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

Hereditary Colorectal Cancer and Polyposis Syndromes Caused by Variants in Uncommon Genes

A substantial number of hereditary colorectal cancer (CRC) and colonic polyposis cannot be explained by alteration in confirmed predisposition genes, such as mismatch repair (MMR) genes, APC and MUTYH. Recently, a certain number of potential predisposition genes have been suggested, involving each a small number of cases reported so far. Here, we describe the detection of rare variants in the NTLH1, AXIN2, RNF43, BUB1, and TP53 genes in nine unrelated patients who were suspected for inherited CRC and/or colonic polyposis. Seven of them were classified as pathogenic or likely pathogenic variants (PV/LPV). Clinical manifestations of carriers were largely consistent with reported cases with, nevertheless, distinct characteristics. PV/LPV in these uncommon gene can be responsible for up to 2.7% of inherited CRC or colonic polyposis syndromes. Our findings provide supporting evidence for the role of these genes in cancer predisposition, and contribute to the determination of related cancer spectrum and cancer risk for carriers, allowing for the establishment of appropriate screening strategy and genetic counseling in affected families.

Comprehensive genomic filtering algorithm to expose the cause of skewed X chromosome inactivation. The proof of concept in female haemophilia expression

BACKGROUND

Exploring the expression of X linked disorders like haemophilia A (HA) in females involves understanding the balance achieved through X chromosome inactivation (XCI). Skewed XCI (SXCI) may be involved in symptomatic HA carriers. We aimed to develop an approach for dissecting the specific cause of SXCI and verify its value in HA.

METHODS

A family involving three females (two symptomatic with severe/moderate HA: I.2, the mother, and II.1, the daughter; one asymptomatic: II.2) and two related affected males (I.1, the father and I.3, the maternal uncle) was studied. The genetic analysis included F8 mutational screening, multiplex ligation-dependent probe amplification, SNP microarray, whole exome sequencing (WES) and Sanger sequencing. XCI patterns were assessed in ectoderm/endoderm and mesoderm-derived tissues using AR-based and RP2-based systems.

RESULTS

The comprehensive family analysis identifies I.2 female patient as a heterozygous carrier of F8:p.(Ser1414Ter) excluding copy number variations. A consistent XCI pattern of 99.5% across various tissues was observed. A comprehensive filtering algorithm for WES data was designed, developed and applied to I.2. A Gly58Arg missense variant in VMA21 was revealed as the cause for SXCI.Each step of the variant filtering system takes advantage of publicly available genomic databases, non-SXCI controls and case-specific molecular data, and aligns with established concepts in the theoretical background of SXCI.

CONCLUSION

This study acts as a proof of concept for our genomic filtering algorithm's clinical utility in analysing X linked disorders. Our findings clarify the molecular aspects of SXCI and improve genetic diagnostics and counselling for families with X linked diseases like HA.

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

Genetic Landscape of SH3TC2 variants in Russian patients with Charcot-Marie-Tooth disease

Introduction

Charcot-Marie-Tooth disease type 4C (CMT4C) OMIM#601596 stands out as one of the most prevalent forms of recessive motor sensory neuropathy worldwide. This disorder results from biallelic pathogenic variants in the SH3TC2 gene.

Methods

Within a cohort comprising 700 unrelated Russian patients diagnosed with Charcot-Marie-Tooth disease, we conducted a gene panel analysis encompassing 21 genes associated with hereditary neuropathies. Among the cohort, 394 individuals exhibited demyelinating motor and sensory neuropathy.

Results and discussion

Notably, 10 cases of CMT4C were identified within this cohort. The prevalence of CMT4C among Russian demyelinating CMT patients lacking the PMP22 duplication is estimated at 2.5%, significantly differing from observations in European populations. In total, 4 novel and 9 previously reported variants in the SH3TC2 gene were identified. No accumulation of a major variant was detected. Three previously reported variants, c.2860C>T p. (Arg954*), p. (Arg658Cys) and c.279G>A p. (Lys93Lys), recurrently detected in unrelated families. Nucleotide alteration p. (Arg954*) is present in most of our patients (30%).

