Copy Number Variants Calls Research Articles

Application of Genetic Origin Analysis of Copy Number Variations in Non-Invasive Prenatal Testing.

This study aimed to assess the application of origin analysis of copy number variations (CNVs) in non-invasive prenatal testing (NIPT) and provide a basis for expanding the clinical application of NIPT. We enrolled 35,317 patients who underwent NIPT between January 2019 and March 2023. Genome sequencing of copy number variation (CNV-Seq) analysis was performed using the CNV calling pipeline to identify subchromosomal abnormalities in maternal plasma. Genetic origin was determined by comparing the chimaerism ratio of CNV and the concentration of cell-free foetal DNA (cffDNA). All pregnant women with a high risk of CNV, as indicated by the NIPT, were informed of their genetic origins. Amniocentesis was recommended for detecting the CNVs in foetal chromosomes, and pregnancy outcomes were tracked. A total of 109 pregnancies showed clinically significant positive results for CNV after NIPT, including 65 cases of maternal/foetal (M/F)-CNVs and 44 cases of F-CNVs. The occurrence of M/F-CNVs was independent of age, screening (serological or ultrasound) indications for abnormalities, and mode of pregnancy. The incidence of pathogenic/likely pathogenic (P/LP)-F-CNVs was high in cases where serological screening indicated intermediate, high-risk, or abnormal US findings (p<0.05). In the M/F-CNV group, most of the P/LP-CNVs were small fragments with low penetrance; 55 (84.62%) were less than 5Mb in size, and nine (13.85%) were between 5 and 10Mb. In the F-CNV group, foetal P/LP-CNV was detected in 36 of 42 cases undergoing prenatal diagnosis, and no significant bias was noted in the size distribution of P/LP-F-CNV fragments. The prenatal diagnostic rate and positive predictive value in the F-CNV group were 95.45% and 85.71%, respectively, which were significantly different from those in the M/F group (26.15% and 52.95%), respectively (p<0.05). Genetic origin analysis of CNV can effectively improve adherence to prenatal diagnosis in pregnant women and the accuracy of prenatal diagnosis.

Copy Number Analysis of Whole Exome Sequencing Data

Abstract Background Genetic alterations implicated in constitutional disorders range in size from single nucleotide substitutions to losses and gains of millions of bases. Whole exome sequencing (WES) captures many clinically relevant variants, but an analytic pipeline optimized for detection of small substitutions, insertions, and deletions may miss pathogenic copy number variants (CNVs). Historically, our laboratory performed separate cytogenetic tests to evaluate for larger genomic imbalances. Addition of copy number analysis to our WES pipeline could improve the assay’s diagnostic yield. This study describes part of our strategy to validate copy number analysis of our constitutional whole exome sequencing data. Methods We identified 18 CNV specimens that underwent both chromosomal microarray analysis (CMA) and WES between 2017 and 2023. Variants included 13 deletions (size range 14Kb-2Mb) and 5 duplications (size range 325Kb-10Mb). The standard method of constitutional copy number analysis in our clinical laboratory is CMA using the Affymetrix Cytoscan HD Array and analysis software. CNVs identified on CMA were reviewed with reference to the Database of Genomic Variants and multiple academic hospital databases. WES was performed on an Illumina sequencing system following capture-based library enrichment with the Integrated DNA Technologies xGen Exome Research, CNV Backbone, Human mtDNA Research, and Human ID Research Panel probes. CNVs identified on WES were called using the Broad Institute’s Genomic Analysis Toolkit (GATK) with a quality score cutoff of 120. Whole exome sequencing data was visualized on the Integrative Genomics Viewer. Results 14 out of 18 CNVs reported on CMA were also detected on WES with a quality score at or above 120 (78%). This includes 10 out of 13 deletions (77%) and 3 out of 5 duplications (60%). For both deletions and duplications, the size ranges of detected variants at or above the cutoff score and undetected variants overlapped. One duplication reported on CMA was detected on WES with a quality score below 120. Visualization of whole exome sequencing data showed variation in probe density that may have affected CNV calling. Conclusions The majority of CNVs reported after CMA (14/18, 78%) were also detected on WES data using a GATK quality score cutoff of 120. The data does not suggest an association between CNV size and detection by WES, although interpretation is limited by the small number of cases. Data visualization may be required to identify some CNVs. Future steps to characterize the limitations of copy number analysis of WES data include assessments of variance in probe density and read depth across the exome. Copy number analysis of WES data from cases with normal CMA results will also be performed for assay validation.

