Copy-Number Evolution Problems: Complexity and Algorithms

El-Kebir, Mohammed; Raphael, Benjamin J.; Shamir, Ron; Sharan, Roded; Zaccaria, Simone; Zehavi, Meirav; Zeira, Ron

doi:10.1007/978-3-319-43681-4_11

Cited by 22 publications

(37 citation statements)

References 14 publications

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“…However, we suspect the problem to NP-hard, as the related unmixed version, the CNT problem, is NPhard [7]. Moreover, other similar deconvolution problems under a tree constraint are NP-hard as well [6,8].…”

Section: Methodsmentioning

confidence: 99%

“…Following the model in [7,25,27], we represent a chromosome as a sequence of m segments. A copy-number profile, or profile for short, specifies the number of copies of each segment in a clone.…”

Section: Copy-number Tree Mixture Deconvolution Problemmentioning

confidence: 99%

“…, c n . Previously, we have shown that this problem, the Copy-Number Tree (CNT) problem, is NPhard and we introduced an ILP formulation for solving it [7]. However, with bulksequencing data the observations correspond to k samples obtained from a single tumor in different regions or at different time points.…”

Section: Copy-number Tree Mixture Deconvolution Problemmentioning

confidence: 99%

“…The second category considers the effects of CNAs on multiple segments as interval events that amplify or delete copies of contiguous segments; the most prominent such approach is MEDICC [25]. Recently, [27] and [7] improved the model in MEDICC. Specifically, [27] formally investigated the effects of interval events on segments of a single clone.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, [27] formally investigated the effects of interval events on segments of a single clone. In [7], the authors formalized the Copy-Number Tree (CNT) problem that aims to find the most parsimonious evolution of clones explained by the minimum number of interval events, and derived an integer linear program (ILP) that solves this problem. However, all of the studies applying these methods either assume that each sample is homogeneous and consisting of a single clone [26,28] or first attempt to infer the clones independently on each sample before performing a phylogenetic analysis of CNAs [19].…”

Section: Introductionmentioning

confidence: 99%

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The Copy-Number Tree Mixture Deconvolution Problem and Applications to Multi-sample Bulk Sequencing Tumor Data

Zaccaria

El-Kebir

Klau

et al. 2017

Lecture Notes in Computer Science

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Abstract.Cancer is an evolutionary process driven by somatic mutation. This process can be represented as a phylogenetic tree. Constructing such a phylogenetic tree from genome sequencing data is a challenging task due to the mutational complexity of cancer and the fact that nearly all cancer sequencing is of bulk tissue, measuring a superposition of somatic mutations present in different cells. We study the problem of reconstructing tumor phylogenies from copy number aberrations (CNAs) measured in bulk-sequencing data. We introduce the CopyNumber Tree Mixture Deconvolution (CNTMD) problem, which aims to find the phylogenetic tree with the fewest number of CNAs that explain the copy number data from multiple samples of a tumor. CNTMD generalizes two approaches that have been researched intensively in recent years: deconvolution/factorization algorithms that aim to infer the number and proportions of clones in a mixed tumor sample; and phylogenetic models of copy number evolution that model the dependencies between copy number events that affect the same genomic loci. We design an algorithm for solving the CNTMD problem and apply the algorithm to both simulated and real data. On simulated data, we find that our algorithm outperforms existing approaches that perform either deconvolution or phylogenetic tree construction under the assumption of a single tumor clone per sample. On real data, we analyze multiple samples from a prostate cancer patient, identifying clones within these samples and a phylogenetic tree that relates these clones and their differing proportions across samples. This phylogenetic tree provides a higher-resolution view of copy number evolution of this cancer than published analyses.

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Section: Methodsmentioning

confidence: 99%

Section: Copy-number Tree Mixture Deconvolution Problemmentioning

confidence: 99%

Section: Copy-number Tree Mixture Deconvolution Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Copy-Number Tree Mixture Deconvolution Problem and Applications to Multi-sample Bulk Sequencing Tumor Data

Zaccaria

El-Kebir

Klau

et al. 2017

Lecture Notes in Computer Science

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Tandem Duplications, Segmental Duplications and Deletions, and Their Applications

Zhu

2020

Computer Science – Theory and Applications

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Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data

2020

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Copy-number aberrations (CNAs) and whole-genome duplications (WGDs) are frequent somatic mutations in cancer but their quantification from DNA sequencing of bulk tumor samples is challenging. Standard methods for CNA inference analyze tumor samples individually; however, DNA sequencing of multiple samples from a cancer patient has recently become more common. We introduce HATCHet (Holistic Allele-specific Tumor Copynumber Heterogeneity), an algorithm that infers allele-and clone-specific CNAs and WGDs jointly across multiple tumor samples from the same patient. We show that HATCHet outperforms current state-of-the-art methods on multi-sample DNA sequencing data that we simulate using MASCoTE (Multiple Allele-specific Simulation of Copy-number Tumor Evolution). Applying HATCHet to 84 tumor samples from 14 prostate and pancreas cancer patients, we identify subclonal CNAs and WGDs that are more plausible than previously published analyses and more consistent with somatic single-nucleotide variants (SNVs) and small indels in the same samples.

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Copy-Number Evolution Problems: Complexity and Algorithms

Cited by 22 publications

References 14 publications

The Copy-Number Tree Mixture Deconvolution Problem and Applications to Multi-sample Bulk Sequencing Tumor Data

The Copy-Number Tree Mixture Deconvolution Problem and Applications to Multi-sample Bulk Sequencing Tumor Data

Tandem Duplications, Segmental Duplications and Deletions, and Their Applications

Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data

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