Interpretation Of Copy Number Variants Research Articles

Abstract PO5-04-13: Liquid Biopsy Testing in a Greek Cohort of ER-positive, HER2-negative metastatic breast cancer patients

Abstract AIM Endocrine therapy (ET) represents the first line treatment for patients with ER+, HER2- breast cancer, however disease progression is observed in many cases. PIK3CA and ESR1 are the most encountered mutated genes that have been associated with targeted molecular treatment, as well as with resistance to Endocrine Therapy. Liquid biopsy is a non-invasive procedure, that can provide “real-time” monitoring of disease progression and response to treatment. The aim of this study is to determine the mutation rate of ESR1 and PIK3CA genes in a selected cohort of 200 Greek breast cancer patients, using liquid biopsies and NGS technology. PATIENTS AND METHODS Liquid biopsies were collected from 200 ER-positive, HER2-negative metastatic breast cancer patients who have received at least one previous line of endocrine treatment. cfDNA was extracted using the QIAamp Circulating Nucleic Acid Kit (Qiagen). Library preparation was performed using Oncomine™ Breast cfDNA Research Assay (Thermofisher Scientific) and sequencing was carried out using Ion GeneStudio™ S5 System (Thermofisher Scientific). Ion Torrent Oncomine Knowledgebase Reporter was used in sequence analysis and interpretation of Copy number variations, SNPs, and indels. RESULTS Preliminary data of the first 49 examined samples demonstrated the prevalence of ESR1 and PIK3CA mutations in 20.4% (10/49) and 38.7% (19/49) of the cases respectively. The most frequently mutated codon in the ESR1 gene is the Y537, while in the PIK3CA gene, the H1047 codon is mainly altered. ESR1 and PIK3CA genes were co-mutated in 10.2% of the cases. Mutations in KRAS (6.1%) and TP53 (24%) were also detected. CONCLUSION More than 20% of the ER-positive, HER2-negative breast cancer patients with metastatic disease are eligible for targeted treatment with elacestrant. In addition, the high prevalence of mutations detected in our cohort indicates that liquid biopsy NGS panel testing can be used to monitor treatment response, track the development of resistance, and identify emerging genetic alterations that may guide treatment adjustments or the selection of alternative targeted therapies in breast cancer patients. REFERENCES Demir Cetinkaya B, Biray Avci C. Molecular perspective on targeted therapy in breast cancer: a review of current status. Med Oncol. 2022 Jul 14;39(10):149. doi: 10.1007/s12032-022-01749-1. PMID: 35834030; PMCID: PMC9281252. Liao H, Huang W, Pei W, Li H. Detection of ESR1 Mutations Based on Liquid Biopsy in Estrogen Receptor-Positive Metastatic Breast Cancer: Clinical Impacts and Prospects. Front Oncol. 2020 Dec 15;10:587671. doi: 10.3389/fonc.2020.587671. PMID: 33384956; PMCID: PMC7770162. https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-elacestrant-er-positive-her2-negative-esr1-mutated-advanced-or-metastatic-breast-cancer Citation Format: Nikolaos Tsoulos, Angeliki Meintani, Georgios Kapetsis, Chrysiis Chatzigiannidou-Florou, Aikaterini Tsantikidi, Stella Maxouri, Eleni Thanou, Kalliopi Aggelaina, Vasiliki Metaxa-Mariatou, Georgios Tsaousis, Ioannis Natsiopoulos, Vasileios Venizelos, Christos Markopoulos, Flora Zagouri, Eleftherios Kampletsas, Eirini Karyda, Dimitrios Tryfonopoulos, Konstantinos Papazisis, Dimitrios Ziogas, Ilias Athanasiadis, Eirini Papadopoulou, Georgios Nasioulas. Liquid Biopsy Testing in a Greek Cohort of ER-positive, HER2-negative metastatic breast cancer patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-04-13.

