exRNA Atlas Analysis Reveals Distinct Extracellular RNA Cargo Types and Their Carriers Present across Human Biofluids.

To develop a map of cell-cell communication mediated by extracellular RNA (exRNA), the NIH Extracellular RNA Communication Consortium created the exRNA Atlas resource (https://exrna-atlas.org). The Atlas version 4P1 hosts 5,309 exRNA-seq and exRNA qPCR profiles from 19 studies and a suite of analysis and visualization tools. To analyze variation between profiles, we apply computational deconvolution. The analysis leads to a model with six exRNA cargo types (CT1, CT2, CT3A, CT3B, CT3C, CT4), each detectable in multiple biofluids (serum, plasma, CSF, saliva, urine). Five of the cargo types associate with known vesicular and non-vesicular (lipoprotein and ribonucleoprotein) exRNA carriers. To validate utility of this model, we re-analyze an exercise response study by deconvolution to identify physiologically relevant response pathways that were not detected previously. To enable wide application of this model, as part of the exRNA Atlas resource, we provide tools for deconvolution and analysis of user-provided case-control studies.

Implementing evidence-based assertions of clinical actionability in the context of secondary findings: Updates from the ClinGen Actionability Working Group.

PURPOSE: The ClinGen Actionability Working Group (AWG) developed an evidence-based framework to generate actionability reports and scores of gene-condition pairs in the context of secondary findings from genome sequencing. Here we describe the expansion of the framework to include actionability assertions. METHODS: Initial development of the actionability rubric was based on previously scored adult gene-condition pairs and individual expert evaluation. Rubric refinement was iterative and based on evaluation, feedback, and discussion. The final rubric was pragmatically evaluated via integration into actionability assessments for 27 gene-condition pairs. RESULTS: The resulting rubric has a 4-point scale (limited, moderate, strong, and definitive) and uses the highest-scoring outcome-intervention pair of each gene-condition pair to generate a preliminary assertion. During AWG discussions, predefined criteria and factors guide discussion to produce a consensus assertion for a gene-condition pair, which may differ from the preliminary assertion. The AWG has retrospectively generated assertions for all previously scored gene-condition pairs and are prospectively asserting on gene-condition pairs under assessment, having completed over 170 adult and 188 pediatric gene-condition pairs. CONCLUSION: The AWG expanded its framework to provide actionability assertions to enhance the clinical value of their resources and increase their utility as decision aids regarding return of secondary findings.

ClinGen's Pediatric Actionability Working Group: Clinical actionability of secondary findings from genome-scale sequencing in children and adolescents.

PURPOSE: Synthesis and curation of evidence regarding the clinical actionability of secondary findings (SFs) from genome-scale sequencing are needed to support decision-making on reporting of these findings. To assess actionability of SFs in children and adolescents, the Clinical Genome Resource established the Pediatric Actionability Working Group (AWG). METHODS: The Pediatric AWG modified the framework of the existing Adult AWG, which included production of summary reports of actionability for genes and associated conditions and consensus actionability scores for specific outcome-intervention pairs. Modification of the adult framework for the pediatric setting included accounting for special considerations for reporting presymptomatic or predictive genetic findings in the pediatric context, such as maintaining future autonomy by not disclosing conditions not actionable until adulthood. The Pediatric AWG then applied this new framework to genes and associated conditions with putative actionability. RESULTS: As of September 2021, the Pediatric AWG applied the new framework to 70 actionability topics representing 143 genes. Reports and scores are publicly available at www.clinicalgenome.org. CONCLUSION: The Pediatric AWG continues to curate gene-condition topics and build an evidence-based resource, supporting clinical communities and decision-makers with policy development on the return of SFs in pediatric populations.

PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery.

We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources, to facilitate collaboration among researchers and to make these data easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols and training materials for processing PDX data. Currently, the portal contains PDXNet model information and data resources from 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI's Patient-Derived Model Repository (PDMR) for public access. These models have 2134 associated sequencing files from 873 samples across 308 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal includes results from freely available, robust, validated and standardized analysis workflows on PDXNet sequencing files and PDMR data (3857 samples from 629 patients across 85 disease types). The PDXNet portal is continuously updated with new data and is of significant utility to the cancer research community as it provides a centralized location for PDXNet resources, which support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials.

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.

Extending gene ontology in the context of extracellular RNA and vesicle communication.

BACKGROUND: To address the lack of standard terminology to describe extracellular RNA (exRNA) data/metadata, we have launched an inter-community effort to extend the Gene Ontology (GO) with subcellular structure concepts relevant to the exRNA domain. By extending GO in this manner, the exRNA data/metadata will be more easily annotated and queried because it will be based on a shared set of terms and relationships relevant to extracellular research. METHODS: By following a consensus-building process, we have worked with several academic societies/consortia, including ERCC, ISEV, and ASEMV, to identify and approve a set of exRNA and extracellular vesicle-related terms and relationships that have been incorporated into GO. In addition, we have initiated an ongoing process of extractions of gene product annotations associated with these terms from Vesiclepedia and ExoCarta, conversion of the extracted annotations to Gene Association File (GAF) format for batch submission to GO, and curation of the submitted annotations by the GO Consortium. As a use case, we have incorporated some of the GO terms into annotations of samples from the exRNA Atlas and implemented a faceted search interface based on such annotations. RESULTS: We have added 7 new terms and modified 9 existing terms (along with their synonyms and relationships) to GO. Additionally, 18,695 unique coding gene products (mRNAs and proteins) and 963 unique non-coding gene products (ncRNAs) which are associated with the terms: "extracellular vesicle", "extracellular exosome", "apoptotic body", and "microvesicle" were extracted from ExoCarta and Vesiclepedia. These annotations are currently being processed for submission to GO. CONCLUSIONS: As an inter-community effort, we have made a substantial update to GO in the exRNA context. We have also demonstrated the utility of some of the new GO terms for sample annotation and metadata search.

Integration of extracellular RNA profiling data using metadata, biomedical ontologies and Linked Data technologies.

The large diversity and volume of extracellular RNA (exRNA) data that will form the basis of the exRNA Atlas generated by the Extracellular RNA Communication Consortium pose a substantial data integration challenge. We here present the strategy that is being implemented by the exRNA Data Management and Resource Repository, which employs metadata, biomedical ontologies and Linked Data technologies, such as Resource Description Framework to integrate a diverse set of exRNA profiles into an exRNA Atlas and enable integrative exRNA analysis. We focus on the following three specific data integration tasks: (a) selection of samples from a virtual biorepository for exRNA profiling and for inclusion in the exRNA Atlas; (b) retrieval of a data slice from the exRNA Atlas for integrative analysis and (c) interpretation of exRNA analysis results in the context of pathways and networks. As exRNA profiling gains wide adoption in the research community, we anticipate that the strategies discussed here will increasingly be required to enable data reuse and to facilitate integrative analysis of exRNA data.