Suppr超能文献

分析 2658 个癌症全基因组中的非编码体细胞驱动因子。

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

机构信息

The Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA.

出版信息

Nature. 2020 Feb;578(7793):102-111. doi: 10.1038/s41586-020-1965-x. Epub 2020 Feb 5.

Abstract

The discovery of drivers of cancer has traditionally focused on protein-coding genes. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.

摘要

癌症驱动因子的传统发现方法主要集中在蛋白编码基因上。在这里,我们分析了国际癌症基因组联盟(ICGC)和癌症基因组图谱(TCGA)的泛癌症全基因组分析(PCAWG)联盟 2658 个基因组中的非编码区域的驱动点突变和结构变异。对于点突变,我们开发了一种从多种驱动发现方法中结合显著性水平的统计上严格的策略,克服了单个方法的局限性。对于结构变异,我们提出了两种驱动发现方法,并确定了受高频断点和体细胞易位影响的区域。我们的分析证实了先前报道的驱动因子,对其他驱动因子提出了质疑,并确定了新的候选因子,包括 TP53 5'区域的点突变、NFKBIZ 和 TOB1 的 3'非翻译区的点突变、BRD4 中的焦点缺失以及 AKR1C 基因座的重排。我们表明,尽管驱动癌症的点突变和结构变异在非编码基因和调控序列中的频率低于蛋白编码基因,但随着更多癌症基因组的出现,将发现更多的这些驱动因子的例子。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e819/7054214/363370c21df0/41586_2020_1965_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验