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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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The intra-tumor phylogeny problem.(A) Molecular profiles obtained from a bulk sequenced heterogeneous tumor are shown. They consist, in this example, of three clones (red squares, blue triangles, and green discs) and normal cells (small grey discs). The intra-tumor phylogeny problem is to infer the population structure of the tumor, i.e., to identify the different clones and to elucidate how they relate to each other. (B) Classical phylogenetic trees and hierarchical clustering methods place the observed molecular profiles at the leaf nodes of a tree, while the inner nodes represent unobserved common ancestors. Here, leaf nodes are defined as the nodes without any child nodes and inner nodes as the nodes that have at least one child node. (C) Unlike classical phylogenetic tree models, BitPhylogeny clusters molecular profiles to identify subclones and places them as both inner (blue triangle) and leaf nodes (red square, green disc) of a tree describing the hierarchy of the tumor cell population.
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Fig1: The intra-tumor phylogeny problem.(A) Molecular profiles obtained from a bulk sequenced heterogeneous tumor are shown. They consist, in this example, of three clones (red squares, blue triangles, and green discs) and normal cells (small grey discs). The intra-tumor phylogeny problem is to infer the population structure of the tumor, i.e., to identify the different clones and to elucidate how they relate to each other. (B) Classical phylogenetic trees and hierarchical clustering methods place the observed molecular profiles at the leaf nodes of a tree, while the inner nodes represent unobserved common ancestors. Here, leaf nodes are defined as the nodes without any child nodes and inner nodes as the nodes that have at least one child node. (C) Unlike classical phylogenetic tree models, BitPhylogeny clusters molecular profiles to identify subclones and places them as both inner (blue triangle) and leaf nodes (red square, green disc) of a tree describing the hierarchy of the tumor cell population.

Mentions: Single-cell studies offer the most direct evidence of tumor heterogeneity, but are often limited to either a small number of genetic markers [11] and genes [12] or a small number of sequenced cells [13] with generally high error rates and high allelic dropout rates [14,15]. Thus, today, the main data source for evolutionary inference is bulk sequencing of mixed tumor samples [3,5,6], which is also the most readily available type of data for clinical applications of evolutionary methods in translational medicine. Whether obtained from single-cell or bulk sequencing, we assume in the following that the sequencing reads provide a statistical sample of the genomes of the underlying cell population. The intra-tumor phylogeny problem is to reconstruct the population structure of a tumor from these data. The problem consists of two tasks, namely (i) identifying the tumor subclones and (ii) estimating their evolutionary relationships (Figure 1).Figure 1


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

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

The intra-tumor phylogeny problem.(A) Molecular profiles obtained from a bulk sequenced heterogeneous tumor are shown. They consist, in this example, of three clones (red squares, blue triangles, and green discs) and normal cells (small grey discs). The intra-tumor phylogeny problem is to infer the population structure of the tumor, i.e., to identify the different clones and to elucidate how they relate to each other. (B) Classical phylogenetic trees and hierarchical clustering methods place the observed molecular profiles at the leaf nodes of a tree, while the inner nodes represent unobserved common ancestors. Here, leaf nodes are defined as the nodes without any child nodes and inner nodes as the nodes that have at least one child node. (C) Unlike classical phylogenetic tree models, BitPhylogeny clusters molecular profiles to identify subclones and places them as both inner (blue triangle) and leaf nodes (red square, green disc) of a tree describing the hierarchy of the tumor cell population.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig1: The intra-tumor phylogeny problem.(A) Molecular profiles obtained from a bulk sequenced heterogeneous tumor are shown. They consist, in this example, of three clones (red squares, blue triangles, and green discs) and normal cells (small grey discs). The intra-tumor phylogeny problem is to infer the population structure of the tumor, i.e., to identify the different clones and to elucidate how they relate to each other. (B) Classical phylogenetic trees and hierarchical clustering methods place the observed molecular profiles at the leaf nodes of a tree, while the inner nodes represent unobserved common ancestors. Here, leaf nodes are defined as the nodes without any child nodes and inner nodes as the nodes that have at least one child node. (C) Unlike classical phylogenetic tree models, BitPhylogeny clusters molecular profiles to identify subclones and places them as both inner (blue triangle) and leaf nodes (red square, green disc) of a tree describing the hierarchy of the tumor cell population.
Mentions: Single-cell studies offer the most direct evidence of tumor heterogeneity, but are often limited to either a small number of genetic markers [11] and genes [12] or a small number of sequenced cells [13] with generally high error rates and high allelic dropout rates [14,15]. Thus, today, the main data source for evolutionary inference is bulk sequencing of mixed tumor samples [3,5,6], which is also the most readily available type of data for clinical applications of evolutionary methods in translational medicine. Whether obtained from single-cell or bulk sequencing, we assume in the following that the sequencing reads provide a statistical sample of the genomes of the underlying cell population. The intra-tumor phylogeny problem is to reconstruct the population structure of a tumor from these data. The problem consists of two tasks, namely (i) identifying the tumor subclones and (ii) estimating their evolutionary relationships (Figure 1).Figure 1

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