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

Two founder variants account for over 90% of pathogenic BRCA alleles in the Orkney and Shetland Isles in Scotland

For breast and ovarian cancer risk assessment in the isolated populations of the Northern Isles of Orkney and Shetland (in Scotland, UK) and their diasporas, quantifying genetically drifted BRCA1 and BRCA2 pathogenic variants is important. Two actionable variants in these genes have reached much higher frequencies than in cosmopolitan UK populations. Here, we report a BRCA2 splice acceptor variant, c.517-2A>G, found in breast and ovarian cancer families from Shetland. We investigated the frequency and origin of this variant in a population-based research cohort of people of Shetland ancestry, VIKING I. The variant segregates with female breast and ovarian cancer in diagnosed cases and is classified as pathogenic. Exome sequence data from 2108 VIKING I participants with three or more Shetlandic grandparents was used to estimate the population prevalence of c.517-2A>G in Shetlanders. Nine VIKING I research volunteers carry this variant, on a shared haplotype (carrier frequency 0.4%). This frequency is ~130-fold higher than in UK Biobank, where the small group of carriers has a different haplotype. Records of birth, marriage and death indicate genealogical linkage of VIKING I carriers to a founder from the Isle of Whalsay, Shetland, similar to our observations for the BRCA1 founder variant c.5207T>C from Westray, Orkney. In total, 93.5% of pathogenic BRCA variant carriers in Northern Isles exomes are accounted for by these two drifted variants. We thus provide the scientific evidence of an opportunity for screening people of Orcadian and Shetlandic origins for each drifted pathogenic variant, particularly women with Westray or Whalsay ancestry.

Evaluation of pathogenicity of WT1 intron variants by in vitro splicing analysis

BACKGROUND

Wilms tumor 1 (WT1; NM_024426) causes Denys-Drash syndrome, Frasier syndrome, or isolated focal segmental glomerulosclerosis. Several WT1 intron variants are pathogenic; however, the pathogenicity of some variants remains undefined. Whether a candidate variant detected in a patient is pathogenic is very important for determining the therapeutic options for the patient.

METHODS

In this study, we evaluated the pathogenicity of WT1 gene intron variants with undetermined pathogenicity by comparing their splicing patterns with those of the wild-type using an in vitro splicing assay using minigenes. The three variants registered as likely disease-causing genes: Mut1 (c.1017-9 T > C(IVS5)), Mut2 (c.1355-28C > T(IVS8)), Mut3 (c.1447 + 1G > C(IVS9)), were included as subjects along the 34 splicing variants registered in the Human Gene Mutation Database (HGMD)®.

RESULTS

The results showed no significant differences in splicing patterns between Mut1 or Mut2 and the wild-type; however, significant differences were observed in Mut3.

CONCLUSION

We concluded that Mut1 and Mut2 do not possess pathogenicity although they were registered as likely pathogenic, whereas Mut3 exhibits pathogenicity. Our results suggest that the pathogenicity of intronic variants detected in patients should be carefully evaluated.

Molecular analysis of BRCA1 and BRCA2 genes in La Rioja (Spain): five new variants

BACKGROUND

To study BRCA1/2 gene variants in La Rioja in the northcentral area of Spain.

METHODS

We performed a molecular analysis of BRCA1 and BRCA2 in 642 individuals from 427 different families from June 2008 to December 2019.

RESULTS

We identified 71 families with pathogenic variants in these genes, 32 families with BRCA1 variants and 39 families with BRCA2 variants. The pathogenic variants c.959delG in BRCA1 and c.1363_1369delTCAGAGA, c.1397dupA, c.4234_4236delACTinsC and c.8387delC in BRCA2 have not been previously described. The c.81-2 A > T variant in BRCA1, detected in two unrelated families, has not been reported previously in the Spanish population. Two large genomic deletions were found in the BRCA1 gene in exons (Ex) 23-24 and Ex1A-1B-2, and one deletion was found in the BRCA2 gene in Ex2. The pathogenic variant c.5123 C > A in BRCA1 was detected in 8 unrelated families and was the most frequent pathogenic variant in our population. The c.6024dupG mutation in BRCA2 was detected in 6 unrelated families; the c.2808_2011delACAA mutation in BRCA2 was found in 5 different families; the c.211 A > G mutation in BRCA1 was found in three different families; and the c.68_69delAG, c81-2 A > T, c.4038_4039delAA, and c.5266dupC variants in BRCA1 and the c.2457delA, c.2701delC, c.5116_5119delAATA, c.6275delTT, c.7558 C > T and c.7617 + 1G > A variants in BRCA2 were found in two different families.

CONCLUSIONS

The spectrum of pathogenic variants in the BRCA1/2 genes in La Rioja is similar to that in other Spanish regions, with some unique characteristics. The pathogenic c.6024dupG variant in the BRCA2 gene was detected in a large number of families and could have a founding effect in the Ebro riverside areas in the regions of La Rioja and Navarra.

TRIAL REGISTRATION

Not applicable.

Co-observation of germline pathogenic variants in breast cancer predisposition genes: Results from analysis of the BRIDGES sequencing dataset

Co-observation of a gene variant with a pathogenic variant in another gene that explains the disease presentation has been designated as evidence against pathogenicity for commonly used variant classification guidelines. Multiple variant curation expert panels have specified, from consensus opinion, that this evidence type is not applicable for the classification of breast cancer predisposition gene variants. Statistical analysis of sequence data for 55,815 individuals diagnosed with breast cancer from the BRIDGES sequencing project was undertaken to formally assess the utility of co-observation data for germline variant classification. Our analysis included expected loss-of-function variants in 11 breast cancer predisposition genes and pathogenic missense variants in BRCA1, BRCA2, and TP53. We assessed whether co-observation of pathogenic variants in two different genes occurred more or less often than expected under the assumption of independence. Co-observation of pathogenic variants in each of BRCA1, BRCA2, and PALB2 with the remaining genes was less frequent than expected. This evidence for depletion remained after adjustment for age at diagnosis, study design (familial versus population-based), and country. Co-observation of a variant of uncertain significance in BRCA1, BRCA2, or PALB2 with a pathogenic variant in another breast cancer gene equated to supporting evidence against pathogenicity following criterion strength assignment based on the likelihood ratio and showed utility in reclassification of missense BRCA1 and BRCA2 variants identified in BRIDGES. Our approach has applicability for assessing the value of co-observation as a predictor of variant pathogenicity in other clinical contexts, including for gene-specific guidelines developed by ClinGen Variant Curation Expert Panels.

Principles of clinical genetics for rheumatologists: clinical indications and interpretation of broad-based genetic testing

Advances in DNA sequencing technologies, especially next-generation sequencing (NGS), which is the basis for whole-exome sequencing (WES) and whole-genome sequencing (WGS), have profoundly transformed immune-mediated rheumatic disease diagnosis. Recently, substantial cost reductions have facilitated access to these diagnostic tools, expanded the capacity of molecular diagnostics and enabled the pursuit of precision medicine in rheumatology. Understanding the fundamental principles of genetics and diversity in genetic variant classification is a crucial milestone in rheumatology. However, despite the growing availability of DNA sequencing platforms, a significant number of autoinflammatory diseases (AIDs), neuromuscular disorders, hereditary collagen diseases, and monogenic bone diseases remain unsolved, and variants of uncertain significance (VUS) pose a formidable challenge to addressing these unmet needs in the coming decades. This article aims to provide an overview of the clinical indications and interpretation of comprehensive genetic testing in the medical field, addressing the related complexities and implications.

Synonymous variant at the terminal nucleotide in exon 3 of F7 causes abnormal splicing: A case report

BACKGROUND

Synonymous variants are non-pathogenic due to non-substitution of amino acids. However, synonymous exonic terminal nucleotide substitutions may affect splicing. Splicing variants are easily analyzed at RNA level for genes expressed in blood cells. Minigene analysis provides another method for splicing variant analysis of genes that are poorly or not expressed in peripheral blood.

