | 97 (77.6) | 579 (95.5) | 1,070 (92.4) | 128 (76.2) | 942 (95.2) | ||
| Yes | 55 (7.5) | 29 (22.4) | 27 (4.5) | 88 (7.6) | 40 (23.8) | 48 (4.8) | ||
| Missing | 0 | 0 | 0 | 33 | 8 | 25 | ||
| Education | 0.1 | 0.1 | ||||||
| Year 12 (age ≤18) | 338 (46.5) | 52 (41.9) | 286 (47.4) | 665 (55.8) | 94 (53.4) | 571 (56.2) | ||
| Technical college (age 18+) | 194 (26.7) | 29 (23.4) | 165 (27.4) | 378 (31.7) | 52 (29.6) | 326 (32.1) | ||
| University (age 18+) | 195 (26.8) | 43 (34.7) | 152 (25.2) | 149 (12.5) | 30 (17.0) | 119 (11.7) | ||
| Missing | 4 | 1 | 3 | 0 | 0 | 0 | ||
| Ethnicity | 0.5 | 0.8 | ||||||
| Caucasian | 603 (83.3) | 103 (82.4) | 500 (83.5) | 1,094 (96.7) | 158 (96.3) | 936 (96.8) | ||
| Asian | 58 (8.0) | 13 (10.4) | 45 (7.5) | 28 (2.5) | 4 (2.4) | 24 (2.5) | ||
| Otherb | 63 (8.7) | 9 (7.2) | 54 (9.0) | 9 (0.8) | 2 (1.2) | 7 (0.7) | ||
| Missing | 7 | 0 | 7 | 61 | 12 | 49 | ||
| Smoking | 0.9 | 0.2 | ||||||
| Never | 396 (54.2) | 70 (56.0) | 326 (53.8) | 717 (60.2) | 99 (56.3) | 618 (60.8) | ||
| Former | 259 (35.4) | 42 (33.6) | 217 (35.8) | 302 (25.3) | 44 (25.0) | 258 (25.4) | ||
| Current | 76 (10.4) | 13 (10.4) | 63 (10.4) | 173 (14.5) | 33 (18.8) | 140 (13.8) | ||
| Physical activity | 0.05 | 0.7 | ||||||
| Low | 232 (39.1) | 44 (43.6) | 188 (38.2) | 334 (29.8) | 51 (31.3) | 283 (29.6) | ||
| Medium | 147 (24.8) | 31 (30.7) | 116 (23.6) | 364 (32.6) | 48 (29.5) | 316 (33.1) | ||
| High | 214 (36.1) | 26 (25.7) | 188 (38.2) | 420 (37.6) | 64 (39.3) | 356 (37.3) | ||
| Missing | 138 | 24 | 114 | 74 | 13 | 61 | ||
| BMI (kg/m2) | 0.5 | 0.9 | ||||||
| Underweight/Normal (<24.9) | 314 (42.9) | 55 (44.0) | 259 (42.7) | 468 (40.7) | 69 (40.1) | 399 (40.8) | ||
| Overweight (25.0-29.9) | 249 (34.1) | 46 (36.8) | 203 (33.5) | 404 (35.1) | 62 (36.1) | 342 (34.9) | ||
| Obese (≥30.0) | 168 (23.0) | 24 (19.2) | 144 (23.8) | 279 (24.2) | 41 (23.8) | 238 (24.3) | ||
| Missing | 0 | 0 | 0 | 41 | 4 | 37 | ||
| Alcohol (g/day) | 0.4 | 0.4 | ||||||
| 0 | 121 (21.0) | 21 (21.6) | 100 (20.9) | 355 (30.7) | 48 (27.9) | 307 (31.2) | ||
| ≤10 | 328 (57.1) | 50 (51.6) | 278 (58.2) | 572 (49.5) | 84 (48.8) | 488 (49.6) | ||
| >10 | 126 (21.9) | 26 (26.8) | 100 (20.9) | 228 (19.7) | 40 (23.3) | 188 (19.1) | ||
| Missing | 156 | 28 | 128 | 37 | 4 | 33 | ||
Abbreviations: BMI = body mass index, FIGO = International Federation of Gynecology and Obstetrics, SD = standard deviation.