Exome copy number variant detection, analysis, and classification in a large cohort of families with undiagnosed rare genetic disease

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and, with new innovative methods, can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the Genomics Research to Elucidate the Genetics of Rare Diseases consortium and analyzed using the seqr platform. The addition of CNV detection to exome analysis identified causal CNVs for 171 families (2.6%). The estimated sizes of CNVs ranged from 293bp to 80 Mb. The causal CNVs consisted of 140 deletions, 15 duplications, 3 suspected complex structural variants (SVs), 3 insertions, and 10 complex SVs, the latter two groups being identified by orthogonal confirmation methods. To classify CNV variant pathogenicity, we used the 2020 American College of Medical Genetics and Genomics/ClinGen CNV interpretation standards and developed additional criteria to evaluate allelic and functional data as well as variants on the X chromosome to further advance the framework. We interpreted 151 CNVs as likely pathogenic/pathogenic and 20 CNVs as high-interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher-resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

Clinical trio genome sequencing facilitates the interpretation of variants in cancer predisposition genes in paediatric tumour patients

The prevalence of pathogenic and likely pathogenic (P/LP) variants in genes associated with cancer predisposition syndromes (CPS) is estimated to be 8-18% for paediatric cancer patients. In more than half of the carriers, the family history is unsuspicious for CPS. Therefore, broad genetic testing could identify germline predisposition in additional children with cancer resulting in important implications for themselves and their families. We thus evaluated clinical trio genome sequencing (TGS) in a cohort of 72 paediatric patients with solid cancers other than retinoblastoma or CNS-tumours. The most prevalent cancer types were sarcoma (n = 26), neuroblastoma (n = 15), and nephroblastoma (n = 10). Overall, P/LP variants in CPS genes were identified in 18.1% of patients (13/72) and P/LP variants in autosomal-dominant CPS genes in 9.7% (7/72). Genetic evaluation would have been recommended for the majority of patients with P/LP variants according to the Jongmans criteria. Four patients (5.6%, 4/72) carried P/LP variants in autosomal-dominant genes known to be associated with their tumour type. With the immediate information on variant inheritance, TGS facilitated the identification of a de novo P/LP in NF1, a gonadosomatic mosaic in WT1 and two pathogenic variants in one patient (DICER1 and PALB2). TGS allows a more detailed characterization of structural variants with base-pair resolution of breakpoints which can be relevant for the interpretation of copy number variants. Altogether, TGS allows comprehensive identification of children with a CPS and supports the individualised clinical management of index patients and high-risk relatives.

Combination of expert guidelines-based and machine learning-based approaches leads to superior accuracy of automated prediction of clinical effect of copy number variations

Clinical interpretation of copy number variants (CNVs) is a complex process that requires skilled clinical professionals. General recommendations have been recently released to guide the CNV interpretation based on predefined criteria to uniform the decision process. Several semiautomatic computational methods have been proposed to recommend appropriate choices, relieving clinicians of tedious searching in vast genomic databases. We have developed and evaluated such a tool called MarCNV and tested it on CNV records collected from the ClinVar database. Alternatively, the emerging machine learning-based tools, such as the recently published ISV (Interpretation of Structural Variants), showed promising ways of even fully automated predictions using broader characterization of affected genomic elements. Such tools utilize features additional to ACMG criteria, thus providing supporting evidence and the potential to improve CNV classification. Since both approaches contribute to evaluation of CNVs clinical impact, we propose a combined solution in the form of a decision support tool based on automated ACMG guidelines (MarCNV) supplemented by a machine learning-based pathogenicity prediction (ISV) for the classification of CNVs. We provide evidence that such a combined approach is able to reduce the number of uncertain classifications and reveal potentially incorrect classifications using automated guidelines. CNV interpretation using MarCNV, ISV, and combined approach is available for non-commercial use at https://predict.genovisio.com/.

Clinical utility of periodic reinterpretation of CNVs of uncertain significance: an 8-year retrospective study

