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.

  • Article
  • Published:

Spontaneous cell fusions as a mechanism of parasexual recombination in tumour cell populations

Nature Ecology & Evolution volume 5, pages 379–391 (2021)Cite this article

Subjects

Abstract

The initiation and progression of cancers reflect the underlying process of somatic evolution, in which the diversification of heritable phenotypes provides a substrate for natural selection, resulting in the outgrowth of the most fit subpopulations. Although somatic evolution can tap into multiple sources of diversification, it is assumed to lack access to (para)sexual recombination—a key diversification mechanism throughout all strata of life. On the basis of observations of spontaneous fusions involving cancer cells, the reported genetic instability of polypoid cells and the precedence of fusion-mediated parasexual recombination in fungi, we asked whether cell fusions between genetically distinct cancer cells could produce parasexual recombination. Using differentially labelled tumour cells, we found evidence of low-frequency, spontaneous cell fusions between carcinoma cells in multiple cell line models of breast cancer both in vitro and in vivo. While some hybrids remained polyploid, many displayed partial ploidy reduction, generating diverse progeny with heterogeneous inheritance of parental alleles, indicative of partial recombination. Hybrid cells also displayed elevated levels of phenotypic plasticity, which may further amplify the impact of cell fusions on the diversification of phenotypic traits. Using mathematical modelling, we demonstrated that the observed rates of spontaneous somatic cell fusions may enable populations of tumour cells to amplify clonal heterogeneity, thus facilitating the exploration of larger areas of the adaptive landscape (relative to strictly asexual populations), which may substantially accelerate a tumour’s ability to adapt to new selective pressures.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Detection of spontaneous cell fusions in vitro and in vivo.
Fig. 2: Phenotypic characterization of hybrids.
Fig. 3: Fusion-mediated genetic diversification.
Fig. 4: Fusion-mediated phenotypic diversification.
Fig. 5: Impact of fusion-mediated recombination on genetic diversity in tumour cell populations.

Similar content being viewed by others

Data availability

All data supporting the findings of this study are available in the article and its Supplementary Information files. All SNP microarray data files along with their associated metadata have been deposited in the GEO database under the accession codes GSE159681 and GSE157845.

Code availability

The code for running the non-spatial and spatial simulations as well as for quantifying single-cell diversity is available online at https://github.com/ebaratch/FusionProject.

References

  1. Greaves, M. & Maley, C. C. Clonal evolution in cancer. Nature 481, 306–313 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Scott, J. & Marusyk, A. Somatic clonal evolution: a selection-centric perspective. Biochim. Biophys. Acta, Rev. Cancer 1867, 139–150 (2017).

    Article  CAS  Google Scholar 

  3. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    Article  CAS  PubMed  Google Scholar 

  4. Becks, L. & Agrawal, A. F. The evolution of sex is favoured during adaptation to new environments. PLoS Biol. 10, e1001317 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. McDonald, M. J., Rice, D. P. & Desai, M. M. Sex speeds adaptation by altering the dynamics of molecular evolution. Nature 531, 233–236 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Sinai, S., Olejarz, J., Neagu, I. A. & Nowak, M. A. Primordial sex facilitates the emergence of evolution. J. R. Soc. Interface 15, 20180003 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Duelli, D. & Lazebnik, Y. Cell fusion: a hidden enemy? Cancer Cell 3, 445–448 (2003).

    Article  CAS  PubMed  Google Scholar 

  8. Lu, X. & Kang, Y. Cell fusion as a hidden force in tumor progression. Cancer Res. 69, 8536–8539 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Platt, J. L., Zhou, X., Lefferts, A. R. & Cascalho, M. Cell fusion in the war on cancer: a perspective on the inception of malignancy. Int. J. Mol. Sci. 17, 1118 (2016).

    Article  PubMed Central  CAS  Google Scholar 

  10. Kuznetsova, A. Y. et al. Chromosomal instability, tolerance of mitotic errors and multidrug resistance are promoted by tetraploidization in human cells. Cell Cycle 14, 2810–2820 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Fujiwara, T. et al. Cytokinesis failure generating tetraploids promotes tumorigenesis in p53-null cells. Nature 437, 1043–1047 (2005).

