Novel diagnostic tool for prediction of variant spliceogenicity derived from a set of 395 combined in silico/in vitro studies: an international collaborative effort

Leman, Raphaël; Gaildrat, Pascaline; Gac, Gérald Le; Ka, Chandran; Fichou, Yann; Audrézet, Marie-Pierre; Caux‐Moncoutier, Virginie; Caputo, Sandrine M.; Boutry‐Kryza, Nadia; Léoné, Mélanie; Mazoyer, Sylvie; Bonnet‐Dorion, Françoise; Sévenet, Nicolas; Guillaud‐Bataille, Marine; Rouleau, Etienne; Paillerets, Brigitte Bressac-de; Wappenschmidt, Barbara; Rossing, Maria; Muller, Danielle; Bourdon, Violaine; Révillon, Françoise; Parsons, Michael T.; Rousselin, Antoine; Davy, Grégoire; Castelain, G; Castéra, Laurent; Sokolowska, Joanna; Coulet, Florence; Férec, Claude; Spurdle, Amanda B.; Martins, Alexandra; Krieger, Sophie; Houdayer, Claude

doi:10.1093/nar/gky979

Cited by 11 publications

(10 citation statements)

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“…Another limitation of our cDNA-based assay is that this approach cannot rule out potential effects on splicing. Importantly, although there are some computational predictors for possible splicing aberrations, either algorithms could not demonstrate adequate reliability for clinical usage, especially for mutations outside of the consensus splice sites 48 . Comprehensive splicing functional assays such as hybrid minigene assay and saturation genome editing technique would reinforce the evidence 15,49,50 .…”

Section: Discussionmentioning

confidence: 99%

“…Eight VUSs located in the last 3 exonic bases of the splice donor site and 5 VUSs located in the first 2 exonic bases of the splice acceptor site were possible candidates for disrupting splice consensus sequences. These VUSs were analyzed using Splicing Prediction in Consensus Element (SPiCE) 48 , the most accurate in silico splice site prediction algorithm for BRCA1/2 . Only 6 variants—V159M (c.475G>A), D23Y (c.67G>T), V211I (c.631G>A), R2336P (c.7007G>C), and R2602T (c.7805G>C)—were predicted to alter canonical splicing, and we assessed the effect of these variants on protein function by the MANO-B method ( Supplementary Table 3 ).…”

Section: Methodsmentioning

confidence: 99%

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High-Throughput Functional Evaluation ofBRCA2Variants of Unknown Significance

Ikegami

Ueno

Momozawa

et al. 2020

Preprint

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Keywords: BRCA2, poly(ADP-ribose) polymerase (PARP) inhibitor, variants of unknown 22 significance (VUSs), companion diagnosis, Bayesian hierarchical model 23 24 ABSTRACT 44Numerous nontruncating missense variants of the BRCA2 gene have been identified, but there 45 is a lack of convincing evidence, such as familial data, demonstrating their clinical relevance 46 and they thus remain unactionable. To assess the pathogenicity of variants of unknown 47The BRCA1 and BRCA2 (BRCA) genes encode key proteins in the homology-directed DNA 60 break repair (HDR) pathway, and their inactivation predisposes individuals to cancer 61 development 1 . Germline loss-of-function variants in BRCA markedly increase the risk of early-62 onset breast and ovarian cancer; in such cases, prophylactic oophorectomy and mastectomy 63 and genetic testing for at-risk relatives must be considered 2, 3, 4 . Tumors with pathogenic 64 mutations within BRCA and defective HDR have been shown to be particularly sensitive to 65 platinum-based chemotherapies and PARP inhibitors, the efficacy of which is mediated 66 through synthetic lethality in cancer cells with BRCA loss-of-function 5, 6 . 67 68The American College of Medical Genetics and Genomics (ACMG) standards and guidelines 69 for the interpretation of sequence variants recommend a process for variant classification based 70 on criteria using population, computational, functional, and segregation data 7 . Family-based 71 studies including a multifactorial model of pathology, a cosegregation profile, and the 72 cooccurrence and family history of cancer may exemplify the most reliable methods for 73 classifying BRCA2 gene variants 8, 9 . Nonsense or frameshift mutations within the coding exons 74 of BRCA markedly alter the structures of the protein products and are presumed to confer loss-75 of-function. However, the vast majority of missense variants are individually rare in both 76 general populations and cancer patients, and case-control studies may not have sufficient 77 statistical significance to classify these variants as pathogenic or benign 10, 11, 12 . No current in 78 silico computational prediction algorithm for missense variants is accurate enough when used 79 alone 13 . Thus, the functional evaluation of missense variants of unknown significance (VUSs) 80 is urgently required to improve the interpretation of variants identified by genetic testing and 81 to support clinical decision-making for their carriers 14 . 82Page 5 of 63 While thousands of BRCA1 VUSs have been assessed by recently developed high-throughput 83 functional assays 15, 16, 17 , a few hundred BRCA2 variants have been evaluated by a conventional 84 functional assay for BRCA2, the HDR assay 18, 19 . The HDR assay has three issues requiring 85 improvement: (i) the throughput is quite low, (ii) it uses a hamster cell line with transient 86 expression of BRCA2 cDNA, and most importantly, (iii) it can evaluate only variants in the 87 DNA-binding domain 14, 20 . To overcome these limitations, we propose herein a high-8...

