Classification Of Genetic Variants Research Articles

SpadaHC: a database to improve the classification of variants in hereditary cancer genes in the Spanish population.

Accurate classification of genetic variants is crucial for clinical decision-making in hereditary cancer. In Spain, genetic diagnostic laboratories have traditionally approached this task independently due to the lack of a dedicated resource. Here we present SpadaHC, a web-based database for sharing variants in hereditary cancer genes in the Spanish population. SpadaHC is implemented using a three-tier architecture consisting of a relational database, a web tool and a bioinformatics pipeline. Contributing laboratories can share variant classifications and variants from individuals in Variant Calling Format (VCF) format. The platform supports open and restricted access, flexible dataset submissions, automatic pseudo-anonymization, VCF quality control, variant normalization and liftover between genome builds. Users can flexibly explore and search data, receive automatic discrepancy notifications and access SpadaHC population frequencies based on many criteria. In February 2024, SpadaHC included 18 laboratory members, storing 1.17 million variants from 4306 patients and 16 343 laboratory classifications. In the first analysis of the shared data, we identified 84 genetic variants with clinically relevant discrepancies in their classifications and addressed them through a three-phase resolution strategy. This work highlights the importance of data sharing to promote consistency in variant classifications among laboratories, so patients and family members can benefit from more accurate clinical management. Database URL: https://spadahc.ciberisciii.es/.

Prioritize Variant Reclassification in Pediatric Long QT Syndrome-Time to Revisit.

Congenital long QT syndrome (LQTS) is an inherited arrhythmia syndrome associated with sudden cardiac death. Accurate interpretation and classification of genetic variants in LQTS patients are crucial for effective management. All patients with LQTS with a positive genetic test over the past 18years (2002-2020) in our single tertiary pediatric cardiac center were identified. Reevaluation of the reported variants in LQTS genes was conducted using the American College of Genetics and Genomics (ACMG) guideline after refinement by the US ClinGen SVI working group and guideline by Walsh et al. on genetic variant reclassification, under multidisciplinary input. Among the 59 variants identified. 18 variants (30.5%) were reclassified. A significant larger portion of variants of unknown significance (VUS) were reclassified compared to likely pathogenic (LP)/pathogenic (P) variants (57.7% vs 9.1%, p < 0.001). The rate of reclassification was significantly higher in the limited/disputed evidence group compared to the definite/moderate evidence group (p = 0.0006). All LP/P variants were downgraded in the limited/disputed evidence group (p = 0.0057). VUS upgrades are associated with VUS located in genes within the definite/moderate evidence group (p = 0.0403) and with VUS present in patients exhibiting higher corrected QT intervals (QTc) (p = 0.0445). A significant number of pediatric LQTS variants were reclassified, particularly for VUS. The strength of the gene-disease association of the genes influences the reclassification performance. The study provides important insights and guidance for pediatricians to seek for reclassification of "outdated variants" in order to facilitate contemporary precision medicine.

Abstract 2267: Assessment of AlphaMissense and structure-function predictions demonstrates efficient reclassification of genetic variants of unknown pathogenicity in inherited myeloid neoplasms

