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在个性化癌症基因调控网络中识别产生新生边的顺式调控突变。

Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks.

作者信息

Kalender Atak Zeynep, Imrichova Hana, Svetlichnyy Dmitry, Hulselmans Gert, Christiaens Valerie, Reumers Joke, Ceulemans Hugo, Aerts Stein

机构信息

Laboratory of Computational Biology, VIB Center for Brain & Disease Research, Leuven, Belgium.

Department of Human Genetics, KU Leuven, Leuven, Belgium.

出版信息

Genome Med. 2017 Aug 30;9(1):80. doi: 10.1186/s13073-017-0464-7.

Abstract

The identification of functional non-coding mutations is a key challenge in the field of genomics. Here we introduce μ-cisTarget to filter, annotate and prioritize cis-regulatory mutations based on their putative effect on the underlying "personal" gene regulatory network. We validated μ-cisTarget by re-analyzing the TAL1 and LMO1 enhancer mutations in T-ALL, and the TERT promoter mutation in melanoma. Next, we re-sequenced the full genomes of ten cancer cell lines and used matched transcriptome data and motif discovery to identify master regulators with de novo binding sites that result in the up-regulation of nearby oncogenic drivers. μ-cisTarget is available from http://mucistarget.aertslab.org .

摘要

功能性非编码突变的识别是基因组学领域的一项关键挑战。在此,我们引入μ-cisTarget,以根据其对潜在“个人”基因调控网络的假定影响来筛选、注释顺式调控突变并对其进行优先级排序。我们通过重新分析T-ALL中的TAL1和LMO1增强子突变以及黑色素瘤中的TERT启动子突变来验证μ-cisTarget。接下来,我们对十个癌细胞系的全基因组进行了重测序,并使用匹配的转录组数据和基序发现来识别具有从头结合位点的主调控因子,这些位点导致附近致癌驱动因子的上调。可从http://mucistarget.aertslab.org获取μ-cisTarget。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f84/5575942/21d9bff5fa2f/13073_2017_464_Fig1_HTML.jpg

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