The mutational heterogeneity observed within tumours poses additional challenges to the development of effective cancer treatments. A thorough understanding of a tumour's subclonal composition and its mutational history is essential to open up the design of treatments tailored to individual patients. Comparative studies on a large number of tumours permit the identification of mutational patterns which may refine forecasts of cancer progression, response to treatment and metastatic potential. The composition of tumours is shaped by evolutionary processes. Recent advances in next-generation sequencing offer the possibility to analyse the evolutionary history and accompanying heterogeneity of tumours at an unprecedented resolution, by sequencing single cells. New computational challenges arise when moving from bulk to single-cell sequencing data, leading to the development of novel modelling frameworks. In this review, we present the state of the art methods for understanding the phylogeny encoded in bulk or single-cell sequencing data, and highlight future directions for developing more comprehensive and informative pictures of tumour evolution. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

中文翻译：

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通过单细胞测序了解肿瘤进化的进展。

肿瘤内观察到的突变异质性对有效癌症治疗的开发提出了额外的挑战。彻底了解肿瘤的亚克隆组成及其突变历史对于开辟针对个体患者的治疗设计至关重要。对大量肿瘤的比较研究可以识别突变模式，从而可以完善对癌症进展、治疗反应和转移潜力的预测。肿瘤的组成是由进化过程决定的。新一代测序的最新进展提供了通过对单细胞进行测序以前所未有的分辨率分析肿瘤的进化历史和伴随的异质性的可能性。当从批量数据转移到单细胞测序数据时，会出现新的计算挑战，从而导致新型建模框架的开发。在这篇综述中，我们提出了用于理解大量或单细胞测序数据编码的系统发育的最先进方法，并强调了开发更全面和信息丰富的肿瘤进化图片的未来方向。本文是罗伯特·A·盖滕比 (Robert A. Gatenby) 博士编辑的特刊的一部分，题为：进化原理 - 癌症的异质性？