Purpose: To estimate the effects of BRCA1 and BRCA2 mutations on ovarian cancer and breast cancer survival.

Experimental Design: We searched PubMed and EMBASE for studies that evaluated the associations between BRCA mutations and ovarian or breast cancer survival. Meta-analysis was conducted to generate combined HRs with 95% confidence intervals (CI) for overall survival (OS) and progression-free survival (PFS).

Results: From 1,201 unique citations, we identified 27 articles that compared prognosis between BRCA mutation carriers and noncarriers in patients with ovarian or breast cancer. Fourteen studies examined ovarian cancer survival and 13 studies examined breast cancer survival. For ovarian cancer, meta-analysis demonstrated that both BRCA1 and BRCA2 mutation carriers had better OS (HR, 0.76; 95% CI, 0.70–0.83 for BRCA1 mutation carriers; HR, 0.58; 95% CI, 0.50–0.66 for BRCA2 mutation carriers) and PFS (HR, 0.65; 95% CI, 0.52–0.81 for BRCA1 mutation carriers; HR, 0.61; 95% CI, 0.47–0.80 for BRCA2 mutation carriers) than noncarriers, regardless of tumor stage, grade, or histologic subtype. Among patients with breast cancer, BRCA1 mutation carriers had worse OS (HR, 1.50; 95% CI, 1.11–2.04) than noncarriers but were not significantly different from noncarriers in PFS. BRCA2 mutation was not associated with breast cancer prognosis.

Conclusions: Our analyses suggest that BRCA mutations are robust predictors of outcomes in both ovarian and breast cancers and these mutations should be taken into account when devising appropriate therapeutic strategies. Clin Cancer Res; 21(1); 211–20. ©2014 AACR.

Translational Relevance

This report compiles data from 14 studies of 9,588 women with ovarian cancer and 13 studies of 10,016 women with breast cancer and confirms the positive prognostic effect of both BRCA1 and BRCA2 mutations on ovarian cancer overall and progression-free survival and adverse prognostic effect of BRCA1 mutation on breast cancer overall survival. The results can be used immediately by health care professionals for patient counseling regarding expected survival. In addition, stratification by BRCA status should be considered for cancer treatments and the design of clinical trials for targeted therapy.

BRCA1 and BRCA2 are 2 distinct tumor suppressor genes that play an integral role in response to cellular stress via the activation of DNA repair processes (1). Individuals with mutations in these 2 genes are at an increased risk of developing breast, ovarian, and other cancers. The lifetime risk of breast cancer among BRCA mutation carriers is 45% to 80% and for ovarian cancer 45% to 60% (2). Several studies have investigated prognoses among BRCA mutation carriers and noncarriers. Patients with ovarian cancer with BRCA mutations have been reported to have better outcomes than noncarriers (3–6). While other studies (7–10) demonstrated that outcomes were superior only among women with BRCA2 mutations, a recent study involving 873 patients did not find survival differences between BRCA1 or BRCA2 mutation carriers and noncarriers (11).

Research on breast cancer prognoses and BRCA mutations has also yielded inconsistent results. Multiple studies have demonstrated that BRCA1-related breast cancers were more likely to be triple-negative, which was correlated with poor prognosis (12–14). A meta-analysis published in 2010 also linked BRCA1 mutation to decreased overall (OS) and progression-free survival (PFS) among patients with breast cancer, although BRCA2 mutation was not associated with differential survival outcomes (15). Additional studies, published since 2010, have generated varying results on the role of BRCA mutations in breast cancer prognoses. While a multi-country study failed to show differences in breast cancer survival rates associated with BRCA mutations (16), Cortesi and colleagues (17) reported favorable 10-year breast cancer outcomes among BRCA1 carriers. Meanwhile, a large-scale study involving 2,967 patients demonstrated adverse effect of BRCA2 mutations on breast cancer–specific survival (18). These inconsistencies in the research may be due to variations in sample size and the relative rarity of mutation carriers.

Despite our increased understanding of the pathophysiology of BRCA-mutated cancers, specific treatments for these diseases have not yet been established. A novel class of anticancer agents, PARP inhibitors, has shown strong activity in BRCA-related cancers by using the inherent homologous recombination defectiveness of these tumors (19, 20). Enhanced understanding of the role of BRCA mutation status in ovarian and breast cancer survival will provide more accurate prognostic information and can improve clinical decision-making with respect to trial design and cancer treatment. The objective of this study was to perform a meta-analysis to evaluate the effects of BRCA1 and BRCA2 mutations on OS and PFS of ovarian and breast cancer.

We followed the PRISMA Statement guidelines to design, analyze, and report our meta-analytic findings (21). Only the study-level summary data were used for the analyses.

Literature search

We reviewed PubMed and EMBASE databases for articles published before February 10, 2014. We identified studies by using Medical Subject Headings (MeSH) including mutation, BRCA1, Genes, BRCA2, Survival, Prognosis, Ovarian Neoplasms, Hereditary Breast and Ovarian Cancer Syndrome, Ovarian epithelial cancer, Breast Neoplasms. These terms were also combined with keyword and manual searches of references in all selected studies.

Selection criteria

The inclusion criteria were as follows: (i) comparisons were made between patients with breast or ovarian cancer with BRCA1 or BRCA2 mutations and noncarriers confirmed by mutation analyses; (ii) outcomes were survival-related, such as OS or PFS; (iii) outcomes involved human subjects; and (iv) articles were published in English. In addition, articles were excluded if (i) control patients not confirmed of noncarriers; (ii) they were review articles; (iii) subjects had metastatic or recurrent cancer; (iv) comparisons were made between carriers or did not evaluate BRCA1 and 2 mutations separately; and (v) studies were for cancer specific–survival only.