METHODS

Whole exome sequencing was performed to screen for potential pathogenic mutations in the proband, which were validated within the family by Sanger sequencing. The pathogenicity of the synonymous mutation was analyzed using the minigene technology.

RESULTS

The proband harbored the compound heterogeneous variants c. [291G >A; 572-50C >T] and c.681 + 1G >T in F7, of which the synonymous variant c.291G >A was located at the terminal position of exon 3. Minigene analysis revealed exon3 skipping due to this mutation, which may have subsequently affected protein sequence, structure, and function.

CONCLUSION

Our finding confirmed the pathogenicity of c.291G >A, thus extending the pathogenic mutation spectrum of F7, and providing insights for effective reproductive counseling.

RNA Panel Sequencing Is an Effective Tool to Help Classify Splice Variants for Clinical Oncogenetic Diagnosis

Routine gene panel analysis identifies pathogenic variants in clinically relevant genes. However, variants of unknown significance (VUSs) are commonly observed, many of which potentially have an impact on mRNA transcription and splicing. Several software programs attempt to predict the impact of variants on splicing and thus make it possible to select the variants for which it is important to study the effect on the transcripts. Transcript analysis is also necessary to show the tandem character of large duplications, and it can be useful for the search for deep intronic variants that are difficult to identify in a DNA panel. We analyzed 53 variants of unknown significance by targeted sequencing of 48 genes using RNA extracted from patient blood samples. RT-PCR and Sanger sequencing of patient mRNA or minigene monoallelic analysis was also carried out when necessary. For the 53 VUSs, 21 could be classified as likely neutral and 10 as pathogenic or likely pathogenic. Data are comprehensively presented for four variants: PTEN c.206+6T>G, MLH1 c.791-489_791-20del, BRCA2 c.68-8_68-7delinsAA, and MSH2 c.(1076+1_1077-1)_(1276+1_1277-1)dup. These four examples illustrate the usefulness of blood RNA panel sequencing in clinical oncogenetics to help classify VUSs with predicted splice effects. It could also be useful for characterizing large duplications and for detecting deep intronic variants with an impact on expressed transcripts.

Tracking updates in clinical databases increases efficiency for variant reanalysis

Purpose

Variant interpretation, guided by American College of Medical Genetics and Genomics guidelines, can inform clinical decision-making. However, interpretations may change over time for a variety of reasons. Periodic reanalysis of previous variant interpretations is important to ensure that reported genetic findings remain accurate according to current knowledge.

Methods

We performed automated filtering by comparing ClinVar variants available in August 2020 with those from August 2021 to screen for potential reanalysis candidates from 3 projects. These variants were subsequently interpreted based on the American College of Medical Genetics and Genomics/Association for Molecular Pathology variant interpretation guideline or ClinGen revised gene-specific guidelines if applicable.

Results

Our method annotated 241 unique variants requiring reanalysis, from 3 projects containing 3,832,210 previously interpreted variants, including those filtered automatically. Among these 241 variants, 43 variants changed interpretation, including 55.81% (N = 24) with upgraded and 44.19% (N = 19) with downgraded classifications. An efficiency study showed that our strategy increased the reanalysis efficiency and saved reviewing time.

Conclusion

We demonstrated an effective high-throughput method, initiating from external data updates, to achieve variant reanalysis in a clinical laboratory. This filtering method reduced the number of variants that need to be reanalyzed, screened potential variants, and saved time and cost for clinical laboratories.

Missense Variants in COL4A1/2 Are Associated with Cerebral Aneurysms: A Case Report and Literature Review

BACKGROUND

Although cerebral aneurysm (CA) is a defining complication of COL4A1/2-related vasculopathy, the specific factors influencing its onset remain uncertain. This study aimed to identify and analyze these factors.

METHODS

We described a family presenting with a novel variant of the COL4A1 gene complicated with CA. Concurrently, an exhaustive review of previously documented patients with COL4A1/2-related vasculopathy was conducted by sourcing data from PubMed, Web of Science, Google Scholar, and Ichushi databases. We compared the variant types and locations between patients with CA (positive group) and those without CA (negative group).

RESULTS

This study included 53 COL4A1/2 variants from 76 patients. Except for one start codon variant, all the identified variants in CA were missense variants. Otherwise, CA was not associated with other clinical manifestations, such as small-vessel disease or other large-vessel abnormalities. A higher frequency of missense variants (95.5% vs. 58.1%, p = 0.0035) was identified in the CA-positive group.

CONCLUSIONS

CA development appears to necessitate qualitative alterations in COL4A1/2, and the underlying mechanism seems independent of small-vessel disease or other large-vessel anomalies. Our findings suggest that a meticulous evaluation of CA is necessary when missense variants in COL4A1/2 are identified.

Parallel DNA/RNA NGS Using an Identical Target Enrichment Panel in the Analysis of Hereditary Cancer Predisposition

Germline DNA testing using the next-gene-ration sequencing (NGS) technology has become the analytical standard for the diagnostics of hereditary diseases, including cancer. Its increasing use places high demands on correct sample identification, independent confirmation of prioritized variants, and their functional and clinical interpretation. To streamline these processes, we introduced parallel DNA and RNA capture-based NGS using identical capture panel CZECANCA, which is routinely used for DNA analysis of hereditary cancer predisposition. Here, we present the analytical workflow for RNA sample processing and its analytical and diagnostic performance. Parallel DNA/RNA analysis allowed credible sample identification by calculating the kinship coefficient. The RNA capture-based approach enriched transcriptional targets for the majority of clinically relevant cancer predisposition genes to a degree that allowed analysis of the effect of identified DNA variants on mRNA processing. By comparing the panel and whole-exome RNA enrichment, we demonstrated that the tissue-specific gene expression pattern is independent of the capture panel. Moreover, technical replicates confirmed high reproducibility of the tested RNA analysis. We concluded that parallel DNA/RNA NGS using the identical gene panel is a robust and cost-effective diagnostic strategy. In our setting, it allows routine analysis of 48 DNA/RNA pairs using NextSeq 500/550 Mid Output Kit v2.5 (150 cycles) in a single run with sufficient coverage to analyse 226 cancer predisposition and candidate ge-nes. This approach can replace laborious Sanger confirmatory sequencing, increase testing turnaround, reduce analysis costs, and improve interpretation of the impact of variants by analysing their effect on mRNA processing.

Functional evaluation of BRCA1/2 variants of unknown significance with homologous recombination assay and integrative in silico prediction model

Numerous variants of unknown significance (VUSs) exist in hereditary breast and ovarian cancers. Although multiple methods have been developed to assess the significance of BRCA1/2 variants, functional discrepancies among these approaches remain. Therefore, a comprehensive functional evaluation system for these variants should be established. We performed conventional homologous recombination (HR) assays for 50 BRCA1 and 108 BRCA2 VUSs and complementarily predicted VUSs using a statistical logistic regression prediction model that integrated six in silico functional prediction tools. BRCA1/2 VUSs were classified according to the results of the integrative in vitro and in silico analyses. Using HR assays, we identified 10 BRCA1 and 4 BRCA2 VUSs as low-functional pathogenic variants. For in silico prediction, the statistical prediction model showed high accuracy for both BRCA1 and BRCA2 compared with each in silico prediction tool individually and predicted nine BRCA1 and seven BRCA2 variants to be pathogenic. Integrative functional evaluation in this study and the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) guidelines strongly suggested that seven BRCA1 variants (p.Glu272Gly, p.Lys1095Glu, p.Val1653Leu, p.Thr1681Pro, p.Phe1761Val, p.Thr1773Ile, and p.Gly1803Ser) and four BRCA2 variants (p.Trp31Gly, p.Ser2616Phe, p.Tyr2660Cys, and p.Leu2792Arg) were pathogenic. This study demonstrates that integrative evaluation using conventional HR assays and optimized in silico prediction comprehensively classified the significance of BRCA VUSs for future clinical applications.

