Preclinical Evaluation of Spatial Frequency Domain-Enabled Wide-Field Quantitative Imaging for Enhanced Glioma Resection

5-Aminolevelunic acid-induced protoporphyrin IX (PpIX) fluorescence-guided resection (FGR) enables maximum safe resection of glioma by providing real-time tumor contrast. However, the subjective visual assessment and the variable intrinsic optical attenuation of tissue limit this technique to reliably delineating only high-grade tumors that display strong fluorescence. We have previously shown, using a fiber-optic probe, that quantitative assessment using noninvasive point spectroscopic measurements of the absolute PpIX concentration in tissue further improves the accuracy of FGR, extending it to surgically curable low-grade glioma. More recently, we have shown that implementing spatial frequency domain imaging with a fluorescent-light transport model enables recovery of two-dimensional images of [PpIX], alleviating the need for time-consuming point sampling of the brain surface. We present first results of this technique modified for in vivo imaging on an RG2 rat brain tumor model. Despite the moderate errors in retrieving the absorption and reduced scattering coefficients in the subdiffusive regime of 14% and 19%, respectively, the recovered [PpIX] maps agree within 10% of the point [PpIX] values measured by the fiber-optic probe, validating its potential as an extension or an alternative to point sampling during glioma resection

Hypoxic Environment and Paired Hierarchical 3D and 2D Models of Pediatric H3.3-Mutated Gliomas Recreate the Patient Tumor Complexity.

BACKGROUND:Pediatric high-grade gliomas (pHGGs) are facing a very dismal prognosis and representative pre-clinical models are needed for new treatment strategies. Here, we examined the relevance of collecting functional, genomic, and metabolomics data to validate patient-derived models in a hypoxic microenvironment. METHODS:From our biobank of pediatric brain tumor-derived models, we selected 11 pHGGs driven by the histone H3.3K28M mutation. We compared the features of four patient tumors to their paired cell lines and mouse xenografts using NGS (next generation sequencing), aCGH (array comparative genomic hybridization), RNA sequencing, WES (whole exome sequencing), immunocytochemistry, and HRMAS (high resolution magic angle spinning) spectroscopy. We developed a multicellular in vitro model of cell migration to mimic the brain hypoxic microenvironment. The live cell technology Incucyte© was used to assess drug responsiveness in variable oxygen conditions. RESULTS:The concurrent 2D and 3D cultures generated from the same tumor sample exhibited divergent but complementary features, recreating the patient intra-tumor complexity. Genomic and metabolomic data described the metabolic changes during pHGG progression and supported hypoxia as an important key to preserve the tumor metabolism in vitro and cell dissemination present in patients. The neurosphere features preserved tumor development and sensitivity to treatment. CONCLUSION:We proposed a novel multistep work for the development and validation of patient-derived models, considering the immature and differentiated content and the tumor microenvironment of pHGGs

Mathematical modelling of movement and glioma invasion

Modelling movement is an important topic in fields ranging from ecology to medicine. In particular, glioma, an often fatal brain tumour is characterised by its diffuse invasion into the surrounding normal brain tissue, enabling the tumour to escape therapy. In this thesis we focus on the mathematical modelling of glioma and movement in general. We begin by exploring the basic structure of the brain, describing glioma classification and the hallmarks of cancer as well as reviewing mathematical models of glioma. In Chapter 2 an Ordinary Differential Equation model is presented to describe the interaction between healthy and mutated cells in vivo and vitro scenarios. The model is extended to a Partial Differential Equation to cover the spatial dynamics of interaction and the possibility of travelling wave solutions. A leading hypothesis suggests that malignant glioma cells switch between proliferating and migrating phenotypes, a mechanism known as the “go or grow” hypothesis. Although the molecular mechanisms that control this switch are uncertain, it is generally assumed to depend on micro-environmental factors. In Chapter 3 we propose a simple mathematical model based on the go or grow hypothesis for brain tumours (gliomas). The model describes the competition between healthy glial cells and malignant cells, with the latter subdivided into invasive and proliferating subpopulations. Simulation and stability analysis is performed for spatial and non-spatial versions of the model. The model incorporates two types of switch between migration and proliferation glioma cells: a constant switch form and a density dependent form. In Chapter 4 we present a framework for modelling a different characteristic movement lengths based on a biased random walk in response to external control species. We use the model to understand different strategies by which a population may locate some resource in its environment. Further we consider a pilot application to glioma, showing how it can be used to model movement along different brain structures. Finally we conclude with a brief discussion that summarises the main results and highlights directions for future work

