Affiliations of authors: H. A. Risch, Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT; S. A. Narod, Centre for Research in Womens Health, Sunnybrook and Womens College Health Sciences Centre, University of Toronto, Toronto, Canada.
Correspondence to: Harvey A. Risch, M.D., Ph.D., Department of Epidemiology and Public Health, Yale University School of Medicine, 60 College St., P.O. Box 208034, New Haven, CT 065208034 (e-mail: Harvey.Risch{at}Yale.edu).
In a recent issue of the Journal (1), Begg describes potential biases in our (2) and other population-based case-screening studies that estimate mutation penetrance through kincohort methods (3). The main issue raised by Begg is that a case series represents individuals selected to have risk factors that place them at excess disease risk. To the extent that any risk factors are overrepresented among case relativesbecause of either genetic or familial reasonsfamily members of both carrier and noncarrier probands will show excess disease incidence, and thus mutation penetrance will be overestimated. We agree with this theoretical argument but question the degree of bias actually present in the published studies of breast and ovarian cancer and mutations in BRCA1 or BRCA2 to which Begg refers (1). In particular, our penetrance estimates of breast cancer based on an ovarian cancer case series are unlikely to be biased (2).
Disease risk to a given age among carrier and noncarrier relatives is found by treating the relatives as a cohort and performing a Cox regression analysis on it, with age at diagnosis, death, or end of follow-up as the time variable and with proband mutation status as the exposure. This method yields an estimate of the relative risk (RR) of disease among relatives associated with proband mutation status, as well as a productlimit estimate of the survivor function for the noncarrier relatives (S0). The estimated mutation penetrance is 1 + S0 2(S0)RR. This method is robust in the usual circumstances of one (or sometimes two) affected first-degree relatives per family, but generalized estimating equation methods can also be used. As Begg notes (1), the estimated RR is not subject to the bias; only S0 is subject to bias. In fact, 1 + S0 2(S0)RR estimates the penetrance for any similar base population to which the RR would apply. Therefore, for studies such as ours (2) that use this method, and in the very usual circumstances where mutation frequency is low in the population, the observed RR values can certainly be used with S0 values taken from published information, such as Surveillance, Epidemiology, and End Results Program (SEER)1 cancer incidence tables (4), to calculate unbiased penetrance estimates for general populations.
Are risk factors overrepresented among case relatives? Empirically, breast cancer risks in relatives of populationbased samples of case patients with ovarian cancer do not differ from risks in the general population. Our study observed a risk of breast cancer to age 70 years of 7.2% among first-degree relatives of ovarian cancer proband case patients not carrying BRCA1 or BRCA2 mutations and of 8.4% for all first-degree relatives (2). These frequencies are comparable to the 8%9% figure seen in recent SEER data (4). None of the dozen cancer sites we examined had risks to family members that differed appreciably or statistically significantly from population risks (2,4). This fact suggests that risk factors (separate from BRCA1 and BRCA2 mutations) that might predispose to breast or other cancers are not overrepresented in patients with ovarian cancer (or that these risk factors do not cluster within families) and that our values for S0 and penetrance are unbiased.
Begg identifies another potential bias in penetrance estimates, caused by associations between mutation status and genetic or familial risk factors. Potential confounding of RR estimates can happen in all observational studies, not just in those that estimate penetrance. At present, there is little evidence for confounding in mutation RR estimates for breast cancer in relatives of patients with ovarian cancer. Ovarian and breast cancer essentially do not share nongenetic risk factors of any importance that can contribute appreciably to familial clustering (5). Some evidence for modification of BRCA1 and BRCA2 risks by polymorphic variation in other genes has been observed for breast cancer, but noneor if anything, an opposite patternhas been observed for ovarian cancer (5).
Penetrance heterogeneity of specific BRCA1 and BRCA2 mutations is likely and would also constitute a heritable modifier of the risk for breast cancer. We observed a greater than fivefold increase in BRCA1-associated breast cancer risk for mutations in the 5' end of the gene compared with those in the 3' end of the gene (2). Begg (1) cited our null results for breast cancer associated with BRCA2 mutations overall, although we reported that penetrance was statistically significantly elevated for BRCA2 mutations outside the "ovarian-cancer cluster region" (nucleotides 40756503), about 50%, comparable to the penetrance of BRCA1 mutations as a whole (2). Other investigators have also found appreciable penetrance heterogeneity for specific BRCA1 and BRCA2 mutations (6,7). Penetrance of BRCA1 and BRCA2 mutations for ovarian cancer, however, does not show any of the same heterogeneity as for breast cancer (2). Thus, case probands with ovarian cancer are unlikely to overrepresent specific BRCA1 or BRCA2 mutations that could be associated with an excess risk of breast cancer.
NOTES
1 Editor's note: SEER is a set of geographically defined, population-based, central cancer registries in the United States, operated by local nonprofit organizations under contract to the National Cancer Institute (NCI). Registry data are submitted electronically without personal identifiers to the NCI on a biannual basis, and the NCI makes the data available to the public for scientific research.
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