Skip to main content
Log in

Klassifizierung von „variants of unknown significance“ (VUS) beim familiären Brust- und Eierstockkrebs

Classification of variants of unknown significance (VUS) in hereditary breast and ovarian cancer

  • Schwerpunktthema: Familiärer Brust- und Eierstockkrebs
  • Published:
medizinische genetik

Zusammenfassung

Die Anwendung von NGS-basierten Verfahren in der molekulargenetischen Diagnostik wird in den nächsten Jahren zur Identifikation einer Vielzahl von Varianten mit unklarer Signifikanz (VUS) führen, deren Relevanz für den untersuchten Phänotyp bestimmt werden muss. In der Diagnostik erblicher Tumorprädispositionserkrankungen wird die VUS-Klassifizierung insbesondere in non-BRCA1/2-Genen in den nächsten Jahren einen hohen Stellenwert einnehmen, eine Herausforderung, die jedoch insbesondere durch internationale wissenschaftliche Kooperationen bewältigt werden kann. Das Deutsche Konsortium Familiärer Brust- und Eierstockkrebs (GC-HBOC) verwendet zur Klassifikation dieser Varianten das international etablierte IARC 5-Klassen-System und kooperiert zur Bewertung seltener Varianten sowie Varianten in bislang weniger gut untersuchten Genen mit zahlreichen internationalen Konsortien und Forschungsgruppen. Vorhersageprogramme können im Kontext von Forschungsprojekten ein nützliches Werkzeug bei der Bewertung beispielsweise der großen Zahl von Varianten in NGS-basierten Untersuchungen sein. Im Rahmen der molekulargenetischen Diagnostik sollte die Klassifizierung der identifizierten Varianten jedoch nicht ausschließlich aufgrund der Vorhersageprogramme erfolgen.

Abstract

In the coming years, procedures based on next-generation sequencing (NGS) in genetic routine diagnostics will lead to a tremendous increase in the number of identified variants of unknown significance (VUS) whose relevance for the analysed phenotype has to be determined. Classification of VUS, especially in non-BRCA1/2 genes, will become one of the key challenges in diagnostics of hereditary tumor predisposition disorders. These can be overcome by international scientific cooperation. Therefore, the German consortium of hereditary breast and ovarian cancer (GC-HBOC) applies the internationally accepted IARC 5-class system and cooperates with numerous international consortia and working groups for the classification of infrequent variants and variants in new risk genes. Prediction programs can be valuable tools for classification of variants especially in the context of NGS-based research projects dealing with large amounts of data. In a diagnostic setting, the classification of variants should not be solely based on in-silico prediction tools.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Literatur

  1. Romero A, Garcia-Garcia F, Lopez-Perolio I, Ruiz de Garibay G, Garcia-Saenz JA, Garre P, Ayllon P, Benito E, Dopazo J, Diaz-Rubio E et al (2015) BRCA1 alternative splicing landscape in breast tissue samples. BMC Cancer 15:219

    Article  PubMed Central  PubMed  Google Scholar 

  2. Yang R, Chen B, Hemminki K, Wappenschmidt B, Engel C, Sutter C, Ditsch N, Weber BH, Niederacher D, Arnold N et al (2009) Polymorphisms in BRCA2 resulting in aberrant codon-usage and their analysis on familial breast cancer risk. Breast Cancer Res Treat 118(2):407–413

    Article  CAS  PubMed  Google Scholar 

  3. Plon SE, Eccles DM, Easton D, Foulkes WD, Genuardi M, Greenblatt MS, Hogervorst FB, Hoogerbrugge N, Spurdle AB, Tavtigian SV et al (2008) Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test results. Hum Mutat 29(11):1282–1291

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  4. Goldgar DE, Easton DF, Deffenbaugh AM, Monteiro AN, Tavtigian SV, Couch FJ, Breast Cancer Information Core Steering C (2004) Integrated evaluation of DNA sequence variants of unknown clinical significance: application to BRCA1 and BRCA2. Am J Hum Genet 75(4):535–544

