Role of Splicing Regulatory Elements and In Silico Tools Usage in the Identification of Deep Intronic Splicing Variants in Hereditary Breast/Ovarian Cancer Genes.

Judith Balmaña,Anna Tenés,Joanna Domènech-Vivó,Orland Diez,Sara Gutiérrez-Enríquez,Alejandro Moles-Fernández

doi:10.3390/cancers13133341

Abstract

Simple SummaryThere is a significant percentage of hereditary breast and ovarian cancer (HBOC) cases that remain undiagnosed, because no pathogenic variant is detected through massively parallel sequencing of coding exons and exon-intron boundaries of high-moderate susceptibility risk genes. Deep intronic regions may contain variants affecting RNA splicing, leading ultimately to disease, and hence they may explain several cases where the genetic cause of HBOC is unknown. This study aims to characterize intronic regions to identify a landscape of “exonizable” zones and test the efficiency of two in silico tools to detect deep intronic variants affecting the mRNA splicing process.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.

Highlights

Pathogenic variants in the tumor suppressor genes BRCA1 and BRCA2 (BRCA1/2) and other genes, mainly involved in DNA repair, have been linked to high or moderate risks of developing hereditary breast and ovarian cancer (HBOC) [1,2]
Exon definition is the initial step in pre-mRNA splicing, and it has been suggested that accurate splice site recognition resides in a Splicing Regulatory Elements (SREs) balance, i.e., exons enriched with Exonic Splicing Enhancers (ESEs) and introns with Intronic Splicing Silencers (ISSs) [10,11,12]
To establish the performance of SpliceAI in predicting deep intronic pseudoexongenerating variants, we interrogated a set of variants collected from the literature, after searching for variants located beyond 20 nucleotides from exon-intron boundaries and for which RNA data was available (Table S1)

Summary

Introduction

Pathogenic variants in the tumor suppressor genes BRCA1 and BRCA2 (BRCA1/2) and other genes, mainly involved in DNA repair, have been linked to high or moderate risks of developing hereditary breast and ovarian cancer (HBOC) [1,2]. The detection of pathogenic variants is addressed mainly by massively parallel sequencing of high-moderate penetrance gene panels. An important number of identified deleterious variants affect pre-mRNA splicing and interestingly, hereditary cancer genes (including some HBOC and Lynch syndrome genes) are enriched for this type of variants [7]. SREs are sequences that act as splicing intronic/exonic enhancers (ISE/ESE) or silencers (ISS/ESS), binding (SR)-rich proteins and heterogeneous nuclear ribonucleoproteins (hnRNPs), respectively [8,9]. Exon definition is the initial step in pre-mRNA splicing, and it has been suggested that accurate splice site recognition resides in a SRE balance, i.e., exons enriched with Exonic Splicing Enhancers (ESEs) and introns with Intronic Splicing Silencers (ISSs) [10,11,12]

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