The estimates will improve BRCA1 and BRCA2 variant classification by using multifactorial likelihood analysis, and inform patient mutation testing and clinical management.
Our results suggest that the addition of tumour immunohistochemical expression can add value to the classification of likely causal BRCA1 variants using multifactorial likelihood analysis and that functional assays are a useful adjunct to multifactorial analysis.
In the case of BRCA1 A1708E, which was previously classified as a UV using multifactorial likelihood analysis based on limited data, revised multifactorial analysis yielded a posterior probability of pathogenicity of 99%.
A comparison of the conclusions from the original multifactorial likelihood analysis, the revised multifactorial analysis incorporating revised evolutionary and co-occurrence components and additional tumour features, and the functional data are shown in Table 3.
Our results show that multifactorial likelihood analysis, which incorporates sequence conservation, co-inheritance, segregation, and tumour immunohistochemical analysis, may improve classification of variants.
We have compared the results from multifactorial likelihood analysis with those from several functional analyses for the four BRCA1 sequence variants A1708E, G1738R, R1699Q, and A1708V.
In this scenario, multifactorial likelihood analysis should exclude tumor-pathology information from individuals who had previously contributed to risk prediction used to prioritize families for mutation screening.
In addition, we have extended our previous multifactorial likelihood analysis and now provide convincing evidence for pathogenicity using this approach, with a posterior probability of pathogenicity of 99%.
Both multifactorial likelihood analysis and functional approaches have been proposed as a means to elucidate likely clinical significance of such variants, but analysis of the comparative value of these methods for classifying all sequence variants has been limited.
