Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. This is a cross-disciplinary challenge, involving ...genomics, proteomics, immunology, and computational approaches. We have built a computational framework called pVACtools that, when paired with a well-established genomics pipeline, produces an end-to-end solution for neoantigen characterization. pVACtools supports identification of altered peptides from different mechanisms, including point mutations, in-frame and frameshift insertions and deletions, and gene fusions. Prediction of peptide:MHC binding is accomplished by supporting an ensemble of MHC Class I and II binding algorithms within a framework designed to facilitate the incorporation of additional algorithms. Prioritization of predicted peptides occurs by integrating diverse data, including mutant allele expression, peptide binding affinities, and determination whether a mutation is clonal or subclonal. Interactive visualization via a Web interface allows clinical users to efficiently generate, review, and interpret results, selecting candidate peptides for individual patient vaccine designs. Additional modules support design choices needed for competing vaccine delivery approaches. One such module optimizes peptide ordering to minimize junctional epitopes in DNA vector vaccines. Downstream analysis commands for synthetic long peptide vaccines are available to assess candidates for factors that influence peptide synthesis. All of the aforementioned steps are executed via a modular workflow consisting of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization, and selection using a graphical Web-based interface (pVACviz), and design of DNA vector-based vaccines (pVACvector) and synthetic long peptide vaccines. pVACtools is available at http://www.pvactools.org.
Recent efforts to design personalized cancer immunotherapies use predicted neoantigens, but most neoantigen prediction strategies do not consider proximal (nearby) variants that alter the peptide ...sequence and may influence neoantigen binding. We evaluated somatic variants from 430 tumors to understand how proximal somatic and germline alterations change the neoantigenic peptide sequence and also affect neoantigen binding predictions. On average, 241 missense somatic variants were analyzed per sample. Of these somatic variants, 5% had one or more in-phase missense proximal variants. Without incorporating proximal variant correction for major histocompatibility complex class I neoantigen peptides, the overall false discovery rate (incorrect neoantigens predicted) and the false negative rate (strong-binding neoantigens missed) across peptides of lengths 8-11 were estimated as 0.069 (6.9%) and 0.026 (2.6%), respectively.
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.
Increasing use of next generation sequencing in cancer generates large volumes of patient variant data for clinical interpretation. Complex intergroup biocuration can help address this problem and includes coordination of group efforts and harmonization of variant formatting. This work describes generation and implementation of such a workflow for collaborative somatic variant curation by ClinGen Somatic Working Groups and the Clinical Interpretation of Variants in Cancer database (CIViC – www.civicdb.org), along with subsequent automated ClinVar submission.
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.
Abstract
The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from publications, databases, and other ...web-based sources. Drug, gene, and interaction data are normalized and merged into conceptual groups. The information contained in this resource is available to users through a straightforward search interface, an application programming interface (API), and TSV data downloads. DGIdb 4.0 is the latest major version release of this database. A primary focus of this update was integration with crowdsourced efforts, leveraging the Drug Target Commons for community-contributed interaction data, Wikidata to facilitate term normalization, and export to NDEx for drug-gene interaction network representations. Seven new sources have been added since the last major version release, bringing the total number of sources included to 41. Of the previously aggregated sources, 15 have been updated. DGIdb 4.0 also includes improvements to the process of drug normalization and grouping of imported sources. Other notable updates include the introduction of a more sophisticated Query Score for interaction search results, an updated Interaction Score, the inclusion of interaction directionality, and several additional improvements to search features, data releases, licensing documentation and the application framework.
Abstract
The Drug–Gene Interaction Database (DGIdb, https://dgidb.org) is a publicly accessible resource that aggregates genes or gene products, drugs and drug–gene interaction records to drive ...hypothesis generation and discovery for clinicians and researchers. DGIdb 5.0 is the latest release and includes substantial architectural and functional updates to support integration into clinical and drug discovery pipelines. The DGIdb service architecture has been split into separate client and server applications, enabling consistent data access for users of both the application programming interface (API) and web interface. The new interface was developed in ReactJS, and includes dynamic visualizations and consistency in the display of user interface elements. A GraphQL API has been added to support customizable queries for all drugs, genes, annotations and associated data. Updated documentation provides users with example queries and detailed usage instructions for these new features. In addition, six sources have been added and many existing sources have been updated. Newly added sources include ChemIDplus, HemOnc, NCIt (National Cancer Institute Thesaurus), Drugs@FDA, HGNC (HUGO Gene Nomenclature Committee) and RxNorm. These new sources have been incorporated into DGIdb to provide additional records and enhance annotations of regulatory approval status for therapeutics. Methods for grouping drugs and genes have been expanded upon and developed as independent modular normalizers during import. The updates to these sources and grouping methods have resulted in an improvement in FAIR (findability, accessibility, interoperability and reusability) data representation in DGIdb.
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