Clinical Variant Interpretation Research Articles

1679P A comparative analysis of cancer NGS reporting practices between Europe and North America

Results of Next Generation Sequencing (NGS) increasingly incorporated in routine clinical oncology. Multiple guidelines and standards have been issued globally to support clinical NGS variant interpretation and are integrated in different ways into cancer NGS reports around the world. However, a thorough capture of how these guidelines are being translated in real-world laboratory reporting has not yet been clearly studied.

WITHDRAWN: Clinical interpretation of PRSS1 gene variants in patients with pancreatitis

The Publisher regrets that this article is an accidental duplication of an article that has already been published in Clinics and Research in Hepatology and Gastroenterology, Volume 45, Issue 1, 2021, 101497. https://doi.org/10.1016/j.clinre.2020.07.004. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal

A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project

BackgroundGenomic variants which disrupt splicing are a major cause of rare genetic diseases. However, variants which lie outside of the canonical splice sites are difficult to interpret clinically. Improving the clinical interpretation of non-canonical splicing variants offers a major opportunity to uplift diagnostic yields from whole genome sequencing data.MethodsHere, we examine the landscape of splicing variants in whole-genome sequencing data from 38,688 individuals in the 100,000 Genomes Project and assess the contribution of non-canonical splicing variants to rare genetic diseases. We use a variant-level constraint metric (the mutability-adjusted proportion of singletons) to identify constrained functional variant classes near exon–intron junctions and at putative splicing branchpoints. To identify new diagnoses for individuals with unsolved rare diseases in the 100,000 Genomes Project, we identified individuals with de novo single-nucleotide variants near exon–intron boundaries and at putative splicing branchpoints in known disease genes. We identified candidate diagnostic variants through manual phenotype matching and confirmed new molecular diagnoses through clinical variant interpretation and functional RNA studies.ResultsWe show that near-splice positions and splicing branchpoints are highly constrained by purifying selection and harbour potentially damaging non-coding variants which are amenable to systematic analysis in sequencing data. From 258 de novo splicing variants in known rare disease genes, we identify 35 new likely diagnoses in probands with an unsolved rare disease. To date, we have confirmed a new diagnosis for six individuals, including four in whom RNA studies were performed.ConclusionsOverall, we demonstrate the clinical value of examining non-canonical splicing variants in individuals with unsolved rare diseases.

Whole genomic approach in mutation discovery of infantile spasms patients.

Infantile spasms (IS) are a clinically and genetically heterogeneous group of epilepsy disorders in early infancy. The genetic backgrounds of IS have been gradually unraveled along with the increased application of next-generation sequencing (NGS). However, to date, only selected genomic regions have been sequenced using a targeted approach in most cases of IS, and the genetic etiologies of the majority of patients remain unknown. We conducted a proof-of-concept study using whole-genome sequencing (WGS) for the genetic diagnosis of IS. We included 16 patients with IS for this study, and WGS was applied as a first-tier test for genetic diagnosis. In total, we sequenced the whole genomes of 28 participants, including the genomes of six patients, which were sequenced with those of their parents. Among variants identified, we focused on those located in epilepsy or seizure-associated genes. We used two different methods to call relevant large deletions from WGS results. We found pathogenic or likely pathogenic variants in four patients (25.0%); a de novo variant in HDAC4, compound heterozygous variants in GRM7, and heterozygous variants in CACNA1E and KMT2E. We also selected two more candidate variants in SOX5 and SHROOM4 intronic regions. Although there are currently several difficulties in applying WGS for genetic diagnosis, especially in clinical interpretation of non-coding variants, we believe that developing sequencing technologies would overcome these hurdles in the near future. Considering the vast genetic heterogeneity and the substantial portion of patients with unknown etiologies, further studies using whole genomic approaches are necessary for patients with IS.

Identifying Lethal Dependencies with HUGE Predictive Power.

