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E-scape: interactive visualization of single-cell phylogenetics and cancer evolution

Nature Methods volume 14, pages 549–550 (2017)Cite this article

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Populations of cancer cells emerge through branched evolutionary processes characterized by variable cell fitness and differential growth. Cell population dynamics fundamentally impact biological properties and clinical endpoints, motivating key questions asked by cancer-focused biomedical investigators. Recently, it has become possible to analyze how mutations are distributed across time1,2 and space3,4 in cell populations, with implications for our understanding of treatment resistance, cancer progression5 and metastasis4.

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Figure 1: The E-scape tool suite.

References

  1. Ding, L. et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481, 506–510 (2012).

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  2. Eirew, P. et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature 518, 422–426 (2015).

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  3. Gundem, G. et al. The evolutionary history of lethal metastatic prostate cancer. Nature 520, 353–357 (2015).

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  4. McPherson, A. et al. Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer. Nat. Genet. 48, 758–767 (2016).

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  5. Kridel, R. et al. Histological transformation and progression in follicular lymphoma: a clonal evolution study. PLoS Med. 13, e1002197 (2016).

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  6. Wang, Y. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).

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Acknowledgements

We wish to acknowledge the generous long-term funding provided by the BC Cancer Foundation. The project was supported by a Canadian Cancer Society Research Institute Innovation grant to S.P.S. (702532), a Genome Canada/Genome BC Disruptive Innovation in Genomics grant to S.P.S. and C.N. (232SCG), and a Terry Fox Research Institute Program Project grant to S.P.S. (1021). S.P.S. is supported by the Canada Research Chairs program, a Canadian Institutes for Health Research Foundation Grant (FDN-143246), and is a Michael Smith Foundation for Health Research scholar. M.A.S. and A.M. are supported by NSERC CGS scholarships. A.R. is supported by a CIHR CGS scholarship.

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Author notes

  1. Maia A Smith and Cydney B Nielsen: These authors contributed equally to this work.

Authors and Affiliations

  1. Bioinformatics Graduate Program, University of British Columbia, Vancouver, British Columbia, Canada

    Maia A Smith

  2. Department of Molecular Oncology, British Columbia Cancer Agency, Vancouver, British Columbia, Canada

    Maia A Smith, Cydney B Nielsen, Fong Chun Chan, Andrew McPherson, Andrew Roth, Hossein Farahani, Daniel Machev, Adi Steif & Sohrab P Shah

  3. Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada

    Cydney B Nielsen & Sohrab P Shah

Authors
  1. Maia A Smith
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Corresponding author

Correspondence to Sohrab P Shah.

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Competing interests

S.S. is a founder and shareholder of Contextual Genomics Inc., a genomics-based molecular diagnostics company.

Integrated supplementary information

Supplementary Figure 1 A schematic of the computational processing workflow of E-scape.

The user prepares the data inputs, which will vary in content and data type depending on the desired visualization. The user then calls the desired E-scape htmlwidget. From this point, the input is validated and any computationally intensive processing occurs before the data are sent to the web component. The data are then restructured for use in d3.js functions, which translate the data into visual elements displayed in the user's browser. The user may share a static visualization by directly pressing the download buttons on the visualization. Alternatively, the user may share an interactive visualization by calling the htmlwidget from an RMarkdown document and then knitting the document to html.

Supplementary Figure 2 TimeScape clone and mutation selection functionality.

(a) Clicking a clone in the clonal phylogeny filters the mutation table to show only those mutations emerging in the clicked clone. (b) Clicking a mutation displays its variant allele frequency (VAF) across time, and highlights the clonal phylogeny branch in which it originated. Selections can be released by clicking the refresh button in the toolbar (circular arrow). For the interactive TimeScape, see link in Supplementary Note 1. Data are from AML patient 933124 from Ding et al. Nature 481, 506–510 (2012).

Supplementary Figure 3 Temporal clonal expansion patterns in transformed follicular lymphomas.

Clones that are more prevalent at time point 1 (T1; diagnostic specimen) are colored light purple; clones that are more prevalent at time point 2 (T2; histologically transformed specimen) are shown in light orange. For the interactive TimeScapes, see link in Supplementary Note 1. Data are from AML patient 933124 from Kridel et al. PLoS Medicine 13(12):e1002197 (2016).

Supplementary Figure 4 Integration of CellScape and TimeScape visualizations.

(a) A CellScape view (top panel) of single cell targeted mutation data and an integrated TimeScape view (bottom panel) automatically generated from the single cell data. (b) Mouseover of time point “X1” highlights the phylogenetic tree nodes and heatmap rows associated with this time point. (c) Mouseover of a clone in the TimeScape clonal phylogeny highlights the corresponding clone in the temporal view, the single cell phylogeny, and the heatmap. (d) Mouseover of a heatmap cell reveals its Variant Allele Frequency (VAF), mutation site, and single cell ID (not shown). For the interactive version, see link in Supplementary Note 1. Data are from Eirew et al. Nature 518, 422–426 (2015).

