Recommendations by the ClinGen Rett/Angelman-like expert panel for gene-specific variant interpretation methods.

Interpretation of genetic variants is most accurate when gene- and disease-specific considerations are considered. The 2015 ACMG/AMP guidelines form the basis for the application of variant interpretation criteria for Mendelian disorders. The Hereditary Breast, Ovarian, and Pancreatic Cancer Variant Curation Expert Panel (HBOP VCEP) has undertaken the process for creating gene- and disease-specific specifications for the interpretation of PALB2 germline sequence variants. The HBOP VCEP is comprised of experts in the fields of clinical and molecular genetics, epidemiology, functional assays, and variant interpretation. The group met regularly to consider each of the codes from the 2015 ACMG/AMP guidelines to determine their relevance for PALB2. After criteria were created using database analysis, literature review, and expert opinion, they were vetted against a diverse set of pilot variants and ultimately finalized. The HBOP VCEP advised against using 13 codes, limited the use of six codes, and tailored nine codes to create the final PALB2 variant interpretation guidelines. Among the 39 pilot variants, 37 were in ClinVar, and using the new specifications concordant classifications resulted for 31 of the variants (84%). Of the 14 variants of uncertain significance/conflicting variants in ClinVar, four were classified by the VCEP, likely due to code combination modifications and refined population frequency cutoffs. The PALB2-specific guidelines put forward by the HBOP VCEP represent a conservative approach to classifying variants in PALB2 and lead to improved classifications relative to current ClinVar entries. Adoption of these specifications will help to harmonize classifications deposited in the public domain.

Four years ago, at BOSC 2019, the Forome Platform team first presented AnFiSA, an open-source variant curation and interpretation tool for analysis of the data produced from genome sequencing. In the last year the work has been published in the Journal of Biomedical Informatics1. One of the focuses of the paper was the creation of a community comprising clinicians, researchers, and professional software developers to reach our goals. We received contributions from numerous organizations and dozens of individual contributors to facilitate this effort. It became evident that for a community to operate efficiently, there must be a shared language that comes naturally to all members. Back in 2019, we argued that representing variant curation guidelines in the form of manually curated decision trees would align the guidelines development workflow with that of software development. We also suggested that developers of these guidelines would benefit from utilizing an Integrated Development Environment (IDE) that offers functionalities such as syntax highlighting, error checking, and visual debugging. Over the past four years, we have made significant progress towards creating such an IDE. We replaced the Vue.js client with one based on React.js and introduced IDE-like features. As the guidelines are often articulated as free-text English prose, we contend that developing a more structured domain-specific language (DSL) that can be grasped by both clinicians and engineers/researchers would be immensely advantageous for the community as well as helpful in mimicking software development process. Our development of the DSL and its corresponding visual representations for editing and debugging allowed us to condense decision trees into ordered sequences of inclusion and exclusion criteria. In this talk, we aim to present our systematic approach for converting free text into complex logical statements and extensive decision trees, which can be further translated into inclusion and exclusion criteria depicted by graphical widgets. We will also demonstrate the utilization of these widgets and dashboards in a debugging process for a particular corpus of data. We would like to emphasize that we view the visualization of the ordered sequences of inclusion and exclusion criteria as a valuable tool, extending beyond variant curation to areas such as patient recruitment and population health research. Our DSL utilizes genetic and sequencing annotations as variables, which encompass both technical and biological information. Technical annotations include provenance and confidence data for specific calls, while biological annotations summarize evidence pertaining to the potential impact of mutations on molecular function and phenotype. These annotations collectively provide clinicians and researchers with information on provenance, confidence, and evidence to aid in decision making. Our work on the DSL revealed the need to structure annotation types systematically to help answer three critical questions: 1) How confident am I that the variant is present in my patient? 2) How confident am I that it affects the phenotype? 3) What evidence supports the association between the observed genotype change and the phenotype? In order to accomplish this objective, in the latest version of AnFiSA we have implemented a classification system for annotations based on their scale and resolution, knowledge domain, and method. Genetic and biological annotations gather relevant evidence from public and proprietary sources about the potential mutation effects on molecular function and phenotype, combining multiple inputs to provide a summary. The strength of evidence heavily relies on the level of resolution of a specific annotation, as well as the knowledge domain and the method of an annotation2. The resolution ranges from granular transcript and variant specific to broader functional units. The knowledge domain refers to areas such as human or animal genetics, or molecular function described by experimental or computational techniques. The methods used for generating annotations vary among statistical genetics inference, bioinformatics predictions, and in vivo and in vitro experiments. The initial implementation of this classification is accessible on GitHub and can be tested in the most recent versions of AnFiSA. Our proposal is to initiate a conversation regarding the semantic and syntax of the DSL for variant curation and interpretation, as well as the most effective and intuitive way to structure annotation types. We believe that developing a standard or at least a guideline, for representation of variant curation rules will help in establishing a unified and systematic approach to reporting clinically relevant and actionable variants and ultimately will bring us closer to evidence based medicine. 1. Bouzinier, M. A. et al. AnFiSA: An open-source computational platform for the analysis of sequencing data for rare genetic disease. J. Biomed. Inform. 133, 104174 (2022). 2. Hufeng Zhou et al. FAVOR: Functional Annotation of Variants Online Resource and Annotator for Variation across the Human Genome. Nucleic Acids Res 2022 Nov 9; gkac966. PMID: 36350676. DOI: 10.1093/nar/gkac966

