Mutational signatures are patterns of mutations that arise during tumorigenesis. We present an enhanced, practical framework for mutational signature analyses. Applying these methods on 3,107 whole genome sequenced (WGS) primary cancers of 21 organs reveals known signatures and nine previously undescribed rearrangement signatures. We highlight inter-organ variability of signatures and present a way of visualizing that diversity, reinforcing our findings in an independent analysis of 3,096 WGS metastatic cancers. Signatures with a high level of genomic instability are dependent on TP53 dysregulation. We illustrate how uncertainty in mutational signature identification and assignment to samples affects tumor classification, reinforcing that using multiple orthogonal mutational signature data is not only beneficial, it is essential for accurate tumor stratification. Finally, we present a reference web-based tool for cancer and experimentally-generated mutational signatures, called Signal (https://signal.mutationalsignatures.com), that also supports performing mutational signature analyses.

中文翻译：

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用于突变特征分析的实用框架和在线工具显示组织间变异和驱动依赖性。

突变特征是肿瘤发生过程中出现的突变模式。我们提出了一个增强的、实用的突变特征分析框架。将这些方法应用于 21 个器官的 3,107 个全基因组测序 (WGS) 原发性癌症，揭示了已知的特征和 9 个先前未描述的重排特征。我们强调了特征的器官间变异性，并提出了一种可视化这种多样性的方法，强化了我们在对 3,096 个 WGS 转移性癌症的独立分析中的发现。具有高度基因组不稳定性的特征取决于 TP53 失调。我们说明了突变特征识别和样本分配的不确定性如何影响肿瘤分类，强调使用多个正交突变特征数据不仅是有益的，而且对于准确的肿瘤分层至关重要。最后，我们提出了一个基于网络的参考工具，用于癌症和实验生成的突变特征，称为 Signal (https://signal.mutationalsignatures.com)，它也支持执行突变特征分析。