    Article  CAS  PubMed  Google Scholar 

  12. Su, Y. et al. Somatic cell fusions reveal extensive heterogeneity in basal-like breast cancer. Cell Rep. 11, 1549–1563 (2015).

    Article  CAS  PubMed  Google Scholar 

  13. Zhou, X. et al. Cell fusion connects oncogenesis with tumor evolution. Am. J. Pathol. 185, 2049–2060 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Erenpreisa, J. & Cragg, M. S. MOS, aneuploidy and the ploidy cycle of cancer cells. Oncogene 29, 5447–5451 (2010).

    Article  CAS  PubMed  Google Scholar 

  15. Bennett, R. J. The parasexual lifestyle of Candida albicans. Curr. Opin. Microbiol. 28, 10–17 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Zuba-Surma, E. K., Kucia, M., Abdel-Latif, A., Lillard, J. W. Jr. & Ratajczak, M. Z. The ImageStream System: a key step to a new era in imaging. Folia Histochem. Cytobiol. 45, 279–290 (2007).

    PubMed  Google Scholar 

  17. Fais, S. & Overholtzer, M. Cell-in-cell phenomena in cancer. Nat. Rev. Cancer 18, 758–766 (2018).

    Article  CAS  PubMed  Google Scholar 

  18. Rappa, G., Mercapide, J. & Lorico, A. Spontaneous formation of tumorigenic hybrids between breast cancer and multipotent stromal cells is a source of tumor heterogeneity. Am. J. Pathol. 180, 2504–2515 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Gast, C. E. et al. Cell fusion potentiates tumor heterogeneity and reveals circulating hybrid cells that correlate with stage and survival. Sci. Adv. 4, eaat7828 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Lu, X. & Kang, Y. Efficient acquisition of dual metastasis organotropism to bone and lung through stable spontaneous fusion between MDA-MB-231 variants. Proc. Natl Acad. Sci. USA 106, 9385–9390 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Duelli, D. M. et al. A virus causes cancer by inducing massive chromosomal instability through cell fusion. Curr. Biol. 17, 431–437 (2007).

    Article  CAS  PubMed  Google Scholar 

  22. Storchova, Z. & Pellman, D. From polyploidy to aneuploidy, genome instability and cancer. Nat. Rev. Mol. Cell Biol. 5, 45–54 (2004).

    Article  CAS  PubMed  Google Scholar 

  23. Forche, A. et al. The parasexual cycle in Candida albicans provides an alternative pathway to meiosis for the formation of recombinant strains. PLoS Biol. 6, e110 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Duncan, A. W. et al. The ploidy conveyor of mature hepatocytes as a source of genetic variation. Nature 467, 707–710 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Skinner, A. M., Grompe, M. & Kurre, P. Intra-hematopoietic cell fusion as a source of somatic variation in the hematopoietic system. J. Cell Sci. 125, 2837–2843 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Marusyk, A., Janiszewska, M. & Polyak, K. Intratumor heterogeneity: the Rosetta Stone of therapy resistance. Cancer Cell 37, 471–484 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Koulakov, A. A. & Lazebnik, Y. The problem of colliding networks and its relation to cell fusion and cancer. Biophys. J. 103, 2011–2020 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2018).

    Article  CAS  Google Scholar 

  29. Ferrall-Fairbanks, M. C., Ball, M., Padron, E. & Altrock, P. M. Leveraging single-cell RNA sequencing experiments to model intratumor heterogeneity. JCO Clin. Cancer Inform. 3, 1–10 (2019).

