Cited by 239BRCA gene mutations in cancerRNA Research and SplicingGenomics and Rare Diseases

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Guidelines for splicing analysis in molecular diagnosis derived from a set of 327 combined in silico/in vitro studies on BRCA1 and BRCA2 variants

Houdayer, Claude, Caux-Moncoutier, Virginie, Krieger, Sophie, Barrois, Michel, Bonnet, Françoise, Bourdon, Violaine, Bronner, Myriam, Buisson, Monique, Coulet, Florence, Gaildrat, Pascaline, Lefol, Cédrick, Léone, Mélanie, Mazoyer, Sylvie, Muller, Danielle, Remenieras, Audrey, Révillion, Françoise, Rouleau, Etienne, Sokolowska, Joanna, Vert, Jean-Philippe, Lidereau, Rosette, Soubrier, Florent, Sobol, Hagay, Sevenet, Nicolas, Bressac-de Paillerets, Brigitte, Hardouin, Agnès, Tosi, Mario, Sinilnikova, Olga M., Stoppa-Lyonnet, Dominique·2012·Human Mutation

DOI 10.1002/humu.22101

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Abstract

Assessing the impact of variants of unknown significance (VUS) on splicing is a key issue in molecular diagnosis. This impact can be predicted by in silico tools, but proper evaluation and user guidelines are lacking. To fill this gap, we embarked upon the largest BRCA1 and BRCA2 splice study to date by testing 272 VUSs (327 analyses) within the BRCA splice network of Unicancer. All these VUSs were analyzed by using six tools (splice site prediction by neural network, splice site finder (SSF), MaxEntScan (MES), ESE finder, relative enhancer and silencer classification by unanimous enrichment, and human splicing finder) and the predictions obtained were compared with transcript analysis results. Combining MES and SSF gave 96% sensitivity and 83% specificity for VUSs occurring in the vicinity of consensus splice sites, that is, the surrounding 11 and 14 bases for the 5' and 3' sites, respectively. This study was also an opportunity to define guidelines for transcript analysis along with a tentative classification of splice variants. The guidelines drawn from this large series should be useful for the whole community, particularly in the context of growing sequencing capacities that require robust pipelines for variant interpretation.

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Listening to silence and understanding nonsense: exonic mutations that affect splicing
Cartegni, L., Chew, S. L., & Krainer, A. R. (2002) · Nature Reviews Genetics · doi:10.1038/nrg775 · Cited by 2,209
Intronic variants in BRCA1 and BRCA2 that affect RNA splicing can be reliably selected by splice-site prediction programs
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“Thus, DNA variants: c.7806-9T > G (exon 17 skipping+ex17-del69+ivs16-ins8) [35], c.7975A > G (partial exon 17 skipping) [36], c.7976G > A and c.7976G > C (total exon 17 skipping) [37,38], c.7988A > T (partial exon 18 skipping) [38], c.7992A > T (partial exon 18 disruption; [+] creation.”
Adding In Silico Assessment of Potential Splice Aberration to the Integrated Evaluation of BRCA Gene Unclassified Variants
Vallée, M., Sera, T. D., Nix, D. A., Paquette, A. M., Parsons, M. T., Bell, R., Hoffman, A., Hogervorst, F. B. L., Goldgar, D. E., Spurdle, A. B., et al. (2016) · Human Mutation · doi:10.1002/humu.22973 · Cited by 59
“From their dataset of sequence variants’ effects on splicing, Houdayer et al. (2012) were able to perform receiver operating characteristic (ROC) analyses on several splice junction analysis programs; they found that MES was the single best performing program, with an area under the ROC curve of…”
MethodResultInfluential
Experimental Assessment of Splicing Variants Using Expression Minigenes and Comparison with In Silico Predictions
Sharma, N., Sosnay, P. R., Ramalho, A. S., Douville, C. B., Franca, A., Gottschalk, L., Park, J., Lee, M., Vecchio-Pagán, B., Raraigh, K. S., et al. (2014) · Human Mutation · doi:10.1002/humu.22624 · Cited by 67
“The discrepancies between experimental results and in silico predictions in this study support the recommendation that use of a single prediction model is not sufficient, as recommended for the BRCA1 and the BRCA2 genes [Spurdle et al., 2008; Acedo et al., 2012; Houdayer et al., 2012].”
MethodResultInfluential
CCM molecular screening in a diagnosis context: novel unclassified variants leading to abnormal splicing and importance of large deletions
Riant, F., Cécillon, M., Saugier-Veber, P., & Tournier-Lasserve, E. (2013) · Neurogenetics · doi:10.1007/s10048-013-0362-0 · Cited by 61
“proposed an analysis pipeline to check the unknown variants with a cutoff ratio of 15 % for MaxEntScan and 5 % for Splice Site finder like [23].”