Abstract Introduction: Classification of genetic variants has significant implications for clinical management. Artificial Intelligence (AI) has the potential to transform classification of genetic variants, such as AlphaMissense, which incorporates structure-function relationships and allele frequencies across large datasets of genetic variants. While this represents a promising new tool, the performance of AlphaMissense has only been compared to known pathogenic and benign variants. Therefore, the discriminatory power for variants of unknown significance (VUS) has yet to be determined. We have performed a comparison of these predictions with variants associated with inherited myeloid neoplasms: 1) to assess the accuracy of these models against VUS; 2) to enhance classification of unpublished VUS by leveraging published information about other variants; and 3) to map the predicted classification onto protein structures to examine spatial patterns. Methods: For 83 genes associated with inherited myeloid diseases, a systematic literature review for missense variants was performed using the Mastermind Genomic Intelligence Platform and ClinVar. We used Alphamissense to characterize VUS missense variants according to predicted pathogenicity scores as pathogenic, ambiguous, or benign. Known pathogenic (n=1594) and benign (n=501) variants served as controls to evaluate the accuracy of the system. AlphaFold2 structures were visualized for structure-function rendering of reclassified VUS and known pathogenic variants to compare and identify discernible patterns of spatial distribution. Results: This variant dataset comprised 1594 pathogenic, 501 benign, and 46676 VUS missense variants. Among VUS, AlphaMissense reclassified a majority of VUS missense mutations and established that the system was 88% accurate in predicting pathogenicity. A higher percentage of variants was classified as pathogenic among tumor suppressor genes e.g. DDX41 and RUNX1 (57.1% and 40.5%) compared to the oncogenes, GATA1 and GATA2 (25.7% and 34.9%). Furthermore, 3D models revealed a majority of variants reclassified to pathogenic were located in regions with defined tertiary structure clustered with known pathogenic variants, while benign variants were located at peripheral positions lacking definite structure. Conclusions: These results demonstrate the discriminatory power of AlphaMissense for VUS pathogenicity prediction for both loss-of-function and gain-of-function disease mechanisms. This work may also have implications for rare or challenging somatic cancer variants and variants in rare diseases. Finally, incorporating information available from empirical and clinical studies for disease-causing variants offers the possibility of significantly enhancing the predictive power of these models. Citation Format: Amina Kurtovic-Kozaric, Asja Campara, Melissa Jahibasic, Amar Mujkic, Adnan Fojnica, Mark J. Kiel. Assessment of AlphaMissense and structure-function predictions demonstrates efficient reclassification of genetic variants of unknown pathogenicity in inherited myeloid neoplasms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2267.

Ser194Leu DSG2 mutation, associated with arrhythmogenic left ventricular cardiomyopathy and ventricular tachycardia.

Arrhythmogenic cardiomyopathy (AC) is an inherited cardiomyopathy characterized by fibro-fatty replacement of cardiomyocytes, leading to life-threatening ventricular arrhythmia and heart failure. Pathogenic variants of desmoglein2 gene (DSG2) have been reported as genetic etiologies of AC. In contrast, many reported DSG2 variants are benign or variants of uncertain significance. Correct genetic variant classification is crucial for determining the best medical therapy for the patient and family members. Pathogenicity of the DSG2 Ser194Leu variant that was identified by whole exome sequencing in a patient, who presented with ventricular tachycardia and was diagnosed with AC, was investigated by electron microscopy and immunohistochemical staining of endomyocardial biopsy sample. Electron microscopy demonstrated a widened gap in the adhering junction and a less well-organized intercalated disk region in the mutated cardiomyocytes compared to the control. Immunohistochemical staining in the proband diagnosed with AC showed reduced expression of desmoglein 2 and connexin 43 and intercalated disc distortion. Reduced expression of DSG2 and Connexin 43 were observed in cellular cytoplasm and gap junctions. Additionally, we detected perinuclear accumulation of DSG2 and Connexin 43 in the proband sample. Ser194Leu is a missense pathogenic mutation of DSG2 gene associated with arrhythmogenic left ventricular cardiomyopathy.

Genetic variant classification by predicted protein structure: A case study on IRF6