Case report: Compound heterozygous variants detected by next-generation sequencing in a Tunisian child with ataxia-telangiectasia

Ataxia-telangiectasia (A-T) is an autosomal recessive primary immunodeficiency disorder (PID) caused by biallelic mutations occurring in the serine/threonine protein kinase (ATM) gene. The major role of nuclear ATM is the coordination of cell signaling pathways in response to DNA double-strand breaks, oxidative stress, and cell cycle checkpoints. Defects in ATM functions lead to A-T syndrome with phenotypic heterogeneity. Our study reports the case of a Tunisian girl with A-T syndrome carrying a compound heterozygous mutation c.[3894dupT]; p.(Ala1299Cysfs3;rs587781823), with a splice acceptor variant: c.[5763-2A>C;rs876659489] in the ATM gene that was identified by next-generation sequencing (NGS). Further genetic analysis of the family showed that the mother carried the c.[5763-2A>C] splice acceptor variant, while the father harbored the c.[3894dupT] variant in the heterozygous state. Molecular analysis provides the opportunity for accurate diagnosis and timely management in A-T patients with chronic progressive disease, especially infections and the risk of malignancies. This study characterizes for the first time the identification of compound heterozygous ATM pathogenic variants by NGS in a Tunisian A-T patient. Our study outlines the importance of molecular genetic testing for A-T patients, which is required for earlier detection and reducing the burden of disease in the future, using the patients' families.

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.

Splicing analysis of 24 potential spliceogenic variants in MMR genes and clinical interpretation based on refined ACMG/AMP criteria

Lynch syndrome (LS) is a common hereditary cancer syndrome caused by heterozygous germline pathogenic variants in DNA mismatch repair (MMR) genes. Splicing defect constitutes one of the major mechanisms for MMR gene inactivation. Using RT-PCR based RNA analysis, we investigated 24 potential spliceogenic variants in MMR genes and determined their pathogenicity based on refined splicing-related American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) criteria. Aberrant transcripts were confirmed in 19 variants and 17 of which were classified as pathogenic including 11 located outside of canonical splice sites. Most of these variants were previously reported in LS patients without mRNA splicing assessment. Thus, our study provides crucial evidence for pathogenicity determination, allowing for appropriate clinical follow-up. We also found that computational predictions were globally well correlated with RNA analysis results and the use of both SPiP and SpliceAI software appeared more efficient for splicing defect prediction.

Deciphering the impact of coding and non-coding SCN1A gene variants on RNA splicing

Variants that disrupt normal pre-mRNA splicing are increasingly being recognized as a major cause of monogenic disorders. The SCN1A gene, a key epilepsy gene that is linked to various epilepsy phenotypes, is no exception. Approximately 10% of all reported variants in the SCN1A gene are designated as splicing variants, with many located outside of the canonical donor and acceptor splice sites, and most have not been functionally investigated. However, given its restricted expression pattern, functional analysis of splicing variants in the SCN1A gene could not be routinely performed. In this study, we conducted a comprehensive analysis of all reported SCN1A variants and their potential to impact SCN1A splicing and conclude that splicing variants are substantially misannotated and under-represented. We created a splicing reporter system consisting of 18 splicing vectors covering all 26 protein-coding exons with different genomic contexts and several promoters of varying strengths in order to reproduce the wild-type splicing pattern of the SCN1A gene, revealing cis-regulatory elements essential for proper recognition of SCN1A exons. Functional analysis of 95 SCN1A variants was carried out, including all 68 intronic variants reported in the literature, located outside of the splice sites canonical dinucleotides; 21 exonic variants of different classes (synonymous, missense, nonsense and in-frame deletion) and six variants observed in patients with epilepsy. Interestingly, almost 20% of tested intronic variants had no influence on SCN1A splicing, despite being reported as causative in the literature. Moreover, we confirmed that the majority of predicted exonic variants affect splicing unravelling their true molecular mechanism. We used functional data to perform genotype-phenotype correlation, revealing distinct distribution patterns for missense and splice-affecting 'missense' variants and observed no difference in the phenotype severity of variants leading to in-frame and out-of-frame isoforms, indicating that the Nav1.1 protein is highly intolerant to structural variations. Our work demonstrates the importance of functional analysis in proper variant annotation and provides a tool for high-throughput delineation of splice-affecting variants in SCN1A in a whole-gene manner.

