Phertilizer: Growing a Clonal Tree from Ultra-low Coverage Single-cell DNA Sequencing of Tumors

Weber, Leah L.; Zhang, Chuanyi; Ochoa, Idoia; El-Kebir, Mohammed

doi:10.1101/2022.04.18.488655

Cited by 2 publications

(3 citation statements)

References 70 publications

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“…We varied nine variables in the simulation for testing SCsnvcna and compared it with the most state-of-the-art method, SCARLET [26], on these nine datasets. We did not compare SC-snvcna with Dorri, et al [8] and Phertilizer [30] mainly because like SCARLET, SCsnvcna infers the SNV placement on a CNA tree, assuming that the CNA tree is given, whereas Dorri, et al [8] and Phertilizer [30] focus on inferring a phylogenetic tree based on CNAs or both CNAs and SNVs. The nine variables are the number of subclones, false positive rates, false negative rates, missing rates, mutation loss rates, number of cells, number of mutations, a variable in the Beta splitting model for CNA tree generation, and lastly, the standard deviation of the CPs between SNV cells and CNA cells.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, BiTSC 2 does not explicitly output the placement of mutations on the tree whereas COMPASS is designed mainly for Mission Bio Tapestri platform data which has been used for targeted PCR and have limited coverage on the genome. Phertilizer [30] infers a phylogenetic tree with both SNVs and CNAs, but the SNVs are inferred from the cells sequenced mainly for CNA detection, leading to a high missing rate in SNVs. Although Phertilizer tries to increase the detection rate of SNVs by inferring the clones of the cells, the SNV detection rate depends heavily on the size of the clones, the sequencing coverage of the cells and the number of cells being sequenced.…”

Section: Introductionmentioning

confidence: 99%

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SCsnvcna: Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data

Zhang

Bass

Irianto

et al. 2022

Preprint

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Single-cell DNA sequencing enables the construction of evolutionary trees that can reveal how tumors gain mutations and grow. Different whole genome amplification (WGA) procedures render genomic materials of different characteristics, often suitable for the detection of either single nucleotide variation (SNV) or for copy number aberration (CNA), but not for both, hindering the placement of both SNVs and CNAs on the same phylogenetic tree for the study of interplay of SNVs and CNAs. SCARLET places SNVs on a CNA tree, a tree derived based on the copy number profiles, while considering SNV loss due to copy number losses. However, SCARLET requires that the SNVs and CNAs are detected from the same sets of cells, which is technically challenging due to the sequencing errors or the low sequencing coverage associated with a particular WGA procedure. Here we presented a novel computational tool, SCsnvcna, that aims at placing SNVs on a CNA tree whereas the sets of cells rendering the SNVs and CNAs are independent, thus is more practical in terms of the technical challenge from single cell WGA process. SCsnvcna is a Bayesian probabilistic model that utilizes both the genotype constraints on the tree and the cellular prevalence (CP) to search the solution that has the highest joint probability. Both simulated and real datasets show that SCsnvcna is highly accurate in predicting the placement of SNVs and SNV cells. In addition, SCsnvcna has a precise prediction of SNV losses due to copy number loss.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SCsnvcna: Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data

Zhang

Bass

Irianto

et al. 2022

Preprint

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“…Multiple evolutionary models have been used to construct tumor phylogenies from scDNA-seq data. Early works [17–21] used SNVs as evolutionary markers, and relied on the infinite-sites model [22] which states that an SNV can be gained only once and never be subsequently lost in the phylogeny. While the same SNV occurring independently more than once is rare [23], loss of SNVs due to copy-number deletions is common in cancer [24].…”

Section: Introductionmentioning

confidence: 99%

ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model

Sashittal

Zhang

Iacobuzio-Donahue

et al. 2023

Preprint

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Tumors consist of subpopulations of cells that harbor distinct collections of somatic mutations. These mutations range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). While many approaches infer tumor phylogenies using SNVs as phylogenetic markers, CNAs that overlap SNVs may lead to erroneous phylogenetic inference. Specifically, an SNV may be lost in a cell due to a deletion of the genomic segment containing the SNV. Unfortunately, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs. For instance, recent targeted scDNA-seq technologies, such as Mission Bio Tapestri, measure SNVs with high fidelity in individual cells, but yield much less reliable measurements of CNAs. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers and partial information about CNAs in the form of clustering of cells with similar copy-number profiles. This copy-number clustering constrains where loss of SNVs can occur in the phylogeny. We develop ConDoR (Constrained Dollo Reconstruction), an algorithm to infer tumor phylogenies from targeted scDNA-seq data using the constrained k-Dollo model. We show that ConDoR outperforms existing methods on simulated data. We use ConDoR to analyze a new multi-region targeted scDNA-seq dataset of 2153 cells from a pancreatic ductal adenocarcinoma (PDAC) tumor and produce a more plausible phylogeny compared to existing methods that conforms to histological results for the tumor from a previous study. We also analyze a metastatic colorectal cancer dataset, deriving a more parsimonious phylogeny than previously published analyses and with a simpler monoclonal origin of metastasis compared to the original study.

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Phertilizer: Growing a Clonal Tree from Ultra-low Coverage Single-cell DNA Sequencing of Tumors

Cited by 2 publications

References 70 publications

SCsnvcna: Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data

SCsnvcna: Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data

ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model

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