MIPUP: minimum perfect unmixed phylogenies for multi-sampled tumors via branchings and ILP

Husić, Edin; Li, Xinyue; Hujdurović, Ademir; Mehine, Miika; Rizzi, Roméo; Mäkinen, Veli; Milanič, Martin; Tomescu, Alexandru I.

doi:10.1093/bioinformatics/bty683

Cited by 13 publications

(14 citation statements)

References 27 publications

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“…Using a breast cancer dataset, we show that CASet and DISC are able to differentiate between trees with differing clonal makeups, demonstrating their topological acuity. In addition, we use CASet and DISC to assess trees reconstructed by MIPUP [8] and LICHeE [3] from a breast cancer xenograft dataset [37]. Our results suggest that the MIPUP tree may not more closely resemble the original hypothesized tree from [37] than the LICHeE tree, as is suggested in [8] based solely on qualitative analysis.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, we use CASet and DISC to assess trees reconstructed by MIPUP [8] and LICHeE [3] from a breast cancer xenograft dataset [37]. Our results suggest that the MIPUP tree may not more closely resemble the original hypothesized tree from [37] than the LICHeE tree, as is suggested in [8] based solely on qualitative analysis. This highlights the importance of using quantitative measures such as CASet and DISC for these types of assessments.…”

Section: Discussionmentioning

confidence: 99%

“…The authors of [8] introduced a new tree reconstruction method MIPUP, and when they compare their results to those of another method, LICHeE [3], on breast cancer xenoengraftment data sample SA501 [37], they use only qualitative analysis to claim their method produces phylogenies closer to those in the original publication. We assessed this claim quantitatively by running CASet, DISC, and MLTED on the SA501 tree from [37] and the corresponding trees reconstructed by MIPUP and LICHeE, as shown in [8]. These trees had around 180 SNV mutations each in 5-7 nodes with slightly different mutation sets between trees.…”

Section: Intra-family Clustering Structurementioning

confidence: 99%

“…Directed edges represent direct ancestral relationships between populations. In recent years, a number of methods have been developed to infer the tree describing a tumor's evolution from single nucleotide variants in bulk sequencing data [2][3][4][5][6][7][8][9] and single cell sequencing data [10][11][12][13][14] (see [15] for a more complete listing). The goal of tree inference is to gain a better understanding of tumor development, which may in turn reveal insights about the mutations that drive a tumor's growth [16,17] and may be targeted for patient treatment [18,19].…”

Section: Introductionmentioning

confidence: 99%

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Distance Measures for Tumor Evolutionary Trees

DiNardo

Tomlinson

Ritz

et al. 2019

Preprint

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In recent years, there has been increased interest in studying cancer by using algorithmic methods to infer the evolutionary tree underlying a tumor's developmental history. Quantitative measures that compare such trees are then vital to benchmarking these algorithmic tree inference methods, understanding the structure of the space of possible trees for a given dataset, and clustering together similar trees in order to evaluate inheritance patterns. However, few appropriate distance measures exist, and those that do exist have low resolution for differentiating trees or do not fully account for the complex relationship between tree topology and how the mutations that label that topology are inherited. Here we present two novel distance measures, Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC), that are specifically designed to account for the subclonal mutation inheritance patterns characteristic of tumor evolutionary trees. We apply CASet and DISC to two simulated and two breast cancer datasets and show that our distance measures allow for more nuanced and accurate delineation between tumor evolutionary trees than existing distance measures. Implementations of CASet and DISC are available at: https://bitbucket.org/oesperlab/stereodist.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Intra-family Clustering Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distance Measures for Tumor Evolutionary Trees

DiNardo

Tomlinson

Ritz

et al. 2019

Preprint

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“…Many of these methods use bulk sequencing data where DNA from millions of tumor (and normal) cells are sequenced together. Tree inference from this type of data is typically based on the use of cancer cell fraction of detected variants -in particular single-nucleotide variants [36,6,11,25,28,8,12,31,27,19], but also copy number (e.g., [39]) and structural variants (e.g., [9,29]). While being cost-effective, the low resolution of bulk sequencing data is a limiting factor in tumor evolution modeling.…”

