Computational Modelling of Metastasis Development in Renal Cell Carcinoma

To improve our understanding of the biology of the metastatic colonization process, weconducted a modelling study based on multi-modal data from an orthotopic murine experimentalsystem of metastatic renal cell carcinoma. The standard theory of metastatic colonization usuallyassumes that secondary tumours, once established at a distant site, grow independently from eachother and from the primary tumour. Using a mathematical model describing the metastaticpopulation dynamics under this assumption, we challenged the theory against our data thatincluded: 1) dynamics of primary tumour cells in the kidney and metastatic cells in the lungs,retrieved by green fluorescent protein tracking, and 2) magnetic resonance images (MRI) informingon the number and size of macroscopic lesions. While the model could fit the primary tumour andtotal metastatic burden, the predicted size distribution was not in agreement with the MRIobservations. Moreover, the model was incompatible with the growth rates of individual metastatictumours.To explain the observed metastatic patterns, we hypothesised that metastatic foci derivedfrom one or a few cells could aggregate, resulting in a similar total mass but a smaller number ofmetastases. This was indeed observed in our data and led us to investigate the effect of spatialinteractions on the dynamics of the global metastatic burden. We derived a novel mathematicalmodel for spatial tumour growth, where the intra-tumour increase in pressure is responsible for theslowdown of the growth rate. The model could fit the growth of lung metastasis visualized bymagnetic resonance imaging. As a non-trivial outcome from this analysis, the model predicted thatthe net growth of two neighbouring tumour lesions that enter in contact is considerably impaired (of31% ± 1.5%, mean ± standard deviation), as compared to the growth of two independent tumours.Together, our results have implications for theories of metastatic development and suggest thatglobal dynamics of metastasis development is dependent on spatial interactions between metastaticlesions

Glioblastoma invasion and cooption depend on IRE1α endoribonuclease activity

International audienceIRE1α is an endoplasmic reticulum (ER)-resident transmembrane signaling protein and a cellular stress sensor. The protein harbors a cytosolic dual kinase/endoribonuclease activity required for adaptive responses to micro-environmental changes. In an orthotopic xenograft model of human glioma, invalidation of IRE1α RNase or/and kinase activities generated tumors with remarkably distinct phenotypes. Contrasting with the extensive angiogenesis observed in tumors derived from control cells, the double kinase/RNase invalidation reprogrammed mesenchymal differentiation of cancer cells and produced avascular and infiltrative glioblastomas with blood vessel co-option. In comparison, selective invalidation of IRE1α RNase did not compromise tumor angiogenesis but still elicited invasive features and vessel co-option. In vitro, IRE1α RNase deficient cells were also endowed with a higher ability to migrate. Constitutive activation of both enzymes led to wild-type-like lesions. The presence of IRE1α, but not its RNase activity, is therefore required for glioblastoma neovascularization, whereas invasion results only from RNase inhibition. In this model, two key mechanisms of tumor progression and cancer cell survival are functionally linked to IRE1

Spatial model fitting.

<p>(A) Top: Coronal MRI data of the lungs at days 19 and 26. Bottom: the simulated growth by the model using the fitted parameters and starting from the real shape of the observed metastasis at day 19 on the coronal MRI slice. Simulations were obtained using Eqs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004626#pcbi.1004626.e006" target="_blank">4</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004626#pcbi.1004626.e009" target="_blank">7</a> with the following parameter values: <i>γ</i>₀ = 0.78 day^-1; <i>Π</i>₀ = 0.0026 Pa; Time of simulation: T = 7 days (B) Volumes compared to simulations by the fitted model for the growth of four individual metastasis. The fits were performed on the volume only, considering the metastases as spherical.</p

The animal model.

<p>At day 14 after GFP+ RENCA cells injection, the first tumour cells were observed in the lungs (in green). At days 18–19, the first macro-metastases were observed by MRI.</p

Parameters values resulting from the population fit of the primary tumour and metastatic dynamics.

<p>CI = confidence interval.</p><p>CV = Coefficient of Variation, in per cent = <math><mrow><mi>s</mi><mi>t</mi><mi>d</mi></mrow><mrow><mi>e</mi><mi>s</mi><mi>t</mi></mrow><mo>×</mo></math>100, with <i>est</i> and <i>std</i> respectively the median value and standard deviation of the estimated lognormal population distribution of the parameters resulting from the nonlinear mixed-effects statistical estimation procedure.</p><p>Parameters values resulting from the population fit of the primary tumour and metastatic dynamics.</p

Number of required merging foci.