Next-generation genome sequencing has revolutionized genetic testing, identifying numerous rare disease-associated gene variants. However, to impute pathogenicity, computational approaches remain inadequate and functional testing of gene variant is required to provide the highest level of evidence. The emergence of AlphaFold2 has transformed the field of protein structure determination, and here we outline a strategy that leverages predicted protein structure to enhance genetic variant classification. We used the gene IRF6 as a case study due to its clinical relevance, its critical role in cleft lip/palate malformation, and the availability of experimental data on the pathogenicity of IRF6 gene variants through phenotype rescue experiments in irf6-/- zebrafish. We compared results from over 30 pathogenicity prediction tools on 37 IRF6 missense variants. IRF6 lacks an experimentally derived structure, so we used predicted structures to explore associations between mutational clustering and pathogenicity. We found that among these variants, 19 of 37 were unanimously predicted as deleterious by computational tools. Comparing in silico predictions with experimental findings, 12 variants predicted as pathogenic were experimentally determined as benign. Even with the recently published AlphaMissense model, 15/18 (83%) of the predicted pathogenic variants were experimentally determined as benign. In comparison, mapping variants to the protein revealed deleterious mutation clusters around the protein binding domain, whereas N-terminal variants tend to be benign, suggesting the importance of structural information in determining pathogenicity of mutations in this gene. In conclusion, incorporating gene-specific structural features of known pathogenic/benign mutations may provide meaningful insights into pathogenicity predictions in a gene-specific manner and facilitate the interpretation of variant pathogenicity.

CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions.

Machine Learning-based scoring and classification of genetic variants aids the assessment of clinical findings and is employed to prioritize variants in diverse genetic studies and analyses. Combined Annotation-Dependent Depletion (CADD) is one of the first methods for the genome-wide prioritization of variants across different molecular functions and has been continuously developed and improved since its original publication. Here, we present our most recent release, CADD v1.7. We explored and integrated new annotation features, among them state-of-the-art protein language model scores (Meta ESM-1v), regulatory variant effect predictions (from sequence-based convolutional neural networks) and sequence conservation scores (Zoonomia). We evaluated the new version on data sets derived from ClinVar, ExAC/gnomAD and 1000 Genomes variants. For coding effects, we tested CADD on 31 Deep Mutational Scanning (DMS) data sets from ProteinGym and, for regulatory effect prediction, we used saturation mutagenesis reporter assay data of promoter and enhancer sequences. The inclusion of new features further improved the overall performance of CADD. As with previous releases, all data sets, genome-wide CADD v1.7 scores, scripts for on-site scoring and an easy-to-use webserver are readily provided via https://cadd.bihealth.org/ or https://cadd.gs.washington.edu/ to the community.

The importance of variant reinterpretation in inherited cardiovascular diseases: Establishing the optimal timeframe.

Inherited cardiovascular diseases are rare diseases that are difficult to diagnose by non-expert professionals. Genetic analyses play a key role in the diagnosis of these diseases, in which the identification of a pathogenic genetic variant is often a diagnostic criterion. Therefore, genetic variant classification and routine reinterpretation as data become available represent one of the main challenges associated with genetic analyses. Using the genetic variants identified in an inherited cardiovascular diseases unit during a 10-year period, the objectives of this study were: 1) to evaluate the impact of genetic variant reinterpretation, 2) to compare the reclassification rates between different cohorts of cardiac channelopathies and cardiomyopathies, and 3) to establish the most appropriate periodicity for genetic variant reinterpretation. All the evaluated cohorts (full cohort of inherited cardiovascular diseases, cardiomyopathies, cardiac channelopathies, hypertrophic cardiomyopathy, dilated cardiomyopathy, arrhythmogenic cardiomyopathy, Brugada syndrome, long QT syndrome and catecholaminergic polymorphic ventricular tachycardia) showed reclassification rates above 25%, showing even higher reclassification rates when there is definitive evidence of the association between the gene and the disease in the cardiac channelopathies. Evaluation of genetic variant reclassification rates based on the year of the initial classification showed that the most appropriate frequency for the reinterpretation would be 2 years, with the possibility of a more frequent reinterpretation if deemed convenient. To keep genetic variant classifications up to date, genetic counsellors play a critical role in the reinterpretation process, providing clinical evidence that genetic diagnostic laboratories often do not have at their disposal and communicating changes in classification and the potential implications of these reclassifications to patients and relatives.

