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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. Including patient-specific information about nearby somatic and germline alterations improves the accuracy of neoantigen prediction, potentially impacting cancer vaccine design.

Background Neoantigen-targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. At least 100 clinical trials involving these therapies have been initiated globally. Accurate identification and prioritization of neoantigens is crucial for designing these trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of massively parallel DNA and RNA sequencing technologies, it is now possible to computationally predict neoantigens based on patient-specific variant information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized therapies. Complexities such as alternative transcript annotations, various binding, presentation and immunogenicity prediction algorithms, and variable peptide lengths/registers all potentially impact the neoantigen selection process. There has been a rapid development of computational tools that attempt to account for these complexities. While these tools generate numerous algorithmic predictions for neoantigen characterization, results from these pipelines are difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. This often leads to over-simplification of pipeline outputs to make them tractable, for example, limiting prediction to a single RNA isoform or only summarizing the top ranked of many possible peptide candidates. In addition to variant detection, gene expression, and predicted peptide binding affinities, recent studies have also demonstrated the importance of mutation location, allele-specific anchor locations, and variation of T-cell response to long versus short peptides. Due to the intricate nature and number of salient neoantigen features, presenting all relevant information to facilitate candidate selection for downstream applications is a difficult challenge that current tools fail to address. Results We have created pVACview, the first interactive tool designed to aid in the prioritization and selection of neoantigen candidates for personalized neoantigen therapies including cancer vaccines. pVACview has a user-friendly and intuitive interface where users can upload, explore, select, and export their neoantigen candidates. The tool allows users to visualize candidates at multiple levels of detail including variant, transcript, peptide, and algorithm prediction information. Conclusions pVACview will allow researchers to analyze and prioritize neoantigen candidates with greater efficiency and accuracy in basic and translational settings. The application is available as part of the pVACtools software at pvactools.org and as an online server at pvacview.org.

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.

Objective The diversity of nomenclature and naming strategies makes therapeutic terminology difficult to manage and harmonize. As the number and complexity of available therapeutic ontologies continues to increase, the need for harmonized cross-resource mappings is becoming increasingly apparent. This study creates harmonized concept mappings that enable the linking together of like-concepts despite source-dependent differences in data structure or semantic representation. Materials and Methods For this study, we created Thera-Py, a Python package and web API that constructs searchable concepts for drugs and therapeutic terminologies using 9 public resources and thesauri. By using a directed graph approach, Thera-Py captures commonly used aliases, trade names, annotations, and associations for any given therapeutic and combines them under a single concept record. Results We highlight the creation of 16 069 unique merged therapeutic concepts from 9 distinct sources using Thera-Py and observe an increase in overlap of therapeutic concepts in 2 or more knowledge bases after harmonization using Thera-Py (9.8%-41.8%). Conclusion We observe that Thera-Py tends to normalize therapeutic concepts to their underlying active ingredients (excluding nondrug therapeutics, eg, radiation therapy, biologics), and unifies all available descriptors regardless of ontological origin. Lay Summary Working with therapeutic terminology in medicine is challenging due to the ambiguity associated with different naming strategies. A therapeutic can have many different types of identifiers across many vocabularies: natural product names, chemical structures, development codes, generic names, brand names, product formulations, or treatment regiments. This diversity of nomenclature makes therapeutic terminology uniquely difficult to manage and the need for harmonized cross-resource mappings is becoming increasingly apparent. To support these mappings, we introduce Thera-Py, a Python package and web API that constructs stable, searchable therapeutic concepts for drugs and therapeutic terminology. By using a directed graph approach, Thera-Py captures commonly used aliases, trade names, annotations, and associations for any given therapeutic and harmonizes them under a single merged concept record. Using this approach, we found that Thera-Py tends to normalize therapeutic concepts to their underlying active ingredients (excluding nondrug therapeutics, eg, radiation therapy, biologics) and unifies all available descriptors regardless of ontological origin. In this report, we highlight the creation of 16 069 unique merged therapeutic concepts from 9 distinct sources and observe an increased overlap of therapeutic concepts in commonly used knowledge bases after harmonization using Thera-Py.

