The Defender against apoptotic cell death (DAD1) is a negative regulator of programmed cell death that was initially identified in the temperature sensitive tsBN7 cell line. It has been shown to be an essential subunit of oligosaccharyltransferase and is localized to the Endoplasmic reticulum (ER) in a normal physiological state. However, our data suggests that DAD1 localizes outside of the cell and alters the apoptotic signaling via FAS ligand to give cancer cells a survival advantage. Although the mechanism is poorly understood, increased expression of DAD1 has been associated with increasing Gleason score in prostate cancer (PCa) patient tumors. Based on the aforementioned evidence, our study attempts to unravel cellular localization and the underlying mechanisms by which DAD1 mediates prostate cancer cell survival, and explore its potential as a biomarker in prostate cancer. As evidenced by qRTPCR, immunocytochemistry, immunohistochemistry, Co-ip, ELISA, and immuno-blot analysis, cancer cells down regulate the expression of the binding partner of DAD1 responsible for retention of DAD1 in the ER, which allows DAD1 to exit the ER and be exocytosed. The exocytosed DAD1 binds to FAS L and prevents apoptotic signaling. Treatment with DAD1 antibody induces significantly higher cell death in prostate cancer cells compared to the non-tumorigenic cells. Combination of DAD1 antibody with currently used chemotherapeutic agents like Docetaxel and Doxorubicin can be used to achieve higher cell death at a lower dose of these drugs to minimize side effects. Further, our immunohistochemistry data in tumor microarray suggests that DAD1 could serve as a potential biomarker marker in PCa. In addition to the tissue, we also examined the expression of DAD1 in prostate cancer patient serum samples using sandwich ELISA; our results indicate DAD1 is a more sensitive and specific prognostic marker for prostate cancer compared to PSA. Our data suggests that localization of DAD1 outside of the cells is crucial to the survival of PCa cells and this phenomenon can be exploited to specifically target prostate cancer cells in therapy and serve as a biomarker in prostate cancer. / 1 / Nobel Bhasin

Despite the global effort to discover and improve ways to detect, treat, and monitor cancer, it still remains the second leading cause of death in the United States and poses a major health and economic burden worldwide. While traditional treatments like surgery, chemotherapy, radiation therapy, and hormone therapy have been successful and have decreased cancer mortality, cancer incidence in all sites continues to rise. Consequently, there is an immediate need to find new therapeutics for the treatment of cancer. In recent years, and with the continuing push towards personalized medicine, cancer biomarkers have become crucial to detect, treat, and monitor cancer. Thymidine kinase 1 (TK1) has been identified as a cancer biomarker with diagnostic, prognostic, and therapeutic potential. TK1 is a nucleotide salvage pathway enzyme responsible for maintaining a balance in the cell nucleotide pool and providing the cell with thymidine monophosphate, which upon further phosphorylation is incorporated into DNA during cell replication. TK1 has been found to be upregulated in the serum of cancer patients. Serum TK1 (sTK1) has been used as an early diagnostic and prognostic biomarker in many types of cancer and has been shown to be a better proliferation biomarker than Ki67. In this dissertation, we described the characterization of TK1 as a cancer biomarker that associates with the plasma membrane of hematological malignancies such as Burkitt's lymphoma, acute lymphoblastic leukemia, acute promyelocytic leukemia, acute T cell lymphoma, and solid malignancies such as lung, breast, and colon cancer. We also describe the different oligomeric TK1 forms that are found on the cell membrane and show that membrane TK1 has activity. We assess the clinical relevance of TK1 in all these malignancies, looking at tissue expression as well as gene expression from patients from The Cancer Genome Atlas database. We find that TK1 is not expressed on the surface of normal cells, whether they are proliferating or not, making TK1 a unique cancer biomarker, with the potential to be used in targeted therapy. We also find that TK1 expressed on the surface may be involved in the invasion potential of cancer cells. The knowledge gained from this study will help researchers working in clinical research and cancer immunotherapeutics to potentially use TK1 as a biomarker and cancer target, and thus providing another weapon against cancer. In this dissertation, we described the characterization of TK1 as a cancer biomarker that associates with the plasma membrane of hematological malignancies such as Burkitt's lymphoma, acute lymphoblastic leukemia, acute promyelocytic leukemia, acute T cell lymphoma, and solid malignancies such as lung, breast, and colon cancer. We also describe the different oligomeric TK1 forms that are found on the cell membrane and show that membrane TK1 has activity. We assess the clinical relevance of TK1 in all these malignancies, looking at tissue expression as well as gene expression from patients from The Cancer Genome Atlas database. We find that TK1 is not expressed on the surface of normal cells, whether they are proliferating or not, making TK1 a unique cancer biomarker, with the potential to be used in targeted therapy. We also find that TK1 expressed on the surface may be involved in the invasion potential of cancer cells. The knowledge gained from this study will help researchers working in clinical research and cancer immunotherapeutics to potentially use TK1 as a biomarker and cancer target, and thus providing another weapon against cancer.

