MEDALT: single-cell copy number lineage tracing enabling gene discovery

Wang, Fang; Wang, Qihan; Mohanty, Vakul; Liang, Shaoheng; Dou, Jun; Han, Jiaying; Minussi, Darlan Conterno; Gao, Ruli; Li, Ding; Navin, Nicholas E.; Chen, Ken

doi:10.1186/s13059-021-02291-5

Cited by 27 publications

(33 citation statements)

References 75 publications

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“…The annotated results were filtered using the list of high-frequency genes in cancers in COSMIC to obtain a list of cancer-related mutations in the sample. A pseudotime evolutionary tree of somatic mutated genes was built with MEDALT 65 , showing the driver gene in each branch. According to the calculated intercellular evolutionary distance, the R package CellScape was used to construct a single-cell cluster phylogenetic tree with the minimum spanning tree algorithm, and the corresponding drive genes were labeled at each evolutionary branch node.…”

Section: Methodsmentioning

confidence: 99%

Genomic alterations and evolution of cell clusters in metastatic invasive micropapillary carcinoma of the breast

et al. 2022

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Invasive micropapillary carcinoma (IMPC) has very high rates of lymphovascular invasion and lymph node metastasis and has been reported in several organs. However, the genomic mechanisms underlying its metastasis are unclear. Here, we perform whole-genome sequencing of tumor cell clusters from primary IMPC and paired axillary lymph node metastases. Cell clusters in multiple lymph node foci arise from a single subclone of the primary tumor. We find evidence that the monoclonal metastatic ancestor in primary IMPC shares high frequency copy-number loss of PRDM16 and IGSF9 and the copy number gain of ALDH2. Immunohistochemistry analysis further shows that low expression of IGSF9 and PRDM16 and high expression of ALDH2 are associated with lymph node metastasis and poor survival of patients with IMPC. We expect these genomic and evolutionary profiles to contribute to the accurate diagnosis of IMPC.

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Section: Methodsmentioning

confidence: 99%

Genomic alterations and evolution of cell clusters in metastatic invasive micropapillary carcinoma of the breast

et al. 2022

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“…lution was simulated on the level of the genome through chromosomal and segmental gains and losses but also copy-number neutral events including inversions and balanced translocations and complex events such as breakagefusion-bridges and WGDs (Methods). From these simulated genomes with varying mutation rates copy-number profiles were generated by counting genomic segments and subjected to different tree reconstruction strategies, including Euclidean and Manhattan distances with the neighbor joining [34] and minimum evolution [35] algorithms, as well as the tailored tool MEDALT [28]. MEDICC2 outperforms other methods for all ranges of mutation rates and tree sizes, especially in the presence of WGDs (Figure 2C, Supplementary Figure 2A-B) and independent of the tree metric used (Supplementary Figure 2C).…”

Section: Inferring Phylogenies From Scnas With Medicc2mentioning

confidence: 99%

“…For neighbor-joining we used the implementation of MEDICC2 and for the minimum evolution tree we used the function fastme.bal from the R package ape [63]. As a representative of algorithms that create minimum spanning trees (MST), we also compared against MEDALT [28] (Supplementary Figure 2A).…”

Section: Simulating Genome Evolutionmentioning

confidence: 99%

“…While several SCNA-based evolutionary inference methods have been proposed in the past [26][27][28][29], they do not model WGD events, frequently make use of the infinite sites assumption [30] and thus cannot infer parallel evolution, and do not deal with statistical dependencies between genomic loci. They are further often restricted to solving the much simpler problem of tree inference with fully sampled data, i.e.…”

Section: Introductionmentioning

confidence: 99%

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MEDICC2: whole-genome doubling aware copy-number phylogenies for cancer evolution

