Identification de nouveaux facteurs pronostiques et de nouvelles cibles thérapeutiques potentielles dans le cancer du rein

Kidney cancer is one of the 10 commonest human cancers. To date, no biomolecular markers are available in this type of cancer, and in the case of metastatic cancer, the therapeutic arsenal is still inefficient. The different processes involved in cancer progression are still poorly understood. Understanding those processes could highlight new therapeutic targets, and new prognostic or diagnostic biomolecular markers of this disease. For a first project, a new innovative model has been generated from a murine RCC cell line as a tool to understand cancer progression mechanisms and to identify new therapeutic target and new biomolecular markers in kidney cancer. This model of sequential reimplantation of cancer cells isolated from primary tumours or metastases allowed us to generate different cell lines showing increased aggressiveness after passages. Using a systems biology strategy, this model will allow us to identify new potential therapeutic targets and new biomolecular markers in RCC. Interleukin-34 is an example of an already selected target, showing the power of the model generated. For a second project, the role of some members of extracellular matrix (collagen type I, fibronectin, matrigel).was studied using this same murine RCC cell line. This study demonstrated the potential pro-invasive and pro-metastatic roles of collagen type I deposition in tumors. Collagen-activated receptors are proposed as mediators of the effect induced by collagen type I in this model. Those two projects have and will continue to contribute to a better understanding of cancer progression mechanisms, and will bring out new biomolecular markers and new therapeutic targets.Le cancer du rein compte parmi les 10 types de cancers les plus fréquents chez l’Homme. Il n’existe aujourd’hui aucun marqueur biomoléculaire dans ce type de cancer, et dans le cas d’un cancer métastatique, l’arsenal thérapeutique aujourd’hui disponible manque d’efficacité. Les différents processus mis en jeu lors de la progression tumorale sont encore mal connus. La connaissance de ces processus pourrait permettre de mettre en évidence de nouvelles cibles thérapeutiques, ainsi que des marqueurs biomoléculaires pronostiques ou diagnostiques de la maladie. Dans un premier projet, et afin de mieux comprendre les mécanismes de la progression tumorale et d’identifier de nouvelles cibles thérapeutiques potentielles et de nouveaux marqueurs biomoléculaires dans le cancer du rein, un nouveau modèle innovant a été généré à partir d’une lignée tumorale de RCC murine. Ce modèle de réimplantations successives de cellules tumorales issues de tumeur primaire ou de métastases a permis de générer différentes lignées cellulaires montrant une agressivité accrue au cours des passages. En utilisant une stratégie de biologie des systèmes, ce modèle pourra permettre de mettre en évidence des cibles d’études prometteuses qui pourraient être de nouvelles cibles thérapeutiques ou de nouveaux marqueurs biomoléculaires dans le RCC. L’interleukine-34 est l’exemple d’une cible d’étude d’ores et déjà été sélectionnée, mettant en évidence la puissance du modèle généré. Dans un second projet, les rôles de certains membres de la matrice extracellulaire tumorale ont été évalués en utilisant cette même lignée de RCC murine (collagène de type I, fibronectine, matrigel). Cette étude a permis de mettre en évidence le potentiel pro-invasif et pro-métastatique du dépôt de collagène de type I dans les tumeurs. Des récepteurs activés par le collagène sont proposés comme potentiellement impliqués dans les effets induits par le collagène de type I dans le modèle. Ces deux projets permettent et permettront de mieux comprendre certains mécanismes de la progression tumorale, ainsi que de mettre en évidence des marqueurs biomoléculaires et de nouvelles cibles thérapeutiques

Identification of new potential prognosis factor and therapeutically targets in kidney cancer