Combining full-length gene assay and SpliceAI to interpret the splicing impact of all possible SPINK1 coding variants

BACKGROUND

Single-nucleotide variants (SNVs) within gene coding sequences can significantly impact pre-mRNA splicing, bearing profound implications for pathogenic mechanisms and precision medicine. In this study, we aim to harness the well-established full-length gene splicing assay (FLGSA) in conjunction with SpliceAI to prospectively interpret the splicing effects of all potential coding SNVs within the four-exon SPINK1 gene, a gene associated with chronic pancreatitis.

RESULTS

Our study began with a retrospective analysis of 27 SPINK1 coding SNVs previously assessed using FLGSA, proceeded with a prospective analysis of 35 new FLGSA-tested SPINK1 coding SNVs, followed by data extrapolation, and ended with further validation. In total, we analyzed 67 SPINK1 coding SNVs, which account for 9.3% of the 720 possible coding SNVs. Among these 67 FLGSA-analyzed SNVs, 12 were found to impact splicing. Through detailed comparison of FLGSA results and SpliceAI predictions, we inferred that the remaining 653 untested coding SNVs in the SPINK1 gene are unlikely to significantly affect splicing. Of the 12 splice-altering events, nine produced both normally spliced and aberrantly spliced transcripts, while the remaining three only generated aberrantly spliced transcripts. These splice-impacting SNVs were found solely in exons 1 and 2, notably at the first and/or last coding nucleotides of these exons. Among the 12 splice-altering events, 11 were missense variants (2.17% of 506 potential missense variants), and one was synonymous (0.61% of 164 potential synonymous variants). Notably, adjusting the SpliceAI cut-off to 0.30 instead of the conventional 0.20 would improve specificity without reducing sensitivity.

CONCLUSIONS

By integrating FLGSA with SpliceAI, we have determined that less than 2% (1.67%) of all possible coding SNVs in SPINK1 significantly influence splicing outcomes. Our findings emphasize the critical importance of conducting splicing analysis within the broader genomic sequence context of the study gene and highlight the inherent uncertainties associated with intermediate SpliceAI scores (0.20 to 0.80). This study contributes to the field by being the first to prospectively interpret all potential coding SNVs in a disease-associated gene with a high degree of accuracy, representing a meaningful attempt at shifting from retrospective to prospective variant analysis in the era of exome and genome sequencing.

Predicting the impact of rare variants on RNA splicing in CAGI6

Variants which disrupt splicing are a frequent cause of rare disease that have been under-ascertained clinically. Accurate and efficient methods to predict a variant's impact on splicing are needed to interpret the growing number of variants of unknown significance (VUS) identified by exome and genome sequencing. Here, we present the results of the CAGI6 Splicing VUS challenge, which invited predictions of the splicing impact of 56 variants ascertained clinically and functionally validated to determine splicing impact. The performance of 12 prediction methods, along with SpliceAI and CADD, was compared on the 56 functionally validated variants. The maximum accuracy achieved was 82% from two different approaches, one weighting SpliceAI scores by minor allele frequency, and one applying the recently published Splicing Prediction Pipeline (SPiP). SPiP performed optimally in terms of sensitivity, while an ensemble method combining multiple prediction tools and information from databases exceeded all others for specificity. Several challenge methods equalled or exceeded the performance of SpliceAI, with ultimate choice of prediction method likely to depend on experimental or clinical aims. One quarter of the variants were incorrectly predicted by at least 50% of the methods, highlighting the need for further improvements to splicing prediction methods for successful clinical application.

Functional Characterization of Two Novel Intron 4 SERPING1 Gene Splice Site Pathogenic Variants in Families with Hereditary Angioedema

Variants that affect splice sites comprise 14.3% of all pathogenic variants in the SERPING1 gene; more than half of them are located outside the canonical sites. To make a clinical decision concerning patients with such variants, it is essential to know the exact way in which the effect of the variant would be realized. The optimal approach to determine the consequences is considered to be mRNA analysis. In the current study, we present the results of functional analysis of two previously non-described variants in the SERPING1 gene (NM_000062.3) affecting intron 4: c.686-1G>A and c.685+4dup, which were detected in members of two Russian families with autosomal dominant inheritance of angioedema type 1. Analysis of the patients' mRNA (extracted from whole blood) showed that the SERPING1(NM_000062.3):c.685+4dup variant leads to the loss of the donor splice site and the activation of the cryptic site in exon 4: r.710_745del (p.Gly217_Pro228del), while the SERPING1(NM_000062.3):c.686-1G>A variant leads to the skipping of exon 5: r.746_949del (p.Asp229_Ser296del).

