Matching Items (19)
Description
The growing field of immunotherapy has generated numerous promising diseasetreatment platforms in recent years. By utilizing the innate capabilities of the immune system, these treatments have provided a unique, simplistic approach to targeting and eliminating cancer. Among these, the bispecific T cell engager (BiTEÒ) model has demonstrated potential as a treatment capable of bringing immune cells into contact with cancer cells of interest and initiating perforin/granzyme-mediated cell death of the tumor. While standard BiTE platforms rely on targeting a tumor-specific receptor via its complementary antibody, no such universal receptor has been reported for glioblastoma (GBM), the most common and aggressive primary brain tumor which boasts a median survival of only 15 months. In addition to its dismal prognosis, GBM deploys several immune-evasion tactics that further complicate treatment and make targeted therapy difficult. However, it has been reported that chlorotoxin, a 36-amino acid peptide found in the venom of Leiurus quinquestriatus, binds specifically to glioma cells while not binding healthy tissue in humans. This specificity positions chlorotoxin as a prime candidate to act as a GBM-targeting moiety as one half of an immunotherapeutic treatment platform resembling the BiTE design which I describe here. Named ACDClx∆15, this fusion protein tethers a truncated chlorotoxin molecule to the variable region of a monoclonal antibody targeted to CD3ε on both CD8+ and CD4+ T cells and is theorized to bring T cells into contact with GBM in order to stimulate an artificial immune response against the tumor. Here I describe the design and production of ACDClx∆15 and test its ability to bind and activate T lymphocytes against murine GBM in vitro. ACDClx∆15 was shown to bind both GBM and T cells without binding healthy cells in vitro but did not demonstrate the ability to activate T cells in the presence of GBM.
ContributorsSchaefer, Braeden Scott (Author) / Mor, Tsafrir (Thesis advisor) / Mason, Hugh (Committee member) / Blattman, Joseph (Committee member) / Arizona State University (Publisher)
Created2021
Description
Drug delivery has made a significant contribution to cancer immunotherapy and can have a tremendous impact on modulating immunometabolism, thereby affecting cancer outcomes. Notably, the science of delivery of cancer vaccines and immunotherapeutics, modulating immune cell functions has inspired development of several successful companies and clinical products. For example, cancer vaccines require activation of dendritic cells (DCs) and tumour associated Mɸs (TAMs) through modulation of their energy metabolism (e.g., glycolysis, glutaminolysis, Krebs cycle). Similar to activated immune cells, cancer cells also upregulate glucose and glutamine transporters for proliferation and survival. Cancer cells having accelerated energy metabolism, which has been exploited as a target for various therapeutic studies. In the first strategy, an immunometabolism strategy based on sustained release of succinate from biomaterials, which incorporate succinate in the backbone of the polymer was developed. This study demonstrates that succinate-based polymeric microparticles act as alarmins by modulating the immunometabolism of DCs and Mɸs to generate robust pro-inflammatory responses for melanoma treatment in immunocompetent young as well as aging mice. In the second strategy, a biomaterial-based strategy was developed to deliver metabolites one-step downstream of the node where the glycolytic pathway is inhibited, to specifically rescue DCs from glycolysis inhibition. The study successfully demonstrated for the first time that the glycolysis of DCs can be rescued both in vitro and in vivo using a biomaterial strategy of delivering metabolites downstream of the inhibitory node. Overall, it is believed that advanced drug delivery strategies will play an important role in marrying the fields of immunometabolism and immunotherapy to generate translatable anti-cancer treatments.
ContributorsInamdar, Sahil (Author) / Acharya, Abhinav P (Thesis advisor) / Rege, Kaushal (Committee member) / Green, Matthew (Committee member) / Curtis, Marion (Committee member) / Seetharam, Mahesh (Committee member) / Arizona State University (Publisher)
Created2022
Description
The treatment of melanoma is dependent on what stage the cancer has developed into. Metastatic melanoma is commonly treated with immune checkpoint inhibitors. Unfortunately, not all patients will respond to the treatment as expected. This paper develops important background knowledge on melanoma, how it is treated for each stage, and immune checkpoint inhibitors.