Single-cell somatic copy number variants in brain using different amplification methods and reference genomes

The presence of somatic mutations, including copy number variants (CNVs), in the brain is well recognized. Comprehensive study requires single-cell whole genome amplification, with several methods available, prior to sequencing. Here we compare PicoPLEX with two recent adaptations of multiple displacement amplification (MDA): primary template-directed amplification (PTA) and droplet MDA, across 93 human brain cortical nuclei. We demonstrate different properties for each, with PTA providing the broadest amplification, PicoPLEX the most even, and distinct chimeric profiles. Furthermore, we perform CNV calling on two brains with multiple system atrophy and one control brain using different reference genomes. We find that 20.6% of brain cells have at least one Mb-scale CNV, with some supported by bulk sequencing or single-cells from other brain regions. Our study highlights the importance of selecting whole genome amplification method and reference genome for CNV calling, while supporting the existence of somatic CNVs in healthy and diseased human brain.

8610 The Role of Adenylyl Cyclase 2 Gene-Dosage Changes in Congenital Male Genitourinary Anomalies

Abstract Disclosure: D.J. Lamb: Advisory Board Member; Self; Ro Health, Inc. Stock Owner; Self; Fellow Health, Inc. M.A. O'Neill: None. Collectively, congenital genitourinary (GU) birth defects are the most common birth defects in males, yet relatively little is known about their cause. In part, this reflects the current clinical perspective that, because most of these birth defects can be surgically repaired, there is nothing to be gained by understanding the cause. Yet our studies show that seemingly simple birth defects like hypospadias or cryptorchidism, may be associated with other more significant underappreciated defects, such as those affecting the kidney, heart, eyes or behavior. We tested the hypothesis that chromosome microdeletions and microduplications (called copy number variations, CNVs) result in gene-dosage changes that are important regulators of GU development and when haploinsufficient or duplicated normal genitourinary development is impacted causing birth defects of the upper and/or lower genitourinary tract. We successfully used this strategy to identify 15 different previously unrecognized genes that when microduplicated or microdeleted result in conditions such as cryptorchidism, hypospadias, disorders of sexual differentiation (DSD) and other anomalies. Remarkably, gene-dosage changes affected major signaling pathways and post-translational modifications in novel, previously unrecognized ways. Herein, we focused on defining the role of a candidate gene commonly microdeleted or microduplicated in patients with cryptorchidism, hypospadias and/or micropenis. Locus mapping allowed identification of adenylate cyclase 2 (ADCY2) as a candidate gene at the 5p15.31 Cri-du-Chat region in patients with male genital tract anomalies. ADCY2 CNVs were more common in non-syndromic patients with micropenis, cryptorchidism, and/or hypospadias (1.6%) than the general population (0.07%; p&amp;lt; 0.001). Whole exome sequence studies revealed a range of phenotypic anomalies in patients with damaging and potentially damaging ADCY2 variants in addition to the male GU anomalies, including congenital anomalies of the kidney and urinary tract. It is our hypothesis that ADCY2 duplications result in a de-sensitization of the LH receptor diminishing testosterone biosynthesis in the testis, whereas deletions diminish LH receptor signaling. ADCY2 CNVs were assessed in 100 men with spermatogenic failure who were born with cryptorchidism. The ADCY2 CNV frequency was the same as in our cohort of boys with GU anomalies, but as adults they also have hypergonadotropic hypogonadism, which we predicted if ADCY2 over-expression was impacting adult Leydig cell steroidogenesis through partial desensitization of the LH receptor. These studies identified an unrecognized cause of GU anomalies. In the future, such knowledge may lead to improved diagnosis and therapeutic approaches to ameliorate these common birth defects. Presentation: 6/2/2024

CNVoyant a machine learning framework for accurate and explainable copy number variant classification

