Assessment of individuals with BRCA1 and BRCA2 large rearrangements in high-risk breast and ovarian cancer families.

Published

Journal Article

BRCA1/2 large rearrangement (LR) testing has been available to patients since 2006. Three existing models commonly used in cancer genetics clinical and research settings (BRCAPRO, Penn II and Myriad II) have not been assessed for their performance in predicting the presence of BRCA1/2 large genomic rearrangements in patients who do not have mutations detectable by the traditional Sanger sequencing approach. This study sought to determine if there is an optimal pre-test probability "cut off" value, calculated using these models, to optimize detection of large rearrangements (LRs). Our cohort consisted of 3,301 probands seen for genetic counseling and BRCA1/2 clinical testing from September 2006 to September 2011. A detailed personal and three-generation family history, including self-reported ethnicity, was taken as part of our standard clinical practice. We applied the BRCAPRO, Penn II, and Myriad II models to the probands with LRs. In our cohort of 3,301 probands, 150 carried a non-Ashkenazi mutation in BRCA1 or BRCA2. Seventeen unrelated probands carried a private BRCA1/2 LR (17/150, 11.3 % of all detectable non-AJ mutations). At a pre-test probability cutoff of 10 %, all three empiric risk models would have failed to identify almost 30 % of probands with LRs. Our study shows that BRCA1/2 LR testing should be offered to all women who meet criteria for BRCA1/2 sequence analysis.

Full Text

Duke Authors

Cited Authors

  • Arnold, AG; Otegbeye, E; Fleischut, MH; Glogowski, EA; Siegel, B; Boyar, SR; Salo-Mullen, E; Amoroso, K; Sheehan, M; Berliner, JL; Stadler, ZK; Kauff, ND; Offit, K; Robson, ME; Zhang, L

Published Date

  • June 2014

Published In

Volume / Issue

  • 145 / 3

Start / End Page

  • 625 - 634

PubMed ID

  • 24825132

Pubmed Central ID

  • 24825132

Electronic International Standard Serial Number (EISSN)

  • 1573-7217

International Standard Serial Number (ISSN)

  • 0167-6806

Digital Object Identifier (DOI)

  • 10.1007/s10549-014-2987-6

Language

  • eng