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.

In silico methods for predicting functional synonymous variants

Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be "silent," but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improvements in computational platforms have led to the development of numerous machine-learning tools, which can be used to advance synonymous SNV research. In this review, we discuss tools that should be used to investigate synonymous variants. We provide supportive examples from seminal studies that demonstrate how these tools have driven new discoveries of functional synonymous SNVs.

Identification and in-silico characterization of splice-site variants from a large cardiogenetic national registry

Splice-site variants in cardiac genes may predispose carriers to potentially lethal arrhythmias. To investigate, we screened 1315 probands and first-degree relatives enrolled in the Canadian Hearts in Rhythm Organization (HiRO) registry. 10% (134/1315) of patients in the HiRO registry carry variants within 10 base-pairs of the intron-exon boundary with 78% (104/134) otherwise genotype negative. These 134 probands were carriers of 57 unique variants. For each variant, American College of Medical Genetics and Genomics (ACMG) classification was revisited based on consensus between nine in silico tools. Due in part to the in silico algorithms, seven variants were reclassified from the original report, with the majority (6/7) downgraded. Our analyses predicted 53% (30/57) of variants to be likely/pathogenic. For the 57 variants, an average of 9 tools were able to score variants within splice sites, while 6.5 tools responded for variants outside these sites. With likely/pathogenic classification considered a positive outcome, the ACMG classification was used to calculate sensitivity/specificity of each tool. Among these, Combined Annotation Dependent Depletion (CADD) had good sensitivity (93%) and the highest response rate (131/134, 98%), dbscSNV was also sensitive (97%), and SpliceAI was the most specific (64%) tool. Splice variants remain an important consideration in gene elusive inherited arrhythmia syndromes. Screening for intronic variants, even when restricted to the ±10 positions as performed here may improve genetic testing yield. We compare 9 freely available in silico tools and provide recommendations regarding their predictive capabilities. Moreover, we highlight several novel cardiomyopathy-associated variants which merit further study.

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.

Analyzing the effects of BRCA1/2 variants on mRNA splicing by minigene assay

As BRCA1/2 gene sequencing become more extensive, a large number VUS (variants of uncertain significance) emerge rapidly. Verifying the splicing effect is an effective means for VUS reclassification. The Minigene Assay platform was established and its reliability was verified in this article. 47 BRCA1 or BRCA2 variants were selected and performed to validate their effect on mRNA splicing. The results showed that, a total of 16 variants were experimentally proved to have effects on mRNA splicing, among which 14 variants were shown to cause truncated proteins by Sanger sequencing. While the other two variants, BRCA2 c.7976 + 3 A > G and BRCA1 c.5152 + 3_5152 + 4insT was analyzed to cause 57 bp and 26 bp base in-frame deletion, respectively. The remaining 31 variants were not shown to cause mRNA splicing abnormity, including several sites at the edge of exons, which were predicted to affect splicing of mRNA by multiple bioinformatic software. Based on our experimental results, 37 variants were reclassified by ACMG rules. Our study showed that experimental splicing analysis was effectual for variants classification, and multiple functional assay or clinical data were also necessary for comprehensive judgment of variants.

The TSC2 c.2742+5G>A variant causes variable splicing changes and clinical manifestations in a family with tuberous sclerosis complex

Background

Tuberous sclerosis complex (TSC) is a genetic, variably expressed, multisystem disease characterized by benign tumors. It is caused by pathogenic variants of the TSC complex subunit 1 gene (TSC1) and the TSC complex subunit 2 gene (TSC2). Genetic testing allows for early diagnosis, genetic counseling, and improved outcomes, but it did not identify a pathogenic variant in up to 25% of all TSC patients. This study aimed to identify the disease-causing variant in a Han-Chinese family with TSC.

Methods

A six-member, three-generation Han-Chinese family with TSC and three unrelated healthy women were recruited. A comprehensive medical examination, a 3-year follow-up, whole exome sequencing, Sanger sequencing, and segregation analysis were performed in the family. The splicing analysis results obtained from six in silico tools, minigene assay, and patients' lymphocyte messenger RNA were compared, and quantitative reverse transcription PCR was used to confirm the pathogenicity of the variant.

Results

Two affected family members had variable clinical manifestations including a rare bilateral cerebellar ataxia symptom. The 3-year follow-up results suggest the effects of a combined treatment of anti-epilepsy drugs and sirolimus for TSC-related epilepsy and cognitive deficits. Whole exome sequencing, Sanger sequencing, segregation analysis, splicing analysis, and quantitative reverse transcription PCR identified the TSC2 gene c.2742+5G>A variant as the genetic cause. This variant inactivated the donor splice site, a cryptic non-canonical splice site was used for different splicing changes in two affected subjects, and the resulting mutant messenger RNA may be degraded by nonsense-mediated decay. The defects of in silico tools and minigene assay in predicting cryptic splice sites were suggested.

Conclusions

This study identified a TSC2 c.2742+5G>A variant as the genetic cause of a Han-Chinese family with TSC and first confirmed its pathogenicity. These findings expand the phenotypic and genetic spectrum of TSC and may contribute to its diagnosis and treatment, as well as a better understanding of the splicing mechanism.

Characterising splicing defects of ABCA4 variants within exons 13-50 in patient-derived fibroblasts

The ATP-binding cassette subfamily A member 4 gene (ABCA4)-associated retinopathy, Stargardt disease, is the most common monogenic inherited retinal disease. Given the pathogenicity of numerous ABCA4 variants is yet to be examined and a significant proportion (more than 15%) of ABCA4 variants are categorized as splice variants in silico, we therefore established a fibroblast-based splice assay to analyze ABCA4 variants in an Australian Stargardt disease cohort and characterize the pathogenic mechanisms of ABCA4 variants. A cohort of 67 patients clinically diagnosed with Stargardt disease was recruited. Genomic DNA was analysed using a commercial panel for ABCA4 variant detection and the consequences of ABCA4 variants were predicted in silico. Dermal fibroblasts were propagated from skin biopsies, total RNA was extracted and the ABCA4 transcript was amplified by RT-PCR. Our analysis identified a total of 67 unique alleles carrying 74 unique variants. The most prevalent splice-affecting complex allele c.[5461-10T>C; 5603A>T] was carried by 10% of patients in a compound heterozygous state. ABCA4 transcripts from exon 13 to exon 50 were readily detected in fibroblasts. In this region, aberrant splicing was evident in 10 out of 57 variant transcripts (18%), carried by 19 patients (28%). Patient-derived fibroblasts provide a feasible platform for identification of ABCA4 splice variants located within exons 13-50. Experimental evidence of aberrant splicing contributes to the pathogenic classification for ABCA4 variants. Moreover, identification of variants that affect splicing processes provides opportunities for intervention, in particular antisense oligonucleotide-mediated splice correction.

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

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. SPiP lends itself to a unique suite for comprehensive prediction of spliceogenicity in the genomic medicine era. SPiP is available at: https://sourceforge.net/projects/splicing-prediction-pipeline/.

Unpredicted Aberrant Splicing Products Identified in Postmortem Sudden Cardiac Death Samples

Molecular screening for pathogenic mutations in sudden cardiac death (SCD)-related genes is common practice for SCD cases. However, test results may lead to uncertainty because of the identification of variants of unknown significance (VUS) occurring in up to 70% of total identified variants due to a lack of experimental studies. Genetic variants affecting potential splice site variants are among the most difficult to interpret. The aim of this study was to examine rare intronic variants identified in the exonic flanking sequence to meet two main objectives: first, to validate that canonical intronic variants produce aberrant splicing; second, to determine whether rare intronic variants predicted as VUS may affect the splicing product. To achieve these objectives, 28 heart samples of cases of SCD carrying rare intronic variants were studied. Samples were analyzed using 85 SCD genes in custom panel sequencing. Our results showed that rare intronic variants affecting the most canonical splice sites displayed in 100% of cases that they would affect the splicing product, possibly causing aberrant isoforms. However, 25% of these cases (1/4) showed normal splicing, contradicting the in silico results. On the contrary, in silico results predicted an effect in 0% of cases, and experimental results showed >20% (3/14) unpredicted aberrant splicing. Thus, deep intron variants are likely predicted to not have an effect, which, based on our results, might be an underestimation of their effect and, therefore, of their pathogenicity classification and family members’ follow-up.

