Matching Items (5)
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Description
Following diagnosis of a glioblastoma (GBM) brain tumor, surgical resection, chemotherapy and radiation together yield a median patient survival of only 15 months. Importantly, standard treatments fail to address the dynamic regulation of the brain tumor microenvironment that actively supports tumor progression and treatment resistance. Moreover, specialized niches within the

Following diagnosis of a glioblastoma (GBM) brain tumor, surgical resection, chemotherapy and radiation together yield a median patient survival of only 15 months. Importantly, standard treatments fail to address the dynamic regulation of the brain tumor microenvironment that actively supports tumor progression and treatment resistance. Moreover, specialized niches within the tumor microenvironment maintain a population of highly malignant glioblastoma stem-like cells (GSCs). GSCs are resistant to traditional chemotherapy and radiation therapy and are likely responsible for near universal rates of tumor recurrence and associated morbidity. Thus, disrupting microenvironmental support for GSCs could be critical to more effective GBM therapies. Three-dimensional (3D) culture models of the tumor microenvironment are powerful tools for identifying key biochemical and biophysical inputs that may support or inhibit malignant behaviors. Here, we developed synthetic poly(N-isopropylacrylamide-co-Jeffamine M-1000® acrylamide) or PNJ copolymers as a model 3D system for culturing GBM cell lines and low-passage patient-derived GSCs in vitro. These temperature responsive scaffolds reversibly transition from soluble to insoluble in aqueous solution by heating from room temperature to body temperature, thereby enabling easy encapsulation and release of cells in a 3D scaffold. We also designed this system with the capacity for presenting the cell-adhesion peptide sequence RGD for adherent culture conditions. Using this system, we identified conditions that promoted GBM proliferation, invasion, GSC phenotypes, and radiation resistance. In particular, using two separate patient-derived GSC models, we observed that PNJ scaffolds regulated self-renewal, provided protection from radiation induced cell death, and may promote stem cell plasticity in response to radiation. Furthermore, PNJ scaffolds produced de novo activation of the transcription factor HIF2α, which is critical to GSC tumorigenicity and stem plasticity. All together, these studies establish the robust utility of PNJ biomaterials as in vitro models for studying microenvironmental regulation of GSC behaviors and treatment resistance.
ContributorsHeffernan, John M. (Author) / Sirianni, Rachael W. (Thesis advisor) / Vernon, Brent L (Thesis advisor) / Mehta, Shwetal (Committee member) / Stabenfeldth, Sarah (Committee member) / Massia, Stephen (Committee member) / Arizona State University (Publisher)
Created2017
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Description
Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and

Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and includes chemotherapy, radiation therapy, and surgical removal if the tumor is surgically accessible. Treatment seldom results in a significant increase in longevity, partly due to the lack of precise information regarding tumor size and location. This lack of information arises from the physical limitations of MR and CT imaging coupled with the diffusive nature of glioblastoma tumors. GBM tumor cells can migrate far beyond the visible boundaries of the tumor and will result in a recurring tumor if not killed or removed. Since medical images are the only readily available information about the tumor, we aim to improve mathematical models of tumor growth to better estimate the missing information. Particularly, we investigate the effect of random variation in tumor cell behavior (anisotropy) using stochastic parameterizations of an established proliferation-diffusion model of tumor growth. To evaluate the performance of our mathematical model, we use MR images from an animal model consisting of Murine GL261 tumors implanted in immunocompetent mice, which provides consistency in tumor initiation and location, immune response, genetic variation, and treatment. Compared to non-stochastic simulations, stochastic simulations showed improved volume accuracy when proliferation variability was high, but diffusion variability was found to only marginally affect tumor volume estimates. Neither proliferation nor diffusion variability significantly affected the spatial distribution accuracy of the simulations. While certain cases of stochastic parameterizations improved volume accuracy, they failed to significantly improve simulation accuracy overall. Both the non-stochastic and stochastic simulations failed to achieve over 75% spatial distribution accuracy, suggesting that the underlying structure of the model fails to capture one or more biological processes that affect tumor growth. Two biological features that are candidates for further investigation are angiogenesis and anisotropy resulting from differences between white and gray matter. Time-dependent proliferation and diffusion terms could be introduced to model angiogenesis, and diffusion weighed imaging (DTI) could be used to differentiate between white and gray matter, which might allow for improved estimates brain anisotropy.
ContributorsAnderies, Barrett James (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Stepien, Tracy (Committee member) / Harrington Bioengineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Description
In this research we consider stochastic models of Glioblastoma Multiforme brain tumors. We first look at a model by K. Swanson et al., which describes the dynamics as random diffusion plus deterministic logistic growth. We introduce a stochastic component in the logistic growth in the form of a random growth