Variant Analysis in LDLR Gene Uncovers Genetic Basis of Familial Hypercholesterolemia: A Case Report

Familial Hypercholesterolemia (FH) is a hereditary disorder characterised by elevated blood cholesterol levels, predominantly Low-density Lipoprotein Cholesterol (LDL-C). This condition poses a significant risk for early-onset atherosclerotic cardiovascular diseases. A critical step toward effective clinical management is the precise identification of pathogenic variants responsible for FH. The present study aimed to unravel the genetic cause of FH through comprehensive variant effect prediction and comparison with clinical manifestations in a nine-year-old girl with hyperlipidemia. Whole Exome Sequencing (WES) was performed on the proband, and a set of three key genes associated with hyperlipidemia {Apolipoprotein E (APOE), Low -density Lipoprotein Receptors (LDLR), Proprotein Convertase Subtilisin /Kexin type 9 (PCSK9)} were evaluated for the presence of pathogenic mutations. The data were meticulously analysed based on the American College of Medical Genetics (ACMG) guidelines for variant classification. The analysis revealed two pathogenic variations in the LDLR gene: c.1A&gt;C (p.Met1Leu) in exon 1 and a splice site variant c.1187-10G&gt;A in intron 8. Sanger sequencing of family members confirmed the presence of one mutation each in the father and mother, while a younger sibling also carried both pathogenic variants. Genetic testing confirmed Heterozygous FH (HeFH) in the parents and Homozygous FH (HoFH) in both siblings. Proper classification of genetic variants is crucial for informed clinical decision-making and patient management. The study provides valuable insights into the molecular basis of FH in an Indian patient and contributes to the growing knowledge of the LDLR gene mutation spectrum.

Gene editing and cardiac disease modelling for the interpretation of genetic variants of uncertain significance in congenital heart disease

BackgroundGenomic sequencing in congenital heart disease (CHD) patients often discovers novel genetic variants, which are classified as variants of uncertain significance (VUS). Functional analysis of each VUS is required in specialised laboratories, to determine whether the VUS is disease causative or not, leading to lengthy diagnostic delays. We investigated stem cell cardiac disease modelling and transcriptomics for the purpose of genetic variant classification using a GATA4 (p.Arg283Cys) VUS in a patient with CHD.MethodsWe performed high efficiency CRISPR gene editing with homology directed repair in induced pluripotent stem cells (iPSCs), followed by rapid clonal selection with amplicon sequencing. Genetic variant and healthy matched control cells were compared using cardiomyocyte disease modelling and transcriptomics.ResultsGenetic variant and healthy cardiomyocytes similarly expressed Troponin T (cTNNT), and GATA4. Transcriptomics analysis of cardiomyocyte differentiation identified changes consistent with the patient’s clinical human phenotype ontology terms. Further, transcriptomics revealed changes in calcium signalling, and cardiomyocyte adrenergic signalling in the variant cells. Functional testing demonstrated, altered action potentials in GATA4 genetic variant cardiomyocytes were consistent with patient cardiac abnormalities.ConclusionsThis work provides in vivo functional studies supportive of a damaging effect on the gene or gene product. Furthermore, we demonstrate the utility of iPSCs, CRISPR gene editing and cardiac disease modelling for genetic variant interpretation. The method can readily be applied to other genetic variants in GATA4 or other genes in cardiac disease, providing a centralised assessment pathway for patient genetic variant interpretation.

Integration of deep learning with Ramachandran plot molecular dynamics simulation for genetic variant classification.

Functional classification of genetic variants is a key for their clinical applications in patient care. However, abundant variant data generated by the next-generation DNA sequencing technologies limit the use of experimental methods for their classification. Here, we developed a protein structure and deep learning (DL)-based system for genetic variant classification, DL-RP-MDS, which comprises two principles: 1) Extracting protein structural and thermodynamics information using the Ramachandran plot-molecular dynamics simulation (RP-MDS) method, 2) combining those data with an unsupervised learning model of auto-encoder and a neural network classifier to identify the statistical significance patterns of the structural changes. We observed that DL-RP-MDS provided higher specificity than over 20 widely used insilico methods in classifying the variants of three DNA damage repair genes: TP53, MLH1, and MSH2. DL-RP-MDS offers a powerful platform for high-throughput genetic variant classification. The software and online application are available at https://genemutation.fhs.um.edu.mo/DL-RP-MDS/.

