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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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Simulation study with five trees (A-E).(First column) Sankey plots of the trees used for simulations. For each node, the width of the in-edge is proportional to the clone frequency. The colors denote different layers of the tree (tree depths). Plots were produced with the R package riverplot. (Second column) Performance of clustering methods for the simulation studies with four different noise levels. Performance measures are based on 10,000 MCMC samples (the box plots in the second column). The MPEAR-summarized predictions (marked as BitPhylogeny) outperform the baseline competitors in all data sets with noise. (Third column) Comparison in terms of the summary statistics maximum tree depth and number of clones. For hierarchical clustering and k-centroids, the trees are constructed as minimum spanning trees from estimated clonal methyltypes.
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Fig3: Simulation study with five trees (A-E).(First column) Sankey plots of the trees used for simulations. For each node, the width of the in-edge is proportional to the clone frequency. The colors denote different layers of the tree (tree depths). Plots were produced with the R package riverplot. (Second column) Performance of clustering methods for the simulation studies with four different noise levels. Performance measures are based on 10,000 MCMC samples (the box plots in the second column). The MPEAR-summarized predictions (marked as BitPhylogeny) outperform the baseline competitors in all data sets with noise. (Third column) Comparison in terms of the summary statistics maximum tree depth and number of clones. For hierarchical clustering and k-centroids, the trees are constructed as minimum spanning trees from estimated clonal methyltypes.

Mentions: We have assessed the performance of BitPhylogeny in the controlled settings of five simulation studies. Based on the review article by Navin and Hicks [46], we chose the simulated trees to be representative of different modes of evolution (Figure 3). One tree reflects monoclonal evolution, three trees are based on a polyclonal mode of evolution, and one tree assumes a mutator phenotype.Figure 3


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

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

Simulation study with five trees (A-E).(First column) Sankey plots of the trees used for simulations. For each node, the width of the in-edge is proportional to the clone frequency. The colors denote different layers of the tree (tree depths). Plots were produced with the R package riverplot. (Second column) Performance of clustering methods for the simulation studies with four different noise levels. Performance measures are based on 10,000 MCMC samples (the box plots in the second column). The MPEAR-summarized predictions (marked as BitPhylogeny) outperform the baseline competitors in all data sets with noise. (Third column) Comparison in terms of the summary statistics maximum tree depth and number of clones. For hierarchical clustering and k-centroids, the trees are constructed as minimum spanning trees from estimated clonal methyltypes.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig3: Simulation study with five trees (A-E).(First column) Sankey plots of the trees used for simulations. For each node, the width of the in-edge is proportional to the clone frequency. The colors denote different layers of the tree (tree depths). Plots were produced with the R package riverplot. (Second column) Performance of clustering methods for the simulation studies with four different noise levels. Performance measures are based on 10,000 MCMC samples (the box plots in the second column). The MPEAR-summarized predictions (marked as BitPhylogeny) outperform the baseline competitors in all data sets with noise. (Third column) Comparison in terms of the summary statistics maximum tree depth and number of clones. For hierarchical clustering and k-centroids, the trees are constructed as minimum spanning trees from estimated clonal methyltypes.
Mentions: We have assessed the performance of BitPhylogeny in the controlled settings of five simulation studies. Based on the review article by Navin and Hicks [46], we chose the simulated trees to be representative of different modes of evolution (Figure 3). One tree reflects monoclonal evolution, three trees are based on a polyclonal mode of evolution, and one tree assumes a mutator phenotype.Figure 3

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