ANOVA for continuous variables and χ2 test for categorical variables.
“Other” includes Aboriginal and Torres Strait Islander, black, mixed ethnicity and other women.
For both studies, BRCA carriers were younger at the time of diagnosis and more likely to have high-grade serous cancer than non-carriers; in AOCS, carriers also had more advanced disease at diagnosis and were more likely than non-carriers to have residual disease left after surgery (66% vs 58%). Overall, a higher proportion of OPAL women had no residual disease after surgery, likely reflecting the advances in surgery between the study periods.
In AOCS, BRCA carriers had significantly more comorbidities than non-carriers but this was entirely due to the fact that the Charlson index included any prior cancer as a comorbidity and there was ahigher proportion of women with previous breast cancer among BRCA carriers than non-carriers (AOCS: 24% vs 5%). When we recalculated the Charlson index excluding prior breast cancer, there was no significant difference by BRCA carrier status. This was not seen in OPAL because the Charlson index was canclulcated including only cancers diagnosed within the 5 years prior to the ovarian cancer diagnosis. BRCA carriers and non-carriers did not differ with respect to grade, smoking, physical activity, BMI or alcohol intake.
Overall, 328 OPAL women (45%) and 654 AOCS women (55%) died in the first 5 years of follow-up. The difference was largely due to the fact that BRCA carriers in OPAL had lower mortality than those in AOCS (27% vs. 49%), likely due to the introduction of PARP inhibitor therapy (about 80% of BRCA carriers in OPAL were treated with PARP inhibitors when they experienced recurrence).
BRCA variant carriers had statistically significantly better long-term survival than non-carriers in both studies (log-rank test p-value: OPAL (maximum 7.5 years) <0.001; AOCS (maximum 10 years) 0.04). However, consistent with previous reports (5, 6), the survival advantage diminished around 5–6 years after diagnosis (results not shown). Hence, unless otherwise specified, all results are for 5-year survival.
Overall, smoking was associated with poorer survival in OPAL (HR 1.44, 95% CI 1.00-2.08; HR 1.33, 95% CI 1.05-1.67, for current and former smokers, respectively), but there was only a weak association with current smoking in AOCS (HR 1.20, 95% CI 0.96-1.50; HR 0.96, 95% CI 0.80-1.15).
In both studies, the association between ever smoking and survival was stronger among BRCA carriers (OPAL: 2.46 [1.22-4.99] vs. 1.22 [0.97-1.53], p-interaction=0.04; AOCS: 1.76 [1.12-2.78] vs. 0.96 [0.81-1.14], p-interaction=0.03). For AOCS, BRCA carriers who were current smokers before diagnosis were over twice (HR 2.28, 95%CI 1.28-4.04) as likely to die as never smokers, with a weaker association for former smokers, whereas there was little association among non-carriers (Table 2). In OPAL, only 13 BRCA carriers were current smokers so the estimates for current and former smoking were imprecise.