DNA methylation signature classification of rare disorders using publicly available methylation data.

Disease-specific DNA methylation patterns (DNAm signatures) have been established for an increasing number of genetic disorders and represent a valuable tool for classification of genetic variants of uncertain significance (VUS). Sample size and batch effects are critical issues for establishing DNAm signatures, but their impact on the sensitivity and specificity of an already established DNAm signature has not previously been tested. Here, we assessed whether publicly available DNAm data can be employed to generate a binary machine learning classifier for VUS classification, and used variants in KMT2D, the gene associated with Kabuki syndrome, together with an existing DNAm signature as proof-of-concept. Using publicly available methylation data for training, a classifier for KMT2D variants was generated, and individuals with molecularly confirmed Kabuki syndrome and unaffected individuals could be correctly classified. The present study documents the clinical utility of a robust DNAm signature even for few affected individuals, and most importantly, underlines the importance of data sharing for improved diagnosis of rare genetic disorders.

Clinical and genetic features of luscan-lumish syndrome associated with a novel de novo variant of SETD2 gene: Case report and literature review.

Introduction: Luscan-Lumish syndrome (LLS) is currently recognized as a rarely-observed condition featured with overgrowth, macrocephaly, obesity, type I Chiari malformation, and linguistic retardation. So far, there have been only a few LLS cases registered worldwide, but with none of them reported from China. To acquire a deeper understanding on the clinical and genetic features of this disease, a Chinese boy with LLS caused by a heterozygous variant in SETD2 gene was investigated in the present study. Methods: The patient was clinically examined and the medical history of his family was collected. Genetic testing was performed to determine the genetic etiology. Results: The proband was a boy aged 5-year-7-month-old, who was referred to our hospital due to "being a slow learner in kindergarten". The child had a history of delayed motor and language development in comparison to his peers. After admission, physical examination revealed tall stature and macrocephaly as the major manifestation, in addition to a relatively lower rating in intelligence assessment as well as abnormal MRI images showing a slightly shorter corpus callosum accompanied by a mildly thinner corpus callosum body. Whole exome sequencing (WES) revealed a heterozygous c.2514_2516delTAG (p.Ser838del) variant in SETD2 gene, which was subsequently identified as a novel de novo variant. According to the standardized genetic variant classification published by the American College of Medical Genetics and Genomics (ACMG), the variant, with a pathogenicity analysis result indicating PS2 + PM2_Supporting + PM4, was determined to be likely pathogenic. Through literature review, the clinical phenotypes of the 15 LLS cases were summarized, including 8 cases of overgrowth (53%), 13 cases of macrocephaly (87%), 11 cases of developmental delay (73%), 8 cases of autism (53%), and 7 cases of special facial features (47%). Besides, abnormal craniocerebral MRI findings were noticed in 7 cases. Despite that the mutation sites of the 15 patients varied from case to case, they showed a uniformly distributed pattern throughout the whole SETD2 gene, including 5 missense mutations, 5 frameshift mutations and 5 non-sense mutations. Conclusion: LLS, not having been recognized till recent years, is identified as an autosomal dominant syndrome triggered by SETD2 gene mutation. As the first report of LLS in China, the case in our study was proved to be associated with a unique type of SETD2 gene mutation that has never been reported previously, which is believed to enrich the mutation spectrum of SETD2 gene and also, deepening the clinicians' understanding on the disease.

From bugs to bedside: functional annotation of human genetic variation for neurological disorders using invertebrate models.

