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PMID: 18824701 Published · ppublish English

Clinically applicable models to characterize BRCA1 and BRCA2 variants of uncertain significance.

Spearman Andrew D, Sweet Kevin, Zhou Xiao-Ping, McLennan Jane, Couch Fergus J, Toland Amanda Ewart

Abstract

Twenty percent of individuals with a strong family and/or personal history of breast and ovarian cancer carry a deleterious mutation in BRCA1 or BRCA2. Identification of mutations in these genes is extremely beneficial for patients pursuing risk reduction strategies. Approximately 7% of individuals who have genetic testing of BRCA1 and BRCA2 carry a variant of uncertain significance (VUS), making clinical management less certain. The majority of identified VUS occur only in one to two individuals; these variants are not able to be classified using current classification models with segregation analysis components.,To develop a clinically applicable method that can predict the pathogenicity of VUS that does not require familial information or segregation analysis, we identified characteristics of breast or ovarian tumors that distinguished sporadic tumors from tumors with BRCA1 or BRCA2 mutations. Study participants included individuals with known deleterious mutations in BRCA1 or BRCA2 and individuals with classified or unclassified BRCA variants.,We applied the models to 57 tumors with 43 different deleterious BRCA mutations and 57 tumors with 54 unique classified and unclassified BRCA variants. Of the 33 previously unclassified VUS studied, we found evidence of neutrality for 21.,Our models showed 98% sensitivity and 76% specificity for predicting classified DNA changes. We classified 64% of unknown variants as neutral. Classification of VUS as neutral will have immediate benefit for those individuals and their family members. These models are adaptable for the clinic and will be useful for individuals with limited available family history.

Article Info
Journal
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Abbr.
J Clin Oncol
Published
2008-12-24
Indexed
2008-11-21
Updated
2016-10-19
Language
English
Country/Region
United States
NLM ID
8309333
Analysis Services
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