Developing a disease-specific annotation protocol for VHL gene curation using Hypothes.is.

Von Hippel-Lindau (VHL) disease is a rare, autosomal dominant disorder that predisposes individuals to developing tumors in many organs. There is significant phenotypic variability and genetic variants encountered within this syndrome, posing a considerable challenge to patient care. The lack of VHL variant data sharing paired with the absence of aggregated genotype-phenotype information results in an arduous process, when characterizing genetic variants and predicting patient prognosis. To address these gaps in knowledge, the Clinical Genome Resource (ClinGen) VHL Variant Curation Expert Panel (VCEP) has been resolving a list of variants of uncertain significance within the VHL gene. Through community curation, we crowdsourced the laborious task of variant annotation by modifying the ClinGen Community Curation (C3)-developed Baseline Annotation protocol and annotating all published VHL cases with the reported genotype-phenotype information in Hypothes.is, an open-access web annotation tool. This process, incorporated into the ClinGen VCEP's workflow, will aid in their curation efforts. To facilitate the curation at all levels of genetics expertise, our team developed a 4-day biocuration training protocol and resource guide. To date, 91.3% of annotations have been completed by undergraduate and high-school students without formal academic genetics specialization. Here, we present our VHL-specific annotation protocol utilizing Hypothes.is, which offers a standardized method to present case-resolution data, and our biocuration training protocol, which can be adapted for other rare disease platforms. By facilitating training for community curation of VHL disease, we increased student engagement with clinical genetics while enhancing knowledge translation in the field of hereditary cancer. Database URL: https://hypothes.is/groups/dKymJJpZ/vhl-hypothesis-annotation.

CIViCdb 2022: evolution of an open-access cancer variant interpretation knowledgebase.

CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants. As clinical sequencing becomes more prevalent in cancer management, the need for cancer variant interpretation has grown beyond the capability of any single institution. CIViC contains peer-reviewed, published literature curated and expertly-moderated into structured data units (Evidence Items) that can be accessed globally and in real time, reducing barriers to clinical variant knowledge sharing. We have extended CIViC's functionality to support emergent variant interpretation guidelines, increase interoperability with other variant resources, and promote widespread dissemination of structured curated data. To support the full breadth of variant interpretation from basic to translational, including integration of somatic and germline variant knowledge and inference of drug response, we have enabled curation of three new Evidence Types (Predisposing, Oncogenic and Functional). The growing CIViC knowledgebase has over 300 contributors and distributes clinically-relevant cancer variant data currently representing >3200 variants in >470 genes from >3100 publications.

Standardized evidence-based approach for assessment of oncogenic and clinical significance of NTRK fusions.

Gene fusions involving the neurotrophic receptor tyrosine kinase genes NTRK1, NTRK2, and NTRK3, are well established oncogenic drivers in a broad range of pediatric and adult tumors. These fusions are also important actionable markers, predicting often dramatic response to FDA approved kinase inhibitors. Accurate interpretation of the clinical significance of NTRK fusions is a high priority for diagnostic laboratories, but remains challenging and time consuming given the rapid pace of new data accumulation, the diversity of fusion partners and tumor types, and heterogeneous and incomplete information in variant databases and knowledgebases. The ClinGen NTRK Fusions Somatic Cancer Variant Curation Expert Panel (SC-VCEP) was formed to systematically address these challenges and create an expert-curated resource to support clinicians, researchers, patients and their families in making accurate interpretations and informed treatment decisions for NTRK fusion-driven tumors. We describe a system for NTRK fusion interpretation (including compilation of key elements and annotations) developed by the NTRK fusions SC-VCEP. We illustrate this stepwise process on examples of LMNA::NTRK1 and KANK1::NTRK2 fusions. Finally, we provide detailed analysis of current representation of NTRK fusions in public fusion databases and the CIViC knowledgebase, performed by the NTRK fusions SC-VCEP to determine existing gaps and prioritize future curation activities.

Standards for the classification of pathogenicity of somatic variants in cancer (oncogenicity): Joint recommendations of Clinical Genome Resource (ClinGen), Cancer Genomics Consortium (CGC), and Variant Interpretation for Cancer Consortium (VICC).