Section: Introductionmentioning

confidence: 99%

Tumor Phylogeny Topology Inference via Deep Learning

Azer

Ebrahimabadi

Malikić

et al. 2020

Preprint

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Motivation: Principled computational approaches for tumor phylogeny reconstruction via single cell sequencing (SCS) typically aim to identify the most likely perfect phylogeny tree through combinatorial optimization or Bayesian inference. Because of the limitations of SCS technologies, such as frequent allele dropout and variable sequence coverage, a noise reduction/elimination process may become necessary to infer a tumor phylogeny. Such noise reduction processes may aim to correct for the most likely/parsimonious set of false negative/false positive variant calls so as to construct a perfect phylogeny. Since these problems are NP-hard, available principled approaches for tumor phylogeny reconstruction are limited in their ability to scale up for handling emergent SCS datasets. In fact, even when the goal is to infer basic topological features of the tumor phylogeny rather than reconstructing it entirely, available techniques may be prohibitively slow. As a result, fast techniques to deduce, e.g. (i) whether the most likely tree has a linear (chain) or branching topology, or (ii) whether a perfect phylogeny is feasible from single-cell genotype matrix, without explicitly testing for the three gametes rule, are highly desirable. Results: In this paper we introduce deep-learning solutions to the above mentioned problems for studying tumor evolution from SCS data. After training with sufficiently many examples: (1) our fully connected neural network for differentiating linear vs branching topologies, can improve the running time of the fastest combinatorial tumor phylogeny reconstruction methods by a factor of ≥ 1000, while achieving an accuracy of ∼ 98% on simulated data including 100 cells and 100 mutations with realistic noise levels (leading to mostly false negatives) of 10 − 15%; (2) similarly, our fully connected neural network for checking whether the input data permits a perfect phylogeny, achieves an accuracy of ∼ 90% on simulated data including 10 cells and 10 mutations, with similar noise levels; (3) finally, our reinforcement learning approach for tumor phylogeny reconstruction can actually eliminate noise and obtain the PP, when false negative/false positive rate ≤ 2%, for a large fraction of evaluation data sets with varying number of cells and mutations, even when trained with fixed size data sets of only 10 cells and 10 mutations -this may be useful for future clinical applications that would employ emerging SCS technologies with lower noise levels.

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Algorithmic approaches to clonal reconstruction in heterogeneous cell populations

2019

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Background: The reconstruction of clonal haplotypes and their evolutionary history in evolving populations is a common problem in both microbial evolutionary biology and cancer biology. The clonal theory of evolution provides a theoretical framework for modeling the evolution of clones. Results: In this paper, we review the theoretical framework and assumptions over which the clonal reconstruction problem is formulated. We formally define the problem and then discuss the complexity and solution space of the problem. Various methods have been proposed to find the phylogeny that best explains the observed data. We categorize these methods based on the type of input data that they use (space-resolved or time-resolved), and also based on their computational formulation as either combinatorial or probabilistic. It is crucial to understand the different types of input data because each provides essential but distinct information for drastically reducing the solution space of the clonal reconstruction problem. Complementary information provided by single cell sequencing or from whole genome sequencing of randomly isolated clones can also improve the accuracy of clonal reconstruction. We briefly review the existing algorithms and their relationships. Finally we summarize the tools that are developed for either directly solving the clonal reconstruction problem or a related computational problem. Conclusions: In this review, we discuss the various formulations of the problem of inferring the clonal evolutionary history from allele frequeny data, review existing algorithms and catergorize them according to their problem formulation and solution approaches. We note that most of the available clonal inference algorithms were developed for elucidating tumor evolution whereas clonal reconstruction for unicellular genomes are less addressed. We conclude the review by discussing more open problems such as the lack of benchmark datasets and comparison of performance between available tools.

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MIPUP: minimum perfect unmixed phylogenies for multi-sampled tumors via branchings and ILP

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 13 publications

References 27 publications

Distance Measures for Tumor Evolutionary Trees

Distance Measures for Tumor Evolutionary Trees

Tumor Phylogeny Topology Inference via Deep Learning

Algorithmic approaches to clonal reconstruction in heterogeneous cell populations

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