Advancements in the micro-and nano-scale fabrication techniques have opened up new avenues for the development of portable, scalable and easier-to-use biosensors. Over the last few years, electrodes made of carbon have been widely used as sensing units in biosensors due to their attractive physiochemical properties. The aim of this research is to investigate different strategies to develop functionalized high surface carbon micro/nano-structures for electrochemical and biosensing devices. High aspect ratio three-dimensional carbon microarrays were fabricated via carbon microelectromechanical systems (C-MEMS) technique, which is based on pyrolyzing pre-patterned organic photoresist polymers. To further increase the surface area of the carbon microstructures, surface porosity was introduced by two strategies, i.e. (i) using F127 as porogen and (ii) oxygen reactive ion etch (RIE) treatment. Electrochemical characterization showed that porous carbon thin film electrodes prepared by using F127 as porogen had an effective surface area (Aeff 185%) compared to the conventional carbon electrode. To achieve enhanced electrochemical sensitivity for C-MEMS based functional devices, graphene was conformally coated onto high aspect ratio three-dimensional (3D) carbon micropillar arrays using electrostatic spray deposition (ESD) technique. The amperometric response of graphene/carbon micropillar electrode arrays exhibited higher electrochemical activity, improved charge transfer and a linear response towards H2O2 detection between 250μM to 5.5mM. Furthermore, carbon structures with dimensions from 50 nano-to micrometer level have been fabricated by pyrolyzing photo-nanoimprint lithography patterned organic resist polymer. Microstructure, elemental composition and resistivity characterization of the carbon nanostructures produced by this process were very similar to conventional photoresist derived carbon. Surface functionalization of the carbon nanostructures was performed using direct amination technique. Considering the need for requisite functional groups to covalently attach bioreceptors on the carbon surface for biomolecule detection, different oxidation techniques were compared to study the types of carbon–oxygen groups formed on the surface and their percentages with respect to different oxidation pretreatment times. Finally, a label-free detection strategy using signaling aptamer/protein binding complex for platelet-derived growth factor oncoprotein detection on functionalized three-dimensional carbon microarrays platform was demonstrated. The sensor showed near linear relationship between the relative fluorescence difference and protein concentration even in the sub-nanomolar range with an excellent detection limit of 5 pmol.

No description available.

