title: Assessing the added value of breast tumor markers in genetic risk prediction model BRCAPRO.
abstract: The BRCAPRO model estimates carrier probabilities for the BRCA1 and BRCA2 genes, and was recently enhanced to use estrogen receptor (ER) and progesterone receptor (PR) status of breast cancer. No independent assessment of the added value of these markers exists. Moreover, earlier versions of BRCAPRO did not use human epidermal growth factor receptor 2 (Her-2/neu) status of breast cancer. Here, we incorporate Her-2/neu in BRCAPRO and validate all the markers. We trained the enhanced model on 406 germline tested individuals, and validated on a separate clinical cohort of 796 individuals for whom test results and family history are available. For model-building, we estimated joint probabilities of ER, PR, and Her-2/neu status for carriers and non-carriers of BRCA1/2 mutations. For validation, we obtained BRCAPRO predictions with and without markers. We calculated area under the receiver operating characteristic curve (AUC), sensitivity, specificity, predictive values, and correct reclassification rates. The AUC for predicting BRCA1 status among individuals who are carriers of at least one mutation improved when ER and PR were used. The AUC for predicting the presence of either mutation improved when Her-2/neu was added. Use of markers also produced highly significant correct reclassification improvements in both cases. Breast tumor markers are useful for prediction of BRCA1/2 mutation status. ER and PR improve discrimination between BRCA1 and BRCA2 mutation carriers while Her-2/neu helps discriminate between carriers and non-carriers, particularly among women who are ER positive and Her-2/neu negative. These results support the use of the enhanced version of BRCAPRO in clinical settings.
authors.affiliation: Department of Biostatistics, School of Public Health, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX 76107-2699, USA. swati.biswas@unthsc.edu
authors.forename: Swati
authors.initials: S
authors.lastname: Biswas
authors.forename: Neelam
authors.initials: N
authors.lastname: Tankhiwale
authors.forename: Amanda
authors.initials: A
authors.lastname: Blackford
authors.forename: Angelica M Gutierrez
authors.initials: AM
authors.lastname: Barrera
authors.forename: Kaylene
authors.initials: K
authors.lastname: Ready
authors.forename: Karen
authors.initials: K
authors.lastname: Lu
authors.forename: Christopher I
authors.initials: CI
authors.lastname: Amos
authors.forename: Giovanni
authors.initials: G
authors.lastname: Parmigiani
authors.forename: Banu
authors.initials: B
authors.lastname: Arun
mesh_terms: D000328:Adult; D019540:Area Under Curve; D019313:BRCA1 Protein / Q000235:genetics; D024682:BRCA2 Protein / Q000235:genetics; D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000378:metabolism*; D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism*; D018567:Breast Neoplasms, Male / Q000235:genetics / Q000378:metabolism; D005260:Female; D006579:Heterozygote; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008957:Models, Genetic; D015999:Multivariate Analysis; D010051:Ovarian Neoplasms / Q000235:genetics; D010375:Pedigree; D012372:ROC Curve; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism*; D011960:Receptors, Estrogen / Q000378:metabolism*; D011980:Receptors, Progesterone / Q000378:metabolism*; D018570:Risk Assessment; D012307:Risk Factors
chemical_list: D019313:BRCA1 Protein; D024682:BRCA2 Protein; D014408:Biomarkers, Tumor; D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone; C492913:BRCA1 protein, human; C551750:BRCA2 protein, human; C508053:ERBB2 protein, human