Single-cell analysis reveals new evolutionary complexity in uveal melanoma

Durante, Michael A.; Rodriguez, Daniel A.; Kurtenbach, Stefan; Kuznetsov, Jeffim N.; Sánchez, Margarita Dorantes; Decatur, Christina L.; Snyder, Helen; Feun, Lynn G.; Livingstone, Alan S.; Harbour, J. William

doi:10.1038/s41467-019-14256-1

Cited by 341 publications

(390 citation statements)

References 39 publications

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“…Besides merely plotting a single list of genes, PieParty also allows for more complex visualizations. As an example, we used scRNA-seq data from uveal melanoma (UM) tumors to demonstrate a more complex application 4 . There are two main classes of UM tumors, class 1, which rarely metastasize, and class 2, which frequently metastasize and have a high mortality rate.…”

Section: Resultsmentioning

confidence: 99%

“…scRNA-seq data for testis and UM are publicly available 3,4 , and were downloaded, analyzed with Seurat (Version 3.2.2) as previously described 4 . The pure macrophage population was generated by extracting cells with CD68 expression > 1.…”

Section: Methodsmentioning

confidence: 99%

“…The pure macrophage population was generated by extracting cells with CD68 expression > 1. The macrophages, monocytes, and undetermined lymphocytes population used for rare cell population identification was generated by extracting cell clusters based on cell type classifications generated from the original analysis 4 .…”

Section: Methodsmentioning

confidence: 99%

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PieParty: Visualizing cells from scRNA-seq data as pie charts

Kurtenbach

Dollar

Durante

et al. 2020

Preprint

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Single cell RNA sequencing (scRNA-seq) has been a transformative technology in many research fields. Dimensional reduction techniques such as UMAP and tSNE are used to visualize scRNA- seq data in two or three dimensions in order for cells to be clustered in biologically meaningful ways. Subsequently, gene expression is frequently mapped onto these plots to show the distribution of expression across cell types. However, plotting each cell with only one color leads to repetitive and unintuitive representations. Here, we present Pie Party, which allows scRNA-seq data to be plotted such that every cell is represented as a pie chart, where every slice in the pie charts correspond to the gene expression of individual genes, which allows for the simultaneous visualization of the expression of multiple genes and gene networks. The resulting figures are information dense, space efficient and highly intuitive. PieParty is publicly available on GitHub at https://github.com/harbourlab/PieParty.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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PieParty: Visualizing cells from scRNA-seq data as pie charts

Kurtenbach

Dollar

Durante

et al. 2020

Preprint

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“…Lastly, it was exciting to observe benefits of our methods on both the scDNA-seq and the scRNA-seq data. Although RNA-derived copy number profiles may not be as accurate as those derived from DNAs, previous studies 54 suggested that they can reasonably distinguish tumor clones. Our study further revealed the value of scRNA-seq data in lineage tracing and supported the notion that genomic profiles, even approximations, are more accurate than transcriptomic profiles in determining biological timing of cells.…”