BackgroundArray-CGH is the first-tier genetic test both in pre- and postnatal developmental disorders worldwide. Variants of uncertain significance (VUS) represent around 10~15% of reported copy number variants (CNVs). Even though VUS reanalysis has become usual in practice, no long-term study regarding CNV reinterpretation has been reported.MethodsThis retrospective study examined 1641 CGH arrays performed over 8 years (2010–2017) to demonstrate the contribution of periodically re-analyzing CNVs of uncertain significance. CNVs were classified using AnnotSV on the one hand and manually curated on the other hand. The classification was based on the 2020 American College of Medical Genetics (ACMG) criteria.ResultsOf the 1641 array-CGH analyzed, 259 (15.7%) showed at least one CNV initially reported as of uncertain significance. After reinterpretation, 106 of the 259 patients (40.9%) changed categories, and 12 of 259 (4.6%) had a VUS reclassified to likely pathogenic or pathogenic. Six were predisposing factors for neurodevelopmental disorder/autism spectrum disorder (ASD). CNV type (gain or loss) does not seem to impact the reclassification rate, unlike the length of the CNV: 75% of CNVs downgraded to benign or likely benign are less than 500 kb in size.ConclusionsThis study’s high rate of reinterpretation suggests that CNV interpretation has rapidly evolved since 2010, thanks to the continuous enrichment of available databases. The reinterpreted CNV explained the phenotype for ten patients, leading to optimal genetic counseling. These findings suggest that CNVs should be reinterpreted at least every 2 years.

CNV-ClinViewer: Enhancing the clinical interpretation of large copy-number variants online.

Pathogenic copy number variants (CNVs) can cause a heterogeneous spectrum of rare and severe disorders. However, most CNVs are benign and are part of natural variation in human genomes. CNV pathogenicity classification, genotype-phenotype analyses, and therapeutic target identification are challenging and time-consuming tasks that require the integration and analysis of information from multiple scattered sources by experts. Here, we introduce the CNV-ClinViewer, an open-source web-application for clinical evaluation and visual exploration of CNVs. The application enables real-time interactive exploration of large CNV datasets in a user-friendly designed interface and facilitates semi-automated clinical CNV interpretation following the ACMG guidelines by integrating the ClassifCNV tool. In combination with clinical judgment the application enables clinicians and researchers to formulate novel hypotheses and guide their decision-making process. Subsequently, the CNV-ClinViewer enhances for clinical investigators patient care and for basic scientists translational genomic research. The web-application is freely available at https://cnv-ClinViewer.broadinstitute.org and the open-source code can be found at https://github.com/LalResearchGroup/CNV-clinviewer. Supplementary data are available at Bioinformatics online.

SCIP: software for efficient clinical interpretation of copy number variants detected by whole-genome sequencing

Copy number variants (CNVs) represent major etiologic factors in rare genetic diseases. Current clinical CNV interpretation workflows require extensive back-and-forth with multiple tools and databases. This increases complexity and time burden, potentially resulting in missed genetic diagnoses. We present the Suite for CNV Interpretation and Prioritization (SCIP), a software package for the clinical interpretation of CNVs detected by whole-genome sequencing (WGS). The SCIP Visualization Module near-instantaneously displays all information necessary for CNV interpretation (variant quality, population frequency, inheritance pattern, and clinical relevance) on a single page—supported by modules providing variant filtration and prioritization. SCIP was comprehensively evaluated using WGS data from 1027 families with congenital cardiac disease and/or autism spectrum disorder, containing 187 pathogenic or likely pathogenic (P/LP) CNVs identified in previous curations. SCIP was efficient in filtration and prioritization: a median of just two CNVs per case were selected for review, yet it captured all P/LP findings (92.5% of which ranked 1st). SCIP was also able to identify one pathogenic CNV previously missed. SCIP was benchmarked against AnnotSV and a spreadsheet-based manual workflow and performed superiorly than both. In conclusion, SCIP is a novel software package for efficient clinical CNV interpretation, substantially faster and more accurate than previous tools (available at https://github.com/qd29/SCIP, a video tutorial series is available at https://bit.ly/SCIPVideos).

Utility and Outcomes of the 2019 American College of Medical Genetics and Genomics–Clinical Genome Resource Guidelines for Interpretation of Copy Number Variants with Borderline Classifications at an Academic Clinical Diagnostic Laboratory

In 2019, the American College of Medical Genetics and Genomics and the Clinical Genome Resource published updated technical standards for the interpretation and reporting of copy number variants (CNVs), introducing a semiquantitative classification system to improve standardization and consistency between laboratories. Evaluation of these guidelines' performance will inform laboratories about the impact of their implementation into clinical practice. A total of 145 difficult-to-classify CNVs, originally assessed by an academic molecular diagnostic laboratory, were re-interpreted/classified according to the American College of Medical Genetics and Genomics-Clinical Genome Resource guidelines. Classifications between interpretation systems were then compared. The concordance rate was 60.7%, and significantly more variants of uncertain significance were obtained when using the guidelines (n=98) versus the laboratory's classification system (n=49; P<0.001). The concordance rate was presumably impacted by the intentionally unclear nature of the selected variants. The difference in variant of uncertain significance rate was largely due to laboratory-specific practices for variant interpretation and reporting and differences in utilization of general population data. Laboratory-specific policies and practices may need to be addressed for true standardization. Challenges to consistent guideline utilization are centered around the general lack of high-quality curated data available for CNV interpretations and the inherent subjectivity in the selection of evidence criteria and application of evidence points. Multiple aspects of the guidelines were highlighted to further improve classification standardization.