Mathematical models for glioma growh and migration inside the brain

284 p.Los gliomas forman el subtipo más prevalente, agresivo e invasivo de tumores cerebrales primarios,caracterizados por una rápida proliferación celular y una elevada capacidad de infiltración. A pesar de los avances de la investigación clínica, estos tumores suelen ser resistentes al tratamiento; la supervivencia media oscila entre 9 y 12 meses, siendo la recurrencia la principal causa de mortalidad.La migración y la invasión de los gliomas en el cerebro son fenómenos complejos y aún se desconocen varios de los mecanismos subyacentes que guían la progresión de estos tumores.En esta tesis, proponemos varios modelos matemáticos para estudiar diversos aspectos de la progresión del glioma en relación con las escalas microscópicas y macroscópicas que caracterizan este proceso. Considerar el carácter intrínsico multiescala de la evolución del glioma permite definir modelos basados en sistemas dinámicos, ecuaciones cinéticas y EDP macroscópicas con diferentes roles dependiendo de los fenómenos a estudiar. Uno de los objetivos principales de esta tesis es integrar datos biológicos y clínicos con los modelos matemáticos. Los datos experimentales utilizados se han obtenido de imágenes por resonancia magnética, de imágenes con tensor de difusión del cerebro humano y de análisis de inmunofluorescencia in vivo de distribuciones de varias proteínas en Drosophila, un modelo fiable para el estudio de la dinámica del glioblastoma.Analizamos las características de anisotropía del tejido nervioso, utilizando los datos del tensor de difusión, y la influencia de la estructura de las fibras en la dinámica de las células tumorales.Mostramos cómo la red de fibras guía la migración celular a lo largo de rutas preferenciales,reproduciendo los patrones ramificados y heterogéneos típicos de la evolución del glioma; asimismo,demostramos cómo los tratamientos multimodales pueden reducir este comportamiento.Estudiamos la interdependencia entre la acidez del microambiente y la vascularización en el proceso de angiogénesis tumoral. Para ello, construimos un modelo capaz de reproducir la influencia de estos mecanismos en el desarrollo de la heterogeneidad intratumoral y de características típicas de la progresión del glioma relacionadas con la hipoxia (e.g. la necrosis). Este estudio permite formular una clasificación de los tumores basada en el nivel de necrosis, así como la investigación de terapias multimodales que incluyan efectos antiangiogénicos.Investigamos la influencia de las protrusiones celulares desde una perspectiva no local.Analizamos su rol en el fenómeno de la guía por contacto y en la manifestación de efectos colaborativos o competitivos entre dos estímulos que determinan cambios de dirección de la velocidad celular.Utilizando el análisis experimental de las distribuciones de varias proteínas, evaluamos la relación de las protrusiones celulares con las integrinas y las proteasas como principales mecanismos de progresión del glioblastoma. Mostramos cómo las interacciones bioquímicas y biomecánicas de estos agentes dan como resultado el desarrollo de frentes de propagación tumoral, que pueden presentar una evolución dinámica y heterogénea en relación a los cambios ambientales.bcam:basque center for applied mathematics; La Caixa Foundatio

