Inferring Cancer Progression from Single-cell Sequencing while Allowing Mutation Losses

Ciccolella, Simone; Gomez, Mauricio Soto; Patterson, Murray; Vedova, Gianluca Della; Hajirasouliha, Iman; Bonizzoni, Paola

doi:10.1101/268243

Cited by 24 publications

(32 citation statements)

References 37 publications

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“…Recently, several methods [28][29][30][31][32][33] allows mutation to be gained more than once. A challenge in using these less stringent evolutionary models is that they increase the ambiguity in phylogenetic reconstruction ( Fig.…”

Section: Missionmentioning

confidence: 99%

“…We evaluated the phylogenies output by the methods by two measures that have been previously used in tumor evolution studies 11,15,23,28,29,41 . First, the mutation matrix error M (B,B) = 1 mn m i=1 n j=1 |b i,j − b i,j | is the normalized Hamming distance between the inferred binary mutation matrixB and the true binary mutation matrix B and assesses the accuracy of the error-corrected mutation profiles for each observed cell.…”

Section: Simulated Datamentioning

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%

Section: Simulated Datamentioning

confidence: 99%

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

Satas

Zaccaria

Mon

et al. 2019

Preprint

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“…We executed SCITE [16] and SPhyR [6] on both the obtained clusters and on the unclustered datasets, to understand the effect of the clustering on the tools. Furthermore we selected our own cancer progression inference tool SASC [4] that is not able to complete in reasonable time on the unclustered data, due to the higher complexity of the search space of the solutions it generates. We performed the inference with all the clustering methods used as a preprocessing step.…”

Section: Assessing the Impact Of A Clusteringmentioning

confidence: 99%

“…While this assumption has only limited biological validity [19,2] it reduces greatly the space of all possible solutions, making feasible several approaches [7,11], which are at least partially inspired by a classical linear time algorithm [10] for reconstructing the phylogeny from noise-free character data: this latter computational problem is called the perfect phylogeny problem.The newer single cell sequencing (SCS) technology provides a much finer level of resolution: in fact we can determine whether or not a given cell has a mutation, therefore avoiding the notion of sample and the approximations implied by the use of samples. Still, SCS is expensive and plagued by high dropout, i.e., missing values, and false negative rates.Nowadays, we are witnessing a decrease in SCS costs, coupled with improvements in the dropout and false negative rates, stimulating the research on tools for tumor evolution inference from SCS data [16,25,4,5,31,6]. We believe that this line of research is going to become even more important in the next few years, since currently available SCS data is associated with a very large solution space.…”

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confidence: 99%

Effective Clustering for Single Cell Sequencing Cancer Data

Ciccolella

Patterson

Bonizzoni

et al. 2019

Preprint

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Background. Single cell sequencing (SCS) technologies provide a level of resolution that makes it indispensable for inferring from a sequenced tumor, evolutionary trees or phylogenies representing an accumulation of cancerous mutations. A drawback of SCS is elevated false negative and missing value rates, resulting in a large space of possible solutions, which in turn makes infeasible using some approaches and tools. While this has not inhibited the development of methods for inferring phylogenies from SCS data, the continuing increase in size and resolution of these data begin to put a strain on such methods. One possible solution is to reduce the size of an SCS instance -usually represented as a matrix of presence, absence and missing values of the mutations found in the different sequenced cells -and infer this tree from the reduced-size instance. Previous approaches have used k-means to this end, clustering groups of mutations and/or cells, and using these means as the reduced instance.Results. In this work, we benchmark a variety of commonly used methods aimed at clustering vector, or matrix data -here representing SCS instances: with a focus on how effective these methods are for the purpose of inferring tumor evolutionary trees from these SCS instances. A trend we observe is that those methods designed for clustering data of a categorical nature (SCS data having three categories: present, absent and missing) -namely k-modes, and a method we have devised called celluloid (see Methods) -perform much better than all of the other methods. We demonstrate that these categorical methods cluster mutations with high precision: never pairing too many mutations that are unrelated in the ground truth, but also obtain accurate results in terms of the phylogeny inferred downstream from the reduced instance produced by such a method. Finally, we demonstrate the usefulness of a clustering step by applying the entire pipeline (clustering + inference method) to a real dataset, showing a significant reduction in the runtime, raising considerably the upper bound on the size of SCS instances which can be solved in practice. BackgroundA tumor, at the time of detection, usually by performing a biopsy on the extracted tissue, is the result of a tumultuous evolutionary process, originating from a single tumor cell -the founder cell [23] -that has acquired a driver mutation, which inhibits control on the proliferation of subsequent cancer cells. From that moment, the combination of unrestrained proliferation and a very hostile environment -as the immune system fights for survival, and so the tumor cells, under extreme selection pressure, have to disguise themselves to avoid being attacked, compete with each other, all while having to thrive while getting low levels of oxygen -produces the accumulation of highly elevated number of mutations, including structural variations. This model of tumor evolution is called the clonal model [23], since a clone is a population of cells carrying the same set of mutations. Understanding this clon...

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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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Inferring Cancer Progression from Single-cell Sequencing while Allowing Mutation Losses

Cited by 24 publications

References 37 publications

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

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

Effective Clustering for Single Cell Sequencing Cancer Data

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

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