Interpretation Of Genomic Variants Research Articles

Abstract 2351: Genomics annotation and interpretation in somatic oncology using structured data from a clinical knowledgebase

Abstract Accurate and comprehensive interpretation of genomic variants has become a bottleneck in clinical sequencing applications due to the accelerating precision oncology and biomedical information explosion. This motivated us to build Ephesus, a framework enabling curation of predictive, prognostic and diagnostic evidence for clinical biomarkers in cancers. Currently Ephesus is the primary content source for Roche navify Mutation Profiler (nMP). The Ephesus’ data model ensures adherence to best practices in clinical genomic content curation. Variant classification follows the AMP guidelines for somatic variant interpretation. Variant interpretations are derived from international regulatory approvals and clinical practice guidelines (FDA, EMA, TGA, eVIQ, etc.) and recommendations (NCCN, ESMO). The data model is mapped to a set of relational tables. To enforce data integrity and validity, strategies such as data normalization based on standard nomenclatural ontologies, a submit-review-approval workflow, and biological constraints are adopted. Ephesus is deployed as a web application used by curators inside and outside Roche. The UI allows users to conduct edits, filtering, sorting and bulk operations on entities by attributes. In order to minimize manual effort and maximize content coverage, inference rules are applied before ingesting into nMP. Currently Ephesus is developed for data collection, browsing and summarizing primary entities such as biomarkers, evidence items, genes, variants, variant groups, and drugs. The content from the 2023-Aug snapshot contains 11,596 directly curated biomarker profiles (including biomarker combinations), and 6M+ expanded profiles for 41 major cancer types. To evaluate the clinical reporting value of the curated knowledge, ~160k real cancer patient samples of the AACR GENIE project were queried on Ephesus. Compared with three other major knowledgebases, Ephesus/NMP is observed showing the highest performance. Table 1. Percentage of patients or biomarkers with at least one interpretation from each knowledgebase CGI (2022-10-17) CIVIC(2023-09-01) ClinVar(2023-09-09) NMP(2023-08-11) AACR-Genie-v14.0 Total patient % with any interpretation 25% (40,545) 46% (73,835) 81% (130,882) 89% (142,905) 160,965 biomarker % (including combos) with any interpretation 0.03% (256) 0.06% (526) 13.26% (122,424) 73.79% (681,128) 923,093 Citation Format: Jian Li, Lili Niu, Shuba krishna, Fnu Kinshuk, Martin Jones, Carlos Hernandez, Michael Clark, Sandra Balladares. Genomics annotation and interpretation in somatic oncology using structured data from a clinical knowledgebase [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 2351.

Clinical Strategies in Gene Screening Counseling for the Healthy General Population.

The burgeoning interest in precision medicine has propelled an increase in the use of genome tests for screening purposes within the healthy population. Gene screening tests aim to pre-emptively identify those individuals who may be genetically predisposed to certain diseases. However, as genetic screening becomes more commonplace, it is essential to acknowledge the unique challenges it poses. A prevalent issue in this regard is the occurrence of falsepositive results, which can lead to unnecessary additional tests or treatments, and psychological distress. Additionally, the interpretation of genomic variants is based on current research evidence, and can accordingly change as new research findings emerge, potentially altering the clinical significance of these variants. Conversely, a further prominent concern regards false assurances in genetic testing, as genetic tests can yield false-negative results, potentially posing a significant clinical risk. Moreover, the results obtained for the same disease can vary among different genetic testing services, due to differences in the types of variants assessed, the scope of tests, analytical methods, and the algorithms used for predicting diseases. Consequently, whereas genetic testing holds significant promise for the future of medicine, it poses unique challenges. If conducted without a full understanding of its implications, genetic testing may fail to achieve its purpose potentially hindering effective health management. Therefore, to ensure a comprehensive understanding of the implications of genetic testing within the general population, sufficient discussion and careful consideration should be given to counseling based on gene test results.

Massive open online courses (MOOCs) in genomic variant interpretation: An innovative education strategy for the growing genetic counselor workforce.

