Matching Items (120)
Description
In this study, we demonstrate the effectiveness of a cancer type specific FrAmeShifT (FAST) vaccine. A murine breast cancer (mBC) FAST vaccine and a murine pancreatic cancer (mPC) FAST vaccine were tested in the 4T1 breast cancer syngeneic mouse model. The mBC FAST vaccine, both with and without check point inhibitors (CPI), significantly slowed tumor growth, reduced pulmonary metastasis and increased the cell-mediated immune response. In terms of tumor volumes, the mPC FAST vaccine was comparable to the untreated controls. However, a significant difference in tumor volume did emerge when the mPC vaccine was used with CPI. The collective data indicated that the immune checkpoint blockade therapy was only beneficial with suboptimal neoantigens. More importantly, the FAST vaccine, though requiring notably less resources, performed similarly to the personalized version of the frameshift breast cancer vaccine in the same mouse model. Furthermore, because the frameshift peptide (FSP) array provided a strong rationale for a focused vaccine, the FAST vaccine can theoretically be expanded and translated to any human cancer type. Overall, the FAST vaccine is a promising treatment that would provide the most benefit to patients while eliminating most of the challenges associated with current personal cancer vaccines.
ContributorsMurphy, Sierra Nicole (Author) / Johnston, Stephen (Thesis director) / Peterson, Milene (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School of Molecular Sciences (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The goal of this paper is to discuss the most efficient method to achieve early detection in lung cancers by reducing the occurrences of false-positive readings. Imaging techniques (computed tomography screenings) have greater impact than non-imaging techniques in early detection for lung cancer. On the other hand, positron emission tomography and non-imaging techniques, such as liquid biopsy, are better at distinguishing cancer stages. Therefore, these techniques are not suitable early detection methods for lung cancer. Based on literature reviews, the combination that is most capable of early lung cancer detection incorporate low-dose computed tomography screenings, thin-section computed tomography screenings, and computer-aided diagnosis. Low-dose computed tomography screenings has lower radiation-associated risks compared to the standard-dose computed tomography. This technique can be used as both at the first examination and the follow-up examinations. Thin-section computed tomography screenings can be used as a supplement to check if there is any nodules that have not yet been discovered. Computer-aided diagnosis is an add-on method to make sure the computed tomography screenings images are being correctly labeled. Identifying other contributing factors to the effectiveness of the early lung cancer detection, such as the amount of forced expiratory volume, forced vital capacity, and the presence of emphysema, could also decrease the percentage of false positive outcomes.
ContributorsChuang, Hao-Yun (Author) / Johnston, Stephen (Thesis director) / Peterson, Milene (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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 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
Description
The objective of this thesis was to determine whether Zika Virus (ZIKV) can be effectively inactivated by Selective Photonic Disinfection (SEPHODIS) and determine whether key proteins involved in the infection process are preserved, making SEPHODIS a possible source for vaccine development. As of January 2018, there have been 3,720 confirmed cases of Congenital Zika Syndrome in infants, making a Zika Vaccine a high priority (Mitchell, 2018). SEPHODIS is a process that involves prolonged exposure of an object to a pulsing laser which can render it ineffective. Initially, ZIKV was subjected to laser inactivation for 6 hours, then a plaque assay was performed on both laser-treated and control samples. ZIKV was inactivated two-fold? after laser treatment, when compared with control, as indicated by the plaque assay results. Additionally, both samples were submitted to ELISA to evaluate antigenicity with a panel of monoclonal and human sera. As a second control, virus inactivated by formaldehyde (2%) was used. ELISA results showed that antigenicity of some proteins were preserved while others were probably disturbed. However, ELISA results show that ZIKV envelope protein (E-protein), the protein responsible for viral entry into cells, was effectively preserved after laser-treatment, implying that if laser parameters were tweaked to obtain more complete inactivation, then SEPHODIS may be an appropriate source for the development of a vaccine.
ContributorsViafora, Ataiyo Blue (Author) / Johnston, Stephen (Thesis director) / Tsen, Kong-Thon (Committee member) / School of Life Sciences (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
PD-L1 blockade has shown recent success in cancer therapy and cancer vaccine regimens. One approach for anti-PD-L1 antibodies has been their application as adjuvants for cancer vaccines. Given the disadvantages of such antibodies, including long half-life and adverse events related to their use, a novel strategy using synbodies in place of antibodies can be tested. Synbodies offer a variety of advantages, including shorter half-life, smaller size, and cheaper cost. Peptides that could bind PD-L1 were identified via peptide arrays and used to construct synbodies. These synbodies were tested with inhibition ELISA assays, SPR, and pull down assays. Additional flow cytometry analysis was done to determine the binding specificity of the synbodies to PD-L1 and the ability of those synbodies to inhibit the PD-L1/PD-1 interaction. Although analysis of permeabilized cells expressing PD-L1 indicated that the synbodies could successfully bind PD-L1, those results were not replicated in non-permeabilized cells. Further assays suggested that the binding of the synbodies was non-specific. Other tests were done to see if the synbodies could inhibit the PD-1/PD-L1 interaction. This assay did not yield any conclusive results and further experimentation is needed to determine the efficacy of the synbodies in inhibiting this interaction.