P490: Meeting the genetic counseling demand for genomic newborn screening Results: A nationwide telehealth service delivery analysis*

The ClinGen PTEN Expert Panel was organized by the ClinGen Hereditary Cancer Clinical Domain Working Group to assemble clinicians, researchers, and molecular diagnosticians with PTEN expertise to develop specifications to the 2015 ACMG/AMP Sequence Variant Interpretation Guidelines for PTEN variant interpretation. We describe finalized PTEN-specific variant classification criteria and outcomes from pilot testing of 42 variants with benign/likely benign (BEN/LBEN), pathogenic/likely pathogenic (PATH/LPATH), uncertain significance (VUS), and conflicting (CONF) ClinVar assertions. Utilizing these rules, classifications concordant with ClinVar assertions were achieved for 14/15 (93.3%) BEN/LBEN and 16/16 (100%) PATH/LPATH ClinVar consensus variants for an overall concordance of 96.8% (30/31). The variant where agreement was not reached was a synonymous variant near a splice donor with noncanonical sequence for which in silico models cannot predict the native site. Applying these rules to six VUS and five CONF variants, adding shared internal laboratory data enabled one VUS to be classified as LBEN and two CONF variants to be as classified as PATH and LPATH. This study highlights the benefit of gene-specific criteria and the value of sharing internal laboratory data for variant interpretation. Our PTEN-specific criteria and expertly reviewed assertions should prove helpful for laboratories and others curating PTEN variants.

With increasing adoption of next generation sequencing into clinical practice, the problem of clinical interpretation of tumor variants arises as a bottleneck for patient care, as effective annotation of these variants draws from a constantly increasing body of largely unstructured clinical and preclinical results. One model to sustain clinical variant curation is to house variant annotation behind a paywall, using access fees to fund further curation effort. An alternate approach is to leverage public curation and expert moderation to create a free public resource to house and distribute this knowledge. The Clinical Interpretation of Variants in Cancer (CIViC, www.civicdb.org) knowledgebase employs the latter approach, and is a free and open-access public resource with an intuitive user interface and flexible public API for programmatic access to all content, which is available to the public with no restrictions on usage. All data is available for retrieval without login, while registration with a free account is required to contribute curation. The provenance of all curation and revision in CIViC is viewable through the web interface, and curators may also leave public comments on all content. Selected expert editors review and revise submitted content, which is clearly labeled as accepted once it has fully undergone moderation. All content in CIViC adheres to a structured data model which follows a published standard operating procedure for curation. This data model incorporates ontologies, standards and guidelines from across the field to promote interoperability and compatibility with other efforts. The CIViC interface also allows curators and organizations to track and display summary statistics of all their activity. CIViC currently has a community of over 190 curators and 16,000 clinical and research users around the world. CIViC continually develops and improves both new and existing features in response to user feedback as well as collaborative and internal development goals. Recently, the drugs and treatment terms used in predictive/therapeutic annotation have been normalized to the NCI Thesaurus. A conflict of interest (COI) statement is now required for all CIViC Editors, and functionality for writing and displaying the COI has been built into the interface. CIViC employs Predictive (Therapeutic), Prognostic, Diagnostic, Predisposing evidence types, and we highlight the recently introduced Functional evidence type, which has seen continued development. We will present the rationale for these changes including demonstrating how adding a Dominant Negative term better supports curation of functional genomics data sets. A focus of functional curation has been TP53, with over 50 evidence items to date. With multiple use cases for this type of data including targeted therapeutics, identification of relevant hotspots, or characterization of cancer driver mechanisms, functional evidence can be used to support conventional concepts of clinical utility and expand the CIViC data model. These developments provide a mechanism for discussion and integration of functional data into somatic variant interpretation guidelines, an area being explored but lacking expert consensus. Citation Format: Arpad Danos, Kilannin Krysiak, Erica K. Barnell, Adam C. Coffman, Joshua F. McMichael, Susanna Kiwala, Nicholas C. Spies, Lana M. Sheta, Shahil P. Pema, Lynzey Kujan, Kaitlin A. Clark, Sydney Anderson, Amber Wollam, Brian Li, Justin Guerra, Shruti Rao, Deborah I. Ritter, Cameron J. Grisdale, Gordana Raca, Alex H. Wagner, Subha Madhavan, Malachi Griffith, Obi L. Griffith. Evolution of the CIViC knowledgebase for community driven curation of clinical variants in cancer [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 3211.

