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- All Subjects: Cancer
Description
Cancer is a heterogeneous disease with discrete oncogenic mechanisms. P53 mutation is the most common oncogenic mutation in many cancers including breast cancer. This dissertation focuses on fundamental genetic alterations enforced by p53 mutation as an indirect target. p53 mutation upregulates the mevalonate pathway genes altering cholesterol biosynthesis and prenylation. Prenylation, a lipid modification, is required for small GTPases signaling cascades. Project 1 demonstrates that prenylation inhibition can specifically target cells harboring p53 mutation resulting in reduced tumor proliferation and migration. Mutating p53 is associated with Ras and RhoA activation and statin prevents this activity by inhibiting prenylation. Ras-related pathway genes were selected from the transcriptomic analysis for evaluating correlation to statin sensitivity. A gene signature of seventeen genes and TP53 genotype (referred to as MPR signature) is generated to predict response to statins. MPR signature is validated through two datasets of drug screening in cell lines. As advancements in targeted gene modification are rising, the CRISPR-Cas9 technology has emerged as a new cancer therapeutic strategy. One of the important risk factors in gene therapy is the immune recognition of the exogenous therapeutic tool, resulting in obstruction of treatment and possibly serious health consequences. Project 2 describes a method development that can potentially improve the safety and efficacy of gene-targeting proteins. A cohort of 155 healthy individuals was screened for pre-existing B cell and T cell immune response to the S. pyogenes Cas9 protein. We detected antibodies against Cas9 in more than 10% of the healthy population and identified two immunodominant T cell epitopes of this protein. A de-immunized Cas9 that maintains the wild-type functionality was engineered by mutating the identified T cell epitopes. The gene signature and method described here have the potential to improve strategies for genome-driven tumor targeting.
ContributorsRoshdi Ferdosi, Shayesteh (Author) / Anderson, Karen S (Thesis advisor) / LaBaer, Joshua (Thesis advisor) / Woodbury, Neel (Committee member) / Borges, Chad (Committee member) / Arizona State University (Publisher)
Created2017
Description
The purpose of this project is to explore the benefit of using prodrugs in chemotherapy, as well as to explain the concept of angiogenesis and the importance of this process to tumor development. Angiogenesis is the formation of new blood capillaries that are necessary for the survival of a tumor, as a tumor cannot grow larger than 1-2 mm3 without developing its own blood supply. Vascular disrupting agents, such as iodocombstatin, a derivative of combretastatin, can be used to effectively cut off the blood supply to a growing neoplasm, effectively inhibiting the supply of oxygen and nutrients needed for cell division Thus, VDAs have a very important implication in terms of the future of chemotherapy. A prodrug, defined as an agent that is inactive in the body until metabolized to yield the drug itself, was synthesized by combining iodocombstatin with a β-glucuronide linker. The prodrug is theoretically hydrolyzed in the body to afford the active drug by β-glucuronidase, an enzyme that is produced five times as much by cancer cells as by normal cells. This effectively creates a “magic-bullet” form of chemotherapy, known as Direct Enzyme Prodrug Therapy (DEPT).
ContributorsClark, Caroline Marie (Author) / Pettit, George Robert (Thesis director) / Melody, Noeleen (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor)
Created2015-05
Description
The immune system plays a dual role during neoplastic progression. It can suppress tumor growth by eliminating cancer cells, and also promote neoplastic expansion by either selecting for tumor cells that are fitter to survive in an immunocompetent host or by establishing the right conditions within the tumor microenvironment. First, I present a model to study the dynamics of subclonal evolution of cancer. I model selection through time as an epistatic process. That is, the fitness change in a given cell is not simply additive, but depends on previous mutations. Simulation studies indicate that tumors are composed of myriads of small subclones at the time of diagnosis. Because some of these rare subclones harbor pre-existing treatment-resistant mutations, they present a major challenge to precision medicine. Second, I study the question of self and non-self discrimination by the immune system, which is fundamental in the field in cancer immunology. By performing a quantitative analysis of the biochemical properties of thousands of MHC class I peptides, I find that hydrophobicity of T cell receptors contact residues is a hallmark of immunogenic epitopes. Based on these findings, I further develop a computational model to predict immunogenic epitopes which facilitate the development of T cell vaccines against pathogen and tumor antigens. Lastly, I study the effect of early detection in the context of Ebola. I develope a simple mathematical model calibrated to the transmission dynamics of Ebola virus in West Africa. My findings suggest that a strategy that focuses on early diagnosis of high-risk individuals, caregivers, and health-care workers at the pre-symptomatic stage, when combined with public health measures to improve the speed and efficacy of isolation of infectious individuals, can lead to rapid reductions in Ebola transmission.
