A Computational Model for Classification of BRCA2 Variants Using Mouse Embryonic Stem Cell-based Functional Assays

Overview

Journal NPJ Genom Med

Specialty Genetics

Date 2020 Dec 9

PMID 33293522

Citations 14

Authors

Kajal Biswas

Gary B Lipton

Stacey Stauffer

Teresa Sullivan

Linda Cleveland

Eileen Southon

Susan Reid

Valentin Magidson

Edwin S Iversen Jr

Shyam K Sharan

Affiliations

Soon will be listed here.

Abstract

Sequencing-based genetic tests to identify individuals at increased risk of hereditary breast and ovarian cancers have resulted in the identification of more than 40,000 sequence variants of BRCA1 and BRCA2. A majority of these variants are considered to be variants of uncertain significance (VUS) because their impact on disease risk remains unknown, largely due to lack of sufficient familial linkage and epidemiological data. Several assays have been developed to examine the effect of VUS on protein function, which can be used to assess their impact on cancer susceptibility. In this study, we report the functional characterization of 88 BRCA2 variants, including several previously uncharacterized variants, using a well-established mouse embryonic stem cell (mESC)-based assay. We have examined their ability to rescue the lethality of Brca2 null mESC as well as sensitivity to six DNA damaging agents including ionizing radiation and a PARP inhibitor. We have also examined the impact of BRCA2 variants on splicing. In addition, we have developed a computational model to determine the probability of impact on function of the variants that can be used for risk assessment. In contrast to the previous VarCall models that are based on a single functional assay, we have developed a new platform to analyze the data from multiple functional assays separately and in combination. We have validated our VarCall models using 12 known pathogenic and 10 neutral variants and demonstrated their usefulness in determining the pathogenicity of BRCA2 variants that are listed as VUS or as variants with conflicting functional interpretation.

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