Department of Computer Science, University of Tübingen, Tübingen, Germany.
Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany.
BMC Bioinformatics. 2023 Mar 8;24(1):88. doi: 10.1186/s12859-023-05194-3.
Personalized oncology represents a shift in cancer treatment from conventional methods to target specific therapies where the decisions are made based on the patient specific tumor profile. Selection of the optimal therapy relies on a complex interdisciplinary analysis and interpretation of these variants by experts in molecular tumor boards. With up to hundreds of somatic variants identified in a tumor, this process requires visual analytics tools to guide and accelerate the annotation process.
The Personal Cancer Network Explorer (PeCaX) is a visual analytics tool supporting the efficient annotation, navigation, and interpretation of somatic genomic variants through functional annotation, drug target annotation, and visual interpretation within the context of biological networks. Starting with somatic variants in a VCF file, PeCaX enables users to explore these variants through a web-based graphical user interface. The most protruding feature of PeCaX is the combination of clinical variant annotation and gene-drug networks with an interactive visualization. This reduces the time and effort the user needs to invest to get to a treatment suggestion and helps to generate new hypotheses. PeCaX is being provided as a platform-independent containerized software package for local or institution-wide deployment. PeCaX is available for download at https://github.com/KohlbacherLab/PeCaX-docker .
个性化肿瘤学代表了癌症治疗从传统方法向针对特定疗法的转变，这些疗法的决策是基于患者特定的肿瘤特征做出的。最佳疗法的选择依赖于分子肿瘤委员会专家对这些变体进行复杂的跨学科分析和解释。由于在肿瘤中鉴定出多达数百种体细胞变体，因此这个过程需要可视化分析工具来指导和加速注释过程。
个人癌症网络浏览器（PeCaX）是一种可视化分析工具，通过功能注释、药物靶点注释以及在生物网络背景下的可视化解释，支持对体细胞基因组变体进行高效注释、导航和解释。从 VCF 文件中的体细胞变体开始，PeCaX 允许用户通过基于网络的图形用户界面来探索这些变体。PeCaX 最突出的特点是将临床变体注释和基因药物网络与交互式可视化相结合。这减少了用户在获得治疗建议方面所需投入的时间和精力，并有助于产生新的假设。PeCaX 作为一个独立于平台的容器化软件包提供，可用于本地或机构范围内的部署。PeCaX 可在 https://github.com/KohlbacherLab/PeCaX-docker 下载。
