Validity of Models for Predicting BRCA1 and BRCA2 Mutations

2 October 2007 | Volume 147 Issue 7 | Pages 441-450

Background: Deleterious mutations of the BRCA1 and BRCA2 genes confer susceptibility to breast and ovarian cancer. At least 7 models for estimating the probabilities of having a mutation are used widely in clinical and scientific activities; however, the merits and limitations of these models are not fully understood.

Objective: To systematically quantify the accuracy of the following publicly available models to predict mutation carrier status: BRCAPRO, family history assessment tool, Finnish, Myriad, National Cancer Institute, University of Pennsylvania, and Yale University.

Design: Cross-sectional validation study, using model predictions and BRCA1 or BRCA2 mutation status of patients different from those used to develop the models.

Setting: Multicenter study across Cancer Genetics Network participating centers.

Patients: 3 population-based samples of participants in research studies and 8 samples from genetic counseling clinics.

Measurements: Discrimination between individuals testing positive for a mutation in BRCA1 or BRCA2 from those testing negative, as measured by the c-statistic, and sensitivity and specificity of model predictions.

Results: The 7 models differ in their predictions. The better-performing models have a c-statistic around 80%. BRCAPRO has the largest c-statistic overall and in all but 2 patient subgroups, although the margin over other models is narrow in many strata. Outside of high-risk populations, all models have high false-negative and false-positive rates across a range of probability thresholds used to refer for mutation testing.

Limitation: Three recently published models were not included.

Conclusions: All models identify women who probably carry a deleterious mutation of BRCA1 or BRCA2 with adequate discrimination to support individualized genetic counseling, although discrimination varies across models and populations.

Editors' Notes Context A computer software program from the University of Texas Southwestern Medical Center (CaGene) has multiple prediction models to estimate BRCA1 and BRCA2 mutation probability, which guides decisions on whether to test for a mutation. A comprehensive quantitative evaluation of how well the prediction models discriminate between persons who carry the mutation and those who do not is lacking. Contribution The authors used 7 prediction models to estimate the probability of a BRCA1/BRCA2 mutation in 3342 families. All 7 models were able to discriminate between mutation-positive and mutation-negative people. Implications The probability that someone carries the BRCA1/BRCA2 mutation should not be considered in isolation when decisions are being made about genetic testing. Other factors should be discussed with the patient and factored into the decision-making process. —The Editors

 

Author and Article Information

From Johns Hopkins University, Baltimore, Maryland; Center for Clinical Epidemiology and Biostatistics and Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania; Duke University, Durham, North Carolina; Massachusetts General Hospital, Boston, Massachusetts; University of California, Irvine, Irvine, California; University of Toronto, Toronto, Ontario, Canada; Fred Hutchinson Cancer Research Center, Seattle, Washington; Baylor College of Medicine and University of Texas M.D. Anderson Cancer Center, Houston, Texas; Lombardi Cancer Center, Georgetown University; University of Texas Southwestern Medical Center, Dallas, Texas; Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah; and National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland.

Acknowledgments: Data were collected within the framework of the NCI's Cancer Genetics Network, combining data previously collected at each Cancer Genetics Network center and at City of Hope National Medical Center. Most data predated Cancer Genetics Network activities, but the Cancer Genetics Network provided the venue for the pooled analysis. The authors thank Connie Griffin for her Cancer Genetics Network leadership at the Johns Hopkins University, Kelly Qu for support with database management at Johns Hopkins University, Jihong Zong for collecting and transmitting data at M.D. Anderson Cancer Center, and Neil Malloy for assistance with data coordination at Massachusetts General Hospital.

Grant Support: In part by the NCI Cancer Genetics Network. Work of the Cancer Genetics Network Statistical Coordinating Center was supported by National Cancer Institute grant CA78284. Work of Drs. Parmigiani and Chen and Ms. Friebel was also supported in part by National Cancer Institute grants P50CA88843, P50CA62924-05, and 5P30 CA06973-39, R01CA105090-01A1; National Institutes of Health grant HL 99-024; and the Hecht Fund. Work of investigators at the Fred Hutchinson Cancer Research Center was supported in part by National Institutes of Health grants R01 CA 36397, R01 CA 63705, and K05 CA-90754. The work of Dr. Weitzel and Ms. Sand was supported in part by California Cancer Research Program of the University of California (grant no. 99-86874) and in part by a General Clinical Research Center grant from National Institutes of Health (M01 RR00043) awarded to the City of Hope National Medical Center. Data from Georgetown University were provided by the Familial Cancer Registry Shared Resource of Lombardi Comprehensive Cancer Center, which is supported in part by the National Institutes of Health (grant P30-CA-51008).

Potential Financial Conflicts of Interest: Employment: B.L. Weber (GlaxoSmithKline). Stock ownership or options (other than mutual funds): B.L. Weber (GlaxoSmithKline).