Benchmarking splice variant prediction algorithms using massively parallel splicing assays

BACKGROUND

Variants that disrupt mRNA splicing account for a sizable fraction of the pathogenic burden in many genetic disorders, but identifying splice-disruptive variants (SDVs) beyond the essential splice site dinucleotides remains difficult. Computational predictors are often discordant, compounding the challenge of variant interpretation. Because they are primarily validated using clinical variant sets heavily biased to known canonical splice site mutations, it remains unclear how well their performance generalizes.

RESULTS

We benchmark eight widely used splicing effect prediction algorithms, leveraging massively parallel splicing assays (MPSAs) as a source of experimentally determined ground-truth. MPSAs simultaneously assay many variants to nominate candidate SDVs. We compare experimentally measured splicing outcomes with bioinformatic predictions for 3,616 variants in five genes. Algorithms' concordance with MPSA measurements, and with each other, is lower for exonic than intronic variants, underscoring the difficulty of identifying missense or synonymous SDVs. Deep learning-based predictors trained on gene model annotations achieve the best overall performance at distinguishing disruptive and neutral variants, and controlling for overall call rate genome-wide, SpliceAI and Pangolin have superior sensitivity. Finally, our results highlight two practical considerations when scoring variants genome-wide: finding an optimal score cutoff, and the substantial variability introduced by differences in gene model annotation, and we suggest strategies for optimal splice effect prediction in the face of these issues.

CONCLUSION

SpliceAI and Pangolin show the best overall performance among predictors tested, however, improvements in splice effect prediction are still needed especially within exons.

Natural history of GM1 gangliosidosis-Retrospective cohort study of 61 French patients from 1998 to 2019

GM1 gangliosidosis is a rare lysosomal storage disorder associated with β-galactosidase enzyme deficiency. There are three types of GM1 gangliosidosis based on age of symptom onset, which correlate with disease severity. In 2019, we performed a retrospective multicentric study including all patients diagnosed with GM1 gangliosidosis in France since 1998. We had access to data for 61 of the 88 patients diagnosed between 1998 and 2019. There were 41 patients with type 1 (symptom onset ≤6 months), 11 with type 2a (symptom onset from 7 months to 2 years), 5 with type 2b (symptom onset from 2 to 3 years), and 4 with type 3 (symptom onset >3 years). The estimated incidence in France was 1/210000. In patients with type 1, the first symptoms were hypotonia (26/41, 63%), dyspnea (7/41, 17%), and nystagmus (6/41, 15%), whereas in patients with type 2a, these were psychomotor regression (9/11, 82%) and seizures (3/11, 27%). In types 2b and 3, the initial symptoms were mild, such as speech difficulties, school difficulties, and progressive psychomotor regression. Hypotonia was observed in all patients, except type 3. The mean overall survival was 23 months (95% confidence interval [CI]: 7, 39) for type 1 and 9.1 years (95% CI: 4.5, 13.5) for type 2a. To the best of our knowledge, this is one of the largest historical cohorts reported, which provides important information on the evolution of all types of GM1 gangliosidosis. These data could be used as a historical cohort in studies assessing potential therapies for this rare genetic disease.

Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: Recommendations from the ClinGen SVI Splicing Subgroup