The Use of Next-generation Sequencing in the Diagnosis of Rare Inherited Anaemias: A Joint BSH/EHA Good Practice Paper.

The Use of Next-generation Sequencing in the Diagnosis of Rare Inherited Anaemias: A Joint BSH/EHA Good Practice Paper.

Prenatal utility of parental source verification on the interpretation of copy number variation identified by chromosomal microarray analysis

To investigate the parental-origin verification of fetal CNVs in the setting of prenatal diagnosis. 182 families were recruited in this study. All the pregnant women underwent transabdominal amniocentesis or cordocentesis, and the amniotic fluid or cord blood samples were then subjected to karyotyping and chromosomal microarray analysis (CMA) respectively. The peripheral blood specimens of fetal parents were also tested by CMA for determining the parental-origin of fetal CNVs. Then we followed up the clinical outcomes of the fetuses. Of the 182 fetuses, 163 (89.6%) had parental-origin CNVs, and 19 (10.4%) had de novo CNVs. 149 (91.4%) of the parental-origin CNVs were classified into the variant of uncertain significance (VUS) before parental-origin tests. After parental source verification, 137 (91.9%) of these VUS cases were changed into likely benign cases. Among the 163 cases of parental-origin CNVs, 122 (74.8%) fetuses were born healthy. Among the 19 de novo cases, 9 of these families chose to terminate the pregnancy. The majority of inherited CNVs detected by CMA had a tendency to be benign, and the parental source verification could assist the elucidation of clinical significance of CNVs in prenatal diagnosis.

OP034: Evaluation of the 2019 ACMG-ClinGen guidelines for interpretation of copy-number variants with borderline classifications at an academic clinical diagnostics laboratory

Copy-number variation is a source of both genetic diversity and Mendelian disease. Analysis of copy-number variants (CNVs) is an essential and frequently utilized component of medical genetics for a wide variety of patient indications. In 2019, the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen) developed new technical standards for the interpretation of CNVs, introducing a semi-quantitative classification system that aims to foster greater standardization and consistency of classifications between laboratories.

A scalable artificial intelligence platform that automatically finds copy number variations (CNVs) in journal articles and transforms them into a database: CNV extraction, transformation, and loading AI (CNV-ETLAI)

BackgroundAlthough copy number variations (CNVs) are infrequent, each anomaly is unique, and multiple CNVs can appear simultaneously. Growing evidence suggests that CNVs contribute to a wide range of diseases. When CNVs are detected, assessment of their clinical significance requires a thorough literature review. This process can be extremely time-consuming and may delay disease diagnosis. Therefore, we have developed CNV Extraction, Transformation, and Loading Artificial Intelligence (CNV-ETLAI), an innovative tool that allows experts to classify and interpret CNVs accurately and efficiently. MethodsWe combined text, table, and image processing algorithms to develop an artificial intelligence platform that automatically extracts, transforms, and organizes CNV information into a database. To validate CNV-ETLAI, we compared its performance to ground truth datasets labeled by a human expert. In addition, we analyzed the CNV data, which was collected using CNV-ETLAI via a crowdsourcing approach. ResultsIn comparison to a human expert, CNV-ETLAI improved CNV detection accuracy by 4% and performed the analysis 60 times faster. This performance can improve even further with upscaling of the CNV-ETLAI database as usage increases. 5,800 CNVs from 2,313 journal articles were collected. Total CNV frequency for the whole chromosome was highest for chromosome X, whereas CNV frequency per 1 Mb of genomic length was highest for chromosome 22. ConclusionsWe have developed, tested, and shared CNV-ETLAI for research and clinical purposes (https://lmic.mgh.harvard.edu/CNV-ETLAI). Use of CNV-ETLAI is expected to ease and accelerate diagnostic classification and interpretation of CNVs.

