Schwartz Russell, Schäffer Alejandro A
Department of Biological Sciences and Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, USA.
Computational Biology Branch, National Center for Biotechnology Information, National Institutes of Health, Bethesda, Maryland 20892, USA.
Nat Rev Genet. 2017 Apr;18(4):213-229. doi: 10.1038/nrg.2016.170. Epub 2017 Feb 13.
Rapid advances in high-throughput sequencing and a growing realization of the importance of evolutionary theory to cancer genomics have led to a proliferation of phylogenetic studies of tumour progression. These studies have yielded not only new insights but also a plethora of experimental approaches, sometimes reaching conflicting or poorly supported conclusions. Here, we consider this body of work in light of the key computational principles underpinning phylogenetic inference, with the goal of providing practical guidance on the design and analysis of scientifically rigorous tumour phylogeny studies. We survey the range of methods and tools available to the researcher, their key applications, and the various unsolved problems, closing with a perspective on the prospects and broader implications of this field.
高通量测序技术的飞速发展以及人们对进化理论在癌症基因组学中重要性的日益认识,导致肿瘤进展系统发育研究大量涌现。这些研究不仅带来了新的见解,还产生了大量实验方法,有时得出相互矛盾或证据不足的结论。在此,我们根据系统发育推断的关键计算原理来审视这一系列工作,旨在为科学严谨的肿瘤系统发育研究的设计与分析提供实用指导。我们概述了研究人员可用的方法和工具范围、它们的关键应用以及各种未解决的问题,最后展望了该领域的前景和更广泛的影响。