The American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) framework for classifying variants uses six evidence categories related to the splicing potential of variants: PVS1, PS3, PP3, BS3, BP4, and BP7. However, the lack of guidance on how to apply such codes has contributed to variation in the specifications developed by different Clinical Genome Resource (ClinGen) Variant Curation Expert Panels. The ClinGen Sequence Variant Interpretation Splicing Subgroup was established to refine recommendations for applying ACMG/AMP codes relating to splicing data and computational predictions. We utilized empirically derived splicing evidence to (1) determine the evidence weighting of splicing-related data and appropriate criteria code selection for general use, (2) outline a process for integrating splicing-related considerations when developing a gene-specific PVS1 decision tree, and (3) exemplify methodology to calibrate splice prediction tools. We propose repurposing the PVS1_Strength code to capture splicing assay data that provide experimental evidence for variants resulting in RNA transcript(s) with loss of function. Conversely, BP7 may be used to capture RNA results demonstrating no splicing impact for intronic and synonymous variants. We propose that the PS3/BS3 codes are applied only for well-established assays that measure functional impact not directly captured by RNA-splicing assays. We recommend the application of PS1 based on similarity of predicted RNA-splicing effects for a variant under assessment in comparison with a known pathogenic variant. The recommendations and approaches for consideration and evaluation of RNA-assay evidence described aim to help standardize variant pathogenicity classification processes when interpreting splicing-based evidence.

Overview of the Genetic Causes of Hereditary Breast and Ovarian Cancer Syndrome in a Large French Patient Cohort

The use of multigene panel testing for patients with a predisposition to Hereditary Breast and Ovarian Cancer syndrome (HBOC) is increasing as the identification of mutations is useful for diagnosis and disease management. Here, we conducted a retrospective analysis of BRCA1/2 and non-BRCA gene sequencing in 4630 French HBOC suspected patients. Patients were investigated using a germline cancer panel including the 13 genes defined by The French Genetic and Cancer Group (GGC)-Unicancer. In the patients analyzed, 528 pathogenic and likely pathogenic variants (P/LP) were identified, including BRCA1 (n = 203, 38%), BRCA2 (n = 198, 37%), PALB2 (n = 46, 9%), RAD51C (n = 36, 7%), TP53 (n = 16, 3%), and RAD51D (n = 13, 2%). In addition, 35 novel (P/LP) variants, according to our knowledge, were identified, and double mutations in two distinct genes were found in five patients. Interestingly, retesting a subset of BRCA1/2-negative individuals with an expanded panel produced clinically relevant results in 5% of cases. Additionally, combining in silico (splicing impact prediction tools) and in vitro analyses (RT-PCR and Sanger sequencing) highlighted the deleterious impact of four candidate variants on splicing and translation. Our results present an overview of pathogenic variations of HBOC genes in the southeast of France, emphasizing the clinical relevance of cDNA analysis and the importance of retesting BRCA-negative individuals with an expanded panel.

Characterization of novel CACNA1A splice variants by RNA-sequencing in patients with episodic or congenital ataxia

Loss of function variants in CACNA1A cause a broad spectrum of neurological disorders, including episodic ataxia, congenital or progressive ataxias, epileptic manifestations or developmental delay. Variants located on the AG/GT consensus splice sites are usually considered as responsible of splicing defects, but exonic or intronic variants located outside of the consensus splice site can also lead to abnormal splicing. We investigated the putative consequences on splicing of 11 CACNA1A variants of unknown significance (VUS) identified in patients with episodic ataxia or congenital ataxia. In silico splice predictions were performed and RNA obtained from fibroblasts was analyzed by Sanger sequencing. The presence of abnormal transcripts was confirmed in 10/11 patients, nine of them were considered as deleterious and one remained of unknown significance. Targeted next-generation RNA sequencing was done in a second step to compare the two methods. This method was successful to obtain the full cDNA sequence of CACNA1A. Despite the presence of several isoforms in the fibroblastic cells, it detected most of the abnormally spliced transcripts. In conclusion, RNA sequencing was efficient to confirm the pathogenicity of nine novel CACNA1A variants. Sanger or Next generation methods can be used depending on the facilities and organization of the laboratories.

Benchmarking splice variant prediction algorithms using massively parallel splicing assays

Background

Variants that disrupt mRNA splicing account for a sizable fraction of the pathogenic burden in many genetic disorders, but identifying splice-disruptive variants (SDVs) beyond the essential splice site dinucleotides remains difficult. Computational predictors are often discordant, compounding the challenge of variant interpretation. Because they are primarily validated using clinical variant sets heavily biased to known canonical splice site mutations, it remains unclear how well their performance generalizes.