The growth in genomic testing in healthcare requires a highly trained specialist workforce to ensure evidence based clinical germline variant interpretation. Genetic counselors form a core part of the clinical genomics multidisciplinary team (MDT) and represent a growing workforce participating in variant interpretation from data analysis to the patient consultation. Standardized, high-quality variant interpretation training for Genetic Counselors has historically been ad hoc and variable, with existing programs lacking capacity to reach the entire workforce. To address the requirement for scalable variant interpretation training for genomics healthcare professionals (HCPs), two Massive Open Online Courses (MOOCs) were developed. We analyzed the data from 17 Genetic counselors, as part of an evaluation cohort completing the first run of these MOOCs. Overall genetic counselors enjoyed the courses, felt they were clinically relevant and would recommend them to colleagues. Common challenges amongst the genetic counseling workforces included utilizing relevant databases and finding time in the workday to complete training. These findings suggest MOOCs could be an acceptable option to ensure a consistent and transferrable high standard of training, complimentary to existing curricula. They also hold the potential to facilitate large-scale education to update the genetic counseling workforce when changes in variant interpretation guidance occur.

Evaluation of two Massive Open Online Courses (MOOCs) in genomic variant interpretation for the NHS workforce

BackgroundThe implementation of the National Genomic Medicine Service in the UK has increased patient access to germline genomic testing. Increased testing leads to more genetic diagnoses but does result in the identification of genomic variants of uncertain significance (VUS). The rigorous process of interpreting these variants requires multi-disciplinary, highly trained healthcare professionals (HCPs). To meet this training need, we designed two Massive Open Online Courses (MOOCs) for HCPs involved in germline genomic testing pathways: Fundamental Principles (FP) and Inherited Cancer Susceptibility (ICS).MethodsAn evaluation cohort of HCPs involved in genomic testing were recruited, with additional data also available from anonymous self-registered learners to both MOOCs. Pre- and post-course surveys and in-course quizzes were used to assess learner satisfaction, confidence and knowledge gained in variant interpretation. In addition, granular feedback was collected on the complexity of the MOOCs to iteratively improve the resources.ResultsA cohort of 92 genomics HCPs, including clinical scientists, and non-genomics clinicians (clinicians working in specialties outside of genomics) participated in the evaluation cohort. Between baseline and follow-up, total confidence scores improved by 38% (15.2/40.0) (95% confidence interval [CI] 12.4–18.0) for the FP MOOC and 54% (18.9/34.9) (95%CI 15.5–22.5) for the ICS MOOC (p < 0.0001 for both). Of those who completed the knowledge assessment through six summative variant classification quizzes (V1–6), a mean of 79% of respondents classified the variants such that correct clinical management would be undertaken (FP: V1 (73/90) 81% Likely Pathogenic/Pathogenic [LP/P]; V2 (55/78) 70% VUS; V3 (59/75) 79% LP/P; V4 (62/72) 86% LP/LP. ICS: V5 (66/91) 73% VUS; V6 (76/88) 86% LP/P). A non-statistically significant higher attrition rate was seen amongst the non-genomics workforce when compared to genomics specialists for both courses. More participants from the non-genomics workforce rated the material as “Too Complex” (FP n = 2/7 [29%], ICS n = 1/5 [20%]) when compared to the specialist genomics workforce (FP n = 1/43 [2%], ICS n = 0/35 [0%]).ConclusionsAfter completing one or both MOOCs, self-reported confidence in genomic variant interpretation significantly increased, and most respondents could correctly classify variants such that appropriate clinical management would be instigated. Genomics HCPs reported higher satisfaction with the level of content than the non-genomics clinicians. The MOOCs provided foundational knowledge and improved learner confidence, but should be adapted for different workforces to maximise the benefit for clinicians working in specialties outside of genetics.

