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Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution


Human cancers, including breast cancers, comprise clones differing in mutation content. Clones evolve dynamically in space and time following principles of Darwinian evolution1,2, underpinning important emergent features such as drug resistance and metastasis3,4,5,6,7. Human breast cancer xenoengraftment is used as a means of capturing and studying tumour biology, and breast tumour xenografts are generally assumed to be reasonable models of the originating tumours8,9,10. However, the consequences and reproducibility of engraftment and propagation on the genomic clonal architecture of tumours have not been systematically examined at single-cell resolution. Here we show, using deep-genome and single-cell sequencing methods, the clonal dynamics of initial engraftment and subsequent serial propagation of primary and metastatic human breast cancers in immunodeficient mice. In all 15 cases examined, clonal selection on engraftment was observed in both primary and metastatic breast tumours, varying in degree from extreme selective engraftment of minor (<5% of starting population) clones to moderate, polyclonal engraftment. Furthermore, ongoing clonal dynamics during serial passaging is a feature of tumours experiencing modest initial selection. Through single-cell sequencing, we show that major mutation clusters estimated from tumour population sequencing relate predictably to the most abundant clonal genotypes, even in clonally complex and rapidly evolving cases. Finally, we show that similar clonal expansion patterns can emerge in independent grafts of the same starting tumour population, indicating that genomic aberrations can be reproducible determinants of evolutionary trajectories. Our results show that measurement of genomically defined clonal population dynamics will be highly informative for functional studies using patient-derived breast cancer xenoengraftment.

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Figure 1: Clonal dynamics over multiple passages in time.
Figure 2: Single-cell determination of clonal genotypes recapitulates population-based prediction of cascading subclonal evolution.
Figure 3: Clonal dynamics are reproduced in replicate transplants.

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Genome data have been deposited at the European Genome-phenome Archive ( under accession number EGAS00001000952. Processed data can be viewed at


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We are grateful to the staff of the CTAG Molecular Pathology facility, members of the Library Technical Development, Library Construction, Sequencing and Bioinformatics teams at the Michael Smith Genome Sciences Centre for technical assistance with data generation, and S. Kalloger for assistance with sample collection. S.A. and S.P.S. are supported by Canada Research Chairs. P.E. is supported by a Michael Smith Foundation for Health Research (MSFHR) Fellowship. A.S. is supported by an NSERC CREATE scholarship through the graduate program in Genome Science and Technology at UBC. S.P.S. is a MSFHR scholar. We acknowledge long-term funding support provided by the BC Cancer Foundation. The S.A., S.P.S. and C.H. groups receive operating funds from the Canadian Breast Cancer Foundation, Canadian Cancer Society Research Institute, Terry Fox Research Institute, Genome Canada and Canadian Institutes for Health Research (CIHR). We thank S. Mullaly for critical reading of the manuscript.

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Authors and Affiliations



S.A. and S.P.S. designed the study and supervised the research. S.A., S.P.S., P.E. and A.S. wrote the paper. P.E., A.Br., J.S., T.A., W.G., H.C., H.X., L.N., Y.W. and D.L. performed transplants and passaging. K.G., S.C. and C.M. recruited patients and performed tissue biopsies. A.S., P.E., G.H., C.N., H.F., A.J.L.R., C.L., A.Ba., C.S., K.S., J.R., R.A., A.M., C.d.S., S.P.S. and S.A. carried out bioinformatics analyses. J.K., D.Y., E.L., J.Br., A.W., J.Bi., K.L., A.J.M., A.O., R.M., Y.Z., C.H. and M.A.M. assisted with sequence generations and single-cell experiments. T.O., J.L. and D.H. contributed to histological analysis. C.J.E., C.H., M.A.M., C.C., S.P.S. and S.A. provided intellectual contributions to design or interpretation.

Corresponding authors

Correspondence to Sohrab P. Shah or Samuel Aparicio.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Transplant history.

Diagrams show the transplant history of each xenograft line. Line segment colours represent the site used for each transplant (blue, subcutaneous; red, subrenal capsule; green, mammary fat pad). Black points indicate the passage of an engrafted xenograft to the next mouse generation. Grey crosses indicate transplants that did not result in palpable tumours. Samples analysed by whole-genome and/or targeted deep sequencing are indicated (black squares and vertical lines, respectively). The cumulative time in vivo is shown on the x axis. The originating tumour site (Met., pleural effusion; Prim., primary breast) and immunohistochemical expression of biomarkers (ER, oestrogen receptor; PR, progesterone receptor; TN, triple negative for ER, PR and HER2) are shown.