Results

We benchmarked eight widely used splicing effect prediction algorithms, leveraging massively parallel splicing assays (MPSAs) as a source of experimentally determined ground-truth. MPSAs simultaneously assay many variants to nominate candidate SDVs. We compared experimentally measured splicing outcomes with bioinformatic predictions for 3,616 variants in five genes. Algorithms' concordance with MPSA measurements, and with each other, was lower for exonic than intronic variants, underscoring the difficulty of identifying missense or synonymous SDVs. Deep learning-based predictors trained on gene model annotations achieved the best overall performance at distinguishing disruptive and neutral variants. Controlling for overall call rate genome-wide, SpliceAI and Pangolin also showed superior overall sensitivity for identifying SDVs. Finally, our results highlight two practical considerations when scoring variants genome-wide: finding an optimal score cutoff, and the substantial variability introduced by differences in gene model annotation, and we suggest strategies for optimal splice effect prediction in the face of these issues.

Conclusion

SpliceAI and Pangolin showed the best overall performance among predictors tested, however, improvements in splice effect prediction are still needed especially within exons.

AXIN2 germline testing in a French cohort validates pathogenic variants as a rare cause of predisposition to colorectal polyposis and cancer

Only a few patients with germline AXIN2 variants and colorectal adenomatous polyposis or cancer have been described, raising questions about the actual contribution of this gene to colorectal cancer (CRC) susceptibility. To assess the clinical relevance for AXIN2 testing in patients suspected of genetic predisposition to CRC, we collected clinical and molecular data from the French Oncogenetics laboratories analyzing AXIN2 in this context. Between 2004 and June 2020, 10 different pathogenic/likely pathogenic AXIN2 variants were identified in 11 unrelated individuals. Eight variants were from a consecutive series of 3322 patients, which represents a frequency of 0.24%. However, loss-of-function AXIN2 variants were strongly associated with genetic predisposition to CRC as compared with controls (odds ratio: 11.89, 95% confidence interval: 5.103-28.93). Most of the variants were predicted to produce an AXIN2 protein devoid of the SMAD3-binding and DIX domains, but preserving the β-catenin-binding domain. Ninety-one percent of the AXIN2 variant carriers who underwent colonoscopy had adenomatous polyposis. Forty percent of the variant carriers developed colorectal or/and other digestive cancer. Multiple tooth agenesis was present in at least 60% of them. Our report provides further evidence for a role of AXIN2 in CRC susceptibility, arguing for AXIN2 testing in patients with colorectal adenomatous polyposis or cancer.

APPLICATION OF THE ACMG/AMP FRAMEWORK TO CAPTURE EVIDENCE RELEVANT TO PREDICTED AND OBSERVED IMPACT ON SPLICING: RECOMMENDATIONS FROM THE CLINGEN SVI SPLICING SUBGROUP

The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) framework for classifying variants uses six evidence categories related to the splicing potential of variants: PVS1 (null variant in a gene where loss-of-function is the mechanism of disease), PS3 (functional assays show damaging effect on splicing), PP3 (computational evidence supports a splicing effect), BS3 (functional assays show no damaging effect on splicing), BP4 (computational evidence suggests no splicing impact), and BP7 (silent change with no predicted impact on splicing). However, the lack of guidance on how to apply such codes has contributed to variation in the specifications developed by different Clinical Genome Resource (ClinGen) Variant Curation Expert Panels. The ClinGen Sequence Variant Interpretation (SVI) Splicing Subgroup was established to refine recommendations for applying ACMG/AMP codes relating to splicing data and computational predictions. Our study utilised empirically derived splicing evidence to: 1) determine the evidence weighting of splicing-related data and appropriate criteria code selection for general use, 2) outline a process for integrating splicing-related considerations when developing a gene-specific PVS1 decision tree, and 3) exemplify methodology to calibrate bioinformatic splice prediction tools. We propose repurposing of the PVS1_Strength code to capture splicing assay data that provide experimental evidence for variants resulting in RNA transcript(s) with loss of function. Conversely BP7 may be used to capture RNA results demonstrating no impact on splicing for both intronic and synonymous variants, and for missense variants if protein functional impact has been excluded. Furthermore, we propose that the PS3 and BS3 codes are applied only for well-established assays that measure functional impact that is not directly captured by RNA splicing assays. We recommend the application of PS1 based on similarity of predicted RNA splicing effects for a variant under assessment in comparison to a known Pathogenic variant. The recommendations and approaches for consideration and evaluation of RNA assay evidence described aim to help standardise variant pathogenicity classification processes and result in greater consistency when interpreting splicing-based evidence.