Combined whole exome sequencing and chromosomal microarray analysis improve clinical interpretation of genomic variants in patients with intellectual disability

IntroductionaCGH determines pathogenic copy number variations (CNVs) in about 10% of patients with intellectual disability (ID). In another 20% of patients, probably pathogenic CNVs or variants with uncertain clinical significance are detected. It may be variants that do not fully explain the patient’s symptoms, aberrations with reduced penetrance or inherited from healthy parents. The use of a sequencing method for such cases is advisable.ObjectivesImprovement of diagnosis of intellectual disability.MethodsaCGH with 60K Agilent microarrays, qPCR, targeted sequencing, whole exome sequencing (WES).Results Six patients with ID and inherited deletions/duplications detected by aCGH and their parents if available were further examined by sequencing. Four patients had maternal CNVs: (1) del1q41 (SPATA17, LINC00210, RRP15), (2) del7q35 (TCAF2, exon 8), (3) dup8p22p21.3 (PSD3, exons 1-11), and (4) del12p11.1 (SYT10, exons 1-2). Two patients had paternal CNVs: (5) dup1q44 (SMYD3, exons 2-5) and (6) del15q11.2 (TUBGCP5, CYFIP1, NIPA1, NIPA2, LOC283683). The severe phenotype of patient (5) with dup1q44 could not be explained by the paternally inherited disruption of the single SMYD3 gene. WES determined probably pathogenic SNV in the MID1 gene associated with Opitz GBBB syndrome (OMIM 300000), which corresponds better to the patient’s phenotype and is likely to be the cause of the disease. Although del1q41 is included in the region of chromosome 1q41-q42 deletion syndrome (OMIM 612530) the phenotype of the patient (1) is much milder; WES in the patient detected two pathogenic (MPO, MAN2C1) and one probably pathogenic (ARID1B) SNVs. In patient (6) with del15q11.2 pat WES detected additional pathogenic SNV in exon 7 of the ARSE gene. In patient (3) with dup8p22p21.3 WES determined two SNVs with uncertain significance in the KIDINS220, FOXG1 genes. No SNVs were detected by WES in patient (2) with del7q35. For patient (4) with del12p11.1 targeted SYT10 sequencing revealed no pathogenic SNVs as well.ConclusionsSometimes aCGH-analysis is sufficient to identify the causes of ID, however, in the case of detection of CNVs with uncertain clinical significance and/or inherited from healthy parents, it may be necessary to further examine the patient using sequencing methods. So, the accurate diagnosis was made by WES for one patient of eight. For another two patients the combination of CNVs and SNPs should be considered. For the last three patients the described aberrations could not explain the phenotype and whole genome sequencing may be the solution.This study was supported by the Russian Science Foundation, grant 21-65-00017, https://rscf.ru/project/21-65-00017/Disclosure of InterestNone Declared

KMT2A-D pathogenicity, prevalence, and variation according to a population database.

The KMT2 family of genes is essential epigenetic regulators promoting gene expression. The gene family contains three subgroups, each with two paralogues: KMT2A and KMT2B; KMT2C and KMT2D; KMT2F and KMT2G. KMT2A-D are among the most frequent somatically altered genes in several different cancer types. Somatic KMT2A rearrangements are well-characterized in infant leukemia (IL), and growing evidence supports the role of additional family members (KMT2B, KMT2C, and KMT2D) in leukemogenesis. Enrichment of rare heterozygous frameshift variants in KMT2A and C has been reported in acute myeloid leukemia (AML), IL, and solid tumors. Currently, the non-synonymous variation, prevalence, and penetrance of these four genes are unknown. This study determined the prevalence of pathogenic/likely pathogenic (P/LP) germline KMT2A-D variants in a cancer-free adult population from the Genome Aggregation Database (gnomAD). Two methods of variant interpretation were utilized: a manual genomic variant interpretation and an automated ACMG pipeline. The ACMG pipeline identified considerably fewer P/LP variants (n=89) compared to the manual method (n=660) in all 4 genes. Consequently, the total P/LP prevalence and allele frequency (AF) were higher in the manual method (1:112, AF=4.46E-03) than in ACMG (1:832, AF=6.01E-04). Multiple ancestry-exclusive P/LP variants were identified along with an increased frequency in males compared to females. Many of these variants identified in this population database are also associated with severe juvenile conditions. These data demonstrate that putatively functional germline variation in these developmentally important genes is more common than previously appreciated and identification in cancer-free adults may indicate incomplete penetrance for many of these variants. Future research should examine a genetic predisposing role in IL and other pediatric cancers.