The precise classification of copy number variants (CNVs) presents a significant challenge in genomic medicine, primarily due to the complex nature of CNVs and their diverse impact on rare genetic diseases (RGDs). This complexity is compounded by the limitations of existing methods in accurately distinguishing between benign, uncertain, and pathogenic CNVs. Addressing this gap, we introduce CNVoyant, a machine learning-based multi-class framework designed to enhance the clinical significance classification of CNVs. Trained on a comprehensive dataset of 52,176 ClinVar entries across pathogenic, uncertain, and benign classifications, CNVoyant incorporates a broad spectrum of genomic features, including genome position, disease-gene annotations, dosage sensitivity, and conservation scores. Models to predict the clinical significance of copy number gains and losses were trained independently. Final models were selected after testing 29 machine learning architectures and 10,000 hyperparameter combinations each for deletions and duplications via fivefold cross-validation. We validate the performance of CNVoyant by leveraging a comprehensive set of 21,574 CNVs from the DECIPHER database, a highly regarded resource known for its extensive catalog of chromosomal imbalances linked to clinical outcomes. Compared to alternative approaches, CNVoyant shows marked improvements in precision-recall and ROC AUC metrics for binary pathogenic classifications while going one step further, offering multi-classification of clinical significance and corresponding SHAP explainability plots. Additionally, when provided germline CNV calls from real-world RGD cases with diagnostic CNV(s), CNVoyant correctly classified all diagnostic CNVs as having pathogenic significance with high confidence. This large-scale validation demonstrates CNVoyant’s superior accuracy and underscores its potential to aid genomic researchers and clinical geneticists in interpreting the clinical implications of real CNVs.

Investigating copy number variants in schizophrenia pedigrees using a new consensus pipeline called PECAN

Copy number variants (CNVs) have been implicated in many human diseases, including psychiatric disorders. Whole genome sequencing offers advantages in CNV calling compared to previous array-based methods. Here we present a robust and transparent CNV calling pipeline, PECAN (PEdigree Copy number vAriaNt calling), for short-read, whole genome sequencing data, comprised of a novel combination of four calling methods and structural variant genotyping. This method is scalable and can incorporate pedigree information to retain lower-confidence CNVs that would otherwise be discarded. We have robustly benchmarked PECAN using gold-standard CNV calls for two well-established evaluation samples, NA12878 and HG002, showing that PECAN performs with high precision and recall on both datasets, outperforming another pedigree-based CNV calling pipeline. As part of this work, we provide a list of high-confidence gold standard CNVs for the NA12878 reference sample, curated from multiple studies. We applied PECAN to a collection of pedigrees multiply affected with schizophrenia and identified a rare deletion that perfectly co-segregates with schizophrenia in one of the pedigrees. The CNV overlaps the gene PITRM1, which has been implicated in a complex phenotype including ataxia, developmental delay, and schizophrenia-like episodes in affected adults.

Enhancing clinical genomic accuracy with panelGC: a novel metric and tool for quantifying and monitoring GC biases in hybridization capture panel sequencing.

Accurate assessment of fragment abundance within a genome is crucial in clinical genomics applications such as the analysis of copy number variation (CNV). However, this task is often hindered by biased coverage in regions with varying guanine-cytosine (GC) content. These biases are particularly exacerbated in hybridization capture sequencing due to GC effects on probe hybridization and polymerase chain reaction (PCR) amplification efficiency. Such GC content-associated variations can exert a negative impact on the fidelity of CNV calling within hybridization capture panels. In this report, we present panelGC, a novel metric, to quantify and monitor GC biases in hybridization capture sequencing data. We establish the efficacy of panelGC, demonstrating its proficiency in identifying and flagging potential procedural anomalies, even in situations where instrument and experimental monitoring data may not be readily accessible. Validation using real-world datasets demonstrates that panelGC enhances the quality control and reliability of hybridization capture panel sequencing.

Comprehensive assessment of long-read sequencing platforms and calling algorithms for detection of copy number variation.

Copy number variations (CNVs) play pivotal roles in disease susceptibility and have been intensively investigated in human disease studies. Long-read sequencing technologies offer opportunities for comprehensive structural variation (SV) detection, and numerous methodologies have been developed recently. Consequently, there is a pressing need to assess these methods and aid researchers in selecting appropriate techniques for CNV detection using long-read sequencing. Hence, we conducted an evaluation of eight CNV calling methods across 22 datasets from nine publicly available samples and 15 simulated datasets, covering multiple sequencing platforms. The overall performance of CNV callers varied substantially and was influenced by the input dataset type, sequencing depth, and CNV type, among others. Specifically, the PacBio CCS sequencing platform outperformed PacBio CLR and Nanopore platforms regarding CNV detection recall rates. A sequencing depth of 10x demonstrated the capability to identify 85% of the CNVs detected in a 50x dataset. Moreover, deletions were more generally detectable than duplications. Among the eight benchmarked methods, cuteSV, Delly, pbsv, and Sniffles2 demonstrated superior accuracy, while SVIM exhibited high recall rates.

