A recent analysis using family history weighting and co-observation classification modeling indicated that BRCA1 c.594-2A>C (IVS9-2A>C), previously described to cause exon 10 skipping (a truncating alteration), displays characteristics inconsistent with those of a high risk pathogenic BRCA1 variant. We used large-scale genetic and clinical resources from the ENIGMA, CIMBA and BCAC consortia to assess pathogenicity of c.594-2A>C. The combined odds for causality considering case-control, segregation and breast tumor pathology information was 3.23 x 10-8. Our data indicate that c.594-2A>C is always in cis with c.641A>G. The spliceogenic effect of c.[594-2A>C;641A>G] was characterized using RNA analysis of human samples and splicing minigenes. As expected, c.[594-2A>C; 641A>G] caused exon 10 skipping, albeit not due to c.594-2A>C impairing the acceptor site but rather by c.641A>G modifying exon 10 splicing regulatory element(s). Multiple blood-based RNA assays indicated that the variant allele did not produce detectable levels of full-length transcripts, with a per allele BRCA1 expression profile composed of70-80% truncating transcripts, and20-30% of in-frame D9,10 transcripts predicted to encode a BRCA1 protein with tumor suppression function. We confirm that BRCA1c.[594-2A>C;641A>G] should not be considered a high-risk pathogenic variant. Importantly, results from our detailed mRNA analysis suggest that BRCA-associated cancer risk is likely not markedly increased for individuals who carry a truncating variant in BRCA1 exons 9 or 10, or any other BRCA1 allele that permits 20-30% of tumor suppressor function. More generally, our findings highlight the importance of assessing naturally occurring alternative splicing for clinical evaluation of variants in disease-causing genes.
CITATION STYLE
de la Hoya, M., Soukarieh, O., López-Perolio, I., Vega, A., Walker, L. C., van Ierland, Y., … Spurdle, A. B. (2016). Combined genetic and splicing analysis of BRCA1 c.[594-2A>C; 641A>G] highlights the relevance of naturally occurring in-frame transcripts for developing disease gene variant classification algorithms. Human Molecular Genetics, 25(11), 2256–2268. https://doi.org/10.1093/hmg/ddw094
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