Accurate protein stability predictions from homology models

Calculating changes in protein stability (ΔΔG) has been shown to be central for predicting the consequences of single amino acid substitutions in protein engineering as well as interpretation of genomic variants for disease risk. Structure-based calculations are considered most accurate, however the tools used to calculate ΔΔGs have been developed on experimentally resolved structures. Extending those calculations to homology models based on related proteins would greatly extend their applicability as large parts of e.g. the human proteome are not structurally resolved. In this study we aim to investigate the accuracy of ΔΔG values predicted on homology models compared to crystal structures. Specifically, we identified four proteins with a large number of experimentally tested ΔΔGs and templates for homology modeling across a broad range of sequence identities, and selected three methods for ΔΔG calculations to test. We find that ΔΔG-values predicted from homology models compare equally well to experimental ΔΔGs as those predicted on experimentally established crystal structures, as long as the sequence identity of the model template to the target protein is at least 40%. In particular, the Rosetta cartesian_ddg protocol is robust against the small perturbations in the structure which homology modeling introduces. In an independent assessment, we observe a similar trend when using ΔΔGs to categorize variants as low or wild-type-like abundance. Overall, our results show that stability calculations performed on homology models can substitute for those on crystal structures with acceptable accuracy as long as the model is built on a template with sequence identity of at least 40% to the target protein.

Exome/Genome-Wide Testing in Newborn Screening: A Proportionate Path Forward.

Population-based newborn screening (NBS) is among the most effective public health programs ever launched, improving health outcomes for newborns who screen positive worldwide through early detection and clinical intervention for genetic disorders discovered in the earliest hours of life. Key to the success of newborn screening programs has been near universal accessibility and participation. Interest has been building to expand newborn screening programs to also include many rare genetic diseases that can now be identified by exome or genome sequencing (ES/GS). Significant declines in sequencing costs as well as improvements to sequencing technologies have enabled researchers to elucidate novel gene-disease associations that motivate possible expansion of newborn screening programs. In this paper we consider recommendations from professional genetic societies in Europe and North America in light of scientific advances in ES/GS and our current understanding of the limitations of ES/GS approaches in the NBS context. We invoke the principle of proportionality—that benefits clearly outweigh associated risks—and the human right to benefit from science to argue that rigorous evidence is still needed for ES/GS that demonstrates clinical utility, accurate genomic variant interpretation, cost effectiveness and universal accessibility of testing and necessary follow-up care and treatment. Confirmatory or second-tier testing using ES/GS may be appropriate as an adjunct to conventional newborn screening in some circumstances. Such cases could serve as important testbeds from which to gather data on relevant programmatic barriers and facilitators to wider ES/GS implementation.

Abstract 1156: Genome Nexus: A comprehensive resource for the annotation and interpretation of genomic variants in cancer

