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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
Magnetic resonance imaging (MRI) data of metastatic brain cancer patients at the Barrow Neurological Institute sparked interest in the radiology department due to the possibility that tumor size distributions might mimic a power law or an exponential distribution. In order to consider the question regarding the growth trends of metastatic

Magnetic resonance imaging (MRI) data of metastatic brain cancer patients at the Barrow Neurological Institute sparked interest in the radiology department due to the possibility that tumor size distributions might mimic a power law or an exponential distribution. In order to consider the question regarding the growth trends of metastatic brain tumors, this thesis analyzes the volume measurements of the tumor sizes from the BNI data and attempts to explain such size distributions through mathematical models. More specifically, a basic stochastic cellular automaton model is used and has three-dimensional results that show similar size distributions of those of the BNI data. Results of the models are investigated using the likelihood ratio test suggesting that, when the tumor volumes are measured based on assuming tumor sphericity, the tumor size distributions significantly mimic the power law over an exponential distribution.
ContributorsFreed, Rebecca (Co-author) / Snopko, Morgan (Co-author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / WPC Graduate Programs (Contributor) / School of Accountancy (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
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Description
Prostate cancer is the second most common kind of cancer in men. Fortunately, it has a 99% survival rate. To achieve such a survival rate, a variety of aggressive therapies are used to treat prostate cancers that are caught early. Androgen deprivation therapy (ADT) is a therapy that is given

Prostate cancer is the second most common kind of cancer in men. Fortunately, it has a 99% survival rate. To achieve such a survival rate, a variety of aggressive therapies are used to treat prostate cancers that are caught early. Androgen deprivation therapy (ADT) is a therapy that is given in cycles to patients. This study attempted to analyze what factors in a group of 79 patients caused them to stick with or discontinue the treatment. This was done using naïve Bayes classification, a machine-learning algorithm. The usage of this algorithm identified high testosterone as an indicator of a patient persevering with the treatment, but failed to produce statistically significant high rates of prediction.
ContributorsMillea, Timothy Michael (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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Description

Over time, tumor treatment resistance inadvertently develops when androgen de-privation therapy (ADT) is applied to metastasized prostate cancer (PCa). To combat tumor resistance, while reducing the harsh side effects of hormone therapy, the clinician may opt to cyclically alternates the patient’s treatment on and off. This method,known as intermittent ADT,

Over time, tumor treatment resistance inadvertently develops when androgen de-privation therapy (ADT) is applied to metastasized prostate cancer (PCa). To combat tumor resistance, while reducing the harsh side effects of hormone therapy, the clinician may opt to cyclically alternates the patient’s treatment on and off. This method,known as intermittent ADT, is an alternative to continuous ADT that improves the patient’s quality of life while testosterone levels recover between cycles. In this paper,we explore the response of intermittent ADT to metastasized prostate cancer by employing a previously clinical data validated mathematical model to new clinical data from patients undergoing Abiraterone therapy. This cell quota model, a system of ordinary differential equations constructed using Droop’s nutrient limiting theory, assumes the tumor comprises of castration-sensitive (CS) and castration-resistant (CR)cancer sub-populations. The two sub-populations rely on varying levels of intracellular androgen for growth, death and transformation. Due to the complexity of the model,we carry out sensitivity analyses to study the effect of certain parameters on their outputs, and to increase the identifiability of each patient’s unique parameter set. The model’s forecasting results show consistent accuracy for patients with sufficient data,which means the model could give useful information in practice, especially to decide whether an additional round of treatment would be effective.

ContributorsBennett, Justin Klark (Author) / Kuang, Yang (Thesis director) / Kostelich, Eric (Committee member) / Phan, Tin (Committee member) / School of Mathematical and Statistical Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description

Redox homeostasis is described as the net physiologic balance between inter-convertible oxidized and reduced equivalents within subcellular compartments that remain in a dynamic equilibrium. This equilibrium is impacted by reactive oxygen species (ROS), which are natural by-products of normal cellular activity. Studies have shown that cancer cells have high ROS

Redox homeostasis is described as the net physiologic balance between inter-convertible oxidized and reduced equivalents within subcellular compartments that remain in a dynamic equilibrium. This equilibrium is impacted by reactive oxygen species (ROS), which are natural by-products of normal cellular activity. Studies have shown that cancer cells have high ROS levels and altered redox homeostasis due to increased basal metabolic activity, mitochondrial dysfunction, peroxisome activity, as well as the enhanced activity of NADPH oxidase, cyclooxygenases, and lipoxygenases. Glioblastoma (GBM) is the most prevalent primary brain tumor in adults with a median survival of 15 months. GBM is characterized by its extreme resistance to therapeutic interventions as well as an elevated metabolic rate that results in the exacerbated production of ROS. Therefore, many agents with either antioxidant or pro-oxidant mechanisms of action have been rigorously employed in preclinical as well as clinical settings for treating GBM by inducing oxidative stress within the tumor. Among those agents are well-known antioxidant vitamin C and small molecular weight SOD mimic BMX-001, both of which are presently in clinical trials on GBM patients. Despite the wealth of investigations, limited data is available on the response of normal brain vs glioblastoma tissue to these therapeutic interventions. Currently, a sensitive and rapid liquid chromatography tandem mass spectrometry (LC-MS/MS) method was established for the quantification of a panel of oxidative stress biomarkers: glutathione (GSH), cysteine (Cys), glutathione disulfide (GSSG), and cysteine disulfide in human-derived brain tumor and mouse brain samples; this method will be enriched with additional oxidative stress biomarkers homocysteine (Hcy), methionine (Met), and cystathionine (Cyst). Using this enriched method, we propose to evaluate the thiol homeostasis and the redox state of both normal brain and GBM in mice after exposure with redox-active therapeutics. Our results showed that, compared to normal brain (in intact mice), GBM tissue has significantly lower GSH/GSSG and Cys/CySS ratios indicating much higher oxidative stress levels. Contralateral “normal” brain tissue collected from the mice with intracranial GBM were also under significant oxidative stress compared to normal brains collected from the intact mice. Importantly, normal brain tissue in both studies retained the ability to restore redox homeostasis after treatment with a redox-active therapeutic within 24 hours while glioblastoma tissue does not. Ultimately, elucidating the differential redox response of normal vs tumor tissue will allow for the development of more redox-active agents with therapeutic benefit.