Comparison of In Silico Tools for Splice-Altering Variant Prediction Using Established Spliceogenic Variants: An End-User’s Point of View

Assessing the impact of variants of unknown significance on splicing has become a critical issue and a bottleneck, especially with the widespread implementation of whole-genome or exome sequencing. Although multiple in silico tools are available, the interpretation and application of these tools are difficult and practical guidelines are still lacking. A streamlined decision-making process can facilitate the downstream RNA analysis in a more efficient manner. Therefore, we evaluated the performance of 8 in silico tools (Splice Site Finder, MaxEntScan, Splice-site prediction by neural network, GeneSplicer, Human Splicing Finder, SpliceAI, Splicing Predictions in Consensus Elements, and SpliceRover) using 114 NF1 spliceogenic variants, experimentally validated at the mRNA level. The change in the predicted score incurred by the variant of the nearest wild-type splice site was analyzed, and for type II, III, and IV splice variants, the change in the prediction score of de novo or cryptic splice site was also analyzed. SpliceAI and SpliceRover, tools based on deep learning, outperformed all other tools, with AUCs of 0.972 and 0.924, respectively. For de novo and cryptic splice sites, SpliceAI outperformed all other tools and showed a sensitivity of 95.7% at an optimal cut-off of 0.02 score change. Our results show that deep learning algorithms, especially those of SpliceAI, are validated at a significantly higher rate than other in silico tools for clinically relevant NF1 variants. This suggests that deep learning algorithms outperform traditional probabilistic approaches and classical machine learning tools in predicting the de novo and cryptic splice sites.

Splicing Analysis of 16 PALB2 ClinVar Variants by Minigene Assays: Identification of Six Likely Pathogenic Variants

PALB2 loss-of-function variants are associated with significant increased risk of breast cancer as well as other types of tumors. Likewise, splicing disruptions are a common mechanism of disease susceptibility. Indeed, we previously showed, by minigene assays, that 35 out of 42 PALB2 variants impaired splicing. Taking advantage of one of these constructs (mgPALB2_ex1-3), we proceeded to analyze other variants at exons 1 to 3 reported at the ClinVar database. Thirty-one variants were bioinformatically analyzed with MaxEntScan and SpliceAI. Then, 16 variants were selected for subsequent RNA assays. We identified a total of 12 spliceogenic variants, 11 of which did not produce any trace of the expected minigene full-length transcript. Interestingly, variant c.49-1G > A mimicked previous outcomes in patient RNA (transcript ∆(E2p6)), supporting the reproducibility of the minigene approach. A total of eight variant-induced transcripts were characterized, three of which (∆(E1q17), ∆(E3p11), and ∆(E3)) were predicted to introduce a premature termination codon and to undergo nonsense-mediated decay, and five (▼(E1q9), ∆(E2p6), ∆(E2), ▼(E3q48)-a, and ▼(E3q48)-b) maintained the reading frame. According to an ACMG/AMP (American College of Medical Genetics and Genomics/Association for Molecular Pathology)-based classification scheme, which integrates mgPALB2 data, six PALB2 variants were classified as pathogenic/likely pathogenic, five as VUS, and five as likely benign. Furthermore, five ±1,2 variants were catalogued as VUS because they produced significant proportions of in-frame transcripts of unknown impact on protein function.

Minigene-based splicing analysis and ACMG/AMP-based tentative classification of 56 ATM variants

The ataxia telangiectasia-mutated (ATM) protein is a major coordinator of the DNA damage response pathway. ATM loss-of-function variants are associated with 2-fold increased breast cancer risk. We aimed at identifying and classifying spliceogenic ATM variants detected in subjects of the large-scale sequencing project BRIDGES. A total of 381 variants at the intron-exon boundaries were identified, 128 of which were predicted to be spliceogenic. After further filtering, we ended up selecting 56 variants for splicing analysis. Four functional minigenes (mgATM) spanning exons 4-9, 11-17, 25-29, and 49-52 were constructed in the splicing plasmid pSAD. Selected variants were genetically engineered into the four constructs and assayed in MCF-7/HeLa cells. Forty-eight variants (85.7%) impaired splicing, 32 of which did not show any trace of the full-length (FL) transcript. A total of 43 transcripts were identified where the most prevalent event was exon/multi-exon skipping. Twenty-seven transcripts were predicted to truncate the ATM protein. A tentative ACMG/AMP (American College of Medical Genetics and Genomics/Association for Molecular Pathology)-based classification scheme that integrates mgATM data allowed us to classify 29 ATM variants as pathogenic/likely pathogenic and seven variants as likely benign. Interestingly, the likely pathogenic variant c.1898+2T>G generated 13% of the minigene FL-transcript due to the use of a noncanonical GG-5'-splice-site (0.014% of human donor sites). Circumstantial evidence in three ATM variants (leakiness uncovered by our mgATM analysis together with clinical data) provides some support for a dosage-sensitive expression model in which variants producing ≥30% of FL-transcripts would be predicted benign, while variants producing ≤13% of FL-transcripts might be pathogenic. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Recommendations by the ClinGen Rett/Angelman-like expert panel for gene-specific variant interpretation methods

The genes MECP2, CDKL5, FOXG1, UBE3A, SLC9A6, and TCF4 present unique challenges for current ACMG/AMP variant interpretation guidelines. To address those challenges, the Rett and Angelman-like Disorders Variant Curation Expert Panel (Rett/AS VCEP) drafted gene-specific modifications. A pilot study was conducted to test the clarity and accuracy of using the customized variant interpretation criteria. Multiple curators obtained the same interpretation for 78 out of the 87 variants (~90%), indicating appropriate usage of the modified guidelines the majority of times by all the curators. The classification of 13 variants changed using these criteria specifications compared to when the variants were originally curated and as present in ClinVar. Many of these changes were due to internal data shared from laboratory members however some changes were because of changes in strength of criteria. There were no two-step classification changes and only 1 clinically relevant change (Likely pathogenic to VUS). The Rett/AS VCEP hopes that these gene-specific variant curation rules and the assertions provided help clinicians, clinical laboratories, and others interpret variants in these genes but also other fully penetrant, early-onset genes associated with rare disorders.

Pathogenicity of missense variants affecting the collagen IV α5 carboxy non-collagenous domain in X-linked Alport syndrome

X-linked Alport syndrome is a genetic kidney disease caused by pathogenic COL4A5 variants, but little is known of the consequences of missense variants affecting the NC1 domain of the corresponding collagen IV α5 chain. This study examined these variants in a normal (gnomAD) and other databases (LOVD, Clin Var and 100,000 Genomes Project) to determine their pathogenicity and clinical significance. Males with Cys substitutions in the collagen IV α5 NC1 domain reported in LOVD (n = 25) were examined for typical Alport features, including age at kidney failure. All NC1 variants in LOVD (n = 86) were then assessed for structural damage using an online computational tool, Missense3D. Variants in the ClinVar, gnomAD and 100,000 Genomes Project databases were also examined for structural effects. Predicted damage associated with NC1 substitutions was then correlated with the level of conservation of the affected residues. Cys substitutions in males were associated with the typical features of X-linked Alport syndrome, with a median age at kidney failure of 31 years. NC1 substitutions predicted to cause structural damage were overrepresented in LOVD (p < 0.001), and those affecting Cys residues or 'buried' Gly residues were more common than expected (both p < 0.001). Most NC1 substitutions in gnomAD (88%) were predicted to be structurally-neutral. Substitutions affecting conserved residues resulted in more structural damage than those affecting non-conserved residues (p < 0.001). Many pathogenic missense variants affecting the collagen IV α5 NC1 domain have their effect through molecular structural damage and 3D modelling is a useful tool in their assessment.

