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Next-generation sequencing technologies have enabled a dramatic expansion of clinical genetic testing both for inherited conditions and diseases such as cancer. Accurate variant calling in NGS data is a critical step upon which virtually all downstream analysis and interpretation processes rely. Just as NGS technologies have evolved considerably over the past 10 years, so too have the software tools and approaches for detecting sequence variants in clinical samples. In this review, I discuss the current best practices for variant calling in clinical sequencing studies, with a particular emphasis on trio sequencing for inherited disorders and somatic mutation detection in cancer patients. I describe the relative strengths and weaknesses of panel, exome, and whole-genome sequencing for variant detection. Recommended tools and strategies for calling variants of different classes are also provided, along with guidance on variant review, validation, and benchmarking to ensure optimal performance. Although NGS technologies are continually evolving, and new capabilities (such as long-read single-molecule sequencing) are emerging, the “best practice” principles in this review should be relevant to clinical variant calling in the long term.

Next‐generation sequencing (NGS) of tumor tissue (ie, clinical sequencing) can guide clinical management by providing information about actionable gene aberrations that have diagnostic and therapeutic significance. Here, we undertook a hospital‐based prospective study (TOP‐GEAR project, 2nd stage) to investigate the feasibility and utility of NGS‐based analysis of 114 cancer‐associated genes (the NCC Oncopanel test). We examined 230 cases (comprising more than 30 tumor types) of advanced solid tumors, all of which were matched with nontumor samples. Gene profiling data were obtained for 187 cases (81.3%), 111 (59.4%) of which harbored actionable gene aberrations according to the Clinical Practice Guidelines for Next Generation Sequencing in Cancer Diagnosis and Treatment (Edition 1.0) issued by 3 major Japanese cancer‐related societies. Twenty‐five (13.3%) cases have since received molecular‐targeted therapy according to their gene aberrations. These results indicate the utility of tumor‐profiling multiplex gene panel testing in a clinical setting in Japan. This study is registered with UMIN Clinical Trials Registry (UMIN 000011141). The results of the TOP‐GEAR project (UMIN 000011141) indicate the utility of tumor‐profiling multiplex gene panel testing in a clinical setting in Japan.

Background The 2015 American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) guidelines for clinical sequence variant interpretation state that “well-established” functional studies can be used as evidence in variant classification. These guidelines articulated key attributes of functional data, including that assays should reflect the biological environment and be analytically sound; however, details of how to evaluate these attributes were left to expert judgment. The Clinical Genome Resource (ClinGen) designates Variant Curation Expert Panels (VCEPs) in specific disease areas to make gene-centric specifications to the ACMG/AMP guidelines, including more specific definitions of appropriate functional assays. We set out to evaluate the existing VCEP guidelines for functional assays. Methods We evaluated the functional criteria (PS3/BS3) of six VCEPs ( CDH1 , Hearing Loss, Inherited Cardiomyopathy- MYH7 , PAH , PTEN , RASopathy). We then established criteria for evaluating functional studies based on disease mechanism, general class of assay, and the characteristics of specific assay instances described in the primary literature. Using these criteria, we extensively curated assay instances cited by each VCEP in their pilot variant classification to analyze VCEP recommendations and their use in the interpretation of functional studies. Results Unsurprisingly, our analysis highlighted the breadth of VCEP-approved assays, reflecting the diversity of disease mechanisms among VCEPs. We also noted substantial variability between VCEPs in the method used to select these assays and in the approach used to specify strength modifications, as well as differences in suggested validation parameters. Importantly, we observed discrepancies between the parameters VCEPs specified as required for approved assay instances and the fulfillment of these requirements in the individual assays cited in pilot variant interpretation. Conclusions Interpretation of the intricacies of functional assays often requires expert-level knowledge of the gene and disease, and current VCEP recommendations for functional assay evidence are a useful tool to improve the accessibility of functional data by providing a starting point for curators to identify approved functional assays and key metrics. However, our analysis suggests that further guidance is needed to standardize this process and ensure consistency in the application of functional evidence.