Method
Beyond DNA: An Integrated and Functional Approach for Classifying Germline Variants in Breast Cancer Genes
Pesaran, T. F., Karam, R., Huether, R., Li, S., Farber-Katz, S., Chamberlin, A. C., Chong, H. K., LaDuca, H., & Elliott, A. M. (2016) · International Journal of Breast Cancer · doi:10.1155/2016/2469523 · Cited by 74
“In general, these in silico tools are more sensitive (∼90–100%) than being specific (∼60–80%) when predicting the impact of a variant on splicing [21, 22].”
Comparative In Vitro and In Silico Analyses of Variants in Splicing Regions of BRCA1 and BRCA2 Genes and Characterization of Novel Pathogenic Mutations
Colombo, M., Vecchi, G. D., Caleca, L., Foglia, C., Ripamonti, C. B., Ficarazzi, F., Barile, M., Varesco, L., Peissel, B., Manoukian, S., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0057173 · Cited by 58
“However, in some instances, different mRNA transcript patterns have been reported in association with the same mutation by diverse studies [18,22].”
Result
Functional Classification of BRCA2 DNA Variants by Splicing Assays in a Large Minigene with 9 Exons
Acedo, A., Hernández-Moro, C., Curiel-García, Á., Díez-Gómez, B., & Velasco, E. (2015) · Human Mutation · doi:10.1002/humu.22725 · Cited by 58
“Also, variant c.8486A>G, in the same position that c.8486A>T tested by us (Table 2), was reported to have the same outcome (exon 19 skipping) [Houdayer et al., 2012].”
Background
Streamlined ion torrent PGM-based diagnostics: BRCA1 and BRCA2 genes as a model
Tarabeux, J., Zeitouni, B., Moncoutier, V., Tenreiro, H., Abidallah, K., Lair, S., Legoix-né, P., Leroy, Q., Rouleau, E., Golmard, L., et al. (2013) · European Journal of Human Genetics · doi:10.1038/ejhg.2013.181 · Cited by 58
Self-citation
Interpretable prioritization of splice variants in diagnostic next-generation sequencing
Danis, D., Jacobsen, J. O. B., Carmody, L., Gargano, M. A., McMurry, J., Hegde, A., Haendel, M., Valentini, G., Smedley, D., & Robinson, P. N. (2021) · bioRxiv · doi:10.1101/2021.01.28.428499 · Cited by 57
“Suggestive evidence exists that non-canonical SAVs might be a more common cause of Mendelian disease than is commonly appreciated.(9,19,21).”
Background
Detecting splicing patterns in genes involved in hereditary breast and ovarian cancer
Davy, G., Rousselin, A., Goardon, N., Castéra, L., Harter, V., Legros, A., Müller, E., Fouillet, R., Brault, B., Henriques, A. S., et al. (2017) · European Journal of Human Genetics · doi:10.1038/ejhg.2017.116 · Cited by 57
Self-citation
Naturally occurring BRCA2 alternative mRNA splicing events in clinically relevant samples
Fackenthal, J., Yoshimatsu, T., Zhang, B.-F., de Garibay, G. R., Colombo, M., Vecchi, G. D., Ayoub, S. C., Lal, K., Olopade, O., Vega, A., et al. (2016) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2015-103570 · Cited by 54
Self-citation
Functional Analysis of a Large set of BRCA2 exon 7 Variants Highlights the Predictive Value of Hexamer Scores in Detecting Alterations of Exonic Splicing Regulatory Elements
Giacomo, D. D., Gaildrat, P., Abulí, A., Abdat, J., Frébourg, T., Tosi, M., & Martins, A. (2013) · Human Mutation · doi:10.1002/humu.22428 · Cited by 56
“It is currently accepted that the most useful programs in molecular diagnostic settings are those devoted to predicting alterations in consensus splice sites, as it has been shown for multiple disease-associated BRCA1 and BRCA2 variants [Houdayer et al., 2012; Spurdle et al., 2008; Thery et al., 2011; Vreeswijk et al.…”
BackgroundInfluentialSelf-citation
Contribution of germline deleterious variants in the RAD51 paralogs to breast and ovarian cancers
Golmard, L., Castéra, L., Krieger, S., Caux‐Moncoutier, V., Abidallah, K., Tenreiro, H., Laugé, A., Tarabeux, J., Millot, G. A., Nicolas, A., et al. (2017) · European Journal of Human Genetics · doi:10.1038/s41431-017-0021-2 · Cited by 52
Self-citation
Evaluation of Bioinformatic Programmes for the Analysis of Variants within Splice Site Consensus Regions
Tang, R., Prosser, D., & Love, D. (2016) · Adv. Bioinformatics · doi:10.1155/2016/5614058 · Cited by 51
GREM 1 and POLE variants in hereditary colorectal cancer syndromes
Rohlin, A., Eiengård, F., Lundstam, U., Zagoras, T., Nilsson, S., Edsjö, A., Pedersen, J. C., Svensson, J.-H., Skullman, S., Karlsson, G., et al. (2015) · Genes Chromosomes and Cancer · doi:10.1002/gcc.22314 · Cited by 53
“…prediction of variants was performed with SpliceSiteFinder-like, MaxEntScan, NNSPLICE, GeneSplicer and Human Splicefinder (Reese et al., 1997; Zhang, 1998; Pertea et al., 2001; Fairbrother et al., 2002; Cartegni et al., 2003; Yeo and Burge, 2004; Desmet et al., 2009; Houdayer et al., 2012).”