Kaufmann

Petković

Watkins

et al. 2021

Preprint

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Chromosomal instability (CIN) and somatic copy number alterations (SCNA) play a key role in the evolutionary process that shapes cancer genomes. SCNAs comprise many classes of clinically relevant events, such as localised amplifications, gains, losses, loss-of-heterozygosity (LOH) events, and recently discovered parallel evolutionary events revealed by multi-sample phasing. These events frequently appear jointly with whole genome doubling (WGD), a transformative event in tumour evolution, which generates tetraploid or near-tetraploid cells. WGD events are often clonal, occuring before the emergence of the most recent common ancestor, and have been associated with increased CIN, poor patient outcome and are currently being investigated as potential therapeutic targets. While SCNAs can provide a rich source of phylogenetic information, so far no method exists for phylogenetic inference from SCNAs that includes WGD events. Here we present MEDICC2, a new phylogenetic algorithm for allele-specific SCNA data based on a minimum-evolution criterion that explicitly models clonal and subclonal WGD events and that takes parallel evolutionary events into account. MEDICC2 can identify WGD events and quantify SCNA burden in single-sample studies and infer phylogenetic trees and ancestral genomes in multi-sample scenarios. In this scenario, it accurately locates clonal and subclonal WGD events as well as parallel evolutionary events in the evolutionary history of the tumour, timing SCNAs relative to each other. We use MEDICC2 to detect WGD events in 2778 tumours with 98.8% accuracy and show its ability to correctly place subclonal WGD events in simulated and real-world multi-sample tumours, while accurately inferring its phylogeny and parallel SCNA events. MEDICC2 is implemented in Python 3 and freely available under GPLv3 at https://bitbucket.org/schwarzlab/medicc2.

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“…In recent years, many computational methods have emerged for inferring the evolutionary histories of tumors from single-cell data. CHISEL [ 8 ], SCICoNE [ 9 ] and MEDALT [ 10 ] are the few methods that perform scCNA detection and also infer evolutionary histories. RobustClone [ 11 ] is a model free method that takes raw scSNV or scCNA genotype matrix as input to recover clone genotypes and infer tumor clone tree.…”

Section: Introductionmentioning

confidence: 99%

BiTSC 2: Bayesian inference of tumor clonal tree by joint analysis of single-cell SNV and CNA data

Chen

Gong

Wan

et al. 2022

Briefings in Bioinformatics

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The rapid development of single-cell DNA sequencing (scDNA-seq) technology has greatly enhanced the resolution of tumor cell profiling, providing an unprecedented perspective in characterizing intra-tumoral heterogeneity and understanding tumor progression and metastasis. However, prominent algorithms for constructing tumor phylogeny based on scDNA-seq data usually only take single nucleotide variations (SNVs) as markers, failing to consider the effect caused by copy number alterations (CNAs). Here, we propose BiTSC$^2$, Bayesian inference of Tumor clonal Tree by joint analysis of Single-Cell SNV and CNA data. BiTSC$^2$ takes raw reads from scDNA-seq as input, accounts for the overlapping of CNA and SNV, models allelic dropout rate, sequencing errors and missing rate, as well as assigns single cells into subclones. By applying Markov Chain Monte Carlo sampling, BiTSC$^2$ can simultaneously estimate the subclonal scCNA and scSNV genotype matrices, subclonal assignments and tumor subclonal evolutionary tree. In comparison with existing methods on synthetic and real tumor data, BiTSC$^2$ shows high accuracy in genotype recovery, subclonal assignment and tree reconstruction. BiTSC$^2$ also performs robustly in dealing with scDNA-seq data with low sequencing depth and variant missing rate. BiTSC$^2$ software is available at https://github.com/ucasdp/BiTSC2.

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MEDALT: single-cell copy number lineage tracing enabling gene discovery

Cited by 27 publications

References 75 publications

Genomic alterations and evolution of cell clusters in metastatic invasive micropapillary carcinoma of the breast

Genomic alterations and evolution of cell clusters in metastatic invasive micropapillary carcinoma of the breast

MEDICC2: whole-genome doubling aware copy-number phylogenies for cancer evolution

BiTSC 2: Bayesian inference of tumor clonal tree by joint analysis of single-cell SNV and CNA data

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