Mathematical models for glioma growh and migration inside the brain

284 p.Los gliomas forman el subtipo más prevalente, agresivo e invasivo de tumores cerebrales primarios,caracterizados por una rápida proliferación celular y una elevada capacidad de infiltración. A pesar de los avances de la investigación clínica, estos tumores suelen ser resistentes al tratamiento; la supervivencia media oscila entre 9 y 12 meses, siendo la recurrencia la principal causa de mortalidad.La migración y la invasión de los gliomas en el cerebro son fenómenos complejos y aún se desconocen varios de los mecanismos subyacentes que guían la progresión de estos tumores.En esta tesis, proponemos varios modelos matemáticos para estudiar diversos aspectos de la progresión del glioma en relación con las escalas microscópicas y macroscópicas que caracterizan este proceso. Considerar el carácter intrínsico multiescala de la evolución del glioma permite definir modelos basados en sistemas dinámicos, ecuaciones cinéticas y EDP macroscópicas con diferentes roles dependiendo de los fenómenos a estudiar. Uno de los objetivos principales de esta tesis es integrar datos biológicos y clínicos con los modelos matemáticos. Los datos experimentales utilizados se han obtenido de imágenes por resonancia magnética, de imágenes con tensor de difusión del cerebro humano y de análisis de inmunofluorescencia in vivo de distribuciones de varias proteínas en Drosophila, un modelo fiable para el estudio de la dinámica del glioblastoma.Analizamos las características de anisotropía del tejido nervioso, utilizando los datos del tensor de difusión, y la influencia de la estructura de las fibras en la dinámica de las células tumorales.Mostramos cómo la red de fibras guía la migración celular a lo largo de rutas preferenciales,reproduciendo los patrones ramificados y heterogéneos típicos de la evolución del glioma; asimismo,demostramos cómo los tratamientos multimodales pueden reducir este comportamiento.Estudiamos la interdependencia entre la acidez del microambiente y la vascularización en el proceso de angiogénesis tumoral. Para ello, construimos un modelo capaz de reproducir la influencia de estos mecanismos en el desarrollo de la heterogeneidad intratumoral y de características típicas de la progresión del glioma relacionadas con la hipoxia (e.g. la necrosis). Este estudio permite formular una clasificación de los tumores basada en el nivel de necrosis, así como la investigación de terapias multimodales que incluyan efectos antiangiogénicos.Investigamos la influencia de las protrusiones celulares desde una perspectiva no local.Analizamos su rol en el fenómeno de la guía por contacto y en la manifestación de efectos colaborativos o competitivos entre dos estímulos que determinan cambios de dirección de la velocidad celular.Utilizando el análisis experimental de las distribuciones de varias proteínas, evaluamos la relación de las protrusiones celulares con las integrinas y las proteasas como principales mecanismos de progresión del glioblastoma. Mostramos cómo las interacciones bioquímicas y biomecánicas de estos agentes dan como resultado el desarrollo de frentes de propagación tumoral, que pueden presentar una evolución dinámica y heterogénea en relación a los cambios ambientales.bcam:basque center for applied mathematics; La Caixa Foundatio

Mathematical models for glioma growth and migration inside the brain

Gliomas are the most prevalent, aggressive, and invasive subtype of primary brain tumors, characterized by rapid cell proliferation and great infiltration capacity. De- spite the advances of clinical research, these tumors are often resistant to treatment, the median survival ranges between 9 and 12 months, and recurrence is the main cause of mortality. Glioma migration and invasion into the brain tissue is a complex phenomenon and little is still known about the underlying mechanisms that lead to tumor progression. In this thesis, we propose several mathematical models studying various aspects of glioma progression in relation to the microscopic and macroscopic scales charac- terizing this process. Exploiting the inherently multiscale nature of glioma evolution allows to define models based on dynamical systems, kinetic equations, and macro- scopic PDEs with different roles depending on the considered phenomena. The in- tegration of biological and clinical data with the mathematical models is one of the key objectives of this thesis. The experimental data at hand are obtained from mag- netic resonance and diffusion tensor images of the human brain and from in-vivo im- munofluorescence analysis of protein distributions in Drosophila, a reliable model for the study of glioblastoma dynamics. We analyze the anisotropic characteristics of the brain tissue, using the diffusion tensor data, and the influence of the fiber structures on tumor cell dynamics. We show how the fiber network directs cell migration along preferential paths, reproducing the branched and heterogeneous patterns typical of glioma evolution, and how multi- modal treatments can reduce this behavior. We study the interdependency of microenvironmental acidity and vasculature in tumor angiogenesis, defining a model capable of reproducing their influence on the emergence of phenotypic heterogeneity and hypoxia-related features (like necrosis) typical of glioma progression. This study enables the testing of a necrosis-based tumor grading and the investigation of multi-modal therapies with anti-angiogenic effects. We investigated the role of cell protrusions from a non-local perspective. We ex- plore their influence on the contact guidance phenomenon and on the emergence of collaborative or competitive effects between two cues driving cell velocity changes. Using the experimental analysis of protein distributions, we evaluate cell protru- sion relationship with integrins and proteases as leading mechanisms of glioblastoma progression. We show how the biochemical and biomechanical interactions of these agents result in the emergence of tumor propagation fronts, which can feature a dy- namical and heterogenous evolution in relation to environmental changes.European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713673. ”la Caixa” Foundation (ID 100010434), with fellowship code LCF/BQ/IN17/11620056

Fluorescence molecular tomography: Principles and potential for pharmaceutical research