Study selection, data extraction, and end points

Initial screening of potentially eligible records was performed by 2 investigators (Q. Zhong, L. Zhang). Subsequent full-text record screening was performed independently by 2 investigators (Q. Zhong, W.-T. Hwang). Disagreements were resolved by consensus. Baseline characteristics and outcomes were extracted from the selected articles. We chose OS and PFS as our endpoints for meta-analysis and treated disease-free survival (DFS) as PFS. Various endpoints for PFS were reported in the selected breast cancer studies, including local recurrence-free survival (LRFS; refs. 22, 23), distant DFS (DDFS; refs. 16, 23, 24), DFS (17, 25, 26), metastasis-free survival (MFS; refs. 22, 27), and contralateral RFS (CRFS; refs. 23, 27). Because DDFS and MFS shared the same definition in these articles, we operationally defined PFS to include also MFS or DDFS for studies that did not provide DFS.

Quality assessment

The quality of each study was independently rated by 2 investigators (Q. Zhong, W.-T. Hwang), using the Newcastle–Ottawa Scale (28) and Altman framework (29).

Statistical analysis

HR was used as a measure of the prognostic value. HR > 1 indicated poor survival for the group with BRCA mutations. HRs and 95% confidence intervals (CI) were extracted from articles. For those studies in which HRs and CIs were not available, we used the method proposed by Parmar and colleagues to derive estimates from survival curves (30). To improve accuracy, we excluded HRs derived from studies if the number of events in one group was less than 5 (31, 32). Heterogeneity was assessed by the χ2 test and expressed by I2 index (33), which describes the percentage of total variation across studies that is due to heterogeneity rather than chance (25% low heterogeneity, 50% medium, 75% high). Random-effects models were initially used to obtain the summary HRs and 95% CIs, and if there was no heterogeneity among studies, fixed-effects models were used to estimate pooled HRs (34). If results of both univariate and multivariate Cox regression analyses were reported, we chose multivariate models for a more accurate estimate of the effect of BRCA mutation. Subgroup analyses were performed on the basis of variables including stage, grade, histologic subtype, optimal debulking rate, tumor size, nodal status, estrogen receptor (ER) status, chemotherapy, and hormone therapy rates and whether a multivariate or univariate Cox regression was used. Publication bias was assessed by inspecting the symmetry of the funnel plot and tested with Begg and Egger's tests (35, 36). STATA 12.0 was used to test for publication bias. All other analyses were conducted using Review Manager 5.2.

Identification of relevant studies

Figure 1 summarizes the identification of relevant studies. We screened 1,201 articles for eligibility and identified 27 studies [including 2 related publications from the same study (refs. 13, 26)] for meta-analysis. Among them, 20 were retrospective cohort studies and 7 were prospective cohort studies. The research quality among the selected studies was high, with a median quality score of 0.86 (range, 0.67–0.92).

Figure 1.

Flow diagram for selection of studies.

Figure 1.

Flow diagram for selection of studies.

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Study characteristics

Ovarian cancer studies.

Fourteen studies were included in the meta-analysis of ovarian cancer. Characteristics of ovarian cancer patient cohorts are presented in Table 1. The studies were conducted in 8 countries (Poland, Italy, Israel, United Kingdom, Canada, United States, Hong Kong, and Australia) and published between 1999 and 2014. The median number of women evaluated per study was 251 (range, 48–3,879), with a total of 9,588 patients, including 1,722 BRCA1 mutation carriers, 659 BRCA2 mutation carriers, and 7,207 noncarriers. The reported mean or median age for studies was similar, ranging from 54.4 to 65.4 years. The percentage of women with serous tumors varied from 31% to 100%, with 2 studies focused exclusively on serous cancers (8, 9). Between 62% and 100% of patients had stage III–IV disease, whereas 12% to 100% of patients had grade 3 tumors. Optimal debulking rate varied from 51% to 89%, but data were missing in 8 studies (3, 4, 7, 10, 37–40). Eight studies (3–5, 7, 10, 11, 37, 41) reported median or mean follow-up time, ranging from 1.5 to 6.9 years. Ten studies (71%; refs. 3–11, 37) conducted a multivariate analysis to adjust for confounding variables such as age, stage, debulking status, cancer or family history, menopausal status, grade, histology, platinum sensitivity, year of diagnosis, or morphology. The most commonly used BRCA mutation detecting methods were PCR and sequencing in selected studies. BRCA-associated ovarian cancers were more likely to be characterized by high-grade serous histology and advanced stage. On the basis of 7 of 14 ovarian cancer studies that had mean age data available, we found that the mean ages for the mutation groups appeared to be younger, particularly for BRCA1 mutation carriers: 43 and 47 years for BRCA1 and BRCA2 carriers, respectively, compared with 49 years for noncarriers (Supplementary Table S1).

Table 1.