In this research we consider stochastic models of Glioblastoma Multiforme brain tumors. We first look at a model by K. Swanson et al., which describes the dynamics as random diffusion plus deterministic logistic growth. We introduce a stochastic component in the logistic growth in the form of a random growth rate defined by a Poisson process. We show that this stochastic logistic growth model leads to a more accurate evaluation of the tumor growth compared its deterministic counterpart. We also discuss future plans to incorporate individual patient geometry, extend the model to three dimensions and to incorporate effects of different treatments into our model, in collaboration with a local hospital.
ContributorsManning, Michael Clare (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Gardner, Carl (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Letters and Sciences (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2013-12
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Description
Glioblastoma multiforme (GBMs) is the most prevalent brain tumor type and causes approximately 40% of all non-metastic primary tumors in adult patients [1]. GBMs are malignant, grade-4 brain tumors, the most aggressive classication as established by the World Health Organization and are marked by their low survival rate; the median

Glioblastoma multiforme (GBMs) is the most prevalent brain tumor type and causes approximately 40% of all non-metastic primary tumors in adult patients [1]. GBMs are malignant, grade-4 brain tumors, the most aggressive classication as established by the World Health Organization and are marked by their low survival rate; the median survival time is only twelve months from initial diagnosis: Patients who live more than three years are considered long-term survivors [2]. GBMs are highly invasive and their diffusive growth pattern makes it impossible to remove the tumors by surgery alone [3]. The purpose of this paper is to use individual patient data to parameterize a model of GBMs that allows for data on tumor growth and development to be captured on a clinically relevant time scale. Such an endeavor is the rst step to a clinically applicable predictions of GBMs. Previous research has yielded models that adequately represent the development of GBMs, but they have not attempted to follow specic patient cases through the entire tumor process. Using the model utilized by Kostelich et al. [4], I will attempt to redress this deciency. In doing so, I will improve upon a family of models that can be used to approximate the time of development and/or structure evolution in GBMs. The eventual goal is to incorporate Magnetic Resonance Imaging (MRI) data into a parameterized model of GBMs in such a way that it can be used clinically to predict tumor growth and behavior. Furthermore, I hope to come to a denitive conclusion as to the accuracy of the Koteslich et al. model throughout the development of GBMs tumors.
ContributorsManning, Miles (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Preul, Mark (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2012-12
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Description
Glioblastoma (GBM) is a highly invasive and deadly late stage tumor that develops from abnormal astrocytes in the brain. With few improvements in treatment over many decades, median patient survival is only 15 months and the 5-year survival rate hovers at 6%. Numerous challenges are encountered in the development of

Glioblastoma (GBM) is a highly invasive and deadly late stage tumor that develops from abnormal astrocytes in the brain. With few improvements in treatment over many decades, median patient survival is only 15 months and the 5-year survival rate hovers at 6%. Numerous challenges are encountered in the development of treatments for GBM. The blood-brain barrier (BBB) serves as a primary obstacle due to its innate ability to prevent unwanted molecules, such as most chemotherapeutics, from entering the brain tissue and reaching malignant cells. The GBM cells themselves serve as a second obstacle, having a high level of genetic and phenotypic heterogeneity. This characteristic improves the probability of a population of cells to have resistance to treatment, which ensures the survival of the tumor. Here, the development and testing of two different modes of therapy for treating GBM is described. These therapeutics were enhanced by pathogenic peptides known to improve entry into brain tissue or to bind GBM cells to overcome the BBB and/or tumor cell heterogeneity. The first therapeutic utilizes a small peptide, RVG-29, derived from the rabies virus glycoprotein to improve brain-specific delivery of nanoparticles encapsulated with a small molecule payload. RVG-29-targeted nanoparticles were observed to reach the brain of healthy mice in higher concentrations 2 hours following intravenous injection compared to control particles. However, targeted camptothecin-loaded nanoparticles were not capable of producing significant treatment benefits compared to non-targeted particles in an orthotopic mouse model of GBM. Peptide degradation following injection was shown to be a likely cause for reduced treatment benefit. The second therapeutic utilizes chlorotoxin, a non-toxic 36-amino acid peptide found in the venom of the deathstalker scorpion, expressed as a fusion to antibody fragments to enhance T cell recognition and killing of GBM. This candidate biologic, known as anti-CD3/chlorotoxin (ACDClx) is expressed as an insoluble protein in Nicotiana benthamiana and Escherichia coli and must be purified in denaturing and reducing conditions prior to being refolded. ACDClx was shown to selectively activate T cells only in the presence of GBM cells, providing evidence that further preclinical development of ACDClx as a GBM immunotherapy is warranted.
ContributorsCook, Rebecca Leanne (Author) / Blattman, Joseph N (Thesis advisor) / Sirianni, Rachael W. (Thesis advisor) / Mor, Tsafrir (Committee member) / Anderson, Karen (Committee member) / Arizona State University (Publisher)
Created2019