PURPOSE: Several professional societies have published guidelines for the clinical interpretation of somatic variants, which specifically address diagnostic, prognostic, and therapeutic implications. Although these guidelines for the clinical interpretation of variants include data types that may be used to determine the oncogenicity of a variant (eg, population frequency, functional, and in silico data or somatic frequency), they do not provide a direct, systematic, and comprehensive set of standards and rules to classify the oncogenicity of a somatic variant. This insufficient guidance leads to inconsistent classification of rare somatic variants in cancer, generates variability in their clinical interpretation, and, importantly, affects patient care. Therefore, it is essential to address this unmet need. METHODS: Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group and ClinGen Germline/Somatic Variant Subcommittee, the Cancer Genomics Consortium, and the Variant Interpretation for Cancer Consortium used a consensus approach to develop a standard operating procedure (SOP) for the classification of oncogenicity of somatic variants. RESULTS: This comprehensive SOP has been developed to improve consistency in somatic variant classification and has been validated on 94 somatic variants in 10 common cancer-related genes. CONCLUSION: The comprehensive SOP is now available for classification of oncogenicity of somatic variants.

ClinGen Variant Curation Interface: a variant classification platform for the application of evidence criteria from ACMG/AMP guidelines.

BACKGROUND: Identification of clinically significant genetic alterations involved in human disease has been dramatically accelerated by developments in next-generation sequencing technologies. However, the infrastructure and accessible comprehensive curation tools necessary for analyzing an individual patient genome and interpreting genetic variants to inform healthcare management have been lacking. RESULTS: Here we present the ClinGen Variant Curation Interface (VCI), a global open-source variant classification platform for supporting the application of evidence criteria and classification of variants based on the ACMG/AMP variant classification guidelines. The VCI is among a suite of tools developed by the NIH-funded Clinical Genome Resource (ClinGen) Consortium and supports an FDA-recognized human variant curation process. Essential to this is the ability to enable collaboration and peer review across ClinGen Expert Panels supporting users in comprehensively identifying, annotating, and sharing relevant evidence while making variant pathogenicity assertions. To facilitate evidence-based improvements in human variant classification, the VCI is publicly available to the genomics community. Navigation workflows support users providing guidance to comprehensively apply the ACMG/AMP evidence criteria and document provenance for asserting variant classifications. CONCLUSIONS: The VCI offers a central platform for clinical variant classification that fills a gap in the learning healthcare system, facilitates widespread adoption of standards for clinical curation, and is available at https://curation.clinicalgenome.org.

Clinical Genetics Lacks Standard Definitions and Protocols for the Collection and Use of Diversity Measures.

Genetics researchers and clinical professionals rely on diversity measures such as race, ethnicity, and ancestry (REA) to stratify study participants and patients for a variety of applications in research and precision medicine. However, there are no comprehensive, widely accepted standards or guidelines for collecting and using such data in clinical genetics practice. Two NIH-funded research consortia, the Clinical Genome Resource (ClinGen) and Clinical Sequencing Evidence-generating Research (CSER), have partnered to address this issue and report how REA are currently collected, conceptualized, and used. Surveying clinical genetics professionals and researchers (n = 448), we found heterogeneity in the way REA are perceived, defined, and measured, with variation in the perceived importance of REA in both clinical and research settings. The majority of respondents (>55%) felt that REA are at least somewhat important for clinical variant interpretation, ordering genetic tests, and communicating results to patients. However, there was no consensus on the relevance of REA, including how each of these measures should be used in different scenarios and what information they can convey in the context of human genetics. A lack of common definitions and applications of REA across the precision medicine pipeline may contribute to inconsistencies in data collection, missing or inaccurate classifications, and misleading or inconclusive results. Thus, our findings support the need for standardization and harmonization of REA data collection and use in clinical genetics and precision health research.

A harmonized meta-knowledgebase of clinical interpretations of somatic genomic variants in cancer.

Precision oncology relies on accurate discovery and interpretation of genomic variants, enabling individualized diagnosis, prognosis and therapy selection. We found that six prominent somatic cancer variant knowledgebases were highly disparate in content, structure and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. We developed a framework for harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations. We demonstrated large gains in overlap between resources across variants, diseases and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 57% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide a freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases.

Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen).

PURPOSE: Copy-number analysis to detect disease-causing losses and gains across the genome is recommended for the evaluation of individuals with neurodevelopmental disorders and/or multiple congenital anomalies, as well as for fetuses with ultrasound abnormalities. In the decade that this analysis has been in widespread clinical use, tremendous strides have been made in understanding the effects of copy-number variants (CNVs) in both affected individuals and the general population. However, continued broad implementation of array and next-generation sequencing-based technologies will expand the types of CNVs encountered in the clinical setting, as well as our understanding of their impact on human health. METHODS: To assist clinical laboratories in the classification and reporting of CNVs, irrespective of the technology used to identify them, the American College of Medical Genetics and Genomics has developed the following professional standards in collaboration with the National Institutes of Health (NIH)-funded Clinical Genome Resource (ClinGen) project. RESULTS: This update introduces a quantitative, evidence-based scoring framework; encourages the implementation of the five-tier classification system widely used in sequence variant classification; and recommends "uncoupling" the evidence-based classification of a variant from its potential implications for a particular individual. CONCLUSION: These professional standards will guide the evaluation of constitutional CNVs and encourage consistency and transparency across clinical laboratories.

