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- Genre: Doctoral Dissertation
Description
Glioblastoma (GBM) is the most common primary brain tumor with an incidence of approximately 11,000 Americans. Despite decades of research, average survival for GBM patients is a modest 15 months. Increasing the extent of GBM resection increases patient survival. However, extending neurosurgical margins also threatens the removal of eloquent brain. For this reason, the infiltrative nature of GBM is an obstacle to its complete resection. We hypothesize that targeting genes and proteins that regulate GBM motility, and developing techniques that safely enhance extent of surgical resection, will improve GBM patient survival by decreasing infiltration into eloquent brain regions and enhancing tumor cytoreduction during surgery. Chapter 2 of this dissertation describes a gene and protein we identified; aquaporin-1 (aqp1) that enhances infiltration of GBM. In chapter 3, we describe a method for enhancing the diagnostic yield of GBM patient biopsies which will assist in identifying future molecular targets for GBM therapies. In chapter 4 we develop an intraoperative optical imaging technique that will assist identifying GBM and its infiltrative margins during surgical resection. The topic of this dissertation aims to target glioblastoma infiltration from molecular and cellular biology and neurosurgical disciplines. In the introduction we; 1. Provide a background of GBM and current therapies. 2. Discuss a protein we found that decreases GBM survival. 3. Describe an imaging modality we utilized for improving the quality of accrued patient GBM samples. 4. We provide an overview of intraoperative contrast agents available for neurosurgical resection of GBM, and discuss a new agent we studied for intraoperative visualization of GBM.
ContributorsGeorges, Joseph F (Author) / Feuerstein, Burt G (Thesis advisor) / Smith, Brian H. (Thesis advisor) / Van Keuren-Jensen, Kendall (Committee member) / Deviche, Pierre (Committee member) / Bennett, Kevin (Committee member) / Arizona State University (Publisher)
Created2014
Description
The phycologist, M. R. Droop, studied vitamin B12 limitation in the flagellate Monochrysis lutheri and concluded that its specific growth rate depended on the concentration of the vitamin within the cell; i.e. the cell quota of the vitamin B12. The Droop model provides a mathematical expression to link growth rate to the intracellular concentration of a limiting nutrient. Although the Droop model has been an important modeling tool in ecology, it has only recently been applied to study cancer biology. Cancer cells live in an ecological setting, interacting and competing with normal and other cancerous cells for nutrients and space, and evolving and adapting to their environment. Here, the Droop equation is used to model three cancers.
First, prostate cancer is modeled, where androgen is considered the limiting nutrient since most tumors depend on androgen for proliferation and survival. The model's accuracy for predicting the biomarker for patients on intermittent androgen deprivation therapy is tested by comparing the simulation results to clinical data as well as to an existing simpler model. The results suggest that a simpler model may be more beneficial for a predictive use, although further research is needed in this field prior to implementing mathematical models as a predictive method in a clinical setting.
Next, two chronic myeloid leukemia models are compared that consider Imatinib treatment, a drug that inhibits the constitutively active tyrosine kinase BCR-ABL. Both models describe the competition of leukemic and normal cells, however the first model also describes intracellular dynamics by considering BCR-ABL as the limiting nutrient. Using clinical data, the differences in estimated parameters between the models and the capacity for each model to predict drug resistance are analyzed.
Last, a simple model is presented that considers ovarian tumor growth and tumor induced angiogenesis, subject to on and off anti-angiogenesis treatment. In this environment, the cell quota represents the intracellular concentration of necessary nutrients provided through blood supply. Mathematical analysis of the model is presented and model simulation results are compared to pre-clinical data. This simple model is able to fit both on- and off-treatment data using the same biologically relevant parameters.
First, prostate cancer is modeled, where androgen is considered the limiting nutrient since most tumors depend on androgen for proliferation and survival. The model's accuracy for predicting the biomarker for patients on intermittent androgen deprivation therapy is tested by comparing the simulation results to clinical data as well as to an existing simpler model. The results suggest that a simpler model may be more beneficial for a predictive use, although further research is needed in this field prior to implementing mathematical models as a predictive method in a clinical setting.
Next, two chronic myeloid leukemia models are compared that consider Imatinib treatment, a drug that inhibits the constitutively active tyrosine kinase BCR-ABL. Both models describe the competition of leukemic and normal cells, however the first model also describes intracellular dynamics by considering BCR-ABL as the limiting nutrient. Using clinical data, the differences in estimated parameters between the models and the capacity for each model to predict drug resistance are analyzed.