Identification of Spliceogenic Variants beyond Canonical GT-AG Splice Sites in Hereditary Cancer Genes

Pathogenic/likely pathogenic variants in susceptibility genes that interrupt RNA splicing are a well-documented mechanism of hereditary cancer syndromes development. However, if RNA studies are not performed, most of the variants beyond the canonical GT-AG splice site are characterized as variants of uncertain significance (VUS). To decrease the VUS burden, we have bioinformatically evaluated all novel VUS detected in 732 consecutive patients tested in the routine genetic counseling process. Twelve VUS that were predicted to cause splicing defects were selected for mRNA analysis. Here, we report a functional characterization of 12 variants located beyond the first two intronic nucleotides using RNAseq in APC, ATM, FH, LZTR1, MSH6, PALB2, RAD51C, and TP53 genes. Based on the analysis of mRNA, we have successfully reclassified 50% of investigated variants. 25% of variants were downgraded to likely benign, whereas 25% were upgraded to likely pathogenic leading to improved clinical management of the patient and the family members.

Presence of rare potential pathogenic variants in subjects under 65 years old with very severe or fatal COVID-19

Rare variants affecting host defense against pathogens could be involved in COVID-19 severity and may help explain fatal outcomes in young and middle-aged patients. Our aim was to report the presence of rare genetic variants in certain genes, by using whole exome sequencing, in a selected group of COVID-19 patients under 65 years who required intubation or resulting in death (n = 44). To this end, different etiopathogenic mechanisms were explored using gene prioritization-based analysis in which genes involved in immune response, immunodeficiencies or blood coagulation were studied. We detected 44 different variants of interest, in 29 different patients (66%). Some of these variants were previously described as pathogenic and were located in genes mainly involved in immune response. A network analysis, including the 42 genes with candidate variants, showed three main components, consisting of 25 highly interconnected genes related to immune response and two additional networks composed by genes enriched in carbohydrate metabolism and in DNA metabolism and repair processes. In conclusion, we have detected candidate variants that may potentially influence COVID-19 outcome in our cohort of patients. Further studies are needed to confirm the ultimate role of the genetic variants described in the present study on COVID-19 severity.

Splicing analyses for variants in MMR genes: best practice recommendations from the European Mismatch Repair Working Group

Over 20% of the DNA mismatch repair (MMR) germline variants in suspected Lynch syndrome patients are classified as variants of uncertain significance (VUS). Well-established functional assays are pivotal for assessing the biological impact of these variants and provide relevant evidence for clinical classification. In our collaborative European Mismatch Repair Working Group (EMMR-WG) we compared three different experimental approaches for evaluating the effect of seven variants on mRNA splicing in MMR genes: (i) RT-PCR of full-length transcripts (FLT), (ii) RT-PCR of targeted transcript sections (TTS), both from patient biological samples and (iii) minigene splicing assays. An overall good concordance was observed between splicing patterns in TTS, FLT and minigene analyses for all variants. The FLT analysis depicted a higher number of different isoforms and mitigated PCR-bias towards shorter isoforms. TTS analyses may miss aberrant isoforms and minigene assays may under/overestimate the severity of certain splicing defects. The interpretation of the experimental findings must be cautious to adequately discriminate abnormal events from physiological complex alternative splicing patterns. A consensus strategy for investigating the impact of MMR variants on splicing was defined. First, RNA should be obtained from patient's cell cultures (such as fresh lymphocyte cultures) incubated with/without a nonsense-mediated decay inhibitor. Second, FLT RT-PCR analysis is recommended to oversee all generated isoforms. Third, TTS analysis and minigene assays are useful independent approaches for verifying and clarifying FLT results. The use of several methodologies is likely to increase the strength of the experimental evidence which contributes to improve variant interpretation.

CI-SpliceAI—Improving machine learning predictions of disease causing splicing variants using curated alternative splice sites

Background

It is estimated that up to 50% of all disease causing variants disrupt splicing. Due to its complexity, our ability to predict which variants disrupt splicing is limited, meaning missed diagnoses for patients. The emergence of machine learning for targeted medicine holds great potential to improve prediction of splice disrupting variants. The recently published SpliceAI algorithm utilises deep neural networks and has been reported to have a greater accuracy than other commonly used methods.

Methods and findings

The original SpliceAI was trained on splice sites included in primary isoforms combined with novel junctions observed in GTEx data, which might introduce noise and de-correlate the machine learning input with its output. Limiting the data to only validated and manual annotated primary and alternatively spliced GENCODE sites in training may improve predictive abilities. All of these gene isoforms were collapsed (aggregated into one pseudo-isoform) and the SpliceAI architecture was retrained (CI-SpliceAI). Predictive performance on a newly curated dataset of 1,316 functionally validated variants from the literature was compared with the original SpliceAI, alongside MMSplice, MaxEntScan, and SQUIRLS. Both SpliceAI algorithms outperformed the other methods, with the original SpliceAI achieving an accuracy of ∼91%, and CI-SpliceAI showing an improvement at ∼92% overall. Predictive accuracy increased in the majority of curated variants.

Conclusions

We show that including only manually annotated alternatively spliced sites in training data improves prediction of clinically relevant variants, and highlight avenues for further performance improvements.

Synonymous Variants: Necessary Nuance in our Understanding of Cancer Drivers and Treatment Outcomes

Once called "silent mutations" and assumed to have no effect on protein structure and function, synonymous variants are now recognized to be drivers for some cancers. There have been significant advances in our understanding of the numerous mechanisms by which synonymous single nucleotide variants (sSNVs) can affect protein structure and function by affecting pre-mRNA splicing, mRNA expression, stability, folding, miRNA binding, translation kinetics, and co-translational folding. This review highlights the need for considering sSNVs in cancer biology to gain a better understanding of the genetic determinants of human cancers and to improve their diagnosis and treatment. We surveyed the literature for reports of sSNVs in cancer and found numerous studies on the consequences of sSNVs on gene function with supporting in vitro evidence. We also found reports of sSNVs that have statistically significant associations with specific cancer types but for which in vitro studies are lacking to support the reported associations. Additionally, we found reports of germline and somatic sSNVs that were observed in numerous clinical studies and for which in silico analysis predicts possible effects on gene function. We provide a review of these investigations and discuss necessary future studies to elucidate the mechanisms by which sSNVs disrupt protein function and are play a role in tumorigeneses, cancer progression, and treatment efficacy. As splicing dysregulation is one of the most well recognized mechanisms by which sSNVs impact protein function, we also include our own in silico analysis for predicting which sSNVs may disrupt pre-mRNA splicing.

Presumed COL4A3/COL4A4 Missense/Synonymous Variants Induce Aberrant Splicing

Background

The incorrect interpretation of missense and synonymous variants can lead to improper molecular diagnosis and subsequent faulty genetic counselling. The aim of this study was to evaluate the pathogenicity of presumed COL4A3/COL4A4 missense and synonymous variants detected by next-generation sequencing to provide evidence for diagnosis and genetic counselling.

Methods

Patients' clinical findings and genetic data were analysed retrospectively. An in vitro minigene assay was conducted to assess the effect of presumed COL4A3/COL4A4 missense and synonymous variants on RNA splicing.

