小柯机器人

美国博德研究所Gad Getz、Rameen Beroukhim、丹麦奥胡斯大学医院Jakob Skou Pedersen、英国桑格研究所Iñigo Martincorena和丹麦哥本哈根大学Joachim Weischenfeldt合作，分析了2,658个癌症全基因组中非编码体细胞驱动因子。这一研究成果在线发表在2020年2月5日的国际学术期刊《自然》上。

研究人员利用国际癌症基因组联合会（ICGC）和癌症基因组图谱（TCGA）的全基因组泛癌基因分析（PCAWG）联合会的数据，对2658个基因组的非编码区中点突变和结构变异进行了分析。对于点突变，研究人员开发了一种严谨的统计策略，利用组合分析方法探究驱动基因的显著水平，从而克服了单个计算方法的局限性。对于结构变体，研究人员利用两种驱动探索方法，并确定了受反复断点和体细胞反复并置显著影响的区域。该分析证实了先前报道的驱动基因，并对其他基因提出了质疑，还鉴定了新的候选物，如TP53 5'区域内的点突变、NFKBIZ和TOB1 3'非翻译区域的突变、BRD4的局灶性缺失和位点AKR1C基因cDNA的重排。该研究显示，虽然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比在蛋白质编码基因中的频率低，但是随着更多癌症基因组数据的出现，将会丰富这些驱动因素。

研究人员表示，一般人们将驱动癌症发生的因素集中在蛋白质编码基因上。

附：英文原文

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

Author: Esther Rheinbay, Morten Muhlig Nielsen, Federico Abascal, Jeremiah A. Wala, Ofer Shapira, Grace Tiao, Henrik Hornshj, Julian M. Hess, Randi Istrup Juul, Ziao Lin, Lars Feuerbach, Radhakrishnan Sabarinathan, Tobias Madsen, Jaegil Kim, Loris Mularoni, Shimin Shuai, Andrs Lanzs, Carl Herrmann, Yosef E. Maruvka, Ciyue Shen, Samirkumar B. Amin, Pratiti Bandopadhayay, Johanna Bertl, Keith A. Boroevich, John Busanovich, Joana Carlevaro-Fita, Dimple Chakravarty, Calvin Wing Yiu Chan, David Craft, Priyanka Dhingra, Klev Diamanti, Nuno A. Fonseca, Abel Gonzalez-Perez, Qianyun Guo, Mark P. Hamilton, Nicholas J. Haradhvala, Chen Hong, Keren Isaev, Todd A. Johnson, Malene Juul, Andre Kahles, Abdullah Kahraman, Youngwook Kim, Jan Komorowski, Kiran Kumar, Sushant Kumar, Donghoon Lee, Kjong-Van Lehmann, Yilong Li, Eric Minwei Liu, Lucas Lochovsky, Keunchil Park, Oriol Pich, Nicola D. Roberts, Gordon Saksena, Steven E. Schumacher, Nikos Sidiropoulos, Lina Sieverling, Nasa Sinnott-Armstrong

Issue&Volume: 2020-02-05

Abstract: The discovery of drivers of cancer has traditionally focused on protein-coding genes1,2,3,4. 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) Consortium5 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 drivers6,7, 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.

DOI: 10.1038/s41586-020-1965-x

Source: https://www.nature.com/articles/s41586-020-1965-x

Nature：《自然》，创刊于1869年。隶属于施普林格·自然出版集团，最新IF：69.504
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