Validity of models for predicting BRCA1 and BRCA2 mutations

Giovanni Parmigiani, Sining Chen, Edwin S. Iversen, Tara M. Friebel, Dianne M. Finkelstein, Hoda Anton-Culver, Argyrios Ziogas, Barbara L. Weber, Andrea Eisen, Kathleen E. Malone, Janet R. Daling, Li Hsu, Elaine A. Ostrander, Leif E. Peterson, Joellen M. Schildkraut, Claudine Isaacs, Camille Corio, Leoni Leondaridis, Gail Tomlinson, Christopher I. AmosLouise C. Strong, Donald A. Berry, Jeffrey N. Weitzel, Sharon Sand, Debra Dutson, Rich Kerber, Beth N. Peshkin, David M. Euhus

Research output: Contribution to journalArticle

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Abstract

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.

Original languageEnglish (US)
Pages (from-to)441-450
Number of pages10
JournalAnnals of Internal Medicine
Volume147
Issue number7
StatePublished - Oct 2 2007
Externally publishedYes

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Mutation
Genetic Counseling
BRCA2 Gene
Population
BRCA1 Gene
National Cancer Institute (U.S.)
Validation Studies
Ovarian Neoplasms
Multicenter Studies
Cross-Sectional Studies
Breast Neoplasms
Sensitivity and Specificity
Research
Neoplasms

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Parmigiani, G., Chen, S., Iversen, E. S., Friebel, T. M., Finkelstein, D. M., Anton-Culver, H., ... Euhus, D. M. (2007). Validity of models for predicting BRCA1 and BRCA2 mutations. Annals of Internal Medicine, 147(7), 441-450.

Validity of models for predicting BRCA1 and BRCA2 mutations. / Parmigiani, Giovanni; Chen, Sining; Iversen, Edwin S.; Friebel, Tara M.; Finkelstein, Dianne M.; Anton-Culver, Hoda; Ziogas, Argyrios; Weber, Barbara L.; Eisen, Andrea; Malone, Kathleen E.; Daling, Janet R.; Hsu, Li; Ostrander, Elaine A.; Peterson, Leif E.; Schildkraut, Joellen M.; Isaacs, Claudine; Corio, Camille; Leondaridis, Leoni; Tomlinson, Gail; Amos, Christopher I.; Strong, Louise C.; Berry, Donald A.; Weitzel, Jeffrey N.; Sand, Sharon; Dutson, Debra; Kerber, Rich; Peshkin, Beth N.; Euhus, David M.

In: Annals of Internal Medicine, Vol. 147, No. 7, 02.10.2007, p. 441-450.

Research output: Contribution to journalArticle

Parmigiani, G, Chen, S, Iversen, ES, Friebel, TM, Finkelstein, DM, Anton-Culver, H, Ziogas, A, Weber, BL, Eisen, A, Malone, KE, Daling, JR, Hsu, L, Ostrander, EA, Peterson, LE, Schildkraut, JM, Isaacs, C, Corio, C, Leondaridis, L, Tomlinson, G, Amos, CI, Strong, LC, Berry, DA, Weitzel, JN, Sand, S, Dutson, D, Kerber, R, Peshkin, BN & Euhus, DM 2007, 'Validity of models for predicting BRCA1 and BRCA2 mutations', Annals of Internal Medicine, vol. 147, no. 7, pp. 441-450.
Parmigiani G, Chen S, Iversen ES, Friebel TM, Finkelstein DM, Anton-Culver H et al. Validity of models for predicting BRCA1 and BRCA2 mutations. Annals of Internal Medicine. 2007 Oct 2;147(7):441-450.
Parmigiani, Giovanni ; Chen, Sining ; Iversen, Edwin S. ; Friebel, Tara M. ; Finkelstein, Dianne M. ; Anton-Culver, Hoda ; Ziogas, Argyrios ; Weber, Barbara L. ; Eisen, Andrea ; Malone, Kathleen E. ; Daling, Janet R. ; Hsu, Li ; Ostrander, Elaine A. ; Peterson, Leif E. ; Schildkraut, Joellen M. ; Isaacs, Claudine ; Corio, Camille ; Leondaridis, Leoni ; Tomlinson, Gail ; Amos, Christopher I. ; Strong, Louise C. ; Berry, Donald A. ; Weitzel, Jeffrey N. ; Sand, Sharon ; Dutson, Debra ; Kerber, Rich ; Peshkin, Beth N. ; Euhus, David M. / Validity of models for predicting BRCA1 and BRCA2 mutations. In: Annals of Internal Medicine. 2007 ; Vol. 147, No. 7. pp. 441-450.
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abstract = "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.",
author = "Giovanni Parmigiani and Sining Chen and Iversen, {Edwin S.} and Friebel, {Tara M.} and Finkelstein, {Dianne M.} and Hoda Anton-Culver and Argyrios Ziogas and Weber, {Barbara L.} and Andrea Eisen and Malone, {Kathleen E.} and Daling, {Janet R.} and Li Hsu and Ostrander, {Elaine A.} and Peterson, {Leif E.} and Schildkraut, {Joellen M.} and Claudine Isaacs and Camille Corio and Leoni Leondaridis and Gail Tomlinson and Amos, {Christopher I.} and Strong, {Louise C.} and Berry, {Donald A.} and Weitzel, {Jeffrey N.} and Sharon Sand and Debra Dutson and Rich Kerber and Peshkin, {Beth N.} and Euhus, {David M.}",
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T1 - Validity of models for predicting BRCA1 and BRCA2 mutations

AU - Parmigiani, Giovanni

AU - Chen, Sining

AU - Iversen, Edwin S.

AU - Friebel, Tara M.

AU - Finkelstein, Dianne M.

AU - Anton-Culver, Hoda

AU - Ziogas, Argyrios

AU - Weber, Barbara L.

AU - Eisen, Andrea

AU - Malone, Kathleen E.

AU - Daling, Janet R.

AU - Hsu, Li

AU - Ostrander, Elaine A.

AU - Peterson, Leif E.

AU - Schildkraut, Joellen M.

AU - Isaacs, Claudine

AU - Corio, Camille

AU - Leondaridis, Leoni

AU - Tomlinson, Gail

AU - Amos, Christopher I.

AU - Strong, Louise C.

AU - Berry, Donald A.

AU - Weitzel, Jeffrey N.

AU - Sand, Sharon

AU - Dutson, Debra

AU - Kerber, Rich

AU - Peshkin, Beth N.

AU - Euhus, David M.

PY - 2007/10/2

Y1 - 2007/10/2

N2 - 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.

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