UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Fantastic Sources Of Tumor Heterogeneity And How To Characterize Them(2021) Patkar, Sushant A; Ruppin, Eytan; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Cancer constantly evolves to evade the host immune system and resist different treatments. As a consequence, we see a wide range of inter and intra-tumor heterogeneity. In this PhD thesis, we present a collection of computational methods that characterize this heterogeneity from diverse perspectives. First, we developed computational frameworks for predicting functional re-wiring events in cancer and imputing the functional effects of protein-protein interactions given genome-wide transcriptomics and genetic perturbation data. Second, we developed a computational framework to characterize intra-tumor genetic heterogeneity in melanoma from bulk sequencing data and study its effects on the host immune response and patient survival independently of the overall mutation burden. Third, we analyzed publicly available genome-wide copy number, expression and methylation data of distinct cancer types and their normal tissues of origin to systematically uncover factors driving the acquisition of cancer type-specific chromosomal aneuploidies. Lastly, we developed a new computational tool: CODEFACS (COnfident Deconvolution For All Cell Subsets) to dissect the cellular heterogeneity of each patient’s tumor microenvironment (TME) from bulk RNA sequencing data, and LIRICS (LIgand Receptor Interactions between Cell Subsets): a supporting statistical framework to discover clinically relevant cellular immune crosstalk. Taken together, the methods presented in this thesis offer a way to study tumor heterogeneity in large patient cohorts using widely available bulk sequencing data and obtain new insights on tumor progression.Item Mathematical Models of Immune Regulation and Cancer Vaccines(2012) Wilson, Shelby Nicole; Levy, Doron; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)An array of powerful mathematical tools can be used to identify the key underlying components and interactions that determine the mechanics of biological systems such as the immune system and its interaction with cancer. In this dissertation, we develop mathematical models to study the dynamics of immune regulation in the context of the primary immune response and tumor growth. Regulatory T cells play a key role in the contraction of the immune response, a phase that follows the peak response to bring cell levels back to normal. To understand how the immune response is regulated, it is imperative to study the dynamics of regulatory cells, and in particular, the conditions under which they are functionally stable. There is conflicting biological evidence regarding the ability of regulatory cells to lose their regulatory capabilities and possibly turn into immune promoting cells. We develop dynamical models to investigate the effects of an unstable regulatory T cell population on the immune response. These models display the usual characteristics of an immune response with the added capabilities of being able to correct for initial imbalances in T cell populations. We also observe an increased robustness of the immune response with respect to key parameters. Similar conclusions are demonstrated with regards to the effects of regulatory T cell switching on immunodominance. TGF-beta is an immunoregulatory protein that contributes to inadequate anti-tumor immune responses in cancer patients. Recent experimental data suggests that TGF-beta inhibition alone, provides few clinical benefits, yet it can significantly amplify the anti-tumor immune response when combined with a tumor vaccine. We develop a mathematical model to gain insight into the cooperative interaction between anti-TGF-beta and vaccine treatments. Using numerical simulations and stability analysis we study the following scenarios: a control case of no treatment, anti-TGF-beta treatment, vaccine treatment, and combined anti-TGF-beta vaccine treatments. Consistent with experimental data, we show that monotherapy alone cannot successfully eradicate a tumor. Tumor eradication requires the combination of these therapeutic approaches. We also demonstrate that our model captures the observed experimental results, and hence can be potentially used in designing future experiments involving this approach to immunotherapy.