The purposes of the study were to establish isometric torque reference values for healthy 6- to 8-year-old children for 6 lower extremity muscles and determine the effects of gender, age, height, weight, and physical activity upon strength. A hand-held dynamometer was used to obtain force/torque values for 154 children. Age-referenced force/torque and cutoff values are provided for each muscle group. Torque increased with age and height for all muscles and with weight for all muscles except knee extensors. No gender differences were found. Children who participated in 3 or more hours per week in organized sports were stronger in 4 of 6 muscles; the number of hours spent in active play did not affect torque. This study provides hand-held dynamometer strength reference values to enable clinicians to determine if clients of the same age, height, and weight have muscle weakness.

Clinical interpretation of DNA sequence variants is a critical step in reporting clinical genetic testing results. Application of next-generation sequencing technology in molecular genetic testing has facilitated diagnoses of genetic disorders in clinical practice. However, the large number of DNA sequence variants detected in clinical specimens, many of which have never been seen before, make clinical interpretation challenging. Recommendations by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) have been widely adopted by clinical laboratories around the world to guide clinical interpretation of sequence variants. The ClinGen Sequence Variant Interpretation Working Group and various disease-specific variant curation expert panels have also developed specifications for the ACMG/AMP recommendations. Despite these efforts to standardize variant interpretation in clinical practice, different laboratories may subjectively use professional judgment to determine which criteria are applicable when classifying a variant. In addition, clinicians and researchers who are not familiar with the variant interpretation process may have difficulty understanding clinical genetic reports and communicating the clinical significance of genetic testing results. Here we provide a step-by-step protocol for clinical interpretation of sequence variants, including practical examples. By following this protocol, clinical laboratory geneticists can interpret the clinical significance of sequence variants according to the ACMG/AMP recommendations and ClinGen framework. Furthermore, this article will help clinicians and researchers to understand variant classification in clinical genetic testing reports and evaluate the quality of the reports. © 2020 by John Wiley & Sons, Inc. Basic Protocol: Interpreting the clinical significance of sequence variants Support Protocol: Reevaluating the clinical significance of sequence variants.

MECP2 duplication syndrome (MDS) is a rare X-linked neurodevelopmental disorder caused by duplications of the dosage-sensitive methyl-CpG-binding protein 2 (MECP2) gene. Developing effective therapies for MDS is particularly challenging due to the variability in MECP2 expression among patients and the potential risk of inducing Rett syndrome through excessive pharmacological intervention. Reducing dosage to optimize silencing levels often compromises durability and necessitates increased dosing frequency. We present here a series of fully chemically modified small interfering RNAs (siRNAs) designed for both isoform-selective and total MECP2 silencing. Among these, we identify six lead siRNA candidates across two distinct chemical scaffolds, achieving targeted total MECP2 expression reductions ranging from 25% to 75%, sustained for at least four months following a single administration. The efficacy and safety of human ortholog silencing were evaluated using two mouse models with distinct levels of human MECP2 transgene expression. In the severe duplication model, a single dose of the total isoform-silencing siRNA fully rescued early mortality and behavioral impairments. Additionally, we show that the isoform-selective targeting strategy may be safer in mild cases of MDS where exaggerated pharmacology may lead to Rett Syndrome. Overall, this study introduces a series of preclinical candidates with the capacity to address the varying levels of MECP2 duplication encountered in clinical settings. Furthermore, it establishes a target selection strategy that may be applied to other dosage-sensitive gene imbalances.