Abstract Interpreting genomic variants in tumor samples presents a challenge in research and the clinical setting. A major barrier is that information about variants is fragmented across disparate databases, and aggregating information from these requires building extensive infrastructure. To this end, we have developed Genome Nexus, a one stop shop for variant annotation, equipped with a powerful API for bulk annotation of variants and a user friendly interface for cancer researchers. Genome Nexus is available at https://www.genomenexus.org. It a) aggregates variant information from a large number of sources that are relevant to cancer research and clinical applications; b) allows high-performance programmatic access to the aggregated data via a unified API; c) provides a search interface and a reference page for individual cancer variants; d) provides user-friendly tools for annotating variants in patients; e) is freely available under an open source license and can be installed in a private cloud or local environment. Genome Nexus contains annotations from more than a dozen resources, including those that provide variant effect information (VEP), protein sequence annotation (Uniprot, Pfam, dbPTM), functional consequence prediction (Polyphen-2, Mutation Assessor, SIFT), population prevalence (gnomAD, dbSNP, ExAC), cancer population prevalence (Cancer Hotspots, SignalDB) and clinical actionability (OncoKB, CIViC, Clinvar). The annotations can be accessed through the website, the API, and a command line client. Genome Nexus is unique in providing a user friendly interface specific to cancer that allows high performance annotation of any variant. It is the main annotation service for the popular cancer genomics tool cBioPortal, which serves thousands of users daily. It is also offered as a standalone tool for annotation, allowing researchers and clinicians as well as genomic infrastructure developers to leverage it directly in their own workflows. For example, a local installation of Genome Nexus is used for annotating all variants in AACR Project GENIE. Citation Format: Ino de Bruijn, Xiang Li, Onur Sumer, Benjamin Gross, Robert Sheridan, Angelica Ochoa, Manda Wilson, Avery Wang, Hongxin Zhang, Aaron Lisman, Adam Abeshouse, Sander Rodenburg, Sjoerd van Hagen, Remond Fijneman, Gerrit Meijer, Nikolaus Schultz, Jianjiong Gao. Genome Nexus: A comprehensive resource for the annotation and interpretation of genomic variants in cancer [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 1156.

Genome Nexus: A Comprehensive Resource for the Annotation and Interpretation of Genomic Variants in Cancer.

Interpretation of genomic variants in tumor samples still presents a challenge in research and the clinical setting. A major issue is that information for variant interpretation is fragmented across disparate databases, and aggregation of information from these requires building extensive infrastructure. To this end, we have developed Genome Nexus, a one-stop shop for variant annotation with a user-friendly interface for cancer researchers and clinicians. Genome Nexus (1) aggregates variant information from sources that are relevant to cancer research and clinical applications, (2) allows high-performance programmatic access to the aggregated data via a unified application programming interface, (3) provides a reference page for individual cancer variants, (4) provides user-friendly tools for annotating variants in patients, and (5) is freely available under an open source license and can be installed in a private cloud or local environment and integrated with local institutional resources. Genome Nexus is available at https://www.genomenexus.org. It displays annotations from more than a dozen resources including those that provide variant effect information (variant effect predictor), protein sequence annotation (Uniprot, Pfam, and dbPTM), functional consequence prediction (Polyphen-2, Mutation Assessor, and SIFT), population prevalences (gnomAD, dbSNP, and ExAC), cancer population prevalences (Cancer hotspots and SignalDB), and clinical actionability (OncoKB, CIViC, and ClinVar). We describe several use cases that demonstrate the utility of Genome Nexus to clinicians, researchers, and bioinformaticians. We cover single-variant annotation, cohort analysis, and programmatic use of the application programming interface. Genome Nexus is unique in providing a user-friendly interface specific to cancer that allows high-performance annotation of any variant including unknown ones. Interpretation of cancer genomic variants is improved tremendously by having an integrated resource for annotations. Genome Nexus is freely available under an open source license.

Predicting genes from phenotypes using human phenotype ontology (HPO) terms.

The interpretation of genomic variants following whole exome sequencing (WES) can be aided using human phenotype ontology (HPO) terms to standardize clinical features and predict causative genes. We performed WES on 453 patients diagnosed prior to 18years of age and identified 114 pathogenic (P) or likely pathogenic (LP) variants in 112 patients. We utilized PhenoDB to extract HPO terms from provider notes and then used Phen2Gene to generate a gene score and gene ranking from each list of HPO terms. We assigned Phen2Gene gene rankings to 6 rank classes, with class 1 covering raw gene rankings of 1 to 10 and class 2 covering rankings from 11 to 50 out of a total of 17,126 possible gene rankings. Phen2Gene ranked causative genes into rank class 1 or 2 in 27.7% of cases and the genes in rank class 1 were all associated with well-characterized phenotypes. We found significant associations between the gene score and the number of years, since the gene was first published, the number of HPO terms with an hierarchical depth greater or equal to 11, and the number of Online Mendelian Inheritance in Man terms associated with the phenotype and gene. We conclude that genes associated with recognizable phenotypes and terms deep in the HPO hierarchy have the best chance of producing a high gene score and ranking in class 1 to 2 using Phen2Gene software with HPO terms. Clinicians and laboratory staff should consider these results when HPO terms are employed to prioritize candidate genes.

