Diffuse pleural mesothelioma (DPM) is a devastating lung cancer most commonly diagnosed at an advanced stage with a poor prognosis for patients. Therapies available to patients after diagnosis currently include surgical resection, radiotherapy, immunotherapy, and chemotherapy. However, these therapies only prolong life for about a year and a half on average. DPM patients desperately need effective therapies in the form of drugs, drug combinations, and miRNA-based therapies, that could lengthen overall survival and provide a better quality of life. I hypothesized that focusing on DPM tumor biology would streamline the process for discovering new therapies that will have a lasting impact for patients. I have applied systems biology methods to mine multiomic data from patient DPM tumors to discover new therapeutic options. I began by developing a somatic mutation integration pipeline, which created a comprehensive somatic mutational profile of DPM tumors from patient genomic and transcriptomic data. The somatic mutational profile was used in the generation of dpmSYGNAL, a disease-relevant gene regulatory network (GRN) trained on patient tumor multiomic data. I integrated this GRN with functional genomics screens performed on two low-passage primary DPM tumor cell lines and identified gene vulnerabilities that could be targeted by FDA-approved inhibitors and drug combinations. I also developed a pipeline to integrate miRNA target genes from biotinylated pulldowns with RNA-seq data from a study re-expressing the miRNA hsa-miR-497-5p in DPM cell lines. I determined that the re-expression of hsa-miR-497-5p had early pro-apoptotic effects and inhibited the cell cycle at later time points. The identification of inhibitors, combinations of inhibitors, and a therapeutic miRNA demonstrates that DPM biology can be used as a guide to discover new therapeutics for DPM.

The complex network of the immune system defends the human body against infection, providing protection from pathogens. This work aims to improve preparation and structural knowledge of two proteins on opposite sides of the immune system spectrum. The first protein, secreted autotransporter toxin (Sat) is a class I serine protease autotransporter of Enterobacteriaceae (SPATE) that has cytotoxic and immunomodulatory effects on the host. Previous studies on Sat show its ability to aid in bacterial colonization and evasion of the immune system. This work improves the stability of Sat by making mutations to the active serine protease motif (GDSGS) while inhibiting remaining activity with reversible and irreversible serine protease inhibitors. Characterization of Sat by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), and size-exclusion chromatography led to the first structural studies of Sat by x-ray crystallography and cryo-EM. Human leukocyte antigen class I proteins play an important role in the adaptive immune system by presenting endogenous viral peptides at the cell surface for CD8+ T cell recognition. In vitro production of HLA-I proteins is a difficult task without endoplasmic reticulum chaperones as present in vivo. Disulfide bond formation, folded light chain and a peptide bound are all key to refolding the HLA-I heavy chain for complex formation. The work presented in this dissertation represents systematic studies aimed at improving the production of HLA-I proteins in vitro in bacterial expression systems. Optimization of every step of the preparation was investigated providing higher expression yields, quality of inclusion bodies, and refolding improvements. With further improvements in the future, this work forms the basis for a more efficient small and large-scale production of HLA-I molecules for functional and structural studies.

Human Papillomavirus (HPV) is the most commonly transmitted STI and isresponsible for an estimated 5% of cancer cases worldwide. HPV infection is implicated in 70% of cervical cancer incidence and is also responsible for a variety of oropharyngeal and anogenital cancers. While vaccination has greatly reduced the cervical cancer burden in developed countries, HPV infection remains high in developing countries due to high cost and poor access to healthcare. Several studies have highlighted the presence of anti-HPV antibodies following infection and their potential use as biomarkers for developing novel screening methods. Progression from initial infection to cancer is slow, thus presenting an opportunity for effective screening programs. Biomarker screening is an important area of cancer detection and Lateral Flow Assays (LFA) are a low cost, easy to use alternative to other screening methods that require extensive training and laboratory space. Therefore, this project proposes as a hypothesis that the development of an LFA screening for HPV specific IgG can provide clinically relevant data for the early detection of cervical dysplasia. This project adapts an LFA in a multiplexed format for fluorescence-based serologic detection of HPV specific IgG in patient plasma.

