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- All Subjects: immunotherapy
- Creators: Borges, Chad
Description
Background: High risk types of human papillomavirus (HPV) are known to cause cancer, including cervical (99%) and oropharyngeal cancer (70%). HPV type 16 is the most common subtype. Three antigens that are critical for integration or tumor progression are E2, E6 and E7. In this study, we developed a systematic approach to identify naturally-processed HPV16-derived HLA class I epitopes for immunotherapy development. Methods: K562 cells, which lack HLA expression, were transduced with each HPV16 antigen using lentivirus and supertransfected with HLA-A2 by nucleofection. Stable cell lines expressing each antigen were selected for and maintained throughout the investigation. In order to establish a Gateway-compatible vector for robust transient gene expression, a Gateway recombination expression cloning cassette was inserted into the commercial Lonza pMAX GFP backbone, which has been experimentally shown to display high transfection expression efficiency. GFP was cloned into the vector and plain K562 cells were transfected with the plasmid by nucleofection. Results: Expression of K562-A2 was tested at various time points by flow cytometry and A2 expression was confirmed. Protein expression was shown for the transduced K562 E7 by Western blot analysis. High transfection efficiency of the pMAX_GFP_Dest vector (up to 97% GFP+ cells) was obtained 48 hours post transfection, comparable to the commercial GFP-plasmid. Conclusion: We have established a rapid system for target viral antigen co-expression with single HLA molecules for analysis of antigen presentation. Using HPV as a model system, our goal is to identify specific antigenic peptide sequences to develop immunotherapeutic treatments for HPV-associated cancers.
ContributorsVarda, Bianca Marie (Author) / Anderson, Karen (Thesis director) / Borges, Chad (Committee member) / Krishna, Sri (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Antibodies are naturally occurring proteins that protect a host during infection through direct neutralization and/or recruitment of the innate immune system. Unfortunately, in some infections, antibodies present unique hurdles that must be overcome for a safer and more efficacious antibody-based therapeutic (e.g., antibody dependent viral enhancement (ADE) and inflammatory pathology). This dissertation describes the utilization of plant expression systems to produce N-glycan specific antibody-based therapeutics for Dengue Virus (DENV) and Chikungunya Virus (CHIKV). The Fc region of an antibody interacts with Fcγ Receptors (FcγRs) on immune cells and components of the innate immune system. Each class of immune cells has a distinct action of neutralization (e.g., antibody dependent cell-mediated cytotoxicity (ADCC) and antibody dependent cell-mediated phagocytosis (ADCP)). Therefore, structural alteration of the Fc region results in novel immune pathways of protection. One approach is to modulate the N-glycosylation in the Fc region of the antibody. Of scientific significance, is the plant’s capacity to express human antibodies with homogenous plant and humanized N-glycosylation (WT and GnGn, respectively). This allows to study how specific glycovariants interact with other components of the immune system to clear an infection, producing a tailor-made antibody for distinct diseases. In the first section, plant-produced glycovariants were explored for reduced interactions with specific FcγRs for the overall reduction in ADE for DENV infections. The results demonstrate a reduction in ADE of our plant-produced monoclonal antibodies in in vitro experiments, which led to a greater survival in vivo of immunodeficient mice challenged with lethal doses of DENV and a sub-lethal dose of DENV in ADE conditions. In the second section, plant-produced glycovariants were explored for increased interaction with specific FcγRs to improve ADCC in the treatment of the highly inflammatory CHIKV. The results demonstrate an increase ADCC activity in in vitro experiments and a reduction in CHIKV-associated inflammation in in vivo mouse models. Overall, the significance of this dissertation is that it can provide a treatment for DENV and CHIKV; but equally importantly, give insight to the role of N-glycosylation in antibody effector functions, which has a broader implication for therapeutic development for other viral infections.
ContributorsHurtado, Jonathan (Author) / Chen, Qiang (Thesis advisor) / Arntzen, Charles (Committee member) / Borges, Chad (Committee member) / Lake, Douglas (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