Standard operating procedure for curation and clinical interpretation of variants in cancer.

Manually curated variant knowledgebases and their associated knowledge models are serving an increasingly important role in distributing and interpreting variants in cancer. These knowledgebases vary in their level of public accessibility, and the complexity of the models used to capture clinical knowledge. CIViC (Clinical Interpretation of Variants in Cancer - www.civicdb.org) is a fully open, free-to-use cancer variant interpretation knowledgebase that incorporates highly detailed curation of evidence obtained from peer-reviewed publications and meeting abstracts, and currently holds over 6300 Evidence Items for over 2300 variants derived from over 400 genes. CIViC has seen increased adoption by, and also undertaken collaboration with, a wide range of users and organizations involved in research. To enhance CIViC's clinical value, regular submission to the ClinVar database and pursuit of other regulatory approvals is necessary. For this reason, a formal peer reviewed curation guideline and discussion of the underlying principles of curation is needed. We present here the CIViC knowledge model, standard operating procedures (SOP) for variant curation, and detailed examples to support community-driven curation of cancer variants.

A case for expert curation: an overview of cancer curation in the Clinical Genome Resource (ClinGen).

We describe the Clinical Genome Resource (ClinGen) cancer-related curation activities and the importance of curation to the evolving state of variant interpretation in a clinical context for both pediatric and adult cancer patients. We highlight specific examples from the CDH1 and PTEN Variant Curation Expert Panels (VCEPs) of the FDA-recognized process by which ClinGen VCEPs specify the American College of Medical Genetics and Genomics/Association of Molecular Pathology evidence code to develop variant classifications. We also review gene curations performed within the Hereditary Cancer Clinical Domain. We describe the parallel efforts for curation of somatic cancer variants from the Somatic Cancer Working Group. The ClinGen Germline/Somatic Committee is working to improve incorporation of both hereditary and somatic variant data to aid clinical interpretation. These ClinGen efforts rely on broad data sharing and detailed phenotypic and molecular information from published case studies to provide expert-curated variant interpretation to the cancer community.

Determining the clinical validity of hereditary colorectal cancer and polyposis susceptibility genes using the Clinical Genome Resource Clinical Validity Framework.

PURPOSE: Gene-disease associations implicated in hereditary colorectal cancer and polyposis susceptibility were evaluated using the ClinGen Clinical Validity framework. METHODS: Forty-two gene-disease pairs were assessed for strength of evidence supporting an association with hereditary colorectal cancer and/or polyposis. Genetic and experimental evidence supporting each gene-disease relationship was curated independently by two trained biocurators. Evidence was reviewed with experts and assigned a final clinical validity classification. RESULTS: Of all gene-disease pairs evaluated, 14/42 (33.3%) were Definitive, 1/42 (2.4%) were Strong, 6/42 (14.3%) were Moderate, 18/42 (42.9%) were Limited, and 3/42 (7.1%) were either No Reported Evidence, Disputed, or Refuted. Of panels in the National Institutes of Health Genetic Testing Registry, 4/26 (~15.4%) contain genes with Limited clinical evidence. CONCLUSION: Clinicians and laboratory diagnosticians should note that <60% of the genes on clinically available panels have Strong or Definitive evidence of association with hereditary colon cancer or polyposis, and >40% have only Moderate, Limited, Disputed, or Refuted evidence. Continuing to expand the structured assessment of the clinical relevance of genes listed on hereditary cancer testing panels will help clinicians and diagnostic laboratories focus the communication of genetic testing results on clinically significant genes.

Clinical validity assessment of genes frequently tested on hereditary breast and ovarian cancer susceptibility sequencing panels.

