DOI: 10.5061/dryad.3j9kd51s6
title: Data from: In vitro competition between two transmissible cancers, and potential implications for their host, the Tasmanian devil

description: Since the emergence of a transmissible cancer, devil facial tumour disease
 (DFT1), in the 1980s, wild Tasmanian devil populations have been in
 decline. In 2016, a second, independently evolved transmissible cancer
 (DFT2) was discovered raising concerns for survival of the host species.
 Here, we applied experimental and modelling frameworks to examine
 competition dynamics between the two transmissible cancers in vitro. Using
 representative cell lines for DFT1 and DFT2, we have found that in
 monoculture, DFT2 grows twice as fast as DFT1 but reaches lower maximum
 cell densities. Using co-cultures, we demonstrate that DFT2 outcompetes
 DFT1: the number of DFT1 cells decreasing over time, never reaching
 exponential growth. This phenomenon could not be replicated when cells
 were grown separated by a semi-permeable membrane, consistent with
 exertion of mechanical stress on DFT1 cells by DFT2. A logistic model and
 a Lotka-Volterra competition model were used to interrogate monoculture
 and co-culture growth curves respectively, suggesting DFT2 is a better
 competitor than DFT1, but also showing that competition outcomes might
 depend on the initial number of cells, at least in the laboratory. We
 provide theories how the in vitro results could be translated to
 observations in the wild and propose that these results may indicate that
 although DFT2 is currently in a smaller geographic area than DFT1, it
 could have the potential to outcompete DFT1. Further, we provide a
 framework for improving the parameterization of epidemiological models
 applied to these cancer lineages, which will inform future disease
 management.
description: <em>Cell cultures </em>
 Representative cell lines for DFT1 (4906, also known as 88 [41])
 and DFT2 (RV also known as TD467 or 202T1 [42]) were cultured as
 previously described [41], [42]. DFT1 and DFT2 cell lines have been shown
 to share many characteristics of in vivo tumours [43], [44], [45], making
 these cell lines a useful <em>in vitro</em> study system.
 Cells were maintained in RPMI-1640 media with GlutaMAX (Gibco, 61870036)
 supplemented with 10% heat-inactivated FBS (Gibco, 10500-064) and 50 ug/mL
 penicillin/streptomycin (Gibco, 15070063) or 50 ug/mL of gentamicin
 (Sigma, G1397) at 35°C and 5% CO2. Upon reaching 80-90% confluency, cells
 were detached using TrypLE Express (Gibco, 12605010) and passaged 1:3.
 Cells were maintained below Passage 30. DFT1 cell lines were tested for
 mycoplasma in [46]. All DFT2 cells were tested for mycoplasma [insert
 details such as kit name] when they entered the lab. They have been
 passaged successfully in a mycoplasma free tissue culture facility since
 then. <em>Transduction (Green Fluorescent Protein
 cell lines) </em> Distinguishing DFT1 and DFT2
 cells using flow cytometry based solely on their size and shape is
 difficult, thus, we labelled one of these cell lines with Green
 Fluorescent Protein (GFP), allowing us to distinguish co-cultured tumour
 cells reliably.<em> </em>The DFT1-GFP (4906-GFP) and DFT2-GFP
 (RV-GFP) cell lines were established using lentiviral transduction.
 HEK293T cells were used to produce lentiviral particles with PLKO-GFP
 (pLKO_TRC001), psPAX2 and pMD2.G (provided by N. Divecha). Lentivirus
 particles were transduced into 4906 and RV cells. Following transduction,
 approximately 10<sup>5</sup> cells expressing high levels of
 GFP relative to untransduced control cells were sorted by Fluorescence
 Activated Cell Sorting (FACS) on a BD FACS Aria II using the gating
 strategy presented in Fig. S1. These sorted cells were then cultured for a
 further two weeks before being sorted for a second time to establish a
 heterogeneous cell line with stable and high expression of GFP and remove
 cells which were not expressing GFP. <em>Direct
 co-cultures </em> DFT1 and DFT2 cells were
 cultured in 12-well plates (Corning, 3513) for 14 days. Cells were plated
 in 1mL of culture media per well (for a concentration of
 10<sup>5</sup> cells/mL of media), which was replaced every 3
 days. Triplicate wells were harvested daily and counted on a Guava®
 easyCyte™ model 6HT. The following culture conditions with varying DFT1 to
 DFT2 ratios at the start of the experiment were performed: giving an
 advantage to DFT1 (70-80% DFT1), giving no advantage to either cell line
 (50-60% DFT1), giving an advantage to DFT2 (30-40% DFT1) and monoculture
 controls (100% DFT1 or 100% DFT2). A total number of