Method
Analysis of PALB2 Gene in BRCA1/BRCA2 Negative Spanish Hereditary Breast/Ovarian Cancer Families with Pancreatic Cancer Cases
Blanco, A., de la Hoya, M., Osório, A., Dı́ez, O., Miramar, M. D., Infante, M., Martínez-Bouzas, C., Torres, A., Lasa, A., Llort, G., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067538 · Cited by 54
“Recently published guidelines for splicing analysis [39] indicate that only variants located in the consensus sites described by Cartegni et al [40], 11 bases for the 59 site (from the 3 last exonic to the 8 first intronic bases) and 14 bases for the 39 site (from the 12 last intronic to the first 2 exonic bases), wou…”
Frequent mutations of RetNet genes in eoHM: Further confirmation in 325 probands and comparison with late‐onset high myopia based on exome sequencing
Zhou, L., Xiao, X.-S., Li, S.-Q., Jia, X.-Y., & Zhang, Q.-J. (2018) · Experimental Eye Research · doi:10.1016/j.exer.2018.02.007 · Cited by 50
A survey of the clinicopathological and molecular characteristics of patients with suspected Lynch syndrome in Latin America
Rossi, B., Palmero, E., López-Kostner, F., Sarroca, C., Vaccaro, C., Spirandelli, F., Ashton-Prolla, P., Rodriguez, Y., Galvão, H. D. C. R., Reis, R., et al. (2017) · BMC Cancer · doi:10.1186/s12885-017-3599-4 · Cited by 48
“For stratification purposes, negative alterations of reference splice sites were deemed important when MaxEntScan scores showed ≥15% decrease relative to corresponding wild-type splice sites [57].”
BackgroundMethod
Variation benchmark datasets: update, criteria, quality and applications
Sarkar, A., Yang, Y., & Vihinen, M. (2019) · bioRxiv · doi:10.1093/database/baz117 · Cited by 47
“Experimental BRCA1 and BRCA2 13, 15, 33, 38, 35, 73 1, 1, 1, 1, 1, 1 (98)”
Cancer Variant Interpretation Group UK (CanVIG-UK): an exemplar national subspecialty multidisciplinary network
Garrett, A., Callaway, A., Durkie, M., Çubuk, C., Alikian, M., Burghel, G., Robinson, R., Izatt, L., Talukdar, S., Side, L., et al. (2020) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2019-106759 · Cited by 45
Classification of variants of uncertain significance in BRCA1 and BRCA2 using personal and family history of cancer from individuals in a large hereditary cancer multigene panel testing cohort
Li, H.-Y., LaDuca, H., Pesaran, T., Chao, E. C., Dolinsky, J. S., Parsons, M., Spurdle, A., Polley, E., Shimelis, H., Hart, S., et al. (2019) · Genetics in Medicine · doi:10.1038/s41436-019-0729-1 · Cited by 45
Genotype–phenotype correlations for COL4A3–COL4A5 variants resulting in Gly substitutions in Alport syndrome
Gibson, J., Huang, M., Dabrera, M. S. C., Shukla, K., Rothe, H., Hilbert, P., Deltas, C., Storey, H., Lipska-Ziętkiewicz, B., Chan, M., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-06525-9 · Cited by 44
CI-SpliceAI—Improving machine learning predictions of disease causing splicing variants using curated alternative splice sites
Strauch, Y. L., Lord, J., Niranjan, M., & Baralle, D. (2022) · PLoS ONE · doi:10.1371/journal.pone.0269159 · Cited by 55
Performance Evaluation of SpliceAI for the Prediction of Splicing of NF1 Variants
Ha, C., Kim, J., & Jang, J. (2021) · Genes · doi:10.3390/genes12091308 · Cited by 42
“In contrast, 5% was used for NNSplice and SSF and 2% was used for Human Splicing Finder [18,31,33,34].”
