CORRESPONDENCE

RESPONSE: Re: Pretest Prediction of BRCA1 or BRCA2 Mutation by Risk Counselors and the Computer Model BRCAPRO

David M. Euhus, Laura Esserman, Patricia A. Ganz, Gordan B. Mills, Gail Tomlinson

Affiliations of authors: D. M. Euhus, G. Tomlinson, The University of Texas Southwestern Medical Center at Dallas; L. Esserman, The University of California, San Francisco; P. A. Ganz, University of California Los Angeles Jonsson Comprehensive Cancer Center, Los Angeles, G. B. Mills, The University of Texas M. D. Anderson Cancer Center, Houston.

Correspondence to: David. M. Euhus, M.D., Division of Surgical Oncology, E6.222, U.T. Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390–9155 (e-mail: david.euhus{at}UTSouthwestern.edu).

The data that Bonadona et al. present in their letter illustrates an important aspect of disease probability prediction that cannot be overemphasized: the predictive value of a test (e.g., the BRCAPRO computer model or the subjective estimate of a risk counselor) is related not only to the sensitivity and specificity of the test but also to the prevalence of the condition of interest (in this case, BRCA gene mutations) being evaluated. Bonadona et al. found that the positive predictive value of BRCAPRO, using the greater than 10% probability threshold was fairly low (25%) when applied to a population-based sample with a low gene mutation prevalence (9.1%). As we emphasized in the "Discussion" section of our article, we evaluated a highly selected sample of women who had already been selected for BRCA gene mutation testing on the basis of a strong family history of breast and/or ovarian cancer. Consequently, the BRCA gene mutation prevalence in our sample (43%) was much higher than that of the population-based sample of Bonadona et al. and, as expected, the positive predictive value of BRCAPRO was also higher (50%).

A look at our receiver operator characteristics curves shows that the overall discrimination between BRCA gene mutation carriers and mutation noncarriers is not outstanding for either BRCAPRO or for the risk counselors, suggesting that performance measures for BRCAPRO and for the risk counselors may depend strongly on the prevalence of BRCA gene mutations in the population being assessed. The data of Bonadona et al. confirm the association between BRCA gene mutation prevalence and the positive predictive value of BRCAPRO. Nevertheless, the 6.1% BRCA gene mutation prevalence for women with a BRCAPRO mutation probability lower than or equal to 10% reported by Bonadona et al. is encouraging. We would emphasize, however, that the lower than or equal to 10% mutation probability threshold is an arbitrary cutoff that is useful for comparing genetic risk counselors and computer models but is not intended as an inflexible criterion for selecting patients for genetic testing. As with any clinical test, the decision to undergo testing must take into account many factors, including the patient’s motivation for testing and the impact that the test result may have on patient management strategies. This reality further emphasizes the need for subjective assessment in addition to mathematical risk estimation for patients considering genetic testing.

Bonadona et al. also report substantial overlap between BRCAPRO mutation probabilities for gene mutation carriers compared with those for mutation noncarriers. This finding is not a useful measure of BRCAPRO performance, however, because one would expect a probability model to generate probability estimates ranging from 0 to 1.0 for any population evaluated. In addition, neither our data nor those of Bonadona et al. are relevant for assessing the accuracy of the BRCA gene mutation penetrance estimates incorporated into BRCAPRO.

Finally, given the current limitations of pretest mutation probability prediction (by computer models or risk counselors), we may be forced to settle for a low positive predictive value as long as we can obtain a reasonable negative predictive value—that is, a low pretest probability estimate provides a measure of reassurance that there really is no mutation. In this regard, the negative predictive value of BRCAPRO in the low-prevalence sample in the study by Bonadona et al. was a respectable 94%, compared with 84% in our high-prevalence sample. More accurate methods for estimating pretest BRCA gene mutation probability would be desirable. Given the current state of the art, however, the value of an experienced genetic risk counselor who understands the limitations of the computer-based probability models cannot be overstated.



             
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