Identifying Robust Subclonal Structures through Tumor Progression Tree Alignment

通过肿瘤进展树比对识别稳健的亚克隆结构

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Abstract

Understanding and comparing tumor evolutionary histories is fundamental to cancer genomics. Clonal trees, used to model tumor progression, are rooted, unordered trees in which each node represents a subclone labeled by a set of distinct mutations. To compare two clonal trees, we introduce omlta , the o ptimal m ulti-label tree a lignment, which removes the minimum number of mutation labels from the trees, so that the remaining trees are isomorphic. Computing omlta is NP-hard. Here, we present an algorithm to compute the omlta , with a running time of where L ≥ 1 is the total number of mutation labels occurring in the input trees and k is the minimum possible number of mutation labels that need to be removed for the alignment. Our implementation ( https://github.com/algo-cancer/omlta ) is the first computational tool for determining the optimal alignment between clonal trees. We applied omlta to 126 cases from the TRACERx study on non-small cell lung cancers and some melanoma single-cell data.

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