ContributorsShaik, Kamal (Author) / LaBaer, Joshua (Thesis director) / Tovmasyan, Artak (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2022-12
Description
With cancer rates increasing and affecting more people every year, I felt it was important to educate the younger generation about the potential factors that could put them at risk of receiving a cancer diagnosis later in life. I thought that this was important to do because most students, especially

With cancer rates increasing and affecting more people every year, I felt it was important to educate the younger generation about the potential factors that could put them at risk of receiving a cancer diagnosis later in life. I thought that this was important to do because most students, especially in rural communities, are not taught the factors that increase your risk of getting cancer in the future. This leads to students not having the tools to think about the repercussions that their actions can have in their distant future in regard to their risk of getting cancer. I went to six schools throughout the valley and the White Mountains of Arizona with differing education levels and demographics to provide them with prevention strategies that they could implement into their daily lives to reduce their risk of getting cancer in the future. Some of the schools had curriculums that included cancer and some of the factors that increase your risk, while others never mention what is happening biologically when a person has cancer. I introduced factors such as no smoking or tobacco use, diet, exercise, sunscreen use, avoiding alcohol, and getting screened regularly. While at each school, I discussed the importance of creating these healthy habits while they are young because cancer is a disease that comes from the accumulation of mutations that can begin occurring in their bodies even now. After my presentation, 98.6% of the 305 students who viewed my presentation felt like they had learned something from the presentation and were almost all willing to implement at least one of the changes into their daily lives.
ContributorsGoforth, Michelle Nicole (Author) / Compton, Carolyn (Thesis director) / Lake, Douglas (Committee member) / Popova, Laura (Committee member) / Dean, W.P. Carey School of Business (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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Description
In cancer, various genetic and epigenetic alterations cause cancer cells to hyperproliferate and to bypass the survival and migration mechanisms that typically regulate healthy cells. The focal adhesion kinase (FAK) gene produces FAK, a protein that has been implicated in tumor progression in various cancers. Compared with normal tissue counterparts,

In cancer, various genetic and epigenetic alterations cause cancer cells to hyperproliferate and to bypass the survival and migration mechanisms that typically regulate healthy cells. The focal adhesion kinase (FAK) gene produces FAK, a protein that has been implicated in tumor progression in various cancers. Compared with normal tissue counterparts, FAK is overexpressed in many cancers. FAK is therefore a promising cancer drug target due to its demonstrated role in cancer invasion and metastasis and inhibition of FAK is important to achieve an optimal tumor response. Small molecule FAK inhibitors have been shown to decrease tumor growth and metastasis in several preclinical trials. However, these inhibitors focus narrowly on the enzymatic portion of FAK and neglect its scaffolding function, leaving FAK’s scaffolding of oncogenic drivers intact. Paxillin, a major focal adhesion-associated protein, binds to FAK, enabling it to localize to focal adhesions, and this is essential for FAK’s activation and function. Therefore, disrupting the protein-protein interaction between FAK and paxillin has been hypothesized to prevent tumor progression. The binding of FAK to paxillin at its focal adhesion targeting (FAT) domain is mediated by two highly conserved leucine-rich sequences, the leucine-aspartic acid (LD) motifs LD2 and LD4. The purpose of this project was to develop novel stapled LD2 peptide analogs that target the protein-protein interaction of FAT to LD2. Peptide stapling was performed to enhance the pharmacological performance of the LD2 peptide analogs. Based on the native LD2 peptide sequence, stapled LD2 peptide analogs were developed with the intent to improve efficacy of cell permeability, while maintaining or improving FAK binding. The LD2 peptide analogs were characterized via surface plasmon resonance, fluorescence polarization, immunofluorescence, and circular dichroism spectroscopy. Successful LD2 stapled peptide analogs can be therapeutically relevant inhibitors of the FAT-LD2 protein-protein interaction in cancer and have the potential for greater efficacy in FAK inhibition, proteolytic resistance, and cell permeability, which is key in preventing tumor progression in cancer.
ContributorsNott, Rohini (Author) / Gould, Ian R. (Thesis director) / Marlowe, Timothy A. (Committee member) / Cance, William G. (Committee member) / School of Life Sciences (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05