<p>There it is the number of required merging foci to obtain the metastatic sizes measured on the MR images for each followed metastasis. Two cases are considered: with and without spatial interactions.</p><p>Number of required merging foci.</p

Tumour-tumour spatial interactions.

<p>(A) Three different configurations with a same initial burden: only one tumour, two close tumours, two far tumours. The dynamics in the three configurations are compared with the parameter set inferred from the fit on one metastatic growth (0.78, 0.0026) day^-1×Pa. (B) The final burdens are compared in two configurations: two close tumours and two independent tumours. The mean burdens over a set of 64 parameters (resulting from an 8 × 8 uniform discretization of the relevant parameter space given by the individual tumour fits, (0.67,1.01) × (5.2 ∙ 10⁻⁴,2.6 ∙ 10⁻³)) are plotted with the standard deviations (difference of 31% ± 1.5% between the two distributions). (C) From left to right: time course of two interacting tumours growing and pushing each other. The parameters were fixed from one of the fitted MRI metastases: <i>γ</i>₀ = 0.78 day^-1; <i>Π</i>₀ = 0.0026 Pa; simulation time: T = 7 days; initial distance between the two metastases: D = 0.2mm; initial surface for each metastasis: S = 0.46 mm². (D) The curve represents the evolution of the final burden with respect to the initial distance between the two interacting tumours. The initial total burden and the parameters were taken to be the same as one of the four fitted metastases (same as C).</p

Simulation of multiple metastatic foci merging (with spatial interactions).

<p>From left to right: time course of merging metastatic germs. Each germ starts from one cell. The germs are randomly located at a distance of 0.03 mm from each other. Simulations were obtained using Eqs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004626#pcbi.1004626.e006" target="_blank">4</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004626#pcbi.1004626.e009" target="_blank">7</a> and the following parameter values: <i>γ</i>₀ = 0.78 day^-1; <i>Π</i>₀ = 0.0026 Pa; time of simulation: T = 7 days; number of germs = 200 in 2D. The corresponding number of cells in 3D is computed under a spherical symmetry assumption and is 2127. Movie of the simulation is available as <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004626#pcbi.1004626.s010" target="_blank">S3 File</a>.</p

Time course of the macro-metastases size distribution: standard model versus observations.

<p>(A) Top row: Simulation of the mathematical formalism of the standard theory (i.e. dissemination and independent growth of the resulting tumour foci), using the parameter values inferred from the data of the total metastatic burden (total GFP signal in the lungs). Only tumours larger than the visible threshold at MRI (0.05 mm³) are plotted. Simulations were obtained using Eqs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004626#pcbi.1004626.e001" target="_blank">1</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004626#pcbi.1004626.e003" target="_blank">2</a> for the time evolution of the density of secondary tumours, endowed with a lognormal distribution of the parameters for inter-animal variability, with the following values (retrieved from the population mixed-effects fit, median ± standard deviation): λ = 0.679 <i>α</i> = 0.417 ± 0.171 day^-1, <i>β</i> = 0.106 ± 0.0478 day^-1 and <i>μ</i> = 9.72 × 10⁻⁶ ± 0.428 × 10⁻⁶ cell∙day^-1. Shown are the results of 1000 simulations, mean + standard deviation. Bottom row: Observations of macro-metastases numbers and sizes in one mouse on MRI data. (B) Comparison of several metrics derived from the metastatic size distributions. For the model, numbers are represented as mean value and standard deviation in parenthesis. The data corresponds to the mouse presented in the upper histogram. (C) Comparison of the largest metastatic size at day 19 between model (<i>n =</i> 1000 simulated animals) and observations (<i>n =</i> 6 animals), log scale. The observed largest metastases are significantly larger than simulated ones (<i>p</i> < 10^-5 by the z-test).</p

Metastases merging.

<p>From left to right: Sagittal slices of the lungs from day 19 until day 26 for the same mouse. Two tumours are growing close to each other and merge between days 21 and 24.</p