4.8 Article

Comprehensive Analysis of Missense Variations in the BRCT Domain of BRCA1 by Structural and Functional Assays

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CANCER RESEARCH
卷 70, 期 12, 页码 4880-4890

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AMER ASSOC CANCER RESEARCH
DOI: 10.1158/0008-5472.CAN-09-4563

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  1. Canadian Cancer Society Research Institute
  2. Howard Hughes International Scholar Program
  3. Alberta Heritage Foundation
  4. CNPq, Brazil
  5. NIH [CA116167]

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Genetic screening of the breast and ovarian cancer susceptibility gene BRCA1 has uncovered a large number of variants of uncertain clinical significance. Here, we use biochemical and cell-based transcriptional assays to assess the structural and functional defects associated with a large set of 117 distinct BRCA1 missense variants within the essential BRCT domain of the BRCA1 protein that have been documented in individuals with a family history of breast or ovarian cancer. In the first method, we used limited proteolysis to assess the protein folding stability of each of the mutants compared with the wild-type. In the second method, we used a phosphopeptide pull-down assay to assess the ability of each of the variants to specifically interact with a peptide containing a pSer-X-X-Phe motif, a known functional target of the BRCA1 BRCT domain. Finally, we used transcriptional assays to assess the ability of each BRCT variant to act as a transcriptional activation domain in human cells. Through a correlation of the assay results with available family history and clinical data, we define limits to predict the disease risk associated with each variant. Forty-two of the variants show little effect on function and are likely to represent variants with little or no clinical significance; 50 display a clear functional effect and are likely to represent pathogenic variants; and the remaining 25 variants display intermediate activities. The excellent agreement between the structure/function effects of these mutations and available clinical data supports the notion that functional and structure information can be useful in the development of models to assess cancer risk. Cancer Res; 70(12); 4880-90. (C)2010 AACR.

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