 10<sup>5</sup> cells per well were plated, meaning a 50:50
 co-culture will start with 0.5x10<sup>5</sup> DFT1 cells and
 0.5x10<sup>5</sup> DFT2 cells; while a monoculture will start
 with 10<sup>5</sup> cells of either DFT1 or DFT2. This
 experiment was performed in duplicate, alternating the use of one GFP-cell
 line and one unlabelled cell line (i.e. DFT1-GFP was co-cultured with
 DFT2, and DFT1 was co-cultured with DFT2-GFP) to eliminate potential
 effects of the GFP-transduction process and selection by FACS on cell
 growth. Imaging of the cells during one representative direct co-culture
 experiment can be found in Fig. S5. <em>Transwell
 co-cultures</em> DFT1 and DFT2 cells were
 co-cultured using 12-well transwell plates (Corning, CLS3460). Transwells
 allow cells to remain in two compartments separated by a semi-permeable
 membrane, permitting small molecules to be exchanged but keeping cell
 lines separated. Monocultures (cells of a same DFT grown in the inserts
 and wells) were compared to co-cultures (cells of one DFT in the inserts
 and of the other DFT in the wells). Cells were plated at a density of
 0.5x10<sup>5 </sup>cells/insert and 10<sup>5</sup>
 cells/well (the inserts having a surface about two times smaller than the
 wells), for both mono-cultures and for co-cultures. Cells were plated in
 0.5mL of culture media in the inserts and 1mL of media in the wells (for a
 final concentration of 10<sup>5</sup> cells/mL of media) which
 was replaced every 3 days. Duplicate wells were harvested and analysed on
 a Guava® easyCyte™ model 6HT every two days. Only cells from the wells
 were counted to avoid any effect of the surface size and type of the
 transwell inserts on cell growth. The experiment was performed in
 duplicate. <em>Flow
 cytometry</em> <em> </em>Cells were
 incubated in the dark on ice for 15 minutes with 1 ug/mL Propidium Iodide
 as a live/dead marker. Cells were run on a Guava® easyCyte™ model 6HT and
 data was analysed using the CytoExploreR R package [47]. Gating performed
 first selected cells from debris (FSC-Height vs SSC-Height), then singlets
 from doublets (SSC-Area, SSC-Height), live from dead cells (FSC vs RED),
 and, for the direct co-cultures, GFP-positive from GFP-negative cells (FSC
 vs GRN) (a representative gating strategy is shown in Fig. S2). The number
 of cells in each well was calculated as follows: (number of gated events /
 volume analysed by the flow cytometer) x volume of cells per well (1
 mL). <em>Growth rate and carrying capacity
 estimation </em> A logistic differential equation
 (Eq. 1) was used to represent the growth of DFT1, DFT1-GFP, DFT2 and
 DFT2-GFP cell lines as this model has been shown to accurately describe
 DFT cell growth <em>in vitro</em> in a preliminary analysis
 [48]and in vivo [49]. In equation (1), <em>N</em> represents
 the number of cells, <em>r</em> the per capita cellular growth
 rate (per day) and <em>K</em>the maximum number of cells the
 space and resources can accommodate (i.e., carrying capacity). 
 (1)     dN/dt = rN(1-N/K) A grid search method
 was used to simulate growth curves using 10 000 combinations of the growth
 rate <em>r</em> (ranging from 0.01 to 1, with a step of 0.01
 per day) and <em>K</em> (ranging from
 10<sup>4</sup> to 10<sup>6</sup>, with a step of
 10<sup>4 </sup>cells). <em>N</em> was initialised
 with the number of cells at day 1 of the experiment, that is once cells
 have had time to attach to the surface of the plate and unattached dead
 cells were removed, to meet the model’s assumption that cell population
 grows with time. The adequacy of each combination of parameter values was
 then assessed on each replicate growth curve of the DFT cells in
 monoculture (direct co-cultures) and in co-culture (transwell co-cultures)
 by calculating the Root Mean Square Error (RMSE) between the simulated and
 the observed population dynamics of each setting. Parameters from
 simulations with the best fit (i.e., lowest RMSE) to the experimental data
 were then selected. Median values of <em>r</em> and
 <em>K</em> were compared between DFT1 and DFT2 cell lines
 using a Wilcoxon rank sum test with continuity correction. The
 <em>r</em> and <em>K</em>parameters were also
 estimated for DFT and DFT-GFP cell lines, which, showed that transduction
 appears to have lowered the carrying capacity of DFT1-GFP cells (Fig.
 S3). <em>Competition coefficient
 estimation </em> The two-species competition
 Lotka-Volterra equations (Eq. 2 and 3 [29], [30]) were used to quantify
 competitive interactions between DFT1 and DFT2 cells.