Requests for Single Reprints: Giovanni Parmigiani, PhD, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, 550 North Broadway, Suite 1103, Baltimore, MD 21205-2011; e-mail, gp{at}jhu.edu.

Current Author Addresses: Dr. Parmigiani: The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, 550 North Broadway, Suite 1103, Baltimore, MD 21205-2011.

Dr. Chen: Department of Environmental Health Sciences, Johns Hopkins School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205.

Dr. Iversen: Department of Statistical Sciences, Duke University, Box 90251, Durham, NC 27708.

Ms. Friebel: Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, 909 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021.

Dr. Finkelstein: Biostatistics Center, Massachusetts General Hospital, 50 Staniford Street, Suite 560, Boston, MA 02114.

Drs. Anton-Culver and Ziogas: University of California, 224 IH, Mail Code 7550, Irvine, CA 92697-7550.

Dr. Weber: GlaxoSmithKline, 2301 Renaissance Boulevard, Building 510, Mailcode RN0510, King of Prussia, PA 19406-2772.

Dr. Eisen: Department of Medicine, University of Toronto, Suite RFE 3-805, 190 Elizabeth Street, Toronto, Ontario M5G 2C4, Canada.

Drs. Malone, Daling, and Hsu: Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109-1024.

Dr. Ostrander: Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, 50 South Drive, MSC 8000, Building 50, Room 5351, Bethesda, MD 20892-8000.

Dr. Peterson: The Methodist Hospital, 6550 Fannin Street, SM-1299, Houston, TX 77030.

Dr. Schildkraut: Duke University Medical Center, Box 2949, Durham, NC 27510.

Dr. Isaacs, Ms. Corio, Ms. Leondaridis, and Ms. Peshkin: Georgetown University, 2233 Wisconsin Avenue NW, Suite 317, Washington, DC 20007.

Drs. Tomlinson and Euhus: University of Texas Southwestern Medical Center at Dallas, 5323 Harry Hines Boulevard, Dallas, TX 75390-9155.

Drs. Amos, Strong, and Berry: M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Box 189, Houston, TX 77030.

Dr. Weitzel and Ms. Sand: City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91001.

Ms. Dutson and Dr. Kerber: Huntsman Cancer Institute, 2000 Circle of Hope, Salt Lake City, UT 84112-5550.

Author Contributions: Conception and design: G. Parmigiani, D.M. Finkelstein, H. Anton-Culver, B.L. Weber, G. Tomlinson, D.M. Euhus.

Analysis and interpretation of the data: G. Parmigiani, S. Chen, E.S. Iversen Jr., A. Ziogas, B.L. Weber, L.E. Peterson, R. Kerber, B.N. Peshkin, D.M. Euhus.

Drafting of the article: G. Parmigiani, H. Anton-Culver, L.E. Peterson.

Critical revision of the article for important intellectual content: S. Chen, E.S. Iversen Jr., K.E. Malone, J.R. Daling, L. Hsu, E.A. Ostrander, C. Isaacs, C.I. Amos, D.A. Berry, J.N. Weitzel, B.N. Peshkin, D.M. Euhus.

Final approval of the article: G. Parmigiani, S. Chen, E.S. Iversen Jr., D.M. Finkelstein, B.L. Weber, A. Eisen, K.E. Malone, J.R. Daling, E.A. Ostrander, J.M. Schildkraut, C. Isaacs, C. Corio, G. Tomlinson, J.N. Weitzel, B.N. Peshkin.

Provision of study materials or patients: D.M. Finkelstein, B.L. Weber, A. Eisen, K.E. Malone, J.R. Daling, J.M. Schildkraut, C. Isaacs, C. Corio, G. Tomlinson, C.I. Amos, L.C. Strong, J.N. Weitzel, D. Dutson, B.N. Peshkin, D.M. Euhus.

Statistical expertise: G. Parmigiani, S. Chen, E.S. Iversen Jr., A. Ziogas, L. Hsu, L. Leondaridis, C.I. Amos, S. Sand, R. Kerber.

Obtaining of funding: G. Parmigiani, H. Anton-Culver, B.L. Weber, J.R. Daling, E.A. Ostrander, J.M. Schildkraut, L.C. Strong.

Administrative, technical, or logistic support: G. Parmigiani, T.M. Friebel, D.M. Finkelstein, A. Ziogas, B.L. Weber, J.R. Daling, D.A. Berry, J.N. Weitzel, D. Dutson, D.M. Euhus.

Collection and assembly of data: T.M. Friebel, D.M. Finkelstein, H. Anton-Culver, B.L. Weber, K.E. Malone, J.R. Daling, E.A. Ostrander, J.M. Schildkraut, C. Isaacs, C. Corio, L. Leondaridis, G. Tomlinson, L.C. Strong, D.M. Euhus.