Confirmation and pathogenicity of small copy number variations incidentally detected via a targeted next-generation sequencing–based preimplantation genetic testing for aneuploidy platform

ObjectiveTo evaluate the technical accuracy, inheritance, and pathogenicity of small copy number variants (CNVs) detected by a targeted next-generation sequencing (NGS)-based PGT-A platform. DesignRetrospective observational study performed between 2020-2022. Subjects12,157 patients who underwent clinical PGT-A performed by targeted NGS for whole chromosome and large segmental aneuploidies. ExposureAn incidental finding was reported when a CNV gain/loss of at least three consecutive amplicons appeared in at least two embryos from the same IVF cycle. Main Outcome MeasuresThe primary outcome measures were the specificity, incidence, inheritance, and pathogenicity of small CNVs detected by the PGT-A platform. Accuracy of the PGT-A platform CNV calls was assessed via concordance with the CNV calls (size and genomic location) on chromosomal microarray of the gamete provider(s). Parental origin of the CNV and pathogenicity classifications were also reported. ResultsIn 75 of 12,157 unique PGT-A patients (0.62%;95%CI:0.5-0.8%), an incidental finding that met reporting criteria was identified. Chromosomal microarray follow-up was requested for all cases and results were received for one or both members of 65 reproductive couples. In all cases, one of the gamete providers was confirmed to have the CNV identified in the embryos (100.0%: N=65/65 95%CI:94.5-100). The identified CNV was of maternal origin in 34 cases (52.3%) and of paternal origin in 31 cases (47.7%). A significant correlation was identified between PGT-A-predicted CNV sizes and chromosomal microarray detected sizes (r=0.81) and genomic coordinates on parental DNA. Twenty-six (40%) of the CNVs were classified as benign/likely benign, 30 (46.2%) as a variant of uncertain significance (VUS), and 9 (13.8%) as pathogenic/likely pathogenic. ConclusionCertain PGT-A platforms may enable the detection of inherited, small CNVs with extremely high specificity without prior knowledge of parental status. The majority of CNVs in this data set were confirmed to be benign/likely benign or a VUS; however, pathogenic/likely pathogenic CNVs associated with a broad range of phenotypic features may also be detected, although a reliable negative predictive value for small CNVs with current PGT-A technologies is unknown due to the many technical challenges.

Copy number variant detection using next-generation sequencing in EYS-associated retinitis pigmentosa.

Retinitis pigmentosa (RP) is the most common inherited retinal dystrophy and a major cause of blindness. RP is caused by several variants of multiple genes, and genetic diagnosis by identifying these variants is important for optimizing treatment and estimating patient prognosis. Next-generation sequencing (NGS), which is currently widely used for diagnosis, is considered useful but is known to have limitations in detecting copy number variations (CNVs). In this study, we re-evaluated CNVs in EYS, the main causative gene of RP, identified via NGS using multiplex ligation-dependent probe amplification (MLPA). CNVs were identified in NGS samples of eight patients. To identify potential CNVs, MLPA was also performed on samples from 42 patients who were undiagnosed by NGS but carried one of the five major pathogenic variants reported in Japanese EYS-RP cases. All suspected CNVs based on NGS data in the eight patients were confirmed via MLPA. CNVs were found in 2 of the 42 NGS-undiagnosed RP cases. Furthermore, results showed that 121 of the 661 patients with RP had EYS as the causative gene, and 8.3% (10/121 patients with EYS-RP) had CNVs. Although NGS using the CNV calling criteria utilized in this study failed to identify CNVs in two cases, no false-positive results were detected. Collectively, these findings suggest that NGS is useful for CNV detection during clinical diagnosis of RP.

