Tree inference for single-cell data

Jahn, Katharina; Kuipers, Jack; Beerenwinkel, Niko

doi:10.1101/047795

Cited by 82 publications

(210 citation statements)

References 42 publications

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“…Methods address this challenge by using an evolutionary model to infer a phylogeny while simultaneously imputing missing data and correcting errors in the observed SNVs. Algorithms such as SCITE 21 , On-coNEM 22 , SciΦ 23 , and B-SCITE 24 use the simplest phylogenetic model for SNVs, the infinite sites model.…”

Section: Missionmentioning

confidence: 99%

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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: Missionmentioning

confidence: 99%

“…as well. Most existing methods 21,22,24,28,[31][32][33] for single-cell phylogeny inference discretize read counts into an observed mutation matrixB, using either two or three genotypes in addition to missing data (i.e, b v,a ∈ {0, 1, ?} orb v,a ∈ {00, 01, 11, ?}).…”

Section: Maximum Likelihood Loss-supported Refinement Problemmentioning

confidence: 99%

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

Satas

Zaccaria

Mon

et al. 2019

Preprint

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“…To account for the noise in the sequencing data, we develop a probabilistic model and an MCMC inference scheme for singlecell read counts (Methods). A major difference to MCMC schemes used for reconstructing trees of point mutations [22,18] is that the infinite sites assumption, which excludes the possibility of multiple mutational hits at the same genomic site, can no longer be made [19]. CNAs may overlap and nest inside each other, so the model developed here allows for arbitrary violations of the infinite sites assumption and arbitrary reoccurrences of amplifications and deletions across different genomic regions.…”

Section: Model Overviewmentioning

confidence: 99%

Single-cell copy number calling and event history reconstruction

Kuipers

Tuncel

Ferreira

et al. 2020

Preprint

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Copy number alterations are driving forces of tumour development and the emergence of intratumour heterogeneity. A comprehensive picture of these genomic aberrations is therefore essential for the development of personalised and precise cancer diagnostics and therapies. Single-cell sequencing offers the highest resolution for copy number profiling down to the level of individual cells. Recent high-throughput protocols allow for the processing of hundreds of cells through shallow whole-genome DNA sequencing. The resulting low read-depth data poses substantial statistical and computational challenges to the identification of copy number alterations. We developed SCICoNE, a statistical model and MCMC algorithm tailored to single-cell copy number profiling from shallow whole-genome DNA sequencing data. SCICoNE reconstructs the history of copy number events in the tumour and uses these evolutionary relationships to identify the copy number profiles of the individual cells. We show the accuracy of this approach in evaluations on simulated data and demonstrate its practicability in applications to a xenograft breast cancer sample. * Contributed equally

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“…We collectively refer to these methods as "ITH methods" in the following. Subclonal reconstruction from single cell sequencing has emerged as a new field, simplifying part of the inference problem, but raising other issues, related to technical limitations (high dropout rate) and high cost, possibly a limitation to the availability of large cohorts [10,11,12,3].…”

Section: Introductionmentioning

confidence: 99%

Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data

Abecassis

Hamy

Laurent

et al. 2018

Preprint

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Tumors are made of evolving and heterogeneous populations of cells which arise from successive appearance and expansion of subclonal populations, following acquisition of mutations conferring them a selective advantage. Those subclonal populations can be sensitive or resistant to different treatments, and provide information about tumor aetiology and future evolution. Hence, it is important to be able to assess the level of heterogeneity of tumors with high reliability for clinical applications.In the past few years, a large number of methods have been proposed to estimate intra-tumor heterogeneity from whole exome sequencing (WES) data, but the accuracy and robustness of these methods on real data remains elusive. Here we systematically apply and compare 6 computational methods to estimate tumor heterogeneity on 1,697 WES samples from the cancer genome atlas (TCGA) covering 3 cancer types (breast invasive carcinoma, bladder urothelial carcinoma, and head and neck squamous cell carcinoma), and two distinct input mutation sets. We observe significant differences between the estimates produced by different methods, and identify several likely confounding factors in heterogeneity assessment for the different methods. We further show that the prognostic value of tumor heterogeneity for survival prediction is limited in those datasets, and find no evidence that it improves over prognosis based on other clinical variables.In conclusion, heterogeneity inference from WES data on a single sample, and its use in cancer prognosis, should be considered with caution. Other approaches to assess intra-tumoral heterogeneity such as those based on multiple samples may be preferable for clinical applications.

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Tree inference for single-cell data

Cited by 82 publications

References 42 publications

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

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

Single-cell copy number calling and event history reconstruction

Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data

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