Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Clonal fitness inferred from time-series modelling of single-cell cancer genomes

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.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Replicate branch of p53 mutant cells and engineered mixture experiment.
Fig. 2: Fitness landscapes of untreated TNBC-SA609 PDX.
Fig. 3: Positive selection in untreated TNBC PDX.
Fig. 4: Fitness landscape reversal in early cisplatin treatment in TNBC PDX models.

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.

References

  1. 1.

    Gerstung, M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Williams, M. J. et al. Quantification of subclonal selection in cancer from bulk sequencing data. Nat. Genet. 50, 895–903 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Salichos, L., Meyerson, W., Warrell, J. & Gerstein, M. Estimating growth patterns and driver effects in tumor evolution from individual samples. Nat. Commun. 11, 732 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Shah, S. P. et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486, 395–399 (2012).

    ADS  CAS  PubMed  Article  Google Scholar 

  5. 5.

    Li, Y. et al. Patterns of somatic structural variation in human cancer genomes. Nature 578, 112–121 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Nik-Zainal, S. et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54 (2016).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Wright, S. The distribution of gene frequencies in populations. Proc. Natl Acad. Sci. USA 23, 307–320 (1937).

    ADS  CAS  PubMed  PubMed Central  MATH  Article  Google Scholar 

  9. 9.

    Tataru, P., Simonsen, M., Bataillon, T. & Hobolth, A. Statistical inference in the Wright–Fisher model using allele frequency data. Syst. Biol. 66, e30–e46 (2017).

    PubMed  Google Scholar 

  10. 10.

    Vasan, N., Baselga, J. & Hyman, D. M. A view on drug resistance in cancer. Nature 575, 299–309 (2019).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Ben-David, U. & Amon, A. Context is everything: aneuploidy in cancer. Nat. Rev. Genet. 21, 44–62 (2020).

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Sunshine, A. B. et al. The fitness consequences of aneuploidy are driven by condition-dependent gene effects. PLoS Biol. 13, e1002155 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  13. 13.

    Sheltzer, J. M., Torres, E. M., Dunham, M. J. & Amon, A. Transcriptional consequences of aneuploidy. Proc. Natl Acad. Sci. USA 109, 12644–12649 (2012).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Williams, M. J., Werner, B., Barnes, C. P., Graham, T. A. & Sottoriva, A. Identification of neutral tumor evolution across cancer types. Nat. Genet. 48, 238–244 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041.e21 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Khan, K. H. et al. Longitudinal liquid biopsy and mathematical modeling of clonal evolution forecast time to treatment failure in the PROSPECT-C phase II colorectal cancer clinical trial. Cancer Discov. 8, 1270–1285 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Gerlinger, M. et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat. Genet. 46, 225–233 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Jamal-Hanjani, M. et al. Tracking the evolution of non–small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).

    CAS  Article  Google Scholar 

  20. 20.

    López, S. et al. Interplay between whole-genome doubling and the accumulation of deleterious alterations in cancer evolution. Nat. Genet. 52, 283–293 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  21. 21.

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

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Good, B. H., McDonald, M. J., Barrick, J. E., Lenski, R. E. & Desai, M. M. The dynamics of molecular evolution over 60,000 generations. Nature 551, 45–50 (2017).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  23. 23.

    Dorri, F. et al. Efficient Bayesian inference of phylogenetic trees from large scale, low-depth genome-wide single-cell data. Preprint at https://doi.org/10.1101/2020.05.06.058180 (2020).

  24. 24.

    Burleigh, A. et al. A co-culture genome-wide RNAi screen with mammary epithelial cells reveals transmembrane signals required for growth and differentiation. Breast Cancer Res. 17, 4 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  25. 25.

    de la Vega, M. R., Chapman, E. & Zhang, D. D. NRF2 and the Hallmarks of cancer. Cancer Cell 34, 21–43 (2018).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  26. 26.

    The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

    ADS  Article  CAS  Google Scholar 

  27. 27.

    Patch, A.-M. et al. Whole-genome characterization of chemoresistant ovarian cancer. Nature 521, 489–494 (2015).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Laks, E. et al. Clonal decomposition and DNA replication states defined by scaled single-cell genome sequencing. Cell 179, 1207–1221 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Hather, G. et al. Growth rate analysis and efficient experimental design for tumor xenograft studies. Cancer Inform. 13, 65–72 (2014).