Results

Five unclassified COL4A3/COL4A4 variants, which were detected in five of 343 patients with hereditary kidney diseases, were analysed. All of them were predicted to affect splicing by Human Splicing Finder. The presumed COL4A3 missense variant c.4793T > G [p. (Leu1598Arg)] resulted in a loss of alternative full-length transcript during the splicing process. The COL4A3 transcript carried synonymous variant c.765G > A [p. (Thr255Thr)], led to an in-frame deletion of exon 13. Nevertheless, variants c.3566G > A [p. (Gly1189Glu)] in COL4A3 and c.3990G > A [p. (Pro1330Pro)], c.4766C > T [p. (Pro1589Leu)] in COL4A4 exhibited no deleterious effect on splicing. Among the five patients harbouring the abovementioned COL4A3/COL4A4 variants, three patients were genetically diagnosed with autosomal recessive Alport syndrome, one patient was highly suspected of having thin basement membrane nephropathy, and the other patient was clinically diagnosed with Alport syndrome.

Conclusions

COL4A3 presumed missense variant p. (Leu1598Arg) and synonymous variant p. (Thr255Thr) affect RNA splicing, which highlights the prime importance of transcript analysis of unclassified exonic sequence variants for better molecular diagnosis and genetic counselling. Meanwhile, the reliability of splicing predictions by predictive tools for exonic substitutions needs to be improved.

A practical guide to interpreting germline variants that drive hematopoietic malignancies, bone marrow failure, and chronic cytopenias

PURPOSE

The American College of Medical Genetics and Genomics and the Association for Molecular Pathology guidelines for germline variant interpretation are implemented as a broad framework by standardizing variant interpretation. These rules were designed to be specified, but this process has not been performed for most of the 200 genes associated with inherited hematopoietic malignancies, bone marrow failure, and cytopenias. Because guidelines on how to perform these gene specifications are lacking, variant interpretation is less reliable and reproducible.

METHODS

We have used a variety of methods such as calculations of minor allele frequencies, quasi-case-control studies to establish thresholds, proband counting, and plotting of receiver operating characteristic curves to compare different in silico prediction tools to design recommendations for variant interpretation.

RESULTS

We herein provide practical recommendations for the creation of thresholds for minor allele frequencies, in silico predictions, counting of probands, identification of functional domains with minimal benign variation, use of constraint Z-scores and functional evidence, prediction of nonsense-mediated decay, and assessment of phenotype specificity.

CONCLUSION

These guidelines can be used by anyone interpreting variants associated with inherited hematopoietic malignancies, bone marrow failure, and cytopenias to develop criteria for reliable, accurate, and reproducible germline variant interpretation.

Empirical prediction of variant-activated cryptic splice donors using population-based RNA-Seq data

Predicting which cryptic-donors may be activated by a splicing variant in patient DNA is notoriously difficult. Through analysis of 5145 cryptic-donors (versus 86,963 decoy-donors not used; any GT or GC), we define an empirical method predicting cryptic-donor activation with 87% sensitivity and 95% specificity. Strength (according to four algorithms) and proximity to the annotated-donor appear important determinants of cryptic-donor activation. However, other factors such as splicing regulatory elements, which are difficult to identify, play an important role and are likely responsible for current prediction inaccuracies. We find that the most frequently recurring natural mis-splicing events at each exon-intron junction, summarised over 40,233 RNA-sequencing samples (40K-RNA), predict with accuracy which cryptic-donor will be activated in rare disease. 40K-RNA provides an accurate, evidence-based method to predict variant-activated cryptic-donors in genetic disorders, assisting pathology consideration of possible consequences of a variant for the encoded protein and RNA diagnostic testing strategies.

Genetic variants affecting the consensus splicing motifs can alter binding of spliceosomal components and induce mis-splicing. Here, the authors develop a method, showing that ranking the most common recurring mis-splicing events in public RNA-Seq data can predict the activation of cryptic-donors.

Splicing predictions, minigene analyses, and ACMG-AMP clinical classification of 42 germline PALB2 splice-site variants

PALB2 loss-of-function variants confer high risk of developing breast cancer. Here we present a systematic functional analysis of PALB2 splice-site variants detected in approximately 113,000 women in the large-scale sequencing project Breast Cancer After Diagnostic Gene Sequencing (BRIDGES; https://bridges-research.eu/). Eighty-two PALB2 variants at the intron-exon boundaries were analyzed with MaxEntScan. Forty-two variants were selected for the subsequent splicing functional assays. For this purpose, three splicing reporter minigenes comprising exons 1-12 were constructed. The 42 potential spliceogenic variants were introduced into the minigenes by site-directed mutagenesis and assayed in MCF-7/MDA-MB-231 cells. Splicing anomalies were observed in 35 variants, 23 of which showed no traces or minimal amounts of the expected full-length transcripts of each minigene. More than 30 different variant-induced transcripts were characterized, 23 of which were predicted to truncate the PALB2 protein. The pathogenicity of all variants was interpreted according to an in-house adaptation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG-AMP) variant classification scheme. Up to 23 variants were classified as pathogenic/likely pathogenic. Remarkably, three ±1,2 variants (c.49-2A>T, c.108+2T>C, and c.211+1G>A) were classified as variants of unknown significance, as they produced significant amounts of either in-frame transcripts of unknown impact on the PALB2 protein function or the minigene full-length transcripts. In conclusion, we have significantly contributed to the ongoing effort of identifying spliceogenic variants in the clinically relevant PALB2 cancer susceptibility gene. Moreover, we suggest some approaches to classify the findings in accordance with the ACMG-AMP rationale. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Consistency of variant interpretations among bioinformaticians and clinical geneticists in hereditary cancer panels

Next-generation sequencing (NGS) is used increasingly in hereditary cancer patients' (HCP) management. While enabling evaluation of multiple genes simultaneously, the technology brings to light the dilemma of variant interpretation. Here, we aimed to reveal the underlying reasons for the discrepancy in the evidence titles used during variant classification according to ACMG guidelines by two different bioinformatic specialists (BIs) and two different clinical geneticists (CGs). We evaluated final reports of 1920 cancer patients and 189 different variants from 285 HCP were enrolled to the study. A total of 173 of these variants were classified as pathogenic (n = 132) and likely pathogenic (n = 41) by the BI and an additional 16 variants, that were classified as VUS by at least one interpreter and their classification would change the clinical management, were compared for their evidence titles between different specialists. The attributed evidence titles and the final classification of the variants among BIs and CGs were compared. The discrepancy between P/LP final reports was 22.5%. The discordance between CGs was 30% whereas the discordance between two BIs was almost 75%. The use of PVS1, PS3, PP3, PP5, PM1, PM2, BP1, BP4 criteria markedly varied from one expert to another. This difference was particularly noticeable in PP3, PP5, and PM1 evidence and mostly in the variants affecting splice sites like BRCA1(NM_007294.4) c.4096 + 1 G > A and CHEK2(NM_007194.4) c.592 + 3 A > T. With recent advancements in precision medicine, the importance of variant interpretations is emerging. Our study shows that variant interpretation is subjective process that is in need of concrete definitions for accurate and standard interpretation.

