A Multi-Labeled Tree Dissimilarity Measure for Comparing “Clonal Trees” of Tumor Progression

Resource type
Date created
2019-07-27
Authors/Contributors
Abstract
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.
Document
Published as
Karpov N, Malikic S, Rahman MK, Sahinalp SC. A multi-labeled tree dissimilarity measure for comparing "clonal trees" of tumor progression. Algorithms Mol Biol. 2019;14:17. Published 2019 Jul 27. doi:10.1186/s13015-019-0152-9. DOI: 10.1186/s13015-019-0152-9.
Publication title
Algorithms Mol Biol
Document title
A Multi-Labeled Tree Dissimilarity Measure for Comparing “Clonal Trees” of Tumor Progression
Date
2019
Publisher DOI
10.1186/s13015-019-0152-9
Copyright statement
Copyright is held by the author(s).
Scholarly level
Peer reviewed?
Yes
Language
English
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s13015-019-0152-9.pdf 3.23 MB

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