文章：

人类癌症基因组的超突变：足迹和机制

Hypermutation in human cancer genomes: footprints and mechanisms

原文发布日期：2014-11-24

DOI: 10.1038/nrc3816

类型: Review Article

开放获取: 否

要点：

1. Increased spontaneous or environmentally enhanced mutagenesis often correlates with increased mutation load and cancer risk. Mutation loads of individual cancer genomes can differ by several orders of magnitude and the top mutation loads are defined as having a hypermutation phenotype.
2. Recent accumulation of cancer genomics information caused a breakthrough in understanding the origin and mechanisms of hypermutation. Mutation rates and distribution across cancer genomes are influenced by features of genome structure and function, such as replication timing, transcription and chromatin structure. Mutation rates also increase in the vicinity of rearrangement breakpoints
3. Mutation rates can vary depending on local DNA sequence and the kind of genetic change. These parameters are defined as mutation signatures. Statistical analysis of somatic mutation signatures in cancer genomes has deciphered new sources of hypermutation in cancer and has confirmed the role of classic carcinogenic mutagens in cancer hypermutation.
4. Mutation signatures can be identified by large-scale statistical analysis (termed non-negative matrix factorization (NMF)) of complex genome-wide mutation catalogues, as well as through selecting a fraction of mutations enriched with a single mutagenic mechanism. The latter can be achieved by concentrating on groups of closely spaced mutations: that is, mutation clusters.
5. Combining NMF methods with analyses that concentrate on clustered mutations helped to identify a new kind of strong and ubiquitous carcinogenic mutagen that acts endogenously — a subclass of apolipoprotein B mRNA editing enzyme catalytic polypeptide-like (APOBEC) cytidine deaminases. The use of these complementary techniques greatly enhanced the statistical power to analyse APOBEC-mediated mutagenesis in cancer.
6. Merging statistical pattern analysis with mechanistic information is feasible for other sources of mutations for which vast mechanistic knowledge has been accumulated over past decades. This can lead to the identification of new environmental, occupational and endogenous sources of hypermutation, as well as to an understanding of their specific affects in different cancer types, and even in individual cancer samples.

要点翻译：

1. 自发性或环境增强的诱变作用增加通常与突变负荷和癌症风险升高相关。个体癌症基因组的突变负荷可能相差数个数量级，其中最高突变负荷被定义为超突变表型。
2. 近期癌症基因组学信息的积累促使人们对超突变的起源和机制有了突破性认识。突变率及其在癌症基因组中的分布受到基因组结构和功能特征的影响，例如复制时序、转录及染色质结构。重排断点附近区域的突变率也会上升。
3. 突变率可能因局部DNA序列和遗传改变类型而异。这些参数被定义为突变特征。对癌症基因组中体细胞突变特征的统计分析破译了癌症中超突变的新来源，并证实了经典致癌诱变剂在癌症超突变中的作用。
4. 突变特征可通过全基因组复杂突变目录的大规模统计分析（称为非负矩阵分解）进行识别，也可通过筛选富集单一诱变机制的突变子集来实现。后者可通过聚焦于紧密相邻的突变群（即突变簇）来完成。
5. 将非负矩阵分解方法与聚焦于成簇突变的分析相结合，有助于识别一类新型强效且普遍存在的内源性致癌诱变物——载脂蛋白B mRNA编辑酶催化多肽样（APOBEC）胞苷脱氨酶亚类。这些互补技术的运用显著增强了分析APOBEC介导的癌症诱变的统计效力。
6. 将统计模式分析与机制信息相融合的方法，也适用于过去数十年已积累大量机制知识的其他突变来源。这有望识别新的环境性、职业性和内源性超突变来源，并理解它们在不同癌症类型乃至个体癌症样本中的特异性影响。

英文摘要：

A role for somatic mutations in carcinogenesis is well accepted, but the degree to which mutation rates influence cancer initiation and development is under continuous debate. Recently accumulated genomic data have revealed that thousands of tumour samples are riddled by hypermutation, broadening support for the idea that many cancers acquire a mutator phenotype. This major expansion of cancer mutation data sets has provided unprecedented statistical power for the analysis of mutation spectra, which has confirmed several classical sources of mutation in cancer, highlighted new prominent mutation sources (such as apolipoprotein B mRNA editing enzyme catalytic polypeptide-like (APOBEC) enzymes) and empowered the search for cancer drivers. The confluence of cancer mutation genomics and mechanistic insight provides great promise for understanding the basic development of cancer through mutations.

摘要翻译： 

体细胞突变在致癌过程中的作用已得到广泛认可，但突变率对癌症起始和发展的影响程度仍在持续争论中。近期积累的基因组数据揭示，成千上万的肿瘤样本中存在大量突变（hypermutation），进一步支持了许多癌症获得“突变体表型”的观点。这一癌症突变数据集的大幅扩展，为突变谱分析提供了前所未有的统计能力，确认了癌症中若干经典的突变来源，突出了新的重要突变来源（如载脂蛋白B mRNA编辑酶催化多肽样（APOBEC）酶），并推动了对癌症驱动因素的寻找。癌症突变基因组学与机制性见解的融合，为通过突变理解癌症的基本发生发展提供了巨大前景。

原文链接：

Hypermutation in human cancer genomes: footprints and mechanisms