Identifying tumor clones in sparse single-cell mutation data

Myers, Matthew; Zaccaria, Simone; Raphael, Benjamin J.

doi:10.1093/bioinformatics/btaa449

Cited by 18 publications

(31 citation statements)

References 39 publications

(109 reference statements)

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“…Observe that the only difference between Eqs. (10) and 5is the use of DCF rather than CCF . To obtain a probabilistic model for the DCF d, we follow a similar procedure described in Section 2.3 above for the CCF, replacing the inverse transformation…”

Section: Descendant Cell Fractionmentioning

confidence: 99%

“…Quantifying the heterogeneity within a tumor is essential for understanding carcinogenesis and devising personalized treatment strategies [2][3][4] . While recent single-cell DNA sequencing technologies enable high-resolution measurements of tumor heterogeneity [5][6][7][8][9][10][11] , the vast majority of cancer studies in research and clinical settings [12][13][14] heterogeneity from SNVs is the Cancer Cell Fraction (CCF) -also known as the cellular prevalence or the mutation cellularity -which is the proportion of cancer cells that contain the SNV. CCFs form the basis for many cancer analyses including: studying tumor heterogeneity 13,15,16 , reconstructing clonal evolution and metastatic progression 13,[17][18][19] , identifying selection [20][21][22] , and analyzing changes in mutational processes over time [23][24][25] .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

DeCiFering the Elusive Cancer Cell Fraction in Tumor Heterogeneity and Evolution

Satas

Zaccaria

El-Kebir

et al. 2021

Preprint

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Most tumors are heterogeneous mixtures of normal cells and cancer cells, with individual cancer cells distinguished by somatic mutations that accumulated during the evolution of the tumor. The fundamental quantity used to measure tumor heterogeneity from somatic single-nucleotide variants (SNVs) is the Cancer Cell Fraction (CCF), or proportion of cancer cells that contain the SNV. However, in tumors containing copy-number aberrations (CNAs) -- e.g., most solid tumors -- the estimation of CCFs from DNA sequencing data is challenging because a CNA may alter the mutation multiplicity, or number of copies of an SNV. Existing methods to estimate CCFs rely on the restrictive Constant Mutation Multiplicity (CMM) assumption that the mutation multiplicity is constant across all tumor cells containing the mutation. However, the CMM assumption is commonly violated in tumors containing CNAs, and thus CCFs computed under the CMM assumption may yield unrealistic conclusions about tumor heterogeneity and evolution. The CCF also has a second limitation for phylogenetic analysis: the CCF measures the presence of a mutation at the present time, but SNVs may be lost during the evolution of a tumor due to deletions of chromosomal segments. Thus, SNVs that co-occur on the same phylogenetic branch may have different CCFs. In this work, we address these limitations of the CCF in two ways. First, we show how to compute the CCF of an SNV under a less restrictive and more realistic assumption called the Single Split Copy Number (SSCN) assumption. Second, we introduce a novel statistic, the descendant cell fraction (DCF), that quantifies both the prevalence of an SNV and the past evolutionary history of SNVs under an evolutionary model that allows for mutation losses. That is, SNVs that co-occur on the same phylogenetic branch will have the same DCF. We implement these ideas in an algorithm named DeCiFer. DeCiFer computes the DCFs of SNVs from read counts and copy-number proportions and also infers clusters of mutations that are suitable for phylogenetic analysis. We show that DeCiFer clusters SNVs more accurately than existing methods on simulated data containing mutation losses. We apply DeCiFer to sequencing data from 49 metastatic prostate cancer samples and show that DeCiFer produces more parsimonious and reasonable reconstructions of tumor evolution compared to previous approaches. Thus, DeCiFer enables more accurate quantification of intra-tumor heterogeneity and improves downstream inference of tumor evolution.

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Section: Descendant Cell Fractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DeCiFering the Elusive Cancer Cell Fraction in Tumor Heterogeneity and Evolution

Satas

Zaccaria

El-Kebir

et al. 2021

Preprint

Self Cite

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“…We compared SECEDO's performance to that of SBMClone (Myers et al, 2020), the current state of the art in SNV-based clustering. Since SBMClone was reported to work only at coverage ≥ 0.2x, and the coverage of the breast cancer dataset is 0.03x, we created higher coverage data in silico by merging sequencing data from cells reported to be in the same cluster by CHISEL.…”