The "extreme phenotype approach" applied to male breast cancer allows the identification of rare variants of ATR as potential breast cancer susceptibility alleles

In oncogenetics, some patients could be considered as "extreme phenotypes", such as those with very early onset presentation or multiple primary malignancies, unusually high numbers of cancers of the same spectrum or rare cancer types in the same parental branch. For these cases, a genetic predisposition is very likely, but classical candidate gene panel analyses often and frustratingly remains negative. In the framework of the EX2TRICAN project, exploring unresolved extreme cancer phenotypes, we applied exome sequencing on rare familial cases with male breast cancer, identifying a novel pathogenic variant of ATR (p.Leu1808*). ATR has already been suspected as being a predisposing gene to breast cancer in women. We next identified 3 additional ATR variants in a cohort of both male and female with early onset and familial breast cancers (c.7762-2A>C; c.2078+1G>A; c.1A>G). Further molecular and cellular investigations showed impacts on transcripts for variants affecting splicing sites and reduction of ATR expression and phosphorylation of the ATR substrate CHEK1. This work further demonstrates the interest of an extended genetic analysis such as exome sequencing to identify very rare variants that can play a role in cancer predisposition in extreme phenotype cancer cases unexplained by classical cancer gene panels testing.

Combining globally search for a regular expression and print matching lines with bibliographic monitoring of genomic database improves diagnosis

Introduction: Exome sequencing has a diagnostic yield ranging from 25% to 70% in rare diseases and regularly implicates genes in novel disorders. Retrospective data reanalysis has demonstrated strong efficacy in improving diagnosis, but poses organizational difficulties for clinical laboratories. Patients and methods: We applied a reanalysis strategy based on intensive prospective bibliographic monitoring along with direct application of the GREP command-line tool (to "globally search for a regular expression and print matching lines") in a large ES database. For 18 months, we submitted the same five keywords of interest [(intellectual disability, (neuro)developmental delay, and (neuro)developmental disorder)] to PubMed on a daily basis to identify recently published novel disease-gene associations or new phenotypes in genes already implicated in human pathology. We used the Linux GREP tool and an in-house script to collect all variants of these genes from our 5,459 exome database. Results: After GREP queries and variant filtration, we identified 128 genes of interest and collected 56 candidate variants from 53 individuals. We confirmed causal diagnosis for 19/128 genes (15%) in 21 individuals and identified variants of unknown significance for 19/128 genes (15%) in 23 individuals. Altogether, GREP queries for only 128 genes over a period of 18 months permitted a causal diagnosis to be established in 21/2875 undiagnosed affected probands (0.7%). Conclusion: The GREP query strategy is efficient and less tedious than complete periodic reanalysis. It is an interesting reanalysis strategy to improve diagnosis.

Computational prediction of human deep intronic variation

BACKGROUND

The adoption of whole-genome sequencing in genetic screens has facilitated the detection of genetic variation in the intronic regions of genes, far from annotated splice sites. However, selecting an appropriate computational tool to discriminate functionally relevant genetic variants from those with no effect is challenging, particularly for deep intronic regions where independent benchmarks are scarce.

RESULTS

In this study, we have provided an overview of the computational methods available and the extent to which they can be used to analyze deep intronic variation. We leveraged diverse datasets to extensively evaluate tool performance across different intronic regions, distinguishing between variants that are expected to disrupt splicing through different molecular mechanisms. Notably, we compared the performance of SpliceAI, a widely used sequence-based deep learning model, with that of more recent methods that extend its original implementation. We observed considerable differences in tool performance depending on the region considered, with variants generating cryptic splice sites being better predicted than those that potentially affect splicing regulatory elements. Finally, we devised a novel quantitative assessment of tool interpretability and found that tools providing mechanistic explanations of their predictions are often correct with respect to the ground - information, but the use of these tools results in decreased predictive power when compared to black box methods.

CONCLUSIONS

Our findings translate into practical recommendations for tool usage and provide a reference framework for applying prediction tools in deep intronic regions, enabling more informed decision-making by practitioners.