Inference of clonal selection in cancer populations using single-cell sequencing data

Skums, Pavel; Tsyvina, Viachaslau; Zelikovsky, Alexander

doi:10.1093/bioinformatics/btz392

Cited by 15 publications

(11 citation statements)

References 47 publications

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“…the evolutionary likelihood of the most probable fitness landscape, as calculated by our recently published tool SCIFIL [ 18 ]. Roughly speaking, this likelihood measures the probability to observe given subclone frequencies when the clonal population evolutionary trajectory over the most likely inferred fitness landscape is described by the tree T .…”

Section: Resultsmentioning

confidence: 99%

“…In cancer genomics, examples of such non-linear behaviour include synthetic lethality [8,11], epistasis [12,13] or genetic interactions [14,15]. When phenotypic effects are associated with the reproductive success, they are often summarized within the concept of fitness landscape [16][17][18][19]. Within this concept, each genotype is assigned a quantitative measure of its replicative success (fitness or height of the landscape).…”

Section: Introductionmentioning

confidence: 99%

“…However, in contrast to the phylogenetic inference, very few computational tools for assessment of cancer evolutionary parameters are currently available [ 19 , 29 , 30 ]. In particular, several studies recently addressed the problem of inference of cancer fitness landscapes [ 18 , 35 ]. In this paper, we expand the cancer evolutionary analysis toolkit by proposing a computational method for inference of mutability landscapes and quantification of genetic instability within clonal cancer populations.…”

Section: Introductionmentioning

confidence: 99%

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Inference of mutability landscapes of tumors from single cell sequencing data

et al. 2020

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One of the hallmarks of cancer is the extremely high mutability and genetic instability of tumor cells. Inherent heterogeneity of intra-tumor populations manifests itself in high variability of clone instability rates. Analogously to fitness landscapes, the instability rates of clonal populations form their mutability landscapes. Here, we present MULAN (MUtability LANdscape inference), a maximum-likelihood computational framework for inference of mutation rates of individual cancer subclones using single-cell sequencing data. It utilizes the partial information about the orders of mutation events provided by cancer mutation trees and extends it by inferring full evolutionary history and mutability landscape of a tumor. Evaluation of mutation rates on the level of subclones rather than individual genes allows to capture the effects of genomic interactions and epistasis. We estimate the accuracy of our approach and demonstrate that it can be used to study the evolution of genetic instability and infer tumor evolutionary history from experimental data. MULAN is available at https://github.com/compbel/MULAN.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Inference of mutability landscapes of tumors from single cell sequencing data

et al. 2020

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“…A recent example integrating population genetics approaches with phylogenetics, is a computational model for inference of fitness landscapes of cancer clone populations using scDNA-seq data, SCIFIL [Skums et al, 2019]. It estimates the maximum likelihood fitness of clone variants by fitting a replicator equation model onto a character-based tumor phylogeny.…”

Section: Statusmentioning

confidence: 99%

12 Grand challenges in single-cell data science

Laehnemann

Köster

Szczurek

et al. 2019

Preprint

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The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology; analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis; together, they give rise to the new realm of 'Single Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single Cell Data Science' for the coming years.

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“…In cancer development, this variation can be used to trace the cellular ancestry of tumour subclones and metastases [8][9][10][11] , and to characterise the evolutionary dynamics of cancer progression 12,13 . In the long run, methods that account for the dynamics of mutational signatures in cellular evolution will improve diagnosis, treatment and prognosis of diseases for which somatic alterations are a key factor.…”

Section: Introductionmentioning

confidence: 99%

ProSolo: Accurate Variant Calling from Single Cell DNA Sequencing Data

Laehnemann

Köster

Fischer

et al. 2020

Preprint

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1Obtaining accurate mutational profiles from single cell DNA is essential for the analysis of genomic cell-to-cell heterogeneity at the finest level of resolution. However, sequencing libraries suitable for genotyping require whole genome amplification, which introduces allelic bias and copy errors. As a result, single cell DNA sequencing data violates the assumptions of variant callers developed for bulk sequencing, which when applied to single cells generate significant numbers of false positives and false negatives. Only dedicated models accounting for amplification bias and errors will be able to provide more accurate calls.We present ProSolo, a probabilistic model for calling single nucleotide variants from multiple displacement amplified single cell DNA sequencing data. It introduces a mechanistically motivated empirical model of amplification bias that improves the quantification of genotyping uncertainty. To account for amplification errors, it jointly models the single cell sample with a bulk sequencing sample from the same cell population-also enabling a biologically relevant imputation of missing genotypes for the single cell.Through these innovations, ProSolo achieves substantially higher performance in calling and genotyping single nucleotide variants in single cells in comparison to all state-of-the-art tools. Moreover, ProSolo implements the first approach to control the false discovery rate reliably and flexibly; not only for single nucleotide variant calls, but also for artefacts of single cell methodology that one may wish to identify, such as allele dropout. ProSolo's model is implemented into a flexible framework, encouraging extensions. The source code and usage instructions are available at: https://github.com/prosolo/prosolo ADO -allele dropout, alt -alternative, err -error, het -heterozygous, hom -homozygous, ref -reference. 6/28 18/28

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Inference of clonal selection in cancer populations using single-cell sequencing data

Cited by 15 publications

References 47 publications

Inference of mutability landscapes of tumors from single cell sequencing data

Inference of mutability landscapes of tumors from single cell sequencing data

12 Grand challenges in single-cell data science

ProSolo: Accurate Variant Calling from Single Cell DNA Sequencing Data

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