Extended Data Figure 2 Comparison of the prevalence of mutations in six originating tumours and subsequent xenografts in SNV and CNA spaces.

a, Density scatter plots showing the WGSS variant allele prevalence of genome-wide high-confidence SNVs in tumours (x axis) and xenografts (y axis). SNVs in clones undergoing neutral dynamics lie along a diagonal, and SNVs in clones undergoing expansion or contraction lie on/towards the y and x axes, respectively. b, Scatter plots showing the mutation cellular prevalence of selected SNVs in tumours and xenografts, inferred by PyClone from population-level targeted deep sequencing. Circles represent individual SNVs, colours indicate clusters of mutations for which mutation cellular prevalences vary together over all sample time points. c, Scatter plots show co-occurrence of CNA/LOH events inferred by TITAN in tumours and xenografts. The z axis height of each bar shows the number of genes belonging to a unique mutation cluster and present at the indicated mutation cellular prevalence in tumour (x axis) and xenograft (y axis).

Extended Data Figure 3 Single-cell determination of clonal genotypes recapitulates population-based prediction of minor clone selection.

DNA prepared from 62 individual lysed SA494 tumour and 58 passage 4 lysed xenograft nuclei was amplified in single reactions using a panel of multiplexed PCR primer pairs targeting amplicons containing 40 SNV and 7 germline variants, and the variant allele ratios were determined by targeted deep sequencing. a, Mutation clusters inferred by the PyClone model from bulk population measurements. b, Bayesian phylogenetic tree derived from multi-locus genotypes of individual nuclei. The tumour and xenograft nuclei group in distinct clades. c, Heat map depicts the multi-locus variant allele prevalences (blue/yellow/red corresponds to wild-type/heterozygous/homozygous loci, respectively) at variant positions (horizontal axis) in individual nuclei (vertical axis, ordered by phylogenetic grouping in b). Upper two blocks show gDNA controls and normal cell nuclei present in tumour samples. The PyClone mutation cluster corresponding to each SNV is indicated by colour in the lowermost horizontal bar. d, Consensus genotypes derived from high-probability splits in the phylogenetic tree confirm a set of high prevalence tumour-specific and xenograft-specific mutations, consistent with the expansion of a minor originating clone to dominance in the xenograft, as well as mutations shared in tumour and xenograft nuclei.

Extended Data Figure 4 Clonal dynamics are reproduced in replicate transplants.

ac, Upper panels, passaging history of SA532, SA429 and SA496, showing transplants that resulted in successful xenografts. The transplant sites (blue, subcutaneous; red, subrenal; green, mammary fat pad; all subcutaneous for SA532) and host mouse strains (blue, NSG; orange, NRG; all NSG for SA429 and SA496) are shown. Boxed nodes indicate passages analysed by WGSS. Lower panels, change in cellular prevalence of mutation clusters over individual transplants. Plots correspond to passages in upper panels. The clusters are inferred by PyClone using grouped data from all passages and correspond to those displayed in Fig. 1. Arrows in SA429 and SA532 show examples of parallel clonal dynamics of the same mutation cluster in multiple replicate transplants. SA496 exhibits less replicated evolution compared with other cases.

Extended Data Figure 5 Correlation of clonal dynamics in replicate transplants of SA429, SA501, SA532, SA496 and SA535.

ae, Scatter plots display the inferred mutation cellular prevalence of all SNVs in pairs of same-passage replicates, for cases SA429, SA501, SA532, SA496 and SA535, respectively. For each replicate, prevalences are inferred by a separate PyClone analysis that excludes data from other same-passage transplants. Colours indicate mutation clusters inferred in each individual PyClone analyses; the SNVs clustered and colours assigned may differ in each plot. The Pearson correlation coefficients are shown, indicating closely related evolution in most pairs.

Supplementary information

Supplementary Information

This file contains Supplementary Methods, Supplementary References, legends for Supplementary Figures 1-15 and Tables 1-15 (see separate files for Supplementary Tables and Figures). (PDF 248 kb)

Supplementary Figures

This file contains Supplementary Figures 1-15 – see Supplementary Information document for legends. (PDF 45314 kb)

Supplementary Tables

This zipped file contains Supplementary Tables 1-10 – see Supplementary Information document for legends. (ZIP 7130 kb)

Supplementary Data

This file contains the script to carry out credible interval significance testing. (ZIP 35 kb)

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

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