The Importance of Copy Number Variant Analysis in Patients with Monogenic Kidney Disease

IntroductionGenetic testing can reveal monogenic causes of kidney diseases, offering diagnostic, therapeutic, and prognostic benefits. While single nucleotide variants (SNVs) and copy number variants (CNVs) can result in kidney disease, CNV analysis is not always included in genetic testing. MethodsWe investigated the diagnostic value of CNV analysis in 2,432 kidney disease patients genetically tested at the University Medical Centre Utrecht between 2014 and May 2022. We combined previous diagnostic testing results, encompassing SNVs and CNVs, with newly acquired results based on retrospective CNV analysis. The reported yield considers both ACMG classification and whether the genotype actually results in disease. ResultsWe report a diagnostic yield of at least 23% for our complete diagnostic cohort. The total diagnostic yield based solely on CNVs was 2.4%. The overall contribution of CNV analysis, defined as the proportion of positive genetic tests requiring CNV analysis, was 10.5% and varied among different disease subcategories, with the highest impact seen in congenital anomalies of the kidney and urinary tract and chronic kidney disease at a young age. We highlight the efficiency of exome-based CNV calling, which reduces the need for additional diagnostic tests. Furthermore, a complex structural variant, likely a COL4A4 founder variant, was identified. Additional findings unrelated to kidney diseases were reported in a small percentage of cases. ConclusionIn summary, this study demonstrates the substantial diagnostic value of CNV analysis, providing insights into its contribution to the diagnostic yield and advocating for its routine inclusion in genetic testing of kidney disease patients.

Genome Sequencing for Diagnosing Rare Diseases

BackgroundGenetic variants that cause rare disorders may remain elusive even after expansive testing, such as exome sequencing. The diagnostic yield of genome sequencing, particularly after a negative evaluation, remains poorly defined.MethodsWe sequenced and analyzed the genomes of families with diverse phenotypes who were suspected to have a rare monogenic disease and for whom genetic testing had not revealed a diagnosis, as well as the genomes of a replication cohort at an independent clinical center.ResultsWe sequenced the genomes of 822 families (744 in the initial cohort and 78 in the replication cohort) and made a molecular diagnosis in 218 of 744 families (29.3%). Of the 218 families, 61 (28.0%) — 8.2% of families in the initial cohort — had variants that required genome sequencing for identification, including coding variants, intronic variants, small structural variants, copy-neutral inversions, complex rearrangements, and tandem repeat expansions. Most families in which a molecular diagnosis was made after previous nondiagnostic exome sequencing (63.5%) had variants that could be detected by reanalysis of the exome-sequence data (53.4%) or by additional analytic methods, such as copy-number variant calling, to exome-sequence data (10.8%). We obtained similar results in the replication cohort: in 33% of the families in which a molecular diagnosis was made, or 8% of the cohort, genome sequencing was required, which showed the applicability of these findings to both research and clinical environments.ConclusionsThe diagnostic yield of genome sequencing in a large, diverse research cohort and in a small clinical cohort of persons who had previously undergone genetic testing was approximately 8% and included several types of pathogenic variation that had not previously been detected by means of exome sequencing or other techniques. (Funded by the National Human Genome Research Institute and others.)

Optical genome mapping unveils hidden structural variants in neurodevelopmental disorders

While short-read sequencing currently dominates genetic research and diagnostics, it frequently falls short of capturing certain structural variants (SVs), which are often implicated in the etiology of neurodevelopmental disorders (NDDs). Optical genome mapping (OGM) is an innovative technique capable of capturing SVs that are undetectable or challenging-to-detect via short-read methods. This study aimed to investigate NDDs using OGM, specifically focusing on cases that remained unsolved after standard exome sequencing. OGM was performed in 47 families using ultra-high molecular weight DNA. Single-molecule maps were assembled de novo, followed by SV and copy number variant calling. We identified 7 variants of interest, of which 5 (10.6%) were classified as likely pathogenic or pathogenic, located in BCL11A, OPHN1, PHF8, SON, and NFIA. We also identified an inversion disrupting NAALADL2, a gene which previously was found to harbor complex rearrangements in two NDD cases. Variants in known NDD genes or candidate variants of interest missed by exome sequencing mainly consisted of larger insertions (> 1kbp), inversions, and deletions/duplications of a low number of exons (1–4 exons). In conclusion, in addition to improving molecular diagnosis in NDDs, this technique may also reveal novel NDD genes which may harbor complex SVs often missed by standard sequencing techniques.