ContributorsStates, Savanna (Author) / Lake, Douglas (Thesis director) / Chang, Yung (Committee member) / Barrett, The Honors College (Contributor) / School of Molecular Sciences (Contributor)
Created2024-05
Description
The success of genetically-modified T-cells in treating hematological malignancies has accelerated the research timeline for Chimeric Antigen Receptor-T (CAR-T) cell therapy. Since there are only two approved products (Kymriah and Yescarta), the process knowledge is limited. This leads to a low efficiency at manufacturing stage with serious challenges corresponding to high cost and scalability. In addition, the individualized nature of the therapy limits inventory and creates a high risk of product loss due to supply chain failure. The sector needs a new manufacturing paradigm capable of quickly responding to individualized demands while considering complex system dynamics.
The research formulates the problem of Chimeric Antigen Receptor-T (CAR-T) manufacturing design, understanding the performance for large scale production of personalized therapies. The solution looks to develop a simulation environment for bio-manufacturing systems with single-use equipment. The result is BioMan: a discrete-event simulation model that considers the role of therapy's individualized nature, type of processing and quality-management policies on process yield and time, while dealing with the available resource constraints simultaneously. The tool will be useful to understand the impact of varying factor inputs on Chimeric Antigen Receptor-T (CAR-T) cell manufacturing and will eventually facilitate the decision-maker to finalize the right strategies achieving better processing, high resource utilization, and less failure rates.
The research formulates the problem of Chimeric Antigen Receptor-T (CAR-T) manufacturing design, understanding the performance for large scale production of personalized therapies. The solution looks to develop a simulation environment for bio-manufacturing systems with single-use equipment. The result is BioMan: a discrete-event simulation model that considers the role of therapy's individualized nature, type of processing and quality-management policies on process yield and time, while dealing with the available resource constraints simultaneously. The tool will be useful to understand the impact of varying factor inputs on Chimeric Antigen Receptor-T (CAR-T) cell manufacturing and will eventually facilitate the decision-maker to finalize the right strategies achieving better processing, high resource utilization, and less failure rates.
ContributorsSharma, Gaurav (Author) / Pedrielli, Giulia (Thesis advisor) / Fainekos, Georgios (Committee member) / Fowler, John (Committee member) / Arizona State University (Publisher)
Created2020
Description
Immunotherapy has received great attention recently, as it has become a powerful tool in fighting certain types of cancer. Immunotherapeutic drugs strengthen the immune system's natural ability to identify and eradicate cancer cells. This work focuses on immune checkpoint inhibitor and oncolytic virus therapies. Immune checkpoint inhibitors act as blocking mechanisms against the binding partner proteins, enabling T-cell activation and stimulation of the immune response. Oncolytic virus therapy utilizes genetically engineered viruses that kill cancer cells upon lysing. To elucidate the interactions between a growing tumor and the employed drugs, mathematical modeling has proven instrumental. This dissertation introduces and analyzes three different ordinary differential equation models to investigate tumor immunotherapy dynamics.
The first model considers a monotherapy employing the immune checkpoint inhibitor anti-PD-1. The dynamics both with and without anti-PD-1 are studied, and mathematical analysis is performed in the case when no anti-PD-1 is administrated. Simulations are carried out to explore the effects of continuous treatment versus intermittent treatment. The outcome of the simulations does not demonstrate elimination of the tumor, suggesting the need for a combination type of treatment.
An extension of the aforementioned model is deployed to investigate the pairing of an immune checkpoint inhibitor anti-PD-L1 with an immunostimulant NHS-muIL12. Additionally, a generic drug-free model is developed to explore the dynamics of both exponential and logistic tumor growth functions. Experimental data are used for model fitting and parameter estimation in the monotherapy cases. The model is utilized to predict the outcome of combination therapy, and reveals a synergistic effect: Compared to the monotherapy case, only one-third of the dosage can successfully control the tumor in the combination case.
Finally, the treatment impact of oncolytic virus therapy in a previously developed and fit model is explored. To determine if one can trust the predictive abilities of the model, a practical identifiability analysis is performed. Particularly, the profile likelihood curves demonstrate practical unidentifiability, when all parameters are simultaneously fit. This observation poses concerns about the predictive abilities of the model. Further investigation showed that if half of the model parameters can be measured through biological experimentation, practical identifiability is achieved.
The first model considers a monotherapy employing the immune checkpoint inhibitor anti-PD-1. The dynamics both with and without anti-PD-1 are studied, and mathematical analysis is performed in the case when no anti-PD-1 is administrated. Simulations are carried out to explore the effects of continuous treatment versus intermittent treatment. The outcome of the simulations does not demonstrate elimination of the tumor, suggesting the need for a combination type of treatment.