Section: Discussionmentioning

confidence: 97%

Single-cell copy number lineage tracing enabling gene discovery

Wang

Mohanty

et al. 2020

Preprint

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Aneuploidy plays critical roles in genome evolution.Alleles, whose dosages affect the fitness of an ancestor, will have altered frequencies in the descendant populations upon perturbation.Single-cell sequencing enables comprehensive genome-wide copy number profiling of thousands of cells at various evolutionary stage and lineage. That makes it possible to discover dosage effects invisible at tissue level, provided that the cell lineages can be accurately reconstructed.Here, we present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles. We also present a statistical routine named lineage speciation analysis (LSA), which facilitates discovery of fitness-associated alterations and genes from SCCN lineage trees.We assessed our approaches using a variety of single-cell datasets. Overall, MEDALT appeared more accurate than phylogenetics approaches in reconstructing copy number lineage.From the single-cell DNA-sequencing data of 20 triple-negative breast cancer patients, our approaches effectively prioritized genes that are essential for breast cancer cell fitness and are predictive of patient survival, including those implicating convergent evolution. Similar benefits Recent advances in single-cell DNA sequencing (e.g., tagmentation based approach 15 and single-cell CNV solution by the 10X Genomics) have enabled large-scale acquisition of singlecell copy number (SCCN) profiles in tens of thousands of cells at around 100 kb resolution (~0.1X sequencing coverage per cell) [16][17][18][19] . Other platforms such as single-cell RNA-sequencing 20, 21 and single-cell ATAC-sequencing 22 have also been utilized for SCCN profiling.These SCCN profiles not only present a rich pool of genetic perturbations that are invisible at tissue level, but also potentiate reconstruction of cellular lineage, based on which the impact of an allele on cellular fitness can be measured. Thus, statistical approaches that integrate cellular lineage tracing with population genetic analysis 23 can enable discovery of novel disease genes and mechanisms of disease progression.So far, studies performing retrospective lineage tracing from single-cell data have largely been utilizing phylogenetics approaches designed to model species evolution, which is quite different from cellular evolution in terms of duration, scale, genetics and dynamics 24, 25 . Many existing phylogenetics approaches assume that genomic sites evolve independently and follow the socalled infinite site assumption (ISA) 26 . But in the context of aneuploidy, a genome site can often be altered repeatedly by different CNAs, due partly to constraints on genome and chromatin structures, properties of DNA replication/repairing 27 and functional selection. To apply conventional maximum parsimony approaches on SCCN data, one has to over-segment genomic regions and represent copy numbers as characters in disjoint intervals, which illrepresents the properties of DNAs and dis...

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“…A similar phase I/II study on mRNA transfected autologous DCs in high-risk uveal melanoma patients has recently been closed due to slow accrual [ 105 ]. Immunotherapy with monoclonal antibodies, such as antiprogrammed cell death protein 1 (PD-1) nivolumab, anticytotoxic T-lymphocyte-associated Protein 4 (CTLA-4) ipilimumab, and anti lymphocyte activation gene 3 (LAG-3) relatlimab, modulated the immune responses in the tumor microenvironment and interfered with tumor growth and spread [ 106 , 107 ]. A small sample size phase I/II pilot trial of adjuvant ipilimumab in high-risk primary uveal melanoma demonstrated that 80% of patients were disease-free at 36 months [ 108 ].…”

Section: Adjuvant Therapies and Surveillance Of Ummentioning

confidence: 99%

Molecular Insights and Emerging Strategies for Treatment of Metastatic Uveal Melanoma

Mallone

Sacchetti

Moramarco

2020

Cancers

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Uveal melanoma (UM) is the most common intraocular cancer. In recent decades, major advances have been achieved in the diagnosis and prognosis of UM allowing for tailored treatments. However, nearly 50% of patients still develop metastatic disease with survival rates of less than 1 year. There is currently no standard of adjuvant and metastatic treatment in UM, and available therapies are ineffective resulting from cutaneous melanoma protocols. Advances and novel treatment options including liver-directed therapies, immunotherapy, and targeted-therapy have been investigated in UM-dedicated clinical trials on single compounds or combinational therapies, with promising results. Therapies aimed at prolonging or targeting metastatic tumor dormancy provided encouraging results in other cancers, and need to be explored in UM. In this review, the latest progress in the diagnosis, prognosis, and treatment of UM in adjuvant and metastatic settings are discussed. In addition, novel insights into tumor genetics, biology and immunology, and the mechanisms underlying metastatic dormancy are discussed. As evident from the numerous studies discussed in this review, the increasing knowledge of this disease and the promising results from testing of novel individualized therapies could offer future perspectives for translating in clinical use.

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Single-cell analysis reveals new evolutionary complexity in uveal melanoma

Cited by 341 publications

References 39 publications

PieParty: Visualizing cells from scRNA-seq data as pie charts

PieParty: Visualizing cells from scRNA-seq data as pie charts

Single-cell copy number lineage tracing enabling gene discovery

Molecular Insights and Emerging Strategies for Treatment of Metastatic Uveal Melanoma

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