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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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BitPhylogenyas a graphical model. Each of a total of N observed marker patterns is denoted by xn (shaded node). The clone membership of each observation is denoted by εn and generated by a tree-structured stick-breaking process with variables νε (clone size) and φε (branching probability), and parameters λ, α0 and γ. For each clone, tε and θε are the branch length and clone parameter, respectively, which determine the local probability distribution of observing a marker pattern from this clone. The function pa(·) denotes the parent of each clone in the tree, except the root clone ∅. The transition probabilities p(θε∣θpa(ε)) have hyperparameters βm, βu, Λ and μ.
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Fig2: BitPhylogenyas a graphical model. Each of a total of N observed marker patterns is denoted by xn (shaded node). The clone membership of each observation is denoted by εn and generated by a tree-structured stick-breaking process with variables νε (clone size) and φε (branching probability), and parameters λ, α0 and γ. For each clone, tε and θε are the branch length and clone parameter, respectively, which determine the local probability distribution of observing a marker pattern from this clone. The function pa(·) denotes the parent of each clone in the tree, except the root clone ∅. The transition probabilities p(θε∣θpa(ε)) have hyperparameters βm, βu, Λ and μ.

Mentions: We have developed BitPhylogeny (Bayesian inference of intra-tumor phylogenies), an integrated approach to address the intra-tumor phylogeny problem. The statistical model is based on simultaneously assigning markers of evolution to clones, which are represented as both inner nodes and leaves of a phylogenetic tree, and on learning the topology and the parameters of the tree. We use a TSSB to construct a prior probability of trees and a Markov chain Monte Carlo (MCMC) inference scheme for sampling from the joint posterior. The relationships between parent and child nodes are derived from a classical phylogeny model. The model is formally defined in Materials and methods and depicted as a graphical model in Figure 2. In the following, we benchmark BitPhylogeny in simulation studies and discuss its application to colon cancer methylation data.Figure 2


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

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

BitPhylogenyas a graphical model. Each of a total of N observed marker patterns is denoted by xn (shaded node). The clone membership of each observation is denoted by εn and generated by a tree-structured stick-breaking process with variables νε (clone size) and φε (branching probability), and parameters λ, α0 and γ. For each clone, tε and θε are the branch length and clone parameter, respectively, which determine the local probability distribution of observing a marker pattern from this clone. The function pa(·) denotes the parent of each clone in the tree, except the root clone ∅. The transition probabilities p(θε∣θpa(ε)) have hyperparameters βm, βu, Λ and μ.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig2: BitPhylogenyas a graphical model. Each of a total of N observed marker patterns is denoted by xn (shaded node). The clone membership of each observation is denoted by εn and generated by a tree-structured stick-breaking process with variables νε (clone size) and φε (branching probability), and parameters λ, α0 and γ. For each clone, tε and θε are the branch length and clone parameter, respectively, which determine the local probability distribution of observing a marker pattern from this clone. The function pa(·) denotes the parent of each clone in the tree, except the root clone ∅. The transition probabilities p(θε∣θpa(ε)) have hyperparameters βm, βu, Λ and μ.
Mentions: We have developed BitPhylogeny (Bayesian inference of intra-tumor phylogenies), an integrated approach to address the intra-tumor phylogeny problem. The statistical model is based on simultaneously assigning markers of evolution to clones, which are represented as both inner nodes and leaves of a phylogenetic tree, and on learning the topology and the parameters of the tree. We use a TSSB to construct a prior probability of trees and a Markov chain Monte Carlo (MCMC) inference scheme for sampling from the joint posterior. The relationships between parent and child nodes are derived from a classical phylogeny model. The model is formally defined in Materials and methods and depicted as a graphical model in Figure 2. In the following, we benchmark BitPhylogeny in simulation studies and discuss its application to colon cancer methylation data.Figure 2

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