Simple SummaryThis work shows that the predictions of lethal dependencies (LEDs) between genes can be dramatically improved by incorporating the “HUb effect in Genetic Essentiality” (HUGE) of gene alterations. In three genome-wide loss-of-function screens—Project Score, CERES score and DEMETER score—LEDs are identified with 75 times larger statistical power than using state-of-the-art methods. In AML, we identified LEDs not recalled by previous pipelines, including FLT3-mutant genotypes sensitive to FLT3 inhibitors. Interestingly, in-vitro validations confirm lethal de-pendencies of either NRAS or PTPN11 depending on the NRAS mutational status.Recent functional genomic screens—such as CRISPR-Cas9 or RNAi screening—have fostered a new wave of targeted treatments based on the concept of synthetic lethality. These approaches identified LEthal Dependencies (LEDs) by estimating the effect of genetic events on cell viability. The multiple-hypothesis problem is related to a large number of gene knockouts limiting the statistical power of these studies. Here, we show that predictions of LEDs from functional screens can be dramatically improved by incorporating the “HUb effect in Genetic Essentiality” (HUGE) of gene alterations. We analyze three recent genome-wide loss-of-function screens—Project Score, CERES score and DEMETER score—identifying LEDs with 75 times larger statistical power than using state-of-the-art methods. Using acute myeloid leukemia, breast cancer, lung adenocarcinoma and colon adenocarcinoma as disease models, we validate that our predictions are enriched in a recent harmonized knowledge base of clinical interpretations of somatic genomic variants in cancer (AUROC > 0.87). Our approach is effective even in tumors with large genetic heterogeneity such as acute myeloid leukemia, where we identified LEDs not recalled by previous pipelines, including FLT3-mutant genotypes sensitive to FLT3 inhibitors. Interestingly, in-vitro validations confirm lethal dependencies of either NRAS or PTPN11 depending on the NRAS mutational status. HUGE will hopefully help discover novel genetic dependencies amenable for precision-targeted therapies in cancer. All the graphs showing lethal dependencies for the 19 tumor types analyzed can be visualized in an interactive tool.

Abstract 5735: Druggable gene alterations in Japanese patients with rare malignancy

Abstract Background: In the absence of a current standard of care, precision oncology with next-generation sequencing provides an important therapeutic option for patients with rare malignancy. Here, we sought to establish the clinical relevancy of comprehensive genomic profiling (CGP) for patients with rare malignancy. Methods: We reviewed 341 patients who underwent CGP (FoundationOne CDx; OncoGuide NCC OncoPanel System)in our institution between 2019 and 2021. Cases were classified as common or rare malignancy based on the Rare Cancers in Europe (RARECARE) definition (incidence of <6 cases per 100,000 individuals). We analyzed the genomic features in the rare malignancy cohort, including gene mutations, gene fusions, tumor mutational burden (TMB), and microsatellite instability (MSI) status. Gene variants, amplifications, and fusions categorized as evidence level A using Clinical Interpretations of Variants in Cancer (CIViC) and MD Anderson Knowledge Base for Precision Oncology were considered as druggable alterations. CGP testing was used to calculate overall survival (OS) to death or at the last follow-up. Results: With the exception of 7 unclassifiable cases, rare malignancy accounted for 149 of the 334 cases (45%), with the most common being female genital cancers (32%), followed by digestive cancers (24%), sarcoma (16%), and others (28%). Many (83%) of the patients with rare malignancy harbored at least one pathogenic/likely-pathogenic variant. The commonly mutated genes were TP53 (41%), PIK3CA (20%), ARID1A (14%), and KRAS (13%). ERBB2amplification was detected in 6% of patients (median copy number, 8). Gene fusion in EWSR1 was found in 4% of patients (sarcoma) and in FGFR2 in 1% (cholangiocarcinoma and salivary duct carcinoma). TMB-high (≥10 mut/Mb) and MSI-high cases were found in 8% and 2% of cases, respectively. Druggable alterations were detected in 36 patients; this percentage was not significantly different to that of common malignancy (24% vs. 17%, P=0.10). The common druggable alterations were BRAF V600E (n=10), ERBB2 amplification (n=9), PIK3CA E542K (n=6), and BRCA1/2 variant (n=6). There was no significant difference in OS between the rare and common malignancy groups (1-year OS rate: 62% vs. 46%, P=0.24). Conclusion: In this study, the ratio of rare malignancy was higher than that typically found in previous epidemiological studies (about 20%-25%). We observed no adverse impact for OS, which might be due to the high number of patients (n=48) with intractable pancreatic cancer. Remarkably, >20% of patients harbored a potential druggable alteration. Our results suggest the clinical relevance of CGP for patients with rare malignancy, and its potential utility to provide clues for future clinical development. Citation Format: Akihiro Ohmoto, Naomi Hayashi, Ippei Fukada, Masumi Yamazaki, Mayu Yunokawa, Akiyoshi Kasuga, Eiji Shinozaki, Arisa Ueki, Akiko Tonooka, Kengo Takeuchi, Seiichi Mori, Kazuma Kiyotani, Shunji Takahashi. Druggable gene alterations in Japanese patients with rare malignancy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5735.