    Article  PubMed  Google Scholar 

  30. Hill, M. O. Diversity and evenness: a unifying notation and its consequences. Ecology 54, 427–432 (1973).

    Article  Google Scholar 

  31. Rosenzweig, M. L. Species Diversity in Space and Time (Cambridge Univ. Press, 1995).

  32. Hinohara, K. et al. KDM5 histone demethylase activity links cellular transcriptomic heterogeneity to therapeutic resistance. Cancer Cell 35, 330–332 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Loeb, L. A. Human cancers express a mutator phenotype: hypothesis, origin, and consequences. Cancer Res. 76, 2057–2059 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Waclaw, B. et al. A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity. Nature 525, 261–264 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kimmel, G. J., Gerlee, P. & Altrock, P. M. Time scales and wave formation in non-linear spatial public goods games. PLoS Comput. Biol. 15, e1007361 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Gallaher, J. A., Enriquez-Navas, P. M., Luddy, K. A., Gatenby, R. A. & Anderson, A. R. A. Spatial heterogeneity and evolutionary dynamics modulate time to recurrence in continuous and adaptive cancer therapies. Cancer Res. 78, 2127–2139 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Noble, R., Burri, D., Kather, J. N. & Beerenwinkel, N. Spatial structure governs the mode of tumour evolution. Preprint at bioRxiv https://doi.org/10.1101/586735 (2019).

  38. Jacobsen, B. M. et al. Spontaneous fusion with, and transformation of mouse stroma by, malignant human breast cancer epithelium. Cancer Res. 66, 8274–8279 (2006).

    Article  CAS  PubMed  Google Scholar 

  39. Mortensen, K., Lichtenberg, J., Thomsen, P. D. & Larsson, L. I. Spontaneous fusion between cancer cells and endothelial cells. Cell. Mol. Life Sci. 61, 2125–2131 (2004).

    Article  CAS  PubMed  Google Scholar 

  40. Melzer, C., von der Ohe, J. & Hass, R. In vivo cell fusion between mesenchymal stroma/stem-like cells and breast cancer cells. Cancers https://doi.org/10.3390/cancers11020185 (2019).

  41. Searles, S. C., Santosa, E. K. & Bui, J. D. Cell–cell fusion as a mechanism of DNA exchange in cancer. Oncotarget 9, 6156–6173 (2018).

    Article  PubMed  Google Scholar 

  42. Zack, T. I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134–1140 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Dewhurst, S. M. et al. Tolerance of whole-genome doubling propagates chromosomal instability and accelerates cancer genome evolution. Cancer Discov. 4, 175–185 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Stephens, P. J. et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 144, 27–40 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Birdsell, J. A. & Wills, C. in Evolutionary Biology (eds Macintyre, R. J. & Clegg, M. T.) 27–138 (Springer US, 2003).

  46. Nieuwenhuis, B. P. & James, T. Y. The frequency of sex in fungi. Phil. Trans. R. Soc. B https://doi.org/10.1098/rstb.2015.0540 (2016).

  47. Powell, A. E. et al. Fusion between intestinal epithelial cells and macrophages in a cancer context results in nuclear reprogramming. Cancer Res. 71, 1497–1505 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Lazebnik, Y. The shock of being united and symphiliosis: another lesson from plants? Cell Cycle 13, 2323–2329 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Mani, S. A. et al. The epithelial–mesenchymal transition generates cells with properties of stem cells. Cell 133, 704–715 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Qiu, W. et al. No evidence of clonal somatic genetic alterations in cancer-associated fibroblasts from human breast and ovarian carcinomas. Nat. Genet. 40, 650–655 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Vitale, I. et al. Multipolar mitosis of tetraploid cells: inhibition by p53 and dependency on Mos. EMBO J. 29, 1272–1284 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Amend, S. R. et al. Polyploid giant cancer cells: unrecognized actuators of tumorigenesis, metastasis, and resistance. Prostate 79, 1489–1497 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Islam, S. et al. Drug-induced aneuploidy and polyploidy is a mechanism of disease relapse in MYC/BCL2-addicted diffuse large B-cell lymphoma. Oncotarget 9, 35875–35890 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Lazova, R. et al. A melanoma brain metastasis with a donor–patient hybrid genome following bone marrow transplantation: first evidence for fusion in human cancer. PLoS ONE 8, e66731 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. LaBerge, G. S., Duvall, E., Grasmick, Z., Haedicke, K. & Pawelek, J. A. Melanoma lymph node metastasis with a donor–patient hybrid genome following bone marrow transplantation: a second case of leucocyte–tumor cell hybridization in cancer metastasis. PLoS ONE 12, e0168581 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Marusyk, A. et al. Spatial proximity to fibroblasts impacts molecular features and therapeutic sensitivity of breast cancer cells influencing clinical outcomes. Cancer Res. 76, 6495–6506 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. AlJanahi, A. A., Danielsen, M. & Dunbar, C. E. An introduction to the analysis of single-cell RNA-sequencing data. Mol. Ther. Methods Clin. Dev. 10, 189–196 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Newman, M. E. Modularity and community structure in networks. Proc. Natl Acad. Sci. USA 103, 8577–8582 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Signorell, A. et al. DescTools. R package v. 0.99.38 (2020).

