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Hum Mutat. 2019 Sep;40(9):1593-1611. doi: 10.1002/humu.23802. Epub 2019 Jul 3.

BRCA1- and BRCA2-specific in silico tools for variant interpretation in the CAGI 5 ENIGMA challenge.

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Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR). Universitat Autònoma de Barcelona, Barcelona, Spain.
Oncogenetics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain.
Area of Clinical and Molecular Genetics, University Hospital of Vall d'Hebron, Barcelona, Spain.
Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.


BRCA1 and BRCA2 (BRCA1/2) germline variants disrupting the DNA protective role of these genes increase the risk of hereditary breast and ovarian cancers. Correct identification of these variants then becomes clinically relevant, because it may increase the survival rates of the carriers. Unfortunately, we are still unable to systematically predict the impact of BRCA1/2 variants. In this article, we present a family of in silico predictors that address this problem, using a gene-specific approach. For each protein, we have developed two tools, aimed at predicting the impact of a variant at two different levels: Functional and clinical. Testing their performance in different datasets shows that specific information compensates the small number of predictive features and the reduced training sets employed to develop our models. When applied to the variants of the BRCA1/2 (ENIGMA) challenge in the fifth Critical Assessment of Genome Interpretation (CAGI 5) we find that these methods, particularly those predicting the functional impact of variants, have a good performance, identifying the large compositional bias towards neutral variants in the CAGI sample. This performance is further improved when incorporating to our prediction protocol estimates of the impact on splicing of the target variant.


bioinformatics; breast cancer; functional assays; gene-specific predictor; homology-directed DNA repair (HDR); molecular diagnosis; ovarian cancer; pathogenicity predictions; protein-specific predictor; splicing predictions

[Available on 2020-09-01]

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