Characteristics of the included studies for ovarian cancer

Number of subjects
Author (ref.) yearCountryBRCA1+BRCA2+NoncarrierMean or median ± SD or range of age, yMedian/range of follow-up, ySerous cancer (%)Stage III–IV (%)Grade 3 (%)Optimal debulking (%)Quality score
Cunningham (11) 2014 USA 30 27 816 62.4 ± 11.7 4.5 (0.01–10) 73 80 86 85 0.83 
Safra (4) 2013 Israel, USA, Italy 71 19 100 55.5d (31–83) 4.7 (0.8–17.8) 69.5 88.9 NR NR 0.92 
McLaughlin (7) 2013 Canada, USA 129 89 1,408 57.2 (19–81) 6.9e (0.3–12) 55.8 64.6 35.1 NR 0.88 
Alsop (5) 2012 Australia 88 53 777 59.8 ± 10.4 5.3 (NA) 70.8 67.8 56.6 62.4 0.92 
Hyman (8) 2012 USA 30 17 143 58.5d (32–78) NR 100 100 100 76.3 0.88 
Bolton (3) 2012a Multicountry 909 304 2,666 56.8 ± 11.4 3.2 (1.5–6.9) 67 61.6 72.2 NR 0.88 
Yang (9) 2011 USA 35 27 219 60.6 ± 0.7 NR 100 96 91 75 0.88 
Chetrit (37) 2008b Israel 159 54 392 NR 6.2 (4.2–9.4) 58.7 100 59 NR 0.88 
Pal (10) 2007 USA 20 12 200 56.7 (18–80) 1.5 (NR) 58.2 70.7 NR NR 0.83 
Majdak (6) 2005 Poland 18 NA 171 NRc NR 64.6 87.8 12.1 88.9 0.92 
Buller (38) 2002 USA 24 NA 24 59.4 (55.3–63.1) NR 74.6 86.4 66.1 NR 0.71 
Ramus (39) 2001 Israel 15 12 71 65.4d (32–88) NR 77.6 82.7 50.5 NR 0.71 
Boyd (41) 2000 USA 67 21 101 59.7 ± 11.5 4.8 (NR) 64 95.8 78.8 50.8 0.75 
Pharoah (40) 1999 UK 127 24 119 54.4 (NR) NR 31 83 24.7 NR 0.71 
Number of subjects
Author (ref.) yearCountryBRCA1+BRCA2+NoncarrierMean or median ± SD or range of age, yMedian/range of follow-up, ySerous cancer (%)Stage III–IV (%)Grade 3 (%)Optimal debulking (%)Quality score
Cunningham (11) 2014 USA 30 27 816 62.4 ± 11.7 4.5 (0.01–10) 73 80 86 85 0.83 
Safra (4) 2013 Israel, USA, Italy 71 19 100 55.5d (31–83) 4.7 (0.8–17.8) 69.5 88.9 NR NR 0.92 
McLaughlin (7) 2013 Canada, USA 129 89 1,408 57.2 (19–81) 6.9e (0.3–12) 55.8 64.6 35.1 NR 0.88 
Alsop (5) 2012 Australia 88 53 777 59.8 ± 10.4 5.3 (NA) 70.8 67.8 56.6 62.4 0.92 
Hyman (8) 2012 USA 30 17 143 58.5d (32–78) NR 100 100 100 76.3 0.88 
Bolton (3) 2012a Multicountry 909 304 2,666 56.8 ± 11.4 3.2 (1.5–6.9) 67 61.6 72.2 NR 0.88 
Yang (9) 2011 USA 35 27 219 60.6 ± 0.7 NR 100 96 91 75 0.88 
Chetrit (37) 2008b Israel 159 54 392 NR 6.2 (4.2–9.4) 58.7 100 59 NR 0.88 
Pal (10) 2007 USA 20 12 200 56.7 (18–80) 1.5 (NR) 58.2 70.7 NR NR 0.83 
Majdak (6) 2005 Poland 18 NA 171 NRc NR 64.6 87.8 12.1 88.9 0.92 
Buller (38) 2002 USA 24 NA 24 59.4 (55.3–63.1) NR 74.6 86.4 66.1 NR 0.71 
Ramus (39) 2001 Israel 15 12 71 65.4d (32–88) NR 77.6 82.7 50.5 NR 0.71 
Boyd (41) 2000 USA 67 21 101 59.7 ± 11.5 4.8 (NR) 64 95.8 78.8 50.8 0.75 
Pharoah (40) 1999 UK 127 24 119 54.4 (NR) NR 31 83 24.7 NR 0.71 

Abbreviations: NA, not applicable; NR, not reported.

aThis study was conducted in the United States, Europe, Israel, Hong Kong, Canada, Australia, and the UK.

bFor article written by Chetrit et al., we chose the results of a subgroup of patients with Ashkenazi Jewish origin and stage III–IV tumors whose survival data were analyzed by multivariate analysis. This study only provided percentage of age period: 24% of patients were younger than 50 years.

cMajdak et al., 39% of patients were younger than 50 years.

dMedian age.

eMean follow-up time.

Breast cancer studies.

Thirteen studies were included in the meta-analysis of breast cancer. Characteristics of breast cancer patient cohorts are presented in Table 2. The studies were conducted in 12 countries (Poland, Italy, Israel, Norway, United Kingdom, Netherlands, Canada, France, Finland, United States, Australia, and Germany) and published between 2000 and 2013. A total of 10,016 patients (ranging from 85 to 3,345 per study) were included, among them 890 BRCA1 mutation carriers, 342 BRCA2 mutation carriers, and 8,784 noncarriers. The reported mean or median age ranged from 42.6 to 62.1 years across eligible studies. The percentage of women with tumor > 2 cm varied from 9% to 66%, with 0% to 49% nude positive, 23% to 49% grade 3 tumors, 37% to 67% ER-positive, 25% to 72% receiving chemotherapy, and 19% to 56% receiving hormone therapy. Among the 9 studies (12, 13, 16, 17, 22–25, 27) that reported median or mean follow-up time, the median duration was 6.3, ranging from 4.5 to 7.9 years. Nine studies (69%; refs. 12, 13, 16, 17, 22, 24, 42–44) conducted a multivariate analysis of HRs to adjust for age, tumor size, nodal or ER status, oophorectomy, chemotherapy, hormone therapy, year of diagnosis, or nuclear grade. PCR and sequencing were the most commonly used BRCA mutation detecting methods across selected studies. After analyzing data from 6 studies (5 for BRCA2) of 13 breast cancer studies that had mean age data available, we found that BRCA mutation carriers were comparatively younger in age, particularly for BRCA1 mutation carriers. The mean age was 52 years for BRCA1 mutation carriers and 54 years for BRCA2 mutation carriers, compared with 58 years for noncarriers. Furthermore, BRCA1 mutation carriers were more likely to be ER- and progesterone receptor (PR)–negative, have lower histologic grade, and receive chemotherapy (Supplementary Table S2).

