Computational Tools for Splicing Defect Prediction in Breast/Ovarian Cancer Genes: How Efficient Are They at Predicting RNA Alterations?

In silico tools for splicing defect prediction have a key role to assess the impact of variants of uncertain significance. Our aim was to evaluate the performance of a set of commonly used splicing in silico tools comparing the predictions against RNA in vitro results. This was done for natural splice sites of clinically relevant genes in hereditary breast/ovarian cancer (HBOC) and Lynch syndrome. A study divided into two stages was used to evaluate SSF-like, MaxEntScan, NNSplice, HSF, SPANR, and dbscSNV tools. A discovery dataset of 99 variants with unequivocal results of RNA in vitro studies, located in the 10 exonic and 20 intronic nucleotides adjacent to exon–intron boundaries of BRCA1, BRCA2, MLH1, MSH2, MSH6, PMS2, ATM, BRIP1, CDH1, PALB2, PTEN, RAD51D, STK11, and TP53, was collected from four Spanish cancer genetic laboratories. The best stand-alone predictors or combinations were validated with a set of 346 variants in the same genes with clear splicing outcomes reported in the literature. Sensitivity, specificity, accuracy, negative predictive value (NPV) and Mathews Coefficient Correlation (MCC) scores were used to measure the performance. The discovery stage showed that HSF and SSF-like were the most accurate for variants at the donor and acceptor region, respectively. The further combination analysis revealed that HSF, HSF+SSF-like or HSF+SSF-like+MES achieved a high performance for predicting the disruption of donor sites, and SSF-like or a sequential combination of MES and SSF-like for predicting disruption of acceptor sites. The performance confirmation of these last results with the validation dataset, indicated that the highest sensitivity, accuracy, and NPV (99.44%, 99.44%, and 96.88, respectively) were attained with HSF+SSF-like or HSF+SSF-like+MES for donor sites and SSF-like (92.63%, 92.65%, and 84.44, respectively) for acceptor sites. We provide recommendations for combining algorithms to conduct in silico splicing analysis that achieved a high performance. The high NPV obtained allows to select the variants in which the study by in vitro RNA analysis is mandatory against those with a negligible probability of being spliceogenic. Our study also shows that the performance of each specific predictor varies depending on whether the natural splicing sites are donors or acceptors.

[1]  Jingyue Ju,et al.  Quantitative evaluation of all hexamers as exonic splicing elements. , 2011, Genome research.

[2]  Filomena Ficarazzi,et al.  Comparative In Vitro and In Silico Analyses of Variants in Splicing Regions of BRCA1 and BRCA2 Genes and Characterization of Novel Pathogenic Mutations , 2013, PloS one.

[3]  H. Kuerer,et al.  Selective surgical localization of axillary lymph nodes containing metastases in patients with breast cancer: a prospective feasibility trial. , 2015, JAMA surgery.

[4]  Eric Boerwinkle,et al.  In silico prediction of splice-altering single nucleotide variants in the human genome , 2014, Nucleic acids research.

[5]  Peter Devilee,et al.  Intronic variants in BRCA1 and BRCA2 that affect RNA splicing can be reliably selected by splice‐site prediction programs , 2009, Human mutation.

[6]  C. Béroud,et al.  Human Splicing Finder: an online bioinformatics tool to predict splicing signals , 2009, Nucleic acids research.

[7]  M. Widera,et al.  Genomic HEXploring allows landscaping of novel potential splicing regulatory elements , 2014, Nucleic acids research.

[8]  Jean-Philippe Vert,et al.  Guidelines for splicing analysis in molecular diagnosis derived from a set of 327 combined in silico/in vitro studies on BRCA1 and BRCA2 variants , 2012, Human mutation.

[9]  Eric Boerwinkle,et al.  In silico tools for splicing defect prediction - A survey from the viewpoint of end-users , 2013, Genetics in Medicine.

[10]  A. Spurdle,et al.  Comprehensive annotation of splice junctions supports pervasive alternative splicing at the BRCA1 locus: a report from the ENIGMA consortium. , 2014, Human molecular genetics.

[11]  Christopher B. Burge,et al.  Maximum Entropy Modeling of Short Sequence Motifs with Applications to RNA Splicing Signals , 2004, J. Comput. Biol..

[12]  Dominique Vaur,et al.  Contribution of bioinformatics predictions and functional splicing assays to the interpretation of unclassified variants of the BRCA genes , 2011, European Journal of Human Genetics.

[13]  B. Frey,et al.  The human splicing code reveals new insights into the genetic determinants of disease , 2015, Science.

[14]  Dominique Stoppa-Lyonnet,et al.  Evaluation of in silico splice tools for decision‐making in molecular diagnosis , 2008, Human mutation.

[15]  Bale,et al.  Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology , 2015, Genetics in Medicine.

[16]  E. Boerwinkle,et al.  dbNSFP v3.0: A One‐Stop Database of Functional Predictions and Annotations for Human Nonsynonymous and Splice‐Site SNVs , 2016, Human mutation.

[17]  T. Frebourg,et al.  Exonic Splicing Mutations Are More Prevalent than Currently Estimated and Can Be Predicted by Using In Silico Tools , 2016, PLoS genetics.

[18]  T. Dörk,et al.  Spectrum of ATM gene mutations in a hospital-based series of unselected breast cancer patients. , 2001, Cancer research.

[19]  D. Nix,et al.  Adding In Silico Assessment of Potential Splice Aberration to the Integrated Evaluation of BRCA Gene Unclassified Variants , 2016, Human mutation.

[20]  Debra O. Prosser,et al.  Evaluation of Bioinformatic Programmes for the Analysis of Variants within Splice Site Consensus Regions , 2016, Adv. Bioinformatics.

[21]  A. Spurdle,et al.  Novel diagnostic tool for prediction of variant spliceogenicity derived from a set of 395 combined in silico/in vitro studies: an international collaborative effort , 2018, Nucleic acids research.

[22]  Christophe Béroud,et al.  Bioinformatics identification of splice site signals and prediction of mutation effects , 2010 .

[23]  S. Bonache,et al.  Germline BRCA testing is moving from cancer risk assessment to a predictive biomarker for targeting cancer therapeutics , 2016, Clinical and Translational Oncology.

[24]  A. Krainer,et al.  Listening to silence and understanding nonsense: exonic mutations that affect splicing , 2002, Nature Reviews Genetics.

[25]  David Haussler,et al.  Improved splice site detection in Genie , 1997, RECOMB '97.

[26]  Kamil J. Cygan,et al.  Hereditary cancer genes are highly susceptible to splicing mutations , 2018, PLoS genetics.

[27]  Marvin B. Shapiro,et al.  RNA splice junctions of different classes of eukaryotes: sequence statistics and functional implications in gene expression. , 1987, Nucleic acids research.

[28]  J. Komorowski,et al.  Identification of candidate regulatory SNPs by combination of transcription-factor-binding site prediction, SNP genotyping and haploChIP , 2009, Nucleic acids research.

[29]  F. Couch,et al.  BRCA1 and BRCA2 genetic testing-pitfalls and recommendations for managing variants of uncertain clinical significance. , 2015, Annals of oncology : official journal of the European Society for Medical Oncology.

[30]  Sigve Nakken,et al.  Effects of intronic mutations in the LDLR gene on pre-mRNA splicing: Comparison of wet-lab and bioinformatics analyses. , 2009, Molecular genetics and metabolism.

[31]  Abramowicz Anna,et al.  Splicing mutations in human genetic disorders: examples, detection, and confirmation , 2018, Journal of Applied Genetics.