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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

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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.

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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.

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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.

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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

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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.

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Correspondence to Andriy Marusyk.

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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.

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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.

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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.

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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

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