Despite decades of research and an enormity of resultant data, cancer remains a significant public health problem. New tools and fresh perspectives are needed to obtain fundamental insights, to develop better prognostic and predictive tools, and to identify improved therapeutic interventions. With increasingly common genome-scale data, one suite of algorithms and concepts with potential to shed light on cancer biology is phylogenetics, a scientific discipline used in diverse fields. From grouping subsets of cancer samples to tracing subclonal evolution during cancer progression and metastasis, the use of phylogenetics is a powerful systems biology approach. Well-developed phylogenetic applications provide fast, robust approaches to analyze high-dimensional, heterogeneous cancer data sets. 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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PhyloOncology：通过系统发育分析了解癌症。

尽管进行了数十年的研究和大量的结果数据，癌症仍然是一个重大的公共卫生问题。需要新的工具和新的观点来获得基本见解，开发更好的预后和预测工具，并确定改进的治疗干预措施。随着越来越普遍的基因组规模数据，一套有可能阐明癌症生物学的算法和概念是系统发育学，这是一门用于不同领域的科学学科。从对癌症样本的子集进行分组到在癌症进展和转移过程中追踪亚克隆进化，系统发育学的使用是一种强大的系统生物学方法。成熟的系统发育应用程序提供了快速、稳健的方法来分析高维、异质的癌症数据集。本文是特刊的一部分，标题为：