Computer Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2756
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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 Underlying Dynamics in Acute and Chronic Immunology(2019) Wyatt, Asia Alexandria; Levy, Doron; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)During an immune response, it is understood that there are key differences between the cells and cytokines that are present in a primary response versus those present in subsequent responses. Specifically, after a primary response, memory cells are present and drive the clearance of antigen in these later immune responses. When comparing acute infections to chronic infections, there are also differences in the dynamics of the immune system. In this dissertation, we develop three mathematical models to explore these differences in the immune response to acute and chronic infections through the creation, activation, regulation, and long term maintenance of T cells. We mimic this biological behavior through the use of delayed differential equation (DDE) models. The first model explores the dynamics of adaptive immunity in primary and secondary responses to acute infections. It is shown that while we observe similar amounts of antigen stimulation from both immune responses, with the incorporation of memory T cells, we see an increase in both the amount of effector T cells present and the speed of activation of the immune system in the secondary response. We conclude that our model is robust and can be applied to study different types of antigen from viral to bacterial. Extending our work to chronic infections, we develop our second and third models to explore breast cancer dormancy and T cell exhaustion. For our breast cancer dormancy model, we find that our model behaves similar to acute infections, but with constant antigen stimulation. Moreover, we observe the importance of immune protection on the long term survival of breast cancer cells. In our third model we find that while memory T cells play a major role in the effectiveness of the immune system in acute infection, in chronic infections, over long periods of time, T cell exhaustion prevents proper immune function and clearance of antigen. We also observe how the lack of long term maintenance of memory T cells plays an important role in the final outcome of the system. Finally, we propose two potential extensions to the three models developed: creating a simplified acute infection model and creating a combined breast cancer dormancy model with T cell exhaustion.Item CHARACTERIZATION OF SURVIVAL ASSOCIATED GENE INTERACTIONS AND LYMPHOCYTE HETEROGENEITY IN CANCER(2019) Magen, Assaf; Hannenhalli, Sridhar; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Cancer is the second leading cause of death globally. Tumors form intricate ecosystems in which malignant and immune cells interact to shape disease progression. Yet, the molecular underpinnings of tumorigenesis and immunological responses to tumors are poorly understood, limiting their manipulation to elicit favorable clinical outcomes. This thesis lays conceptual frameworks for investigating the molecular interactions taking place in tumors as well as the diversity of the immune response to cancer. In the molecular level of individual cancer cells, the phenotypic effect of perturbing a gene’s activity depends on the activity level of other genes, reflecting the notion that phenotypes are emergent properties of a network of functionally interacting genes. In the context of cancer, contemporary investigations have primarily focused on just one type of functional genetic interaction (GI) – synthetic lethality (SL). However, there may be additional types of GIs whose systematic identification would enrich the molecular and functional characterization of cancer. This thesis describes a novel data-driven approach called EnGIne, that applied to large-scale cancer data identifies 71,946 GIs spanning 12 distinct types, only a small minority of which are SLs. The detected GIs explain cancer driver genes’ tissue- specificity and differences in patients’ response to drugs, and stratify breast cancer tumors into refined subtypes. These results expand the scope of cancer GIs and lay a conceptual and computational basis for future studies of additional types of GIs and their translational applications. Furthermore, tumor growth is continuously shaped by the immune response. However, T cells typically adopt a dysfunctional phenotype may be reversed using immunotherapy strategies. Most current tumor immunotherapies leverage cytotoxic CD8+ T cells to elicit an effective anti-tumor response. Despite evidence for clinical potential of CD4+ tumor-infiltrating lymphocytes (TILs), their functional diversity has limited our ability to harness their anti-tumor activity. To address this issue, we have used single-cell mRNA sequencing (scRNAseq) to analyze the response of CD4+ T cells specific for a defined recombinant tumor antigen, both in the tumor microenvironment and draining lymph nodes (dLN). New computational approaches to characterize subpopulations identified TIL transcriptomic patterns strikingly distinct from those elicited by responses to infection, and dominated by diversity among T-bet-expressing T helper type 1 (Th1)-like cells. In contrast, the dLN response includes Follicular helper (Tfh)-like cells but lacks Th1 cells. We identify an interferon-driven signature in Th1-like TILs, and show that it is found in human liver cancer and melanoma, in which it is negatively associated with response to checkpoint therapy. Our study unveils unsuspected differences between tumor and virus CD4+ T cell responses, and provides a proof-of-concept methodology to characterize tumor- control CD4+ T cell effector programs. Targeting these programs should help improve immunotherapy strategies.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.