Genotype-phenotype correlations for COL4A3-COL4A5 variants resulting in Gly substitutions in Alport syndrome

Alport syndrome is the commonest inherited kidney disease and nearly half the pathogenic variants in the COL4A3-COL4A5 genes that cause Alport syndrome result in Gly substitutions. This study examined the molecular characteristics of Gly substitutions that determine the severity of clinical features. Pathogenic COL4A5 variants affecting Gly in the Leiden Open Variation Database in males with X-linked Alport syndrome were correlated with age at kidney failure (n = 157) and hearing loss diagnosis (n = 80). Heterozygous pathogenic COL4A3 and COL4A4 variants affecting Gly (n = 304) in autosomal dominant Alport syndrome were correlated with the risk of haematuria in the UK 100,000 Genomes Project. Gly substitutions were stratified by exon location (1 to 20 or 21 to carboxyl terminus), being adjacent to a non-collagenous region (interruption or terminus), and the degree of instability caused by the replacement residue. Pathogenic COL4A5 variants that resulted in a Gly substitution with a highly destabilising residue reduced the median age at kidney failure by 7 years (p = 0.002), and age at hearing loss diagnosis by 21 years (p = 0.004). Substitutions adjacent to a non-collagenous region delayed kidney failure by 19 years (p = 0.014). Heterozygous pathogenic COL4A3 and COL4A4 variants that resulted in a Gly substitution with a highly destabilising residue (Arg, Val, Glu, Asp, Trp) were associated with an increased risk of haematuria (p = 0.018), and those adjacent to a non-collagenous region were associated with a reduced risk (p = 0.046). Exon location had no effect. In addition, COL4A5 variants adjacent to non-collagenous regions were over-represented in the normal population in gnomAD (p < 0.001). The nature of the substitution and of nearby residues determine the risk of haematuria, early onset kidney failure and hearing loss for Gly substitutions in X-linked and autosomal dominant Alport syndrome.

BRCA1/2 variants of unknown significance in hereditary breast and ovarian cancer (HBOC) syndrome: looking for the hidden meaning

Hereditary breast and ovarian cancer syndrome is caused by germline mutations in BRCA1/2 genes. These genes are very large and their mutations are heterogeneous and scattered throughout the coding sequence. In addition to the above-mentioned mutations, variants of uncertain/unknown significance (VUSs) have been identified in BRCA genes, which make more difficult the clinical management of the patient and risk assessment. In the last decades, several laboratories have developed different databases that contain more than 2000 variants for the two genes and integrated strategies which include multifactorial prediction models based on direct and indirect genetic evidence, to classify the VUS and attribute them a clinical significance associated with a deleterious, high-low or neutral risk. This review provides a comprehensive overview of literature studies concerning the VUSs, in order to assess their impact on the population and provide new insight for the appropriate patient management in clinical practice.

Characterization of Synonymous BRCA1:c.132C>T as a Pathogenic Variant

Breast cancer gene 1 (BRCA1) and BRCA2 are tumor suppressors involved in DNA damage response and repair. Carriers of germline pathogenic or likely pathogenic variants in BRCA1 or BRCA2 have significantly increased lifetime risks of breast cancer, ovarian cancer, and other cancer types; this phenomenon is known as hereditary breast and ovarian cancer (HBOC) syndrome. Accurate interpretation of BRCA1 and BRCA2 variants is important not only for disease management in patients, but also for determining preventative measures for their families. BRCA1:c.132C>T (p.Cys44=) is a synonymous variant recorded in the ClinVar database with “conflicting interpretations of its pathogenicity”. Here, we report our clinical tests in which we identified this variant in two unrelated patients, both of whom developed breast cancer at an early age with ovarian presentation a few years later and had a family history of relevant cancers. Minigene assay showed that this change caused a four-nucleotide loss at the end of exon 3, resulting in a truncated p.Cys44Tyrfs*5 protein. Reverse transcription-polymerase chain reaction identified two fragments (123 and 119 bp) using RNA isolated from patient blood samples, in consistency with the results of the minigene assay. Collectively, we classified BRCA1:c.132C>T (p.Cys44=) as a pathogenic variant, as evidenced by functional studies, RNA analysis, and the patients’ family histories. By analyzing variants recorded in the BRCA Exchange database, we found synonymous changes at the ends of exons could potentially influence splicing; meanwhile, current in silico tools could not predict splicing changes efficiently if the variants were in the middle of an exon, or in the deep intron region. Future studies should attempt to identify variants that influence gene expression and post-transcription modifications to improve our understanding of BRCA1 and BRCA2, as well as their related cancers.

Value of the loss of heterozygosity to BRCA1 variant classification

At least 10% of the BRCA1/2 tests identify variants of uncertain significance (VUS) while the distinction between pathogenic variants (PV) and benign variants (BV) remains particularly challenging. As a typical tumor suppressor gene, the inactivation of the second wild-type (WT) BRCA1 allele is expected to trigger cancer initiation. Loss of heterozygosity (LOH) of the WT allele is the most frequent mechanism for the BRCA1 biallelic inactivation. To evaluate if LOH can be an effective predictor of BRCA1 variant pathogenicity, we carried out LOH analysis on DNA extracted from 90 breast and seven ovary tumors diagnosed in 27 benign and 55 pathogenic variant carriers. Further analyses were conducted in tumors with PVs yet without loss of the WT allele: BRCA1 promoter hypermethylation, next-generation sequencing (NGS) of BRCA1/2, and BRCAness score. Ninety-seven tumor samples were analyzed from 26 different BRCA1 variants. A relatively stable pattern of LOH (65.4%) of WT allele for PV tumors was observed, while the allelic balance (63%) or loss of variant allele (15%) was generally seen for carriers of BV. LOH data is a useful complementary argument for BRCA1 variant classification.

Identification and Characterization of an Exonic Duplication in PALB2 in a Man with Synchronous Breast and Prostate Cancer

PALB2 (partner and localizer of BRCA2), as indicated by its name, is a BRCA2-interacting protein that plays an important role in homologous recombination (HR) and DNA double-strand break (DSB) repair. While pathogenic variants of PALB2 have been well proven to confer an increased risk of breast cancer, data on its involvement in prostate cancer (PrC) have not been clearly demonstrated. We investigated, using targeted next generation sequencing (NGS), a 59-year-old Caucasian man who developed synchronous breast and prostate cancers. This genetic investigation allowed to identify an intragenic germline heterozygous duplication in PALB2, implicating intronic repetitive sequences spanning exon 11. This variant was confirmed by multiplex ligation probe amplification (MLPA), and genomic breakpoints have been identified and characterized at the nucleotide level (c.3114-811_3202-1756dup) using an approach based on walking PCR, long range PCR, and Sanger sequencing. RT-PCR using mRNA extracted from lymphocytes and followed by Sanger sequencing revealed a tandem duplication r.3114_3201dup; p.(Gly1068Glufs * 14). This duplication results in the synthesis of a truncated, and most-likely, non-functional protein. These findings expand the phenotypic spectrum of PALB2 variants and may improve the yield of genetic diagnoses in this field.

Pathogenic neurofibromatosis type 1 (NF1) RNA splicing resolved by targeted RNAseq

Neurofibromatosis type 1 (NF1) is caused by loss-of-function variants in the NF1 gene. Approximately 10% of these variants affect RNA splicing and are either missed by conventional DNA diagnostics or are misinterpreted by in silico splicing predictions. Therefore, a targeted RNAseq-based approach was designed to detect pathogenic RNA splicing and associated pathogenic DNA variants. For this method RNA was extracted from lymphocytes, followed by targeted RNAseq. Next, an in-house developed tool (QURNAs) was used to calculate the enrichment score (ERS) for each splicing event. This method was thoroughly tested using two different patient cohorts with known pathogenic splice-variants in NF1. In both cohorts all 56 normal reference transcript exon splice junctions, 24 previously described and 45 novel non-reference splicing events were detected. Additionally, all expected pathogenic splice-variants were detected. Eleven patients with NF1 symptoms were subsequently tested, three of which have a known NF1 DNA variant with a putative effect on RNA splicing. This effect could be confirmed for all 3. The other eight patients were previously without any molecular confirmation of their NF1-diagnosis. A deep-intronic pathogenic splice variant could now be identified for two of them (25%). These results suggest that targeted RNAseq can be successfully used to detect pathogenic RNA splicing variants in NF1.

