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BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies.

Yuan K, Sakoparnig T, Markowetz F, Beerenwinkel N - Genome Biol. (2015)

Bottom Line: Here, we present BitPhylogenyBitPhylogeny, a probabilistic framework to reconstruct intra-tumor evolutionary pathways.Using a full Bayesian approach, we jointly estimate the number and composition of clones in the sample as well as the most likely tree connecting them.We validate our approach in the controlled setting of a simulation study and compare it against several competing methods.

View Article: PubMed Central - PubMed

ABSTRACT
Cancer has long been understood as a somatic evolutionary process, but many details of tumor progression remain elusive. Here, we present BitPhylogenyBitPhylogeny, a probabilistic framework to reconstruct intra-tumor evolutionary pathways. Using a full Bayesian approach, we jointly estimate the number and composition of clones in the sample as well as the most likely tree connecting them. We validate our approach in the controlled setting of a simulation study and compare it against several competing methods. In two case studies, we demonstrate how BitPhylogeny BitPhylogeny reconstructs tumor phylogenies from methylation patterns in colon cancer and from single-cell exomes in myeloproliferative neoplasm.

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Consensus node-based shortest path distances for all simulated trees. Each box plot is summarized for the distance measures across four noise levels (0%, 1%, 2% and 5%). The suffixes L, M and H for the polyclonal tree type refer to the polyclonal-low, -medium and -high trees in Figure 3. BitPhylogeny consistently outperforms the two baseline methods.
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Fig4: Consensus node-based shortest path distances for all simulated trees. Each box plot is summarized for the distance measures across four noise levels (0%, 1%, 2% and 5%). The suffixes L, M and H for the polyclonal tree type refer to the polyclonal-low, -medium and -high trees in Figure 3. BitPhylogeny consistently outperforms the two baseline methods.

Mentions: To compare the tree topologies explicitly, we developed a distance measure called consensus node-based shortest path distance (see Materials and methods for details). The performance of BitPhylogeny is examined based on the empirical MAP solution (see Materials and methods). The results for all synthetic data sets (five clone compositions and four noise levels) are presented in Figure 4. For all clonal compositions and noise levels, BitPhylogeny constructs trees that are much closer to the true tree than both baseline methods. For the monoclonal tree, all three methods are able to reconstruct the two clones accurately. However, as clonal composition becomes more complex, the performance of the two baseline methods starts to degrade quickly. The baseline methods overestimated the number of clones and produce much deeper trees for most synthetic data sets. As a result, they perform poorly when the complexity of clone composition increases.Figure 4


BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies.

Yuan K, Sakoparnig T, Markowetz F, Beerenwinkel N - Genome Biol. (2015)

Consensus node-based shortest path distances for all simulated trees. Each box plot is summarized for the distance measures across four noise levels (0%, 1%, 2% and 5%). The suffixes L, M and H for the polyclonal tree type refer to the polyclonal-low, -medium and -high trees in Figure 3. BitPhylogeny consistently outperforms the two baseline methods.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4359483&req=5

Fig4: Consensus node-based shortest path distances for all simulated trees. Each box plot is summarized for the distance measures across four noise levels (0%, 1%, 2% and 5%). The suffixes L, M and H for the polyclonal tree type refer to the polyclonal-low, -medium and -high trees in Figure 3. BitPhylogeny consistently outperforms the two baseline methods.
Mentions: To compare the tree topologies explicitly, we developed a distance measure called consensus node-based shortest path distance (see Materials and methods for details). The performance of BitPhylogeny is examined based on the empirical MAP solution (see Materials and methods). The results for all synthetic data sets (five clone compositions and four noise levels) are presented in Figure 4. For all clonal compositions and noise levels, BitPhylogeny constructs trees that are much closer to the true tree than both baseline methods. For the monoclonal tree, all three methods are able to reconstruct the two clones accurately. However, as clonal composition becomes more complex, the performance of the two baseline methods starts to degrade quickly. The baseline methods overestimated the number of clones and produce much deeper trees for most synthetic data sets. As a result, they perform poorly when the complexity of clone composition increases.Figure 4

Bottom Line: Here, we present BitPhylogenyBitPhylogeny, a probabilistic framework to reconstruct intra-tumor evolutionary pathways.Using a full Bayesian approach, we jointly estimate the number and composition of clones in the sample as well as the most likely tree connecting them.We validate our approach in the controlled setting of a simulation study and compare it against several competing methods.

View Article: PubMed Central - PubMed

ABSTRACT
Cancer has long been understood as a somatic evolutionary process, but many details of tumor progression remain elusive. Here, we present BitPhylogenyBitPhylogeny, a probabilistic framework to reconstruct intra-tumor evolutionary pathways. Using a full Bayesian approach, we jointly estimate the number and composition of clones in the sample as well as the most likely tree connecting them. We validate our approach in the controlled setting of a simulation study and compare it against several competing methods. In two case studies, we demonstrate how BitPhylogeny BitPhylogeny reconstructs tumor phylogenies from methylation patterns in colon cancer and from single-cell exomes in myeloproliferative neoplasm.

Show MeSH
Related in: MedlinePlus