Abstract 1193: Enhancing pediatric cancer variant curation and representation through standardized classification and automation

Abstract Childhood cancers present unique challenges for variant interpretation in a clinical context due to their rarity, low mutation burden, diversity of molecular alterations, and heterogeneity among patients. Consequently, genes and variants associated with childhood tumors are under-represented in public cancer databases and knowledgebases. A focused effort is needed for the structured curation of genetic variant-level data to document diagnostic, prognostic, and therapeutic biomarkers for childhood cancers. The Pediatric Cancer Curation Advancement Subcommittee (PCCAS), a collaboration between the Clinical Interpretations of Variants in Cancer knowledgebase (CIViC; civicdb.org), the ClinGen Somatic Pediatric Cancer Taskforce, Disease Ontology (DO; disease-ontology.org) and CIViCmine (bionlp.bcgsc.ca/civicmine/), is addressing this challenge through enhanced curation, tagging, and automation. PCCAS created a pediatric specific curation standard operating procedure (SOP) to harmonize pediatric evidence entered in CIViC. Our SOP provides general guidance and considerations to define and classify childhood cancers and to represent childhood cancer evidence on a spectrum of age-related incidence and presentation. For instance, pediatric evidence in CIViC is now tagged using Human Phenotype Ontology (HPO) age of onset terms, allowing pediatric evidence to be easily searched, tracked, and sorted. We also initiated the addition of new age of onset terms to enhance the granularity of these tags. WHO ICD-O nomenclature has been chosen for pediatric disease classification in CIViC. ICD-O provides updated terminology including specific genetic subtypes, which are important in pediatric cancers where their underlying molecular profiles often define the disease. To aid curator selection of disease, we verified pediatric relevant ICD-O terms inclusion in DO and restructured DO disease hierarchies to ensure proper mapping. CIViC highlights our pediatric cancer initiative in multiple areas including a homepage feature linking directly to a dedicated pediatric advanced search that returns all evidence tagged with pediatric or young adult age of onset. Most importantly, our childhood specific SOP and initiatives are included in all ClinGen Somatic Cancer and CIViC training sessions for consistent implementation. CIViCmine supports CIViC by using natural language processing to identify important cancer biomarkers in the literature. To better identify pediatric biomarkers, we are adapting and refining CIViCmine to use MeSH terms and other approaches to enhance accuracy in the identification of childhood evidence in both the literature and CIViC. In conclusion, implementation of these procedures, features, and automation are pushing to make childhood cancer variant evidence more accessible and interpretable. Citation Format: Jason Saliba, Jake Lever, Kilannin Krysiak, Arpad Danos, Alex Wagner, Heather E. Williams, Laveniya Satgunaseelan, David Meredith, Cameron J. Grisdale, Chimene Kesserwan, Jianling Ji, Shruti Rao, Catherine Cottrell, Alanna Church, Mark Evans, Yasmina Jaufeerally-Fakim, Lynn M. Schriml, Angshumoy Roy, Gordana Raca, Malachi Griffith, Obi L. Griffith. Enhancing pediatric cancer variant curation and representation through standardized classification and automation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1193.