In the past several years, single-molecule sequencing platforms, such as those by Pacific Biosciences and Oxford Nanopore Technologies, have become available to researchers and are currently being tested for clinical applications. They offer exceptionally long reads that permit direct sequencing through regions of the genome inaccessible or difficult to analyze by short-read platforms. This includes disease-causing long repetitive elements, extreme GC content regions, and complex gene loci. Similarly, these platforms enable structural variation characterization at previously unparalleled resolution and direct detection of epigenetic marks in native DNA. Here, we review how these technologies are opening up new clinical avenues that are being applied to pathogenic microorganisms and viruses, constitutional disorders, pharmacogenomics, cancer, and more. Single-molecule sequencing shows great promise for future genomic medicine. These technologies offer long read lengths, high consensus accuracies, direct identification of base modifications, direct RNA sequencing, portability, and more democratized access to sequencing platforms. Long reads enable near reference-quality genome assemblies, discovery of novel disease-causing structural variation, and the ability to sequence through previously ‘unsequenceable’ repetitive DNA contents of clinical utility. Single-molecule sequencing is revolutionizing clinical tests, such as direct sequencing of FMR1 alleles for length determination and detecting AGG interruptions, HLA typing at unparalleled resolution, disentangling chromothripsis genomes, determining the allelic distribution of low-frequency mutations in cancer genes such as TP53 and BCR-ABL1, and enabling in-field genetic testing.

Background The success of the clinical use of sequencing based tests (from single gene to genomes) depends on the accuracy and consistency of variant interpretation. Aiming to improve the interpretation process through practice guidelines, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) have published standards and guidelines for the interpretation of sequence variants. However, manual application of the guidelines is tedious and prone to human error. Web-based tools and software systems may not only address this problem but also document reasoning and supporting evidence, thus enabling transparency of evidence-based reasoning and resolution of discordant interpretations. Results In this report, we describe the design, implementation, and initial testing of the Clinical Genome Resource (ClinGen) Pathogenicity Calculator, a configurable system and web service for the assessment of pathogenicity of Mendelian germline sequence variants. The system allows users to enter the applicable ACMG/AMP-style evidence tags for a specific allele with links to supporting data for each tag and generate guideline-based pathogenicity assessment for the allele. Through automation and comprehensive documentation of evidence codes, the system facilitates more accurate application of the ACMG/AMP guidelines, improves standardization in variant classification, and facilitates collaborative resolution of discordances. The rules of reasoning are configurable with gene-specific or disease-specific guideline variations (e.g. cardiomyopathy-specific frequency thresholds and functional assays). The software is modular, equipped with robust application program interfaces (APIs), and available under a free open source license and as a cloud-hosted web service, thus facilitating both stand-alone use and integration with existing variant curation and interpretation systems. The Pathogenicity Calculator is accessible at http://calculator.clinicalgenome.org . Conclusions By enabling evidence-based reasoning about the pathogenicity of genetic variants and by documenting supporting evidence, the Calculator contributes toward the creation of a knowledge commons and more accurate interpretation of sequence variants in research and clinical care.

Cancer is a complex genetic disease that develops from the accumulation of genomic alterations in which germline variations predispose individuals to cancer and somatic alterations initiate and trigger the progression of cancer. For the past 2 decades, genomic research has advanced remarkably, evolving from single‐gene to whole‐genome screening by using genome‐wide association study and next‐generation sequencing that contributes to big genomic data. International collaborative efforts have contributed to curating these data to identify clinically significant alterations that could be used in clinical settings. Focusing on breast cancer, the present review summarizes the identification of genomic alterations with high‐throughput screening as well as the use of genomic information in clinical trials that match cancer patients to therapies, which further leads to cancer precision medicine. Furthermore, cancer screening and monitoring were enhanced greatly by the use of liquid biopsies. With the growing data complexity and size, there is much anticipation in exploiting deep machine learning and artificial intelligence to curate integrative “−omics” data to refine the current medical practice to be applied in the near future. Focusing on breast cancer, this review summarizes the discovery of germline variations and somatic alterations with genome‐wide association studies and next‐generation sequencing that contributes to big genomic data. These genetic biomarkers could be integrated in clinical settings to identify individuals who are at risk for cancer, drug‐induced toxicity, as well as match cancer patients to therapies, which further leads to cancer precision medicine. This review will also discuss the potential use of liquid biopsies in cancer screening and monitoring.