Neurodevelopmental Disorders in Patients With Complex Phenotypes and Potential Complex Genetic Basis Involving Non-Coding Genes, and Double CNVs.

Neurodevelopmental disorders (NDDs) are a heterogeneous class of brain diseases, with a complex genetic basis estimated to account for up to 50% of cases. Nevertheless, genetic diagnostic yield is about 20%. Array-comparative genomic hybridization (array-CGH) is an established first-level diagnostic test able to detect pathogenic copy number variants (CNVs), however, most identified variants remain of uncertain significance (VUS). Failure of interpretation of VUSs may depend on various factors, including complexity of clinical phenotypes and inconsistency of genotype-phenotype correlations. Indeed, although most NDD-associated CNVs are de novo, transmission from unaffected parents to affected children of CNVs with high risk for NDDs has been observed. Moreover, variability of genetic components overlapped by CNVs, such as long non-coding genes, genomic regions with long-range effects, and additive effects of multiple CNVs can make CNV interpretation challenging. We report on 12 patients with complex phenotypes possibly explained by complex genetic mechanisms, including involvement of antisense genes and boundaries of topologically associating domains. Eight among the 12 patients carried two CNVs, either de novo or inherited, respectively, by each of their healthy parents, that could additively contribute to the patients’ phenotype. CNVs overlapped either known NDD-associated or novel candidate genes (PTPRD, BUD13, GLRA3, MIR4465, ABHD4, and WSCD2). Bioinformatic enrichment analyses showed that genes overlapped by the co-occurring CNVs have synergistic roles in biological processes fundamental in neurodevelopment. Double CNVs could concur in producing deleterious effects, according to a two-hit model, thus explaining the patients’ phenotypes and the incomplete penetrance, and variable expressivity, associated with the single variants. Overall, our findings could contribute to the knowledge on clinical and genetic diagnosis of complex forms of NDD.

CNVxplorer: a web tool to assist clinical interpretation of CNVs in rare disease patients.

Copy Number Variants (CNVs) are an important cause of rare diseases. Array-based Comparative Genomic Hybridization tests yield a ∼12% diagnostic rate, with ∼8% of patients presenting CNVs of unknown significance. CNVs interpretation is particularly challenging on genomic regions outside of those overlapping with previously reported structural variants or disease-associated genes. Recent studies showed that a more comprehensive evaluation of CNV features, leveraging both coding and non-coding impacts, can significantly improve diagnostic rates. However, currently available CNV interpretation tools are mostly gene-centric or provide only non-interactive annotations difficult to assess in the clinical practice. Here, we present CNVxplorer, a web server suited for the functional assessment of CNVs in a clinical diagnostic setting. CNVxplorer mines a comprehensive set of clinical, genomic, and epigenomic features associated with CNVs. It provides sequence constraint metrics, impact on regulatory elements and topologically associating domains, as well as expression patterns. Analyses offered cover (a) agreement with patient phenotypes; (b) visualizations of associations among genes, regulatory elements and transcription factors; (c) enrichment on functional and pathway annotations and (d) co-occurrence of terms across PubMed publications related to the query CNVs. A flexible evaluation workflow allows dynamic re-interrogation in clinical sessions. CNVxplorer is publicly available at http://cnvxplorer.com.

Critical exon indexing improves clinical interpretation of copy number variants in neurodevelopmental disorders.

ObjectiveTo evaluate a new tool to aid interpretation of copy number variants (CNVs) in individuals with neurodevelopmental disabilities.MethodsCritical exon indexing (CEI) was used to identify genes with critical exons (CEGs) from clinically reported CNVs, which may contribute to neurodevelopmental disorders (NDDs). The 742 pathogenic CNVs and 1,363 variants of unknown significance (VUS) identified by chromosomal microarray analysis in 5,487 individuals with NDDs were subjected to CEI to identify CEGs. CEGs identified in a subsequent random series of VUS were evaluated for relevance to CNV interpretation.ResultsCEI identified a total of 2,492 unique CEGs in pathogenic CNVs and 953 in VUS compared with 259 CEGs in 6,965 CNVs from 873 controls. These differences are highly significant (p < 0.00001) whether compared as frequency, average, or normalized by CNV size. Twenty-one percent of VUS CEGs were not represented in Online Mendelian Inheritance in Man, highlighting limitations of existing resources for identifying potentially impactful genes within CNVs. CEGs were highly correlated with other indices and known pathways of relevance. Separately, 136 random VUS reports were reevaluated, and 76% of CEGs had not been commented on. In multiple cases, further investigation yielded additional relevant literature aiding interpretation. As one specific example, we discuss GTF2I as a CEG, which likely alters interpretation of several reported duplication VUS in the Williams-Beuren region.ConclusionsApplication of CEI to CNVs in individuals with NDDs can identify genes of potential clinical relevance, aid laboratories in effectively searching the clinical literature, and support the clinical reporting of poorly annotated VUS.

