Breast Cancer Research and Treatment

, Volume 147, Issue 1, pp 119–132 | Cite as

Development and validation of a new algorithm for the reclassification of genetic variants identified in the BRCA1 and BRCA2 genes

  • Dmitry Pruss
  • Brian Morris
  • Elisha Hughes
  • Julie M. Eggington
  • Lisa Esterling
  • Brandon S. Robinson
  • Aric van Kan
  • Priscilla H. Fernandes
  • Benjamin B. Roa
  • Alexander Gutin
  • Richard J. Wenstrup
  • Karla R. BowlesEmail author


BRCA1 and BRCA2 sequencing analysis detects variants of uncertain clinical significance in approximately 2 % of patients undergoing clinical diagnostic testing in our laboratory. The reclassification of these variants into either a pathogenic or benign clinical interpretation is critical for improved patient management. We developed a statistical variant reclassification tool based on the premise that probands with disease-causing mutations are expected to have more severe personal and family histories than those having benign variants. The algorithm was validated using simulated variants based on approximately 145,000 probands, as well as 286 BRCA1 and 303 BRCA2 true variants. Positive and negative predictive values of ≥99 % were obtained for each gene. Although the history weighting algorithm was not designed to detect alleles of lower penetrance, analysis of the hypomorphic mutations c.5096G>A (p.Arg1699Gln; BRCA1) and c.7878G>C (p.Trp2626Cys; BRCA2) indicated that the history weighting algorithm is able to identify some lower penetrance alleles. The history weighting algorithm is a powerful tool that accurately assigns actionable clinical classifications to variants of uncertain clinical significance. While being developed for reclassification of BRCA1 and BRCA2 variants, the history weighting algorithm is expected to be applicable to other cancer- and non-cancer-related genes.


BRCA1 BRCA2 Breast cancer Ovarian cancer Variant classification 



We would like to thank Kirstin Roundy for assistance with manuscript editing and submission.

Conflict of interest

All authors are employees of Myriad Genetics, Inc. and Myriad Genetic Laboratories, Inc. and receive salaries and stock options as compensation.

Supplementary material

10549_2014_3065_MOESM1_ESM.docx (83 kb)
Supplementary material 1 (DOCX 84 kb)


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Dmitry Pruss
    • 1
  • Brian Morris
    • 1
  • Elisha Hughes
    • 1
  • Julie M. Eggington
    • 2
  • Lisa Esterling
    • 2
  • Brandon S. Robinson
    • 2
  • Aric van Kan
    • 2
  • Priscilla H. Fernandes
    • 2
  • Benjamin B. Roa
    • 2
  • Alexander Gutin
    • 1
  • Richard J. Wenstrup
    • 2
  • Karla R. Bowles
    • 2
    Email author
  1. 1.Myriad Genetics, Inc.Salt Lake CityUSA
  2. 2.Myriad Genetic Laboratories, Inc.Salt Lake CityUSA

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