  61. Gatenbee, C. D., Schenck, R. O., Bravo, R. R. & Anderson, A. R. A. EvoFreq: visualization of the evolutionary frequencies of sequence and model data. BMC Bioinform. 20, 710 (2019).

    Article  Google Scholar 

  62. Bravo, R. R. et al. Hybrid Automata Library: a flexible platform for hybrid modeling with real-time visualization. PLoS Comput. Biol. 16, e1007635 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank A. Goldman for sharing his unpublished observations of the frequent occurrence of cell fusions in his experimental studies, which confirmed our observations and helped motivate this work. We thank Y. Lazebnik, A. Goldman and D. Tabassum for providing thoughtful feedback. We thank R. Bravo for the help he provided for using the framework HAL. We thank the Flow Cytometry, Analytic Microscopy, Tissue Histology, Biostatistics and Bioinformatics Shared Resource and Molecular Genomic Core Facilities at the H. Lee Moffitt Cancer Center and Research Institute, an NCI designated Comprehensive Cancer Center (P30-CA076292). We thank the Moffitt SPARK programme for supporting an internship for M.A.L. This work was supported by the Susan G. Komen Breast Cancer Foundation grant no. CCR17481976 (A.M.), a Moffitt Cancer Biology and Evolution programme pilot award (A.M.), an Integrative Mathematical Oncology Workshop award (A.M. and D.B.), an American Cancer Society ACS-IRG award (P.M.A.), A PSON U01 CA244101 (D.B.), an MSM U01 CA202958 (D.B. and E.B.) and the Bankhead-Coley Cancer Research Program, award no. 20B06 (P.M.A., D.B. and A.M.).

Author information

Author notes

  1. These authors contributed equally: Daria Miroshnychenko, Etienne Baratchart, David Basanta, Andriy Marusyk.

Authors and Affiliations

  1. Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    Daria Miroshnychenko, Robert Vander Velde, Mark A. Laurie & Andriy Marusyk

  2. Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    Etienne Baratchart, Meghan C. Ferrall-Fairbanks, Philipp M. Altrock & David Basanta

  3. Department of Molecular Medicine, University of South Florida, Tampa, FL, USA

    Robert Vander Velde & Andriy Marusyk

  4. Department of Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    Marilyn M. Bui

  5. Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    Aik Choon Tan

Authors
  1. Daria Miroshnychenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Etienne Baratchart
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Meghan C. Ferrall-Fairbanks
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Robert Vander Velde
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mark A. Laurie
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Marilyn M. Bui
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Aik Choon Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Philipp M. Altrock
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. David Basanta
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Andriy Marusyk
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.M. and D.M. designed the studies. D.M. and M.A.L. performed the experimental studies and analysed the data. E.B., M.C.F.-F., P.M.A. and D.B. developed the mathematical models. M.C.F.-F., R.V.V. and A.C.T. performed the bioinformatical analyses. M.M.B. provided the pathology analysis. A.M., E.B., P.M.A., D.M. and D.B. wrote the manuscript.