Table 2:
Association between pre-diagnosis lifestyle factors and overall survival (to 5 years) among women with ovarian cancer, by germline BRCA status: OPAL and AOCS studies
| Variable | OPAL | AOCS | OPAL and AOCS | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BRCA carrier | BRCA non-carrier | BRCA carrier | BRCA non-carrier | BRCA carrier | BRCA non-carrier | ||||||||||
| N | HRa (95% CI) | N | HRa (95% CI) | pb | N | HRa (95% CI) | N | HRa (95% CI) | pa | N | HRa (95% CI) | N | HRa (95% CI) | pa | |
| Smoking c | 0.04 | 0.08 | 0.02 | ||||||||||||
| Never smoked | 70 | 1.00 | 326 | 1.00 | 99 | 1.00 | 618 | 1.00 | 169 | 1.00 | 944 | 1.00 | |||
| Ever smoked | 55 | 2.46 (1.22-4.99) | 280 | 1.22 (0.97-1.53) | 77 | 1.76 (1.12-2.78) | 398 | 0.96 (0.81-1.14) | 132 | 1.89 (1.31-2.75) | 678 | 1.05 (0.92-1.20) | |||
| Former | 42 | 2.91 (1.41-6.01) | 217 | 1.17 (0.92-1.50) | 44 | 1.47 (0.85-2.53) | 258 | 0.91 (0.75-1.11) | 86 | 1.85 (1.22-2.81) | 475 | 1.00 (0.86-1.17) | |||
| Current | 13 | 1.21 (0.33-4.41) | 63 | 1.41 (0.96-2.06) | 33 | 2.28 (1.28-4.04) | 140 | 1.09 (0.85-1.41) | 46 | 1.98 (1.20-3.27) | 203 | 1.18 (0.96-1.46) | |||
| Physical activity | 0.9 | 0.2 | 0.5 | ||||||||||||
| Low | 44 | 1.00 | 188 | 1.00 | 51 | 1.00 | 283 | 1.00 | 95 | 1.00 | 471 | 1.00 | |||
| Moderate | 31 | 1.13 (0.46-2.78) | 116 | 0.89 (0.63-1.25) | 48 | 0.68 (0.37-1.25) | 316 | 0.90 (0.72-1.12) | 79 | 0.83 (0.51-1.36) | 432 | 0.89 (0.74-1.07) | |||
| High | 26 | 0.91 (0.33-2.55) | 188 | 0.90 (0.66-1.21) | 64 | 0.81 (0.47-1.41) | 356 | 1.01 (0.81-1.25) | 90 | 0.85 (0.52-1.39) | 544 | 0.97 (0.81-1.15) | |||
| BMI d | 0.9 | 0.4 | 0.6 | ||||||||||||
| Underweight/ normal | 55 | 1.00 | 259 | 1.00 | 69 | 1.00 | 399 | 1.00 | 124 | 1.00 | 658 | 1.00 | |||
| Overweight | 46 | 0.71 (0.32-1.58) | 203 | 0.80 (0.61-1.05) | 62 | 0.95 (0.55-1.64) | 342 | 1.07 (0.88-1.30) | 108 | 0.82 (0.53-1.29) | 545 | 0.96 (0.82-1.13) | |||
| Obese | 24 | 0.81 (0.32-2.04) | 144 | 1.05 (0.78-1.41) | 41 | 1.49 (0.83-2.66) | 238 | 1.07 (0.86-1.34) | 65 | 1.17 (0.73-1.89) | 382 | 1.07 (0.89-1.27) | |||
| Alcohol (g/day) | 0.6 | 0.3 | 0.7 | ||||||||||||
| 0 | 21 | 1.00 | 100 | 1.00 | 48 | 1.00 | 307 | 1.00 | 69 | 1.00 | 407 | 1.00 | |||
| ≤10 | 50 | 1.76 (0.51-6.02) | 278 | 1.00 (0.71-1.41) | 84 | 1.07 (0.65-1.77) | 488 | 1.09 (0.89-1.33) | 134 | 1.11 (0.70-1.76) | 766 | 1.05 (0.89-1.25) | |||
| >10 | 26 | 3.29 (0.82-13.3) | 100 | 1.29 (0.86-1.94) | 40 | 0.76 (0.41-1.41) | 188 | 1.14 (0.89-1.46) | 66 | 1.01 (0.59-1.72) | 288 | 1.17 (0.95-1.44) | |||
Abbreviations: BMI = body mass index, CI = confidence interval, HR = hazard ratio.
