Abstract
Several unclassified variants (UVs) have been identified in splicing regions of disease-associated genes and their characterization as pathogenic mutations or benign polymorphisms is crucial for the understanding of their role in disease development. In this study, 24 UVs located at BRCA1 and BRCA2 splice sites were characterized by transcripts analysis. These results were used to evaluate the ability of nine bioinformatics programs in predicting genetic variants causing aberrant splicing (spliceogenic variants) and the nature of aberrant transcripts. Eleven variants in BRCA1 and 8 in BRCA2, including 8 not previously characterized at transcript level, were ascertained to affect mRNA splicing. Of these, 16 led to the synthesis of aberrant transcripts containing premature termination codons (PTCs), 2 to the up-regulation of naturally occurring alternative transcripts containing PTCs, and one to an in-frame deletion within the region coding for the DNA binding domain of BRCA2, causing the loss of the ability to bind the partner protein DSS1 and ssDNA. For each computational program, we evaluated the rate of non-informative analyses, i.e. those that did not recognize the natural splice sites in the wild-type sequence, and the rate of false positive predictions, i.e., variants incorrectly classified as spliceogenic, as a measure of their specificity, under conditions setting sensitivity of predictions to 100%. The programs that performed better were Human Splicing Finder and Automated Splice Site Analyses, both exhibiting 100% informativeness and specificity. For 10 mutations the activation of cryptic splice sites was observed, but we were unable to derive simple criteria to select, among the different cryptic sites predicted by the bioinformatics analyses, those actually used. Consistent with previous reports, our study provides evidences that in silico tools can be used for selecting splice site variants for in vitro analyses. However, the latter remain mandatory for the characterization of the nature of aberrant transcripts.
Highlights
It is estimated that approximately 5% to 10% of all breast cancers occur in women with a positive family history, and that approximately 15% to 25% of familial aggregations are due to deleterious germline mutations affecting either the BRCA1 (MIM# 113705) or BRCA2 (MIM# 600185) genes [1,2]
Spliceogenic mutations of group A included 5 that had been already analyzed in previous studies (c.547+2T.A in BRCA1, and c.47622A.G, c.700822A.T, c.875521G.A and c.895421_8955delGTTinsAA in BRCA2) [18,22,43,44,45] and 6 not previously characterized (441+2T.G, c.4986+1G.T, c.498721G.A, c.527822delA, c.5332+1G.A in BRCA1and c.475+1G.A in BRCA2)
BRCA1 c.547+2T.A, c.498721G.A and c.5332+1G.A caused the loss of the whole exons 8, 17 and 21, respectively (Fig. 1A–C), and BRCA2 c.475+1G.A resulted in the loss of exon 5 (Fig. 1D)
Summary
It is estimated that approximately 5% to 10% of all breast cancers occur in women with a positive family history, and that approximately 15% to 25% of familial aggregations are due to deleterious germline mutations affecting either the BRCA1 (MIM# 113705) or BRCA2 (MIM# 600185) genes [1,2] Carriers of these mutations have a 40% to 80% probability of developing breast cancer in their life [3] and show an increased risk of other cancers, ovarian carcinoma. To increase the informativeness of genetic testing in breast/ovarian cancer families, multifactorial likelihood models for the classification of UVs have been developed and applied (reviewed in [5,6]). These models take into account several factors. This provides a strong rationale for the use of functional assays for the characterization of UVs under the assumption that they are highly sensitive and specific in detecting deleterious mutations
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