Table 2.

Characteristics of the included studies for breast cancer

Number of subjects
Author (ref.) yearCountryBRCA1+BRCA2+NoncarrierMean or median ± SD or range of age, yMedian/Range of follow-up, yTumor size > 2 cm (%)Node positive (%)Grade 3 (%)ER+ (%)Chemo-therapy (%)Hormone therapy (%)Quality score
Huzarski (12) 2013 Poland 233 NA 3,112 43.9 (21–50) 7.4f (0.1–15.8) 41.4 49.2 NR 58.7 72.1 40.6 0.92 
Goodwin (16) 2012a Multicountry 94 72 1,550 45.3 ± 9.8 7.9 (NR) 38.7 39.7 43.1 66.7 62.2 44.8 0.92 
Cortesi (17) 2010 Italy 80 NA 931 NR 6 (NR) 53.1 NR NR 65.5 38.9 55.5 0.79 
Budroni (42) 2009 Italy NA 44 464 NRe NR 44 NR 74 NR NR 0.88 
Rennert (43) 2007b Israel 76 52 1,189 62.1 ± 13.5 NR 66 47 NR 63 25.1 NR 0.88 
Moller (14) 2007 Norway, UK 89 35 318 48.8 (NR) NR NR 23 49 57 NR NR 0.67 
Brekelmans (23) 2007 Netherlands 170 90 238 44.8 (23–85) 4.5 (0.2–24.5) 40.4 44.1 46.6 43 45.2 18.9 0.92 
Bonadona (27) 2007 France 15 211 NRe 6.8 (0.1–9.3) NR 40.5 36.3 53 56.9 26.3 0.83 
Goffin (24) 2003 Canada 30 NA 248 53.4g (27–65) 8 (NR) NR 44 33 63 47 50 0.92 
Eerola (44) 2001 Finland 32 43 284 NRe NR NR NR NR NA NR NR 0.83 
Stoppa-Lyonnet (22) 2000c France 19 NA 91 42.6 ± 10.2 4.8 (0.5–17.5) 56.8 28.1 23.4 36.5 NR NR 0.83 
Hamann (25) 2000 Germany 36 NA 49 43g (19–73) 5.6 (NR) 37.6 32.9 23.5 NA NR NR 0.75 
Foulkes (13) 2000d Canada 16 NA 99 53.9g (28–65) 6.3 (0.8–11.1) NR 33 59.1 35.7 NR 0.83 
Number of subjects
Author (ref.) yearCountryBRCA1+BRCA2+NoncarrierMean or median ± SD or range of age, yMedian/Range of follow-up, yTumor size > 2 cm (%)Node positive (%)Grade 3 (%)ER+ (%)Chemo-therapy (%)Hormone therapy (%)Quality score
Huzarski (12) 2013 Poland 233 NA 3,112 43.9 (21–50) 7.4f (0.1–15.8) 41.4 49.2 NR 58.7 72.1 40.6 0.92 
Goodwin (16) 2012a Multicountry 94 72 1,550 45.3 ± 9.8 7.9 (NR) 38.7 39.7 43.1 66.7 62.2 44.8 0.92 
Cortesi (17) 2010 Italy 80 NA 931 NR 6 (NR) 53.1 NR NR 65.5 38.9 55.5 0.79 
Budroni (42) 2009 Italy NA 44 464 NRe NR 44 NR 74 NR NR 0.88 
Rennert (43) 2007b Israel 76 52 1,189 62.1 ± 13.5 NR 66 47 NR 63 25.1 NR 0.88 
Moller (14) 2007 Norway, UK 89 35 318 48.8 (NR) NR NR 23 49 57 NR NR 0.67 
Brekelmans (23) 2007 Netherlands 170 90 238 44.8 (23–85) 4.5 (0.2–24.5) 40.4 44.1 46.6 43 45.2 18.9 0.92 
Bonadona (27) 2007 France 15 211 NRe 6.8 (0.1–9.3) NR 40.5 36.3 53 56.9 26.3 0.83 
Goffin (24) 2003 Canada 30 NA 248 53.4g (27–65) 8 (NR) NR 44 33 63 47 50 0.92 
Eerola (44) 2001 Finland 32 43 284 NRe NR NR NR NR NA NR NR 0.83 
Stoppa-Lyonnet (22) 2000c France 19 NA 91 42.6 ± 10.2 4.8 (0.5–17.5) 56.8 28.1 23.4 36.5 NR NR 0.83 
Hamann (25) 2000 Germany 36 NA 49 43g (19–73) 5.6 (NR) 37.6 32.9 23.5 NA NR NR 0.75 
Foulkes (13) 2000d Canada 16 NA 99 53.9g (28–65) 6.3 (0.8–11.1) NR 33 59.1 35.7 NR 0.83 

Abbreviations: NA, not applicable; NR, not reported.

aThis study was conducted in Canada, the United States, and Australia.

bFor article written by Rennert et al., we chose a subgroup of patients of Ashkenazi Jewish origin whose survival data were analyzed by multivariate analysis.

cFor article written by Stoppa-Lyonnet et al., we chose the results of a subgroup of patients with ≤36 months' interval between diagnosis and genetic counseling whose survival data were analyzed by multivariate analysis.

dFoulkes et al. had 2 related publications from the same study (13, 26).

eBudroni et al., 42% of patients were younger than 40 years. Bonadona et al., 50% of patients were younger than 40 years. Eerola et al., 36% of patients were younger than 50 years.

fMean follow-up time.

gMedian age.