Last, a simple model is presented that considers ovarian tumor growth and tumor induced angiogenesis, subject to on and off anti-angiogenesis treatment. In this environment, the cell quota represents the intracellular concentration of necessary nutrients provided through blood supply. Mathematical analysis of the model is presented and model simulation results are compared to pre-clinical data. This simple model is able to fit both on- and off-treatment data using the same biologically relevant parameters.
ContributorsEverett, Rebecca Anne (Author) / Kuang, Yang (Thesis advisor) / Nagy, John (Committee member) / Milner, Fabio (Committee member) / Crook, Sharon (Committee member) / Jackiewicz, Zdzislaw (Committee member) / Arizona State University (Publisher)
Created2015
Description
This research investigated using impedance as a minimally invasive oral cancer-screening tool by modeling healthy and diseased tissue. This research developed an ultra-structurally based tissue model for oral mucosa that is versatile enough to be easily modified to mimic the passive electrical impedance responses of multiple benign and cancerous tissue types. This new model provides answers to biologically meaningful questions related to the impedance response of healthy and diseased tissues. This model breaks away from the old empirical top down "black box" Thèvinin equivalent model. The new tissue model developed here was created from a bottom up perspective resulting in a model that is analogous to having a "Transparent Box" where each network element relating to a specific structural component is known. This new model was developed starting with sub cellular ultra-structural components such as membranes, proteins and electrolytes. These components formed the basic network elements and topology of the organelles. The organelle networks combine to form the cell networks. The cell networks combine to make networks of cell layers and the cell layers were combined into tissue networks. This produced the complete "Transparent Box" model for normal tissue. This normal tissue model was modified for disease based on the ultra-structural pathology of each disease. The diseased tissues evaluated include cancers type one through type three; necrotic-inflammation, hyperkeratosis and the compound condition of hyperkeratosis over cancer type two. The impedance responses for each of the disease were compared side by side with the response of normal healthy tissue. Comparative evidence from the models showed the structural changes in cancer produce a unique identifiable impedance "finger print." The evaluation of the "Transparent Box" model for normal tissues and diseased tissues show clear support for using comparative impedance measurements as a clinical tool for oral cancer screening.
ContributorsPelletier, Peter Robert (Author) / Kozicki, Michael (Thesis advisor) / Towe, Bruce (Committee member) / Saraniti, Marco (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2012
Description
CpG methylation is an essential requirement for the normal development of mammals, but aberrant changes in the methylation can lead to tumor progression and cancer. An in-depth understanding of this phenomenon can provide insights into the mechanism of gene repression. We present a study comparing methylated DNA and normal DNA wrt its persistence length and contour length. Although, previous experiments and studies show no difference between the physical properties of the two, the data collected and interpreted here gives a different picture to the methylation phenomena and its effect on gene silencing. The study was extended to the artificially reconstituted chromatin and its interactions with the methyl CpG binding proteins were also probed.
ContributorsKaur, Parminder (Author) / Lindsay, Stuart (Thesis advisor) / Ros, Robert (Committee member) / Tao, Nongjian (Committee member) / Vaiana, Sara (Committee member) / Beckenstein, Oliver (Committee member) / Arizona State University (Publisher)
Created2012
Description
Malignant brain tumors are devastating despite aggressive treatments such as surgical resection, chemotherapy and radiation therapy. The average life expectancy of patients with newly diagnosed glioblastoma is approximately 15 months. One novel therapeutic strategy involves using a ketogenic diet (KD) which increases circulating ketones and reduces circulating glucose. While the preclinical work has shown that the KD increases survival, enhances radiation and alters several pathways in malignant gliomas, its impact on the anti-tumor immune response has yet to be examined. This dissertation demonstrates that mice fed the KD had increased tumor-reactive innate and adaptive immune responses, including increased cytokine production and cytolysis via tumor-reactive CD8+ T cells. Additionally, we saw that mice maintained on the KD had increased CD4 infiltration, while T regulatory cell numbers stayed consistent. Lastly, mice fed the KD had a significant reduction in immune inhibitory receptor expression as well as decreased inhibitory ligand expression on glioma cells, namely programmed death receptor -1 (PD-1) and its ligand programmed death receptor ligand -1 (PD-L1). Further, it is demonstrated that the ketone body beta-hydroxybutyrate (BHB) reduces expression of PD-L1 on glioma cells in vitro suggesting it may be responsible in part for immune-related changes elicited by the KD. Finally this dissertation also shows that the KD increases the expression of microRNAs predicted to target PD-L1 suggesting a potential mechanism to explain the ability of the KD to modulate immune inhibitory checkpoint pathways. Taken together these studies shed important light on the mechanisms underlying the KD and provide additional support for its use an adjuvant therapy for malignant glioma.