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  5. Easton DF, Deffenbaugh AM, Pruss D, Frye C, Wenstrup RJ, Allen-Brady K, Tavtigian SV, Monteiro AN, Iversen ES, Couch FJ et al (2007) A systematic genetic assessment of 1,433 sequence variants of unknown clinical significance in the BRCA1 and BRCA2 breast cancer-predisposition genes. Am J Hum Genet 81(5):873–883

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  6. Goldgar DE, Easton DF, Byrnes GB, Spurdle AB, Iversen ES, Greenblatt MS, Group IUGVW (2008) Genetic evidence and integration of various data sources for classifying uncertain variants into a single model. Human Mutat 29(11):1265–1272

    Article  Google Scholar 

  7. Spurdle AB (2010) Clinical relevance of rare germline sequence variants in cancer genes: evolution and application of classification models. Curr Opin Genet Dev 20(3):315–323

    Article  CAS  PubMed  Google Scholar 

  8. Domchek SM, Tang J, Stopfer J, Lilli DR, Hamel N, Tischkowitz M, Monteiro AN, Messick TE, Powers J, Yonker A et al (2013) Biallelic deleterious BRCA1 mutations in a woman with early-onset ovarian cancer. Cancer Discov 3(4):399–405

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. Sawyer SL, Tian L, Kahkonen M, Schwartzentruber J, Kircher M, University of Washington Centre for Mendelian G, Consortium FC, Majewski J, Dyment DA, Innes AM et al (2015) Biallelic mutations in BRCA1 cause a new fanconi anemia subtype. Cancer Discov 5(2):135–142

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  10. Cybulski C, Wokolorczyk D, Jakubowska A, Huzarski T, Byrski T, Gronwald J, Masojc B, Deebniak T, Gorski B, Blecharz P et al (2011) Risk of breast cancer in women with a CHEK2 mutation with and without a family history of breast cancer. J Clin Oncol 29(28):3747–3752

    Article  CAS  PubMed  Google Scholar 

  11. Antoniou AC, Casadei S, Heikkinen T, Barrowdale D, Pylkas K, Roberts J, Lee A, Subramanian D, De Leeneer K, Fostira F et al (2014) Breast-cancer risk in families with mutations in PALB2. N Engl J Med 371(6):497–506

    Article  PubMed Central  PubMed  Google Scholar 

  12. Keimling M, Deniz M, Varga D, Stahl A, Schrezenmeier H, Kreienberg R, Hoffmann I, Konig J, Wiesmuller L (2012) The power of DNA double-strand break (DSB) repair testing to predict breast cancer susceptibility. FASEB J 26(5):2094–2104

    Article  CAS  PubMed  Google Scholar 

  13. Keimling M, Volcic M, Csernok A, Wieland B, Dork T, Wiesmuller L (2011) Functional characterization connects individual patient mutations in ataxia telangiectasia mutated (ATM) with dysfunction of specific DNA double-strand break-repair signaling pathways. FASEB J 25(11):3849–3860

    Article  CAS  PubMed  Google Scholar 

  14. Millot GA, Carvalho MA, Caputo SM, Vreeswijk MP, Brown MA, Webb M, Rouleau E, Neuhausen SL, Hansen T, Galli A et al (2012) A guide for functional analysis of BRCA1 variants of uncertain significance. Hum Mutat 33(11):1526–1537

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  15. Spurdle AB, Healey S, Devereau A, Hogervorst FB, Monteiro AN, Nathanson KL, Radice P, Stoppa-Lyonnet D, Tavtigian S, Wappenschmidt B et al (2012) 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 33(1):2–7

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  16. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR (2010) A method and server for predicting damaging missense mutations. Nat Methods 7(4):248–249

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  17. Ng PC, Henikoff S (2003) SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res 31(13):3812–3814

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. Schwarz JM, Cooper DN, Schuelke M, Seelow D (2014) MutationTaster2: mutation prediction for the deep-sequencing age. Nature Methods 11(4):361–362