The sample size used varies for each variable depending on data availability. All models were adjusted for age, comorbidity and stratified by stage. All models except smoking were additionally adjusted for smoking. Models for smoking, physical activity and alcohol were additionally adjusted for BMI. Models for BMI were additionally adjusted for physical activity.
p-value for the interaction between the variable and BRCA status, in the model stratified by BRCA status. For smoking, the p-value for the interaction was between never/former/current smoking and BRCA status.
smoking status 1 year prior to diagnosis.
for OPAL BMI was as at 5 years prior to diagnosis, for AOCS it was as at 1 year prior to diagnosis or at diagnosis where 1 year prior was unavailable.
When we combined AOCS and OPAL, among BRCA carriers, current smokers were twice (95% CI 1.20-3.27) as likely as never smokers to die during the first 5 years after diagnosis, compared with 1.2 (95% CI 0.96-1.46) times for non-carriers (p-interaction=0.02). Restricting to include only high-grade serous cancers resulted in similar estimates.
Among BRCA variant carriers, the association with current smoking was stronger for BRCA2 (N=118, HR 3.48, 95% CI 1.38-8.77) than for BRCA1 (N=182, HR 1.66, 95% CI 0.90-3.10) but the numbers were small, so the estimates were imprecise and the difference was not statistically significant (p-interaction=0.6).
There were no clear associations between PA, BMI or alcohol intake before diagnosis and survival in these studies, and this did not differ between BRCA variant carriers and non-carriers (Table 2).
Similar patterns were seen when we used longer-term follow up (OPAL: 7.5 years; AOCS: 10 years; combined: 7.5 years), but the hazard ratios for all exposures were generally closer to 1.0 in all groups (Supplementary Table 1).
MAYO/SEARCH validation and meta-analysis
We then sought to confirm the observed difference for smoking using two independent data-sets (Supplementary Table 1). Pathogenic germline variants in BRCA1/2were identified in 13% of women in MAYO and 12% in SEARCH. In both studies, BRCA carriers were younger at the time of diagnosis, more likely to have high-grade serous cancer and had better 5-year survival than non-carriers (log-rank test p-value=0.01). In SEARCH only, the BRCA carriers also had significantly lower BMI than non-carriers.
In MAYO, there was no overall association between current smoking and survival (HR 1.09, 95% CI 0.82-1.45). However, there was a suggestion that current smokers had worse survival among BRCA variant carriers (HR 1.75, 95% CI 0.83-3.70), with no association among non-carriers (HR 1.00, 95% CI 0.74-1.37) (Table 3) although the difference was not statistically significant (p=0.3).
Table 3:
Association between pre-diagnosis smoking and overall survival (to 5 years) among women with ovarian cancer, by germline BRCA status: MAYO, SEARCH and validation set (MAYO/SEARCH) pooled analysis
| MAYOa | SEARCHa | Validation set pooled analysis | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BRCA carrier | BRCA non-carrier | BRCA carrier | BRCA non-carrier | BRCA carrier | BRCA non-carrier | |||||||
| N | HRa (95% CI) | N | HRa (95% CI) | pb | N | HRa (95% CI) | N | HRa (95% CI) | pb | pHR (95% CI) | pHR (95% CI) | |
| Never smoked | 115 | 1.00 | 726 | 1.00 | 0.3 | 62 | 1.00 | 425 | 1.00 | 0.6 | 1.00 | 1.00 |
| Ever smoked | 65 | 1.44 (0.88-2.36) | 445 | 1.07 (0.90-1.26) | 39 | 0.77 (0.28-2.11) | 291 | 1.13 (0.87-1.48) | 1.24 (0.73-2.09) | 1.09 (0.94-1.25) | ||
| Former | 50 | 1.34 (0.77-2.32) | 354 | 1.08 (0.91-1.30) | 27 | 0.59 (0.18-1.91) | 205 | 1.26 (0.95-1.67) | 1.06 (0.51-2.19) | 1.13 (0.97-1.31) | ||
| Current | 15 | 1.75 (0.83-3.70) | 91 | 1.00 (0.74-1.37) | 12 | 1.80 (0.35-9.31) | 86 | 0.82 (0.51-1.32) | 1.76 (0.89-3.47) | 0.94 (0.73-1.22) | ||
Abbreviations: CI = confidence interval, HR = hazard ratio, pHR = pooled hazard ratio.
