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OncoVar:癌症中致癌驱动变异的综合数据库和分析平台。

OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers.

机构信息

Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410083, China.

Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China.

出版信息

Nucleic Acids Res. 2021 Jan 8;49(D1):D1289-D1301. doi: 10.1093/nar/gkaa1033.

Abstract

The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar (https://oncovar.org/), which employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and driver genes. We identified 20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC). OncoVar provides four points of view, 'Mutation', 'Gene', 'Pathway' and 'Cancer', to help researchers to visualize the relationships between cancers and driver variants. Importantly, identification of actionable driver alterations provides promising druggable targets and repurposing opportunities of combinational therapies. OncoVar provides a user-friendly interface for browsing, searching and downloading somatic driver mutations, driver genes and pathogenic pathways in various cancer types. This platform will facilitate the identification of cancer drivers across individual cancer cohorts and helps to rank mutations or genes for better decision-making among clinical oncologists, cancer researchers and the broad scientific community interested in cancer precision medicine.

摘要

癌症细胞群体中中性突变的普遍性阻碍了致癌驱动突变与乘客突变的区分。为了系统地优先考虑体细胞突变和癌症基因的致癌能力,我们构建了一个有用的平台 OncoVar(https://oncovar.org/),该平台采用了已发表的生物信息学算法,并结合了已知的驱动事件来识别驱动突变和驱动基因。我们通过重新分析来自癌症基因组图谱(TCGA)中 33 种癌症类型的 10769 个外显子和国际癌症基因组联盟(ICGC)中 18 种癌症类型的 1942 个基因组,以高可信度鉴定了 20162 个癌症驱动突变、814 个驱动基因和 2360 个致病途径。OncoVar 提供了四个视角,即“突变”、“基因”、“途径”和“癌症”,以帮助研究人员可视化癌症与驱动变异之间的关系。重要的是,可操作的驱动突变的鉴定为联合治疗提供了有前途的药物靶点和重新利用机会。OncoVar 为浏览、搜索和下载各种癌症类型的体细胞驱动突变、驱动基因和致病途径提供了用户友好的界面。该平台将有助于在个体癌症队列中识别癌症驱动因素,并帮助临床肿瘤学家、癌症研究人员和对癌症精准医学感兴趣的广大科学界更好地对突变或基因进行排名,从而做出更好的决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4916/7778899/28cc2a62ea4b/gkaa1033fig1.jpg

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