Abstract
Progress in defining genomic fitness landscapes in cancer, especially those defined by copy number alterations (CNAs), has been impeded by lack of time-series single-cell sampling of polyclonal populations and temporal statistical models1,2,3,4,5,6,7. Here we generated 42,000 genomes from multi-year time-series single-cell whole-genome sequencing of breast epithelium and primary triple-negative breast cancer (TNBC) patient-derived xenografts (PDXs), revealing the nature of CNA-defined clonal fitness dynamics induced by TP53 mutation and cisplatin chemotherapy. Using a new Wright–Fisher population genetics model8,9 to infer clonal fitness, we found that TP53 mutation alters the fitness landscape, reproducibly distributing fitness over a larger number of clones associated with distinct CNAs. Furthermore, in TNBC PDX models with mutated TP53, inferred fitness coefficients from CNA-based genotypes accurately forecast experimentally enforced clonal competition dynamics. Drug treatment in three long-term serially passaged TNBC PDXs resulted in cisplatin-resistant clones emerging from low-fitness phylogenetic lineages in the untreated setting. Conversely, high-fitness clones from treatment-naive controls were eradicated, signalling an inversion of the fitness landscape. Finally, upon release of drug, selection pressure dynamics were reversed, indicating a fitness cost of treatment resistance. Together, our findings define clonal fitness linked to both CNA and therapeutic resistance in polyclonal tumours.
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Data availability
Raw sequencing data for DLP+ and 10x scRNA-seq are available from the European Genome-Phenome under study ID EGAS00001004448. Single-cell data from this report may be visualized in an instance of our scWGS exploration platform, Alhena, available at https://www.cellmine.org. Source data are provided with this paper.
Code availability
The software implementation of fitClone is available at https://github.com/UBC-Stat-ML/fitclone.
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Acknowledgements
This project was generously supported by the BC Cancer Foundation at BC Cancer and Cycle for Survival supporting Memorial Sloan Kettering Cancer Center. S.P.S. holds the Nicholls Biondi Chair in Computational Oncology and is a Susan G. Komen Scholar (GC233085). S.A. holds the Nan and Lorraine Robertson Chair in Breast Cancer and is a Canada Research Chair in Molecular Oncology (950-230610). Additional funding provided by the Terry Fox Research Institute Grant 1082, Canadian Cancer Society Research Institute Impact program Grant 705617, CIHR Grant FDN-148429, Breast Cancer Research Foundation award (BCRF-18-180, BCRF-19-180 and BCRF-20-180), MSK Cancer Center Support Grant/Core Grant (P30 CA008748), National Institutes of Health Grant (1RM1 HG011014-01), CCSRI Grant (705636), the Cancer Research UK Grand Challenge Program, Canada Foundation for Innovation (40044) to S.A., S.P.S. and A.B.-C. We thank S. P. Otto, E. Laks, D. Min and E. Zaikova for their contribution to the project.
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S.P.S. and S.A.: project conception and oversight, manuscript writing, senior responsible authors; A.B.-C.: statistical inference method development and oversight; S.S.: computational method development, data analysis, manuscript writing; F.K.: mouse modelling, tissue procurement, data generation, manuscript writing; N.C., M.A., M.J.W., K.R.C., A.W.Z., F.D., J.P., D. Gee, D.L., A.M.: computational biology, data analysis; D. Grewal, C.O., T.M., B.W., J. Brimhall., J. Biele, J.T., H.L., T.R.d.A., S.R.L., B.Y.C.C., P.E., T.K.: tissue procurement, biological substrates and data generation; R.M., A.J.M., M.A.M.: genome sequencing; N.R.: manuscript editing.
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S.P.S. and S.A. are shareholders and consultants of Canexia Health Inc.
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Extended data figures and tables
Extended Data Fig. 1 Schematic overview of experimental design for quantitatively modelling clone-specific fitness.
a, b, Time-series sampling from in vitro (a) and PDX (b) systems. Grey circles represent un-treated, blue represents cisplatin treated and grey with a blue outline denotes drug-holiday samples. c, Clonal dynamics of cell populations observed over time. Whole genome single cell sequencing of time-series samples gives copy number (left) that in turn is used to infer a phylogenetic tree (middle), and clonal fractions over time (right). d, fitClone: mathematical modelling of fitness with diffusion approximation to the K-type Wright–Fisher model. e, fitClone inputs of clonal dynamics measured over time series (left), and inferred trajectories (middle) and posterior distributions of fitness coefficients (right). Box plots are as defined in Fig. 1b.
Extended Data Fig. 2 Impact of p53 mutation on fitness in 184hTERT cells.
a, Heatmap representation of copy number profiles of 2,713 p53WT cells, grouped in 6 phylogenetic clades. b, Phylogeny of cells over the time series p53WT where nodes are groups of cells (scaled in size by number) with shared copy number genotype and edges represent distinct genomic breakpoints. Shaded areas represent clones. Tree root is denoted by the red circle. c, Observed clonal fractions over time, inferred trajectories and quantiles of the posterior distributions over selection coefficients of fitClone model fits to p53WT with respect to the reference clone F. d, Analogous to a but for p53−/−a (n = 3,264 p53−/−a cells). e, Clonal genotypes of three representative clones for p53−/−b showing high level amplification of TSHZ2 in clone D, chromosome 4 loss in clone E. Reference diploid clone I is shown for comparison. f, g, Analogous to b, c, but for p53−/−b (n = 4,881 p53−/−b cells; reference clone I). h, Number of segments per clone in hTERT WT and p53−/−a and p53−/−b branches. i, Number of mutations in p53−/−a and p53−/−b branches. Box plots are as defined in Fig. 1b.