A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritization

Genomic variant interpretation is a critical step of the diagnostic procedure, often supported by the application of tools that may predict the damaging impact of each variant or provide a guidelines-based classification. We propose the application of Machine Learning methodologies, in particular Penalized Logistic Regression, to support variant classification and prioritization. Our approach combines ACMG/AMP guidelines for germline variant interpretation as well as variant annotation features and provides a probabilistic score of pathogenicity, thus supporting the prioritization and classification of variants that would be interpreted as uncertain by the ACMG/AMP guidelines. We compared different approaches in terms of variant prioritization and classification on different datasets, showing that our data-driven approach is able to solve more variant of uncertain significance (VUS) cases in comparison with guidelines-based approaches and in silico prediction tools.

Technological Improvements in the Genetic Diagnosis of Rett Syndrome Spectrum Disorders.

Rett syndrome (RTT) is a severe neurodevelopmental disorder that constitutes the second most common cause of intellectual disability in females worldwide. In the past few years, the advancements in genetic diagnosis brought by next generation sequencing (NGS), have made it possible to identify more than 90 causative genes for RTT and significantly overlapping phenotypes (RTT spectrum disorders). Therefore, the clinical entity known as RTT is evolving towards a spectrum of overlapping phenotypes with great genetic heterogeneity. Hence, simultaneous multiple gene testing and thorough phenotypic characterization are mandatory to achieve a fast and accurate genetic diagnosis. In this review, we revise the evolution of the diagnostic process of RTT spectrum disorders in the past decades, and we discuss the effectiveness of state-of-the-art genetic testing options, such as clinical exome sequencing and whole exome sequencing. Moreover, we introduce recent technological advancements that will very soon contribute to the increase in diagnostic yield in patients with RTT spectrum disorders. Techniques such as whole genome sequencing, integration of data from several “omics”, and mosaicism assessment will provide the tools for the detection and interpretation of genomic variants that will not only increase the diagnostic yield but also widen knowledge about the pathophysiology of these disorders.

Heterogeneity of Accompanying Phenotypes and Genomic Variants Involved in Microtia.

The symptoms associated with microtia are ever-changing and not to stick to 1 pattern. The symptoms associated with microtia are constantly changing and are not set in stone. The aim of this article was to describe the various phenotypes from multiple systems found in microtitis patients included in the DatabasE of genomiC varIation and Phenotype in Humans using Ensembl Resources database, and to analyze possible pathogenic mutations. DatabasE of genomiC varIation and Phenotype in Humans using Ensembl Resources is an interactive web-based database, which incorporates a suite of tools designed to aid the interpretation of genomic variants. The term "microtia" was used as the search term, and the data extracted from the DatabasE of genomiC varIation and Phenotype in Humans using Ensembl Resources for this study was updated until October 2020. Pearson chi-squared test was used to test associations between types of genomic variants and the pathogenicity of variants. Of the 386 cases enrolled in the study, 99% (n = 382) had 1 or more associated abnormalities. The most frequently detected abnormalities were those of the face and neck (n = 362 [93.8% of all cases]); musculoskeletal system (n = 337 [87.3%]); and nervous system (n = 334 [86.5%]), followed by abnormalities of limbs (n = 252 [65.3%]); the eye (n = 212 [54.9%]); and the integument (n = 200 [51.8%]). Besides, a total of 479 genomic variants were determined, including sequence variants and copy number variants (loss and gain). The pathogenicity of loss-type variants was significantly higher among other types (P < 0.001). Twelve sharing variants had more than 5 repeats, and the repeated fragments were concentrated on chromosome 3, 7, 9, 10, 11, 15, 17, 18, and 22. Identification of the relation between phenotypes and genotypes will facilitate the uncovering of the mechanism of microtia and the study of potential therapeutic targets.