Abstract LB152: The Critical Assessment of Genome Interpretation: A community experiment that informs use of methods for germline cancer variant impact prediction

Abstract Interpretation of genomic variation plays an essential role in the analysis of cancer and monogenic disease, with applications ranging from basic research to clinical decisions. Yet the field lacks a clear consensus on the appropriate level of confidence to place in variant impact and interpretation methods, for both well-established oncogenes as well as less understood genes. The Critical Assessment of Genome Interpretation (CAGI, \'kā-jē\) is a community experiment to objectively assess computational methods for predicting the phenotypic impacts of genomic variation. CAGI participants are provided genetic variants and make blind predictions of resulting phenotype. Independent assessors evaluate the predictions by comparing them against experimental and clinical data. Six CAGI editions involving over 60 experiments have taken place to date. Results have been described in two special issues of Human Mutation (vol. 38(9) and vol. 40(9)). About a quarter of CAGI experiments have involved genes implicated in cancer, including variants in BRCA1, BRCA2, CHEK2, the MRN complex (RAD50, MRE11, and NBS1), FXN, NSMCE2 (coding for SUMO ligase), NPM-ALK CDKN2A, PTEN, TPMT, STK11, p16 and TP53. In the ENIGMA consortium challenge, a total of 326 germline BRCA1/BRCA2 missense and in-frame indel variants were provided. The most successful predictions achieved a ROC AUC of 0.94 and were derived from a metapredictor that explicitly considered the population frequency of each variant, trained on cancer data, and included the role of altered splicing. In challenges exploring the role of protein stability in cancer progression, biophysical and structure-based methods are among the top performers and have provided insights into underlying molecular mechanisms. This was the case for the p16 challenge involving variants of unknown significance in familial malignant melanoma, the PTEN and TPMT challenges that measured variant effect on protein stability via intracellular abundance in a high-throughput assay, and the p53 rescue challenge in which predictors managed to successfully identify a handful of double mutants, among a set of 14,668 total variants, that reactivated the damaged protein. Cancer challenges involving breast cancer pharmacogenomics, case-controls, splicing and polygenic risk scores (PRS) have been moderately successful but show promise for the future. A key finding from CAGI is that for most missense challenges, including cancer, it is possible to relate phenotype values to a pathogenicity threshold, and so deduce potential performance in a clinical setting. The results suggest that computational methods are generally more reliable than recognized in the current clinical use guidelines, warranting a reevaluation of their role in clinical variant interpretation. Results from the ENIGMA and other BRCA challenges support this observation but more data are needed. Overall, CAGI has helped establishing the state of art in genome interpretation, encouraged new methodological developments, and informed the clinical application of computational predictors. Detailed information about CAGI may be found at https://genomeinterpretation.org. Citation Format: Constantina Bakolitsa, Shantanu Jain, Gaia Andreoletti, Roger A. Hoskins, Predrag Radivojac, John Moult, Steven E. Brenner, CAGI Participants. The Critical Assessment of Genome Interpretation: A community experiment that informs use of methods for germline cancer variant impact prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB152.

