Philosophiae Doctor - PhD / There are over 100 different types of cancer, and each of these cancers are classified by the type of cell that it initially affects. For the purpose of this research we will be focussing on prostate cancer (PC). Prostate cancer is the second most common form of cancer in men around the world and annually approximately 4500 men in South Africa are diagnosed making PC a global epidemic. Prostate cancer is a type of cancer which starts in the prostate it is normally a walnut-sized gland found right below the bladder. PC follows a natural course, starting as a tiny group of cancer cells that can grow into a tumour. In some men if PC is not treated it may spread to surrounding tissue by a process called direct invasion/ spread and could lead to death. Current diagnostic tests for prostate cancer have low specificity and poor sensitivity. Although many PC's are slow growing there is currently no test to distinguish between these and cancers that will become aggressive and life threatening. Therefore the need for a less invasive early detection method with the ability to overcome the lack of specificity and sensitivity of current available diagnostic test is required. Biomarkers have recently been identified as a viable option for early detection of disease for example biological indicators ie. DNA, RNA, proteins and microRNAs (miRNAs). Since first described in the 1990s, circulating miRNAs have provided an active and rapidly evolving area of research that has the potential to transform cancer diagnostics and prognostics. In particular, miRNAs could provide potentially new biomarkers for PC as diagnostic molecules. Circulating miRNAs are highly stable and are both detectable and quantifiable in a range of accessible bio-fluids, having the potential to be useful as diagnostic, prognostic and predictive biomarkers. In this study we aimed to identify miRNAs as potential biomarkers to detect and distinguish between various types of PC in its earliest stage. The major objectives of the study were to identify miRNAs and their gene targets that play a critical role in disease onset and progression to further understand their mechanism of action in PC using several in silico methods, and to validate the potential diagnostic miRNAs using qRT-PCR in several cell lines. The identification of specific miRNAs and their targets was done using an "in-house" designed pipeline. Bioinformatic analyses was done using a number of databases including STRING, DAVID, DIANA and mFold database, and these combined with programming and statistical analyses was used for the identification of potential miRNAs specific to PC. Our study identified 40 miRNAs associated with PC using our "in-house" parameters in comparison to the 20-30 miRNAs known to be involved in PC found in public databases e.g. miRBase. A comparison between our parameters and those used in public databases showed a higher degree of specificity for the identification PC-associated miRNAs. These selected miRNAs were analysed using different bioinformatics tools, and were confirmed to be novel miRNAs associated with PC. The identified miRNAs were experimentally validated using qRT-PCR to generate expression profiles for PC as well as various other cancers. Prostate lines utilised in this study included PNT2C2 (normal) which was compared to BPH1 (Benign) and LNCaP (Metastatic). In the study the expression profiles of eight potential miRNA biomarkers for the detection of PC was determined using qRT-PCR, and to distinguish PC from other cancers. QRT-PCR data showed that miRNA-3 and -5 were up-regulated in the BPH1 and LNCaP when compared to PNT2C2. In addition miRNA-8 was also shown to be up-regulated in LNCaP. Based on these results it was shown that a miRNA profile could be established to distinguish between BPH1 and the LNCaP prostate cell lines. The results suggest that one miRNA as a diagnostic marker may be sufficient to differentiate between different cancer cell lines. Furthermore by creating a unique profile for each cancer cell line by using a combination of miRNAs could be a suitable approach as well. Finally, it was shown that through the use of a single or combination of all eight miRNAs a unique profile for all the cancer cell lines tested in this study can be created. This is an important finding which could have potential diagnostic or prognostic implications in clinical practice.

Breast cancer (BC) is the second most diagnosed cancer in Canadian women. Early detection of this cancer is critical to improve patient survival and prognoses. Exosomes are proposed to be involved in tumor proliferation through the transfer of diverse biomolecules, including metabolites. The use of exosomes as biomarkers for early diagnosis of BC has recently garnered interest due to them having unique biomolecules in diseased cohorts. Hence, an untargeted metabolomic analysis of BC exosomes was performed using nano high-performance liquid chromatography coupled to tandem mass spectrometry (nLC-MS/MS) for BC diagnostic biomarker discovery. A total of 9 independent metabolite samples from non-tumorogenic MCF10A and highly metastatic MDA-MB-231 cell lines were analyzed. Bioinformatic analysis revealed 27 potential metabolite candidates unique to MDA-MB-231. Amongst 4 metabolites tested, one, N-Acetyl-L-Phenylalanine, was successfully validated. Overall, this study reveals that exosomes possess metabolites that can be candidates for early BC diagnosis.