ContributorsMujahed, Tala (Author) / Johnston, Stephen (Thesis director) / Blattman, Joseph (Committee member) / Diehnelt, Chris (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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 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
Description
The devastating 2014 Ebola virus outbreak in Western Africa demonstrated the lack of therapeutic approaches available for the virus. Although monoclonal antibodies (mAb) and other molecules have been developed that bind the virus, no therapeutic has shown the efficacy needed for FDA approval. Here, a library of 50 peptide based ligands that bind the glycoprotein of the Zaire Ebola virus (GP) were developed. Using whole virus screening of vesicular stomatitis virus pseudotyped with GP, low affinity peptides were identified for ligand construction. In depth analysis showed that two of the peptide based molecules bound the Zaire GP with <100 nM KD. One of these two ligands was blocked by a known neutralizing mAb, 2G4, and showed cross-reactivity to the Sudan GP. This work presents ligands with promise for therapeutic applications across multiple variants of the Ebola virus.
ContributorsRabinowitz, Joshua Avraam (Author) / Diehnelt, Chris (Thesis director) / Johnston, Stephen (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Both technological and scientific fields continue to revolutionize in a similar fashion; however, a major difference is that high-tech corporations have found models to continue progressions while still keeping product costs low. The main objective was to identify which, if any, components of certain technological models could be used with the vaccine and pharmaceutical markets to significantly lower their costs. Smartphones and computers were the two main items investigated while the two main items from the scientific standpoint were vaccines and pharmaceuticals. One concept had the ability to conceivably decrease the costs of vaccines and drugs and that was "market competition". If the United States were able to allow competition within the vaccine and drug companies, it would allow for the product prices to be best affected. It would only take a few small companies to generate generic versions of the drugs and decrease the prices. It would force the larger competition to most likely decrease their prices. Furthermore, the PC companies use a cumulative density function (CDF) to effectively divide their price setting in each product cycle. It was predicted that if this CDF model were applied to the vaccine and drug markets, the prices would no longer have to be extreme. The corporations would be able to set the highest price for the wealthiest consumers and then slowly begin to decrease the costs for the middle and lower class. Unfortunately, the problem within the vaccine and pharmaceutical markets was not the lack of innovation or business models. The problem lied with their liberty to choose product costs due to poor U.S. government regulations.
ContributorsCalderon, Gerardo (Author) / Johnston, Stephen (Thesis director) / Diehnelt, Chris (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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 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
Description
ABSTRACT Peptide microarrays may prove to be a powerful tool for proteomics research and clinical diagnosis applications. Fodor et al. and Maurer et al. have shown proof-of-concept methods of light- and electrochemically-directed peptide microarray fabrication on glass and semiconductor microchips respectively. In this work, peptide microarray fabrication based on the abovementioned techniques were optimized. In addition, MALDI mass spectrometry based peptide synthesis characterization on semiconductor microchips was developed and novel applications of a CombiMatrix (CBMX) platform for electrochemically controlled synthesis were explored. We have investigated performance of 2-(2-nitrophenyl)propoxycarbonyl (NPPOC) derivatives as photo-labile protecting group. Specifically, influence of substituents on 4 and 5 positions of phenyl ring of NPPOC group on the rate of photolysis and the yield of the amine was investigated. The results indicated that substituents capable of forming a π-network with the nitro group enhanced the rate of photolysis and yield. Once such properly substituted NPPOC groups were used, the rate of photolysis/yield depended on the nature of protected amino group indicating that a different chemical step during the photo-cleavage process became the rate limiting step. We also focused on electrochemically-directed parallel synthesis of high-density peptide microarrays using the CBMX technology referred to above which uses electrochemically generated acids to perform patterned chemistry. Several issues related to peptide synthesis on the CBMX platform were studied and optimized, with emphasis placed on the reactions of electro-generated acids during the deprotection step of peptide synthesis. We have developed a MALDI mass spectrometry based method to determine the chemical composition of microarray synthesis, directly on the feature. This method utilizes non-diffusional chemical cleavage from the surface, thereby making the chemical characterization of high-density microarray features simple, accurate, and amenable to high-throughput. CBMX Corp. has developed a microarray reader which is based on electro-chemical detection of redox chemical species. Several parameters of the instrument were studied and optimized and novel redox applications of peptide microarrays on CBMX platform were also investigated using the instrument. These include (i) a search of metal binding catalytic peptides to reduce overpotential associated with water oxidation reaction and (ii) an immobilization of peptide microarrays using electro-polymerized polypyrrole.
ContributorsKumar, Pallav (Author) / Woodbury, Neal (Thesis advisor) / Allen, James (Committee member) / Johnston, Stephen (Committee member) / Arizona State University (Publisher)
Created2013