A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer

Abstract

BackgroundSequencing of both healthy and disease singletons yields many novel and low frequency variants of uncertain significance (VUS). Complete gene and genome sequencing by next generation sequencing (NGS) significantly increases the number of VUS detected. While prior studies have emphasized protein coding variants, non-coding sequence variants have also been proven to significantly contribute to high penetrance disorders, such as hereditary breast and ovarian cancer (HBOC). We present a strategy for analyzing different functional classes of non-coding variants based on information theory (IT) and prioritizing patients with large intragenic deletions.MethodsWe captured and enriched for coding and non-coding variants in genes known to harbor mutations that increase HBOC risk. Custom oligonucleotide baits spanning the complete coding, non-coding, and intergenic regions 10 kb up- and downstream of ATM, BRCA1, BRCA2, CDH1, CHEK2, PALB2, and TP53 were synthesized for solution hybridization enrichment. Unique and divergent repetitive sequences were sequenced in 102 high-risk, anonymized patients without identified mutations in BRCA1/2. Aside from protein coding and copy number changes, IT-based sequence analysis was used to identify and prioritize pathogenic non-coding variants that occurred within sequence elements predicted to be recognized by proteins or protein complexes involved in mRNA splicing, transcription, and untranslated region (UTR) binding and structure. This approach was supplemented by in silico and laboratory analysis of UTR structure.Results15,311 unique variants were identified, of which 245 occurred in coding regions. With the unified IT-framework, 132 variants were identified and 87 functionally significant VUS were further prioritized. An intragenic 32.1 kb interval in BRCA2 that was likely hemizygous was detected in one patient. We also identified 4 stop-gain variants and 3 reading-frame altering exonic insertions/deletions (indels).ConclusionsWe have presented a strategy for complete gene sequence analysis followed by a unified framework for interpreting non-coding variants that may affect gene expression. This approach distills large numbers of variants detected by NGS to a limited set of variants prioritized as potential deleterious changes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-016-0178-5) contains supplementary material, which is available to authorized users.

BRCA1 and BRCA2 5′ noncoding region variants identified in breast cancer patients alter promoter activity and protein binding

“…All rare variants were analyzed in silico using an IT-based method Mucaki et al, 2016) and a modified version of the Shannon pipeline utilizing TF information models built from ENCODE ChIP-seq datasets (Lu, Mucaki, & Rogan, 2017) to assess potential effects of variants on TF binding. Details of analyses are contained in Supporting Information Methods.…”

Expression Changes Confirm Genomic Variants Predicted to Result in Allele-Specific, Alternative mRNA Splicing

“…This facilitates prediction of phenotypic severity (Rogan and Schneider, 1995;von Kodolitsch et al, 1999;von Kodolitsch et al, 2006). The effects of splicing mutations can be predicted in silico by information theory (Rogan and Schneider, 1995;Kannabiran et al, 1998;Rogan et al, 1998;Svojanovsky et al, 2000;Rogan et al, 2003;Caminsky et al, 2014;Dorman et al, 2014;Viner et al, 2014;Caminsky et al, 2016;Mucaki et al, 2016;Shirley et al, 2019), and these predictions can be confirmed by in vitro experimental studies (Vockley et al, 2000;Lamba et al, 2003;Rogan et al, 2003;Khan et al, 2004;Susani et al, 2004;Hobson et al, 2006;Caux-Moncoutier et al, 2009;Olsen et al, 2014;Vemula et al, 2014;Peterlongo et al, 2015). Strengths of one or more splice sites may be altered and, in some instances, concomitant with amino acid changes in coding sequences (Rogan et al, 1998;Peterlongo et al, 2015).…”

Non-Coding Variants in BRCA1 and BRCA2 Genes: Potential Impact on Breast and Ovarian Cancer Predisposition

“…As an alternative for non-coding variants, bioinformatics in silico analysis are useful to prioritize the variants that are located in DNase I, FAIRE peaks of open chromatin and TF consensus binding sites. Additionally, Information theory analysis has been used to evaluate if the binding strength of several TFs are predicted to be altered by BRCA1/2 variants [ 75 ]. Moreover, the potential effect of a single nucleotide variant on RNA secondary structure can be tested by the prediction software SNPfold [ 76 ] and confirmed by the detection of covalent adducts in mRNA by the SHAPE assay [ 77 ].…”