    Article  CAS  PubMed  Google Scholar 

  19. Schwarz JM, Rodelsperger C, Schuelke M, Seelow D (2010) MutationTaster evaluates disease-causing potential of sequence alterations. Nature Methods 7(8):575–576

    Article  CAS  PubMed  Google Scholar 

  20. Tavtigian SV, Deffenbaugh AM, Yin L, Judkins T, Scholl T, Samollow PB, de Silva D, Zharkikh A, Thomas A (2006) Comprehensive statistical study of 452 BRCA1 missense substitutions with classification of eight recurrent substitutions as neutral. J Med Genet 43(4):295–305

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  21. Thusberg J, Olatubosun A, Vihinen M (2011) Performance of mutation pathogenicity prediction methods on missense variants. Hum Mutat 32(4):358–368

    Article  PubMed  Google Scholar 

  22. Baker M (2012) Functional genomics: the changes that count. Nature 482(7384):257, 259–262

  23. Yeo G, Burge CB (2004) Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J Comput Biol 11(2–3):377–394

    Article  CAS  PubMed  Google Scholar 

  24. Desmet FO, Hamroun D, Lalande M, Collod-Beroud G, Claustres M, Beroud C (2009) Human splicing finder: an online bioinformatics tool to predict splicing signals. Nucleic Acids Res 37(9):e67

    Article  PubMed Central  PubMed  Google Scholar 

  25. Wappenschmidt B, Becker AA, Hauke J, Weber U, Engert S, Kohler J, Kast K, Arnold N, Rhiem K, Hahnen E et al (2012) Analysis of 30 putative BRCA1 splicing mutations in hereditary breast and ovarian cancer families identifies exonic splice site mutations that escape in silico prediction. PloS One 7(12):e50800

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  26. Whiley PJ, de la Hoya M, Thomassen M, Becker A, Brandao R, Pedersen IS, Montagna M, Menendez M, Quiles F, Gutierrez-Enriquez S et al (2014) Comparison of mRNA splicing assay protocols across multiple laboratories: recommendations for best practice in standardized clinical testing. Clin Chem 60(2):341–352

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  27. de Garibay GR, Acedo A, Garcia-Casado Z, Gutierrez-Enriquez S, Tosar A, Romero A, Garre P, Llort G, Thomassen M, Diez O et al (2014) Capillary electrophoresis analysis of conventional splicing assays: IARC analytical and clinical classification of 31 BRCA2 genetic variants. Hum Mutat 35(1):53–57

    Article  PubMed  Google Scholar 

  28. Spurdle AB, Couch FJ, Hogervorst FB, Radice P, Sinilnikova OM (2008) Prediction and assessment of splicing alterations: implications for clinical testing. Hum Mutat 29(11):1304–1313

    Article  PubMed Central  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jan Hauke.

Ethics declarations

Interessenkonflikt

PD Dr. Eric Hahnen weist auf folgende Beziehung hin: Er erhielt Honorare für die Teilnahme an Scientific Advisory Board Treffen der Firma AstraZeneca. Jan Hauke, Christoph Engel, Barbara Wappenschmidt und Clemens R. Müller geben an, dass kein Interessenkonflik besteht.

Alle im vorliegenden Manuskript beschriebenen Untersuchungen am Menschen wurden mit Zustimmung der zuständigen Ethik-Kommission, im Einklang mit nationalem Recht sowie gemäß der Deklaration von Helsinki von 1975 (in der aktuellen, überarbeiteten Fassung) durchgeführt. Von allen beteiligten Patienten liegt eine Einverständniserklärung vor.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hauke, J., Engel, C., Wappenschmidt, B. et al. Klassifizierung von „variants of unknown significance“ (VUS) beim familiären Brust- und Eierstockkrebs. medgen 27, 211–216 (2015). https://doi.org/10.1007/s11825-015-0049-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11825-015-0049-z

Schlüsselwörter

Keywords

Navigation