Originally conceived as a way to scaffold molecules of interest into three-dimensional (3D) crystalline lattices for X ray crystallography, the field of deoxyribonucleic acid (DNA) nanotechnology has dramatically evolved since its inception. The unique properties of DNA nanostructures have promoted their use not only for X ray crystallography, but for a suite of biomedical applications as well. The work presented in this dissertation focuses on both of these exciting applications in the field: 1) Nucleic acid nanostructures as multifunctional drug and vaccine delivery platforms, and 2) 3D DNA crystals for structure elucidation of scaffolded guest molecules.Chapter 1 illustrates how a wide variety of DNA nanostructures have been developed for the delivery of drugs and vaccine components. However, their applications are limited under physiological conditions due to their lack of stability in low salt environments, susceptibility to enzymatic degradation, and tendency for endosomal entrapment. To address these issues, Chapter 2 describes a PEGylated peptide coating molecule was designed to electrostatically adhere to and protect DNA origami nanostructures and to facilitate their cytosolic delivery by peptide-mediated endosomal escape. The development of this molecule will aid in the use of nucleic acid nanostructures for biomedical purposes, such as the delivery of messenger ribonucleic acid (mRNA) vaccine constructs. To this end, Chapter 3 discusses the fabrication of a structured mRNA nanoparticle for more cost-efficient mRNA vaccine manufacture and proposes a multi-epitope mRNA nanostructure vaccine design for targeting human papillomavirus (HPV) type 16-induced head and neck cancers. DNA nanotechnology was originally envisioned to serve as three-dimensional scaffolds capable of positioning proteins in a rigid array for their structure elucidation by X ray crystallography. Accordingly, Chapter 4 explores design parameters, such as sequence and Holliday junction isomeric forms, for efficient crystallization of 3D DNA lattices. Furthermore, previously published DNA crystal motifs are used to site-specifically position and structurally evaluate minor groove binding molecules with defined occupancies. The results of this study provide significant advancement towards the ultimate goal of the field.

The purpose of this paper is to create awareness around breast cancer risk factorsand screening methods. Five overarching intrinsic risk factors, including: the patient’s age at the time of diagnosis, race, familial susceptibility, and the role of natural hormone changes, and one extrinsic risk factor, dietary habits, were selected for consideration. Along with risk factors, four screening methods were taken into consideration. These included self-breast exams, mammograms, magnetic resonance imaging (MRI), and ultrasound. The recommendation of screening methods was then determined in relation to a women’s risk for breast cancer. Two categories of risk (average and high risk) were defined and the recommended screening methods were determined based on the risk. Overall, mammography was found to be a useful tool in both average and high risk women. For high risk women, mammography with MRI had a greater sensitivity and was able to detect more breast cancers. More research needs to be conducted on the efficacy of Breast MRI, Ultrasound, and breast self-exams as supplemental tools to mammography in both average and high-risk women

Life history theory offers a powerful framework to understand evolutionary selection pressures and explain how adaptive strategies use the life history trade-off and differences in cancer defenses across the tree of life. There is often some cost to the phenotype of therapeutic resistance and so sensitive cells can usually outcompete resistant cells in the absence of therapy. Adaptive therapy, as an evolutionary and ecologically inspired paradigm in cancer treatment, uses the competitive interactions between drug-sensitive, and drug-resistant subclones to help suppress the drug-resistant subclones. However, there remain several open challenges in designing adaptive therapies, particularly in extending this approach to multiple drugs. Furthermore, the immune system also plays a role in preventing and controlling cancers. Life history theory may help to explain the variation in immune cell levels across the tree of life that likely contributes to variance in cancer prevalence across vertebrates. However, this has not been previously explored. This work 1) describes resistance management for cancer, lessons cancer researchers learned from farmers since adaptive evolutionary strategies were inspired by the management of resistance in agricultural pests, 2) demonstrates how adaptive therapy protocols work with gemcitabine and capecitabine in a hormone-refractory breast cancer mouse model, 3) tests for a relationship between life history strategy and the immune system, and tests for an effect of immune cells levels on cancer prevalence across vertebrates, and 4) provides a novel approach to improve the teaching of life history theory. This work applies lessons that cancer researchers learned from pest managers, who face similar issues of pesticide resistance, to control cancers. It represents the first time that multiple drugs have been used in adaptive therapy for cancer, and the first time that adaptive therapy has been used on hormone-refractory breast cancer. I found that this evolutionary approach to cancer treatment prolongs survival in mice and also selects for the slow life history strategy. I also discovered that species with slower life histories have higher concentrations of white blood cells and a higher percentage of heterophils, monocytes and segmented neutrophils. Moreover, larger platelet size is associated with higher cancer prevalence in mammals.