The exponential accumulation of DNA sequencing data has opened new avenues for discovering the causative roles of single-nucleotide polymorphisms (SNPs) in neurological diseases. The opportunities emerging from this are staggering, yet only as good as our abilities to glean insights from this surplus of information. Whereas computational biology continues to improve with respect to predictions and molecular modeling, the differences between in silico and in vivo analysis remain substantial. Invertebrate in vivo model systems represent technically advanced, experimentally mature, high-throughput, efficient and cost-effective resources for investigating a disease. With a decades-long track record of enabling investigators to discern function from DNA, fly (Drosophila) and worm (Caenorhabditis elegans) models have never been better poised to serve as living engines of discovery. Both of these animals have already proven useful in the classification of genetic variants as either pathogenic or benign across a range of neurodevelopmental and neurodegenerative disorders-including autism spectrum disorders, ciliopathies, amyotrophic lateral sclerosis, Alzheimer's and Parkinson's disease. Pathogenic SNPs typically display distinctive phenotypes in functional assays when compared with null alleles and frequently lead to protein products with gain-of-function or partial loss-of-function properties that contribute to neurological disease pathogenesis. The utility of invertebrates is logically limited by overt differences in anatomical and physiological characteristics, and also the evolutionary distance in genome structure. Nevertheless, functional annotation of disease-SNPs using invertebrate models can expedite the process of assigning cellular and organismal consequences to mutations, ascertain insights into mechanisms of action, and accelerate therapeutic target discovery and drug development for neurological conditions.

AmazonForest: In Silico Metaprediction of Pathogenic Variants.

Simple SummaryClinVar is a valuable platform that stores a large set of relevant genetic associations with complex phenotypes. However, the functional impact of a partial set of such associations remains misinterpreted, due to the presence of variants with uncertain significance or with conflicting pathogenicity interpretations. To fill this gap, we present AmazonForest: a metaprediction model based on Random Forest for pathogenicity prediction. AmazonForest was used to reclassify a set of ∼101,000 variants that were predicted as having high pathogenic probability. AmazonForest is available as a web tool with a simple web interface, and also as an R object for pathogenicity predictions.ClinVar is a web platform that stores ∼789,000 genetic associations with complex diseases. A partial set of these cataloged genetic associations has challenged clinicians and geneticists, often leading to conflicting interpretations or uncertain clinical impact significance. In this study, we addressed the (re)classification of genetic variants by AmazonForest, which is a random-forest-based pathogenicity metaprediction model that works by combining functional impact data from eight prediction tools. We evaluated the performance of representation learning algorithms such as autoencoders to propose a better strategy. All metaprediction models were trained with ClinVar data, and genetic variants were annotated with eight functional impact predictors cataloged with SnpEff/SnpSift. AmazonForest implements the best random forest model with a one hot data-encoding strategy, which shows an Area Under ROC Curve of ≥0.93. AmazonForest was employed for pathogenicity prediction of a set of ∼101,000 genetic variants of uncertain significance or conflict of interpretation. Our findings revealed ∼24,000 variants with high pathogenic probability (). In addition, we show results for Alzheimer’s Disease as a demonstration of its application in clinical interpretation of genetic variants in complex diseases. Lastly, AmazonForest is available as a web tool and R object that can be loaded to perform pathogenicity predictions.

Genetic Diagnosis in Hereditary Hemochromatosis: Discovering and Understanding the Biological Relevance of Variants.

Hereditary hemochromatosis (HH) is a genetic disease, leading to iron accumulation and possible organ damage. Patients are usually homozygous for p. Cys282Tyr in the homeostatic iron regulator gene but may have mutations in other genes involved in the regulation of iron. Next-generation sequencing is increasingly being utilized for the diagnosis of patients, leading to the discovery of novel genetic variants. The clinical significance of these variants is often unknown. Determining the pathogenicity of such variants of unknown significance is important for diagnostics and genetic counseling. Predictions can be made using in silico computational tools and population data, but additional evidence is required for a conclusive pathogenicity classification. Genetic disease models, such as in vitro models using cellular overexpression, induced pluripotent stem cells or organoids, and in vivo models using mice or zebrafish all have their own challenges and opportunities when used to model HH and other iron disorders. Recent developments in gene-editing technologies are transforming the field of genetic disease modeling. In summary, this review addresses methods and developments regarding the discovery and classification of genetic variants, from in silico tools to in vitro and in vivo models, and presents them in the context of HH. It also explores recent gene-editing developments and how they can be applied to the discussed models of genetic disease.

