Major hallmarks of cancer include metastasis and evading the immune system. Despite cutting edge treatments developed in an era of extensive cancer research, immunotherapy has not been proven efficient enough in solid tumors, and metastasis still accounts for the majority of cancer deaths. The overall unsatisfactory response rates to immunotherapy are mainly due to the lack of biomarkers that can predict a patient’s response and the lack of a good understanding of the different immune cell infiltration trends observed in tumors. To address these gaps in breast and prostate cancer, RNA sequenced data for breast and prostate cancer samples were obtained from The Cancer Genome Atlas (TCGA) and analyzed to identify immune evasion mechanisms and understand immune cell infiltration. Breast and prostate cancer populations were each clustered into different immune evasion groups. Then biomarkers predictive of the identified clusters were identified and could be used as predictors of immune evasion and the corresponding immunotherapy options. In breast cancer, 77.4% of the clustered tumor specimens showed evasion through transforming growth factor-beta (TGF-β), 57.8% through decoy receptor 3 (DcR3), 48.0% through cytotoxic T-lymphocyte-associated protein 4 (CTLA4), and 34.3% through programmed cell death-1 (PD-1). Prostate cancer clustering showed immunologic ignorance in 89.77% of samples, upregulated CTLA4 in 58.8%, and upregulated DcR3 expression in 51.6%. However, in most clusters, there were different combinations of evasion mechanisms, which could explain the failure of immune monotherapy approaches. The immune profiling of breast cancer samples suggests that immunologically cold tumors are not only less immunogenic than hot tumors, but also have a high abundance of the pro-tumorigenic M2 macrophages and a stiff matrix, all of which can impede immune cell infiltration. Thus, M2 is a novel prognostic factor in breast cancer and a promising drug target. Epithelial-mesenchymal transition (EMT) is a critical early step in cancer metastasis. Further understanding of this process may shed light on how to stop the spreading of cancer cells. Androgen-repressed prostate cancer (ARCaP) cell lines representative of the epithelial (ARCaP-E) and mesenchymal (ARCaP-M) phenotypes were used and their secretome was investigated using proteomics approaches. High levels of proteins involved in bone remodeling and extracellular matrix degradation were detected in the ARCaP-M cells, indicative of a bone metastatic phenotype. LC-MS/MS analysis showed that the serine protease granzyme B (GZMB) was 800-fold higher in ARCaP-M conditioned media. Quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) and Western blot further showed that GZMB was expressed and translated in ARCaP-M cells and the protein is only detected extracellularly. ARCaP-M cells with the GZMB gene knockdown using RNA interference showed a markedly reduced invasiveness as demonstrated by the Matrigel invasion assay. Our findings indicate a novel role for GZMB in prostate cancer invasion and extracellular matrix degradation. / A Dissertation submitted to the Department of Chemistry and Biochemistry in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / 2019 / November 4, 2019. / Breast cancer, Epithelial-mesenchymal transition, Immune evasion, Metastasis, Prostate Cancer / Includes bibliographical references. / Qing-Xiang Sang, Professor Co-Directing Dissertation; Jinfeng Zhang, Professor Co-Directing Dissertation; Fanxiu Zhu, University Representative; Timothy Logan, Committee Member; Christian Bleiholder, Committee Member.

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As knowledge of evolutionary processes has expanded over the years, we havedeepened our understanding about how they drive organismal, cellular, and molecular biology and the factors beyond natural selection that are involved. Nevertheless, selection maintains a role in fixing and maintaining successful adaptations to new niches, whether from environmental change or organismal migration. Adaptation should not be considered solely on the level of individual genes and point substitutions as selection occurs on multiple levels. Examination on these multiple levels can further aid in understanding the constraints on evolution and how organisms can attain a phenotype. Here we present two packages of tools for the examination of selection on the levels of protein structure and genetic pathways as well as on the individual gene and sequence levels., followed by examples of potential applications. First, we present a package of Application Programming Interface (API) tools that simplifies use of The Adaptive Evolutionary Database. Second, we present a package of tools implemented in the Rust programming language for fast and reliable analysis of phylogenetic data. Then we describe the phenotypic data and methodology for use of these tools to analyze evolution on multiple levels, where genomic data is available. A broad scale analysis of the protein structural properties of evolutionary genetic changes in proteins is developed and described. We also present an organization of phenotypic data for mammals in the arctic biome, an ancestral reconstruction of the evolution of the phenotypic traits under study, and demonstrate a methodology to apply the tool packages to this cohort when sufficient genomic data is available. / Biology / Accompanied by two compressed .zip files: 1) Titled Charts 2) Appendices

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