A comparison of genomic laboratory reports and observations that may enhance their clinical utility for providers and patients.

PurposeTo assess clinical chromosomal microarray (CMA) genomic testing reports for the following: (a) usage of reporting elements consistent with 2011 ACMG guidelines and other elements identified in the primary literature, (b) information quality, and (c) readability.MethodsWe retrospectively analyzed genomic testing reports from 2011 to 2016 provided to, or by our laboratory to aid in clinical detection and interpretation of copy number variants. Analysis was restricted to the following sections: interpretation, recommendations, limitations, and citations. Analysis included descriptive characteristics, reporting elements, reading difficulty using the Simple Measure of Gobbledygook (SMOG), and quality ratings using a subset of questions adapted from the DISCERN‐Genetics questionnaire.ResultsThe analysis included 44 unique reports from 26 laboratories comprising four groups: specialty laboratories (SL; N = 9), reference laboratories (RL; N = 12), hospital laboratories (HL; N = 10), and university‐based laboratories (UL; N = 13). There were 23 abnormal/pathogenic reports and 21 of uncertain/unknown significance. Nine laboratories did not include one or more pieces of information based on ACMG guidelines; only one of ten laboratories reported condition‐specific management/treatment information when available and relevant. Average quality ratings and readability scores were not significantly different between laboratory types or result classification.ConclusionsReporting practices for most report elements varied widely; however, readability and quality did not differ significantly between laboratory types. Management and treatment information, even for well‐known conditions, are rarely included. Effectively communicating test results may be improved if certain reporting elements are incorporated. Recommendations to improve laboratory reports are provided.

ClinTAD: a tool for copy number variant interpretation in the context of topologically associated domains.

Standard clinical interpretation of DNA copy number variants (CNVs) identified by cytogenomic microarray involves examining protein-coding genes within the region and comparison to other CNVs. Emerging basic research suggests that CNVs can also exert a pathogenic effect through disruption of DNA structural elements such as topologically associated domains (TADs). To begin to integrate these discoveries with current practice, we developed ClinTAD, a free browser-based tool to assist with interpretation of CNVs in the context of TADs ( www.clintad.com ). We used ClinTAD to examine 209 randomly selected single-nucleotide polymorphism microarray cases with a total of 236 CNVs. We compared 118 CNVs classified as variants of uncertain clinical significance (VUS), where additional insight into pathogenicity of these CNVs would be of greatest utility, to 118 CNVs classified as benign. We found that a higher proportion of VUS had at least two genes in a nearby TAD related to a phenotype seen in the patient based on Human Phenotype Ontology (HPO) annotation. We present example cases demonstrating scenarios where ClinTAD may either increase or decrease clinical suspicion of pathogenicity for VUS, depending on disruption of TAD boundaries and HPO phenotype match. ClinTAD is an easy-to-use tool, based on emerging research in chromatin architecture, that can help inform CNV interpretation.

Genome-Wide Array Analysis Reveals Novel Genomic Regions and Candidate Gene for Intellectual Disability.