Le cancer du rein compte parmi les 10 types de cancers les plus fréquents chez l’Homme. Il n’existe aujourd’hui aucun marqueur biomoléculaire dans ce type de cancer, et dans le cas d’un cancer métastatique, l’arsenal thérapeutique aujourd’hui disponible manque d’efficacité. Les différents processus mis en jeu lors de la progression tumorale sont encore mal connus. La connaissance de ces processus pourrait permettre de mettre en évidence de nouvelles cibles thérapeutiques, ainsi que des marqueurs biomoléculaires pronostiques ou diagnostiques de la maladie. Dans un premier projet, et afin de mieux comprendre les mécanismes de la progression tumorale et d’identifier de nouvelles cibles thérapeutiques potentielles et de nouveaux marqueurs biomoléculaires dans le cancer du rein, un nouveau modèle innovant a été généré à partir d’une lignée tumorale de RCC murine. Ce modèle de réimplantations successives de cellules tumorales issues de tumeur primaire ou de métastases a permis de générer différentes lignées cellulaires montrant une agressivité accrue au cours des passages. En utilisant une stratégie de biologie des systèmes, ce modèle pourra permettre de mettre en évidence des cibles d’études prometteuses qui pourraient être de nouvelles cibles thérapeutiques ou de nouveaux marqueurs biomoléculaires dans le RCC. L’interleukine-34 est l’exemple d’une cible d’étude d’ores et déjà été sélectionnée, mettant en évidence la puissance du modèle généré. Dans un second projet, les rôles de certains membres de la matrice extracellulaire tumorale ont été évalués en utilisant cette même lignée de RCC murine (collagène de type I, fibronectine, matrigel). Cette étude a permis de mettre en évidence le potentiel pro-invasif et pro-métastatique du dépôt de collagène de type I dans les tumeurs. Des récepteurs activés par le collagène sont proposés comme potentiellement impliqués dans les effets induits par le collagène de type I dans le modèle. Ces deux projets permettent et permettront de mieux comprendre certains mécanismes de la progression tumorale, ainsi que de mettre en évidence des marqueurs biomoléculaires et de nouvelles cibles thérapeutiques.Kidney cancer is one of the 10 commonest human cancers. To date, no biomolecular markers are available in this type of cancer, and in the case of metastatic cancer, the therapeutic arsenal is still inefficient. The different processes involved in cancer progression are still poorly understood. Understanding those processes could highlight new therapeutic targets, and new prognostic or diagnostic biomolecular markers of this disease. For a first project, a new innovative model has been generated from a murine RCC cell line as a tool to understand cancer progression mechanisms and to identify new therapeutic target and new biomolecular markers in kidney cancer. This model of sequential reimplantation of cancer cells isolated from primary tumours or metastases allowed us to generate different cell lines showing increased aggressiveness after passages. Using a systems biology strategy, this model will allow us to identify new potential therapeutic targets and new biomolecular markers in RCC. Interleukin-34 is an example of an already selected target, showing the power of the model generated. For a second project, the role of some members of extracellular matrix (collagen type I, fibronectin, matrigel).was studied using this same murine RCC cell line. This study demonstrated the potential pro-invasive and pro-metastatic roles of collagen type I deposition in tumors. Collagen-activated receptors are proposed as mediators of the effect induced by collagen type I in this model. Those two projects have and will continue to contribute to a better understanding of cancer progression mechanisms, and will bring out new biomolecular markers and new therapeutic targets

Practical identifiability analysis of a mechanistic model for the time to distant metastatic relapse and its application to renal cell carcinoma

International audienceDistant metastasis-free survival (DMFS) curves are widely used in oncology. They are classically analyzed using the Kaplan-Meier estimator or agnostic statistical models from survival analysis. Here we report on a method to extract more information from DMFS curves using a mathematical model of primary tumor growth and metastatic dissemination. The model depends on two parameters, α and μ, respectively quantifying tumor growth and dissemination. We assumed these to be lognormally distributed in a patient population. We propose a method for identification of the parameters of these distributions based on leastsquares minimization between the data and the simulated survival curve. We studied the practical identifiability of these parameters and found that including the percentage of patients with metastasis at diagnosis was critical to ensure robust estimation. We also studied the impact and identifiability of covariates and their coefficients in α and μ, either categorical or continuous, including various functional forms for the latter (threshold, linear or a combination of both). We found that both the functional form and the coefficients could be determined from DMFS curves. We then applied our model to a clinical dataset of metastatic relapse from kidney cancer with individual data of 105 patients. We show that the model was able to describe the data and illustrate our method to disentangle the impact of three covariates on DMFS: a categorical one (Fuhrman grade) and two continuous ones (gene expressions of the macrophage mannose receptor 1 (MMR) and the G Protein-Coupled Receptor Class C Group 5 Member A (GPRC5a) gene). We found that all had an influence in metastasis dissemination (μ), but not on growth (α)

