Emergent Properties of Tumor Microenvironment in a Real-life Model of Multicell Tumor Spheroids

Multicellular tumor spheroids are an important {\it in vitro} model of the pre-vascular phase of solid tumors, for sizes well below the diagnostic limit: therefore a biophysical model of spheroids has the ability to shed light on the internal workings and organization of tumors at a critical phase of their development. To this end, we have developed a computer program that integrates the behavior of individual cells and their interactions with other cells and the surrounding environment. It is based on a quantitative description of metabolism, growth, proliferation and death of single tumor cells, and on equations that model biochemical and mechanical cell-cell and cell-environment interactions. The program reproduces existing experimental data on spheroids, and yields unique views of their microenvironment. Simulations show complex internal flows and motions of nutrients, metabolites and cells, that are otherwise unobservable with current experimental techniques, and give novel clues on tumor development and strong hints for future therapies.Comment: 20 pages, 10 figures. Accepted for publication in PLOS One. The published version contains links to a supplementary text and three video file

Exploiting Clinical Trial Data Drastically Narrows the Window of Possible Solutions to the Problem of Clinical Adaptation of a Multiscale Cancer Model

The development of computational models for simulating tumor growth and response to treatment has gained significant momentum during the last few decades. At the dawn of the era of personalized medicine, providing insight into complex mechanisms involved in cancer and contributing to patient-specific therapy optimization constitute particularly inspiring pursuits. The in silico oncology community is facing the great challenge of effectively translating simulation models into clinical practice, which presupposes a thorough sensitivity analysis, adaptation and validation process based on real clinical data. In this paper, the behavior of a clinically-oriented, multiscale model of solid tumor response to chemotherapy is investigated, using the paradigm of nephroblastoma response to preoperative chemotherapy in the context of the SIOP/GPOH clinical trial. A sorting of the model's parameters according to the magnitude of their effect on the output has unveiled the relative importance of the corresponding biological mechanisms; major impact on the result of therapy is credited to the oxygenation and nutrient availability status of the tumor and the balance between the symmetric and asymmetric modes of stem cell division. The effect of a number of parameter combinations on the extent of chemotherapy-induced tumor shrinkage and on the tumor's growth rate are discussed. A real clinical case of nephroblastoma has served as a proof of principle study case, demonstrating the basics of an ongoing clinical adaptation and validation process. By using clinical data in conjunction with plausible values of model parameters, an excellent fit of the model to the available medical data of the selected nephroblastoma case has been achieved, in terms of both volume reduction and histological constitution of the tumor. In this context, the exploitation of multiscale clinical data drastically narrows the window of possible solutions to the clinical adaptation problem

Computational challenges of tumor spheroid modeling

The speed and the versatility of today's computers open up new opportunities to simulate complex biological systems. Here we review a computational approach recently proposed by us to model large tumor cell populations and spheroids, and we put forward general considerations that apply to any fine-grained numerical model of tumors. We discuss ways to bypass computational limitations and discuss our incremental approach, where each step is validated by experimental observations on a quantitative basis. We present a few results on the growth of tumor cells in closed and open environments and of tumor spheroids. This study suggests new ways to explore the initial growth phase of solid tumors and to optimize anti-tumor treatments.Comment: 19 pages, 4 figures, 2 tables. Accepted for publication in Journal of Bioinformatics and Computational Biolog

Tumor growth analysis using cellular automata based on the cancer hallmarks

Programa Oficial de Doutoramento en Computación . 5009V01[Resumen]En esta tesis se ha realizado un modelado del crecimiento tumoral, considerando éste consecuencia emergente de las interacciones entre las células y su entorno. El modelado se ha considerado en el nivel de comportamiento celular, modelando los procesos de mitosis y muerte celular en función de la adquisición de una serie de rasgos característicos del cáncer (hallmarks) y del entorno inmediato de cada célula. Para el modelado hemos considerado la herramienta de Autómata Celular (AC). En la tesis se ha analizado la relevancia de los diferentes hallmarks en diferentes escenarios, las transiciones de comportamientos al aplicar un tratamiento, además de introducir la modelización de células madre de cáncer (CSCs). Al incorporar CSCs en el modelado se analizan además diferentes estrategias de tratamientos en el contexto de CSC, teniendo en cuenta la capacidad de recrecimiento del tumor debido a la presencia de CSCs. Finalmente, hemos aplicado optimización evolutiva para la obtención automática de los tratamientos que minimicen el efecto de la recidiva.[Abstract] In this thesis we used computational models based on cellular automata and the abstract model of cancer hallmarks to analyze the emergent behavior of tumor growth at cellular level. Tumor growth is modeled with a cellular automaton which determines cell mitotic and apoptotic behaviors. These behaviors depend on the cancer hallmarks acquired in each cell as consequence of mutations. The presence of the cancer hallmarks defines cell states and cell mitotic behaviors. Additionally, these hallmarks are associated with a series of parameters, and depending on their values and the activation of the hallmarks in each of the cells, the system can evolve to different dynamics. With the simulation tool we performed an analysis of the first phases of cancer growth. Firstly, we studied the evolution of cancer cells and hallmarks in different representative situations regarding initial conditions and parameters, analyzing the relative importance of the hallmarks for tumor progression; Secondly, we focused on the analysis of the effect of killing cancer cells, inspecting the time evolution of the multicellular system under such conditions and the possible behavioral transitions between the predominance of cancer and healthy cells. Later, we analyzed the effect of treatment applications on cancer growth taking into account the presence of Cancer Stem Cells (CSCs) and their regrowth capacity. Finally, we used evolutionary computing to analyze the implications of treatment strategies in a CSC context. In this way, we determined the best strategies of treatment applications in terms of intensity, duration and periodicity considering the regrowth capacity of CSCs