Chromosome 20p11.2 deletions cause congenital hyperinsulinism via the loss of FOXA2 or its regulatory elements.

Persistent congenital hyperinsulinism (HI) is a rare genetically heterogeneous condition characterised by dysregulated insulin secretion leading to life-threatening hypoglycaemia. For up to 50% of affected individuals screening of the known HI genes does not identify a disease-causing variant. Large deletions have previously been used to identify novel regulatory regions causing HI. Here, we used genome sequencing to search for novel large (>1 Mb) deletions in 180 probands with HI of unknown cause and replicated our findings in a large cohort of 883 genetically unsolved individuals with HI using off-target copy number variant calling from targeted gene panels. We identified overlapping heterozygous deletions in five individuals (range 3-8 Mb) spanning chromosome 20p11.2. The pancreatic beta-cell transcription factor gene, FOXA2, a known cause of HI was deleted in two of the five individuals. In the remaining three, we found a minimal deleted region of 2.4 Mb adjacent to FOXA2 that encompasses multiple non-coding regulatory elements that are in conformational contact with FOXA2. Our data suggests that the deletions in these three children may cause disease through the dysregulation of FOXA2 expression. These findings provide new insights into the regulation of FOXA2 in the beta-cell and confirm an aetiological role for chromosome 20p11.2 deletions in syndromic HI.

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.

Abstract 4895: Optical genome mapping analysis across multiple solid tumor types in Bionano VIATM software

Abstract Solid tumors can show highly complex somatic structural and copy number variants (CNVs) of multiple classes. These variants can reveal mechanisms behind carcinogenesis, including the amplification of oncogenes, deletion or inactivation of tumor suppressor genes, and fusions to create new oncogenes. High degrees of heterogeneity within solid tumors can complicate analysis. Optical Genome Mapping (OGM) is a comprehensive technology which combines the resolution of molecular-level data with the scale of cytogenomic analysis. ≈5-15mg sections of freshly frozen tumor are homogenized in buffer to release ultra-high molecular weight DNA. Long fragments are then labeled at a regular 6mer, then linearized and imaged with native long-range structure (≥150kbp) preserved. In silico molecules are assembled into maps and aligned to a reference genome to detect structural variants with sensitivity to ≥5% allele fraction. Bionano VIATM software provides tools for visualization and interpretation of structural variants detected with OGM. VIA also provides CNV calling and detection of Absence of Heterozygosity (AOH) and allelic imbalance using SNP-FASST3, a segmentation algorithm for detecting mosaic events in OGM and other types of data. VIA calculates a B-Allele frequency (BAF) distribution from OGM data and adds a genome-wide BAF track to the applications suite, enabling calling of allelic imbalance events such as AOH. Visualized together, the overlay of structural variant information on copy number data provides context for chromosome fusions underlying copy number change. Additionally, balanced fusions are revealed consistently in cases where the impact is copy neutral. To demonstrate the utility of integrative analysis of structural, copy number, and allelic imbalance variants in solid tumor datasets, we processed a pilot set of six diverse solid tumors through Bionano software. Brain, breast, kidney, lung, ovarian, and prostate tumor data which had undergone previous analysis in Bionano Solve 3.7 were freshly analyzed in Solve 3.8 and VIA 7.0. In the lung tumor dataset there are clear allelic imbalance segments spanning most of chromosome 4 with no concomitant copy number change. In VIA, these segments can be merged on examination, converted to AOH calls, and further specified to reflect mosaic copy neutral loss of heterozygosity (CN-LOH). In the complex kidney tumor dataset, it is difficult to resolve ploidy state based on copy number data alone. This ambiguity is resolvable with VIA, by leveraging the copy number and BAF-based allelic imbalance distributions and recentering probes to a manually defined diploid region. The comprehensive OGM data approach facilitated by VIA can streamline analysis in challenging solid tumor datasets. Structural variant visualization, CNV detection and AOH detection are available in VIA 7.0. Citation Format: Samer I. Al-Saffar, Benjamin Clifford, Aliz Raksi, Alex Hastie, Neil Miller, Alka Chaubey. Optical genome mapping analysis across multiple solid tumor types in Bionano VIATM software [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 4895.