Lung cancer is the leading cause of death from cancer in the world. First, we hypothesized that microRNA expression is altered in the bronchial epithelium of patients with lung cancer and that incorporating microRNA expression into an existing mRNA biomarker may improve its performance. Using bronchial brushings collected from current and former smokers, we profiled microRNA expression via small RNA sequencing for 347 patients with available mRNA data. We found that four microRNAs were under-expressed in cancer patients compared to controls (p<0.002, FDR<0.2). We explored the role of these microRNAs and their gene targets in cancer. In addition, we found that adding a microRNA feature to an existing 23-gene biomarker significantly improves its performance (AUC) in a test set (p<0.05). Next, we generalized the biomarker discovery process, and developed a visualization tool for biomarker selection. We built upon an existing biomarker discovery pipeline and created a web-based interface to visualize the performance of multiple predictors. The “visualization” component is the key to sorting through a thousand potential biomarkers, and developing clinically useful molecular predictors. Finally, we explored the molecular events leading to the development of COPD and ILD, two heterogeneous diseases with high mortality. We hypothesized that integrative genetic and expression networks can help identify drivers and elucidate mechanisms of genetic susceptibility. We utilized 262 lung tissue specimens profiled with microRNA sequencing, microarray gene expression and SNP chip genotyping. Next, we built condition specific integrative networks using a causality inference test for predicting SNP-microRNA-mRNA associations, where the microRNA is a predicted mediator of the SNP’s effect on gene expression. We identified the microRNAs predicted to affect the most genes within each network. Members of miR-34/449 family, known to promote airway differentiation by repressing the Notch pathway, were among the top ranked microRNAs in COPD and ILD networks, but not in the non-disease network. In addition, the miR-34/449 gene module was enriched among genes that increase in expression over time when airway basal cells are differentiated at an air-liquid interface and among genes that increase in expression with the airway wall thickening in patients with emphysema. / 2019-07-31T00:00:00Z

New affinity molecules such as nucleic acid aptamers are in demand in the science and medical fields. Current aptamer selection technologies can generate unique aptamers with desired properties to targets of interest. My thesis describes a series of investigations on the protection of an oncolytic virus, the isolation of target cells from biological fluids, and aptamer-facilitated biomarker discovery. We tested individual aptamers and constructed a tetramer aptamer structure (quadramer) to increase virus infectivity. The quadramer protects vesicular stomatitis virus (VSV) during freeze–thaw cycles, shields the virus from neutralizing antibodies and increases viral active units. In addition to aptamers, we screened carbohydrate-based ice recrystallization inhibitors for the possible elimination of the cold chain of Vaccinia virus, VSV, and Herpes virus-1. N-octyl-gluconamide provides the longest shelf life for Vaccinia virus and Herpes virus-1 as tested according to the World Health Organization’s requirements for viral vaccines efficiency during transportation and distribution. We generated switchable aptamers capable of isolating cells expressing LIFR, NRP1, DLL4, uPAR, or PTCH1. These aptamers bind to the receptor positive cells in the presence of Mg2+ and Ca2+, and release the pure cells upon addition of EDTA. The aptamers were applied for a sequential positive immunomagnetic isolation of cells from mice bone marrow. We also utilized fluorescence-activated cell sorting (FACS) in our aptamer selections to develop switchable aptamers to positive isolation of monocytes from human blood. Moreover, we have selected non-switchable aptamers as an affinity probe to the cells expressing Axl receptor for immunofluorescent analysis and cell sorting. We determined aptamers to CD107a and applied them for biomarker discovery with mass spectrometry and found that CD107a was co-expressing with PD-1. Furthermore, we identified CD91 as binding partners to our aptamers in human monocytes using FACS and orbitrap mass spectrometry.

