On the Non-uniqueness of Solutions to the Perfect Phylogeny Mixture Problem

Pradhan, Dikshant; El-Kebir, Mohammed

doi:10.1007/978-3-030-00834-5_16

Cited by 12 publications

(18 citation statements)

References 28 publications

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“…The evolution of a tumor is typically described by a phylogenetic tree, or phylogeny, whose leaves represent the cells observed at the present time and whose internal nodes represent ancestral cells. Tumor phylogenies are challenging to reconstruct using DNA sequencing data from bulk tumor samples, since this data contains mixtures of mutations from thousandsmillions of heterogeneous cells in the sample [3][4][5][6][7][8][9][10][11][12][13][14][15] . Recently, single-cell DNA sequencing (scDNA-seq) of tumors has become more common, and new technologies such as those from 10X Genomics 16…”

Section: Introductionmentioning

confidence: 99%

Single-cell tumor phylogeny inference with copy-number constrained mutation losses

Satas

Zaccaria

Mon

et al. 2019

Preprint

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Motivation: Single-cell DNA sequencing enables the measurement of somatic mutations in individual tumor cells, and provides data to reconstruct the evolutionary history of the tumor. Nearly all existing methods to construct phylogenetic trees from single-cell sequencing data use single-nucleotide variants (SNVs) as markers. However, most solid tumors contain copy-number aberrations (CNAs) which can overlap loci containing SNVs. Particularly problematic are CNAs that delete an SNV, thus returning the SNV locus to the unmutated state. Such mutation losses are allowed in some models of SNV evolution, but these models are generally too permissive, allowing mutation losses without evidence of a CNA overlapping the locus. Results:We introduce a novel loss-supported evolutionary model, a generalization of the infinite sites and Dollo models, that constrains mutation losses to loci with evidence of a decrease in copy number.We design a new algorithm, Single-Cell Algorithm for Reconstructing the Loss-supported Evolution of Tumors (SCARLET), that infers phylogenies from single-cell tumor sequencing data using the losssupported model and a probabilistic model of sequencing errors and allele dropout. On simulated data, we show that SCARLET outperforms current single-cell phylogeny methods, recovering more accurate trees and correcting errors in SNV data. On single-cell sequencing data from a metastatic colorectal cancer patient, SCARLET constructs a phylogeny that is both more consistent with the observed copynumber data and also reveals a simpler monooclonal seeding of the metastasis, contrasting with published reports of polyclonal seeding in this patient. SCARLET substantially improves single-cell phylogeny inference in tumors with CNAs, yielding new insights into the analysis of tumor evolution.Availability: Software is available at

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Section: Introductionmentioning

confidence: 99%

Single-cell tumor phylogeny inference with copy-number constrained mutation losses

Satas

Zaccaria

Mon

et al. 2019

Preprint

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“…Typically, more than one phylogenetic tree can be inferred from the same sequencing data (Jamal-Hanjani et al, 2017;Pradhan and El-Kebir, 2018). If the data can be described by multiple trees, the user should be able to observe the spatial consistency between visualizations from different trees.…”

Section: Analysis Tasksmentioning

confidence: 99%

ClonArch: Visualizing the Spatial Clonal Architecture of Tumors

El-Kebir

2020

Preprint

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Motivation:Cancer is caused by the accumulation of somatic mutations that lead to the formation of distinct populations of cells, called clones. The resulting clonal architecture is the main cause of relapse and resistance to treatment. With decreasing costs in DNA sequencing technology, rich cancer genomics datasets with many spatial sequencing samples are becoming increasingly available, enabling the inference of high-resolution tumor clones and prevalences across different spatial coordinates. While temporal and phylogenetic aspects of tumor evolution, such as clonal evolution over time and clonal response to treatment, are commonly visualized in various clonal evolution diagrams, visual analytics methods that reveal the spatial clonal architecture are missing. Results: This paper introduces ClonArch, a web-based tool to interactively visualize the phylogenetic tree and spatial distribution of clones in a single tumor mass. ClonArch uses the marching squares algorithm to draw closed boundaries representing the presence of clones in a real or simulated tumor. ClonArch enables researchers to examine the spatial clonal architecture of a subset of relevant mutations at different prevalence thresholds and across multiple phylogenetic trees. In addition to simulated tumors with varying number of biopsies, we demonstrate the use of ClonArch on a hepatocellular carcinoma tumor with ∼280 sequencing biopsies. ClonArch provides an automated way to interactively examine the spatial clonal architecture of a tumor, facilitating clinical and biological interpretations of the spatial aspects of intratumor heterogeneity. Availability: https://github.com/elkebir-group/ClonArch

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“…Previous work has shown that many different hypothesized mutation trees can be consistent with data from a single patient [21,22]. Clustering these trees is a compelling use case for tumor evolution distance measures as it has the potential to reveal structure in the space of compatible trees of a single dataset.…”

Section: Clustering Clonal Treesmentioning

confidence: 99%

“…For example, the GraPhyC method [20] uses a distance measure to create a consensus tumor history from several input histories. Lastly, there have been growing questions about the structure of the space of possible evolutionary histories consistent with the underlying sequence data [21][22][23] and how tumor evolutionary histories across patients can be used to identify patterns of tumor evolution [24,25]. Further analysis of these questions would be aided by distance measures tuned to the intricacies of tumor evolution histories.…”

Section: Introductionmentioning

confidence: 99%

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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On the Non-uniqueness of Solutions to the Perfect Phylogeny Mixture Problem

Cited by 12 publications

References 28 publications

Single-cell tumor phylogeny inference with copy-number constrained mutation losses

Single-cell tumor phylogeny inference with copy-number constrained mutation losses

ClonArch: Visualizing the Spatial Clonal Architecture of Tumors

Distance Measures for Tumor Evolutionary Trees

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