Characterization of a novel germline BRCA1 splice variant, c.5332+4delA

Value of the loss of heterozygosity to BRCA1 variant classification

At least 10% of the BRCA1/2 tests identify variants of uncertain significance (VUS) while the distinction between pathogenic variants (PV) and benign variants (BV) remains particularly challenging. As a typical tumor suppressor gene, the inactivation of the second wild-type (WT) BRCA1 allele is expected to trigger cancer initiation. Loss of heterozygosity (LOH) of the WT allele is the most frequent mechanism for the BRCA1 biallelic inactivation. To evaluate if LOH can be an effective predictor of BRCA1 variant pathogenicity, we carried out LOH analysis on DNA extracted from 90 breast and seven ovary tumors diagnosed in 27 benign and 55 pathogenic variant carriers. Further analyses were conducted in tumors with PVs yet without loss of the WT allele: BRCA1 promoter hypermethylation, next-generation sequencing (NGS) of BRCA1/2, and BRCAness score. Ninety-seven tumor samples were analyzed from 26 different BRCA1 variants. A relatively stable pattern of LOH (65.4%) of WT allele for PV tumors was observed, while the allelic balance (63%) or loss of variant allele (15%) was generally seen for carriers of BV. LOH data is a useful complementary argument for BRCA1 variant classification.

Novel bioinformatics quality control metric for next-generation sequencing experiments in the clinical context

As the use of next-generation sequencing (NGS) for the Mendelian diseases diagnosis is expanding, the performance of this method has to be improved in order to achieve higher quality. Typically, performance measures are considered to be designed in the context of each application and, therefore, account for a spectrum of clinically relevant variants. We present EphaGen, a new computational methodology for bioinformatics quality control (QC). Given a single NGS dataset in BAM format and a pre-compiled VCF-file of targeted clinically relevant variants it associates this dataset with a single arbiter parameter. Intrinsically, EphaGen estimates the probability to miss any variant from the defined spectrum within a particular NGS dataset. Such performance measure virtually resembles the diagnostic sensitivity of given NGS dataset. Here we present case studies of the use of EphaGen in context of BRCA1/2 and CFTR sequencing in a series of 14 runs across 43 blood samples and 504 publically available NGS datasets. EphaGen is superior to conventional bioinformatics metrics such as coverage depth and coverage uniformity. We recommend using this software as a QC step in NGS studies in the clinical context. Availability: https://github.com/m4merg/EphaGen or https://hub.docker.com/r/m4merg/ephagen.

BRCA1-No Matter How You Splice It

BRCA1 (breast cancer 1, early onset), a well-known breast cancer susceptibility gene, is a highly alternatively spliced gene. BRCA1 alternative splicing may serve as an alternative regulatory mechanism for the inactivation of the BRCA1 gene in both hereditary and sporadic breast cancers, and other BRCA1-associated cancers. The alternative transcripts of BRCA1 can mimic known functions, possess unique functions compared with the full-length BRCA1 transcript, and in some cases, appear to function in opposition to full-length BRCA1 In this review, we will summarize the functional "naturally occurring" alternative splicing transcripts of BRCA1 and then discuss the latest next-generation sequencing-based detection methods and techniques to detect alternative BRCA1 splicing patterns and their potential use in cancer diagnosis, prognosis, and therapy.

Integrating somatic variant data and biomarkers for germline variant classification in cancer predisposition genes

In its landmark paper about Standards and Guidelines for the Interpretation of Sequence Variants, the American College of Medical Genetics and Genomics (ACMG), and Association for Molecular Pathology (AMP) did not address how to use tumor data when assessing the pathogenicity of germline variants. The Clinical Genome Resource (ClinGen) established a multidisciplinary working group, the Germline/Somatic Variant Subcommittee (GSVS) with this focus. The GSVS implemented a survey to determine current practices of integrating somatic data when classifying germline variants in cancer predisposition genes. The GSVS then reviewed and analyzed available resources of relevant somatic data, and performed integrative germline variant curation exercises. The committee determined that somatic hotspots could be systematically integrated into moderate evidence of pathogenicity (PM1). Tumor RNA sequencing data showing altered splicing may be considered as strong evidence in support of germline pathogenicity (PVS1) and tumor phenotypic features such as mutational signatures be considered supporting evidence of pathogenicity (PP4). However, at present, somatic data such as focal loss of heterozygosity and mutations occurring on the alternative allele are not recommended to be systematically integrated, instead, incorporation of this type of data should take place under the advisement of multidisciplinary cancer center tumor-normal sequencing boards.