BackgroundMethod
Genetic profiles of cervical tumors by high‐throughput sequencing for personalized medical care
Müller, E., Brault, B., Holmes, A. M., Legros, A., Jeannot, E., Campitelli, M., Rousselin, A., Goardon, N., Frébourg, T., Krieger, S., et al. (2015) · Cancer Medicine · doi:10.1002/cam4.492 · Cited by 40
“Variants were retrieved from CanDiD database according to five criteria: (1) the variant was found on a list of preferred transcripts; (2) the variant was in targeted zones; (3) the variant was within the coding sequence or ±10 bp within the intronic sequences; (4) the variant was in a canonical splicing mutation site…”
MethodSelf-citation
Evaluation of a 5‐Tier Scheme Proposed for Classification of Sequence Variants Using Bioinformatic and Splicing Assay Data: Inter‐Reviewer Variability and Promotion of Minimum Reporting Guidelines
Walker, L., Whiley, P., Houdayer, C., Hansen, T. V. O., Vega, A., Santamariña, M., Blanco, A., Fachal, L., Southey, M., Lafferty, A., et al. (2013) · Human Mutation · doi:10.1002/humu.22388 · Cited by 40
“, 2009], and guidelines for the utilization of commonly used bioinformatic tools were recently proposed based on a large-scale assessment of 272 unclassified variants in the BRCA1/BRCA2 genes using in silico and transcript analyses [Houdayer et al., 2012].”
MethodSelf-citation
Assessment of branch point prediction tools to predict physiological branch points and their alteration by variants
Leman, R., Tubeuf, H., Raad, S., Tournier, I., Derambure, C., Lanos, R., Gaildrat, P., Castelain, G., Hauchard, J., Killian, A., et al. (2019) · BMC Genomics · doi:10.1186/s12864-020-6484-5 · Cited by 39
Self-citation
Genetics of movement disorders in the next‐generation sequencing era
Olgiati, S., Quadri, M., & Bonifati, V. (2016) · Movement Disorders · doi:10.1002/mds.26521 · Cited by 43
Protein haploinsufficiency drivers identify MYBPC3 variants that cause hypertrophic cardiomyopathy
Suay-Corredera, C., Pricolo, M., Herrero-Galán, E., Velázquez-Carreras, D., Sánchez-Ortiz, D., García-Giustiniani, D., Delgado, J., Galano-Frutos, J., García-Cebollada, H., Vilches, S., et al. (2021) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2021.100854 · Cited by 38
“Positive hits result in at least two tools predicting >10% decrease (for native sites) or increase (for new sites) in the splicing score, in agreement with published guidelines (73).”
Result
Mass-array screening of frequent mutations in cancers reveals RB1 alterations in aggressive adrenocortical carcinomas.
Ragazzon, B., Libé, R., Assié, G., Tissier, F., Barreau, O., Houdayer, C., Perlemoine, K., Audebourg, A., Clauser, E., René-Corail, F., et al. (2014) · European Journal of Endocrinology · doi:10.1530/eje-13-0778 · Cited by 37
Self-citation
Large‐scale comparative evaluation of user‐friendly tools for predicting variant‐induced alterations of splicing regulatory elements
Tubeuf, H., Charbonnier, C., Soukarieh, O., Blavier, A., Lefebvre, A., Dauchel, H., Frébourg, T., Gaildrat, P., & Martins, A. (2020) · Human Mutation · doi:10.1002/humu.24091 · Cited by 43
“Like, and SPiCE) were evaluated in other studies (Houdayer et al., 2012; Leman et al., 2018; Moles ‐ Fernández et al., 2018).”