 <em>N<sub>i</sub></em>,
 <em>r<sub>i</sub></em>, and
 <em>K<sub>i</sub></em> represent the number of
 cells, growth rate and carrying capacity for DFT1 (i = 1) and DFT2 (i =
 2). The ? parameter represents the competitive impact of DFT1 on the
 growth rate of DFT2, and vice-versa for ?. If a competition coefficient (?
 or ?) is close to zero, a tumour line does not influence the growth of the
 other; if ? or ? is bigger than 1, a tumour line negatively impacts the
 growth of the other; and if ? or ? is lower than zero, a tumour line
 facilitates the growth of the other. Hence, ? and ? inform the type of
 interaction between DFT1 and DFT2: competition (alpha and beta are
 positive), mutualism (alpha and beta are negative), commensalism (alpha is
 negative and beta is close to or equal to zero, or vice versa) or
 parasitism (alpha is positive and beta is negative, or vice
 versa). (2)     dN1/dt = r1N1(1-(N1+aN2)/K1)
 (3)     dN1/dt = r2N2(1-(N2+bN1)/K2) Again, a
 grid search method was used to simulate growth curves using 10 404
 combinations of ? and ? (each ranging from -100 to 100, with a step of 1).
 Mean values of <em>r<sub>1</sub></em>,
 <em>r<sub>2</sub></em>,
 <em>K<sub>1</sub></em> and
 <em>K<sub>2</sub></em> estimated on the
 monocultures, as described above, were fixed in the equations to only
 estimate the competition coefficients. The estimation of these parameters
 values was performed using the same approach than previously but using
 this time the population dynamics of both DFTs in direct co-culture.
 Median values of ? and ? were compared using a Wilcoxon rank sum test with
 continuity correction. Model fitting and statistical analyses were
 performed in R (R version 4.1.3 [50]).
 <em>Predicting competition outcome </em>
 Competition outcome of the Lotka-Volterra model can be predicted
 by examining zero-growth isoclines of the two competing species, e.g.
 [51]. Briefly, the number of cells at which the DFT1 or DFT2 population
 stops growing can be found by solving for
 d<em>N<sub>i</sub></em>/dt = 0. The trajectory of
 both populations can then be represented on a phase diagram in which the
 zero net growth isoclines are given by Eq. 4 and 5. The coordinates of the
 isoclines correspond to the intercepts of both axis (i.e.,
 [<em>N<sub>1,t</sub></em>=0,
 <em>N<sub>2,t</sub></em>=0]). DFT1’s zero net
 growth isocline has the coordinates [0,
 <em>K<sub>1</sub></em>/?] and
 [<em>K<sub>1</sub></em>, 0], and DFT2’s [0,
 <em>K<sub>2</sub></em>] and
 [<em>K<sub>2</sub></em>/?, 0]. From these, we can
 determine the following outcomes: one of the tumour lines always
 outcompetes the other, competition outcome depends on initial conditions
 (i.e. the number of DFT1 and DFT2 cells at the start of the experiment) or
 both tumour lines co-exist (Table 1). (4)    
 N1=K1-aN2 (5)     N2=K2-bN1
description: # In vitro competition between two transmissible cancers, and potential
 implications for their host, the Tasmanian devil
 [https://doi.org/10.5061/dryad.3j9kd51s6](https://doi.org/10.5061/dryad.3j9kd51s6) This dataset contains the results of two experiments: (1) direct DFT1 & DFT2 co-cultures and (2) transwell DFT1 & DFT2 co-cultures. ## Description of the data and file structure Flow cytometry dataset consists of **.fcs** files with associated **settings.csv** files. Gating events in these allowed to calculate the number of cells for the two experiments, which can be found in the files described below. Coordinates for each gate can be found in the **gating.csv** files. **(1) Direct co-culture** File name: **DFT1&DFT2_cocultures.xlsx** File structure: long format Exel table with the following columns: * Experiment: experiment ID - categorical variable * Replicate: replicate number, experiments were performed in triplicates - categorical variable * Culture: ratio of DFT1 to DFT2 cells at the start of the experiment (e.g. "4060" means 40% DFT1 and 60% DFT2) - categorical variable * DFT: whether the sample belongs to DFT1 or DFT2 - categorical variable * Cells: number of cells measured - continuous variable * Time: timepoint at which the number of cells was measured, in hours - categorical variable **(2) Transwell co-cultures** File name: **transwells.xlsx** File structure: long format Exel table with the following columns: * culture: cell line (4906 = DFT1, rv = DFT2) and culture conditions - categorical variable * "4906" refers to the number of DFT1 cells in monoculture * "4906co" refers the number of DFT1 cells being co-cultured with DFT2 * "rv" refers to the number of DFT2 cells in monoculture * "rvco" refers the number of DFT2 cells being co-cultured with DFT1 * replicate: replicate number, experiments were performed in triplicates - categorical variable * time: timepoint at which the number of cells was measured, in days - categorical variable * cells_per_mL: number of cells measured - continuous variable * DFT: whether the sample belongs to DFT1 or DFT2 - categorical variable * experiment: experiment ID - categorical variable ## Code/Software Code was written in the R programming language (version 4.1.3) to perform the following analyses and associated plots: 1. **flowcyto.R** gating of flow cytometry and counting the number of cells for each sample 2. **gridsearch.R** for model fitting and estimating growth parameters r and K 3. **stats.R** for statistical analysis