Clinical, Splicing, and Functional Analysis to Classify BRCA2 Exon 3 Variants: Application of a Points-based ACMG/AMP Approach

Skipping of BRCA2 exon 3 (∆E3) is a naturally occurring splicing event, complicating clinical classification of variants that may alter ∆E3 expression. This study used multiple evidence types to assess pathogenicity of 85 variants in/near BRCA2 exon 3. Bioinformatically predicted spliceogenic variants underwent mRNA splicing analysis using minigenes and/or patient samples. ∆E3 was measured using quantitative analysis. A mouse embryonic stem cell (mESC) based assay was used to determine the impact of 18 variants on mRNA splicing and protein function. For each variant, population frequency, bioinformatic predictions, clinical data, and existing mRNA splicing and functional results were collated. Variant class was assigned using a gene-specific adaptation of ACMG/AMP guidelines, following a recently proposed points-based system. mRNA and mESC analysis combined identified six variants with transcript and/or functional profiles interpreted as loss of function. Cryptic splice site use for acceptor site variants generated a transcript encoding a shorter protein that retains activity. Overall, 69/85 (81%) variants were classified using the points-based approach. Our analysis shows the value of applying gene-specific ACMG/AMP guidelines using a points-based approach and highlights the consideration of cryptic splice site usage to appropriately assign PVS1 code strength.

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References

Mesman R, Calleja F, de la Hoya M, Devilee P, van Asperen C, Vrieling H . Alternative mRNA splicing can attenuate the pathogenicity of presumed loss-of-function variants in BRCA2. Genet Med. 2020; 22(8):1355-1365. PMC: 7394881. DOI: 10.1038/s41436-020-0814-5. View

de la Hoya M, Soukarieh O, Lopez-Perolio I, Vega A, Walker L, van Ierland Y . Combined genetic and splicing analysis of BRCA1 c.[594-2A>C; 641A>G] highlights the relevance of naturally occurring in-frame transcripts for developing disease gene variant classification algorithms. Hum Mol Genet. 2016; 25(11):2256-2268. PMC: 5081057. DOI: 10.1093/hmg/ddw094. View

Ruiz de Garibay G, Acedo A, Garcia-Casado Z, Gutierrez-Enriquez S, Tosar A, Romero A . Capillary electrophoresis analysis of conventional splicing assays: IARC analytical and clinical classification of 31 BRCA2 genetic variants. Hum Mutat. 2013; 35(1):53-7. DOI: 10.1002/humu.22456. View

Li H, LaDuca H, Pesaran T, Chao E, Dolinsky J, Parsons M . Classification of variants of uncertain significance in BRCA1 and BRCA2 using personal and family history of cancer from individuals in a large hereditary cancer multigene panel testing cohort. Genet Med. 2019; 22(4):701-708. PMC: 7118020. DOI: 10.1038/s41436-019-0729-1. View

Caputo S, Telly D, Briaux A, Sesen J, Ceppi M, Bonnet F . 5' Region Large Genomic Rearrangements in the Gene in French Families: Identification of a Tandem Triplication and Nine Distinct Deletions with Five Recurrent Breakpoints. Cancers (Basel). 2021; 13(13). PMC: 8268747. DOI: 10.3390/cancers13133171. View

Tavtigian S, Harrison S, Boucher K, Biesecker L . Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines. Hum Mutat. 2020; 41(10):1734-1737. PMC: 8011844. DOI: 10.1002/humu.24088. View

Sanz D, Acedo A, Infante M, Duran M, Perez-Cabornero L, Esteban-Cardenosa E . A high proportion of DNA variants of BRCA1 and BRCA2 is associated with aberrant splicing in breast/ovarian cancer patients. Clin Cancer Res. 2010; 16(6):1957-67. DOI: 10.1158/1078-0432.CCR-09-2564. View

