Advancing Cancer Research and Medicine with Single-Cell Genomics

Lim, Bora; Lin, Yiyun; Navin, Nicholas E.

doi:10.1016/j.ccell.2020.03.008

Cited by 220 publications

(187 citation statements)

References 147 publications

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“…Pr 0 (x) is the probability of observing the true genotype x : (6) and Pr 1 (x, y) is the probability of observing a genotype y with a d(x, y) of 1 from the true genotype x :…”

Section: Single-cell Genotype Error Modelmentioning

confidence: 99%

CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data

Kozlov

Alves

Stamatakis

et al. 2020

Preprint

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We have developed a maximum likelihood framework called CellPhy for inferring phylogenetic trees from single-cell DNA sequencing (scDNA-seq) data, that can be directly applied to somatic cells and clones. CellPhy is based on a finite-site Markov nucleotide substitution model with 10 diploid states, akin to those typically used in statistical phylogenetics. It includes a dedicated error function for single cells that explicitly incorporates amplification/sequencing error and allelic dropout (ADO). Moreover, it can explicitly consider the uncertainty of the variant calling process by using genotype likelihoods as input. We implemented CellPhy in a widely used open-source phylogenetic inference package (RAxML-NG) that provides statistical confidence measurements on the estimated tree and scales particularly well on large phylogenies with hundreds or even thousands of cells. To benchmark CellPhy, we carried out 19,400 coalescent simulations of cell samples from exponentially-growing tumors for which the true phylogeny was known. We evolved single-cell diploid DNA genotypes along the simulated genealogies under different scenarios including infinite- and finite-sites nucleotide mutation models, trinucleotide mutational signatures, sequencing and amplification errors, allele dropouts, and doublet cells. Our simulations suggest that CellPhy is robust to amplification/sequencing errors and to ADO and that it outperforms the state-of-the-art methods under realistic scDNA-seq scenarios both in terms of accuracy and speed. In addition, we sequenced 24 single-cell whole genomes from a colorectal cancer, and together with three published scDNA-seq data sets, analyzed them to illustrate how CellPhy can provide more reliable biological insights than competing methods. CellPhy is freely available at https://github.com/amkozlov/cellphy.

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“…Pr 0 (x) is the probability of observing the true genotype x : (6) and Pr 1 (x, y) is the probability of observing a genotype y with a d(x, y) of 1 from the true genotype x :…”

Section: Single-cell Genotype Error Modelmentioning

confidence: 99%

CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data

Kozlov

Alves

Stamatakis

et al. 2020

Preprint

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“…Since scRNA-seq technology is still an evolving field requiring constant improvement in technologies starting from source of tissues to dissociation protocols, sequencing techniques, and bioinformatics tools (Lim et al, 2020), it is likely that clusters that we identified here will undergo further refinement in the future. Thus, it is appropriate to compare what has been done in the past to the current data.…”

Section: Complexities In Breast Epithelial Cell Types; Past and The Pmentioning

confidence: 99%

“…Since normal tissue itself is composed of multiple cell types, fine mapping of these cell types and identifying potential cancer vulnerable cell population in normal tissues would aid in identifying and characterizing organ-specific cell-of-origin of cancers. Recent advances in single cell techniques including scRNA-seq, sc-Epigenetics, scDNA-seq and scProteomics-atlas are enabling further refinement of cell types within normal and diseased tissues (Lim et al, 2020). For example, using reduction mammoplasty samples and cells flow sorted based on CD49f/EpCAM, Nguyen et al identified three epithelial cell types in the normal breasts (Nguyen et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…However, Bach et al observed 15 epithelial cell types in the mouse mammary gland (Bach et al, 2017). A concern has been raised about the reproducibility of data, which is likely influenced by the types of tissues used, duration between tissue collection and sequencing, and dissociation protocol (Lim et al, 2020). While these issues can be standardized for studies involving mouse tissues, standardizing is difficult for studies that utilize human tissues collected after a surgical procedure.…”

Section: Introductionmentioning

confidence: 99%

“…While these issues can be standardized for studies involving mouse tissues, standardizing is difficult for studies that utilize human tissues collected after a surgical procedure. In this regard, in an elegant review, Lim et al recently proposed the need to establish a rapid tissue dissociation program to advance single cell technology for clinical applications (Lim et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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A single cell atlas of the healthy breast tissues reveal clinically relevant clusters of breast epithelial cells

Bhat‐Nakshatri

Gao

McGuire

et al. 2020

Preprint

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Significance:This study elucidates different epithelial cell types of the normal breasts and identifies a minor subpopulation of cells from which the majority of breast cancers may originate. This observation should help to develop methods to characterize breast tumors based on cell-of-origin. Although it was suggested that intrinsic subtypes of breast cancers have distinct cells of origins, this study suggests multiple cell-of-origin for an intrinsic subtype of breast cancer, including for hormone responsive breast cancers. Cell-of-origin signatures allowed survival-associated subclassification of intrinsic subtypes. Critically, this normal breast cell atlas would allow for the classification of genes differentially expressed in a breast tumor compared to normal breast due to the cell-of-origin of tumor and those that are acquired due to genomic aberrations.

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