ContributorsChowell-Puente, Diego (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Anderson, Karen S (Thesis advisor) / Maley, Carlo C (Committee member) / Wilson Sayres, Melissa A (Committee member) / Blattman, Joseph N (Committee member) / Arizona State University (Publisher)
Created2016
Description
Resistance to existing anti-cancer drugs poses a key challenge in the field of medical oncology, in that it results in the tumor not responding to treatment using the same medications to which it responded previously, leading to treatment failure. Adaptive therapy utilizes evolutionary principles of competitive suppression, leveraging competition between drug resistant and drug sensitive cells, to keep the population of drug resistant cells under control, thereby extending time to progression (TTP), relative to standard treatment using maximum tolerated dose (MTD). Development of adaptive therapy protocols is challenging, as it involves many parameters, and the number of parameters increase exponentially for each additional drug. Furthermore, the drugs could have a cytotoxic (killing cells directly), or a cytostatic (inhibiting cell division) mechanism of action, which could affect treatment outcome in important ways. I have implemented hybrid agent-based computational models to investigate adaptive therapy, using either a single drug (cytotoxic or cytostatic), or two drugs (cytotoxic or cytostatic), simulating three different adaptive therapy protocols for treatment using a single drug (dose modulation, intermittent, dose-skipping), and seven different treatment protocols for treatment using two drugs: three dose modulation (DM) protocols (DM Cocktail Tandem, DM Ping-Pong Alternate Every Cycle, DM Ping-Pong on Progression), and four fixed-dose (FD) protocols (FD Cocktail Intermittent, FD Ping-Pong Intermittent, FD Cocktail Dose-Skipping, FD Ping-Pong Dose-Skipping). The results indicate a Goldilocks level of drug exposure to be optimum, with both too little and too much drug having adverse effects. Adaptive therapy works best under conditions of strong cellular competition, such as high fitness costs, high replacement rates, or high turnover. Clonal competition is an important determinant of treatment outcome, and as such treatment using two drugs leads to more favorable outcome than treatment using a single drug. Switching drugs every treatment cycle (ping-pong) protocols work particularly well, as well as cocktail dose modulation, particularly when it is feasible to have a highly sensitive measurement of tumor burden. In general, overtreating seems to have adverse survival outcome, and triggering a treatment vacation, or stopping treatment sooner when the tumor is shrinking seems to work well.
ContributorsSaha, Kaushik (Author) / Maley, Carlo C (Thesis advisor) / Forrest, Stephanie (Committee member) / Anderson, Karen S (Committee member) / Cisneros, Luis H (Committee member) / Arizona State University (Publisher)
Created2023
Cancer autoantibody biomarker discovery and validation using nucleic acid programmable protein array
Description
Currently in the US, many patients with cancer do not benefit from the population-based screening, due to challenges associated with the existing cancer screening scheme. Blood-based diagnostic assays have the potential to detect diseases in a non-invasive way. Proteins released from small early tumors may only be present intermittently and get diluted to tiny concentrations in the blood, making them difficult to use as biomarkers. However, they can induce autoantibody (AAb) responses, which can amplify the signal and persist in the blood even if the antigen is gone. Circulating autoantibodies is a promising class of molecules that have potential to serve as early detection biomarkers for cancers. This Ph.D thesis aims to screen for autoantibody biomarkers for the early detection of two deadly cancer, basal-like breast cancer and lung adenocarcinoma. First, a method was developed to display proteins in both native and denatured conformation on protein array. This method adopted a novel protein tag technology, called HaloTag, to covalently immobilize proteins on glass slide surface. The covalent attachment allowed these proteins to endure harsh treatment without getting dissociated from slide surface, which enabled the profiling of antibody responses against both conformational and linear epitopes. Next, a plasma screening protocol was optimized to significantly increase signal to noise ratio of protein array based AAb detection. Following this, the AAb responses in basal-like breast cancer were explored using nucleic acid programmable protein arrays (NAPPA) containing 10,000 full-length human proteins in 45 cases and 45 controls. After verification in a large sample set (145 basal-like breast cancer cases / 145 controls / 70 non-basal breast cancer) by ELISA, a 13-AAb classifier was developed to differentiate patients from controls with a sensitivity of 33% at 98% specificity. Similar approach was also applied to the lung cancer study to identify AAbs that distinguished lung cancer patients from computed-tomography positive benign pulmonary nodules (137 lung cancer cases, 127 smoker controls, 170 benign controls). In this study, two panels of AAbs were discovered that showed promising sensitivity and specificity. Six out of eight AAb targets were also found to have elevated mRNA level in lung adenocarcinoma patients using TCGA data. These projects as a whole provide novel insights on the association between AAbs and cancer, as well as general B cell antigenicity against self-proteins.
ContributorsWang, Jie (Author) / LaBaer, Joshua (Thesis advisor) / Anderson, Karen S (Committee member) / Lake, Douglas F (Committee member) / Chang, Yung (Committee member) / Arizona State University (Publisher)
Created2015