Identification and Characterization of New Alu Element Insertion in the BRCA1 Exon 14 Associated with Hereditary Breast and Ovarian Cancer

Hereditary breast and ovarian cancer syndrome (HBOC) is an autosomal dominant cancer predisposition syndrome characterized by an increased risk of breast and ovarian cancers. Germline pathogenic variants in BRCA1 are found in about 7–10% of all familial breast cancers and 10% of ovarian cancers. Alu elements are the most abundant mobile DNA element in the human genome and are known to affect the human genome by different mechanisms leading to human disease. We report here the detection, by next-generation sequencing (NGS) analysis coupled with a suitable bioinformatics pipeline, of an AluYb8 element in exon 14 of the BRCA1 gene in a family with HBOC history first classified as BRCA-negative by Sanger sequencing and first NGS analysis. The c.4475_c.4476insAluYb8 mutation impacts splicing and induces the skipping of exon 14. As a result, the produced mRNA contains a premature stop, leading to the production of a short and likely non-functional protein (pAla1453Glyfs*10). Overall, our study allowed us to identify a novel pathogenic variant in BRCA1 and showed the importance of bioinformatics tool improvement and versioning.

The Interpretation of Sequence Variants in Myeloid Neoplasms

OBJECTIVES

To provide an overview of the challenges encountered during the interpretation of sequence variants detected by next-generation sequencing (NGS) in myeloid neoplasms, as well as the limitations of the technology with the goal of preventing the over- or undercalling of alterations that may have a significant effect on patient management.

METHODS

Review of the peer-reviewed literature on the interpretation, reporting, and technical challenges of NGS assays for myeloid neoplasms.

RESULTS

NGS has been integrated widely and rapidly into the standard evaluating of myeloid neoplasms. Review of the literature reveals that myeloid sequence variants are challenging to detect and interpret. Large insertions and guanine-cytosine-heavy areas prove technically challenging while frameshift and truncating alterations may be classified as variants of uncertain significance by tertiary analysis informatics pipelines due to their absence in the literature and databases.

CONCLUSIONS

The analysis and interpretation of NGS results in myeloid neoplasia are challenging due to the varied number of detectable gene alterations. Familiarity with the genomic landscape of myeloid malignancies and knowledge of the tools available for the interpretation of sequence variants are essential to facilitate translation into clinical and therapy decisions.

Mutations at a split codon in the GTPase-encoding domain of OPA1 cause dominant optic atrophy through different molecular mechanisms

Exonic (i.e. coding) variants in genes associated with disease can exert pathogenic effects both at the protein and mRNA level, either by altering the amino acid sequence or by affecting pre-mRNA splicing. The latter is often neglected due to the lack of RNA analyses in genetic diagnostic testing. In this study we considered both pathomechanisms and performed a comprehensive analysis of nine exonic nucleotide changes in OPA1, which is the major gene underlying autosomal dominant optic atrophy (DOA) and is characterized by pronounced allelic heterogeneity. We focused on the GTPase-encoding domain of OPA1, which harbors most of the missense variants associated with DOA. Given that the consensus splice sites extend into the exons, we chose a split codon, namely codon 438, for our analyses. Variants at this codon are the second most common cause of disease in our large cohort of DOA patients harboring disease-causing variants in OPA1. In silico splice predictions, heterologous splice assays, analysis of patient’s RNA when available, and protein modeling revealed different molecular outcomes for variants at codon 438. The wildtype aspartate residue at amino acid position 438 is directly involved in the dimerization of OPA1 monomers. We found that six amino acid substitutions at codon 438 (i.e. all substitutions of the first and second nucleotide of the codon) destabilized dimerization while only substitutions of the first nucleotide of the codon caused exon skipping. Our study highlights the value of combining RNA analysis and protein modeling approaches to accurately assign patients to future precision therapies.

mRNA analysis identifies deep intronic variants causing Alport syndrome and overcomes the problem of negative results of exome sequencing

Mutations in COL4A3, COL4A4 and COL4A5 genes lead to Alport syndrome (AS). However, pathogenic variants in some AS patients are not detected by exome sequencing. The aim of this study was to identify the underlying genetic causes of five unrelated AS probands with negative next-generation sequencing (NGS) test results. Urine COL4A3–5 mRNAs were analyzed in the probands with an uncertain inherited mode of AS, and COL4A5 mRNA of skin fibroblasts was analyzed in the probands with X-linked AS. RT-PCR and direct sequencing were performed to detect mRNA abnormalities. PCR and direct sequencing were used to analyze the exons with flanking intronic sequences corresponding to mRNA abnormalities. Six novel deep intronic splicing variants in COL4A4 and COL4A5 genes that cannot be captured by exome sequencing were identified in the four AS probands. Skipping of an exon was caused by an intronic variant, and retention of an intron fragment caused by five variants. In the remaining AS proband, COL4A5 variants c.2677 + 646 C > T and r.2678_r.2767del were detected at the DNA and RNA level, respectively, whereas it is unclear whether c.2677 + 646 C > T may not lead to r.2678_r.2767del. Our results reveal that mRNA analysis for AS genes from either urine or skin fibroblasts can resolve genetic diagnosis in AS patients with negative NGS results. We recommend analyzing COL4A3–5 mRNA from urine as the first choice for these patients because it is feasible and non-invasive.

Interpretable prioritization of splice variants in diagnostic next-generation sequencing

A critical challenge in genetic diagnostics is the computational assessment of candidate splice variants, specifically the interpretation of nucleotide changes located outside of the highly conserved dinucleotide sequences at the 5' and 3' ends of introns. To address this gap, we developed the Super Quick Information-content Random-forest Learning of Splice variants (SQUIRLS) algorithm. SQUIRLS generates a small set of interpretable features for machine learning by calculating the information-content of wild-type and variant sequences of canonical and cryptic splice sites, assessing changes in candidate splicing regulatory sequences, and incorporating characteristics of the sequence such as exon length, disruptions of the AG exclusion zone, and conservation. We curated a comprehensive collection of disease-associated splice-altering variants at positions outside of the highly conserved AG/GT dinucleotides at the termini of introns. SQUIRLS trains two random-forest classifiers for the donor and for the acceptor and combines their outputs by logistic regression to yield a final score. We show that SQUIRLS transcends previous state-of-the-art accuracy in classifying splice variants as assessed by rank analysis in simulated exomes, and is significantly faster than competing methods. SQUIRLS provides tabular output files for incorporation into diagnostic pipelines for exome and genome analysis, as well as visualizations that contextualize predicted effects of variants on splicing to make it easier to interpret splice variants in diagnostic settings.

Case report: Antenatal diagnostic of a polymalformative syndrome due to biallelic BRCA2 mutations

Testing the partner of a BRCA2 carrier must always be discussed. If both members of the couple are BRCA2 carriers, they should be informed about the high risks of polymalformative syndromes.

Performance Evaluation of SpliceAI for the Prediction of Splicing of NF1 Variants

Neurofibromatosis type 1, characterized by neurofibromas and café-au-lait macules, is one of the most common genetic disorders caused by pathogenic NF1 variants. Because of the high proportion of splicing mutations in NF1, identifying variants that alter splicing may be an essential issue for laboratories. Here, we investigated the sensitivity and specificity of SpliceAI, a recently introduced in silico splicing prediction algorithm in conjunction with other in silico tools. We evaluated 285 NF1 variants identified from 653 patients. The effect on variants on splicing alteration was confirmed by complementary DNA sequencing followed by genomic DNA sequencing. For in silico prediction of splicing effects, we used SpliceAI, MaxEntScan (MES), and Splice Site Finder-like (SSF). The sensitivity and specificity of SpliceAI were 94.5% and 94.3%, respectively, with a cut-off value of Δ Score > 0.22. The area under the curve of SpliceAI was 0.975 (p < 0.0001). Combined analysis of MES/SSF showed a sensitivity of 83.6% and specificity of 82.5%. The concordance rate between SpliceAI and MES/SSF was 84.2%. SpliceAI showed better performance for the prediction of splicing alteration for NF1 variants compared with MES/SSF. As a convenient web-based tool, SpliceAI may be helpful in clinical laboratories conducting DNA-based NF1 sequencing.