BackgroundMethodInfluentialSelf-citation
Mis‐splicing in breast cancer: identification of pathogenic BRCA2 variants by systematic minigene assays
Fraile-Bethencourt, E., Valenzuela-Palomo, A., Díez-Gómez, B., Goina, E., Acedo, A., Buratti, E., & Velasco, E. (2019) · Journal of Pathology · doi:10.1002/path.5268 · Cited by 35
Leveraging splice‐affecting variant predictors and a minigene validation system to identify Mendelian disease‐causing variants among exon‐captured variants of uncertain significance
Soens, Z. T., Branch, J., Wu, S., Yuan, Z., Li, Y., Li, H., Wang, K., Xu, M., Rajan, L., Motta, F. L., et al. (2017) · Human Mutation · doi:10.1002/humu.23294 · Cited by 42
“The eight scores were also chosen based on recommendations of performance drawn from 11 different publications that utilized, reported on, or compared the performance of these tools (Colombo et al., 2013; Houdayer et al., 2012;Hunt, Simhadri, Iandoli, Sauna,&Kimchi-Sarfaty, 2014; Jian et al., 2014; Jian, Boerwinkle, &…”
Method
Targeted RNA‐seq successfully identifies normal and pathogenic splicing events in breast/ovarian cancer susceptibility and Lynch syndrome genes
Brandão, R. D., Mensaert, K., López‐Perolio, I., Tserpelis, D., Xenakis, M. N., Lattimore, V., Walker, L. C., Kvist, A., Vega, A. I., Gutiérrez‐Enríquez, S., et al. (2019) · International Journal of Cancer · doi:10.1002/ijc.32114 · Cited by 43
“In the targeted RNA-seq on LCLs we obtained an average of 18,868 reads [9389-33,956], whereas it was 347 [118–484] for non-targeted LCLs, 134 [44–226] for normal breast tissue and 10 [2-16] for normal fimbria tissue.”
BackgroundResult
Alternative mRNA splicing can attenuate the pathogenicity of presumed loss-of-function variants in BRCA2
Mesman, R. L. S., Calléja, F., de la Hoya, M., Devilee, P., van Asperen, C. V., Vrieling, H., & Vreeswijk, M. (2020) · Genetics in Medicine · doi:10.1038/s41436-020-0814-5 · Cited by 33
About 1% of the breast and ovarian Spanish families testing negative for BRCA1 and BRCA2 are carriers of RAD51D pathogenic variants
Gutiérrez‐Enríquez, S., Bonache, S., Ponce, G. R. D. G., Osório, A., Santamariña, M., y Cajal, T. R., Esteban‐Cardeñosa, E., Tenés, A., Yanowsky, K., Barroso, A., et al. (2013) · International Journal of Cancer · doi:10.1002/ijc.28540 · Cited by 32
Spanish Guidelines for the use of targeted deep sequencing in myelodysplastic syndromes and chronic myelomonocytic leukaemia
Palomo, L., Ibáñez, M., Abáigar, M., Vázquez, I., Álvarez, S., Cabezón, M., Tazón‐Vega, B., Rapado, I., Fuster‐Tormo, F., Cervera, J., et al. (2019) · British Journal of Haematology · doi:10.1111/bjh.16175 · Cited by 44
“In silico tools have also been developed to evaluate the consequence of splicing variants related to the creation or loss of splice sites at the exonic or intronic regions (Thusberg et al, 2011; Houdayer et al, 2012).”
Method
A Classification Model Relative to Splicing for Variants of Unknown Clinical Significance: Application to the CFTR Gene
Raynal, C., Baux, D., Thèze, C., Bareil, C., Taulan, M., Roux, A., Claustres, M., Tuffery-Giraud, S., & Georges, M. D. (2013) · Human Mutation · doi:10.1002/humu.22291 · Cited by 32
“They were shown to act as efﬁcient ﬁlters [Houdayer et al., 2012; Vreeswijk et al., 2009], however their performance could be improved as they generate some false positive or false negative predictions [Le Guedard-Mereuze et al., 2010; Thery et al., 2011].”
BackgroundMethod
In silico methods for predicting functional synonymous variants
Lin, B. C., Katneni, U., Jankowska, K. I., Meyer, D., & Kimchi‐Sarfaty, C. (2023) · Genome biology · doi:10.1186/s13059-023-02966-1 · Cited by 41
“Use of a combination of tools predicting both SSs and SREs and employing different algorithms is recommended to overcome potential deficiencies of a single tool and is expected to improve predictive values [12, 132, 133].”
Method
Variant effect on splicing regulatory elements, branchpoint usage, and pseudoexonization: Strategies to enhance bioinformatic prediction using hereditary cancer genes as exemplars
Canson, D. M., Glubb, D. M., & Spurdle, A. B. (2020) · Human Mutation · doi:10.1002/humu.24074 · Cited by 37
“ACTIVE EXONIC SREs: BRCA1/2 AS EXEMPLARS It has been recommended that a precise and detailed map of active SREs be established for each gene of interest in order for SRE prediction to be useful in clinical diagnostics (Houdayer et al., 2012).”
