包括个性化疫苗在内的新抗原靶向疗法在癌症治疗中显示出希望。新抗原的准确识别/优先排序与设计临床试验高度相关,预测治疗反应,了解抵抗机制。随着大规模平行测序技术的出现,现在可以根据患者特异性变异信息预测新抗原.然而,在将新抗原优先用于个性化治疗时,必须考虑多种因素.复杂性,如替代转录本注释,各种绑定,表达和免疫原性预测算法,和可变的肽长度/寄存器都潜在地影响新抗原选择过程。虽然计算工具为新抗原表征生成了许多算法预测,这些管道的结果很难导航,需要对底层工具的广泛了解才能准确解释。由于复杂的性质和数量的突出的新抗原特征,提供所有相关信息以促进下游应用程序的候选选择是当前工具无法解决的难题。我们已经创建了pVACview,第一个交互式工具,旨在帮助个性化新抗原治疗的新抗原候选物的优先排序和选择。pVACview具有用户友好和直观的界面,用户可以上传,探索,选择并输出他们的新抗原候选物。该工具允许用户使用变体可视化候选人,转录物和肽信息。pVACview将允许研究人员在基础和翻译设置中以更高的效率和准确性分析和优先考虑新抗原候选物。该应用程序可作为pVACtools管道的一部分在pvactools.org和作为在线服务器在pvacview.org。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.