Rett Syndrome is a rare genetic disorder exclusively seen in girls. Approximately 95% of RTT cases is caused by mutations in the MeCP2 gene which codes for Methyl-CpG-binding protein 2 (MeCP2). In this review, first, a brief introductory review of Rett Syndrome, MeCP2 protein structure and function, mutation types and frequencies, and phenotype–genotype relationships were provided. After that, the current knowledge on the wild-type and mutant MeCP2 protein structure and dynamics as well as its binding to DNA is reviewed. The review particularly focuses on computational (such as molecular dynamics) and experimental (such as electrophoretic mobility shift assays) studies on the MeCP2 binding to different types of DNA as well as the computational and experimental (such as circular dichroism) studies on the stability changes upon mutations. In the end, a brief opinion on future outlook for further computational studies is provided.

BackgroundThe American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) clinical variant interpretation guidelines established criteria for different types of evidence. This includes the strong evidence codes PS3 and BS3 for “well-established” functional assays demonstrating a variant has abnormal or normal gene/protein function, respectively. However, they did not provide detailed guidance on how functional evidence should be evaluated, and differences in the application of the PS3/BS3 codes are a contributor to variant interpretation discordance between laboratories. This recommendation seeks to provide a more structured approach to the assessment of functional assays for variant interpretation and guidance on the use of various levels of strength based on assay validation.MethodsThe Clinical Genome Resource (ClinGen) Sequence Variant Interpretation (SVI) Working Group used curated functional evidence from ClinGen Variant Curation Expert Panel-developed rule specifications and expert opinions to refine the PS3/BS3 criteria over multiple in-person and virtual meetings. We estimated the odds of pathogenicity for assays using various numbers of variant controls to determine the minimum controls required to reach moderate level evidence. Feedback from the ClinGen Steering Committee and outside experts were incorporated into the recommendations at multiple stages of development.ResultsThe SVI Working Group developed recommendations for evaluators regarding the assessment of the clinical validity of functional data and a four-step provisional framework to determine the appropriate strength of evidence that can be applied in clinical variant interpretation. These steps are as follows: (1) define the disease mechanism, (2) evaluate the applicability of general classes of assays used in the field, (3) evaluate the validity of specific instances of assays, and (4) apply evidence to individual variant interpretation. We found that a minimum of 11 total pathogenic and benign variant controls are required to reach moderate-level evidence in the absence of rigorous statistical analysis.ConclusionsThe recommendations and approach to functional evidence evaluation described here should help clarify the clinical variant interpretation process for functional assays. Further, we hope that these recommendations will help develop productive partnerships with basic scientists who have developed functional assays that are useful for interrogating the function of a variety of genes.

Introduction: The Clinical Genome Resource (ClinGen) Somatic Working Group (sWG) is a multi-institution team engaged in developing processes, resources, and standards to support accurate classification of somatic variants in cancer. Existing decision support resources in cancer knowledgebases are heavily skewed towards genes and variants relevant in adult cancers; however, information to support variant interpretation in childhood cancers is limited. Here we report on the goals and progress of the Pediatric Cancer Taskforce, created within the ClinGen sWG, to lead curation efforts of actionable alterations in childhood cancers. Methods: The ClinGen sWG Pediatric Cancer Taskforce (PCT) consists of a core group of twelve members comprising geneticists, pathologists, and oncologists with expertise in different pediatric cancers and with representation from 9 leading pediatric institutions. The taskforce has a total of 35 members including volunteer-curators who work under guidance of the expert members. Curation of childhood cancer variants is conducted in collaboration with the Clinical Interpretation of Variants in Cancer (CIViC) team at Washington University in Saint Louis, using the CIViC knowledgebase (civicdb.org) and the ClinVar database as open-access curation and data-sharing platforms. Diagnostic, prognostic, and therapeutic evidence is tiered according to the AMP/ASCO/CAP guidelines for the clinical interpretation of somatic variants. PCT members are assigned specific genetic variant-tumor type associations for curation, which are then reviewed in monthly conferences to finalize assertions in CIViC. Results: The PCT has prioritized 40 genetic alterations relevant to pediatric cancer for curation based on their clinical relevance and the lack of sufficient existing curated evidence in clinical knowledgebases. To date, 4 assertions have been created and added to the database: HEY1-NCOA2 fusion in mesenchymal chondrosarcoma, KIAA1549-BRAF fusion and ACVR1 p.G328V variant in pediatric glioma, and EBF1-PDGFRB fusion in pediatric B-cell precursor acute lymphoblastic leukemia. Active curation has been initiated for NTRK fusions agnostic of tissue histology, targetable kinase fusions in Ph-like B-lymphoblastic leukemia, and common variants in selected pediatric sarcomas and brain tumors, focusing heavily on driver gene fusions in childhood cancers. 119 evidence items have been created in CIViC by the members. The PCT also works to implement more standardized and accurate classification of pediatric cancers in CIViC and other cancer resources, and to enhance search for pediatric-specific data through appropriate tagging of evidence using ontology terms. Conclusions: As molecular alterations are increasingly relevant to the care of children with cancer, the ClinGen PCT will work to develop standards, processes, and resources for efficient and accurate determination of clinical relevance of pediatric cancer variants. Citation Format: Alanna J. Church, Shruti Rao, Deborah Ritter, Arpad Danos, Kilann Krysiak, Laura B. Corson, Kevin E. Fisher, Matthew Hiemenz, Katherine A. Janeway, Jianling Ji, Chimene A. Kesserwan, Theodore W. Laetsch, Donald W. Parsons, Ryan J. Schmidt, Kristen L. Sund, Wan-Hsin Lin, Malachi Griffith, Obi L. Griffith, Shashikant Kulkarni, Subha Madhavan, Angshumoy Roy, Gordana Raca. Curation of pediatric cancer variants within the Clinical Genome Resource (ClinGen) [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A58.