ContributorsWoolf, Eric Christopher (Author) / Compton, Carolyn C. (Thesis advisor) / Scheck, Adrienne C (Committee member) / Preul, Mark C (Committee member) / Blattman, Joseph N (Committee member) / Mehta, Shwetal (Committee member) / Arizona State University (Publisher)
Created2018
Description
CD8+ T-lymphocytes (CTLs) are central to the immunologic control of infections and are currently at the forefront of strategies that enhance immune based treatment of a variety of tumors. Effective T-cell based vaccines and immunotherapies fundamentally rely on the interaction of CTLs with peptide-human leukocyte antigen class I (HLA-I) complexes on the infected/malignant cell surface. However, how CTLs are able to respond to antigenic peptides with high specificity is largely unknown. Also unknown, are the different mechanisms underlying tumor immune evasion from CTL-mediated cytotoxicity. In this dissertation, I investigate the immunogenicity and dysfunction of CTLs for the development of novel T-cell therapies. Project 1 explores the biochemical hallmarks associated with HLA-I binding peptides that result in a CTL-immune response. The results reveal amino acid hydrophobicity of T-cell receptor (TCR) contact residues within immunogenic CTL-epitopes as a critical parameter for CTL-self
onself discrimination. Project 2 develops a bioinformatic and experimental methodology for the identification of CTL-epitopes from low frequency T-cells against tumor antigens and chronic viruses. This methodology is employed in Project 3 to identify novel immunogenic CTL-epitopes from human papillomavirus (HPV)-associated head and neck cancer patients. In Project 3, I further study the mechanisms of HPV-specific T-cell dysfunction, and I demonstrate that combination inhibition of Indoleamine 2, 3-dioxygenase (IDO-1) and programmed cell death protein (PD-1) can be a potential immunotherapy against HPV+ head and neck cancers. Lastly, in Project 4, I develop a single-cell assay for high-throughput identification of antigens targeted by CTLs from whole pathogenome libraries. Thus, this dissertation contributes to fundamental T-cell immunobiology by identifying rules of T-cell immunogenicity and dysfunction, as well as to translational immunology by identifying novel CTL-epitopes, and therapeutic targets for T-cell immunotherapy.
onself discrimination. Project 2 develops a bioinformatic and experimental methodology for the identification of CTL-epitopes from low frequency T-cells against tumor antigens and chronic viruses. This methodology is employed in Project 3 to identify novel immunogenic CTL-epitopes from human papillomavirus (HPV)-associated head and neck cancer patients. In Project 3, I further study the mechanisms of HPV-specific T-cell dysfunction, and I demonstrate that combination inhibition of Indoleamine 2, 3-dioxygenase (IDO-1) and programmed cell death protein (PD-1) can be a potential immunotherapy against HPV+ head and neck cancers. Lastly, in Project 4, I develop a single-cell assay for high-throughput identification of antigens targeted by CTLs from whole pathogenome libraries. Thus, this dissertation contributes to fundamental T-cell immunobiology by identifying rules of T-cell immunogenicity and dysfunction, as well as to translational immunology by identifying novel CTL-epitopes, and therapeutic targets for T-cell immunotherapy.
