The rapid development of next-generation sequencing (NGS) technology, including advances in sequencing chemistry, sequencing technologies, bioinformatics, and data interpretation, has facilitated its ...wide clinical application in precision medicine. This review describes current sequencing technologies, including short- and long-read sequencing technologies, and highlights the clinical application of NGS in inherited diseases, oncology, and infectious diseases. We review NGS approaches and clinical diagnosis for constitutional disorders; summarize the application of U.S. Food and Drug Administration-approved NGS panels, cancer biomarkers, minimal residual disease, and liquid biopsy in clinical oncology; and consider epidemiological surveillance, identification of pathogens, and the importance of host microbiome in infectious diseases. Finally, we discuss the challenges and future perspectives of clinical NGS tests.
Widespread clinical laboratory implementation of next-generation sequencing-based cancer testing has highlighted the importance and potential benefits of standardizing the interpretation and ...reporting of molecular results among laboratories. A multidisciplinary working group tasked to assess the current status of next-generation sequencing-based cancer testing and establish standardized consensus classification, annotation, interpretation, and reporting conventions for somatic sequence variants was convened by the Association for Molecular Pathology with liaison representation from the American College of Medical Genetics and Genomics, American Society of Clinical Oncology, and College of American Pathologists. On the basis of the results of professional surveys, literature review, and the Working Group's subject matter expert consensus, a four-tiered system to categorize somatic sequence variations based on their clinical significances is proposed: tier I, variants with strong clinical significance; tier II, variants with potential clinical significance; tier III, variants of unknown clinical significance; and tier IV, variants deemed benign or likely benign. Cancer genomics is a rapidly evolving field; therefore, the clinical significance of any variant in therapy, diagnosis, or prognosis should be reevaluated on an ongoing basis. Reporting of genomic variants should follow standard nomenclature, with testing method and limitations clearly described. Clinical recommendations should be concise and correlate with histological and clinical findings.
It remains an important challenge to predict the functional consequences or clinical impacts of genetic variants in human diseases, such as cancer. An increasing number of genetic variants in cancer ...have been discovered and documented in public databases such as COSMIC, but the vast majority of them have no functional or clinical annotations. Some databases, such as CiVIC are available with manual annotation of functional mutations, but the size of the database is small due to the use of human annotation. Since the unlabeled data (millions of variants) typically outnumber labeled data (thousands of variants), computational tools that take advantage of unlabeled data may improve prediction accuracy.
To leverage unlabeled data to predict functional importance of genetic variants, we introduced a method using semi-supervised generative adversarial networks (SGAN), incorporating features from both labeled and unlabeled data. Our SGAN model incorporated features from clinical guidelines and predictive scores from other computational tools. We also performed comparative analysis to study factors that influence prediction accuracy, such as using different algorithms, types of features, and training sample size, to provide more insights into variant prioritization. We found that SGAN can achieve competitive performances with small labeled training samples by incorporating unlabeled samples, which is a unique advantage compared to traditional machine learning methods. We also found that manually curated samples can achieve a more stable predictive performance than publicly available datasets.
By incorporating much larger samples of unlabeled data, the SGAN method can improve the ability to detect novel oncogenic variants, compared to other machine-learning algorithms that use only labeled datasets. SGAN can be potentially used to predict the pathogenicity of more complex variants such as structural variants or non-coding variants, with the availability of more training samples and informative features.
Pediatric low-grade gliomas (pLGG) are frequently driven by genetic alterations in the RAS-mitogen-activated protein kinase (RAS/MAPK) pathway yet show unexplained variability in their clinical ...outcome. To address this, we characterized a cohort of >1,000 clinically annotated pLGG. Eighty-four percent of cases harbored a driver alteration, while those without an identified alteration also often exhibited upregulation of the RAS/MAPK pathway. pLGG could be broadly classified based on their alteration type. Rearrangement-driven tumors were diagnosed at a younger age, enriched for WHO grade I histology, infrequently progressed, and rarely resulted in death as compared with SNV-driven tumors. Further sub-classification of clinical-molecular correlates stratified pLGG into risk categories. These data highlight the biological and clinical differences between pLGG subtypes and opens avenues for future treatment refinement.