Deep learning model for automatic segmentation of lungs and pulmonary metastasis in small animal MR images

Lungs are the most frequent site of metastases growth. The amount and size of pulmonary metastases acquired from MRI imaging data are the important criteria to assess the efficacy of new drugs in preclinical models. While efficient solutions both for MR imaging and the downstream automatic segmentation have been proposed for human patients, both MRI lung imaging and segmentation in preclinical animal models remains challenging due to the physiological motion (respiratory and cardiac movements), to the low amount of protons in this organ and to the particular challenge of precise segmentation of metastases. As a consequence post-mortem analysis is currently required to obtain information on metastatic volume. In this work, we have developed a complete methodological pipeline for automated analysis of lungs and metastases in mice, consisting of an MR sequence for image acquisition and a deep learning method for automatic segmentation of both lungs and metastases. On one hand, we optimized an MR sequence for mouse lung imaging with high contrast for high detection sensitivity. On the other hand we developed DeepMeta, a multiclass U-Net 3+ deep learning model to automatically segment the images. To assess if the proposed deep learning pipeline is able to provide an accurate segmentation of both lungs and pulmonary metastases, we have longitudinally imaged mice with fast- and slow-growing metastasis. Fifty-five balb/c mice were injected with two different derivatives of renal carcinoma cells. Mice were imaged with a SG-bSSFP (self-gated balanced steady state free precession) sequence at different time points after the injection of cancer cells. Both lung and metastases segmentations were manually performed by experts. DeepMeta was trained to perform lung and metastases segmentation based on the resulting ground truth annotations. Volumes of lungs and of pulmonary metastases as well as the number of metastases per mouse were measured on a separate test dataset of MR images. Thanks to the SG method, the 3D bSSFP images of lungs were artifact-free, enabling the downstream detection and serial follow-up of metastases. Moreover, both lungs and metastases segmentation was accurately performed by DeepMeta as soon as they reached the volume of ∼ 0.02 m m 3 . Thus we were able to distinguish two groups of mice in terms of number and volume of pulmonary metastases as well as in terms of the slow versus fast patterns of growth of metastases. We have shown that our methodology combining SG-bSSFP with deep learning, enables processing of the whole animal lungs and is thus a viable alternative to histology alone

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

DDR1 and DDR2 physical interaction leads to signaling interconnection but with possible distinct functions

<p>Discoidin domain receptors 1 and 2 (DDR1 and DDR2) are members of the tyrosine kinase receptors activated after binding with collagen. DDRs are implicated in numerous physiological and pathological functions such as proliferation, adhesion and migration. Little is known about the expression of the two receptors in normal and cancer cells and most of studies focus only on one receptor. Western blot analysis of DDR1 and DDR2 expression in different tumor cell lines shows an absence of high co-expression of the two receptors suggesting a deleterious effect of their presence at high amount. To study the consequences of high DDR1 and DDR2 co-expression in cells, we over-express the two receptors in HEK 293T cells and compare biological effects to HEK cells over-expressing DDR1 or DDR2. To distinguish between the intracellular dependent and independent activities of the two receptors we over-express an intracellular truncated dominant-negative DDR1 or DDR2 protein (DDR1DN and DDR2DN). No major differences of Erk or Jak2 activation are found after collagen I stimulation, nevertheless Erk activation is higher in cells co-expressing DDR1 and DDR2. DDR1 increases cell proliferation but co-expression of DDR1 and DDR2 is inhibitory. DDR1 but not DDR2 is implicated in cell adhesion to a collagen I matrix. DDR1, and DDR1 and DDR2 co-expression inhibit cell migration. Moreover a DDR1/DDR2 physical interaction is found by co-immunoprecipitation assays. Taken together, our results show a deleterious effect of high co-expression of DDR1 and DDR2 and a physical interaction between the two receptors.</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

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

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