The contribution of deep intronic splice-altering variants to hereditary breast and ovarian cancer (HBOC) is unknown. Current computational in silico tools to predict spliceogenic variants leading to pseudoexons have limited efficiency. We assessed the performance of the SpliceAI tool combined with ESRseq scores to identify spliceogenic deep intronic variants by affecting cryptic sites or splicing regulatory elements (SREs) using literature and experimental datasets. Our results with 233 published deep intronic variants showed that SpliceAI, with a 0.05 threshold, predicts spliceogenic deep intronic variants affecting cryptic splice sites, but is less effective in detecting those affecting SREs. Next, we characterized the SRE profiles using ESRseq, showing that pseudoexons are significantly enriched in SRE-enhancers compared to adjacent intronic regions. Although the combination of SpliceAI with ESRseq scores (considering ∆ESRseq and SRE landscape) showed higher sensitivity, the global performance did not improve because of the higher number of false positives. The combination of both tools was tested in a tumor RNA dataset with 207 intronic variants disrupting splicing, showing a sensitivity of 86%. Following the pipeline, five spliceogenic deep intronic variants were experimentally identified from 33 variants in HBOC genes. Overall, our results provide a framework to detect deep intronic variants disrupting splicing.

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.

Clinical exome sequencing has the potential to identify pathogenic variants unrelated to the purpose of the study (secondary findings, SFs). Data describing actual choices of SFs in participants in a clinical setting and factors influencing their decision are virtually non-existant in Europe. In this work, we report the acceptance rate of SFs, calculate their prevalence and study factors associated with the decision in a cohort of patients affected with a rare genetic disorder in a Spanish Hospital. Finally, we re-examine the presence of previously non reported family history in positive cases. We retrospectively reviewed informed consent choices and SF results from 824 unrelated probands affected with rare genetic disorders who underwent whole-genome or exome sequencing. Ninety percent of families (740/824) affected with rare disorders wished to be informed of SFs. Declining SFs was associated with a prenatal setting (30% vs. 8.7%, p = 0.025), consanguinity (19% vs. 8.7%, p = 0.013), male gender (10.6% vs. 1.5%, p = 0.00865) and the proband being a minor (10.6% vs. 1.5%, p = 0.014). Overall, 27 pathogenic or likely pathogenic variants were identified in 27 individuals, with an SF prevalence of 3.6%. Disclosure of SFs increased the percentage of positive family histories and resulted in early diagnosis or changes in the management of 10 individuals from five families. We show that the acceptance of SFs in Spain is high and the disclosure of SFs leads to a clinically meaningful change in the medical management of individuals.

Outside Front Cover: The cover image is based on the Special Article BRCA1‐ and BRCA2‐specific in silico tools for variant interpretation in the CAGI 5 ENIGMA challenge by Natàlia Padilla et al., https://doi.org/10.1002/humu.23802. Cover design: Selen Özkan and Xavier de la Cruz.