Prognostic value of BRCA mutations for ovarian and breast cancer

Ovarian cancer prognosis.

Fourteen studies were included in the analysis of the effect of BRCA1 mutation on ovarian cancer OS. The results of our meta-analysis showed that BRCA1 mutation carriers were associated with better OS than noncarriers, with a pooled HR of 0.76 (95% CI, 0.70–0.83). No significant heterogeneity was found across the studies (I2 = 17%, P = 0.27). A stronger effect was found using 12 studies assessing OS between BRCA2 carriers and noncarriers. BRCA2 mutation carriers were associated with even better OS than noncarriers, with a pooled HR of 0.58 (95% CI, 0.50–0.66). There was an indication of slight heterogeneity across the studies, but it did not reach statistical significance (I2 = 40%, P = 0.08). Associations between BRCA1 or BRCA2 mutations and ovarian cancer PFS were also evaluated, respectively. Meta-analyses results (3 studies for BRCA1 mutation, 2 studies for BRCA2 mutation) demonstrated that both BRCA1 and BRCA2 mutation carriers were associated with better PFS than noncarriers, with pooled HR of 0.65 (95% CI, 0.52–0.81) for BRCA1 mutation carriers and 0.61 (95% CI, 0.47–0.80) for BRCA2 mutation carriers. No significant heterogeneity was found across the studies (I2 = 0% for BRCA1 mutation carriers and I2 = 59% for BRCA2 mutation carriers, all P > 0.10; Fig. 2).

Figure 2.

Forest plots of associations between BRCA mutations and ovarian cancer survival. A, effect of BRCA1 mutation on OS. B, effect of BRCA1 mutation on PFS. C, effect of BRCA2 mutation on OS. D, effect of BRCA2 mutation on PFS.

Figure 2.

Forest plots of associations between BRCA mutations and ovarian cancer survival. A, effect of BRCA1 mutation on OS. B, effect of BRCA1 mutation on PFS. C, effect of BRCA2 mutation on OS. D, effect of BRCA2 mutation on PFS.

Close modal

Breast cancer prognosis.

Meta-analysis of 11 studies showed a worse breast cancer OS among BRCA1 mutation carriers than noncarriers. Because a significant heterogeneity was found across the studies (I2 = 59%, P = 0.007), the pooled HR of 1.5 (95% CI, 1.11–2.04) was calculated on the basis of a random-effects model. The result of meta-analysis of 5 studies comparing breast cancer OS between BRCA2 carriers and noncarriers indicated no association between mutation and OS, with a pooled HR of 0.97 (95% CI, 0.78–1.22), and no significant heterogeneity was found across the studies (I2 = 0%). Eight and 2 studies were included to assess the association between BRCA1 or BRCA2 mutations and breast cancer PFS, respectively. Meta-analyses results did not reveal any association between these 2 mutations and PFS, with a pooled HR of 1.35 (95% CI, 0.95–1.90) for BRCA1 mutation carriers and 0.95 (95% CI, 0.69–1.30) for BRCA2 mutation carriers. A random-effects model was used for the BRCA1 mutation meta-analysis due to a significant heterogeneity among studies (I2 = 55%, P = 0.03). No significant heterogeneity among studies was found in the meta-analysis of BRCA2 mutation (I2 = 0%; Fig. 3).

Figure 3.

Forest plots of associations between BRCA mutations and breast cancer survival. A, effect of BRCA1 mutation on OS. B, effect of BRCA1 mutation on PFS. C, effect of BRCA2 mutation on OS. D, effect of BRCA2 mutation on PFS.

Figure 3.

Forest plots of associations between BRCA mutations and breast cancer survival. A, effect of BRCA1 mutation on OS. B, effect of BRCA1 mutation on PFS. C, effect of BRCA2 mutation on OS. D, effect of BRCA2 mutation on PFS.

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Subgroup analysis of effect of BRCA mutations on ovarian and breast cancer OS

Subgroup analysis was conducted to investigate potential sources of heterogeneity among studies and to assess the consistency of conclusions among different subpopulations of patients.

Ovarian cancer subgroup analysis.

For ovarian cancer, BRCA1 and BRCA2 mutation carriers have significantly longer OS than noncarriers, regardless of tumor stage, grade, or histologic subtype.

Breast cancer subgroup analysis.

The results of subgroup analyses for BRCA1 mutation in breast cancer outcomes were less consistent due to the limited number of studies available. Interestingly, a subgroup of studies using multivariate analysis showed that BRCA1 mutation carriers had borderline poorer breast cancer OS (HR, 1.40; 95% CI, 1.00–1.98; P = 0.05) than noncarriers, whereas studies involving univariate analysis yielded no association between BRCA1 mutation and breast cancer OS (Table 3).

Table 3.

