Prioritizing Variants in Complete Hereditary Breast and Ovarian Cancer Genes in Patients Lacking Known BRCA Mutations. Issue 7 (18th March 2016)

• Record Type: Journal Article
• Authors: Caminsky, Natasha G.; Mucaki, Eliseos J.; Perri, Ami M.; Lu, Ruipeng; Knoll, Joan H. M.; Rogan, Peter K.
• Is Part Of: Human mutation. Volume 37:Issue 7(2016)
• Page Start: 640
• Page End: 652
• Publication Date: 2016-03-18
• Subjects: 3′ untranslated region -- breast neoplasm -- ovarian; tumor suppressor -- genetic testing -- information theory -- RNA stability; Human chromosome abnormalities -- Periodicals; Mutation (Biology) -- Periodicals; 616.04205
• DOI: 10.1002/humu.22972
• Languages: English
• ISSNs: 1059-7794

Abstract

Abstract : Sequencing of complete hereditary breast and ovarian cancer genes and flanking regions identified 38, 372 unique variants in 287 patients. Information theory‐based methods were used to predict and prioritize non‐coding variants of uncertain significance affecting transcription, RNA binding and splice site recognition in regulatory, coding, and intronic regions. This approach combined with segregation analysis prioritizes a limited set of variants in each patient for a wide spectrum of functional mutation types. ABSTRACT: BRCA1 and BRCA2 testing for hereditary breast and ovarian cancer (HBOC) does not identify all pathogenic variants. Sequencing of 20 complete genes in HBOC patients with uninformative test results ( N = 287), including noncoding and flanking sequences of ATM, BARD1, BRCA1, BRCA2, CDH1, CHEK2, EPCAM, MLH1, MRE11A, MSH2, MSH6, MUTYH, NBN, PALB2, PMS2, PTEN, RAD51B, STK11, TP53, and XRCC2, identified 38, 372 unique variants. We apply information theory (IT) to predict and prioritize noncoding variants of uncertain significance in regulatory, coding, and intronic regions based on changes in binding sites in these genes. Besides mRNA splicing, IT provides a common framework to evaluate potential affinity changes in transcription factor (TFBSs), splicing regulatory (SRBSs), and RNA‐binding protein (RBBSs) binding sites following mutation. We prioritized variants affecting the strengths of 10 splice sites (four natural, six cryptic), 148 SRBS, 36 TFBS, and 31 RBBS. Three variants were also prioritized based on their predicted effects on mRNA secondary (2°) structure and 17 for pseudoexon activation. Additionally, four frameshift, two in‐frame deletions, and five stop‐gain mutations were identified. When combined with pedigree information, complete gene sequence analysis can focus attention on a limited set of variants in a wide spectrum of functional mutation types for downstream functional and co‐segregation analysis.