ContributorsKrishna, Sri (Author) / Anderson, Karen S (Thesis advisor) / LaBaer, Joshua (Committee member) / Jacobs, Bertram L (Committee member) / Lake, Douglas F (Committee member) / Arizona State University (Publisher)
Created2017
Description
Achieving effective drug concentrations within the central nervous system (CNS) remains one of the greatest challenges for the treatment of brain tumors. The presence of the blood-brain barrier and blood-spinal cord barrier severely restricts the blood-to-CNS entry of nearly all systemically administered therapeutics, often leading to the development of peripheral toxicities before a treatment benefit is observed. To circumvent systemic barriers, intrathecal (IT) injection of therapeutics directly into the cerebrospinal fluid (CSF) surrounding the brain and spinal cord has been used as an alternative administration route; however, its widespread translation to the clinic has been hindered by poor drug pharmacokinetics (PK), including rapid clearance, inadequate distribution, as well as toxicity. One strategy to overcome the limitations of free drug PK and improve drug efficacy is to encapsulate drug within nanoparticles (NP), which solubilize hydrophobic molecules for sustained release in physiological environments. In this thesis, we will develop NP delivery strategies for brain tumor therapy in two model systems: glioblastoma (GBM), the most common and deadly malignant primary brain tumor, and medulloblastoma, the most common pediatric brain tumor. In the first research chapter, we developed 120 nm poly(lactic acid-co-glycolic acid) NPs encapsulating the chemotherapy, camptothecin, for intravenous delivery to GBM. NP encapsulation of camptothecin was shown to reduce the drug’s toxicity and enable effective delivery to orthotopic GBM. To build off the success of intravenous NP, the second research chapter explored the utility of 100 nm PEGylated NPs for use with IT administration. Using in vivo imaging and ex vivo tissue slices, we found the NPs were rapidly transported by the convective forces of the CSF along the entire neuraxis and were retained for over 3 weeks. Based on their wide spread delivery and prolonged circulation, we examine the ability of the NPs to localize with tumor lesions in a leptomeningeal metastasis (LM) model of medulloblastoma. NPs administered to LM bearing mice were shown to penetrate into LM mets seeded within the meninges around the brain. These data show the potential to translate our success with intravenous NPs for GBM to improve IT chemotherapy delivery to LM.
ContributorsHouseholder, Kyle Thomas (Author) / Sirianni, Rachael W. (Thesis advisor) / Stabenfeldt, Sarah (Committee member) / Vernon, Brent (Committee member) / Caplan, Michael (Committee member) / Wechsler-Reya, Robert (Committee member) / Arizona State University (Publisher)
Created2018
Description
Biomarkers find a wide variety of applications in oncology from risk assessment to diagnosis and predicting and monitoring recurrence and response to therapy. Developing clinically useful biomarkers for cancer is faced with several challenges, including cancer heterogeneity and factors related to assay development and biomarker performance. Circulating biomarkers offer a rapid, cost-effective, and minimally-invasive window to disease and are ideal for population-based screening. Circulating immune biomarkers are stable, measurable, and can betray the underlying antigen when present below detection levels or even no longer present. This dissertation aims to investigate potential circulating immune biomarkers with applications in cancer detection and novel therapies. Over 600,000 cancers each year are attributed to the human papillomavirus (HPV), including cervical, anogenital and oropharyngeal cancers. A key challenge in understanding HPV immunobiology and developing immune biomarkers is the diversity of HPV types and the need for multiplexed display of HPV antigens. In Project 1, nucleic acid programmable protein arrays displaying the proteomes of 12 HPV types were developed and used for serum immunoprofiling of women with cervical lesions or invasive cervical cancer. These arrays provide a valuable high-throughput tool for measuring the breadth, specificity, heterogeneity, and cross-reactivity of the serologic response to HPV. Project 2 investigates potential biomarkers of immunity to the bacterial CRISPR/Cas9 system that is currently in clinical trials for cancer. Pre-existing B cell and T cell immune responses to Cas9 were detected in humans and Cas9 was modified to eliminate immunodominant epitopes while preserving its function and specificity. This dissertation broadens our understanding of the immunobiology of cervical cancer and provides insights into the immune profiles that could serve as biomarkers of various applications in cancer.
ContributorsEwaisha, Radwa Mohamed Emadeldin Mahmoud (Author) / Anderson, Karen S (Thesis advisor) / LaBaer, Joshua (Committee member) / Lake, Douglas F (Committee member) / Stout, Valerie (Committee member) / Arizona State University (Publisher)
Created2018
Description
Combination therapy has shown to improve success for cancer treatment. Oncolytic virotherapy is cancer treatment that uses engineered viruses to specifically infect and kill cancer cells, without harming healthy cells. Immunotherapy boosts the body's natural defenses towards cancer. The combination of oncolytic virotherapy and immunotherapy is explored through deterministic systems of nonlinear differential equations, constructed to match experimental data for murine melanoma. Mathematical analysis was done in order to gain insight on the relationship between cancer, viruses and immune response. One extension of the model focuses on clinical needs, with the underlying goal to seek optimal treatment regimens; for both frequency and dose quantity. The models in this work were first used to estimate parameters from preclinical experimental data, to identify biologically realistic parameter values. Insight gained from the mathematical analysis in the first model, allowed for numerical analysis to explore optimal treatment regimens of combination oncolytic virotherapy and dendritic vaccinations. Permutations accounting for treatment scheduled were done to find regimens that reduce tumor size. Observations from the produced data lead to in silico exploration of immune-viral interactions. Results suggest under optimal settings, combination treatment works better than monotherapy of either type. The most optimal result suggests treatment over a longer period of time, with fractioned doses, while reducing the total dendritic vaccination quantity, and maintaining the maximum virotherapy used in the experimental work.