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•KIAA1549-BRAF, BRAF p.V600E, and NF1 mutations account for 2/3 of pLGG•Activation of the RAS/MAPK pathway is nearly universal in pLGG•pLGG comprise two distinct clinical subgroups: rearrangement- or SNV-driven•Risk stratification based on alteration type effectively predicts patient outcome
Ryall et al. perform a comprehensive analysis of the molecular underpinnings and clinical correlates of 1000 pediatric low-grade gliomas. They uncover unique clinical features based on the type of molecular alteration identified and provide a risk based stratification to help infer treatment decisions.
Next-generation sequencing (NGS) technology has revolutionized genomic research by decreasing the cost of sequencing while increasing the throughput. The focus now is on potential clinical ...applications of NGS technology for diagnostics and therapeutics. Clinical applications of NGS in cancer can detect clinically actionable genetic/genomic alterations that are critical for cancer care. These alterations can be of diagnostic, prognostic, or therapeutic significance. In certain cancers, patient risk and prognosis can be predicted based on the mutation profile identified by NGS. Many targeted therapies have been developed for cancer patients who bear specific mutations; however, choosing the right NGS technique for the appropriate clinical application can be challenging, especially in clinical oncology, where the material for NGS tests is often limited and the turnaround time (TAT) for cancer tests is constrained to a few days. Currently, amplicon-based NGS approaches have emerged as the best fit for clinical oncology. In this review, we focus on amplicon-based library preparation, sequencing, sequence data alignment and annotation, and post-analytic interpretation and reporting.
We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of ...childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.
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•Proteogenomics characterization of 218 pediatric brain tumor samples of 7 histologies•Proteomic clusters reveal actionable biological features spanning histological boundaries•Proteomics reveal downstream effects of DNA alterations not evident in transcriptomics•Kinase activity analyses provide insights into pathway activities and druggable targets
Integrative proteogenomics analysis of pediatric tumors identifies common underlying biological processes and potential treatments as well as the functional effects of somatic mutations and CNVs driving tumorigenesis.
Information on the heterogeneity of phosphaturic mesenchymal tumor, a rare entity associated with tumor-induced osteomalacia, is limited. In this retrospective analysis of 222 phosphaturic ...mesenchymal tumors, 22 cases exhibited mixed mesenchymal and epithelial elements, which we propose to term "phosphaturic mesenchymal tumor, mixed epithelial, and connective tissue type." Phosphaturic mesenchymal tumor of the mixed epithelial and connective tissue type showed a distinctive and significant male predominance (male:female = 2.67:1), with most patients diagnosed at <40 years old. Moreover, all tumors were mainly located in the alveolar bone with focal invasion into surrounding soft tissue and oral mucosa, which could be detected preoperatively by oral examination. The mesenchymal component, composed of spindled cells resembling fibroblasts or myofibroblasts arranged in a storiform or fascicular pattern, exhibited a less prominent vasculature and lower cellularity than the typical phosphaturic mesenchymal tumor (mixed connective tissue type). The epithelial component was typically haphazardly and diffusely distributed throughout the tumor, forming small, irregular nests resembling odontogenic epithelial nests. All cases were immunoreactive for fibroblast growth factor-23, somatostatin receptor 2A, and NSE in both components. Mostly also demonstrated positive staining for CD99 (21/22, 96%), CD56 (16/22, 73%), Bcl-2 (21/22, 96%), and D2-40 (19/22, 86%) in one or both components. S100 was positive in both components in one of seven cases. Interestingly, immunoreactivity was typically stronger and more diffuse in the epithelial than in the paired mesenchymal components. The mesenchymal component was also diffusely positive for CD68 (17/17, 100%) and showed variable focal staining for SMA (15/22, 68%) and CD34 (9/19, 47 %). These results indicate that phosphaturic mesenchymal tumor of the mixed epithelial and connective tissue type has distinctive clinicopathological characteristics and a polyimmunophenotypic profile.
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