Associations between BRCA mutation and OS grouped by selected factors

ComparisonStudies (n)Patients (n)HR (95% CI)ConsistencyOverall effects PI2
Ovarian cancer OS 
BRCA1+ vs. noncarrier 
  Multivariate analysis 10 8,965 0.73 (0.67–0.81) Consistent <0.00001 19% 
  Univariate analysis 605 0.88 (0.73–1.06) Inconsistent 0.18 0% 
  Stage III–IV tumor ≥ 80% 10 2,933 0.82 (0.72–0.93) Consistent 0.003 22% 
  Stage III–IV tumor < 80% 6,655 0.72 (0.64–0.81) Consistent <0.00001 0% 
  Serous tumor ≥ 70% 2,408 0.77 (0.61–0.97) Consistent 0.03 14% 
  Serous tumor < 70% 7,180 0.76 (0.69–0.84) Consistent <0.00001 29% 
  Grade 3 tumor ≥ 70% 5,034 0.75 (0.66–0.84) Consistent <0.00001 0% 
  Grade 3 tumor < 70% 3,754 0.81 (0.71–0.93) Consistent 0.003 21% 
  Optimal debulking rate ≥ 75% 1,533 0.82 (0.61–1.11) Inconsistent 0.21 39% 
  Optimal debulking rate < 75% 1,107 0.62 (0.46–0.82) Consistent 0.001 0% 
BRCA2+ vs. noncarrier 
  Multivariate analysis 8,776 0.53 (0.46–0.62) Consistent <0.00001 32% 
  Univariate analysis 557 0.83 (0.60–1.15) Inconsistent 0.26 0% 
  Stage III–IV tumor ≥ 80% 2,696 0.60 (0.49–0.74) Consistent <0.00001 41% 
  Stage III–IV tumor < 80% 6,655 0.56 (0.47–0.67) Consistent <0.00001 50% 
  Serous tumor ≥ 70% 2,360 0.62 (0.47–0.81) Consistent 0.0005 51% 
  Serous tumor < 70% 6,991 0.56 (0.48–0.66) Consistent <0.00001 38% 
  Grade 3 tumor ≥ 70% 5,034 0.50 (0.41–0.60) Consistent <0.00001 21% 
  Grade 3 tumor < 70% 3,517 0.71 (0.58–0.87) Consistent 0.0008 0% 
  Optimal debulking rate ≥ 75% 1,344 0.46 (0.31–0.69) Consistent 0.0001 52% 
  Optimal debulking rate < 75% 1,107 0.75 (0.53–1.07) Inconsistent 0.12 0% 
Breast cancer OS 
BRCA1+ vs. noncarrier 
  Multivariate analysis 8,251 1.40 (1.00–1.98) Consistent 0.05 58% 
  Univariate analysis 1,025 1.89 (0.79–4.52) Inconsistent 0.15 71% 
  Tumor > 2 cm proportion ≥ 10% 7,997 1.29 (0.91–1.84) Inconsistent 0.15 62% 
  Tumor > 2cm proportion < 10% 85 1.09 (0.34–3.46) Inconsistent 0.88 NA 
  Node-positive tumor ≥ 25% 7,349 1.37 (1.07–1.75) Consistent 0.01 32% 
  Node-positive tumor < 25% 557 4.34 (2.20–8.54) Consistent <0.00001 76% 
  Grade 3 tumor ≥ 25% 3,049 1.78 (1.05–3.01) Consistent 0.03 69% 
  Grade 3 tumor < 25% 195 2.13 (1.01–4.53) Consistent 0.05 56% 
  ER(+) tumor ≥ 50% 8,224 1.50 (0.98–2.30) Inconsistent 0.06 71% 
  ER(+) tumor < 50% 608 1.57 (1.03–2.39) Consistent 0.04 68% 
  Chemotherapy rate ≥ 50% 5,061 1.36 (0.76–2.46) Inconsistent 0.3 75% 
  Chemotherapy rate < 50% 3,219 1.24 (0.96–1.59) Inconsistent 0.10 53% 
  Hormone therapy rate ≥ 40% 6,350 1.17 (0.71–1.92) Inconsistent 0.55 65% 
  Hormone therapy rate < 40% 498 1.31 (0.82–2.09) Inconsistent 0.25 NA 
BRCA2+ vs. noncarrier 
  Multivariate analysis 3,900 0.95 (0.74–1.22) Consistent 0.68 20% 
  Univariate analysis 498 1.08 (0.66–1.76) Consistent 0.53 NA 
  Tumor > 2 cm proportion ≥ 10% 3,531 1.14 (0.87–1.49) Consistent 0.35 0% 
  Tumor > 2 cm proportion < 10% 508 0.70 (0.46–1.07) Consistent 0.1 NA 
  Node-positive tumor ≥ 25% 4,039 0.99 (0.79–1.24) Consistent 0.91 20% 
  Node-positive tumor < 25% NA NA NA NA NA 
  Grade 3 tumor ≥ 25% 2,214 1.10 (0.78–1.55) Consistent 0.58 0% 
  Grade 3 tumor < 25% NA NA NA NA NA 
  ER(+) tumor ≥ 50% 3,541 0.96 (0.75–1.24) Consistent 0.77 44% 
  ER(+) tumor < 50% 498 1.08 (0.66–1.76) Consistent 0.53 NA 
  Chemotherapy rate ≥ 50% 1,716 1.12 (0.70–1.79) Consistent 0.64 NA 
  Chemotherapy rate < 50% 1,815 1.14 (0.82–1.59) Consistent 0.42 0% 
  Hormone therapy rate ≥ 40% 1,550 1.12 (0.70–1.79) Consistent 0.64 NA 