Background Massively parallel DNA sequencing generates staggering amounts of data. Decreasing cost, increasing throughput, and improved annotation have expanded the diversity of genomics applications in research and clinical practice. This expanding scale creates analytical challenges: accommodating peak compute demand, coordinating secure access for multiple analysts, and sharing validated tools and results. Results To address these challenges, we have developed the Mercury analysis pipeline and deployed it in local hardware and the Amazon Web Services cloud via the DNAnexus platform. Mercury is an automated, flexible, and extensible analysis workflow that provides accurate and reproducible genomic results at scales ranging from individuals to large cohorts. Conclusions By taking advantage of cloud computing and with Mercury implemented on the DNAnexus platform, we have demonstrated a powerful combination of a robust and fully validated software pipeline and a scalable computational resource that, to date, we have applied to more than 10,000 whole genome and whole exome samples.

Background: Neurological diseases are phenotypically and genotypically heterogeneous. Clinical exome sequencing (CES) has been shown to provide a high diagnostic yield for these disorders in the European population but remains to be demonstrated for the Indian population. Objective: The study aimed to understand the utility of clinical exome sequencing for the diagnosis of neurodevelopmental disorders. Materials and Methods: A cohort of 19 idiopathic patients with neurological phenotypes, primarily intellectual disability and developmental delay, were recruited. CES covering 4620 genes was performed on all patients. Candidate variants were validated by Sanger sequencing. Results: CES in 19 patients provided identified 21 variants across 16 genes which have been associated with different neurological disorders. Fifteen variants were reported previously and 6 variants were novel to our study. Eleven patients were diagnosed with autosomal dominant de novo variants, 7 with autosomal recessive and 1 with X-linked recessive variants. CES provided definitive diagnosis to 10 patients; hence, the diagnostic yield was 53%. Conclusion: Our study suggests that the diagnostic yield of CES in the Indian population is comparable to that reported in the European population. CES together with deep phenotyping could be a cost-effective way of diagnosing rare neurological disorders in the Indian population.

Precision medicine is a promising strategy for cancer treatment. In this study, we developed an in‐house clinical sequencing system to perform a comprehensive cancer genomic profiling test as a clinical examination and analyzed the utility of this system. Genomic DNA was extracted from tumor tissues and peripheral blood cells collected from 161 patients with different stages and types of cancer. A comprehensive targeted amplicon exome sequencing for 160 cancer‐related genes was performed using next‐generation sequencing (NGS). The sequencing data were analyzed using an original bioinformatics pipeline, and multiple cancer‐specific gene alterations were identified. The success rate of our test was 99% (160/161), while re‐biopsy was required for 24% (39/161) of the cases. Potentially actionable and actionable gene alterations were detected in 91% (145/160) and 46% (73/160) of the patients, respectively. The actionable gene alterations were frequently detected in PIK3CA (9%), ERBB2 (8%), and EGFR (4%). High tumor mutation burden (TMB) (≥10 mut/Mb) was observed in 12% (19/160) of the patients. The secondary findings in germline variants considered to be associated with hereditary tumors were detected in 9% (15/160) of the patients. Seventeen patients (11%, 17/160) were treated with genotype‐matched therapeutic agents, and the response rate was 47% (8/17). The median turnaround time for physicians was 20 days, and the median survival time after the initial visit was 8.7 months. The results of the present study prove the feasibility of implementing in‐house clinical sequencing as a promising laboratory examination technique for precision cancer medicine. In‐house clinical sequencing system is a promising laboratory examination for precision cancer medicine.

Implementing genome and exome sequencing in clinical practice presents challenges, including obtaining meaningful informed consent. Consent may be challenging due to test limitations such as uncertainties associated with test results and interpretation, complexity created by the potential for additional findings and high patient expectations. We drew on the experiences of research teams within the Clinical Sequencing Exploratory Research (CSER1) Consortium on informed consent for clinical genome and exome sequencing (CGES) to negotiate consensus considerations. We present six considerations for clinicians and 12 key points to communicate as they support patients in deciding whether to undergo CGES. These considerations and key points provide a helpful starting point for informed consent to CGES, grounded in the Clinical Sequencing Exploratory Research (CSER1) experience.