Genome-wide analysis of somatic noncoding mutation patterns in cancer.
Genome-wide analysis of somatic noncoding mutation patterns in cancer.
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癌症体细胞非编码突变模式的全基因组分析。
DOI：
10.1126/science.abg5601
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发表时间：
2022-04-08
期刊：
影响因子：
56.9
通讯作者：
Van Allen, Eliezer M.
中科院分区：
文献类型：
--
作者：
Dietlein, Felix;Wang, Alex B.;Fagre, Christian;Tang, Anran;Besselink, Nicolle J. M.;Cuppen, Edwin;Li, Chunliang;Sunyaev, Shamil R.;Neal, James T.;Van Allen, Eliezer M.
我们建立了代表19种肿瘤类型的3949个完整的癌症基因组中的体细胞突变事件的全基因组概要。蛋白质编码事件捕捉到了公认的驱动因素。组织特异性基因附近的非编码事件，如肝脏中的ALB或前列腺中的KLK3，表征了局部的乘客突变模式，并可能反映了肿瘤细胞的印记。调控启动子和增强子区域的非编码事件经常涉及BCL6、FGFR2、RAD51B、SMC6、TERT和XBP1等癌症相关基因，代表着可能的驱动因素。与大多数非编码调控事件不同，XBP1突变主要积累在基因启动子之外，我们使用CRISPR干扰筛选和荧光素酶报告分析验证了它们对基因表达的影响。总的来说，我们的研究为捕获整个基因组的突变事件提供了一张蓝图，以指导生物发现、治疗和诊断方面的进展。肿瘤发展的一个中心特征是癌细胞在其基因组中获得正常组织中不存在的体细胞突变。一些突变是驱动因素，有助于肿瘤细胞的生长，但其他许多突变是乘客，对肿瘤生物学没有明显影响。在过去的十年里，通过分析数以千计的肿瘤-正常对的测序数据，驱动突变在蛋白质编码的基因组区域得到了全面的表征。蛋白质编码区的这种特征已经对肿瘤生物学产生了丰富的见解，包括许多受基因组启发的药物靶点。然而，体细胞突变在其他98%的癌症基因组--非编码基因组--中的作用仍然不完全清楚。许多统计学方法通过比较对每个基因中的蛋白质编码序列有影响和没有影响的突变的数量，来检测驱动因素是反复发生的突变事件。因此，这些方法在蛋白质编码区以外是不适用的，因为在蛋白质编码区，体细胞突变的作用仍然不太清楚。非编码基因组包含一系列不同的元素，包括基因表达的调节区，这些区域在不同的肿瘤类型中其位置和活动不同。为了将我们对突变的理解扩展到蛋白质编码区之外，我们设计并实现了一种全基因组的滑动窗口方法，该方法检测突变事件，而不考虑它们在调控元件中的位置或对蛋白质编码序列的影响。我们开发了一种三种方法的组合来检测整个基因组中的反复突变事件，这些患者包括19种癌症类型和6120万个体细胞突变。这种方法根据突变事件在基因组中的位置，自动将突变事件分成不同的类别。在蛋白质编码区，我们确定了每种癌症类型平均7.5个事件，并恢复了公认的驱动突变。在非编码基因组中，每种癌症类型的3.7个事件发生在特定组织类型(肝脏中的ALB、前列腺中的KLK3、肺中的SFTPB、肾脏中的SLC5A12、甲状腺组织中的TG等)中唯一表达的基因附近。这些组织特异性事件不太可能是典型的驱动因素，因为它们源于一种突变过程，该过程只在这些基因周围活跃，而不是反映起源的肿瘤细胞表达程序的可能印记。此外，我们在表达的调节区中发现了每种癌症类型的3.8个非编码事件，其中许多涉及与癌症相关的基因(BCL6、FGFR2、RAD51B、SMC6、TERT、XBP1等)。与大多数调控区域的事件相反，XBP1附近的乳腺癌突变主要积累在其启动子以外的调控区域。我们通过进行CRISPR干扰筛选和荧光素酶报告分析验证了它们对基因表达的调控作用，揭示了全基因组方法与协调测序队列相结合的潜力，以全面捕获非编码基因组的已知和未知元件中的突变模式。我们的研究建立了一个全基因组范围的突变模式纲要，这些模式塑造了19种主要癌症类型的基因组，包括在肿瘤生物学中已知作用的基因附近的事件，以及一些显示出实验验证的对基因表达的影响。我们的结果表明，非编码突变与广泛的不同生物过程有关，它们在基因组中的位置对于它们的准确解释是必不可少的。总的来说，我们的研究为解释全基因组测序数据提供了一张蓝图，并为未来将非编码突变与肿瘤发展联系起来的实验努力奠定了基础，最终为针对非编码癌症基因组量身定做的治疗铺平了道路。人类癌症体细胞突变模式的全基因组纲要。我们分析了19种癌症类型的3949名患者的6120万个突变(TOP)。使用滑动窗口方法，我们检测了整个癌症基因组中的突变事件，并根据它们的基因组位置对它们进行了分类(中间)。对于系统的跟踪，我们使用了计算和实验两种策略(下图)。全基因组的泛癌分析；哈特威格医学基金会。
We established a genome-wide compendium of somatic mutation events in 3949 whole cancer genomes representing 19 tumor types. Protein-coding events captured well-established drivers. Noncoding events near tissue-specific genes, such as ALB in the liver or KLK3 in the prostate, characterized localized passenger mutation patterns and may reflect tumor-cell-of-origin imprinting. Noncoding events in regulatory promoter and enhancer regions frequently involved cancer-relevant genes such as BCL6, FGFR2, RAD51B, SMC6, TERT, and XBP1 and represent possible drivers. Unlike most noncoding regulatory events, XBP1 mutations primarily accumulated outside the gene’s promoter, and we validated their effect on gene expression using CRISPR-interference screening and luciferase reporter assays. Broadly, our study provides a blueprint for capturing mutation events across the entire genome to guide advances in biological discovery, therapies, and diagnostics. A central hallmark of tumor development is that cancer cells acquire somatic mutations in their genomes that are not present in normal tissue. Some mutations are drivers and contribute to the growth of tumor cells, but many others are passengers without apparent effects on tumor biology. Over the past decade, driver mutations have been comprehensively characterized in protein-coding genomic regions by analyzing sequencing data from thousands of tumor-normal pairs. This characterization in protein-coding regions has yielded a wealth of insights into tumor biology, including many genome-inspired drug targets. However, the role of somatic mutations in the other 98% of the cancer genome—the noncoding genome—remains incompletely understood. Many