pVACview:高效新抗原优先排序和选择的交互式可视化工具

Huiming Xia, My Hoang, Evelyn Schmidt, Susanna Kiwala, Joshua McMichael, Zachary L. Skidmore, Bryan Fisk, Jonathan J. Song, Jasreet Hundal, Thomas Mooney, Jason R. Walker, S. Peter Goedegebuure, Christopher A. Miller, William E. Gillanders, Obi L. Griffith, Malachi Griffith
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引用次数: 0

摘要

包括个性化疫苗在内的新抗原靶向疗法在治疗癌症方面大有可为。准确识别/优先选择新抗原与设计临床试验、预测治疗反应和了解抗药性机制密切相关。随着大规模并行测序技术的发展,根据患者特异性变异信息预测内抗原已成为可能。然而,在确定用于个体化疗法的新抗原的优先级时,必须考虑许多因素。替代转录本注释、各种结合、表达和免疫原性预测算法以及可变的肽长度/序列等复杂因素都可能影响新抗原的选择过程。虽然计算工具能生成大量用于新抗原特征描述的算法预测结果,但这些管道产生的结果难以驾驭,需要对基础工具有广泛的了解才能准确解读。由于新抗原特征错综复杂且数量众多,如何呈现所有相关信息以方便下游应用的候选筛选是一个艰巨的挑战,而目前的工具无法解决这一问题。我们创建了 pVACview,这是第一款交互式工具,旨在帮助确定个性化新抗原疗法的新抗原候选物的优先级并进行筛选。该工具允许用户使用变体、转录本和肽信息对候选基因进行可视化处理。pVACview 将允许研究人员在基础和转化环境中高效、准确地分析和优先处理新抗原候选基因。该应用程序可作为 pVACtools pipeline 的一部分在 pvactools.org 上使用,也可作为在线服务器在 pvacview.org 上使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection
Neoantigen targeting therapies including personalized vaccines have shown promise in the treatment of cancers. Accurate identification/prioritization of neoantigens is highly relevant to designing clinical trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of massively parallel sequencing technologies, it is now possible to 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. While computational 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. 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. 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 using 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.
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