Fluorescence microscopic imaging is widely used in biomedical research to study molecular and cellular processes in cell culture or tissue samples. This is motivated by the high inherent sensitivity of fluorescence techniques, the spatial resolution that compares favorably with cellular dimensions, the stability of the fluorescent labels used and the sophisticated labeling strategies that have been developed for selectively labeling target molecules. More recently, two and three-dimensional optical imaging methods have also been applied to monitor biological processes in intact biological organisms such as animals or even humans. These whole body optical imaging approaches have to cope with the fact that biological tissue is a highly scattering and absorbing medium. As a consequence, light propagation in tissue is well described by a diffusion approximation and accurate reconstruction of spatial information is demanding. While in vivo optical imaging is a highly sensitive method, the signal is strongly surface weighted, i.e., the signal detected from the same light source will become weaker the deeper it is embedded in tissue, and strongly depends on the optical properties of the surrounding tissue. Derivation of quantitative information, therefore, requires tomographic techniques such as fluorescence molecular tomography (FMT), which maps the three-dimensional distribution of a fluorescent probe or protein concentration. The combination of FMT with a structural imaging method such as X-ray computed tomography (CT) or Magnetic Resonance Imaging (MRI) will allow mapping molecular information on a high definition anatomical reference and enable the use of prior information on tissue’s optical properties to enhance both resolution and sensitivity. Today many of the fluorescent assays originally developed for studies in cellular systems have been successfully translated for experimental studies in animals. The opportunity of monitoring molecular processes non-invasively in the intact organism is highly attractive from a diagnostic point of view but even more so for the drug developer, who can use the techniques for proof-of-mechanism and proof-of-efficacy studies. This review shall elucidate the current status and potential of fluorescence tomography including recent advances in multimodality imaging approaches for preclinical and clinical drug development