Revisiting the UK Genetic Severity Score for NF2: a proposal for the addition of a functional genetic component

BackgroundNeurofibromatosis type 2 (NF2) is an autosomal dominant disorder characterised by the development of multiple schwannomas, especially on vestibular nerves, and meningiomas. The UK NF2 Genetic Severity Score (GSS) is...

Abstract 210: Advancing knowledgebase representation of pediatric cancer variants through ClinGen/CIViC collaboration

Abstract Childhood cancers are driven by unique profiles of somatic genetic alterations, with a significant contribution from predisposing germline variants. Understanding the genomic landscape of pediatric cancers is complicated by their rarity, the heterogeneity of variation within a given disease, and the complex forms of structural variation they contain. Variants in childhood disease may differ from those in adult versions of the same cancer type, or may have different clinical significance. Currently, pediatric variants are underrepresented in cancer variant databases, and an urgent need exists for their publicly available expert curation. To address this, the Pediatric Cancer Taskforce (PCT) was formed within the Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group (CDWG) (https://www.clinicalgenome.org/working-groups/somatic/). The PCT is a multi-institutional group of 39 members with broad experience in childhood cancer and variant curation, whose work consists of standardization and classification of genetic variants in pediatric cancers. The CIViC knowledgebase (www.civicdb.org) is a freely available resource for Clinical Interpretation of Variants in Cancer, which leverages public curation and expert moderation to address the problem of annotating the large volume of clinically actionable cancer variants. PCT curators work together with PCT expert members and the CIViC team on variant curation, and have submitted over 230 Evidence Items and over 10 Assertions to CIViC. To further address issues specific to pediatric curation, the PCT is working with CIViC to develop new pediatric-specific CIViC features and modifications of the data model that will aid in pediatric curation. A pediatric user interface, as well as representation of large scale structural and copy number variation are being developed for version two of CIViC, expected to be released in 1-2 years, which will enable curation of a new class of structural variants often encountered in pediatric cancer. A novel standard operating procedure for childhood cancer curation in CIViC is being developed by PCT experts, curators and the CIViC team. This SOP will cover topics including curation of structural variants, as well as pediatric-specific variant tiering guidelines which take into account the sparse nature of evidence in pediatric cases. A companion resource, CIViCmine (http://bionlp.bcgsc.ca/civicmine/), will be further developed to incorporate pediatric data. These and other joint efforts of the PCT and CIViC will significantly enhance pediatric variant representation for public use, to support the care of children with cancer. Citation Format: Arpad Danos, Wan-Hsin Lin, Jason Saliba, Angshumoy Roy, Alanna J. Church, Shruti Rao, Deborah Ritter, Kilannin Krysiak, Alex Wagner, Erica Barnell, Lana Sheta, Adam Coffman, Susanna Kiwala, Joshua F. McMichael, Laura Corson, Kevin Fisher, Heather E. Williams, Matthew Hiemenz, Katherine A. Janeway, Jianling Ji, Kesserwan A. Chimene, Laura Fuqua, Lisa Dyer, Huiling Xu, Jeffrey Jean, Laveniya Satgunaseelan, Liying Zhang, Ted W. Laetsch, Donald W. Parsons, Ryan Schmidt, Lynn M. Schriml, Kristen L. Sund, Shashikant Kulkarni, Subha Madhavan, Xinjie Xu, Rashmi Kanagal-Shamana, Marian Harris, Yasmine Akkari, Nurit Paz Yacov, Panieh Terraf, Malachi Griffith, Obi L. Griffith, Gordana Raca. Advancing knowledgebase representation of pediatric cancer variants through ClinGen/CIViC collaboration [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 210.

The Science and Art of Clinical Genetic Variant Classification and Its Impact on Test Accuracy.