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Bielski, C. M. et al. Genome doubling shapes the evolution and prognosis of advanced cancers. Nat. Genet. 50, 1189–1195 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Bakhoum, S. F. et al. Chromosomal instability drives metastasis through a cytosolic DNA response. Nature 553, 467–472 (2018).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Davoli, T., Uno, H., Wooten, E. C. & Elledge, S. J. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science 355, eaaf8399 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  33. 33.

    Acar, A. et al. Exploiting evolutionary steering to induce collateral drug sensitivity in cancer. Nat. Commun. 11, 1923 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

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

    ADS  CAS  PubMed  Article  Google Scholar 

  35. 35.

    Li, D. et al. Enhanced tumor suppression by adenoviral PTEN gene therapy combined with cisplatin chemotherapy in small-cell lung cancer. Cancer Gene Ther. 20, 251–259 (2013).

    ADS  CAS  PubMed  Article  Google Scholar 

  36. 36.

    Wang, Y. et al. Klotho sensitizes human lung cancer cell line to cisplatin via PI3k/Akt pathway. PLoS ONE 8, e57391 (2013).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    O’Flanagan, C. H. et al. Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. Genome Biol. 20, 210 (2019).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Roth, A. et al. PyClone: statistical inference of clonal population structure in cancer. Nat. Methods 11, 396–398 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Beaumont, M. A., Cornuet, J.-M., Marin, J.-M. & Robert, C. P. Adaptive approximate Bayesian computation. Biometrika 96, 983–990 (2009).

    MathSciNet  MATH  Article  Google Scholar 

  40. 40.

    Foll, M. et al. Influenza virus drug resistance: a time-sampled population genetics perspective. PLoS Genet. 10, e1004185 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  41. 41.

    Bollback, J. P., York, T. L. & Nielsen, R. Estimation of 2Nes from temporal allele frequency data. Genetics 179, 497–502 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Malaspinas, A.-S., Malaspinas, O., Evans, S. N. & Slatkin, M. Estimating allele age and selection coefficient from time-serial data. Genetics 192, 599–607 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Ferrer-Admetlla, A., Leuenberger, C., Jensen, J. D. & Wegmann, D. An approximate Markov model for the Wright–Fisher diffusion and its application to time series data. Genetics 203, 831–846 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Beskos, A. et al. Exact simulation of diffusions. Ann. Appl. Probab. 15, 2422–2444 (2005).

    MathSciNet  MATH  Article  Google Scholar 

  45. 45.

    Pollock, M. et al. On the exact and ε-strong simulation of (jump) diffusions. Bernoulli 22, 794–856 (2016).

    MathSciNet  MATH  Article  Google Scholar 

  46. 46.

    Jenkins, P. A. et al. Exact simulation of the Wright–Fisher diffusion. Ann. Appl. Probab. 27, 1478–1509 (2017).

    MathSciNet  MATH  Article  Google Scholar 

  47. 47.

    Blanchet, J. Exact simulation for multivariate Itô diffusions. Adv. Appl. Probab. 52, 1003-1 (2020).

    Article  Google Scholar 

  48. 48.

    Jorde, P., Palm, S. & Ryman, N. Estimating genetic drift and effective population size from temporal shifts in dominant gene marker frequencies. Mol. Ecol. 8, 1171–1178 (1999).

    Article  Google Scholar 

Download references

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.

Author information

Affiliations

Authors

Consortia

Contributions

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.

Corresponding authors

Correspondence to Samuel Aparicio or Sohrab P. Shah.

Ethics declarations

Competing interests

S.P.S. and S.A. are shareholders and consultants of Canexia Health Inc.

Additional information

Peer review information Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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. bd, 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.

Source data

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. df, Analogous to ac but for HER2+ SA535 (n = 1,549 cells; reference clone C). gi, Analogous to ac 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).

Supplementary information

Supplementary Information

This file contains Supplementary Methods, Supplementary Figures 1-6 a guide to Supplementary Tables 1-8 and Supplementary References.

Reporting Summary

Supplementary Tables

This file contains Supplementary Tables 1-8 – see Supplementary Information document for full guide.

Source Data

This file contains Source Data for Supplementary Figure 1.

Peer Review File

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links