PeerJ | 2019

Next-generation sequencing of BRCA1 and BRCA2 genes for rapid detection of germline mutations in hereditary breast/ovarian cancer

 
 
 
 
 
 
 
 
 
 
 
 
 

Abstract


Background Conventional methods used to identify BRCA1 and BRCA2 germline mutations in hereditary cancers, such as Sanger sequencing/multiplex ligation-dependent probe amplification (MLPA), are time-consuming and expensive, due to the large size of the genes. The recent introduction of next-generation sequencing (NGS) benchtop platforms offered a powerful alternative for mutation detection, dramatically improving the speed and the efficiency of DNA testing. Here we tested the performance of the Ion Torrent PGM platform with the Ion AmpliSeq BRCA1 and BRCA2 Panel in our clinical routine of breast/ovarian hereditary cancer syndrome assessment. Methods We first tested the NGS approach in a cohort of 11 patients (training set) who had previously undergone genetic diagnosis in our laboratory by conventional methods. Then, we applied the optimized pipeline to the consecutive cohort of 136 uncharacterized probands (validation set). Results By minimal adjustments in the analytical pipeline of Torrent Suite Software we obtained a 100% concordance with Sanger results regarding the identification of single nucleotide alterations, insertions, and deletions with the exception of three large genomic rearrangements (LGRs) contained in the training set. The optimized pipeline applied to the validation set (VS), identified pathogenic and polymorphic variants, including a novel BRCA2 pathogenic variant at exon 3, 100% of which were confirmed by Sanger in their correct zygosity status. To identify LGRs, all negative samples of the VS were subjected to MLPA analysis. Discussion Our experience strongly supports that the Ion Torrent PGM technology in BRCA1 and BRCA2 germline variant identification, combined with MLPA analysis, is highly sensitive, easy to use, faster, and cheaper than traditional (Sanger sequencing/MLPA) approaches.

Volume 7
Pages None
DOI 10.7717/peerj.6661
Language English
Journal PeerJ

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