Abstract 1194: Redesigning CIViC: Enhancing the structured curation of complex cancer variant data

Abstract The Clinical Interpretation of Variants in Cancer (CIViC; www.civicdb.org) knowledgebase is a curation platform designed to capture evidence from the published literature which support or refute the significance of genomic variants in various cancer types. Since the launch of the beta user interface in 2014, this knowledgebase has undergone substantial evolution and redesign to support the needs of the clinical and research communities. Significant work has been done by the community to release guidelines and resources to support cancer variant interpretation. CIViC is a crowd sourced, expert moderated resource with a community of over 300 active curators. In response to community feedback, evolving guidelines and improved understanding of the genetics of cancer, we have developed CIViC 2.0, a major redesign that expands the capabilities of this widely used resource. CIViC 2.0 has been designed to support complex variant relationships (termed molecular profiles in CIViC) such as the poor prognostic impact of the combination of variants in FLT3, DNMT3A and NPM1 in acute myeloid leukemia. These highly structured molecular profiles link details about each constituent variant, including genomic coordinates, HGVS, aliases, and additional metadata. Molecular profiles will also support the absence of a variant, which has become critical in targeted therapy decision making such as colorectal cancer where EGFR expression without a KRAS variant is an indication for targeted inhibitor therapy. These molecular profiles also support structural variants. While fusions have always been supported, CIViC 2.0 will support complex genomic coordinate or cytoband specific queries. Significant technical changes have improved the speed and performance of the UI and the underlying API. A complete redesign of the API, utilizing GraphQL, empowers users to ask more complex questions and more deeply explore this highly curated data. More robust data schemas and comprehensive validation of evidence structure will allow for the programmatic submission of new evidence. By utilizing site-wide full text faceted search, identifying relevant content in summaries, evidence items, or even curator comments is straightforward. The CIViC 2.0 redesign supports the ever-increasing complexity of cancer variant information and provides a powerful tool to explore and utilize the carefully curated data within the knowledgebase for a multitude of questions related to basic, translational, and clinical research. Citation Format: Kilannin C. Krysiak, Adam C. Coffman, Susanna Kiwala, Joshua F. McMichael, Arpad M. Danos, Jason Saliba, Cameron J. Grisdale, Jake Lever, Lana Sheta, Shruti Rao, Alex H. Wagner, Malachi Griffith, Obi L. Griffith. Redesigning CIViC: Enhancing the structured curation of complex cancer variant data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1194.

A calibrated functional patch-clamp assay to enhance clinical variant interpretation in KCNH2-related long QT syndrome

Modern sequencing technologies have revolutionized our detection of gene variants. However, in most genes, including KCNH2, the majority of missense variants are currently classified as variants of uncertain significance (VUSs). The aim of this study was to investigate the utility of an automated patch-clamp assay for aiding clinical variant classification in KCNH2. The assay was designed according to recommendations proposed by the Clinical Genome Sequence Variant Interpretation Working Group. Thirty-one variants (17 pathogenic/likely pathogenic, 14 benign/likely benign) were classified internally as variant controls. They were heterozygously expressed in Flp-In HEK293 cells for assessing the effects of variants on current density and channel gating in order to determine the sensitivity and specificity of the assay. All 17 pathogenic variant controls had reduced current density, and 13 of 14 benign variant controls had normal current density, which enabled determination of normal and abnormal ranges for applying evidence of moderate or supporting strength for VUS reclassification. Inclusion of functional assay evidence enabled us to reclassify 6 out of 44 KCNH2 VUSs as likely pathogenic. The high-throughput patch-clamp assay can provide moderate-strength evidence for clinical interpretation of clinical KCNH2 variants and demonstrates the value of developing automated patch-clamp assays for functional characterization of ion channel gene variants.

Expanding the phenotype of ATP6AP1 deficiency.

