We introduce a new dissimilarity measure between a pair of "clonal trees", each representing the progression and mutational heterogeneity of a tumor sample, constructed by the use of single cell or bulk high throughput sequencing data. In a clonal tree, each vertex represents a specific tumor clone, and is labeled with one or more mutations in a way that each mutation is assigned to the oldest clone that harbors it. Given two clonal trees, our multi-labeled tree dissimilarity (MLTD) measure is defined as the minimum number of mutation/label deletions, (empty) leaf deletions, and vertex (clonal) expansions, applied in any order, to convert each of the two trees to the maximum common tree. We show that the MLTD measure can be computed efficiently in polynomial time and it captures the similarity between trees of different clonal granularity well.

中文翻译：

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用于比较肿瘤进展的“克隆树”的多标记树相异性测量。

我们在一对“克隆树”之间引入了一种新的差异性度量，每个“克隆树”代表肿瘤样本的进展和突变异质性，通过使用单细胞或批量高通量测序数据构建。在克隆树中，每个顶点代表一个特定的肿瘤克隆，并用一个或多个突变标记，每个突变都分配给包含它的最旧的克隆。给定两棵克隆树，我们的多标记树相异性（MLTD）度量被定义为突变/标签删除、（空）叶子删除和顶点（克隆）扩展的最小数量，以任何顺序应用，以转换每个两棵树的最大公共树。我们证明了 MLTD 度量可以在多项式时间内有效地计算，并且它很好地捕获了不同克隆粒度的树之间的相似性。