phyC: Clustering cancer evolutionary trees.
phyC: Clustering cancer evolutionary trees.
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DOI：
10.1371/journal.pcbi.1005509
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发表时间：
2017-05
影响因子：
4.3
通讯作者：
Shimamura T
中科院分区：
文献类型：
--
作者：
Matsui Y;Niida A;Uchi R;Mimori K;Miyano S;Shimamura T
多区域测序为从进化的角度研究常见肿瘤内部或之间的遗传异质性提供了新的机会。已经提出了几种最先进的方法来基于多区域测序数据重建癌症进化树，以建立癌症进化模型。然而，对一组癌症进化树进行比较的研究很少。我们提出了一种用于癌症进化树的聚类方法(PhyC)，在该方法中，根据拓扑和边长属性来识别树的子组。作为解释，我们还提出了一种评估聚类中树木的亚克隆多样性的方法，这为亚克隆扩展的加速提供了洞察力。仿真结果表明，该方法能够以足够的精度检测出真实的簇。将该方法应用于实际的肾透明细胞癌和非小细胞肺癌的多区域测序数据，允许检测与癌症类型或表型相关的簇。Https://github.com/ymatts/phyC.使用R(≥3.2.2)实现了PhyC阐明患者之间癌症进化模式的差异在个体化医学中具有重要价值，因为治疗反应主要取决于癌症进化过程。最近，计算方法被广泛地研究以重建患者体内的癌症进化模式，该模式被可视化为由多区域测序数据构建的所谓的癌症进化树。然而，很少有研究比较一组癌症进化树，以更好地了解一组癌症进化模式和患者表型之间的关系。在给定多个患者的一组树对象的情况下，我们提出了一种无监督学习方法，通过对各自的癌症进化树进行聚类来识别患者亚组。使用这种方法，我们在模拟分析中有效地识别了不同进化模式的模式，并成功地检测出与表型相关和癌症类型相关的子组，以使用实际数据集来表征子组内的树结构。我们相信，随着越来越多的患者癌症演变数据集的出现，我们工作的价值和影响将会增长。
Multi-regional sequencing provides new opportunities to investigate genetic heterogeneity within or between common tumors from an evolutionary perspective. Several state-of-the-art methods have been proposed for reconstructing cancer evolutionary trees based on multi-regional sequencing data to develop models of cancer evolution. However, there have been few studies on comparisons of a set of cancer evolutionary trees. We propose a clustering method (phyC) for cancer evolutionary trees, in which sub-groups of the trees are identified based on topology and edge length attributes. For interpretation, we also propose a method for evaluating the sub-clonal diversity of trees in the clusters, which provides insight into the acceleration of sub-clonal expansion. Simulation showed that the proposed method can detect true clusters with sufficient accuracy. Application of the method to actual multi-regional sequencing data of clear cell renal carcinoma and non-small cell lung cancer allowed for the detection of clusters related to cancer type or phenotype. phyC is implemented with R(≥3.2.2) and is available from https://github.com/ymatts/phyC. Elucidating the differences between cancer evolutionary patterns among patients is valuable in personalized medicine, since therapeutic response mostly depends on cancer evolution process. Recently, computational methods have been extensively studied to reconstruct a cancer evolutionary pattern within a patient, which is visualized as a so-called “cancer evolutionary tree” constructed from multi-regional sequencing data. However, there have been few studies on comparisons of a set of cancer evolutionary trees to better understand the relationship between a set of cancer evolutionary patterns and patient phenotypes. Given a set of tree objects for multiple patients, we propose an unsupervised learning approach to identify subgroups of patients through clustering the respective cancer evolutionary trees. Using this approach, we effectively identified the patterns of different evolutionary modes in a simulation analysis, and also successfully detected the phenotype-related and cancer type-related subgroups to characterize tree structures within subgroups using actual datasets. We believe that the value and impact of our work will grow as more and more datasets for the cancer evolution of patients become available.