Vacuolar ATPases (V-ATPases) are large multisubunit proton pumps conserved among all eukaryotic cells that are involved in diverse functions including acidification of membrane-bound intracellular compartments. The ATP6AP1 gene encodes an accessory subunit of the vacuolar (V)-ATPase protein pump. Pathogenic variants in ATP6AP1 have been described in association with a congenital disorder of glycosylation (CDG), which are highly variable, but often characterized by immunodeficiency, hepatopathy, and neurologic manifestations. Although the most striking and common clinical feature is hepatopathy, the phenotypic and genotypic spectrum of ATP6AP1-CDG continues to expand. Here, we report identical twins who presented with acute liver failure and jaundice. Prenatal features included cystic hygroma, atrial septal defect, and ventriculomegaly. Postnatal features included pectus carinatum, connective tissue abnormalities, and hypospadias. Whole-exome sequencing (WES) revealed a novel de novo in-frame deletion in the ATP6AP1 gene (c.230_232delACT;p.Tyr77del). Although both twins have the commonly reported clinical feature of hepatopathy seen in other individuals with ATP6AP1-CDG-related disorder, they do not have neurological sequelae. This report expands the phenotypic spectrum of ATP6AP1-CDG-related disorder with both probands exhibiting unique prenatal and postnatal features, including fetal ventriculomegaly, umbilical hernia, pectus carinatum, micropenis, and hypospadias. Furthermore, this case affirms that neurological features described in the initial case series on ATP6AP1-CDG do not appear to be central, whereas the prenatal and connective tissue manifestations may be more common than previously thought. This emphasizes the importance of long-term clinical follow-up and variant interpretation using current updated recommendations.

Determinants of trafficking, conduction, and disease within a K+ channel revealed through multiparametric deep mutational scanning.

A long-standing goal in protein science and clinical genetics is to develop quantitative models of sequence, structure, and function relationships to understand how mutations cause disease. Deep mutational scanning (DMS) is a promising strategy to map how amino acids contribute to protein structure and function and to advance clinical variant interpretation. Here, we introduce 7429 single-residue missense mutations into the inward rectifier K+ channel Kir2.1 and determine how this affects folding, assembly, and trafficking, as well as regulation by allosteric ligands and ion conduction. Our data provide high-resolution information on a cotranslationally folded biogenic unit, trafficking and quality control signals, and segregated roles of different structural elements in fold stability and function. We show that Kir2.1 surface trafficking mutants are underrepresented in variant effect databases, which has implications for clinical practice. By comparing fitness scores with expert-reviewed variant effects, we can predict the pathogenicity of 'variants of unknown significance' and disease mechanisms of known pathogenic mutations. Our study in Kir2.1 provides a blueprint for how multiparametric DMS can help us understand the mechanistic basis of genetic disorders and the structure-function relationships of proteins.

Large scale genotype- and phenotype-driven machine learning in Von Hippel-Lindau disease.

Von Hippel-Lindau (VHL) disease is a hereditary cancer syndrome where individuals are predisposed to tumor development in the brain, adrenal gland, kidney, and other organs. It is caused by pathogenic variants in the VHL tumor suppressor gene. Standardized disease information has been difficult to collect due to the rarity and diversity of VHL patients. Over 4100 unique articles published until October 2019 were screened for germline genotype-phenotype data. Patient data weretranslated into standardized descriptions using Human Genome Variation Societygene variant nomenclature and Human Phenotype Ontologyterms and has been manually curated into an open-access knowledgebase called Clinical Interpretation of Variants in Cancer. In total, 634 unique VHL variants, 2882 patients, and 1991 families from 427 papers were captured. We identified relationship trends between phenotype and genotype data using classic statistical methods and spectral clustering unsupervised learning. Our analyses reveal earlier onset of pheochromocytoma/paraganglioma and retinal angiomas, phenotype co-occurrences and genotype-phenotype correlations including hotspots. It confirms existing VHL associations and can be used to identify new patterns and associations in VHL disease. Our database serves as an aggregate knowledge translation tool to facilitate sharing information about the pathogenicity of VHL variants.

CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer.

Several knowledgebases are manually curated to support clinical interpretations of thousands of hotspot somatic mutations in cancer. However, discrepancies or even conflicting interpretations are observed among these databases. Furthermore, many previously undocumented mutations may have clinical or functional impacts on cancer but are not systematically interpreted by existing knowledgebases. To address these challenges, we developed CancerVar to facilitate automated and standardized interpretations for 13 million somatic mutations based on the AMP/ASCO/CAP 2017 guidelines. We further introduced a deep learning framework to predict oncogenicity for these variants using both functional and clinical features. CancerVar achieved satisfactory performance when compared to several independent knowledgebases and, using clinically curated datasets, demonstrated practical utility in classifying somatic variants. In summary, by integrating clinical guidelines with a deep learning framework, CancerVar facilitates clinical interpretation of somatic variants, reduces manual work, improves consistency in variant classification, and promotes implementation of the guidelines.