Amélioration de l'image et la segmentation (applications en imagerie médicale)

Avancement dans l'acquisition d'image et le progrès dans les méthodes de traitement d'image ont apporté les mathématiciens et les informaticiens dans les domaines qui sont d'une importance énorme pour les médecins et les biologistes. Le diagnostic précoce de maladies (comme la cécité, le cancer et les problèmes digestifs) ont été des domaines d'intérêt en médecine. Développement des équipements comme microscope bi-photonique à balayage laser et microscope de fluorescence par réflexion totale interne fournit déjà une bonne idée des caractéristiques très intéressantes sur l'objet observé. Cependant, certaines images ne sont pas appropriés pour extraire suffisamment d'informations sur de cette image. Les méthodes de traitement d'image ont été fournit un bon soutien à extraire des informations utiles sur les objets d'intérêt dans ces images biologiques. Rapide méthodes de calcul permettent l'analyse complète, dans un temps très court, d'une série d'images, offrant une assez bonne idée sur les caractéristiques souhaitées. La thèse porte sur l'application de ces méthodes dans trois séries d'images destinées à trois différents types de diagnostic ou d'inférence. Tout d'abord, Images de RP-muté rétine ont été traités pour la détection des cônes, où il n'y avait pas de bâtonnets présents. Le logiciel a été capable de détecter et de compter le nombre de cônes dans chaque image. Deuxièmement, un processus de gastrulation chez la drosophile a été étudié pour observer toute la mitose et les résultats étaient cohérents avec les recherches récentes. Enfin, une autre série d'images ont été traités où la source était une vidéo à partir d'un microscopie photonique à balayage laser. Dans cette vidéo, des objets d'intérêt sont des cellules biologiques. L'idée était de suivre les cellules si elles subissent une mitose. La position de la cellule, la dispersion spatiale et parfois le contour de la membrane cellulaire sont globalement les facteurs limitant la précision dans cette vidéo. Des méthodes appropriées d'amélioration de l'image et de segmentation ont été choisies pour développer une méthode de calcul pour observer cette mitose. L'intervention humaine peut être requise pour éliminer toute inférence fausse.Advancement in Image Acquisition Equipment and progress in Image Processing Methods have brought the mathematicians and computer scientists into areas which are of huge importance for physicians and biologists. Early diagnosis of diseases like blindness, cancer and digestive problems have been areas of interest in medicine. Development of Laser Photon Microscopy and other advanced equipment already provides a good idea of very interesting characteristics of the object being viewed. Still certain images are not suitable to extract sufficient information out of that image. Image Processing methods have been providing good support to provide useful information about the objects of interest in these biological images. Fast computational methods allow complete analysis, in a very short time, of a series of images, providing a reasonably good idea about the desired characteristics. The thesis covers application of these methods in 3 series of images intended for 3 different types of diagnosis or inference. Firstly, Images of RP-mutated retina were treated for detection of rods, where there were no cones present. The software was able to detect and count the number of cones in each frame. Secondly, a gastrulation process in drosophila was studied to observe any mitosis and results were consistent with recent research. Finally, another series of images were treated where biological cells were observed to undergo mitosis. The source was a video from a photon laser microscope. In this video, objects of interest were biological cells. The idea was to track the cells if they undergo mitosis. Cell position, spacing and sometimes contour of the cell membrane are broadly the factors limiting the accuracy in this video. Appropriate method of image enhancement and segmentation were chosen to develop a computational method to observe this mitosis. Cases where human intervention may be required have been proposed to eliminate any false inference.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Multi-scale Models of Tumor Growth and Invasion

Cancer is a complex, multi-scale disease marked by unchecked cellular growth and proliferation. As a tumor grows, it is known to lose its capacity to maintain a compact structure. This stage of development, known as invasion, is marked by the disaggregation and dispersion of peripheral cells, and the formation of finger-like margins. This thesis provides an overview of three multi-scale models of tumor growth and invasion. The hybrid discrete-continuum (HDC) model couples a cellular automaton approach, which is used to direct the behavior and interactions of individual cells, with a system of reaction-diffusion-chemotaxis equations that describe the micro-environment. The evolutionary hybrid cellular automaton (EHCA) model maintains the core of the HDC approach, but employs an artificial response network to describe cellular dynamics. In contrast to these two, the immersed boundary (IBCell) model describes cells as fully deformable, viscoelastic entities that interact with each other using membrane bound receptors. As part of this thesis, the HDC model has been modified to examine the role of the ECM as a barrier to cellular expansion. The results of these simulations will be presented and discussed in the context of tumor progression

Cell-scale biophysical determinants of cell competition in epithelia

How cells with different genetic makeups compete in tissues is an outstanding question in developmental biology and cancer research. Studies in recent years have revealed that cell competition can either be driven by short-range biochemical signalling or by long-range mechanical stresses in the tissue. To date, cell competition has generally been characterised at the population scale, leaving the single-cell-level mechanisms of competition elusive. Here, we use high time-resolution experimental data to construct a multi-scale agent-based model for epithelial cell competition and use it to gain a conceptual understanding of the cellular factors that governs competition in cell populations within tissues. We find that a key determinant of mechanical competition is the difference in homeostatic density between winners and losers, while differences in growth rates and tissue organisation do not affect competition end result. In contrast, the outcome and kinetics of biochemical competition is strongly influenced by local tissue organisation. Indeed, when loser cells are homogenously mixed with winners at the onset of competition, they are eradicated; however, when they are spatially separated, winner and loser cells coexist for long times. These findings suggest distinct biophysical origins for mechanical and biochemical modes of cell competition