MAYO and SEARCH models were adjusted for age, BMI, year of diagnosis and stratified by stage.
p-value for the interaction between the variable and BRCA status, in the model stratified by BRCA status.
In contrast to the previous studies, pre-diagnosis current smokers in the subset of the SEARCH cohort included in this analysis did not experience worse survival up to 5 years (HR 0.86, 95% CI 0.55-1.35). There was also no significant variation by BRCA status (p=0.6), but the estimate for current smoking was again elevated in variant carriers and null in non-carriers, although the small numbers of BRCA carriers who smoked meant the estimates were very imprecise. After combining the results from the two validation cohorts, smoking was again associated with higher mortality among BRCA carriers than non-carriers, but neither the association among carriers nor the difference between carriers and non-carriers reached statistical significance (pHR 1.76, 95% CI 0.89-3.47 vs pHR 0.94, 95% CI 0.72-1.22 for non-carriers; p-interaction=0.09) (Table 3).
Meta-analyses of all four studies gave a pooled HR of 1.94 (95% CI 1.28-2.94) for current smoking among BRCA variant carriers (Figure 1a) compared with 1.08 (95% CI 0.90-1.29) for non-carriers (Figure 1b) (p=0.01). A smaller non-signficant difference was seen for former smokers (carriers: pHR 1.50, 95% CI 0.93-2.34; non-carriers: pHR 1.07, 95% CI 0.94-1.23) (p=0.2). Among BRCA variant carriers, the association with ever-smoking was similar for BRCA1 (pHR 1.67, 95% CI 1.13-2.47) and BRCA2 (pHR 1.46, 95%CI 0.62-3.40) (p=0.8).
Figure 1: Forest plots showing the association between pre-diagnosis cigarette smoking and overall survival following a diagnosis of epithelial ovarian cancer, by study site and overall for (a) BRCA carriers and (b) BRCA non-carriers.
Study-specific hazard ratios and 95% confidence intervals were estimated using Cox regression models adjusted for age, BMI and stratified by stage. AOCS and OPAL models were additionally adjusted for comorbidities (MAYO and SEARCH were not as data was unavailable). MAYO and SEARCH models were additionally adjusted for year of diagnosis. The pooled hazard ratios and 95% CIs were estimated using a random effects model.
Discussion
This study is the first to evaluate the association between pre-diagnosis smoking status and survival after a diagnosis of ovarian cancer separately by pathogenic BRCA germline variant status, and the results suggest that the adverse effects of smoking may be greater for BRCA variant carriers than non-carriers. Findings were consistent across the OPAL and AOCS cohorts and one of the two validation cohorts; the lack of an overall association with smoking in the fourth cohort made those results harder to interpret. Pooled results for all four studies suggested that for BRCA variant carriers, mortality was almost two-fold higher among current smokers than never smokers, with little association among non-carriers. Among OPAL and AOCS, a stronger association between smoking and survival was seen for BRCA2 carriers than BRCA1 carriers, but this was not seen in the pooled results from all four studies.
Previous research suggests that the adverse effects of smoking on ovarian cancer survival are greatest for women with mucinous ovarian cancers (14) and this is also the histotype that shows the strongest etiologic association with smoking (33). In contrast, history of smoking has not been associated with high-grade serous ovarian cancer, and inverse associations have been seen for endometrioid and clear cell cancers (33). However, most women included in those analyses would have been BRCA-wildtype and the one study conducted among BRCA carriers, did report a positive association between smoking and risk of ovarian cancer (current: HR 1.25, 95% CI 0.73-2.12; former: HR 1.69, 95% CI 1.06-2.71) (34). This adds further weight to support our observation that the association between smoking and survival is largely restricted to those carrying a pathogenic germline BRCA variant.