Corresponding author

Correspondence to Andriy Marusyk.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Ecology and Evolution thanks James DeGregori and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Detection of putative spontaneous cell fusions.

a. Live-cell fluorescence microscopy images of 2D co-cultures between differentially labelled (nuclear GFP and mCherry) cell lines. Arrowheads indicate cells that express both labels. b. Live-cell fluorescence microscopy images of 3D Matrigel co-cultures between MCF10DCIS breast carcinoma cells and a primary breast CAF isolate labelled with cytoplasmic GFP and dsRED, respectively. Arrowheads indicate cells that express both labels. c,d Time-lapse live fluorescence microscopy images of mCherry+ SUM159PT cells co-cultured with GFP+ MDA-MB-231 cells (c) and CAFs (d). The labels indicate time after plating. Black arrowheads show fusion parents, and white arrowheads show double-positive hybrid cells and their progeny. e. Quantification of flow cytometry detection of double-positive events in the co-cultures between GFP+ CAFs and indicated breast cancer cell lines labelled with mCherry. Error bars represent SD, each dot represents an independent biological replicate. *and ** denote p values below 0.05 and 0.01 for two-tailed unpaired t-test, respectively.

Extended Data Fig. 2 ImageStream detection of spontaneous cell fusions.

a. Gating strategy for the detection of double-positive cells with ImageStream imaging-based flow cytometry platform. b. Quantitation of different classes of double-positive events in 3-day co-cultures between 50/50 mixes of GFP/mCherry labelled MCF7 cells, with examples of diffedrent classes of events provided in Fig. 1e. c. Comparison of frequency of double-positive events detected from the same samples of co-cultures of differentially labelled MDA-MB-231 (mCherry+) and SUM159PT (GFP+) cells and freshly mixed controls using FACS and ImageStream platforms (validated true positives percentages are plotted for ImageStream analyses). Error bars represent SD, dots represent biological replicates. d. Distribution of cell diameters of the parental and double-positive cells from ImageStream data shown in (c) measured in bright field and plotted using IDEAS software (ImageStream).

Extended Data Fig. 3 Phenotypic characterization of hybrid cells.

a. Growth rates of the indicated cell lines and their hybrids, at the indicated passages post-antibiotic-selection. b. Representative images of stained membranes from Boyden chamber cell invasion/migration assay. c. Quantitation of Boyden chamber cell invasion/migration assay data. d. Quantification of area of lung metastases formed after tail vein injection of MCFDCIS and MCFDCIS/SUM159PT hybrids. Data from individual mice are plotted separately. *, **, ***, *** denote p values below 0.05, 0.01, 0.001 and 0.0001, respectively, for the two-tailed unpaired t-test. Error bars represent SD, dots represent biological replicates (a,c,d).

Extended Data Fig. 4 DNA content analysis of hybrid cells.

a. FACS analysis of DNA content of the indicated parental cell lines and their hybrids. P1-12 indicate passage number of the mixed hybrid populations; for hybrid subclones the number indicates passage of the mixed culture, used for isolation of single cell subclones. Black contours indicate DNA content profiles for parental cell lines and their G1 and G2 peaks are used as reference points, filled histograms indicate DNA content profiles of the hybrids. “Unstable genomes” and “stable genomes” refer to hybrids that, respectively, did and did not show evidence of reduced DNA content between early and extended passages. Hybrids with stable genomes demonstrate G1 peak closer to G2 peak of parents and no observed difference in DNA content between passages and single cell clones. Inset indicates axes and shows components of the merged plots. b. FACS analysis of retention of GFP and mCherry fluorescence in hybrid cells, recovered from metastasis-bearing lungs of the indicated animals depicted in Fig. 2g.

Extended Data Fig. 5 Copy number analyses of cell line-specific SNP alleles.

SNP copy number and inheritance data were obtained with Illumina CytoSPN-12 platform. Copy number data status is shown for all of the cell line-specific SNP alleles across the genome for the indicated sublones. For the indicated selected zoom-in insets, correspondence between SNP copy numbers and detected inheritance is plotted. Turquoise color indicates mixed inheritance. a. Analyses of the SUM159/MCFDCIS hybrids shown in Fig. 3g and Supplementary Fig. 4. b. Analyses of subclones from MDA-MB-231/SUM159 hybrids, seeded from 1-t passage. SM1-3 and MS1-3 represent subclones isolated from two distinct hybrid populations, using opposite fluorophore/antibiotic labels of the parental cells.