Supplementary Figure 5 Alternate representations of clonal prevalences in MapScape.

a) The default view of MapScape displays clonal prevalences per site as cellular aggregates where the colored areas are proportional to the clonal prevalence of the corresponding clone. (b) Alternatively, MapScape can display clonal prevalences per site as donut charts (toggle using the arrow button in the toolbar). For the interactive MapScape, see link in Supplementary Note 1. Data are from patient A21 from Gundem et al. Nature 520, 353–357 (2015).

Supplementary Figure 6 Interactivity in MapScape.

(a) A MapScape view of metastatic prostate cancer. (b) The user may reorder the samples by clicking and dragging. (c) Clicking a mutation displays its sample-specific variant allele frequencies (VAFs) in the corresponding samples. (d) A MapScape visualization of metastatic ovarian cancer. (e) Mouseover of a phylogenetic classification label highlights all tumor samples following this classification. (f) Mouseover of a clone in the clonal phylogeny highlights the clone throughout the view and displays its prevalence in each sample. (g) Mouseover of a phylogenetic branch highlights the descendant clones throughout the view. For the interactive MapScapes, see links in Supplementary Note 1. Metastatic prostate cancer and metastatic ovarian cancer data are from patient A21 from Gundem et al. Nature 520, 353–357 (2015) and patient 1 from McPherson et al. Nature Genetics 48(7):758–67 (2016), respectively.

Supplementary Figure 7 Side-by-side MapScape and TimeScape visualizations for analysis of metastatic progression in a single patient.

(a) A MapScape visualization displays samples taken from a variety of metastases over three time points, (1) intraperitoneal diagnosis, (2) brain metastasis, and (3) intraperitoneal relapse (Supplementary Methods Link 7). (b) A TimeScape visualization shows the clonal composition at each sampling time point. The plotted clonal prevalences are the averages of the clonal prevalences from all samples at each time point (n=8 for intraperitoneal diagnosis, n=2 for brain metastasis, and n=2 for intraperitoneal relapse). For the interactive MapScape and TimeScape, see links in Supplementary Note 1. Data are from patient 7 from McPherson et al. Nature Genetics48(7):758-67 (2016).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7, Supplementary Methods and Supplementary Note 1 (PDF 13418 kb)

Supplementary Table 1

Clonal prevalences, clonal phylogeny, and mutations used to generate the TimeScape views of acute myeloid leukemia Patient UPN 933124 in Figure 1 and Supplementary Figure 2. (XLSX 69 kb)

Supplementary Table 2

Single cell phylogeny, single cell annotations, clonal phylogeny, and mutations used to generate the CellScape and TimeScape views of xenograft SA501 in Supplementary Figure 4. (XLSX 371 kb)

Supplementary Table 3

Clonal prevalences, clonal phylogeny, mutations, and pixel coordinates used to generate the MapScape view of prostate cancer Patient A21 in Figure 1, Supplementary Figures 5 and 6a-c. (XLSX 58 kb)

Supplementary Table 4

Clonal prevalences, clonal phylogeny, mutations, and pixel coordinates used to generate the MapScape view of ovarian cancer patient 1 in Supplementary Figure 6d-g. (XLSX 114 kb)

Supplementary Table 5

Clonal prevalences, clonal phylogeny, mutations, and pixel coordinates used to generate MapScape and TimeScape views of ovarian cancer patient 7 in Supplementary Figure 7. (XLSX 130 kb)

Supplementary Table 6

Clonal prevalences and clonal phylogeny used to generate the TimeScape views of the transformed follicular lymphoma patients in Supplementary Figure 3. (XLSX 37 kb)

Supplementary Table 7

Single cell phylogeny, single cell annotations, and copy number alternations used to generate the CellScape view of a triple-negative breast cancer patient in Figure 1. (XLSX 180 kb)

Supplementary Table 8

Descriptions of CellScape parameters and input formats. (XLSX 39 kb)

Supplementary Table 9

Descriptions of TimeScape parameters and input formats. (XLSX 19 kb)

Supplementary Table 10

Descriptions of MapScape parameters and input formats. (XLSX 18 kb)

Supplementary Software

Code for the E-scape suite (CellScape, TimeScape, and MapScape). (ZIP 7616 kb)

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Smith, M., Nielsen, C., Chan, F. et al. E-scape: interactive visualization of single-cell phylogenetics and cancer evolution. Nat Methods 14, 549–550 (2017). https://doi.org/10.1038/nmeth.4303

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