The underlying mechanism for this is unclear but smoking affects the immune system (35) and may also adversely influence chemotherapy response (36-38). Overall, the homologous recombination deficiency in BRCA variant carriers appears to drive their generally improved response to chemotherapy (4), and they may also have a stronger immune response to ovarian cancers than non-carriers, particularly for BRCA1 (39, 40). However, it is possible that this may also make them more susceptible to the adverse effects of cigarette smoke. Our results also suggest the strength of the association may differ between BRCA1 and BRCA2 variant carriers, possibly due to differences in immune response (39, 40).
In the two Australian cohorts, there were no clear associations between physical activity, BMI or alcohol intake and survival and no differences by BRCA variant status. It is, however, important to note that, overall, alcohol intake was low in the study populations (about 75% of women drank ≤1 standard drink/day) so we were unable to examine the effect of high alcohol consumption on survival. As we saw no association in our primary analyses and these data were not consistently available, we did not repeat these analyses in the validation cohorts.
Our study has several strengths. We combined data collected using identical or similar questions from two cohorts of Australian women aged 18-79 who share similar lifestyle, clinical and socio-demographic characteristics to increase the sample size and statistical power of our initial analyses. In both studies, clinical data, including both treatment and outcomes, were collected annually from medical records ensuring they were accurate and complete and we minimised the potential for immortal time bias by left-truncating to the date a woman completed the baseline questionnaire. We also sought to validate the smoking results in independent studies (MAYO, SEARCH). While the results from SEARCH were somewhat different from those seen for AOCS, OPAL and MAYO, the lack of an overall association between smoking and five-year survival in the SEARCH cohort may have contributed to this. (The adverse effects of smoking in SEARCH were not seen until over 10 years postdiagnosis; data not shown.) Additionally, the mean time from diagnosis to recruitment in SEARCH was more than two years compared to only 2-6 months for AOCS, OPAL and MAYO.
The retrospective self-reporting of pre-diagnosis lifestyle variables is a limitation of the study and potentially introduced recall error. However, this error is likely to be non-differential, in that it is unlikely to differ by BRCA mutation status or by survival, and so would tend to increase the similarity between the study groups and result in underestimates of the true associations between lifestyle and survival, as well as the differences between groups. An additional limitation is that we only considered germline BRCA variant status and it is likely that some non-carriers will have somatic changes or other deficiencies in the homologous recombination repair pathway. This would again tend to make the groups look more similar leading to underestimates of the difference between carriers and non-carriers. Additionally, due to the observational design of the studies, there is the potential for confounding due to unknown factors. However, for an unknown confounder to have an appreciable effect on our estimates, it would need to be strongly associated with both the lifestyle factors and survival and differ by BRCA status suggesting uncontrolled confounding is unlikely to explain our results. A further limitation was the relatively small sample of BRCA variant carriers which reduced the power of this study as well as the precision of the estimates for this group. We also had to exclude a number of eligible women because they were missing lifestyle or BRCA status. Due to the small numbers of current and former smokers and, in three studies, availability of data for smoking prior to diagnosis only, we were unable to assess the effect of duration of smoking or timing of cessation on survival. The findings of this study provide rationale for a larger analysis to further investigate differences in the associations between lifestyle and survival by BRCA status and to extend this to consider lifestyle after diagnosis.
In conclusion, this study suggests that the relationship between smoking and ovarian cancer survival may differ by pathogenic germline BRCA variant status and that the association between current or former smoking and poorer survival may be stronger among BRCA variant carriers than non-carriers. Further studies are needed to confirm these results and investigate how smoking cessation after diagnosis of ovarian cancer affects survival among BRCA carriers and non-carriers.