Extended Data Fig. 6 Impact of fusions on diversification.

a. Estimates of fusion probabilities from in vitro data, with assumptions of no proliferation of hybrids, or proliferation at the same rates as the parental cells. b. Estimates of fusion probabilities from in vivo data for Gompertz and Logistic growth. c-e. Comparisons are drawn between results of in silico simulations involving mutations only versus mutation and fusion. Clonal diversity is captured by Shannon (c), Simpson (d) and GDI (c) diversity indexes. Mutation and fusion rates are indicated in the figures. Indicated p values denote the results for Kolmogorov-Smirnov test for the final timepoint of simulations (c, d), or for the all of the <1 q values (e); growth rate a=0.67/day; carrying capacity K=104 cells; number of genes G=300.

Extended Data Fig. 7 Parameter sweep analysis for the impact of mutation and fusion rates on clonal richness.

Graphs depict results of in silico simulations with branching birth-death model showing clonal richness over time for the indicated mutation (μ) and fusion (f) rates; p values indicate the results of Kolmogorov-Smirnov test comparing clonal richness at the end of the simulation (only shown for differences that has reached the 0.05 significance threshold); growth rate a=0.67/day; carrying capacity K=104 cells; number of genes G=300.

Extended Data Fig. 8 Impact of population size, cell turnover and starting genetic heterogeneity on diversification by fusion-mediated recombination.

a. Exploration of the impact of maximal tumor population size (carrying capacity) on diversification over indicated mutation and fusion rates. Left: carrying capacity K=104 cells; Right: carrying capacity K=106 cells; growth rate a=0.67/day; number of genes G=300 b. Exploration of the impact of tumor proliferation/turnover rates on diversification over indicated mutation and fusion rates; carrying capacity K=104 cells; number of genes G=50 c. Relationship between the initial clonal richness, and clonal richness at the indicated mutation and fusion rates. In all the panels, clonal richness at the end of the 1095 days of in silico growth is depicted; growth rate a=0.67/day; carrying capacity K=104 cells; number of genes G=300.

Extended Data Fig. 9

Flowchart for agent based model for spatial simulations.

Supplementary information

Supplementary Information

Supplementary Tables 1–3, Figs. 1–5 and mathematical methods.

Reporting Summary

Peer Review Information

Supplementary Video 1

Time-lapse videos of co-cultures between MDA-MB-231 cells labelled with cytoplasmic GFP and nuclear mCherry. Images were acquired every three hours over five days.

Supplementary Video 2

Time-lapse videos of co-cultures between MDA-MB-231 cells labelled with cytoplasmic GFP and nuclear mCherry. Images were acquired every three hours over five days.

Supplementary Video 3

Time-lapse videos of co-cultures between MDA-MB-231 cells labelled with cytoplasmic GFP and SUM159PT cells labelled with nuclear mCherry. Images were acquired every three hours over five days.

Supplementary Video 4

Time-lapse videos of co-cultures between MDA-MB-231 cells labelled with cytoplasmic GFP and SUM159PT cells labelled with nuclear mCherry. Images were acquired every three hours over five days.

Supplementary Video 5

Time-lapse videos of co-cultures between SUM159PT labelled with nuclear mCherry and CAFs labelled with nuclear GFP. Images were acquired every three hours over five days.

Supplementary Video 6

Video of the entire simulation corresponding to Fig. 5g (2D mutation only).

Supplementary Video 7

Video of the entire simulation corresponding to Fig. 5g (2D mutation and fusion).

Supplementary Video 8

Video of the entire simulation corresponding to Fig. 5g (3D mutation only).

Supplementary Video 9

Video of the entire simulation corresponding to Fig. 5g (3D mutation and fusion).

Supplementary Video 10

Video of three-day in vitro spatial simulation for the inferences of fusion rates.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Miroshnychenko, D., Baratchart, E., Ferrall-Fairbanks, M.C. et al. Spontaneous cell fusions as a mechanism of parasexual recombination in tumour cell populations. Nat Ecol Evol 5, 379–391 (2021). https://doi.org/10.1038/s41559-020-01367-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-020-01367-y

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer