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Multiplexed assays of variant effect (MAVEs) have emerged as a powerful approach for interrogating thousands of genetic variants in a single experiment. The flexibility and widespread adoption of these techniques across diverse disciplines have led to a heterogeneous mix of data formats and descriptions, which complicates the downstream use of the resulting datasets. To address these issues and promote reproducibility and reuse of MAVE data, we define a set of minimum information standards for MAVE data and metadata and outline a controlled vocabulary aligned with established biomedical ontologies for describing these experimental designs.

Background The Human Genome Variation Society (HGVS) Nomenclature is the global standard for describing and communicating variants in DNA, RNA, and protein sequences in clinical and research genomics. This manuscript details recent updates to the HGVS Nomenclature, highlighting improvements in governance, community engagement, website functionality, and underlying implementation of the standard. Methods The HGVS Variant Nomenclature Committee (HVNC) now operates under the Human Genome Organization (HUGO), facilitating broader community feedback and collaboration with related standards organizations. The website has been redesigned using modern documentation tools and practices. The specification was updated to include guidance for transcript selection and to align with recent cross-consortia recommendations for the representation of gene fusions. A formal computational grammar was introduced to improve the precision and consistency of variant descriptions. Results Major improvements in HGVS Nomenclature v. 21.1 include a redesigned website with enhanced navigation, search functionality, and mobile responsiveness; a new versioning policy aligned with software management practices; formal mechanisms for community feedback and change proposals; and adoption of Extended Backus-Naur Form (EBNF) for defining syntax. The specification now recommends MANE Select transcripts where appropriate and includes updated guidance for representing adjoined transcripts and gene fusions. All content is freely available under permissive licenses at hgvs-nomenclature.org. Conclusions These advancements establish a more sustainable foundation for maintaining and evolving the HGVS Nomenclature while improving its accessibility and utility. The introduction of formal computational grammar marks a crucial step toward unambiguous variant descriptions that can be reliably processed by both humans and machines. Combined with enhanced community engagement mechanisms and improved guidance, these changes position the HGVS Nomenclature to better serve the evolving needs of clinical and research genomics while maintaining the stability that users require.

Large-scale cancer genomics discovery projects such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) have systematically characterized the molecular lesions in human cancer genomes, thereby laying the foundation for precision cancer medicine. However, a curated set of somatic variants with established relevance to cancer biology is essential for clinical annotation and for use in computational data analysis. We have created a database of curated mutations in cancer.

Background Experimental data from functional assays have a critical role in interpreting the impact of genetic variants. Assay data must be unambiguously mapped to a reference genome to make it accessible, but it is often reported relative to assay-specific sequences, complicating downstream use and integration of variant data across resources. To make multiplexed assays of variant effect (MAVE) data more broadly available to the research and clinical communities, the Atlas of Variant Effects Alliance mapped MAVE data from the MaveDB community database to human reference sequences, creating an extensive set of machine-readable homology mappings that are incorporated into widely used human genomics applications. Results Here, we map approximately 9.0 million individual protein and nucleotide variants in MaveDB to the human genome, describing the examined variants with respect to human reference sequences while preserving the data provenance of the original MAVE sequences. We then disseminate the results to major genomic resources including the Genomics 2 Proteins Portal, UCSC Genome Browser, Ensembl Variant Effect Predictor, and DECIPHER platform. Within these applications, MAVE variants can now be visualized and integrated with other relevant clinical and biological data, making additional knowledge available when performing variant interpretation and conducting other research activities. Conclusions Mapping MAVE variants to human reference sequences and sharing the mapped dataset with several key human genomics applications enables a new and diverse set of applications for MAVE data. This study provides increased access to functional data that can assist in clinical variant interpretation pipelines and enable biomedical research and discovery.

The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools . The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.

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.

BackgroundCholangiocarcinoma (CCA) is a primary malignancy of the biliary tract with a dismal prognosis. Recently, several actionable genetic aberrations were identified with significant enrichment in intrahepatic CCA, including FGFR2 gene fusions with a prevalence of 10–15%. Recent clinical data demonstrate that these fusions are druggable in a second-line setting in advanced/metastatic disease and the efficacy in earlier lines of therapy is being evaluated in ongoing clinical trials. This scenario warrants standardised molecular profiling of these tumours.MethodsA detailed analysis of the original genetic data from the FIGHT-202 trial, on which the approval of Pemigatinib was based, was conducted.ResultsComparing different detection approaches and displaying representative cases, we described the genetic landscape and architecture of FGFR2 fusions in iCCA and show biological and technical aspects to be considered for their detection. We elaborated parameters, including a suggestion for annotation, that should be stated in a molecular diagnostic FGFR2 report to allow a complete understanding of the analysis performed and the information provided.ConclusionThis study provides a detailed presentation and dissection of the technical and biological aspects regarding FGFR2 fusion detection, which aims to support molecular pathologists, pathologists and clinicians in diagnostics, reporting of the results and decision-making.

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. This analysis presents a harmonized meta-knowledgebase to facilitate clinical interpretation of somatic genomic variants in cancer. This community-based project highlights the need for cooperative efforts to curate clinical interpretations of somatic variants for robust practice of precision oncology.

Nearly all patients with small cell lung cancer (SCLC) eventually relapse with chemoresistant disease. The molecular mechanisms driving chemoresistance in SCLC remain un-characterized. Here, we describe whole-exome sequencing of paired SCLC tumor samples procured at diagnosis and relapse from 12 patients, and unpaired relapse samples from 18 additional patients. Multiple somatic copy number alterations, including gains in ABCC1 and deletions in MYCL, MSH2 , and MSH6 , are identifiable in relapsed samples. Relapse samples also exhibit recurrent mutations and loss of heterozygosity in regulators of WNT signaling, including CHD8 and APC . Analysis of RNA-sequencing data shows enrichment for an ASCL1-low expression subtype and WNT activation in relapse samples. Activation of WNT signaling in chemosensitive human SCLC cell lines through APC knockdown induces chemoresistance. Additionally, in vitro-derived chemoresistant cell lines demonstrate increased WNT activity. Overall, our results suggest WNT signaling activation as a mechanism of chemoresistance in relapsed SCLC. Small cell lung cancer (SCLC) patients frequently relapse and become resistant to chemotherapy. Here, the authors analyse the genomic and transcriptomic landscape of primary and relapsed SCLC patients as well as in vitro models, and discover that activation of WNT signalling can drive chemotherapy resistance.