Clinical genetic variant classification science is a growing subspecialty of clinical genetics and genomics. The field's continued improvement is essential for the success of precision medicine in both germline (hereditary) and somatic (oncology) contexts. This review focuses on variant classification for DNA next-generation sequencing tests. We first summarize current limitations in variant discovery and definition, and then describe the current five- and four-tier classification systems outlined in dominant standards and guideline publications for germline and somatic tests, respectively. We then discuss measures of variant classification discordance and the field's bias for positive results, as well as considerations for panel size and population screening in the context of estimates of positive predictive value thatincorporate estimated variant classification imperfections. Finally, we share opinions on the current state of variant classification from some of the authors of the most widely used standards and guideline publications and from other domain experts.

Spinocerebellar ataxia type 14: refining clinicogenetic diagnosis in a rare adult-onset disorder.

ObjectivesGenetic variant classification is a challenge in rare adult‐onset disorders as in SCA‐PRKCG (prior spinocerebellar ataxia type 14) with mostly private conventional mutations and nonspecific phenotype. We here propose a refined approach for clinicogenetic diagnosis by including protein modeling and provide for confirmed SCA‐PRKCG a comprehensive phenotype description from a German multi‐center cohort, including standardized 3D MR imaging.MethodsThis cross‐sectional study prospectively obtained neurological, neuropsychological, and brain imaging data in 33 PRKCG variant carriers. Protein modeling was added as a classification criterion in variants of uncertain significance (VUS).ResultsOur sample included 25 cases confirmed as SCA‐PRKCG (14 variants, thereof seven novel variants) and eight carriers of variants assigned as VUS (four variants) or benign/likely benign (two variants). Phenotype in SCA‐PRKCG included slowly progressive ataxia (onset at 4–50 years), preceded in some by early‐onset nonprogressive symptoms. Ataxia was often combined with action myoclonus, dystonia, or mild cognitive‐affective disturbance. Inspection of brain MRI revealed nonprogressive cerebellar atrophy. As a novel finding, a previously not described T2 hyperintense dentate nucleus was seen in all SCA‐PRKCG cases but in none of the controls.InterpretationIn this largest cohort to date, SCA‐PRKCG was characterized as a slowly progressive cerebellar syndrome with some clinical and imaging features suggestive of a developmental disorder. The observed non‐ataxia movement disorders and cognitive‐affective disturbance may well be attributed to cerebellar pathology. Protein modeling emerged as a valuable diagnostic tool for variant classification and the newly described T2 hyperintense dentate sign could serve as a supportive diagnostic marker of SCA‐PRKCG.

Frequency of germline BRCA1/2 mutations and association with clinicopathological characteristics in Turkish women with epithelial ovarian cancer.

This study aims to determine the frequency of germline BRCA 1/2 mutations in Turkish women with epithelial ovarian cancer (EOC) and evaluate its relationship with clinicopathological characteristics. In this cross-sectional study, all women with recently diagnosed EOC presenting to Zekai Tahir Burak Women's Health Training and Research Hospital Medical Oncology Clinic between 2016 and 2019 were referred for BRCA testing. Peripheral blood samples were obtained from 76 patients applying to Medical Genetics and BRCA1/2 genes were sequenced using next-generation sequencing. The American College of Medical Genetics and Genomics 2015 criteria were followed for classification of genetic variants. Twenty-four women (31.6%) had pathogenic germline BRCA1/2 mutations. Of these, 17 patients (22.4%) harbored germline BRCA1 mutations and 7 (9.2%) had BRCA2 mutations. When we compared the patients with and without BRCA mutations, there was significant difference in terms of family history (41.7% vs 9.6%, respectively, P=.001). Among all patients, 15 (19.7%) had history of breast or ovarian cancer in first- or second-degree relatives. Germline BRCA1/2 mutations were detected in 66.7% of patients with family history, while these mutations were found in 22.9% of patients without family history (P=.001). In this sample 31.6% of Turkish women with EOC harbored germline BRCA1/2 mutations, which seems higher compared to other ethnic groups except for the Ashkenazi Jews population. All women with EOC should be referred for BRCA testing regardless of family history, age at diagnosis, and histological subtype.