title: Pretest prediction of BRCA1 or BRCA2 mutation by risk counselors and the computer model BRCAPRO.
abstract: BACKGROUND
  Because BRCA gene mutation testing is costly, occasionally uninformative, and frequently associated with ethical and legal issues, careful patient selection is required prior to testing. Estimation of BRCA gene mutation probability is an important component of pretest counseling, but the accuracy of these estimates is currently unknown. We measured the performance of eight cancer risk counselors and of a computer model, BRCAPRO, at identifying families likely to carry a BRCA gene mutation.
  
  
  METHODS
  Eight cancer risk counselors and the computer model BRCAPRO estimated BRCA gene mutation probabilities for 148 pedigrees selected from an initial sample of 272 pedigrees. The final sample was limited to pedigrees with a proband affected by breast or ovarian cancer and BRCA1 and BRCA2 gene sequencing results unequivocally reported as negative or positive for a deleterious mutation. Sensitivity, specificity, negative predictive value, positive predictive value, and areas under receiver operator characteristics (ROC) curves were calculated for each risk counselor and for BRCAPRO. All statistical tests were two sided.
  
  
  RESULTS
  Using a greater-than-10% BRCA gene mutation probability threshold, the median sensitivity for identifying mutation carriers was 94% (range = 81% to 98%) for the eight risk counselors and 92% (range = 91% to 92%) for BRCAPRO. Median specificity at this threshold was 16% (range = 6% to 34%) for the risk counselors and 32% (range = 30% to 34%) for BRCAPRO (P =.04). Median area under the ROC curves was 0.671 for the risk counselors (range = 0.620 to 0.717) and 0.712 (range = 0.706 to 0.720) for BRCAPRO (P =.04). There was a slight, but not statistically significant, improvement in all counselor performance measures when BRCAPRO-assigned gene mutation probability information was included with the pedigrees.
  
  
  CONCLUSIONS
  Sensitivity for identifying BRCA gene mutation carriers is similar for experienced risk counselors and the computer model BRCAPRO. Because the computer model consistently demonstrated superior specificity, overall discrimination between BRCA gene mutation carriers and BRCA gene mutation noncarriers was slightly better for BRCAPRO.
authors.affiliation: The University of Texas Southwestern Medical Center at Dallas, 75390-9155, USA. david.euhus@UTSouthwestern.edu
authors.forename: David M
authors.initials: DM
authors.lastname: Euhus
authors.forename: Kristin C
authors.initials: KC
authors.lastname: Smith
authors.forename: Linda
authors.initials: L
authors.lastname: Robinson
authors.forename: Amy
authors.initials: A
authors.lastname: Stucky
authors.forename: Olufunmilayo I
authors.initials: OI
authors.lastname: Olopade
authors.forename: Shelly
authors.initials: S
authors.lastname: Cummings
authors.forename: Judy E
authors.initials: JE
authors.lastname: Garber
authors.forename: Anu
authors.initials: A
authors.lastname: Chittenden
authors.forename: Gordon B
authors.initials: GB
authors.lastname: Mills
authors.forename: Paula
authors.initials: P
authors.lastname: Rieger
authors.forename: Laura
authors.initials: L
authors.lastname: Esserman
authors.forename: Beth
authors.initials: B
authors.lastname: Crawford
authors.forename: Kevin S
authors.initials: KS
authors.lastname: Hughes
authors.forename: Connie A
authors.initials: CA
authors.lastname: Roche
authors.forename: Patricia A
authors.initials: PA
authors.lastname: Ganz
authors.forename: Joyce
authors.initials: J
authors.lastname: Seldon
authors.forename: Carol J
authors.initials: CJ
authors.lastname: Fabian
authors.forename: Jennifer
authors.initials: J
authors.lastname: Klemp
authors.forename: Gail
authors.initials: G
authors.lastname: Tomlinson
mesh_terms: D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics*; D003198:Computer Simulation; D005260:Female; D019398:Genes, BRCA1; D024522:Genes, BRCA2; D005817:Genetic Counseling / Q000379:methods*; D020022:Genetic Predisposition to Disease; D005820:Genetic Testing / Q000191:economics / Q000379:methods*; D006579:Heterozygote; D006801:Humans; D008297:Male; D009154:Mutation / Q000235:genetics*; D010051:Ovarian Neoplasms / Q000175:diagnosis / Q000235:genetics; D010375:Pedigree; D011237:Predictive Value of Tests; D011336:Probability; D012680:Sensitivity and Specificity