No description available.

Lung cancer (LC) is the leading cause of cancer death in the US, claiming over 160,000 lives annually. Although CT screening has been shown to be efficacious in reducing mortality, the limited access to screening programs among high-risk individuals and the high number of false positives contribute to low survival rates and increased healthcare costs. As a result, there is an urgent need for preventative therapeutics and novel interception biomarkers that would enhance current methods for detection of early-stage LC. This thesis addresses this challenge by examining the hypothesis that transcriptomic changes preceding the onset of LC can be identified by studying bronchial premalignant lesions (PMLs) and the normal-appearing airway epithelial cells altered in their presence (i.e., the PML-associated airway field of injury). PMLs are the presumed precursors of lung squamous cell carcinoma (SCC) whose presence indicates an increased risk of developing SCC and other subtypes of LC. Here, I leverage high-throughput mRNA and miRNA sequencing data from bronchial brushings and lesion biopsies to develop biomarkers of PML presence and progression, and to understand regulatory mechanisms driving early carcinogenesis. First, I utilized mRNA sequencing data from normal-appearing airway brushings to build a biomarker predictive of PML presence. After verifying the power of the 200-gene biomarker to detect the presence of PMLs, I evaluated its capacity to predict PML progression and detect presence of LC (Aim 1). Next, I identified likely regulatory mechanisms associated with PML severity and progression, by evaluating miRNA expression and gene coexpression modules containing their targets in bronchial lesion biopsies (Aim2). Lastly, I investigated the preservation of the PML-associated miRNAs and gene modules in the airway field of injury, highlighting an emergent link between the airway field and the PMLs (Aim 3). Overall, this thesis suggests a multi-faceted utility of PML-associated genomic signatures as markers for stratification of high-risk smokers in chemoprevention trials, markers for early detection of lung cancer, and novel chemopreventive targets, and yields valuable insights into early lung carcinogenesis by characterizing mRNA and miRNA expression alterations that contribute to premalignant disease progression towards LC. / 2020-01-25

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. / High-throughput technique such as transcriptomics, proteomics and metabolomics is widely used to generate ‘big’ data for cancer biomarker discovery. Typically, differential expression analysis is performed to identify cancer biomarkers. However, discrepancies from independent studies for the same clinical types of samples using differential expression analysis are observed. This may be attributed to that biomolecules such as genes, proteins and metabolites 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. In this dissertation, we propose to identify cancer biomarker candidates using network-based approaches. We start by proposing a novel data-driven network model (i.e., LOPC) to reconstruct sparse biological networks. Then we propose a novel method (i.e., wgLASSO) to incorporate prior biological knowledge from public available databases into purely data-driven network model to build biologically relevant networks. In addition, a novel differential network analysis method (i.e., dwgLASSO) is proposed to identify cancer biomarkers. Finally, we propose a novel network-based approach (i.e., INDEED) to integrate differential expression and differential network analyses. INDEED is further expanded as INDEED-M to utilize omic data at different levels of human biological system (e.g., transcriptomics, proteomics, and metabolomics) to identify cancer biomarkers from a systems biology perspective. Matlab and R packages for the proposed methods are developed and shared with the research community.

This thesis is about protein profiling in blood-derived samples using suspension bead ar- rays built with protein affinity reagents, and the evaluation of binding characteristics and potential disease relation of such profiles. A central aim of the presented work was to discover and verify disease associated protein profiles in blood-derived samples such as serum or plasma. This was based on immobiliz- ing antigens or antibodies on color-coded beads for a multiplexed analysis. This concept generally allow for a dual multiplexing because hundreds of samples can be screened for hundreds of proteins in a miniaturized and parallelized fashion. At first, protein antigens were used to study humoral immune responses in cattle suffering from a mycoplasma infec- tion (Paper I). Here, the most immunogenic of the applied antigens were identified based on reactivity profiles from the infected cattle, and were combined into an antigen cocktail to serve as a diagnostic assay in a standard ELISA set-up. Next, antibodies and their em- ployment in assays with directly labeled human samples was initiated. This procedure was applied in a study of kidney disorders where screening of plasma resulted in the discovery of a biomarker candidate, fibulin-1 (Paper II). In parallel to the disease related applica- tions, systematic evaluations of the protein profiles were conducted. Protein profiles from 2,300 antibodies were classified on the bases of binding properties in relation to sample heating and stringent washing (Paper III). With a particular focus on heat dependent de- tectability, a method was developed to visualize those proteins that were captured to the beads in an immunoassay by using Western blotting (Paper IV). In conclusion, this thesis presents examples of the possibilities of comparative plasma profiling enabled by protein bead arrays. / <p>QC 20130208</p>