Lattice-gas cellular automata for the analysis of cancer invasion

Cancer cells display characteristic traits acquired in a step-wise manner during carcinogenesis. Some of these traits are autonomous growth, induction of angiogenesis, invasion and metastasis. In this thesis, the focus is on one of the latest stages of tumor progression, tumor invasion. Tumor invasion emerges from the combined effect of tumor cell-cell and cell-microenvironment interactions, which can be studied with the help of mathematical analysis. Cellular automata (CA) can be viewed as simple models of self-organizing complex systems in which collective behavior can emerge out of an ensemble of many interacting "simple" components. In particular, we focus on an important class of CA, the so-called lattice-gas cellular automata (LGCA). In contrast to traditional CA, LGCA provide a straightforward and intuitive implementation of particle transport and interactions. Additionally, the structure of LGCA facilitates the mathematical analysis of their behavior. Here, the principal tools of mathematical analysis of LGCA are the mean-field approximation and the corresponding Lattice Boltzmann equation. The main objective of this thesis is to investigate important aspects of tumor invasion, under the microscope of mathematical modeling and analysis: Impact of the tumor environment: We introduce a LGCA as a microscopic model of tumor cell migration together with a mathematical description of different tumor environments. We study the impact of the various tumor environments (such as extracellular matrix) on tumor cell migration by estimating the tumor cell dispersion speed for a given environment. Effect of tumor cell proliferation and migration: We study the effect of tumor cell proliferation and migration on the tumor’s invasive behavior by developing a simplified LGCA model of tumor growth. In particular, we derive the corresponding macroscopic dynamics and we calculate the tumor’s invasion speed in terms of tumor cell proliferation and migration rates. Moreover, we calculate the width of the invasive zone, where the majority of mitotic activity is concentrated, and it is found to be proportional to the invasion speed. Mechanisms of tumor invasion emergence: We investigate the mechanisms for the emergence of tumor invasion in the course of cancer progression. We conclude that the response of a microscopic intracellular mechanism (migration/proliferation dichotomy) to oxygen shortage, i.e. hypoxia, maybe responsible for the transition from a benign (proliferative) to a malignant (invasive) tumor. Computing in vivo tumor invasion: Finally, we propose an evolutionary algorithm that estimates the parameters of a tumor growth LGCA model based on time-series of patient medical data (in particular Magnetic Resonance and Diffusion Tensor Imaging data). These parameters may allow to reproduce clinically relevant tumor growth scenarios for a specific patient, providing a prediction of the tumor growth at a later time stage.Krebszellen zeigen charakteristische Merkmale, die sie in einem schrittweisen Vorgang während der Karzinogenese erworben haben. Einige dieser Merkmale sind autonomes Wachstum, die Induktion von Angiogenese, Invasion und Metastasis. Der Schwerpunkt dieser Arbeit liegt auf der Tumorinvasion, einer der letzten Phasen der Tumorprogression. Die Tumorinvasion ensteht aus der kombinierten Wirkung von den Wechselwirkungen Tumorzelle-Zelle und Zelle-Mikroumgebung, die mit die Hilfe von mathematischer Analyse untersucht werden können. Zelluläre Automaten (CA) können als einfache Modelle von selbst-organisierenden komplexen Systemen betrachtet werden, in denen kollektives Verhalten aus einer Kombination von vielen interagierenden "einfachen" Komponenten entstehen kann. Insbesondere konzentrieren wir uns auf eine wichtige CA-Klasse, die sogenannten Zelluläre Gitter-Gas Automaten (LGCA). Im Gegensatz zu traditionellen CA bieten LGCA eine einfache und intuitive Umsetzung der Teilchen und Wechselwirkungen. Zusätzlich erleichtert die Struktur der LGCA die mathematische Analyse ihres Verhaltens. Die wichtigsten Werkzeuge der mathematischen Analyse der LGCA sind hier die Mean-field Approximation und die entsprechende Lattice - Boltzmann - Gleichung. Das wichtigste Ziel dieser Arbeit ist es, wichtige Aspekte der Tumorinvasion unter dem Mikroskop der mathematischen Modellierung und Analyse zu erforschen: Auswirkungen der Tumorumgebung: Wir stellen einen LGCA als mikroskopisches Modell der Tumorzellen-Migration in Verbindung mit einer mathematischen Beschreibung der verschiedenen Tumorumgebungen vor. Wir untersuchen die Auswirkungen der verschiedenen Tumorumgebungen (z. B. extrazellulären Matrix) auf die Migration von Tumorzellen dürch Schätzung der Tumorzellen-Dispersionsgeschwindigkeit in einem gegebenen Umfeld. Wirkung von Tumor-Zellenproliferation und Migration: Wir untersuchen die Wirkung von Tumorzellenproliferation und Migration auf das invasive Verhalten der Tumorzellen durch die Entwicklung eines vereinfachten LGCA Tumorwachstumsmodells. Wir leiten die entsprechende makroskopische Dynamik und berechnen die Tumorinvasionsgeschwindigkeit im Hinblick auf die Tumorzellenproliferation- und Migrationswerte. Darüber hinaus berechnen wir die Breite der invasiven Zone, wo die Mehrheit der mitotischer Aktivität konzentriert ist, und es wird festgestellt, dass diese proportional zu den Invasionsgeschwindigkeit ist. Mechanismen der Tumorinvasion Entstehung: Wir untersuchen Mechanismen, die für die Entstehung von Tumorinvasion im Verlauf des Krebs zuständig sind. Wir kommen zu dem Schluss, dass die Reaktion eines mikroskopischen intrazellulären Mechanismus (Migration/Proliferation Dichotomie) zu Sauerstoffmangel, d.h. Hypoxie, möglicheweise für den Übergang von einem gutartigen (proliferative) zu einer bösartigen (invasive) Tumor verantwortlich ist. Berechnung der in-vivo Tumorinvasion: Schließlich schlagen wir einen evolutionären Algorithmus vor, der die Parameter eines LGCA Modells von Tumorwachstum auf der Grundlage von medizinischen Daten des Patienten für mehrere Zeitpunkte (insbesondere die Magnet-Resonanz-und Diffusion Tensor Imaging Daten) ermöglicht. Diese Parameter erlauben Szenarien für einen klinisch relevanten Tumorwachstum für einen bestimmten Patienten zu reproduzieren, die eine Vorhersage des Tumorwachstums zu einem späteren Zeitpunkt möglich machen

Penetration of Endothelial Cell Coated Multicellular Tumor Spheroids by Iron Oxide Nanoparticles

Iron oxide nanoparticles are a useful diagnostic contrast agent and have great potential for therapeutic applications. Multiple emerging diagnostic and therapeutic applications and the numerous versatile parameters of the nanoparticle platform require a robust biological model for characterization and assessment. Here we investigate the use of iron oxide nanoparticles that target tumor vasculature, via the tumstatin peptide, in a novel three-dimensional tissue culture model. The developed tissue culture model more closely mimics the in vivo environment with a leaky endothelium coating around a glioma tumor mass. Tumstatin-iron oxide nanoparticles showed penetration and selective targeting to endothelial cell coating on the tumor in the three-dimensional model, and had approximately 2 times greater uptake in vitro and 2.7 times tumor neo-vascularization inhibition. Tumstatin provides targeting and therapeutic capabilities to the iron oxide nanoparticle diagnostic contrast agent platform. And the novel endothelial cell-coated tumor model provides an in vitro microtissue environment to evaluate nanoparticles without moving into costly and time-consuming animal models