SiFit: A Method for Inferring Tumor Trees from Single-Cell Sequencing Data under Finite-site Models

Zafar, Hamim; Tzen, Anthony; Navin, Nicholas E.; Chen, Ken; Nakhleh, Luay

doi:10.1101/091595

Cited by 5 publications

(7 citation statements)

References 63 publications

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“…Further noise stems from doublet mutation profiles, which occur when two cells are accidentally sequenced together [27]. Classic approaches for phylogeny reconstruction are not suitable for dealing with these SCS specific noise profiles, and a number of probabilistic approaches have been developed to specifically account for the error types found in SCS data [28,29,30,31,32].A major difference between the evolutionary histories of tumours inferred from bulk and SCS data is that the former typically are clonal trees where mutations with similar frequencies are clustered together and represented in a single tree node (Figure 1a), while trees derived from SCS data are fully resolved trees that can be either cell lineage trees, binary trees where the cells form the leaves and mutations occur along tree branches, or mutation trees (Figure 1b) that depict the partial temporal order in which mutations were acquired [34]. For cell lineage trees a heuristic has been proposed for clustering cells into clones in a post-processing step [29] which results in trees that are closer to bulk clonal trees.Another difference is that the VAFs obtained from a bulk sample, are well suited for inferring the temporal order of mutations (by ordering mutations by decreasing VAF), but of limited use for the identification of branching events.…”

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Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data

Malikić

Jahn

Kuipers

et al. 2017

Preprint

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Understanding the evolutionary history and subclonal composition of a tumour represents one of the key challenges in overcoming treatment failure due to resistant cell populations. Most of the current data on tumour genetics stems from short read bulk sequencing data. While this type of data is characterised by low sequencing noise and cost, it consists of aggregate measurements across a large number of cells. It is therefore of limited use for the accurate detection of the distinct cellular populations present in a tumour and the unambiguous inference of their evolutionary relationships. Single-cell DNA sequencing instead provides data of the highest resolution for studying intra-tumour heterogeneity and evolution, but is characterised by higher sequencing costs and elevated noise rates. In this work, we develop the first computational approach that infers trees of tumour evolution from combined single-cell and bulk sequencing data. Using a comprehensive set of simulated data, we show that our approach systematically outperforms existing methods with respect to tree reconstruction accuracy and subclone identification. High fidelity reconstructions are obtained even with a modest number of single cells. We also show that combining single-cell and bulk sequencing data provides more realistic mutation histories for real tumours.

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confidence: 99%

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confidence: 99%

Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data

Malikić

Jahn

Kuipers

et al. 2017

Preprint

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“…An alternative model was proposed in Zafar et al [2016a] for building single cell trees, but again from point mutations. This method is based on maximum likelihood instead of Bayes (but using an unconventional MCMC-based search procedure).…”

Section: Single Cell Methods Based On Point Mutationsmentioning

confidence: 99%

Cancer phylogenetic tree inference at scale from 1000s of single cell genomes

Dorri

Salehi²,

Chern³

et al. 2020

Preprint

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A new generation of scalable single cell whole genome sequencing (scWGS) methods [Zahn et al., 2017, Laks et al., 2019, allows unprecedented high resolution measurement of the evolutionary dynamics of cancer cells populations. Phylogenetic reconstruction is central to identifying sub-populations and distinguishing mutational processes. The ability to sequence tens of thousands of single genomes at high resolution per experiment [Laks et al., 2019] is challenging the assumptions and scalability of existing phylogenetic tree building methods and calls for tailored phylogenetic models and scalable inference algorithms. We propose a phylogenetic model and associated Bayesian inference procedure which exploits the specifics of scWGS data. A first highlight of our approach is a novel phylogenetic encoding of copy-number data providing an attractive statistical-computational trade-off by simplifying the site dependencies induced by rearrangements while still forming a sound foundation to phylogenetic inference. A second highlight is an innovative phylogenetic tree exploration move which makes the cost of MCMC iterations bounded by O(|C| + |L|), where |C| is the number of cells and |L| is the number of loci. In contrast, existing off-the-shelf likelihood-based methods incur iteration cost of O(|C| |L|). Moreover, the novel move considers an exponential number of neighbouring trees whereas offthe-shelf moves consider a polynomial size set of neighbours. The third highlight is a novel * Equal contribution 1 mutation calling method that incorporates the copy-number data and the underlying phylogenetic tree to overcome the missing data issue. This framework allows us to realistically consider routine Bayesian phylogenetic inference at the scale of scWGS data.

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“…MIPUP [23] takes a binary input matrix that represents the presence or absence of a variation in a sample based on a given threshold of VAF and attempts to find the minimum perfect phylogeny. SiFit [48], SCITE [49], OncoNEM [50] and SPhyR [51] are designed for clonal reconstruction in single cell sequencing data. TargetClone [36] is a method specifically designed for targeted sequencing data obtained from microdissected tumor samples.…”

Section: Other Approachesmentioning

confidence: 99%

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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SiFit: A Method for Inferring Tumor Trees from Single-Cell Sequencing Data under Finite-site Models

Cited by 5 publications

References 63 publications

Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data

Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data

Cancer phylogenetic tree inference at scale from 1000s of single cell genomes

Algorithmic approaches to clonal reconstruction in heterogeneous cell populations

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