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Cancer evolution: mathematical models and computational inference.

Beerenwinkel N, Schwarz RF, Gerstung M, Markowetz F - Syst. Biol. (2014)

Bottom Line: Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development.We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations.Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.

View Article: PubMed Central - PubMed

Affiliation: Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB20RE, United Kingdom Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB20RE, United Kingdom niko.beerenwinkel@bsse.ethz.ch.

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Inferring tumor phylogeny from next-generation sequencing data. a) Subclones are related to each other by an evolutionary process of acquisition of mutations. In this example, the three clones (leaf nodes) are characterized by different combinations of the four single nucleotide variant (SNV) sets , , , and . The percentages on the edges of the tree indicate the fraction of cells with this particular set of SNVs, e.g., 70% of all cells carry , 40% additionally carry , and only 7% carry , , and . b) The evolutionary history of a tumor gives rise to a heterogeneous collection of normal cells (small discs) and cancer subclones (large discs, triangles, squares). Internal nodes that have been fully replaced by their descendants (like the one carrying SNV sets  and  without  or ) are no longer part of the tumor. c) Sequencing data consist of short reads covering (parts of) the cancer genome. Comparison to the germline DNA of the same patient allows to identify SNVs and other genomic aberrations. Since reads are short, most will only cover a single SNV. In few cases, pairs of SNVs are covered, which allows to assess patterns of co-occurrence and mutual exclusivity between SNVs. d) The sets of SNVs distinguishing the subclones cluster in the SNV frequency distribution. The mean of each cluster (-axis) is the fraction of cells carrying this set of SNVs. The goal of tumor phylogenetics is to infer the evolutionary tree (a) from the mutations observed in the sequencing data (c) and their frequencies (d).
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Figure 3: Inferring tumor phylogeny from next-generation sequencing data. a) Subclones are related to each other by an evolutionary process of acquisition of mutations. In this example, the three clones (leaf nodes) are characterized by different combinations of the four single nucleotide variant (SNV) sets , , , and . The percentages on the edges of the tree indicate the fraction of cells with this particular set of SNVs, e.g., 70% of all cells carry , 40% additionally carry , and only 7% carry , , and . b) The evolutionary history of a tumor gives rise to a heterogeneous collection of normal cells (small discs) and cancer subclones (large discs, triangles, squares). Internal nodes that have been fully replaced by their descendants (like the one carrying SNV sets and without or ) are no longer part of the tumor. c) Sequencing data consist of short reads covering (parts of) the cancer genome. Comparison to the germline DNA of the same patient allows to identify SNVs and other genomic aberrations. Since reads are short, most will only cover a single SNV. In few cases, pairs of SNVs are covered, which allows to assess patterns of co-occurrence and mutual exclusivity between SNVs. d) The sets of SNVs distinguishing the subclones cluster in the SNV frequency distribution. The mean of each cluster (-axis) is the fraction of cells carrying this set of SNVs. The goal of tumor phylogenetics is to infer the evolutionary tree (a) from the mutations observed in the sequencing data (c) and their frequencies (d).

Mentions: Sequencing a population of cancer cells allows to infer SNVs and their allele frequencies (Fig. 3). The allele frequencies need to be corrected for copy number alterations, LOH, and normal contamination to estimate the percentage of cancer cells carrying the SNV (Shah et al. 2012; Roth et al. 2014). Because it is unknown in which genome a given SNV occurred, prior to tree reconstruction, SNVs are clustered into sets of mutations with common frequency (Fig. 3d). This clustering is often performed using Bayesian mixture models, either finite ones (Larson and Fridley 2013) or nonparametric ones in which the number of mixture components is estimated together with their frequencies and densities (Shah et al. 2012; Nik-Zainal et al. 2012b; Roth et al. 2014).


Cancer evolution: mathematical models and computational inference.

Beerenwinkel N, Schwarz RF, Gerstung M, Markowetz F - Syst. Biol. (2014)

Inferring tumor phylogeny from next-generation sequencing data. a) Subclones are related to each other by an evolutionary process of acquisition of mutations. In this example, the three clones (leaf nodes) are characterized by different combinations of the four single nucleotide variant (SNV) sets , , , and . The percentages on the edges of the tree indicate the fraction of cells with this particular set of SNVs, e.g., 70% of all cells carry , 40% additionally carry , and only 7% carry , , and . b) The evolutionary history of a tumor gives rise to a heterogeneous collection of normal cells (small discs) and cancer subclones (large discs, triangles, squares). Internal nodes that have been fully replaced by their descendants (like the one carrying SNV sets  and  without  or ) are no longer part of the tumor. c) Sequencing data consist of short reads covering (parts of) the cancer genome. Comparison to the germline DNA of the same patient allows to identify SNVs and other genomic aberrations. Since reads are short, most will only cover a single SNV. In few cases, pairs of SNVs are covered, which allows to assess patterns of co-occurrence and mutual exclusivity between SNVs. d) The sets of SNVs distinguishing the subclones cluster in the SNV frequency distribution. The mean of each cluster (-axis) is the fraction of cells carrying this set of SNVs. The goal of tumor phylogenetics is to infer the evolutionary tree (a) from the mutations observed in the sequencing data (c) and their frequencies (d).
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Figure 3: Inferring tumor phylogeny from next-generation sequencing data. a) Subclones are related to each other by an evolutionary process of acquisition of mutations. In this example, the three clones (leaf nodes) are characterized by different combinations of the four single nucleotide variant (SNV) sets , , , and . The percentages on the edges of the tree indicate the fraction of cells with this particular set of SNVs, e.g., 70% of all cells carry , 40% additionally carry , and only 7% carry , , and . b) The evolutionary history of a tumor gives rise to a heterogeneous collection of normal cells (small discs) and cancer subclones (large discs, triangles, squares). Internal nodes that have been fully replaced by their descendants (like the one carrying SNV sets and without or ) are no longer part of the tumor. c) Sequencing data consist of short reads covering (parts of) the cancer genome. Comparison to the germline DNA of the same patient allows to identify SNVs and other genomic aberrations. Since reads are short, most will only cover a single SNV. In few cases, pairs of SNVs are covered, which allows to assess patterns of co-occurrence and mutual exclusivity between SNVs. d) The sets of SNVs distinguishing the subclones cluster in the SNV frequency distribution. The mean of each cluster (-axis) is the fraction of cells carrying this set of SNVs. The goal of tumor phylogenetics is to infer the evolutionary tree (a) from the mutations observed in the sequencing data (c) and their frequencies (d).
Mentions: Sequencing a population of cancer cells allows to infer SNVs and their allele frequencies (Fig. 3). The allele frequencies need to be corrected for copy number alterations, LOH, and normal contamination to estimate the percentage of cancer cells carrying the SNV (Shah et al. 2012; Roth et al. 2014). Because it is unknown in which genome a given SNV occurred, prior to tree reconstruction, SNVs are clustered into sets of mutations with common frequency (Fig. 3d). This clustering is often performed using Bayesian mixture models, either finite ones (Larson and Fridley 2013) or nonparametric ones in which the number of mixture components is estimated together with their frequencies and densities (Shah et al. 2012; Nik-Zainal et al. 2012b; Roth et al. 2014).

Bottom Line: Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development.We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations.Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.

View Article: PubMed Central - PubMed

Affiliation: Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB20RE, United Kingdom Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB20RE, United Kingdom niko.beerenwinkel@bsse.ethz.ch.

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