Supplementary Material
Statement of significance:
The adverse effects of smoking on ovarian cancer survival may be stronger for women with a pathogenic germline BRCA variant than those without; smoking cessation may provide greater benefits for variant carriers.
Highlights.
It is unknown whether associations between lifestyle factors and ovarian cancer survival differ by BRCA variant status
The adverse effects of smoking on ovarian cancer survival may be stronger for women with a BRCA variant than those without
There was no differential association in survival by BRCA variant status for physical activity, BMI or alcohol intake
Acknowledgements
We acknowledge the OPAL Study team, all the clinicians and participating institutions and consumer representatives Karen Livingstone, Hélène O'Neill and Merran Williams who helped make this study possible (see opalstudy.qimrberghofer.edu.au for a complete list). The AOCS acknowledges the cooperation of the participating institutions in Australia and acknowledges the contribution of the study nurses, research assistants and all clinical and scientific collaborators to the study. The complete AOCS Study Group can be found at www.aocstudy.org. We acknowledge Ovarian Cancer Australia and thank all of the women who participated in these research programs. We thank the many individuals who have contributed to SEARCH over many years.
Funding:
OCAC Funding:
The Ovarian Cancer Association Consortium is funded by the generous contributions of its research investigators. It has also been supported by a grant from the Ovarian Cancer Research Fund thanks to donations by the family and friends of Kathryn Sladek Smith (PPD/RPCI.07). The scientific development and funding for this project were in part supported by the US National Cancer Institute GAME-ON Post-GWAS Initiative (U19-CA148112). This study made use of data generated by the Wellcome Trust Case Control consortium that was funded by the Wellcome Trust under award 076113.
Funding for individual studies:
The OPAL Study was funded by the National Health and Medical Research Council (NHMRC) of Australia (GNT1025142, GNT1120431); blood collection was partly funded by a grant from the Brisbane Women’s Club. The Australian Ovarian Cancer Study Group 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). The Australian Ovarian Cancer Study gratefully acknowledges additional support from Ovarian Cancer Australia and the Peter MacCallum Foundation. Funding for BRCA testing was provided by the Australian Government (Public Health and Chronic Disease Grant Program), the US Department of Defense (W81XWH-08-1-0684 and W81XWH-08-1-0685), Cancer Australia (509303) and the Peter MacCallum Foundation. MAYO was funded by the National Institutes of Health (R01-CA122443, R01-CA248288, P50-CA136393). SEARCH was funded by Cancer Research UK (C490/A10119 C490/A10124); UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge. University of Cambridge received salary support for PDPP from the NHS in the East of England through the Clinical Academic Reserve. PMW was supported by an NHMRC Fellowship (GNT1173346)
List of abbreviations
- AOCS
Australian Ovarian Cancer Study
- BMI
body mass index
- CI
confidence interval
- FIGO
International Federation of Obstetricians and Gynecologists
- MAYO
Mayo Clinic Case-Only Ovarian Cancer Study/Mayo Clinic Ovarian Cancer Case Control Study
- METhrs
metabolic equivalent hours
- OCAC
Ovarian Cancer Association Consortium
- OPAL
Ovarian cancer Prognosis and Lifestyle
- PA
physical activity
- pHR
pooled hazard ratio
- SEARCH
Study of Epidemiology and Risk Factors in Cancer Heredity study
Footnotes
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Conflict of interest disclosure statement: PMW, AdF, KA and SF have received grant funding from AstraZeneca for an unrelated study of ovarian cancer. MF has received honoraria for advisory boards from AstraZeneca, MSD, Novartis, GlaxoSmithKline, Takeda and Lilly; research funding to his institution from AstraZeneca, BEIGENE and Novartis; travel expenses from AstraZeneca; and speakers fees from AstraZeneca, GSK and ACT Genomics. The other authors declare no potential conflicts of interest.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data analysed were obtained through OCAC and are not publicly available due to privacy and ethical restrictions.