The druggable genome encompasses the genes that are known or predicted to interact with drugs. The Drug-Gene Interaction Database (DGIdb) provides an integrated resource for discovering and contextualizing these interactions, supporting a broad range of research and clinical applications. DGIdb is currently accessed through structured web interfaces and API calls, requiring users to translate natural-language questions into database-specific query patterns. To allow for the use of DGIdb through natural language, we developed the DGIdb Model Context Protocol (MCP) server, which allows large language models (LLMs) access to up-to-date information through the DGIdb API. We demonstrate that the MCP server greatly enhances an LLM's ability to answer questions requiring accurate, up-to-date biomedical knowledge drawn from structured external resources. The DGIdb MCP server is detailed at https://github.com/griffithlab/dgidb-mcp-server and includes instructions for accessing the server through the Claude desktop app.

The Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase provides a community-driven, open-source platform for discussing the biological and Clinical Significance of molecular variants in cancer. To enable users to make complex connections between CIViC information, we developed the CIViC Model Context Protocol (MCP) server, which allows large language models (LLMs) to directly interface with the CIViC API through natural language, facilitating the rapid summarization of expertly curated cancer variant interpretations. The CIViC MCP server is detailed at https://github.com/griffithlab/civic-mcp-server with archived code and evaluation data deposited in Zenodo (DOI: 10.5281/zenodo.17344050). The repository is a fork of https://github.com/QuentinCody/civic-mcp-server (QuentinCody 2025) and includes instructions for accessing the server through the Claude desktop app and hosting it locally with GPT-5. We also provide a Python script for directly querying the MCP server. Supplementary data are available at online.

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) consolidates, organizes, and presents drug-gene interactions and gene druggability information from papers, databases, and web resources. DGIdb normalizes content from more than thirty disparate sources and allows for user-friendly advanced browsing, searching and filtering for ease of access through an intuitive web user interface, application programming interface (API), and public cloud-based server image. DGIdb v3.0 represents a major update of the database. Nine of the previously included twenty-eight sources were updated. Six new resources were added, bringing the total number of sources to thirty-three. These updates and additions of sources have cumulatively resulted in 56,309 interaction claims. This has also substantially expanded the comprehensive catalogue of druggable genes and antineoplastic drug-gene interactions included in the DGIdb. Along with these content updates, v3.0 has received a major overhaul of its codebase, including an updated user interface, preset interaction search filters, consolidation of interaction information into interaction groups, greatly improved search response times, and upgrading the underlying web application framework. In addition, the expanded API features new endpoints which allow users to extract more detailed information about queried drugs, genes, and drug-gene interactions, including listings of PubMed IDs (PMIDs), interaction type, and other interaction metadata.

Neoantigen targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. At least 100 clinical trials involving these therapies are underway globally. Accurate identification and prioritization of neoantigens is highly relevant to designing these trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of massively parallel DNA and RNA sequencing technologies, it is now possible to computationally predict neoantigens based on patient-specific variant information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized therapies. Complexities such as alternative transcript annotations, various binding, presentation and immunogenicity prediction algorithms, and variable peptide lengths/registers all potentially impact the neoantigen selection process. There has been a rapid development of computational tools that attempt to account for these complexities. While these tools generate numerous algorithmic predictions for neoantigen characterization, results from these pipelines are difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. This often leads to over-simplification of pipeline outputs to make them tractable, for example limiting prediction to a single RNA isoform or only summarizing the top ranked of many possible peptide candidates. In addition to variant detection, gene expression and predicted peptide binding affinities, recent studies have also demonstrated the importance of mutation location, allele-specific anchor locations, and variation of T-cell response to long versus short peptides. Due to the intricate nature and number of salient neoantigen features, presenting all relevant information to facilitate candidate selection for downstream applications is a difficult challenge that current tools fail to address. We have created pVACview, the first interactive tool designed to aid in the prioritization and selection of neoantigen candidates for personalized neoantigen therapies including cancer vaccines. pVACview has a user-friendly and intuitive interface where users can upload, explore, select and export their neoantigen candidates. The tool allows users to visualize candidates across three different levels, including variant, transcript and peptide information. pVACview will allow researchers to analyze and prioritize neoantigen candidates with greater efficiency and accuracy in basic and translational settings The application is available as part of the pVACtools pipeline at pvactools.org and as an online server at pvacview.org.