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

High-Throughput Functional Evaluation ofBRCA2Variants of Unknown Significance

Ikegami

Ueno

Momozawa

et al. 2020

Preprint

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“…Recently, SPiCE [16] has been proposed as a method to predict the probability of a splice site variant affecting splicing. SPiCE is a logistic regression model trained from 142 manually collected and experimentally tested variants.…”

Section: Mmsplice Improves the Prediction Of Splice Variant Pathogenimentioning

confidence: 99%

“…To compare MMSplice with SPiCE [16], we restricted to the regions that SPiCE scores, i.e. [-12, 2] nt around the acceptor or [-3, 8] nt around the donor of protein coding genes.…”

Section: Variant Pathogenicity Predictionmentioning

confidence: 99%

“…Variants can be scored with respect to these regulatory elements by comparing predictions for the reference sequence and the alternative sequence containing the genetic variant of interest. However, although methods combining several of these scores have been proposed, including Human Splicing Finder [14], MutPred splice [15], and more recently SPiCE [16], the resulting physical and quantitative effect of these variants on splicing remains difficult to assess with these algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Modular modeling improves the predictions of genetic variant effects on splicing

Cheng

Tyd

et al. 2018

Preprint

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Predicting the effects of genetic variants on splicing is highly relevant for human genetics. We describe the framework MMSplice (modular modeling of splicing) with which we built the winning model of the CAGI 2018 exon skipping prediction challenge. The MMSplice modules are neural networks scoring exon, intron, and splice sites, trained on distinct large-scale genomics datasets. These modules are combined to predict effects of variants on exon skipping, alternative donor and acceptor sites, splicing efficiency, and pathogenicity, with matched or higher performance than state-of-the-art. Our models, available in the repository Kipoi, apply to variants including indels directly from VCF files.

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SPiP: Splicing Prediction Pipeline, a machine learning tool for massive detection of exonic and intronic variant effects on mRNA splicing

et al. 2022

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Modeling splicing is essential for tackling the challenge of variant interpretation as each nucleotide variation can be pathogenic by affecting pre-mRNA splicing via disruption/creation of splicing motifs such as 5′/3′ splice sites, branch sites, or splicing regulatory elements. Unfortunately, most in silico tools focus on a specific type of splicing motif, which is why we developed the Splicing Prediction Pipeline (SPiP) to perform, in one single bioinformatic analysis based on a machine learning approach, a comprehensive assessment of the variant effect on different splicing motifs. We gathered a curated set of 4616 variants scattered all along the sequence of 227 genes, with their corresponding splicing studies. The Bayesian analysis provided us with the number of control variants, that is, variants without impact on splicing, to mimic the deluge of variants from high-throughput sequencing data.Results show that SPiP can deal with the diversity of splicing alterations, with 83.13% sensitivity and 99% specificity to detect spliceogenic variants. Overall performance as measured by area under the receiving operator curve was 0.986, better than SpliceAI and SQUIRLS (0.965 and 0.766) for the same data set.

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Novel diagnostic tool for prediction of variant spliceogenicity derived from a set of 395 combined in silico/in vitro studies: an international collaborative effort

Cited by 11 publications

References 0 publications

High-Throughput Functional Evaluation ofBRCA2Variants of Unknown Significance

High-Throughput Functional Evaluation ofBRCA2Variants of Unknown Significance

Modular modeling improves the predictions of genetic variant effects on splicing

SPiP: Splicing Prediction Pipeline, a machine learning tool for massive detection of exonic and intronic variant effects on mRNA splicing

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