Short amplicon reverse transcription-polymerase chain reaction detects aberrant splicing in genes with low expression in blood missed by ribonucleic acid sequencing analysis for clinical diagnosis.

Use of blood RNA sequencing (RNA‐seq) as a splicing analysis tool for clinical interpretation of variants of uncertain significance (VUSs) found via whole‐genome and exome sequencing can be difficult for genes that have low expression in the blood due to insufficient read count coverage aligned to specific genes of interest. Here, we present a short amplicon reverse transcription‐polymerase chain reaction(RT‐PCR) for the detection of genes with low blood expression. Short amplicon RT‐PCR, is designed to span three exons where an exon harboring a variant is flanked by one upstream and one downstream exon. We tested short amplicon RT‐PCRs for genes that have median transcripts per million (TPM) values less than one according to the genotype‐tissue expression database. Median TPM values of genes analyzed in this study are SYN1 = 0.8549, COL1A1 = 0.6275, TCF4 = 0.4009, DSP = .2894, TTN = 0.2851, COL5A2 = 0.1036, TERT = 0.04452, NTRK2 = 0.0344, ABCA4 = 0.00744, PRPH = 0, and WT1 = 0. All these genes show insufficient exon‐spanning read coverage in our RNA‐seq data to allow splicing analysis. We successfully detected all genes tested except PRPH and WT1. Aberrant splicing was detected in SYN1, TCF4, NTRK2, TTN, and TERT VUSs. Therefore, our results show short amplicon RT‐PCR is a useful alternative for the analysis of splicing events in genes with low TPM in blood RNA for clinical diagnostics.

DrugCVar: a platform for evidence-based drug annotation for genetic variants in cancer

Targeted therapy for cancer-related genetic variants is critical for precision medicine. Although several databases including The Clinical Interpretation of Variants in Cancer (CIViC), The Oncology Knowledge Base (OncoKB), The Cancer Genome Interpreter (CGI) and My Cancer Genome (MCG) provide clinical interpretations of variants in cancer, the clinical evidence was limited and miscellaneous. In this study, we developed the DrugCVar database, which integrated our manually curated cancer variant-drug targeting evidence from literature and the interpretations from the public resources. In total, 7830 clinical evidences for cancer variant-drug targeting were integrated and classified into 10 evidence tiers. Searching and browsing functions were provided for quick queries of cancer variant-drug targeting evidence. Also, batch annotation module was developed for user-provided massive genetic variants in various formats. Details, such as the mutation function, location of the variants in gene and protein structures and mutation statistics of queried genes in various tumor types, were also provided for further investigations. Thus, DrugCVar could serve as a comprehensive annotation tool to interpret potential drugs for cancer variants especially the massive ones from clinical cancer genomics studies. The database is available at http://drugcvar.omicsbio.info. Supplementary data are available at Bioinformatics online.

Mutation Analysis of the RPGR Gene in a Chinese Cohort.