Personalized neoantigen vaccines represent a promising immunotherapy approach that harnesses tumor-specific antigens to stimulate anti-tumor immune responses. However, the design of these vaccines requires sophisticated computational workflows to predict and prioritize neoantigen candidates from patient sequencing data, coupled with rigorous review to ensure candidate quality. While numerous computational tools exist for neoantigen prediction, to our knowledge, there are no established protocols detailing the complete process from raw sequencing data through systematic candidate selection. Here, we present ImmunoNX (Immunogenomics Neoantigen eXplorer), an end-to-end protocol for neoantigen prediction and vaccine design that has supported over 185 patients across 11 clinical trials. The workflow integrates tumor DNA/RNA and matched normal DNA sequencing data through a computational pipeline built with Workflow Definition Language (WDL) and executed via Cromwell on Google Cloud Platform. ImmunoNX employs consensus-based variant calling, in-silico HLA typing, and pVACtools for neoantigen prediction. Additionally, we describe a two-stage immunogenomics review process with prioritization of neoantigen candidates, enabled by pVACview, followed by manual assessment of variants using the Integrative Genomics Viewer (IGV). This workflow enables vaccine design in under three months. We demonstrate the protocol using the HCC1395 breast cancer cell line dataset, identifying 78 high-confidence neoantigen candidates from 322 initial predictions. Although demonstrated here for vaccine development, this workflow can be adapted for diverse neoantigen therapies and experiments. Therefore, this protocol provides the research community with a reproducible, version-controlled framework for designing personalized neoantigen vaccines, supported by detailed documentation, example datasets, and open-source code.

IMPORTANCE: The Drug-Gene Interaction Database (DGIdb) has a long history of driving hypothesis generation for biomedical research through the careful curation of drug-gene interaction data from primary and secondary sources with supporting literature. Recent advances in large-language model (LLM) and artificial intelligence (AI) technologies have enabled new paradigms for knowledge extraction and biocuration. The accelerating growth of biomedical literature presents a significant challenge for maintaining up-to-date interaction data. With more than 38 million citations indexed in PubMed alone, new strategies must evolve to identify and incorporate new interaction data into DGIdb. OBJECTIVE: Identify new cost-effective AI curation strategies for incorporating new drug-gene interactions into DGIdb. METHODS: We present a methodology that leverages deterministic natural language processing techniques, existing harmonization frameworks, and AI-assisted curation to systematically narrow the literature space and identify new drug-gene interactions from published studies for inclusion in DGIdb. RESULTS: We demonstrate the use of lemmatization to prioritize a set of 100 abstracts containing high amounts of interaction words for downstream AI curation. From our set of abstracts, we were then able to identify 137 drug-gene interactions via an AI curation task, with 121 (88.3%) of these interactions being completely novel to DGIdb. A human expert evaluator reviewed this interaction set and was able to validate 134 of 137 (97.8%) interactions as being valid based on the text provided. CONCLUSION: Taken together, our results highlight a promising, cost-effective method of ingesting new interactions into DGIdb.Competing Interest StatementThe authors have declared no competing interest.Funder Information DeclaredNational Human Genome Research Institute, https://ror.org/00baak391, R00HG010157