Recent advances in high-throughput technique enables the generation of a large amount of omic data such as genomics, transcriptomics, proteomics, metabolomics, glycomics etc. Typically, differential expression analysis (e.g., student's t-test, ANOVA) is performed to identify biomolecules (e.g., genes, proteins, metabolites, glycans) with significant changes on individual level between biologically disparate groups (disease cases vs. healthy controls) for cancer biomarker discovery. However, differential expression analysis on independent studies for the same clinical types of patients often led to different sets of significant biomolecules and had only few in common. This may be attributed to the fact that biomolecules are members of strongly intertwined biological pathways and highly interactive with each other. Without considering these interactions, differential expression analysis could lead to biased results. Network-based methods provide a natural framework to study the interactions between biomolecules. Commonly used data-driven network models include relevance network, Bayesian network and Gaussian graphical models. In addition to data-driven network models, there are many publicly available databases such as STRING, KEGG, Reactome, and ConsensusPathDB, where one can extract various types of interactions to build knowledge-driven networks. While both data- and knowledge-driven networks have their pros and cons, an appropriate approach to incorporate the prior biological knowledge from publicly available databases into data-driven network model is desirable for more robust and biologically relevant network reconstruction. Recently, there has been a growing interest in differential network analysis, where the connection in the network represents a statistically significant change in the pairwise interaction between two biomolecules in different groups. From the rewiring interactions shown in differential networks, biomolecules that have strongly altered connectivity between distinct biological groups can be identified. These biomolecules might play an important role in the disease under study. In fact, differential expression and differential network analyses investigate omic data from two complementary perspectives: the former focuses on the change in individual biomolecule level between different groups while the latter concentrates on the change in pairwise biomolecules level. Therefore, an approach that can integrate differential expression and differential network analyses is likely to discover more reliable and powerful biomarkers. To achieve these goals, we start by proposing a novel data-driven network model (i.e., LOPC) to reconstruct sparse biological networks. The sparse networks only contains direct interactions between biomolecules which can help researchers to focus on the more informative connections. Then we propose a novel method (i.e., dwgLASSO) to incorporate prior biological knowledge into data-driven network model to build biologically relevant networks. Differential network analysis is applied based on the networks constructed for biologically disparate groups to identify cancer biomarker candidates. Finally, we propose a novel network-based approach (i.e., INDEED) to integrate differential expression and differential network analyses to identify more reliable and powerful cancer biomarker candidates. INDEED is further expanded as INDEED-M to utilize omic data at different levels of human biological system (e.g., transcriptomics, proteomics, metabolomics), which we believe is promising to increase our understanding of cancer. Matlab and R packages for the proposed methods are developed and available at Github (https://github.com/Hurricaner1989) to share with the research community. / Ph. D.

Telomerase, discovered in 1985, is considered a near-universal marker of malignancy and therefore has a potential use in cancer therapeutics and diagnostics. In this study, I used several approaches to gain a better understanding of telomerase and its potential applications in the canine context, for both cancer therapeutics and diagnosis. Having already developed an effective siRNA viral vector in vitro, the challenge still remained to deliver it efficiently in vivo. Thus, I initially investigated two possible approaches for in vivo delivery. First, I investigated a cell-based system for direct delivery to the tumours. Specifically I optimised a system for efficient gene-transfer to endothelial cells using a green fluorescent protein plasmid vector, and monitored systemic delivery by ex vivo imaging of dye-labelled cells in a canine xenograft tumour mouse model. In parallel, in vitro I investigated the gene transfer mediated by a novel dendrimer vector that can form nanoparticles with DNA and accumulate in tumour sites in vivo after i.v. administration. In order to utilize these delivery systems, I developed a DNA plasmid-based siRNA vector and tested its efficacy on canine tumour cells. To investigate telomerase as a cancer biomarker, I conducted a study that aimed to detect circulating telomerase reverse transcriptase (TERT) mRNA in serum taken from canine cancer patients. For this I developed several systems for effective RNA isolation from serum and used both conventional and quantitative PCR assays to detect TERT expression. Although for the first time I can confirm the existence of mRNA in serum of canine cancer patients, in this clinical study, I could only detect telomerase transcripts in a very small proportion of canine cancer patients. In a final pilot study to investigate anti-ageing technologies, I looked at the potential for drug-dependant telomerase induction rather than inhibition. For this I investigated the ability of three candidate drugs to induce TERT mRNA activation in canine embryonic fibroblasts. In this study, telomerase induction was measured using the quantitative PCR method that I had developed for serum detection. In summary, I have demonstrated that a cell-based delivery vehicle has a potential application in cancer therapy, but that more development is required before it can be applied clinically. I have also reported here that PPIG3 dendrimer-based gene transfer in vitro is low in canine cancer cells and thus require more optimisation and development before it can be utilised as an efficient systemic delivery vehicle. For the siRNA experiment, unfortunately, I did not observe any telomerase genesilencing in canine cancer cells using the plasmid-based siRNA expression vector, and therefore the gene sequence of cTR that we were targeting as well as the siRNA plasmid-vector that we used needs further validation in canine cells. I also suggest that TERT mRNA may not be a good serum biomarker for canine cancer diagnostics as I did not find TERT transcript in most of our serum samples from canine cancer patients, although circulating mRNA of a housekeeping gene was detected. Finally, in a pilot study, I have demonstrated that telomerase can be induced in normal canine somatic cells using small molecules. However, the long-term effects of telomerase induction on ageing must be determined in future studies.