Spurdle A, Healey S, Devereau A, Hogervorst F, Monteiro A, Nathanson K . ENIGMA--evidence-based network for the interpretation of germline mutant alleles: an international initiative to evaluate risk and clinical significance associated with sequence variation in BRCA1 and BRCA2 genes. Hum Mutat. 2011; 33(1):2-7. PMC: 3240687. DOI: 10.1002/humu.21628. View

Gayther S, Mangion J, Russell P, Seal S, Barfoot R, Ponder B . Variation of risks of breast and ovarian cancer associated with different germline mutations of the BRCA2 gene. Nat Genet. 1997; 15(1):103-5. DOI: 10.1038/ng0197-103. View

10.

Ikegami M, Kohsaka S, Ueno T, Momozawa Y, Inoue S, Tamura K . High-throughput functional evaluation of BRCA2 variants of unknown significance. Nat Commun. 2020; 11(1):2573. PMC: 7244490. DOI: 10.1038/s41467-020-16141-8. View

11.

Goldgar D, Easton D, Byrnes G, Spurdle A, Iversen E, Greenblatt M . Genetic evidence and integration of various data sources for classifying uncertain variants into a single model. Hum Mutat. 2008; 29(11):1265-72. PMC: 2936773. DOI: 10.1002/humu.20897. View

12.

Xia B, Sheng Q, Nakanishi K, Ohashi A, Wu J, Christ N . Control of BRCA2 cellular and clinical functions by a nuclear partner, PALB2. Mol Cell. 2006; 22(6):719-729. DOI: 10.1016/j.molcel.2006.05.022. View

13.

Dines J, Shirts B, Slavin T, Walsh T, King M, Fowler D . Systematic misclassification of missense variants in BRCA1 and BRCA2 "coldspots". Genet Med. 2020; 22(5):825-830. PMC: 7200594. DOI: 10.1038/s41436-019-0740-6. View

14.

Shamsani J, Kazakoff S, Armean I, McLaren W, Parsons M, Thompson B . A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity. Bioinformatics. 2018; 35(13):2315-2317. PMC: 6596880. DOI: 10.1093/bioinformatics/bty960. View

15.

Caputo S, Leone M, Damiola F, Ehlen A, Carreira A, Gaidrat P . Full in-frame exon 3 skipping of confers high risk of breast and/or ovarian cancer. Oncotarget. 2018; 9(25):17334-17348. PMC: 5915120. DOI: 10.18632/oncotarget.24671. View

16.

Yeo G, Burge C . Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J Comput Biol. 2004; 11(2-3):377-94. DOI: 10.1089/1066527041410418. View

17.

Hartford S, Chittela R, Ding X, Vyas A, Martin B, Burkett S . Interaction with PALB2 Is Essential for Maintenance of Genomic Integrity by BRCA2. PLoS Genet. 2016; 12(8):e1006236. PMC: 4973925. DOI: 10.1371/journal.pgen.1006236. View

18.

Canson D, Glubb D, Spurdle A . Variant effect on splicing regulatory elements, branchpoint usage, and pseudoexonization: Strategies to enhance bioinformatic prediction using hereditary cancer genes as exemplars. Hum Mutat. 2020; 41(10):1705-1721. DOI: 10.1002/humu.24074. View

19.

Dorling L, Carvalho S, Allen J, Gonzalez-Neira A, Luccarini C, Wahlstrom C . Breast Cancer Risk Genes - Association Analysis in More than 113,000 Women. N Engl J Med. 2021; 384(5):428-439. PMC: 7611105. DOI: 10.1056/NEJMoa1913948. View

20.

Spurdle A, Couch F, Parsons M, McGuffog L, Barrowdale D, Bolla M . Refined histopathological predictors of BRCA1 and BRCA2 mutation status: a large-scale analysis of breast cancer characteristics from the BCAC, CIMBA, and ENIGMA consortia. Breast Cancer Res. 2015; 16(6):3419. PMC: 4352262. DOI: 10.1186/s13058-014-0474-y. View