  Hormone therapy rate < 40% 498 1.08 (0.66–1.76) Consistent 0.76 NA 
ComparisonStudies (n)Patients (n)HR (95% CI)ConsistencyOverall effects PI2
Ovarian cancer OS 
BRCA1+ vs. noncarrier 
  Multivariate analysis 10 8,965 0.73 (0.67–0.81) Consistent <0.00001 19% 
  Univariate analysis 605 0.88 (0.73–1.06) Inconsistent 0.18 0% 
  Stage III–IV tumor ≥ 80% 10 2,933 0.82 (0.72–0.93) Consistent 0.003 22% 
  Stage III–IV tumor < 80% 6,655 0.72 (0.64–0.81) Consistent <0.00001 0% 
  Serous tumor ≥ 70% 2,408 0.77 (0.61–0.97) Consistent 0.03 14% 
  Serous tumor < 70% 7,180 0.76 (0.69–0.84) Consistent <0.00001 29% 
  Grade 3 tumor ≥ 70% 5,034 0.75 (0.66–0.84) Consistent <0.00001 0% 
  Grade 3 tumor < 70% 3,754 0.81 (0.71–0.93) Consistent 0.003 21% 
  Optimal debulking rate ≥ 75% 1,533 0.82 (0.61–1.11) Inconsistent 0.21 39% 
  Optimal debulking rate < 75% 1,107 0.62 (0.46–0.82) Consistent 0.001 0% 
BRCA2+ vs. noncarrier 
  Multivariate analysis 8,776 0.53 (0.46–0.62) Consistent <0.00001 32% 
  Univariate analysis 557 0.83 (0.60–1.15) Inconsistent 0.26 0% 
  Stage III–IV tumor ≥ 80% 2,696 0.60 (0.49–0.74) Consistent <0.00001 41% 
  Stage III–IV tumor < 80% 6,655 0.56 (0.47–0.67) Consistent <0.00001 50% 
  Serous tumor ≥ 70% 2,360 0.62 (0.47–0.81) Consistent 0.0005 51% 
  Serous tumor < 70% 6,991 0.56 (0.48–0.66) Consistent <0.00001 38% 
  Grade 3 tumor ≥ 70% 5,034 0.50 (0.41–0.60) Consistent <0.00001 21% 
  Grade 3 tumor < 70% 3,517 0.71 (0.58–0.87) Consistent 0.0008 0% 
  Optimal debulking rate ≥ 75% 1,344 0.46 (0.31–0.69) Consistent 0.0001 52% 
  Optimal debulking rate < 75% 1,107 0.75 (0.53–1.07) Inconsistent 0.12 0% 
Breast cancer OS 
BRCA1+ vs. noncarrier 
  Multivariate analysis 8,251 1.40 (1.00–1.98) Consistent 0.05 58% 
  Univariate analysis 1,025 1.89 (0.79–4.52) Inconsistent 0.15 71% 
  Tumor > 2 cm proportion ≥ 10% 7,997 1.29 (0.91–1.84) Inconsistent 0.15 62% 
  Tumor > 2cm proportion < 10% 85 1.09 (0.34–3.46) Inconsistent 0.88 NA 
  Node-positive tumor ≥ 25% 7,349 1.37 (1.07–1.75) Consistent 0.01 32% 
  Node-positive tumor < 25% 557 4.34 (2.20–8.54) Consistent <0.00001 76% 
  Grade 3 tumor ≥ 25% 3,049 1.78 (1.05–3.01) Consistent 0.03 69% 
  Grade 3 tumor < 25% 195 2.13 (1.01–4.53) Consistent 0.05 56% 
  ER(+) tumor ≥ 50% 8,224 1.50 (0.98–2.30) Inconsistent 0.06 71% 
  ER(+) tumor < 50% 608 1.57 (1.03–2.39) Consistent 0.04 68% 
  Chemotherapy rate ≥ 50% 5,061 1.36 (0.76–2.46) Inconsistent 0.3 75% 
  Chemotherapy rate < 50% 3,219 1.24 (0.96–1.59) Inconsistent 0.10 53% 
  Hormone therapy rate ≥ 40% 6,350 1.17 (0.71–1.92) Inconsistent 0.55 65% 
  Hormone therapy rate < 40% 498 1.31 (0.82–2.09) Inconsistent 0.25 NA 
BRCA2+ vs. noncarrier 
  Multivariate analysis 3,900 0.95 (0.74–1.22) Consistent 0.68 20% 
  Univariate analysis 498 1.08 (0.66–1.76) Consistent 0.53 NA 
  Tumor > 2 cm proportion ≥ 10% 3,531 1.14 (0.87–1.49) Consistent 0.35 0% 
  Tumor > 2 cm proportion < 10% 508 0.70 (0.46–1.07) Consistent 0.1 NA 
  Node-positive tumor ≥ 25% 4,039 0.99 (0.79–1.24) Consistent 0.91 20% 
  Node-positive tumor < 25% NA NA NA NA NA 
  Grade 3 tumor ≥ 25% 2,214 1.10 (0.78–1.55) Consistent 0.58 0% 
  Grade 3 tumor < 25% NA NA NA NA NA 
  ER(+) tumor ≥ 50% 3,541 0.96 (0.75–1.24) Consistent 0.77 44% 
  ER(+) tumor < 50% 498 1.08 (0.66–1.76) Consistent 0.53 NA 
  Chemotherapy rate ≥ 50% 1,716 1.12 (0.70–1.79) Consistent 0.64 NA 
  Chemotherapy rate < 50% 1,815 1.14 (0.82–1.59) Consistent 0.42 0% 
  Hormone therapy rate ≥ 40% 1,550 1.12 (0.70–1.79) Consistent 0.64 NA 
  Hormone therapy rate < 40% 498 1.08 (0.66–1.76) Consistent 0.76 NA 

