Affiliations 

  • 1 Department of Clinical Genetics, Odense University Hospital, Odence C, Denmark
  • 2 Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
  • 3 Department of Clinical Genetics, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
  • 4 Hereditary Cancer Program, Catalan Institute of Oncology, ONCOBELL-IDIBELL-IDTP, CIBERONC, Hospitalet de Llobregat, Spain
  • 5 Center for Genomic Medicine, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
  • 6 Molecular Oncology Laboratory, CIBERONC, Hospital Clinico San Carlos, IdISSC (Instituto de Investigación Sanitaria del Hospital Clínico San Carlos), Madrid, Spain
  • 7 Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
  • 8 Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
  • 9 Molecular Diagnostics, Aalborg University Hospital, Aalborg, Denmark
  • 10 Breast Cancer Research Programme, Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
  • 11 Department of Tumour Biology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
  • 12 Department of Clinical Genetics, Aarhus University Hospital, Aarhus N, Denmark
  • 13 Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
  • 14 Department of OB/GYN and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
  • 15 Fundación Pública Galega de Medicina Xenómica, Santiago de Compostela, Spain
  • 16 Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, the Netherlands
  • 17 Department of Biochemistry and Molecular Biology and the Villum Center for Bioanalytical Sciences, University of Southern Denmark, Odense, Denmark
  • 18 Department of Dermatology, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah, USA
  • 19 Ambry Genetics, Aliso Viejo, California, USA
  • 20 Hereditary Cancer Clinic, Nelune Comprehensive Cancer Care Centre, Sydney, New South Wales, Australia
  • 21 Parkville Familial Cancer Centre, Peter MacCallum Cancer Center, Melbourne, Victoria, Australia
  • 22 Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
  • 23 Middlesex Health Shoreline Cancer Center, Westbrook, Connecticut, USA
  • 24 Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
  • 25 Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
  • 26 Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
  • 27 Centre for Medical Genetics, Ghent University, Gent, Belgium
  • 28 Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic
  • 29 Division of Functional Onco-genomics and Genetics, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, Aviano, Italy
  • 30 Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
Hum Mutat, 2022 Dec;43(12):1921-1944.
PMID: 35979650 DOI: 10.1002/humu.24449

Abstract

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.