Abstract 169: Ephesus - A curated content knowledgebase for the clinical interpretation of genomic variants

Abstract Accurate and comprehensive interpretation of genomic variants has become a bottleneck in clinical sequencing applications due to the implementation of precision oncology and the growth of biomedical findings. We therefore were motivated to build Ephesus, a framework enabling curation of predictive, prognostic and diagnostic evidence for clinical biomarkers in somatic cancers. Currently Ephesus is the primary content source for Roche NAVIFY Mutation Profiler (RUO). The Ephesus' data model ensures adherence to best practices in the field of clinical genomic content curation. Somatic variant classification followed the AMP guidelines. Approvals and recommendations from regulatory agencies and clinical practice guidelines were applied on a regional basis. The data model is mapped to a set of relational tables. Strategies such as data normalization according to standard ontologies, a submit-review-approval workflow with change logs, and biologically relevant data constraints are adopted to enforce data integrity. Ephesus is deployed as a web application whose UI allows users to conduct edits, sorting, filtering, and bulk operations on entities attributes. In order to minimize the manual effort and maximize content coverage, a number of inference rules are applied before the content is ingested into NMP. Currently Ephesus is developed to guide data collection, browsing and summarizing related to primary entities such as Biomarkers, Evidence items, Genes, Variants, Variant groups, and Drugs. The content from 10/28/20 snapshot contains 233k biomarker profiles (including biomarker combinations), in the context of 17 major cancer types. To evaluate the clinical reporting value of the curated knowledge, ~60k clinical cancer patient samples from the AACR GENIE project were queried against Ephesus. Compared our results against those from 3 other major knowledgebases (snapshotted at the same time as Ephesus), we see the performance of Ephesus/NMP exceeds all these knowledgebases. Patients% with at least one interpretation (numbers in parenthesis include matching by inferences)KnowledgebaseVariantVariant &amp; diseaseTier I variant &amp; diseaseEphesus53 (91)40 (89)19 (25)CGI3573CIViC402215ClinVar452313 Citation Format: Jian Li, Lili Niu, Fnu Kinshuk, Bernie Chu, John Mu, Nicole Greene, Vivian Douglas, Caitlin Schartner, Kalpana Kannan, Priyanka Rao, Shuba Krishna. Ephesus - A curated content knowledgebase for the clinical interpretation of genomic variants [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 169.

EP402 - Retrospective evaluation of in silico prediction tools, REVEL and CADD, for supporting level evidence (PP3/BP4) of genomic variant interpretation

eP402 - Retrospective evaluation of in silico prediction tools, REVEL and CADD, for supporting level evidence (PP3/BP4) of genomic variant interpretation

Combining evidence for and against pathogenicity for variants in cancer susceptibility genes: CanVIG-UK consensus recommendations

Accurate classification of variants in cancer susceptibility genes (CSGs) is key for correct estimation of cancer risk and management of patients. Consistency in the weighting assigned to individual elements of...

Assessing performance of pathogenicity predictors using clinically relevant variant datasets

BackgroundPathogenicity predictors are integral to genomic variant interpretation but, despite their widespread usage, an independent validation of performance using a clinically relevant dataset has not been undertaken.MethodsWe derive two validation...

Abstract 5462: Adding CIViC knowledge to variant annotation pipelines with CIViCpy

