Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL

Zaccaria, Simone; Raphael, Benjamin J.

doi:10.1038/s41587-020-0661-6

Cited by 123 publications

(251 citation statements)

References 75 publications

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“…Since MEDICC2 is agnostic of the sequencing modality used to generate its input, future advances that increase CNP resolution will be usable by MEDICC2 without modification. Here, single-cell sequencing technologies will prove very useful and are beginning to enter the 15 mainstream [47] for both total copy number based [48] and allele-specific CNPs [49] . While tailored adaptations of the MED principle for single-cell data have recently been proposed in the form of MEDALT [10] , it does not take allele-specific SCNAs or WGD events into account.…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

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

Kaufmann

Petković

Watkins

et al. 2021

Preprint

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“…On the other hand, germline variants (a.k.a., single nucleotide polymorphisms, SNPs) are more widely observed in single-cell sequencing data, even in shallow droplet-based platforms, e.g., 10x Genomics, thanks to the large candidate list (around 7 million SNPs in human population with frequency > 5% (1000 Genomes Project Consortium, 2015)). Germline SNPs are not only perfect natural barcodes when multiplexing cells from multiple individuals (Huang et al, 2019), but also important in implying functional regulation via cellular eQTL analysis or allele specific expression (Cuomo et al, 2020), and allelic imbalance caused by copy number variation (Fan et al, 2018;Zaccaria and Raphael, 2020).…”

Section: Introductionmentioning

confidence: 99%

Cellsnp-lite: an efficient tool for genotyping single cells

Huang¹,

Huang

2021

Preprint

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SummarySingle-cell sequencing is an increasingly used technology and has promising applications in basic research and clinical translations. However, genotyping methods developed for bulk sequencing data have not been well adapted for single-cell data, in terms of both computational parallelization and simplified user interface. Here we introduce a software, cellsnp-lite, implemented in C/C++ and based on well supported package htslib, for genotyping in single-cell sequencing data for both droplet and well based platforms. On various experimental data sets, it shows substantial improvement in computational speed and memory efficiency with retaining highly concordant results compared to existing methods. Cellsnp-lite therefore lightens the genetic analysis for increasingly large single-cell data.AvailabilityThe source code is freely available at https://github.com/single-cell-genetics/cellsnp-lite.Contactyuanhua@hku.hk

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“…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%

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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Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL

Cited by 123 publications

References 75 publications

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

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

Cellsnp-lite: an efficient tool for genotyping single cells

DeCiFering the Elusive Cancer Cell Fraction in Tumor Heterogeneity and Evolution

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