Section: Secedo Recovers Tumor Subclones With Average Precision Of 97% On Simulated Datamentioning

confidence: 99%

“…Hence, its use has been limited to the inference of copy number variations (CNVs) and alterations (CNAs) (10X Genomics, 2018;Durante et al, 2020;Velazquez-Villarreal et al, 2020;Zaccaria and Raphael, 2021). The attempts to also use these data for the identification of tumor subclones based solely on SNVs have so far failed to provide a solution that would be able to recover the clonal composition at the original sequencing depth (Myers et al, 2020); in particular, SBMClone, the algorithm of Myers et al (2020), requires a minimum coverage of ≥ 0.2x per cell, roughly four times more than what is currently achievable using the 10X Genomics technology (10X Genomics, 2018;Velazquez-Villarreal et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing

Rozhoňová

Danciu

Stark

et al. 2021

Preprint

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Recently developed single-cell DNA sequencing technologies enable whole-genome, amplification-free sequencing of thousands of cells at the cost of ultra-low coverage of the sequenced data (< 0.05x per cell), which mostly limits their usage to the identification of copy number alterations (CNAs) in multi-megabase segments. Aside from CNA-based subclone detection, single-nucleotide variant (SNV)-based subclone detection may contribute to a more comprehensive view on intra-tumor heterogeneity. Due to the low coverage of the data, the identification of SNVs is only possible when superimposing the sequenced genomes of hundreds of genetically similar cells. Here we present Single Cell Data Tumor Clusterer (SECEDO, lat. ‘to separate’), a new method to cluster tumor cells based solely on SNVs, inferred on ultra-low coverage single-cell DNA sequencing data. The core aspects of the method are an efficient Bayesian filtering of relevant loci and the exploitation of read overlaps and phasing information. We applied SECEDO to a synthetic dataset simulating 7,250 cells and eight tumor subclones from a single patient, and were able to accurately reconstruct the clonal composition, detecting 92.11% of the somatic SNVs, with the smallest clusters representing only 6.9% of the total population. When applied to four real single-cell sequencing datasets from a breast cancer patient, SECEDO was able to recover the major clonal composition in each dataset at the original sequencing depth of 0.03x per cell, an 8-fold improvement relative to the state of the art. Variant calling on the resulting clusters recovered more than twice as many SNVs with double the allelic ratio compared to calling on all cells together, demonstrating the utility of SECEDO.SECEDO is implemented in C++ and is publicly available at https://github.com/ratschlab/secedo.

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“…There has been a great interest of developing computational tools for reasoning tumor trees by addressing the aforementioned issues in SCS data (Jahn et al, 2016;Kuipers et al, 2017;Zafar et al, 2017Zafar et al, , 2019El-Kebir, 2018;Chen et al, 2020;Myers et al, 2020;Sadeqi Azer et al, 2020). SCITE (Jahn et al, 2016) exploits a Markov Chain Monte Carlo (MCMC) based approach to jointly search the best scoring mutation tree and FN rate.…”

Section: Introductionmentioning

confidence: 99%

GRMT: Generative Reconstruction of Mutation Tree From Scratch Using Single-Cell Sequencing Data

Yu¹,

Liu²,

Du³

et al. 2021

Front. Genet.

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Single-cell sequencing (SCS) now promises the landscape of genetic diversity at single cell level, and is particularly useful to reconstruct the evolutionary history of tumor. There are multiple types of noise that make the SCS data notoriously error-prone, and significantly complicate tumor tree reconstruction. Existing methods for tumor phylogeny estimation suffer from either high computational intensity or low-resolution indication of clonal architecture, giving a necessity of developing new methods for efficient and accurate reconstruction of tumor trees. We introduce GRMT (Generative Reconstruction of Mutation Tree from scratch), a method for inferring tumor mutation tree from SCS data. GRMT exploits the k-Dollo parsimony model to allow each mutation to be gained once and lost at most k times. Under this constraint on mutation evolution, GRMT searches for mutation tree structures from a perspective of tree generation from scratch, and implements it to an iterative process that gradually increases the tree size by introducing a new mutation per time until a complete tree structure that contains all mutations is obtained. This enables GRMT to efficiently recover the chronological order of mutations and scale well to large datasets. Extensive evaluations on simulated and real datasets suggest GRMT outperforms the state-of-the-arts in multiple performance metrics. The GRMT software is freely available at https://github.com/qasimyu/grmt.

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Identifying tumor clones in sparse single-cell mutation data

Cited by 18 publications

References 39 publications

DeCiFering the Elusive Cancer Cell Fraction in Tumor Heterogeneity and Evolution

DeCiFering the Elusive Cancer Cell Fraction in Tumor Heterogeneity and Evolution

SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing

GRMT: Generative Reconstruction of Mutation Tree From Scratch Using Single-Cell Sequencing Data

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