BackgroundMethod
Mutations of Pre-mRNA Splicing Regulatory Elements: Are Predictions Moving Forward to Clinical Diagnostics?
Grodecká, L., Buratti, E., & Freiberger, T. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18081668 · Cited by 37
“[17], as this recommended a particular (specific) re-classification of class 3 variants (or variants of unknown significance) into three other classes (1S, 2S, 3S).”
BackgroundInfluential
High-frequency actionable pathogenic exome variants in an average-risk cohortPreprint
Rego, S. M., Dagan‐Rosenfeld, O., Zhou, W., Sailani, M. R., Limcaoco, P., Colbert, E., Avina, M., Wheeler, J., Craig, C. M., Salins, D. N., et al. (2018) · Molecular Case Studies · doi:10.1101/mcs.a003178 · Cited by 36
“Splice site predictors are only slightly more accurate (Vreeswijk et al. 2008; Houdayer et al. 2012).”
Background
mRNA analysis identifies deep intronic variants causing Alport syndrome and overcomes the problem of negative results of exome sequencing
Wang, X.-Y., Zhang, Y.-Q., Ding, J., & Wang, F. (2021) · Scientific Reports · doi:10.1038/s41598-021-97414-0 · Cited by 29
Functional Categorization of BRCA1 Variants of Uncertain Clinical Significance in Homologous Recombination Repair Complementation Assays
Bouwman, P., van der Heijden, I., van der Gulden, H. M. L., de Bruijn, R., Braspenning, M. E., Moghadasi, S., Wessels, L. F. A., workgroup, T. D.-B. V., Vreeswijk, M. P. G., & Jonkers, J. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-0255 · Cited by 37
A Deep Intronic Variant in LDLR in Familial Hypercholesterolemia
Reeskamp, L. F., Hartgers, M. L., Peter, J., Dallinga‐Thie, G. M., Zuurbier, L. C., Defesche, J. C., Grefhorst, A., & Hovingh, G. K. (2018) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.118.002385 · Cited by 29
Quantitative Analysis of BRCA1 and BRCA2 Germline Splicing Variants Using a Novel RNA-Massively Parallel Sequencing Assay
Farber-Katz, S., Hsuan, V., Wu, S.-T., Landrith, T. A., Vuong, H., Xu, D.-D., Li, B., Hoo, J., Lam, S., Nashed, S., et al. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00286 · Cited by 29
“A specific challenge is the large percentage of VUS in the BRCA1 and BRCA2 genes predicted to affect splicing by in silico tools, but lack RNA evidence (26).”
Background
Splicing analysis of 14 BRCA1 missense variants classifies nine variants as pathogenic
Ahlborn, L. B., Dandanell, M., Steffensen, A. Y., Jønson, L., Nielsen, F., & Hansen, T. (2015) · Breast Cancer Research and Treatment · doi:10.1007/s10549-015-3313-7 · Cited by 29
“Asp1778Asn variant are in agreement with recent splicing data using RNA from patient blood samples [27].”
Background
Implementation of next-generation sequencing for molecular diagnosis of hereditary breast and ovarian cancer highlights its genetic heterogeneity
Pinto, P., Paulo, P., Santos, C., Rocha, P., Pinto, C., Veiga, I., Pinheiro, M., Peixoto, A., & Teixeira, M. R. (2016) · Breast Cancer Research and Treatment · doi:10.1007/s10549-016-3948-z · Cited by 27
“Although these tools cannot be used for classification of variants per se, they are useful for prioritization of VUS for further segregation and functional studies [23, 46].”
MethodResult
Combined Computational-Experimental Analyses ofCFTRExon Strength Uncover Predictability of Exon-Skipping Level
Aissat, A., de Becdelièvre, A., Golmard, L., Vasseur, C., Costa, C., Chaoui, A., Martin, N., Costes, B., Goossens, M., Girodon, E., et al. (2013) · Human Mutation · doi:10.1002/humu.22300 · Cited by 31
“These observations also emphasize the necessity of using a minigene approach to adjust in silico analysis [Houdayer et al., 2012].”