An estimated 1,735,350 new cancer diagnosis and 609,640 cancer related deaths are predicted to occur in the United States in 2018. To improve patient prognosis, biomarkers are needed to identify cancer in early stages. When diagnosed at an early stage, cancer is more likely to respond to treatments and patients have a higher survival rate. Consequently, there is an ever-present need to identify biomarkers that can aid in the detection of cancer. Additionally, there is a paradigm shift in the field of cancer treatment towards immunotherapy. Traditional cancer treatments include chemotherapy, radiation, and hormone therapy and are not cancer-specific, which leads to bystander effects on the patient<&trade>s normal organs that often harm the patient and create unnecessary hardship. To alleviate this, immunotherapy utilizes a patient<&trade>s own immune cells to attack and destroy cancer cells via cancer-specific biomarkers. These biomarkers are ideally on the surface of cancer cells and absent from the patient<&trade>s normal cells to avoid healthy tissue destruction. With this new therapy, there is a recent push to find surface antigens for immunotherapy techniques.This dissertation describes the characterization of HPRT as a diagnostic and therapeutic biomarker for the detection and possible treatment of hematological and solid malignancies. We describe the general upregulation of HPRT upon malignancy and show that this elevation in protein expression is independent of stage, which indicates that it would be useful as an early stage diagnostic companion tool. We have preliminarily linked the elevation in HPRT to a mutation in one of its prime transcription factors, p53. Specific mutation in p53 called Gain of Function mutations have shown to influence salvage pathway enzyme expression, and we have shown that mutations in p53 are relevant to the elevated levels of HPRT within several cancer types. In addition, we also found that HPRT associates significantly with the membrane of several cancer cell lines as well as patient samples. We found that HPRT has insignificant expression on normal cells, which suggests it may be useful as a targetable biomarker for immunotherapy. Throughout our analysis, we also determined that HPRT might have a role in immune regulation as an elevation of the protein correlates to the decrease of several pro-inflammatory genes involved in immune activation. The knowledge gained from the data presented in this dissertation have opened up new functions for HPRT outside of simple nucleotide production and have confirmed that HPRT has a unique role in cancer that has not been previously reported.

In this dissertation, we discuss a reading profiling system, a biological data visualization system and a sports visualization system. Self-tracking is getting increasingly popular in the field of personal informatics. Reading profiling can be used as a personal data collection method. We present UUAT, an unintrusive user attention tracking system. In UUAT, we used user interaction data to develop technologies that help to pinpoint a users reading region (RR). Based on computed RR and user interaction data, UUAT can identify a readers reading struggle or interest. A biomarker is a measurable substance that may be used as an indicator of a particular disease. We developed CancerVis for visual and interactive analysis of cancer data and demonstrate how to apply this platform in cancer biomarker research. CancerVis provides interactive multiple views from different perspectives of a dataset. The views are synchronized so that users can easily link them to a same data entry. Furthermore, CancerVis supports data mining practice in cancer biomarker, such as visualization of optimal cutpoints and cutthrough exploration. Tennis match summarization helps after-live sports consumers assimilate an interested match. We developed TennisVis, a comprehensive match summarization and visualization platform. TennisVis offers chart- graph for a client to quickly get match facts. Meanwhile, TennisVis offers various queries of tennis points to satisfy diversified client preferences (such as volley shot, many-shot rally) of tennis fans. Furthermore, TennisVis offers video clips for every single tennis point and a recommendation rating is computed for each tennis play. A case study shows that TennisVis identifies more than 75% tennis points in full time match.

Our goal was to find new diagnostic and prognostic biomarkers in bladder cancer (BCa), and to predict molecular mechanisms and processes involved in BCa development and progression. Notably, the data collection is an inevitable step and time-consuming work. Furthermore, identification of the complementary results and considerable literature retrieval were requested. Here, we provide detailed information of the used datasets, the study design, and on data mining. We analyzed differentially expressed genes (DEGs) in the different datasets and the most important hub genes were retrieved. We report on the meta-data information of the population, such as gender, race, tumor stage, and the expression levels of the hub genes. We include comprehensive information about the gene ontology (GO) enrichment analyses and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. We also retrieved information about the up- and down-regulation of genes. All in all, the presented datasets can be used to evaluate potential biomarkers and to predict the performance of different preclinical biomarkers in BCa.

No description available.