CORRESPONDENCE

RESPONSE: Re: On the Use of Familial Aggregation in Population-Based Case Probands for Calculating Penetrance

Colin B. Begg

Correspondence to: Colin B. Begg, Ph.D., Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 1275 York Ave., New York, NY 10021 (e-mail: beggc{at}mskcc.org).

I am grateful to the correspondents for their interesting and insightful comments. Amos addresses two issues. First, he laments the fact that ascertainment corrections have not been widely used. However, available ascertainment adjustment techniques may have poor statistical properties in the context of risk heterogeneity. Standard ascertainment corrections use a conditional likelihood in which data from individual pedigrees are, in effect, weighted inversely by their probabilities of ascertainment. To be valid, these weights need to accurately reflect the impact of the unknown contributors to interindividual risk variation, but the information for making these inferences is contained in the patterns of breast cancer occurrence in the relatively few families with more than one occurrence of the disease. In the population-based setting, the preponderance of case probands will have no first-degree relatives with breast cancer and only a small proportion will have two or more (1). Any resulting inferences about the contributions of the unknown factors to the risk distribution may be highly model dependent, especially in the context of the small samples available for studying BRCA mutations, notwithstanding the encouraging simulation results reported recently by Epstein et al. (2) in a setting in which the model is fully specified and assumed known. Indeed, studies using high-risk families have consistently produced penetrance estimates that are much higher than those from the population-based studies, even with corrections for ascertainment. This fact provides empirical evidence of the inadequacy of available methods for ascertainment correction. More research is certainly needed on this topic.

Amos's second point concerns errors in the reporting of family history of breast cancer. There is no question that we should be concerned about this issue, and efforts to verify the accuracy of reported information can only improve the validity of these kinds of studies. However, I am a little confused by his argument that misclassification errors must lead to a downward bias. He argues that overreporting can be effectively ignored because the false-positive rate is low. However, the vast majority (>90%) of family members are typically unaffected, and so the impact of false-positive errors on the total frequency of errors is proportionally larger than the impact of false-negative errors. Even if we assume that the false-positive rate is zero, the underestimation of penetrance in published population-based studies based on the false-negative rates quoted is only a small fraction of their discrepancy with the penetrances reported in the studies of high-risk families.

Risch and Narod argue that there is likely to be little bias in studies that use probands with ovarian cancer rather than breast cancer, such as in their study. They may well be correct. The relevant questions in this case are: 1) To what extent do breast and ovarian cancer cosegregate? and 2) Can mutations in BRCA1 and BRCA2 explain all of this association? Risch and Narod refer to the fact that the overall risk of breast cancer in the kindred of case probands with ovarian cancer in their own study was similar to Surveillance, Epidemiology, and End Results Program (SEER)1 rates of breast cancer. This observation seems a little surprising. Insight into the presence of common risk factors can also be gleaned by studying rates of second primary cancers (3). If we make use of data from SEER and eliminate all occurrences of second primary cancers up to 1 year after diagnosis of the first primary cancer, the standardized incidence ratio of breast cancer in women with a prior ovarian cancer is 1.5 (95% confidence interval [CI] = 1.3 to 1.6). Conversely, the standardized incidence ratio of ovarian cancer after breast cancer is similar, at 1.6 (95% CI = 1.5 to 1.7). As indicated in the "Discussion" section of my article, these estimates are in the middle of the range that we would expect to be induced solely by BRCA1/BRCA2 mutations if the population prevalence of mutation is 0.1% and the (common) relative risks for breast and ovarian cancer are in the range of 10–20. Thus, on the basis of these admittedly highly approximate guesstimates, and recognizing that the impact of treatment of the first primary cancer may perturb these rates, the cosegregation of breast and ovarian cancer is plausibly explained by the common influence of BRCA1 and BRCA2 mutations.

Risch and Narod also make the interesting suggestion of adapting the kin–cohort methodology by anchoring the baseline incidence rates on the population rates rather than the rates observed in the kin–cohort and by using the observed data only to estimate the relative risks. They also cite evidence of penetrance heterogeneity between specific BRCA1 and BRCA2 mutations, an issue also mentioned by Pharoah et al.

Whittemore and Gong argue that the likely magnitude of the bias caused by risk heterogeneity is relatively small. They use as evidence hypothetical calculations of the bias induced by specific postulated degrees of risk variation. I have a minor quibble with them in that they calculate the bias in relatives rather than in probands. Although the kin–cohort calculations involve calculating the incidence in relatives, the penetrance estimators, including those of Wacholder et al. (4), adjust back to the probands, and so I believe that, for example, the bias in the third row of their Table 2 should more properly be reported as 7% rather than 3.5%. My more important concern is with their conclusion that "multiple-case families yield considerably more precise penetrance estimates than . . . population-based studies." Their logic is based on two arguments: the presumed low magnitude of bias caused by heterogeneity and the increased statistical power resulting from the higher numbers of events (i.e., breast cancer occurrences) in multiple-case families. Studies of high-risk families, or indeed any collection of families selectively obtained on the basis of observed outcomes, require correction for ascertainment bias, as indicated in the comments of Amos. That is, they must account for the fact that the occurrence of the multiple cases is the reason they appear in the statistical sample in the first place. If this correction is accomplished with full success and if there is no risk heterogeneity in gene carriers, then the penetrance estimates from high-risk families should be the same as those from population-based studies. In fact, the population-based studies have consistently demonstrated much lower estimates. I do not believe that wide confidence intervals are the reason for this disparity. It is caused by risk heterogeneity, by the failure of the statistical methods used to adequately correct for ascertainment, or by both. I suspect both.

Finally, I have only one comment on the interesting remarks of Pharoah et al. They state that virtually "all genetic testing is conducted on women in families with multiple cases of the disease . . ." or others that have evidence of a high-risk phenotype. Although this observation may be largely true at the present time, the rapid development of the technology for genetic testing and its promotion by commercial interests may well result in its much broader application in the foreseeable future.

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. Back

REFERENCES

1 Collaborative Group on Hormonal Factors in Breast Cancer. Familial breast cancer: collaborative reanalysis of individual data from 52 epidemiological studies including 58,209 women with breast cancer and 101,986 women without the disease. Lancet 2001;358:1389–99.[CrossRef][Medline]

2 Epstein MP, Lin X, Boehnke M. Ascertainment-adjusted parameter estimates revisited. Am J Hum Genet 2002;70:886–95.[Medline]

3 Begg CB, Zhang ZF, Sun M, Herr HW, Schantz SP. Methodology for evaluating the incidence of second primary cancers with application to smoking-related cancers from the Surveillance Epidemiology and End Results (SEER) Program. Am J Epidemiol 1995;142:653–65.[Abstract]

4 Wacholder S, Hartge P, Struewing JP, Pee D, McAdams M, Brody L, et al. The kin-cohort study for estimating penetrance. Am J Epidemiol 1998;148:623–30.[Abstract]



             
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