IOsteosarcoma is the most common bone cancer and typically affects patients in the second decade of life. Current treatment methods have not proven effective for treating reoccurring or metastatic osteosarcoma (mOS) given the 5-year survival rate of 15-30%. Previous work showed that using the immune system to fight the cancer significantly improved survival of mOS in mice, but approximately 40-50% of treated mice still succumbed to disease. To further improve immunotherapy, I analyzed immune cells in the tumor bed and observed high numbers of a rare T cell subtype: CD4hiCD8αhi, or double positive (DP), T cells. While previous literature found mature DP T cells in chronic diseases, the associations and functions of this rare T cell subtype varied between studies and were unknown for mOS. Controlling for age, chronicity of disease, and environmental exposure, I found DP T cells composed a higher percentage of T cells in the cancer as tumor burden increased. I then tested whether the DP cells were pro- or anti-tumor. I found that DP cells produced the cytokines IFNγ and IL-2 when exhaustion was overcome. They also expressed FasL for cytotoxic function, although the target is unknown. These findings suggest DP T cells have multifunctionality, which could be advantageous when responding to high antigen load. II Course-based undergraduate research experiences (CUREs) offer students opportunities to engage in critical thinking and problem solving. However, quantitating the impact that incorporating research into undergraduate courses has on student learning has been difficult since most CUREs lack a comparable traditional course as a control. Because the overall class structure remained unaltered when our upper division immunology course transitioned to a CURE class, we realized retrospectively that we were in a unique position to quantitate the impact of incorporating research on student performance. I then analyzed the summative assessments used to assess student learning and found that students in the CURE format class performed significantly better on quizzes, exams, and reports. There were no significant differences in academic levels, degree programs, or grade point averages, suggesting improved performance was due to increased engagement of students in research.

Computational models have long been used to describe and predict the outcome of complex immunological processes. The dissertation work described here centers on the construction of multiscale computational immunology models that derives biological insights at the population, systems, and atomistic levels. First, SARS-CoV-2 mortality is investigated through the lens of the predicted robustness of CD8+ T cell responses in 23 different populations. The robustness of CD8+ T cell responses in a given population was modeled by predicting the efficiency of endemic MHC-I protein variants to present peptides derived from SARS-CoV-2 proteins to circulating T cells. To accomplish this task, an algorithm, called EnsembleMHC, was developed to predict viral peptides with a high probability of being recognized by CD T cells. It was discovered that there was significant variation in the efficiency of different MHC-I protein variants to present SARS-CoV-2 derived peptides, and countries enriched with variants with high presentation efficiency had significantly lower mortality rates. Second, a biophysics-based MHC-I peptide prediction algorithm was developed. The MHC-I protein is the most polymorphic protein in the human genome with polymorphisms in the peptide binding causing striking changes in the amino acid compositions, or binding motifs, of peptide species capable of stable binding. A deep learning model, coined HLA-Inception, was trained to predict peptide binding using only biophysical properties, namely electrostatic potential. HLA-Inception was shown to be extremely accurate and efficient at predicting peptide binding motifs and was used to determine the peptide binding motifs of 5,821 MHC-I protein variants. Finally, the impact of stalk glycosylations on NL63 protein dynamics was investigated. Previous data has shown that coronavirus crown glycans play an important role in immune evasion and receptor binding, however, little is known about the role of the stalk glycans. Through the integration of computational biology, experimental data, and physics-based simulations, the stalk glycans were shown to heavily influence the bending angle of spike protein, with a particular emphasis on the glycan at position 1242. Further investigation revealed that removal of the N1242 glycan significantly reduced infectivity, highlighting a new potential therapeutic target. Overall, these investigations and associated innovations in integrative modeling.

Adaptive therapy utilizes competitive interactions between resistant and sensitive cells by keeping some sensitive cells to control tumor burden with the aim of increasing overall survival and time to progression. The use of adaptive therapy to treat breast cancer, ovarian cancer, and pancreatic cancer in preclinical models has shown significant results in controlling tumor growth. The adaptive therapy model comes from the integrated pest management agricultural strategy, predator prey model, and the unique intra- and inter-tumor heterogeneity of tumors. The purpose of this thesis is to analyze and compare gemcitabine dose response on hormone refractory breast cancer cells retrieved from mice using an adaptive therapy strategy with standard therapy treatment. In this study, we compared intermittent (drug holiday) adaptive therapy with maximum tolerated dose therapy. The MCF7 resistant cell lines to both fulvestrant and palbociclib were injected into the mammary fat pads of 8 weeks old NOD/SCID gamma (NSG) mice which were then treated with gemcitabine. Tumor burden graphs were made to track tumor growth/decline during different treatments while Drug Dose Response (DDR) curves were made to test the sensitivity of the cell lines to the drug gemcitabine. The tumor burden graphs showed success in controlling the tumor burden with intermittent treatment. The DDR curves showed a positive result in using the adaptive therapy treatment method to treat mice with gemcitabine. Due to some fluctuating DDR results, the sensitivity of the cell lines to gemcitabine needs to be further studied by repeating the DDR experiment on the other mice cell lines for stronger results.