Maximum Parsimony Analysis of Gene Copy Number Changes

Zhou, Jun; Lin, Yu; Rajan, Vaibhav; Hoskins, William; Tang, Jijun

doi:10.1007/978-3-662-48221-6_8

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…The majority of published tools for single-cell phylogenetics are still based on pre-scSeq technologies 84,148,151–153 , with just a handful having been developed specifically for scSeq. Kim and Simon 89 introduced the muttree program, which uses a custom combinatorial inference to find trees optimized for a specialized probabilistic model that differs from the models used by other tools which accept the same input.…”

Section: Variations On Tumour Phylogeneticsmentioning

confidence: 99%

The evolution of tumour phylogenetics: principles and practice

2017

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Rapid advances in high-throughput sequencing and a growing realization of the importance of evolutionary theory to cancer genomics have led to a proliferation of phylogenetic studies of tumour progression. These studies have yielded not only new insights but also a plethora of experimental approaches, sometimes reaching conflicting or poorly supported conclusions. Here, we consider this body of work in light of the key computational principles underpinning phylogenetic inference, with the goal of providing practical guidance on the design and analysis of scientifically rigorous tumour phylogeny studies. We survey the range of methods and tools available to the researcher, their key applications, and the various unsolved problems, closing with a perspective on the prospects and broader implications of this field.

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Section: Variations On Tumour Phylogeneticsmentioning

confidence: 99%

The evolution of tumour phylogenetics: principles and practice

2017

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“…The problem can be solved in pseudo-polynomial time O(n 2 N 7 ), where n is the CNP size and N the maximum copy number. Other distances and phylogenetic approaches are discussed in [12,10,13,11,26,27] Our results. The above CNP comparison frameworks limit events to alter copynumbers by 1 or −1.…”

Section: Introductionmentioning

confidence: 77%

“…This question has recently led to the development of several phylogenetic algorithms tailored for cancer evolution. Most of them use either information of single nucleotide variants obtained from bulk [2,3,4,5] or single-cell [6,7,8] sequencing data, or copy-number alter-arXiv:2002.11271v1 [q-bio.GN] 26 Feb 2020 ations [9,10,11,12,13] (usually in the context of single-cell data). We refer the reader to [14] for a survey of these methods.…”

Section: Introductionmentioning

confidence: 99%

Comparing copy-number profiles under multi-copy amplifications and deletions

Cordonnier

Lafond

2020

BMC Genomics

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Background: during cancer progression, malignant cells accumulate somatic mutations that can lead to genetic aberrations. In particular, evolutionary events akin to segmental duplications or deletions can alter the copy-number profile (CNP) of a set of genes in a genome. Our aim is to compute the evolutionary distance between two cells for which only CNPs are known. This asks for the minimum number of segmental amplifications and deletions to turn one CNP into another. This was recently formalized into a model where each event is assumed to alter a copy-number by 1 or −1, even though these events can affect large portions of a chromosome.Results: we propose a general cost framework where an event can modify the copy-number of a gene by larger amounts. We show that any cost scheme that allows segmental deletions of arbitrary length makes computing the distance strongly NP-hard. We then devise a factor 2 approximation algorithm for the problem when copy-numbers are non-zero and provide an implementation called cnp2cnp. We evaluate our approach experimentally by reconstructing simulated cancer phylogenies from the pairwise distances inferred by cnp2cnp and compare it against two other alternatives, namely the MEDICC distance and the Euclidean distance.Conclusions: the experimental results show that our distance yields more accurate phylogenies on average than these alternatives if the given CNPs are error-free, but that the MEDICC distance is slightly more robust against error in the data. In all cases, our experiments show that either our approach or the MEDICC approach should preferred over the Euclidean distance.

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“…However, this method was tested only in one pilot study with two probes with an implementation customized to the characteristics of that data set. More recent work on single-cell FISH data has made a number of improvements in models and algorithms for inference of copy number phylogenies, but has either analyzed data sets that did not contain a ploidy estimate, or ignored any ploidy estimate that was available and used only copy numbers of localized gene probes for inference [ 54 – 58 ]. Other phylogenetic studies with FISH data used conventional phylogenetic methods that are designed to model species evolution and that are not necessarily suitable for cancer evolution (e.g., [ 11 , 59 ]).…”

Section: Introductionmentioning

confidence: 99%

FISHtrees 3.0: Tumor Phylogenetics Using a Ploidy Probe

et al. 2016

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Advances in fluorescence in situ hybridization (FISH) make it feasible to detect multiple copy-number changes in hundreds of cells of solid tumors. Studies using FISH, sequencing, and other technologies have revealed substantial intra-tumor heterogeneity. The evolution of subclones in tumors may be modeled by phylogenies. Tumors often harbor aneuploid or polyploid cell populations. Using a FISH probe to estimate changes in ploidy can guide the creation of trees that model changes in ploidy and individual gene copy-number variations. We present FISHtrees 3.0, which implements a ploidy-based tree building method based on mixed integer linear programming (MILP). The ploidy-based modeling in FISHtrees includes a new formulation of the problem of merging trees for changes of a single gene into trees modeling changes in multiple genes and the ploidy. When multiple samples are collected from each patient, varying over time or tumor regions, it is useful to evaluate similarities in tumor progression among the samples. Therefore, we further implemented in FISHtrees 3.0 a new method to build consensus graphs for multiple samples. We validate FISHtrees 3.0 on a simulated data and on FISH data from paired cases of cervical primary and metastatic tumors and on paired breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC). Tests on simulated data show improved accuracy of the ploidy-based approach relative to prior ploidyless methods. Tests on real data further demonstrate novel insights these methods offer into tumor progression processes. Trees for DCIS samples are significantly less complex than trees for paired IDC samples. Consensus graphs show substantial divergence among most paired samples from both sets. Low consensus between DCIS and IDC trees may help explain the difficulty in finding biomarkers that predict which DCIS cases are at most risk to progress to IDC. The FISHtrees software is available at ftp://ftp.ncbi.nih.gov/pub/FISHtrees.

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Maximum Parsimony Analysis of Gene Copy Number Changes

Cited by 7 publications

References 37 publications

The evolution of tumour phylogenetics: principles and practice

The evolution of tumour phylogenetics: principles and practice

Comparing copy-number profiles under multi-copy amplifications and deletions

FISHtrees 3.0: Tumor Phylogenetics Using a Ploidy Probe

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