Method
Splicing in the pathogenesis, diagnosis and treatment of ciliopathies
Wheway, G., Lord, J., & Baralle, D. (2019) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2019.194433 · Cited by 27
Extensive Alternative Splicing of KIR Transcripts
Bruijnesteijn, J., van der Wiel, M. K. H., de Groot, N., Otting, Ń., de Vos‐Rouweler, A. J. M., Lardy, N. M., de Groot, N. G., & Bontrop, R. E. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.02846 · Cited by 28
Alport syndrome cold cases: Missing mutations identified by exome sequencing and functional analysis
Chiereghin, C., Robusto, M., Mastrangelo, A., Castorina, P., Montini, G., Giani, M., Duga, S., Asselta, R., & Soldà, G. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178630 · Cited by 27
Clinical and molecular characterization of cystinuria in a French cohort: relevance of assessing large‐scale rearrangements and splicing variants
Gaildrat, P., Lebbah, S., Tebani, A., Sudrié‐Arnaud, B., Tostivint, I. N., Bollée, G., Tubeuf, H., Charles, T., Bertholet‐Thomas, A., Goldenberg, A., et al. (2017) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.294 · Cited by 31
“These data confirm the good performance of this type of bioinformatics prediction, as previously shown, in particular for large datasets of BRCA1 and BRCA2 variants (Houdayer et al. 2012).”
ResultSelf-citation
Capillary Electrophoresis Analysis of Conventional Splicing Assays: IARC Analytical and Clinical Classification of 31 BRCA2 Genetic Variants
de Garibay, G. R., Acedo, A., García-Casado, Z., Gutiérrez-Enríquez, S., Tosar, A., Romero, A., Garre, P., Llort, G., Thomassen, M., Díez, O., et al. (2014) · Human Mutation · doi:10.1002/humu.22456 · Cited by 27
“Undoubtedly, in silico analyses might provide high-accuracy predictions, but they do not detect all splicing defects, especially in variants located outside the 5′ and 3′ consensus sites (Houdayer et al., 2012).”
Background
Criteria of the German Consortium for Hereditary Breast and Ovarian Cancer for the Classification of Germline Sequence Variants in Risk Genes for Hereditary Breast and Ovarian Cancer
Wappenschmidt, B., Hauke, J., Faust, U., Niederacher, D., Wiesmüller, L., Schmidt, G., Groß, E., Gehrig, A., Sutter, C., Ramser, J., et al. (2020) · Geburtshilfe und Frauenheilkunde · doi:10.1055/a-1110-0909 · Cited by 25
RNA splicing analysis in genomic medicine
Wai, H. A., Douglas, A. G. L., & Baralle, D. (2018) · The International Journal of Biochemistry & Cell Biology · doi:10.1016/j.biocel.2018.12.009 · Cited by 33
Combination of Whole Genome Sequencing, Linkage, and Functional Studies Implicates a Missense Mutation in Titin as a Cause of Autosomal Dominant Cardiomyopathy With Features of Left Ventricular Noncompaction
Hastings, R. K., de Villiers, C. P., Hooper, C. L., Ormondroyd, E., Pagnamenta, A. T., Lise, S., Salatino, S., Knight, S. J. L., Taylor, J. C., Thomson, K. L., et al. (2016) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.116.001431 · Cited by 84
Russian Association of Endocrinologists clinical practice guidelines for diagnosis and treatment of pheochromocytoma and paraganglioma
Mel'nichenko, G., Troshina, E., Bel'tsevich, D., Kuznetsov, N. S., & Yukina, M. (2015) · Endocrine Surgery · doi:10.14341/serg2015315-33 · Cited by 26
Comparison of In Silico Tools for Splice-Altering Variant Prediction Using Established Spliceogenic Variants: An End-User's Point of View
Jang, W., Park, J., Chae, H., & Kim, M. (2022) · International Journal of Genomics · doi:10.1155/2022/5265686 · Cited by 25
“22, and performed better than the combined analysis of MES/SSF [19].”
BackgroundMethodInfluential
Empirical prediction of variant-activated cryptic splice donors using population-based RNA-Seq data
Dawes, R., Joshi, H., & Cooper, S. T. (2022) · Nature Communications · doi:10.1038/s41467-022-29271-y · Cited by 24
Splicing predictions, minigene analyses, and ACMG‐AMP clinical classification of 42 germline PALB2 splice‐site variants
Valenzuela-Palomo, A., Bueno-Martínez, E., Sanoguera-Miralles, L., Lorca, V., Fraile-Bethencourt, E., Esteban-Sánchez, A., Gómez-Barrero, S., Carvalho, S., Allen, J., García-Álvarez, A., et al. (2021) · Journal of Pathology · doi:10.1002/path.5839 · Cited by 24
“[26 – 28]; (3) creation of de novo alternative splice sites (cut-off ≥ 3.0); (4) regardless of MES predictions, variants at other conserved positions of the acceptor (Y 11 NCAG j G) and donor (MAG j GTRAGT) consensussequences,suchaspyrimidinetopurinechanges…”
BackgroundInfluential
Urine‐derived podocytes‐lineage cells: A promising tool for precision medicine in Alport Syndrome
Daga, S., Baldassarri, M., Rizzo, C. L., Fallerini, C., Imperatore, V., Longo, I., Frullanti, E., Landucci, E., Massella, L., Pecoraro, C., et al. (2018) · Human Mutation · doi:10.1002/humu.23364 · Cited by 24
Minigene‐based splicing analysis and ACMG/AMP‐based tentative classification of 56 ATM variants
Bueno-Martínez, E., Sanoguera-Miralles, L., Valenzuela-Palomo, A., Esteban-Sánchez, A., Lorca, V., Llinares-Burguet, I., Allen, J., García-Álvarez, A., Pérez-Segura, P., Durán, M., et al. (2022) · Journal of Pathology · doi:10.1002/path.5979 · Cited by 23
“We selected likely spliceogenic variants based on: (i) ≥20% decrease of MES scores [21,22], (ii) creation of putative de novo sites (MES cutoff ≥3.”