statistical approaches detect drivers as recurrent mutation events by comparing the number of mutations with and without effects on protein-coding sequences in each gene. These approaches are therefore inapplicable outside of protein-coding regions, where the roles of somatic mutations remain less well understood. The noncoding genome encompasses a diverse spectrum of elements, including regulatory regions of gene expression that differ in their locations and activities between tumor types. To expand our understanding of mutations beyond protein-coding regions, we designed and implemented a genome-wide, sliding-window approach that detects mutation events irrespective of their locations in regulatory elements or effects on protein-coding sequences. We developed a composite of three methods to detect recurrent mutation events across the whole genomes of 3949 patients with 19 cancer types and 61.2 million somatic mutations. This approach automatically stratified mutation events into different categories on the basis of their position in the genome. In protein-coding regions, we identified an average of 7.5 events per cancer type and recovered well-established driver mutations. In the noncoding genome, 3.7 events per cancer type occurred adjacent to genes exclusively expressed in specific tissue types (ALB in liver, KLK3 in prostate, SFTPB in lung, SLC5A12 in kidney, TG in thyroid tissue, and many others). These tissue-specific events were unlikely to be prototypical drivers because they stemmed from a mutagenic process that was exclusively active around these genes, instead reflecting possible imprints of the expression programs of the tumor cells of origin. Moreover, we found 3.8 noncoding events per cancer type in regulatory regions of expression, many involving cancer-relevant genes (BCL6, FGFR2, RAD51B, SMC6, TERT, XBP1, and many others). In contrast to most events in regulatory regions, breast cancer mutations near XBP1 mainly accumulated in a regulatory region outside of its promoter. We validated their regulatory effects on gene expression by performing CRISPR-interference screening and luciferase reporter assays, illuminating the potential of genome-wide approaches paired with harmonized sequencing cohorts to comprehensively capture mutation patterns in both known and unknown elements of the noncoding genome. Our study establishes a genome-wide compendium of the diverse mutation patterns that shape the genomes of 19 major cancer types, including events near genes with known roles in tumor biology and some exhibiting experimentally validated effects on gene expression. Our results demonstrate that noncoding mutations are associated with a broad spectrum of different biological processes and that their location in the genome is essential for their accurate interpretation. Broadly, our study provides a blueprint for interpreting whole-genome sequencing data and lays the foundation for future experimental endeavors to implicate noncoding mutations in tumor development, ultimately paving the way for therapies tailored to the noncoding cancer genome. Genome-wide compendium of somatic mutation patterns in human cancer. We analyzed 61.2 million mutations from 3949 patients of 19 cancer types (top). Using a sliding-window approach, we detected mutation events across the entire cancer genome and classified them by their genomic locations (middle). For systematic follow-up, we used both computational and experimental strategies (bottom). PCAWG, Pan-Cancer Analysis of Whole Genomes; HMF, Hartwig Medical Foundation.
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DOI：
10.1016/j.tig.2015.11.001
发表时间：
2016-01
期刊：
Trends in genetics : TIG
影响因子：
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DOI：
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发表时间：
2018-07-15
期刊：
Bioinformatics (Oxford, England)
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