Predicting BRCA1 and BRCA2 gene mutation carriers: comparison of PENN II model to previous study

Fam Cancer. 2010 Dec;9(4):495-502. doi: 10.1007/s10689-010-9348-3.

Abstract

A number of models have been developed to predict the probability that a person carries a detectable germline mutation in the BRCA1 or BRCA2 genes. Their relative performance in a clinical setting is variable. To compare the performance characteristics of a web-based BRCA1/BRCA2 gene mutation prediction model: the PENNII model ( www.afcri.upenn.edu/itacc/penn2 ), with studies done previously at our institution using four other models including LAMBDA, BRCAPRO, modified PENNI (Couch) tables, and Myriad II tables collated by Myriad Genetics Laboratories. Proband and family cancer history data were analyzed from 285 probands from unique families (27 Ashkenazi Jewish; 277 female) seen for genetic risk assessment in a multispecialty tertiary care group practice. All probands had clinical testing for BR.CA1 and BRCA2 mutations conducted in the same single commercial laboratory. The performance for PENNII results were assessed by the area under the receiver operating characteristic curve (AUC) of sensitivity versus 1-specificity, as a measure of ranking. The AUCs of the PENNII model were higher for predicting BRCA1 than for BRCA2 (81 versus 72%). The overall AUC was 78.7%. PENN II model for BRCA1/2 prediction performed well in this population with higher AUC compared with our experience using four other models. The ease of use of the PENNII model is compatible with busy clinical practices.

Publication types

  • Comparative Study

MeSH terms

  • Adult
  • Area Under Curve
  • BRCA1 Protein / genetics*
  • BRCA2 Protein / genetics*
  • Breast Neoplasms / diagnosis
  • Breast Neoplasms / epidemiology
  • Breast Neoplasms / genetics*
  • Computer Simulation
  • Female
  • Genetic Predisposition to Disease
  • Germ-Line Mutation / genetics*
  • Heterozygote
  • Humans
  • Jews / genetics
  • Middle Aged
  • Models, Statistical*
  • Ovarian Neoplasms / diagnosis
  • Ovarian Neoplasms / epidemiology
  • Ovarian Neoplasms / genetics*
  • Risk Factors
  • Sensitivity and Specificity
  • Software

Substances

  • BRCA1 Protein
  • BRCA2 Protein