Intellectual disability (ID) is often sporadic, and its complex etiology makes clinical diagnosis extremely difficult. The aims of this study were to detect copy number variations (CNVs) in patients with ID and to analyze the correlation between pathogenic CNVs and clinical phenotype. After cases of ID caused by metabolic dysfunction or environmental factors were excluded, 64 patients with moderate to severe ID were enrolled. Karyotype and single nucleotide polymorphism (SNP) array analyses were performed for all patients. The relationship between CNVs and phenotype was identified with genotype-phenotype comparisons and by searching CNV databases. Karyotype analysis showed four patients with chromosomal aneuploidy and seven with chromosomal structural abnormality. After excluding the four cases with chromosomal aneuploidy, the remaining 60 cases were analyzed using SNP array. The results revealed 87 CNVs in 45 cases, including 16 pathogenic CNVs in 12 individuals, with a diagnostic yield of 20.0% (12/60). We found large deletions at 16q22.2q23.1 and 3q24q25.32 in two patients, respectively, in whom specific syndromes had not been defined. Our array analysis showed one case carried a 210kb deletion at 1p21.2p21.3, which included only one coding gene LPPR4, which might be a candidate gene for ID phenotype. Use of the genome-wide array method can improve the detection rate of CNVs, reveal chromosomal abnormalities that have not been well-characterized by cytology, and provide a new way to locate genes for patients with the ID phenotype. Interpretation of CNVs remains a major challenge. Sharing of CNVs and phenotype information from different laboratories in public databases is important.

Chromosomal microarray in clinical diagnosis: a study of 337 patients with congenital anomalies and developmental delays or intellectual disability.

AimTo determine the diagnostic yield and criteria that could help to classify and interpret the copy number variations (CNVs) detected by chromosomal microarray (CMA) technique in patients with congenital and developmental abnormalities including dysmorphia, developmental delay (DD) or intellectual disability (ID), autism spectrum disorders (ASD) and congenital anomalies (CA).MethodCMA analysis was performed in 337 patients with DD/ID with or without dysmorphism, ASD, and/or CA. In 30 of 337 patients, chromosomal imbalances had previously been detected by classical cytogenetic and molecular cytogenetic methods.ResultsIn 73 of 337 patients, clinically relevant variants were detected and better characterized. Most of them were >1 Mb. Variants of unknown clinical significance (VOUS) were discovered in 35 patients. The most common VOUS size category was <300 kb (40.5%). Deletions and de novo imbalances were more frequent in pathogenic CNV than in VOUS category. CMA had a high diagnostic yield of 43/307, excluding patients previously detected by other methods.ConclusionCMA was valuable in establishing the diagnosis in a high proportion of patients. Criteria for classification and interpretation of CNVs include CNV size and type, mode of inheritance, and genotype-phenotype correlation. Agilent ISCA v2 Human Genome 8x60 K oligonucleotide microarray format proved to be reasonable resolution for clinical use, particularly in the regions that are recommended by the International Standard Cytogenomic Array (ISCA) Consortium and associated with well-established syndromes.

Formation of new chromatin domains determines pathogenicity of genomic duplications.

Chromosome conformation capture methods have identified subchromosomal structures of higher-order chromatin interactions called topologically associated domains (TADs) that are separated from each other by boundary regions. By subdividing the genome into discrete regulatory units, TADs restrict the contacts that enhancers establish with their target genes. However, the mechanisms that underlie partitioning of the genome into TADs remain poorly understood. Here we show by chromosome conformation capture (capture Hi-C and 4C-seq methods) that genomic duplications in patient cells and genetically modified mice can result in the formation of new chromatin domains (neo-TADs) and that this process determines their molecular pathology. Duplications of non-coding DNA within the mouse Sox9 TAD (intra-TAD) that cause female to male sex reversal in humans, showed increased contact of the duplicated regions within the TAD, but no change in the overall TAD structure. In contrast, overlapping duplications that extended over the next boundary into the neighbouring TAD (inter-TAD), resulted in the formation of a new chromatin domain (neo-TAD) that was isolated from the rest of the genome. As a consequence of this insulation, inter-TAD duplications had no phenotypic effect. However, incorporation of the next flanking gene, Kcnj2, in the neo-TAD resulted in ectopic contacts of Kcnj2 with the duplicated part of the Sox9 regulatory region, consecutive misexpression of Kcnj2, and a limb malformation phenotype. Our findings provide evidence that TADs are genomic regulatory units with a high degree of internal stability that can be sculptured by structural genomic variations. This process is important for the interpretation of copy number variations, as these variations are routinely detected in diagnostic tests for genetic disease and cancer. This finding also has relevance in an evolutionary setting because copy-number differences are thought to have a crucial role in the evolution of genome complexity.