Ten studies met our search criteria. Studies that provided risk-related information but failed to satisfy the criteria are listed in Appendix Table A1 (online only), with reasons for exclusion. In , we give a synopsis of the included studies, briefly describing study population, design, mutation testing information, and risk estimation methods.
Characteristics of Eligible Studies
Heterogeneity was observed among the reported risks (). Visual pairwise comparisons of CIs include many that overlap as well as some that do not. To quantify this study-to-study variation, we performed tests of heterogeneity5
for all age-specific risks, after logit transformation. With two cancer sites, two genes, and six age intervals, a total of 24 tests were performed. For ovarian cancer, nine of the 12 P
values ranged from .11 to .92. The other three P
values were .02, .04, and < .001; all occurred at age 30 years or younger, where the risk estimates are low and unstable. Therefore, we conclude that there is not enough evidence for heterogeneous ovarian cancer risks. Breast cancer risks are more heterogeneous. For BRCA1
carriers, all P
values ranged from .001 to .045. For BRCA2
carriers, the P
values were .23 and .22 at ages 20 and 30 years and between .02 and .05 at later ages.
Fig 1 (A) Breast cancer risk for BRCA1 carriers, (B) breast cancer for BRCA2 carriers, (C) ovarian cancer for BRCA1 carriers, and (D) ovarian cancer for BRCA2 carriers. The cumulative risk estimates from published studies (thin vertical bars) and the meta-analytic (more ...)
Next, we searched for systematic sources of heterogeneity from various aspects of study characteristics. Systematic differences could arise from the mutation type, the study population, or the design/analysis strategy. Regarding mutation type, Hopper et al7
was the only study that exclusively looked at protein-truncating mutations. All other studies included carriers of a mixed pool of mutation types. If penetrance was mutation specific, we would wish to learn about the penetrance(s) associated with each distinct type of mutation(s). However, it is not presently feasible to separate the effects of mutations from these studies. Instead, a reachable goal is to learn about the average risk among a group of carriers with a representative mix of mutations in a population. Because Hopper et al9
looked only at protein-truncating mutations, which are reported to confer lower risks than other types of mutations, we conducted our meta-analysis of this risk both including and excluding this study. The issue of study populations is similar to that of mutation type in that different populations (by ethnicity, eg, Ashkenazi Jew v
not, or geographic locations) may segregate different mutations or share different risk factors. Currently, there are studies containing more than one subpopulation; however, they provide limited evidence of population-specific variation in penetrance, either by geographical region or by ethnicity.13,16,17
Regarding design and analysis, as shown in , each study used an analysis method that addressed ascertainment mechanism in its design. Although it has been conjectured that the designs and analyses used in the studies may result in biases,8,9,13,18
which could generate the observed heterogeneity, some of the empirical evidence also suggests the contrary. For example, the Breast Cancer Linkage Consortium studied families with higher logarithm of the odds scores and also demonstrated that the penetrance estimates are equally high when families with low logarithm of the odds scores are included. Meanwhile, King et al14
used case series data and arrived at similar estimates. Scott et al,12
Marroni et al,15
and Chen et al16
used a similar design and analysis as Ford et al6
and reported lower penetrance. In summary, as the number of studies grows, there is no clear systematic trend attributable to the design and analysis.
Motivated by the lack of systematic heterogeneity among current penetrance estimates, we summarized them with a random effects model using the DerSimonian and Laird approach.5
The resulting consensus estimates are weighted averages of the risks reported by all studies, whereas their SEs take into account both within-study SEs and study-to-study heterogeneity. This approach relies on an assumption of normality of the random effects, which is reasonable because there is no pronounced asymmetry and no study is an obvious outlier.
On the basis of our analysis, we report the mean and standard deviation of the meta-analytic penetrance by 10-year age intervals, as shown in . In a separate analysis, we excluded the Hopper et al9
study. However, the difference was minimal (< 0.1 percentage point at all age intervals).
For comparison, we also obtained the estimate of the penetrance assuming no interstudy variation. The resulting cumulative risks by age 70 years are as follows: breast cancer risk of 55% (95% CI, 50% to 59%) for BRCA1 and 47% (95% CI, 42% to 51%) for BRCA2 mutation carriers;and ovarian cancer risk of 39% (95% CI,34% to 45%) for BRCA1 and 17% (95% CI,13% to 21%) for BRCA2 mutation carriers. Compared with the random effects model, the point estimates are within 2 percentage points of each other. However, the CIs for breast cancer risks become much narrower by ignoring existing heterogeneity, whereas those for the ovarian cancer risks remain similar.
Penetrance curves based on these results have been incorporated in the genetic counseling and risk prediction software BayesMendel,3
which includes the BRCA
mutation prediction tool BRCAPRO,19
and will be incorporated in the next version of CancerGene.20
Note that penetrance by definition is the net risk in the absence of any competing risks. We also derived the future risks of developing cancer for currently asymptomatic carriers after taking into account deaths as the competing cause (death hazard was obtained from Surveillance, Epidemiology, and End Results 13 Incidence and Mortality, 2000–2002; http://seer.cancer.gov/canques/mortality.html
). We report those risks in . An at-risk individual can directly read her prospective risks from this table, depending on her current age, and use them to make clinical decisions such as those regarding prophylactic surgeries.
Predicted Mean Cancer Risk to Currently Unaffected BRCA1/2 Mutation Carriers