描述肿瘤演化模式的系统发育树模空间
Moduli Spaces of Phylogenetic Trees Describing Tumor Evolutionary Patterns
摘要：
癌症遵循克隆达尔文进化论，更健康的亚克隆取代更多的静止细胞，最终导致肉眼可见的疾病。高通量基因组学提供了研究这些过程并确定驱动疾病进展的特定遗传改变的机会。患者癌症的基因组取样提供了分子史，由系统发育树表示。患者队列代表了相关系统发育结构的森林。为了提取临床相关信息，必须表示和统计比较这些树的集合。我们提出了一个框架的基础上的应用程序的工作Billera，霍姆斯和Vogtmann的系统发育树空间的情况下，无根树内的个人癌症组织样本。我们观察到，这些树空间是全局非正弯曲的，允许对患者病史的人群进行统计推断。一个投影树空间的引入，允许可视化的聚合进化行为。在我们的框架内探讨了三种类型的人类恶性肿瘤的已发表数据。
英文摘要：
Cancers follow a clonal Darwinian evolution, with fitter subclones replacing more quiescent cells, ultimately giving rise to macroscopic disease. High-throughput genomics provides the opportunity to investigate these processes and determine specific genetic alterations driving disease progression. Genomic sampling of a patient's cancer provides a molecular history, represented by a phylogenetic tree. Cohorts of patients represent a forest of related phylogenetic structures. To extract clinically relevant information, one must represent and statistically compare these collections of trees. We propose a framework based on an application of the work by Billera, Holmes and Vogtmann on phylogenetic tree spaces to the case of unrooted trees of intra-individual cancer tissue samples. We observe that these tree spaces are globally nonpositively curved, allowing for statistical inference on populations of patient histories. A projective tree space is introduced, permitting visualizations of aggregate evolutionary behavior. Published data from three types of human malignancies are explored within our framework.
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