Disorders of somatic mosaicism (DoSMs) are rare genetic disorders arising from postzygotic alteration leading to segmental/nonsegmental disease. Current professional guidelines for standardized variant interpretation focus on germline and cancer variants, making them suboptimal for DoSM variant interpretation. The Brain Malformations Variant Curation Expert Panel (BMVCEP) modified existing guidelines to account for brain-specific disorders of somatic mosaicism, but applicability to other DoSM presentations is limited. At Washington University in St. Louis School of Medicine, we have adopted the BMVCEP interpretation framework but suggested alterations that make it more suitable for generalized DoSM variant classification. These modifications include (1) expanding applicability beyond genes associated with brain malformations, (2) introduction of a criterion to interpret truncating variants at the C-terminus of gain of function genes, (3) establishment of a variant allele fraction (VAF) cutoff for applying de novo criteria, and (4) demonstration that in silico prediction tools are relevant to interpretation of gain of function missense variants. Furthermore, modifications to BMVCEP guidelines reduce the number of variants classified as uncertain. The variant classification considerations that we propose have the potential to improve the accuracy of somatic variant classification, better inform clinical care, and may benefit clinical laboratories also conducting DoSM testing.

BackgroundDuchenne muscular dystrophy (Duchenne) is caused by pathogenic variants in the DMD gene. Antisense oligonucleotides (AONs) are one emerging precision medicine treatment for Duchenne. DMD molecular genetic testing results guide precision‐therapy molecular eligibility, requiring healthcare providers to perform analyses currently uncommon in clinical laboratory and medical practices. Clear DMD variant notation and interpretation are key components of clinical care with the availability of precision medicine.MethodsThe DMD Open‐access Variant Explorer (DOVE) is a web‐based aid for DMD variant interpretation which additionally reports variant‐specific predicted molecular eligibility for therapy. DOVE was developed in Python and adapted to the Django Web framework, integrates existing open‐access tools, and does not rely on previous variant report/classification.ResultsDOVE [www.dmd.nl/DOVE] interprets colloquial and HGMD inputs of DMD variants to output HGMD variant nomenclature, theoretical molecular eligibility for therapy, and any predicted deleterious molecular consequences of therapy. DOVE relies on holistic in silico prediction of molecular eligibility for therapy in lieu of reference to an empirically defined, “variant‐eligible” list. Examples illustrate the advantage and necessity for holistic variant interpretation.ConclusionDOVE may prove useful for variant interpretation both at patient‐level and in large‐scale programs such as newborn screening and has broad application in concept to molecular genetic test result interpretation.

130. The Myeloid Malignancy Variant Curation Expert Panel: Investigating RUNX1, GATA2, and DDX41

A Phenotype-Enhanced Variant Classification Framework to Decrease the Burden of Variants of Uncertain Significance in Type 2 Long QT Syndrome.