Analysis of a set of missense, frameshift, and in-frame deletion variants of BRCA1

Abstract

Germline mutations that inactivate BRCA1 are responsible for breast and ovarian cancer susceptibility. One possible outcome of genetic testing for BRCA1 is the finding of a genetic variant of uncertain significance for which there is no information regarding its cancer association. This Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final ci form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. outcome leads to problems in risk assessment, counseling and preventive care. The purpose of the present study was to functionally evaluate seven unclassified variants of BRCA1 including a genomic deletion that leads to the in-frame loss of exons 16/17 (Δ exons 16/17) in the mRNA, an insertion that leads to a frameshift and an extended carboxy-terminus (5673insC), and five missense variants (K1487R, S1613C, M1652I, Q1826H and V1833M). We analyzed the variants using a functional assay based on the transcription activation property of BRCA1 combined with supervised learning computational models. Functional analysis indicated that variants S1613C, Q1826H, and M1652I are likely to be neutral, whereas variants V1833M, Δ exons 16/17, and 5673insC are likely to represent deleterious variants. In agreement with the functional analysis, the results of the computational analysis also indicated that the latter three variants are likely to be deleterious. Taken together, a combined approach of functional and bioinformatics analysis, plus structural modeling, can be utilized to obtain valuable information pertaining to the effect of a rare variant on the structure and function of BRCA1. Such information can, in turn, aid in the classification of BRCA1 variants for which there is a lack of genetic information needed to provide reliable risk assessment. NIH Public Access

A Computational Method to Classify Variants of Uncertain Significance Using Functional Assay Data with Application to BRCA1

“…We focus on data from an in vitro assay designed to determine whether a BRCA1 variant affects the transcriptional activity of the C–terminal BRCT domains of BRCA1 . The assay provides a direct measure of structural integrity and an indirect measure of function (7; 8). …”

Multimodel assessment of BRCA1 mutations in Taiwanese (ethnic Chinese) women with early-onset, bilateral or familial breast cancer

“…However, PolyPhen and PANTHER may overpredict damaging classes and SIFT (CanPredict) may overpredict tolerated classes of mutations. 35 These limitations of the classification systems are observed in the benign control S1613G, which is predicted as damaging, although the impact score is lower than true mutants ( Table 2, T1691K and M1775R). Therefore, bioinformatics analyses provide the predictive value, but the functional assay is still required to validate the mutation.…”

Clinical relevance of rare germline sequence variants in cancer genes: evolution and application of classification models

“…Such validated functional assays will then provide quantitative information that can be readily included in multifactorial models. It is encouraging to note that this is likely to occur in the near future for variants lying in specific domains of BRCA1 [47,48] or BRCA2 [10], where sufficient assay results and segregation information are being accumulated to provide the relevant information for validation. Moreover, considerable progress in this area of research is anticipated for the analysis of MMR gene variants, especially with the recent development of rapid and easily quantifiable assays of function [45].…”