Abstract 2426: Liquid biopsy for a predictive biomarker of resistance to immunotherapy

Abstract Immunotherapy has become a mainstay for cancer treatment. However, immune editing is a well-known resistance paradigm that limits the efficacy of immunotherapy in many patients. One type of immune editing that occurs in 1/6 of all cancer patients, with prevalence over 40% in some cancer types, is human leukocyte antigen loss of heterozygosity (HLA LOH). Liquid biopsies provide a non-invasive and inexpensive way of guiding treatment decision-making. We have developed a liquid biopsy assay for detecting HLA LOH in cancer patients, with the goal of utilizing the assay for deciding whether immunotherapy is the right course of treatment for a given patient. The assay was tested utilizing sample sets from cancer patients, where each sample set from a patient included a solid tumor biopsy, a liquid biopsy plasma sample, and PBMC sample for control. Tumor HLA LOH results were used for ground truth, and HLA LOH results from the liquid biopsy sample were compared to those from the tumor sample from the same patient. Whole exome sequencing (WES) was performed on tumor DNA, cell-free DNA (cfDNA) from plasma, and germline DNA from PBMCs, and we ran our novel liquid biopsy HLA LOH detection algorithm on the plasma WES data. Standard copy number variant (CNV) calling tools were utilized for calling HLA LOH from the tumor sequencing data. In both cases - tumor and plasma - germline PBMC DNA was used as control. We tested the liquid biopsy HLA LOH detection method on sample sets from 2 HPV-negative primary head and neck cancer patients, 14 castration-resistant prostate cancer patients, and 27 metastatic breast cancer patients. The samples from the head and neck cancer patients were obtained from patients on clinical trials at NCI, while WES data from prostate cancer patient samples and breast cancer patient samples were downloaded from NCBI Database of Genotypes and Phenotypes (dbGaP) after applying for access to dataset phs001417.v1.p1 (Adalsteinsson, et al., Nature Communications, 2017). The method for liquid biopsy HLA LOH detection utilizes a novel mathematical framework that takes into account biological and experimental variables to generate allele-specific HLA LOH information from sequencing data. In the first two cohorts tested - the head and neck cancer cohort and the prostate cancer cohort - the method was 100% consistent when comparing liquid biopsy HLA LOH results from our method to CNV calling results from matched tumors (4 true positives and 36 true negatives; 0 false negatives and 0 false positives). In the breast cancer cohort, some false negatives and false positives were observed (6 true positives and 48 true negatives; 5 false negatives and 8 false positives). Experiments are ongoing in a CLIA lab to establish sensitivity, specificity, and limit of detection (LoD) for the assay. Upon improvements and validation of the assay, the goal is to utilize the liquid biopsy HLA LOH detection method in the clinic for treatment selection in the context of immunotherapy. Citation Format: Andrew Sinkoe, James L. Gulley. Liquid biopsy for a predictive biomarker of resistance to immunotherapy [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 2426.

Abstract 3492: Comprehensive analysis of hematological malignancies with optical genome mapping and Bionano VIA™Software