PURPOSE: Several genes on hereditary breast and ovarian cancer susceptibility test panels have not been systematically examined for strength of association with disease. We employed the Clinical Genome Resource (ClinGen) clinical validity framework to assess the strength of evidence between selected genes and breast or ovarian cancer. METHODS: Thirty-one genes offered on cancer panel testing were selected for evaluation. The strength of gene-disease relationship was systematically evaluated and a clinical validity classification of either Definitive, Strong, Moderate, Limited, Refuted, Disputed, or No Reported Evidence was assigned. RESULTS: Definitive clinical validity classifications were made for 10/31 and 10/32 gene-disease pairs for breast and ovarian cancer respectively. Two genes had a Moderate classification whereas, 6/31 and 6/32 genes had Limited classifications for breast and ovarian cancer respectively. Contradictory evidence resulted in Disputed or Refuted assertions for 9/31 genes for breast and 4/32 genes for ovarian cancer. No Reported Evidence of disease association was asserted for 5/31 genes for breast and 11/32 for ovarian cancer. CONCLUSION: Evaluation of gene-disease association using the ClinGen clinical validity framework revealed a wide range of classifications. This information should aid laboratories in tailoring appropriate gene panels and assist health-care providers in interpreting results from panel testing.

Integrating somatic variant data and biomarkers for germline variant classification in cancer predisposition genes.

In its landmark paper about Standards and Guidelines for the Interpretation of Sequence Variants, the American College of Medical Genetics and Genomics (ACMG), and Association for Molecular Pathology (AMP) did not address how to use tumor data when assessing the pathogenicity of germline variants. The Clinical Genome Resource (ClinGen) established a multidisciplinary working group, the Germline/Somatic Variant Subcommittee (GSVS) with this focus. The GSVS implemented a survey to determine current practices of integrating somatic data when classifying germline variants in cancer predisposition genes. The GSVS then reviewed and analyzed available resources of relevant somatic data, and performed integrative germline variant curation exercises. The committee determined that somatic hotspots could be systematically integrated into moderate evidence of pathogenicity (PM1). Tumor RNA sequencing data showing altered splicing may be considered as strong evidence in support of germline pathogenicity (PVS1) and tumor phenotypic features such as mutational signatures be considered supporting evidence of pathogenicity (PP4). However, at present, somatic data such as focal loss of heterozygosity and mutations occurring on the alternative allele are not recommended to be systematically integrated, instead, incorporation of this type of data should take place under the advisement of multidisciplinary cancer center tumor-normal sequencing boards.

Adapting crowdsourced clinical cancer curation in CIViC to the ClinGen minimum variant level data community-driven standards.

Harmonization of cancer variant representation, efficient communication, and free distribution of clinical variant-associated knowledge are central problems that arise with increased usage of clinical next-generation sequencing. The Clinical Genome Resource (ClinGen) Somatic Working Group (WG) developed a minimal variant level data (MVLD) representation of cancer variants, and has an ongoing collaboration with Clinical Interpretations of Variants in Cancer (CIViC), an open-source platform supporting crowdsourced and expert-moderated cancer variant curation. Harmonization between MVLD and CIViC variant formats was assessed by formal field-by-field analysis. Adjustments to the CIViC format were made to harmonize with MVLD and support ClinGen Somatic WG curation activities, including four new features in CIViC: (1) introduction of an assertions feature for clinical variant assessment following the Association of Molecular Pathologists (AMP) guidelines, (2) group-level curation tracking for organizations, enabling member transparency, and curation effort summaries, (3) introduction of ClinGen Allele Registry IDs to CIViC, and (4) mapping of CIViC assertions into ClinVar submission with automated submissions. A generalizable workflow utilizing MVLD and new CIViC features is outlined for use by ClinGen Somatic WG task teams for curation and submission to ClinVar, and provides a model for promoting harmonization of cancer variant representation and efficient distribution of this information.

The clinical imperative for inclusivity: Race, ethnicity, and ancestry (REA) in genomics.

The Clinical Genome Resource (ClinGen) Ancestry and Diversity Working Group highlights the need to develop guidance on race, ethnicity, and ancestry (REA) data collection and use in clinical genomics. We present quantitative and qualitative evidence to characterize: (1) acquisition of REA data via clinical laboratory requisition forms, and (2) information disparity across populations in the Genome Aggregation Database (gnomAD) at clinically relevant sites ascertained from annotations in ClinVar. Our requisition form analysis showed substantial heterogeneity in clinical laboratory ascertainment of REA, as well as marked incongruity among terms used to define REA categories. There was also striking disparity across REA populations in the amount of information available about clinically relevant variants in gnomAD. European ancestral populations constituted the majority of observations (55.8%), allele counts (59.7%), and private alleles (56.1%) in gnomAD at 550 loci with "pathogenic" and "likely pathogenic" expert-reviewed variants in ClinVar. Our findings highlight the importance of implementing and supporting programs to increase diversity in genome sequencing and clinical genomics, as well as measuring uncertainty around population-level datasets that are used in variant interpretation. Finally, we suggest the need for a standardized REA data collection framework to be developed through partnerships and collaborations and adopted across clinical genomics.