Extended Data Fig. 3 PDX tumour growth and clonal dynamics with cisplatin.
a, Experimental design of cisplatin treatment in PDX. The solid blue colour representing cisplatin treated tumours (UT,UTT,UTTT,UTTTT); blue outlined in grey as drug holiday (UTU,UTTU,UTTTU); grey as untreated series. b–d, Tumour response curves in TNBC-SA609, TNBC-SA535 and TNBC-SA1035 treated with Cisplatin (blue), in drug Holiday (green) and untreated (red) where each tumour replicate is shown in a different shade. The vertical axis on the right denotes the status of tumours and on the left denotes the tumour volumes. The top horizontal axis represents number of cisplatin cycles and at the bottom days from palpable tumours to collection. The red arrows indicate the start of treatment and the black arrows indicate the tumour sampled for scDNaseq. The bottom horizontal axis shows the tumour passage number. Each line in the big box is an individual tumour showing the growth over time. e, Top, clonal trajectories of the clone with the highest inferred selection coefficient in the treatment regime (solid black line) and the drug holiday counterpart (dashed red line) at each time point, in the three TNBC PDX time series; bottom, as the top row, but for a clone that grows back in the holiday regime.
Extended Data Fig. 4 Comparison of fitness landscapes of breast cancer PDX models.
a, Heatmap representation of copy number profiles of 2,015 cells from TNBC-SA1035, grouped in 11 phylogenetic clades. b, Phylogeny for TNBC-SA1035. c, Observed clonal fractions, inferred fitClone trajectories and quantiles of the selection coefficients with respect to the reference clone A for the TNBC-SA1035 UnRx model. d–f, Analogous to a–c but for HER2+ SA535 (n = 1,549 cells; reference clone C). g–i, Analogous to a–c but for HER2+ SA532 (n = 2,193 cells; reference clone A). Box plots are as defined in Fig. 1b.
Extended Data Fig. 5 Impact of pharmacologic perturbation with cisplatin on fitness landscapes in TNBC-SA1035.
a, Copy number genotype of clone E from the untreated time series. b, Copy number genotype of clone H from treated time series (arrows indicate differences to clone E). c, Evolution in absence of treatment and as a function of drug treatment. For each sample, the phylogeny with clonal abundance from DLP+ is shown, reflecting selection. d, e, The observed clonal abundances (d) and the summarized clonal phylogenetic tree (e).
Extended Data Fig. 6 Tumour evolution in absence of pharmacologic perturbation in TNBC-SA609 line 1.
a, b, Copy number genotype of clone E (a) and copy number genotype of clone C, the reference clone (arrows indicate differences to clone E) (b). c, Evolution in absence of treatment. For each sample, the phylogeny with clonal abundance from DLP+ is shown, reflecting selection. d, e, The observed clonal abundances (d) and the summarized clonal phylogenetic tree (e).
Extended Data Fig. 7 Mixture experiment in TNBC-SA609 PDX Line 1.
a, Clonal proportions of TNBC-SA609 Line 1 X3 and X8 used to generate the initial mixture M0 and subsequent serial passaging, yielding 5 samples for mixture experiment b (mixture b). b, Forward simulations from the original time series and starting population proportions in the initial experimental mixture b. Simulated trajectories are shown superimposed with mean simulation (red line) and observed clonal fractions (blue dots). The observation time is adjusted to match the simulation diffusion time. c, Summary phylogenetic tree, inferred trajectories and fitness coefficients (relative to reference clone C) for mixture a. d, As in c but for mixture b (relative to reference clone C). Box plots are as defined in Fig. 1b.
Extended Data Fig. 8 Fitness landscape reversal in early cisplatin treatment in TNBC PDX models.
In each column, the left and right sub-panels are from the untreated and treated branches respectively. a, Phylogenetic trees annotated with fittest clones in −Rx and Rx. b, c, Inferred trajectories, first coloured by clonal assignment, and then coloured by fitness rank (b), and quantiles of selection coefficients of fitClone model fits to each branch with respect to the reference Clone C in TNBC-SA609, Clone C in TNBC-SA535, and clone A in TNBC-SA1035 (c). d, Distribution over the probability of positive selection over pairs of clones for each series. Box plots are as defined in Fig. 1b.
Extended Data Fig. 9 Impact of pharmacologic perturbation with cisplatin on fitness landscapes in TNBC-SA609.
a, Copy number genotype of clone H from untreated time series. b, Copy number genotype of clone A from the treated time series (arrows indicate differences to clone H). c, Evolution in absence of treatment (top) and as a function of treatment (bottom). For each sample, the phylogeny with clonal abundance from DLP+ is shown, reflecting selection. d, The observed clonal abundances. Starred time points are identical and reproduced to denote the identical starting point. e, Summarized clonal phylogenetic tree.
Extended Data Fig. 10 Impact of pharmacologic perturbation with cisplatin on fitness landscapes in TNBC-SA535.
a, Copy number genotype of clone G from untreated time series. b, Copy number genotype of clone A from treated time series (arrows indicate differences to clone E). c, Evolution in absence of treatment and as a function of drug treatment. For each sample, the phylogeny with clonal abundance from DLP+ is shown, reflecting selection. d, e, The observed clonal abundances (d) and the summarized clonal phylogenetic tree (e).
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Salehi, S., Kabeer, F., Ceglia, N. et al. Clonal fitness inferred from time-series modelling of single-cell cancer genomes. Nature 595, 585–590 (2021). https://doi.org/10.1038/s41586-021-03648-3
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DOI: https://doi.org/10.1038/s41586-021-03648-3
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