Abstract The current standard for analysis of samples with suspected hematological cancer is karyotyping, fluorescence in situ hybridization (FISH), and/or chromosomal microarray (CMA). However, these methods have several limitations for the detection of structural variants (SVs) or copy number variants (CNV) that are smaller than 5 Mbp (karyotyping). Additionally, they are limited in their ability to detect low allele fraction (LAF) events and cannot detect the absence of heterozygosity (AOH) or the loss of heterozygosity (AOH/LOH - FISH and karyotyping). Optical genome mapping (OGM) from Bionano can enable accurate detection of SV and CNV events of sizes down to 5 kbp, with high sensitivity to detect LAF events down to 5% variant allele fraction. Bionano’s VIA™ software provides visualization and analysis support for OGM SV events including an improved Circos plot and ideogram for whole genome review. VIA incorporates CNV calling from OGM data using the SNP-FASST3 algorithm which also allows for accurate copy number calling at allele fractions as low as 10%. CNV detection performance was evaluated using 280 CNV events ranging in size from 175 kbp to 8 Mbp on the 22 autosomal chromosomes. Sensitivity and PPV were calculated for each size range separately. Using SNP-FASST3, VIA also adds the ability to detect AOH/LOH events from OGM data. By analyzing locations where known SNPs disrupt the specific nucleotide sequence motif used by the DLE-1 labeling enzyme, a B-allele frequency can be calculated and enable AOH/LOH detection at low allelic fractions. AOH/LOH performance using SNP-FASST3 was evaluated using a cohort of constitutional and cancer samples analyzed with orthogonal methods as well as with simulated events. Sensitivity for 20-25 Mbp AOH events was 92% with PPV greater than 90% at 25% aberrant cell fraction. The VIA 7.0 release also includes the ability to create a customizable decision tree using supplied reference annotation files for CNV, AOH and SV event pre-classification. VIA resources include a panel of target variants derived from various medical societies for clinical analysis guidelines specific to Acute Myeloid Leukemia (AML), Myelodysplastic Syndrome (MDS) and an aggregation of all hematological malignancy guidelines (pan-heme). These methods, combined with tools for expert review and reclassification of variants, allow users to conduct whole genome interpretation and report results. These new features in VIA provide a streamlined workflow for analyzing heterogeneous hematological cancer samples and demonstrate the analytical performance of updated approaches for detection of CNV and AOH/LOH regions at low allele fractions using optical genome mapping alone. Citation Format: Jacob Wisotsky, Aliz Raski, Westley Sherman, Megan Roytman, Samer Al-Saffar, Jen Hauenstein, Andy Wing Chun Pang, Viren Wasnikar, Neil Miller. Comprehensive analysis of hematological malignancies with optical genome mapping and Bionano VIA™Software [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 3492.

Harnessing Next-Generation Sequencing as a Timely and Accurate Second-Tier Screening Test for Newborn Screening of Inborn Errors of Metabolism.

In this study, we evaluated the implementation of a second-tier genetic screening test using an amplicon-based next-generation sequencing (NGS) panel in our laboratory during the period of 1 September 2021 to 31 August 2022 for the newborn screening (NBS) of six conditions for inborn errors of metabolism: citrullinemia type II (MIM #605814), systemic primary carnitine deficiency (MIM #212140), glutaric acidemia type I (MIM #231670), beta-ketothiolase deficiency (#203750), holocarboxylase synthetase deficiency (MIM #253270) and 3-hydroxy-3-methylglutaryl-CoA lyase deficiency (MIM # 246450). The custom-designed NGS panel can detect sequence variants in the relevant genes and also specifically screen for the presence of the hotspot variant IVS16ins3kb of SLC25A13 by the copy number variant calling algorithm. Genetic second-tier tests were performed for 1.8% of a total of 22,883 NBS samples. The false positive rate for these six conditions after the NGS second-tier test was only 0.017%, and two cases of citrullinemia type II would have been missed as false negatives if only biochemical first-tier testing was performed. The confirmed true positive cases were citrullinemia type II (n = 2) and systemic primary carnitine deficiency (n = 1). The false positives were later confirmed to be carrier of citrullinemia type II (n = 2), carrier of glutaric acidemia type I (n = 1) and carrier of systemic primary carnitine deficiency (n = 1). There were no false negatives reported. The incorporation of a second-tier genetic screening test by NGS greatly enhanced our program's performance with 5-working days turn-around time maintained as before. In addition, early genetic information is available at the time of recall to facilitate better clinical management and genetic counseling.

Visualization for Diagnostic Review of Copy Number Variants in Complex DNA Sequencing Data.

Genomics is at the core of precision medicine, and there are high expectations on genomics-enabled improvement of patient outcomes in the years to come. Around the world, initiatives to increase the use of DNA sequencing in clinical routine are being deployed, such as the use of broad panels in the standard care for oncology patients. Such a development comes at the cost of increased demands on throughput in genomic data analysis. In this paper, we use the task of copy number variant (CNV) analysis as a context for exploring visualization concepts for clinical genomics. CNV calls are generated algorithmically, but time-consuming manual intervention is needed to separate relevant findings from irrelevant ones in the resulting large call candidate lists. We present a visualization environment, named Copycat, to support this review task in a clinical scenario. Key components are a scatter-glyph plot replacing the traditional list visualization, and a glyph representation designed for at-a-glance relevance assessments. Moreover, we present results from a formative evaluation of the prototype by domain specialists, from which we elicit insights to guide both prototype improvements and visualization for clinical genomics in general.