Method
Pathogenic neurofibromatosis type 1 (NF1) RNA splicing resolved by targeted RNAseq
Koster, R., Brandão, R. D., Tserpelis, D., van Roozendaal, C. E. P., van Oosterhoud, C. N., Claes, K., Paulussen, A. D. C., Sinnema, M., Vreeburg, M., van der Schoot, V., et al. (2021) · npj Genomic Medicine · doi:10.1038/s41525-021-00258-w · Cited by 25
HRness in Breast and Ovarian Cancers
Santos, E. S. D., Lallemand, F., Petitalot, A., Caputo, S. M., & Rouleau, É. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21113850 · Cited by 24
Self-citation
BRCA1-No Matter How You Splice It
Li, D., Harlan-Williams, L. M., Kumaraswamy, E., & Jensen, R. (2019) · Cancer Research · doi:10.1158/0008-5472.can-18-3190 · Cited by 23

Authors: Houdayer, C., Caux-Moncoutier, V., Krieger, S., Barrois, M., Bonnet, F., Bourdon, V., Bronner, M., Buisson, M., Coulet, F., Gaildrat, P., Lefol, C., Léone, M., Mazoyer, S., Muller, D., Remenieras, A., Révillion, F., Rouleau, E., Sokolowska, J., Vert, J.-P., Lidereau, R., Soubrier, F., Sobol, H., Sevenet, N., Bressac-de Paillerets, B., Hardouin, A., Tosi, M., Sinilnikova, O. M., & Stoppa-Lyonnet, D.
Affiliations: Sorbonne Paris Cité (FR)
Centre François Baclesse (FR)
Institut Gustave Roussy (FR)
Inserm (FR)
Institut Paoli-Calmettes (FR)
Centre Hospitalier Régional et Universitaire de Nancy (FR)
Lyon 1 Université (FR)
Assistance Publique – Hôpitaux de Paris (FR)
Hôpital René Huguenin (FR)
Institut Strauss (FR)
Corresponding author: Houdayer, Claude
ORCID: Houdayer, Claude
Krieger, Sophie
Bourdon, Violaine
Bronner, Myriam
Coulet, Florence
Gaildrat, Pascaline
Mazoyer, Sylvie
Vert, Jean-Philippe
Soubrier, Florent
Sobol, Hagay
Venue: Human Mutation
Publisher: Hindawi Limited
Published: 2012-05-11
Volume: 33
Issue: 8
Pages: 1228-1238
ISSN: 1059-7794 (ISSN-L)
DOI: 10.1002/humu.22101
OpenAlex: W1975196485
Cited by: 239
References: 0
Citations by year: 2026: 3 · 2025: 5 · 2024: 11 · 2023: 6 · 2022: 25 · 2021: 27 · 2020: 22 · 2019: 23 · 2018: 27 · 2017: 23 · 2016: 14 · 2015: 22 · 2014: 11 · 2013: 17 · 2012: 3
FWCI: 10.21
Type: journal article
Language: en
Open access: closed
Licence: doi.wiley.com/10.1002/tdm_license_1.1 (tdm, 1208 d)
Locations: Human Mutation (publishedVersion)
PubMed (publishedVersion)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Keywords: BRCA1, BRCA2, variants of uncertain significance, splicing, splice site prediction, in silico tools, transcript analysis, variant interpretation, molecular diagnosis
MeSH terms: Exons, Female, Humans, RNA Splicing, BRCA1 Protein, BRCA2 Protein, Pathology, Molecular

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Guidelines for splicing analysis in molecular diagnosis derived from a set of 327 combined in silico/in vitro studies on BRCA1 and BRCA2 variants

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