Purpose: The purpose of this study was to investigate the clinical and genetic characteristics of the retinitis pigmentosa GTPase regulatory factor gene (RPGR) in a Chinese cohort. Methods: A retrospective analysis was performed on 80 subjects with RPGR-retinal dystrophy (RPGR-RD) for detailed genetic and clinical characterization. The panel-based next-generation sequencing of 792 causative genes involved in common genetic eye diseases was conducted in all individuals, followed by clinical variant interpretation. Information, including age, sex, geographic distribution, family history, consanguineous marriage, age at symptom onset, disease duration, best corrected visual acuity (BCVA), and complete ophthalmologic examination results, was collected. Results: This cohort (41 men and 39 women) included 26 families (26 probands and their available family members) and 13 sporadic cases. The average age of these participants was 36.35 ± 17.68 years, and the majority of the families were from eastern China (28 families, 71.79%). The average duration of disease in the probands was 22.68 ± 15.80 years. In addition, the average BCVA values of the right and left eyes in the probands were 0.96 ± 0.77 and 1.00 ± 0.77, respectively. A total of 34 RPGR variants were identified, including 6 reported variants and 28 novel variants. Among these variants, NM_001034853.1: c.2899_2902delGAAG and c.2744_2745ins24 were considered de novo variants. The majority of the RPGR variants were classified as likely pathogenic, accounting for 70.59% of the variants (24 variants). The most common nucleotide and amino acid changes identified in this study were deletions (16 variants, 45.06%) and frameshifts (17 variants, 50.00%), respectively. Genetic analysis revealed that these RPGR variants were distributed in 10 different subregions of RPGR, and 70.59% of the RPGR variants (24 variants) were located in exon 15. Four RPGR variants, NM_001034853.1: c.2405_2406delAG, c.1345C > T, c.2218G > T and c.2236_2237delGA, occurred at a very high frequency of 28.21% (11 families) among 39 unrelated families. Conclusion: This study expands the known mutational spectrum of RPGR, and we provide a new reference for the genetic diagnosis of RPGR variants.

AmazonForest: In Silico Metaprediction of Pathogenic Variants.

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

Middle Eastern Genetic Variation Improves Clinical Annotation of the Human Genome.

Genetic variation in populations of Middle Eastern origin remains highly underrepresented in most comprehensive genomic databases. This underrepresentation hampers the functional annotation of the human genome and challenges accurate clinical variant interpretation. To highlight the importance of capturing genetic variation in the Middle East, we aggregated whole exome and genome sequencing data from 2116 individuals in the Middle East and established the Middle East Variation (MEV) database. Of the high-impact coding (missense and loss of function) variants in this database, 53% were absent from the most comprehensive Genome Aggregation Database (gnomAD), thus representing a unique Middle Eastern variation dataset which might directly impact clinical variant interpretation. We highlight 39 variants with minor allele frequency >1% in the MEV database that were previously reported as rare disease variants in ClinVar and the Human Gene Mutation Database (HGMD). Furthermore, the MEV database consisted of 281 putative homozygous loss of function (LoF) variants, or complete knockouts, of which 31.7% (89/281) were absent from gnomAD. This set represents either complete knockouts of 83 unique genes in reportedly healthy individuals, with implications regarding disease penetrance and expressivity, or might affect dispensable exons, thus refining the clinical annotation of those regions. Intriguingly, 24 of those genes have several clinically significant variants reported in ClinVar and/or HGMD. Our study shows that genetic variation in the Middle East improves functional annotation and clinical interpretation of the genome and emphasizes the need for expanding sequencing studies in the Middle East and other underrepresented populations.

A clinician's guide to omics resources in dermatology.

SummaryWith recent advances in high‐throughput technologies, we are now in an era where the use of large‐scale datasets of biological samples and individual diseases can be analysed using omics methodologies. These include genomics, transcriptomics, proteomics, metabolomics, lipidomics and epigenomics. Omics approaches have been developed to deliver a holistic understanding of systems biology, to identify key biomarkers, and to aid in the interpretation of molecular, biochemical and environmental interactions. Navigating through the plethora of online datasets to find useful and concise information for comparison of data can be complex and overwhelming. The purpose of this article is to review the current repositories and databases, and to evaluate their application in dermatological research and their relevance to clinical practice. For this study, an extensive review of online platforms used in dermatology research was undertaken. Online resources for genetic disease information, genetic disease connection platforms for patients and researchers, clinical interpretation of variants, genome and DNA databases, and omics data repositories and resources were collected. This study provides a comprehensive overview of relevant databases that will aid clinicians and scientists using omics data in dermatology.