An extension of the aforementioned model is deployed to investigate the pairing of an immune checkpoint inhibitor anti-PD-L1 with an immunostimulant NHS-muIL12. Additionally, a generic drug-free model is developed to explore the dynamics of both exponential and logistic tumor growth functions. Experimental data are used for model fitting and parameter estimation in the monotherapy cases. The model is utilized to predict the outcome of combination therapy, and reveals a synergistic effect: Compared to the monotherapy case, only one-third of the dosage can successfully control the tumor in the combination case.
Finally, the treatment impact of oncolytic virus therapy in a previously developed and fit model is explored. To determine if one can trust the predictive abilities of the model, a practical identifiability analysis is performed. Particularly, the profile likelihood curves demonstrate practical unidentifiability, when all parameters are simultaneously fit. This observation poses concerns about the predictive abilities of the model. Further investigation showed that if half of the model parameters can be measured through biological experimentation, practical identifiability is achieved.
ContributorsNikolopoulou, Elpiniki (Author) / Kuang, Yang (Thesis advisor) / Gardner, Carl (Committee member) / Gevertz, Jana (Committee member) / Kang, Yun (Committee member) / Kostellich, Eric (Committee member) / Arizona State University (Publisher)
Created2020
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 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
Description
This dissertation presents three novel algorithms with real-world applications to genomic oncology. While the methodologies presented here were all developed to overcome various challenges associated with the adoption of high throughput genomic data in clinical oncology, they can be used in other domains as well. First, a network informed feature ranking algorithm is presented, which shows a significant increase in ability to select true predictive features from simulated data sets when compared to other state of the art graphical feature ranking methods. The methodology also shows an increased ability to predict pathological complete response to preoperative chemotherapy from genomic sequencing data of breast cancer patients utilizing domain knowledge from protein-protein interaction networks.
Second, an algorithm that overcomes population biases inherent in the use of a human reference genome developed primarily from European populations is presented to classify microsatellite instability (MSI) status from next-generation-sequencing (NGS) data. The methodology significantly increases the accuracy of MSI status prediction in African and African American ancestries.
Finally, a single variable model is presented to capture the bimodality inherent in genomic data stemming from heterogeneous diseases. This model shows improvements over other parametric models in the measurements of receiver-operator characteristic (ROC) curves for bimodal data. The model is used to estimate ROC curves for heterogeneous biomarkers in a dataset containing breast cancer and cancer-free specimen.
ContributorsSaul, Michelle (Author) / Dinu, Valentin (Thesis advisor) / Liu, Li (Committee member) / Wang, Junwen (Committee member) / Arizona State University (Publisher)
Created2021
Description
Fusion protein immunotherapies such as the bispecific T cell engager (BiTE) have displayed promising potential as cancer treatments capable of engaging the immune system against tumor cells. It has been shown that chlorotoxin, a 36-amino peptide found in the venom of the deathstalker scorpion (Leiurus quinquestriatus), binds specifically to glioblastoma (GBM) cells without binding healthy tissue, making it an ideal GBM cell binding moiety for a BiTE-like molecule. However, chlorotoxin’s four disulfide bonds pose a folding challenge outside of its natural context and impede production of the recombinant protein in various expression systems, including those relying on bacteria and plants. To overcome this difficulty, we have engineered a truncated chlorotoxin variant (Cltx∆15) that contains just two of the original eight cystine residues, thereby capable of forming only a single disulfide bond while maintaining its ability to bind GBM cells. We further created a BiTE (ACDClx∆15) which tethers Cltx∆15 to a single chain ⍺-CD3 antibody in order to bring T cells into contact with GBM cells. The gene for ACDClx∆15 was cloned into a pET-11a vector for expression in Escherichia coli and isolated from inclusion bodies before purification via affinity chromatography. Immunoblot analyses confirmed that ACDClx∆15 can be expressed in E. coli and purified with high yield and purity; moreover, flow cytometry indicated that ACDClx∆15 is capable of binding GBM cells. These data warrant further investigation into the ability of ACDClx∆15 to activate T cells against GBM cells.
ContributorsSchaefer, Braeden Scott (Author) / Mor, Tsafrir (Thesis director) / Mason, Hugh (Committee member) / Cook, Rebecca (Committee member) / School of Life Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Immunotherapy is an effective treatment for cancer which enables the patient's immune system to recognize tumor cells as pathogens. In order to design an individualized treatment, the t cell receptors (TCR) which bind to a tumor's unique antigens need to be determined. We created a convolutional neural network to predict the binding affinity between a given TCR and antigen to enable this.
ContributorsCai, Michael Ray (Author) / Lee, Heewook (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-12