Abstract Precision oncology is dependent upon the matching of tumor variants to relevant knowledge describing the clinical significance of those variants. We recently developed the Clinical Interpretations for Variants in Cancer (CIViC; civicdb.org) crowd-sourced, expert-moderated, and open-access knowledgebase. CIViC provides a structured framework for evaluating genomic variants of various types (e.g., fusions, SNVs) for their clinical utility based on therapeutic, prognostic, predisposing, diagnostic, and functional evidence. CIViC has a documented API for accessing CIViC records: Assertions, Evidence, Variants, and Genes. Third-party tools that analyze or access the contents of this knowledgebase must programmatically leverage this API, often reimplementing redundant functionality in the pursuit of common analysis tasks that are beyond the scope of the CIViC web application. We recently published CIViCpy v1.0 (civicpy.org), a software development kit (SDK) for extracting and analyzing the contents of the CIViC knowledgebase. CIViCpy enables users to query CIViC content as dynamic objects in Python. Here we highlight new features and informative analyses introduced since the original publication. New features include an extended variant search functionality that supports queries by variant name, HGVS, and ClinGen Allele Registry ID (CAID). In addition to the CIViC-native GRCh37 coordinates, CIViCpy now supports variant queries from multiple alternative human genome builds, including hg18 and GRCh38. CIViCpy also includes multiple easy-to-use tools for straightforward installation in a variety of compute environments. A commonly requested feature is the annotation of user-provided variants in Variant Call Format (VCF) with CIViC data. CIViCpy now provides convenient methods, including a command line interface (CLI), for performing this task. We illustrate the use of newly added features by annotating pan-cancer VCF files from The Cancer Genome Atlas (TCGA). We show that CIViCpy is able to quickly and efficiently annotate patient variants in the context of the reported disease type, appending data about the associated CIViC Evidence and Assertions to the provided VCF files. The clinical interpretation of genomic variants in cancers requires high-throughput tools for interoperability and analysis of variant interpretation knowledge. These needs are met by CIViCpy, an SDK for downstream applications and rapid analysis. The new features discussed here allow users to easily incorporate CIViC knowledge into their variant annotation pipelines. CIViCpy (civicpy.org) is fully documented, open-source, and freely available online. Citation Format: Susanna Kiwala, Alex H. Wagner, Adam C. Coffman, Joshua F. McMichael, Kelsy C. Cotto, Thomas B. Mooney, Erica K. Barnell, Kilannin Krysiak, Arpad M. Danos, Jason M. Walker, Obi L. Griffith, Malachi Griffith. Adding CIViC knowledge to variant annotation pipelines with CIViCpy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5462.

Ephesus: A curation workflow tool for the clinical interpretation of genomic variants.

e14074 Background: Accurate and comprehensive interpretation of genomic variants has become a bottleneck in clinical sequencing applications due to the accelerated implementation of precision oncology and the rapid growth of relevant biomedical findings. We therefore are motivated to build Ephesus, a framework enabling curation of clinical evidence for biomarkers in somatic cancers. Currently Ephesus is the primary content source for Roche NAVIFY Mutation Profiler (NMP). Methods: Ephesus’ data model ensures adherence to best practice in clinical genomic content curation. Variant classification followed the AMP guidelines for somatic variant interpretation. Approvals and recommendations from regulatory agencies and organizations (FDA, NCCN, etc.) are applied on a regional basis. The data model is mapped to a set of PostgreSQL relational tables. Strategies such as data normalization according to standard ontologies, a submit-review-approval workflow with change logs, and biologically relevant datatype constraints are adopted to enforce data integrity. Ephesus is deployed as a web application, of which the UI allows users to conduct edits, filtering, sorting and bulk operations on entities by attributes. To maximize curation efficiency, a number of inference rules are applied before the content is ingested into NMP. Results: Currently Ephesus is developed to guide data collection, browsing and summarizing related to these primary entities: Biomarkers, Evidence items, Genes, Variants, Variant groups, and Drugs. The content exported on 2020/01/27 contains &gt; 170K biomarker profiles (including combinations), in the context of 13 major cancer types. To evaluate the clinical reporting value of the curated knowledge, more than 60k real clinical cancer patient samples from the AACR GENIE project were queried against Ephesus. We compared the results to the latest content from several major knowledgebases. We see that the performance of Ephesus/NMP excels the others. Conclusions: We have developed Ephesus, a practical content curation framework which provides a highly structured and user-friendly environment for clinically relevant somatic biomarkers. The framework improved the productivity and quality of the manual curation, met clinical interpretation scope and complexity, and assures data integrity and auditability. [Table: see text]