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.

中文翻译：

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pVACtools：用于识别和可视化癌症新抗原的计算工具包。

新抗原的鉴定是预测检查点封锁治疗反应和设计个性化癌症疫苗的关键步骤。这是一个跨学科的挑战，涉及基因组学、蛋白质组学、免疫学和计算方法。我们构建了一个名为 pVACtools 的计算框架，当与完善的基因组学管道配合使用时，可以为新抗原表征提供端到端的解决方案。pVACtools 支持从不同机制识别改变的肽，包括点突变、框内和移码插入和删除以及基因融合。肽：MHC 结合的预测是通过在一个旨在促进其他算法合并的框架内支持 MHC I 类和 II 类结合算法的集合来完成的。通过整合不同的数据来确定预测肽的优先级，包括突变等位基因表达、肽结合亲和力以及确定突变是克隆还是亚克隆。通过网络界面的交互式可视化允许临床用户有效地生成、审查和解释结果，为个体患者疫苗设计选择候选肽。其他模块支持竞争性疫苗输送方法所需的设计选择。其中一个模块优化了肽排序，以最大限度地减少 DNA 载体疫苗中的连接表位。合成长肽疫苗的下游分析命令可用于评估影响肽合成的因素的候选者。所有上述步骤均通过模块化工作流程执行，该工作流程包括从体细胞改变（pVACseq 和 pVACfuse）预测新抗原、使用基于图形网络的界面 (pVACviz) 进行优先级划分和选择的工具，以及基于 DNA 载体的疫苗的设计（ pVACvector）和合成长肽疫苗。pVACtools 可从 http://www.pvactools.org 获取。