ContributorsSummer, Ilyssa Aimee (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Nagy, John (Thesis advisor) / Mubayi, Anuj (Committee member) / Kang, Yun (Committee member) / Arizona State University (Publisher)
Created2016
Description
Cancer is a major health problem in the world today and is expected to become an even larger one in the future. Although cancer therapy has improved for many cancers in the last several decades, there is much room for further improvement. Mathematical modeling has the advantage of being able to test many theoretical therapies without having to perform clinical trials and experiments. Mathematical oncology will continue to be an important tool in the future regarding cancer therapies and management.
This dissertation is structured as a growing tumor. Chapters 2 and 3 consider spheroid models. These models are adept at describing 'early-time' tumors, before the tumor needs to co-opt blood vessels to continue sustained growth. I consider two partial differential equation (PDE) models for spheroid growth of glioblastoma. I compare these models to in vitro experimental data for glioblastoma tumor cell lines and other proposed models. Further, I investigate the conditions under which traveling wave solutions exist and confirm numerically.
As a tumor grows, it can no longer be approximated by a spheroid, and it becomes necessary to use in vivo data and more sophisticated modeling to model the growth and diffusion. In Chapter 4, I explore experimental data and computational models for describing growth and diffusion of glioblastoma in murine brains. I discuss not only how the data was obtained, but how the 3D brain geometry is created from Magnetic Resonance (MR) images. A 3D finite-difference code is used to model tumor growth using a basic reaction-diffusion equation. I formulate and test hypotheses as to why there are large differences between the final tumor sizes between the mice.
Once a tumor has reached a detectable size, it is diagnosed, and treatment begins. Chapter 5 considers modeling the treatment of prostate cancer. I consider a joint model with hormonal therapy as well as immunotherapy. I consider a timing study to determine whether changing the vaccine timing has any effect on the outcome of the patient. In addition, I perform basic analysis on the six-dimensional ordinary differential equation (ODE). I also consider the limiting case, and perform a full global analysis.
This dissertation is structured as a growing tumor. Chapters 2 and 3 consider spheroid models. These models are adept at describing 'early-time' tumors, before the tumor needs to co-opt blood vessels to continue sustained growth. I consider two partial differential equation (PDE) models for spheroid growth of glioblastoma. I compare these models to in vitro experimental data for glioblastoma tumor cell lines and other proposed models. Further, I investigate the conditions under which traveling wave solutions exist and confirm numerically.
As a tumor grows, it can no longer be approximated by a spheroid, and it becomes necessary to use in vivo data and more sophisticated modeling to model the growth and diffusion. In Chapter 4, I explore experimental data and computational models for describing growth and diffusion of glioblastoma in murine brains. I discuss not only how the data was obtained, but how the 3D brain geometry is created from Magnetic Resonance (MR) images. A 3D finite-difference code is used to model tumor growth using a basic reaction-diffusion equation. I formulate and test hypotheses as to why there are large differences between the final tumor sizes between the mice.
Once a tumor has reached a detectable size, it is diagnosed, and treatment begins. Chapter 5 considers modeling the treatment of prostate cancer. I consider a joint model with hormonal therapy as well as immunotherapy. I consider a timing study to determine whether changing the vaccine timing has any effect on the outcome of the patient. In addition, I perform basic analysis on the six-dimensional ordinary differential equation (ODE). I also consider the limiting case, and perform a full global analysis.
ContributorsRutter, Erica Marie (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric J (Thesis advisor) / Frakes, David (Committee member) / Gardner, Carl (Committee member) / Jackiewicz, Zdzislaw (Committee member) / Arizona State University (Publisher)
Created2016