Abbreviation: NA, not applicable.

Publication bias

Formal investigation using Begg and Egger's tests indicated no publication bias in the meta-analyses for associations of BRCA1 or BRCA2 mutations with ovarian cancer OS and BRCA1 mutation with breast cancer OS and PFS. We did not test publication bias for the meta-analyses of ovarian cancer PFS and breast cancer OS and PFS for BRCA2 mutation because too few studies were available to make a valid statistical test (Supplementary Fig. S1).

This is the first meta-analysis that investigated both ovarian and breast cancer survival in patients with BRCA1 or BRCA2 mutations compared with noncarriers in one report. Our results demonstrated that BRCA mutations had a differential impact on ovarian and breast cancer survival. Both BRCA1 and BRCA2 mutations were positively associated with ovarian cancer OS and PFS and BRCA2 mutation appeared to have a greater effect upon OS, regardless of tumor stage, grade, or histologic subtype. In breast cancer outcomes, the impact of BRCA mutations was just the opposite. BRCA1 mutation carriers had worse prognoses than noncarriers for OS but were not different from noncarriers in PFS. BRCA2 mutation was not associated with breast cancer prognosis.

Our results, with respect to ovarian cancer OS, were consistent with the previous meta-analysis by Sun and colleagues (45). For ovarian cancer PFS, although Sun and colleagues did not separate BRCA1 and BRCA2 mutations in their analysis, they still found a favorable PFS in BRCA1/2 mutations carriers, which was consistent with our finding. After we incorporated 4 additional studies (12, 16, 17, 27) into our meta-analysis, our breast cancer OS results were also consistent with the previous meta-analysis published by Lee and colleagues (15), but we did not replicate their findings on the effect of BRCA1 mutation on breast cancer PFS. We believed this may be due to the fact that Lee and colleagues analyzed short-term (i.e., <5 years) and long-term (i.e., >5 years) PFS separately and fewer studies were analyzed.

At this time, the mechanism driving the association between BRCA1 or BRCA2 mutation and survival is not clear. It may be rooted in different functions of the BRCA genes in the dsDNA repair pathway. As we know, postoperative platinum-based chemotherapy is the main treatment for ovarian cancer. Most studies suggested that the ovarian cancer survival advantage observed among BRCA carriers could be mediated through improved response to platinum-based agents, due to an impaired ability to repair dsDNA breaks through homologous recombination (46). The fact that BRCA2 mutation improved ovarian cancer survival may be due to the gene's more direct involvement in homologous recombination which can attenuate or abolish the interaction with RAD51, resulting in failure to load RAD51 to DNA damage sites. The BRCA1 mutation may have less impact on RAD51-mediated homologous recombination, which could account for the disparity in outcomes between BRCA1 and BRCA2 carriers (47, 48). For breast cancer, we often rely upon different types of treatments, including hormone therapy, radiotherapy, and chemotherapy. Research on breast cancer also indicated that BRCA1 mutation carriers had more p53 mutations, resulting in worse prognosis (49). The different breast cancer outcomes observed among BRCA1 and BRCA2 carriers could also be due to their distinct clinical characteristics. As mentioned previously, BRCA1-related breast cancers were more likely to be triple-negative, which made treatment more difficult.

Certain limitations must be considered when interpreting study findings. First, our meta-analysis only encompassed a total of 27 studies. Studies specific to BRCA2 mutations for breast cancer were less common in the literature and are therefore underrepresented in this analysis. The limited availability of BRCA2 mutation studies complicated our ability to test publication bias. This is partially due to the fact that we excluded a number of studies that did not evaluate BRCA1 and BRCA2 separately or compared mutation carriers to sporadic patients with cancer without testing mutation status on control patients. Second, because of the variety of endpoints reported in breast cancer studies, we operationally defined breast cancer PFS to include MFS or DDFS for studies that did not provide DFS and had to exclude LRFS and CRFS results (which were considered to be parts of PFS for some studies) from a few studies. Third, most studies were focused on the Western population; only one study included Asian women (3). Additional studies are needed to better understand the effects of BRCA1 and BRCA2 mutations on survival among carriers in Asia and other ethnic or geographic regions. Finally, nearly half of the studies selected for our breast cancer meta-analysis failed to provide sufficient data on patient characteristics, which may have impacted the validity of subgroup analysis.

Despite these limitations, our meta-analysis rigorously evaluated the effects of BRCA1 and BRCA2 mutations on both ovarian and breast cancer survival using all eligible studies in the same setting that included intensive subgroup analyses. Our results may have important implications for the clinical management of both ovarian and breast cancer. The results can be used immediately by health care professionals for patient counseling regarding expected survival. Also, there is a widespread agreement that PARP inhibitors (PARPi) can affect BRCA-related cancers by using the inherent homologous recombination defectiveness of the tumors in a synthetically lethal manner. Many phase I and II trials have reported the successful applications of PARPi in patients with ovarian and breast cancer with BRCA mutations and phase III trials are under way (19, 20, 50). Given the prognostic information provided by different BRCA status and the possibility for personalized treatments in carriers and noncarriers, the next step would be to see how this knowledge can be used to optimize treatments and testing targeted therapy in the future. The design of upcoming clinical trials could be stratified by BRCA status to avoid potential bias introduced by unequal numbers of carriers in treatment groups or between study cohorts. Routine testing of BRCA1 and BRCA2 mutation status of ovarian cancer and BRCA1 mutation status of breast cancer may now be warranted.

No potential conflicts of interest were disclosed.

Conception and design: X. Zhao, L. Zhang, W.-T. Hwang

Development of methodology: Q. Zhong, H.-L. Peng, W.-T. Hwang

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): Q. Zhong, W.-T. Hwang

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): Q. Zhong, H.-L. Peng, X. Zhao, W.-T. Hwang

Writing, review, and/or revision of the manuscript: Q. Zhong, H.-L. Peng, L. Zhang, W.-T. Hwang

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): W.-T. Hwang

Study supervision: L. Zhang, W.-T. Hwang

This work was supported, in whole or in part, by NIH (P50-CA83638 to W.-T. Hwang and L. Zhang; R01-CA142776 to L. Zhang); Department of Defense (W81XWH-10-1-0082 to L. Zhang); Ovarian Cancer Research Fund (to L. Zhang); the Basser Research Center for BRCA grant (to L. Zhang); and Marsha Rivkin Center for Ovarian Cancer Research grant (to L. Zhang). Q. Zhong was supported by a scholarship from Sichuan University and the National Natural Science Foundation of China (0040215401068).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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