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Exploration of Coding and Non-coding Variants in Cancer Using GenomePaint
Cancer Cell ( IF 50.3 ) Pub Date : 2021-01-11 , DOI: 10.1016/j.ccell.2020.12.011
Xin Zhou 1 , Jian Wang 1 , Jaimin Patel 1 , Marc Valentine 2 , Ying Shao 1 , Scott Newman 1 , Edgar Sioson 1 , Liqing Tian 1 , Yu Liu 1 , Samuel W Brady 1 , Diane Flasch 1 , Xiaotu Ma 1 , Yanling Liu 1 , Robin Paul 1 , Michael N Edmonson 1 , Michael C Rusch 1 , Chunliang Li 3 , Suzanne J Baker 4 , John Easton 1 , Jinghui Zhang 1
Affiliation  

GenomePaint (https://genomepaint.stjude.cloud/) is an interactive visualization platform for whole-genome, whole-exome, transcriptome, and epigenomic data of tumor samples. Its design captures the inter-relatedness between DNA variations and RNA expression, supporting in-depth exploration of both individual cancer genomes and full cohorts. Regulatory non-coding variants can be inspected and analyzed along with coding variants, and their functional impact further explored by examining 3D genome data from cancer cell lines. Further, GenomePaint correlates mutation and expression patterns with patient outcomes, and supports custom data upload. We used GenomePaint to unveil aberrant splicing that disrupts the RING domain of CREBBP, discover cis activation of the MYC oncogene by duplication of the NOTCH1-MYC enhancer in B-lineage acute lymphoblastic leukemia, and explore the inter- and intra-tumor heterogeneity at EGFR in adult glioblastomas. These examples demonstrate that deep multi-omics exploration of individual cancer genomes enabled by GenomePaint can lead to biological insights for follow-up validation.



中文翻译:

使用 GenomePaint 探索癌症中的编码和非编码变体

GenomePaint (https://genomepaint.stjude.cloud/) 是一个交互式可视化平台,用于肿瘤样本的全基因组、全外显子组、转录组和表观基因组数据。它的设计捕捉了 DNA 变异和 RNA 表达之间的相互关联,支持对个体癌症基因组和完整队列的深入探索。可以与编码变体一起检查和分析调节性非编码变体,并通过检查来自癌细胞系的 3D 基因组数据进一步探索其功能影响。此外,GenomePaint 将突变和表达模式与患者结果相关联,并支持自定义数据上传。我们使用 GenomePaint 揭示了破坏CREBBP环域的异常剪接,发现了MYC 的顺式激活通过在 B 系急性淋巴细胞白血病中复制 NOTCH1-MYC 增强子的致癌基因,并探索成人胶质母细胞瘤中EGFR的肿瘤间和肿瘤内异质性。这些例子表明,通过 GenomePaint 对单个癌症基因组进行深入的多组学探索可以为后续验证带来生物学见解。

更新日期:2021-01-11
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