Proteins are essential building blocks in every living cell, and since the complete human genome was sequenced in 2004, researchers have attempted to map the human proteome, which is the functional representation of the genome. One such initiative is the Human Protein Atlas programme (HPA), which generates monospecific antibodies towards all human proteins and uses these for high-throughput tissue profiling on tissue microarrays (TMAs). The results are publically available at the website www.proteinatlas.org. In this thesis, TMAs were used for analysis of expression patterns in various research areas. Different search queries in the HPA were tested and evaluated, and a number of potential biomarkers were identified, e.g. proteins exclusively expressed in islets of Langerhans, but not in exocrine glandular cells or other abdominal organs close to pancreas. The identified candidates were further analyzed on TMAs with pancreatic tissues from normal and diabetic individuals, and colocalization studies with insulin and glucagon revealed that several of the investigated proteins (DGCR2, GBF1, GPR44 and SerpinB10) appeared to be beta cell specific. Moreover, a set of proteins differentially expressed in lung cancer stroma was further analyzed on a clinical lung cancer cohort in the TMA format, and one protein (CD99) was significantly associated with survival. In addition, TMAs with tissue samples from different species were generated, e.g. for mapping of influenza virus attachment in various human and avian tissues. The results showed that the gull influenza virus H16N3 attached to human respiratory tract and eye, suggesting possible transmission of the virus between gull and human. TMAs were also used for analysis of protein expression differences between humans and other primates, and two proteins (TCF3 and SATB2) proved to be significantly differentially expressed on the human lineage at both the protein level and the RNA level.   In conclusion, this thesis exemplifies the potential of the TMA technology, which can be used for analysis of expression patterns in a large variety of research fields, such as biomarker discovery, influenza virus research or further understanding of human evolution.

abstract: Signal processing techniques have been used extensively in many engineering problems and in recent years its application has extended to non-traditional research fields such as biological systems. Many of these applications require extraction of a signal or parameter of interest from degraded measurements. One such application is mass spectrometry immunoassay (MSIA) which has been one of the primary methods of biomarker discovery techniques. MSIA analyzes protein molecules as potential biomarkers using time of flight mass spectrometry (TOF-MS). Peak detection in TOF-MS is important for biomarker analysis and many other MS related application. Though many peak detection algorithms exist, most of them are based on heuristics models. One of the ways of detecting signal peaks is by deploying stochastic models of the signal and noise observations. Likelihood ratio test (LRT) detector, based on the Neyman-Pearson (NP) lemma, is an uniformly most powerful test to decision making in the form of a hypothesis test. The primary goal of this dissertation is to develop signal and noise models for the electrospray ionization (ESI) TOF-MS data. A new method is proposed for developing the signal model by employing first principles calculations based on device physics and molecular properties. The noise model is developed by analyzing MS data from careful experiments in the ESI mass spectrometer. A non-flat baseline in MS data is common. The reasons behind the formation of this baseline has not been fully comprehended. A new signal model explaining the presence of baseline is proposed, though detailed experiments are needed to further substantiate the model assumptions. Signal detection schemes based on these signal and noise models are proposed. A maximum likelihood (ML) method is introduced for estimating the signal peak amplitudes. The performance of the detection methods and ML estimation are evaluated with Monte Carlo simulation which shows promising results. An application of these methods is proposed for fractional abundance calculation for biomarker analysis, which is mathematically robust and fundamentally different than the current algorithms. Biomarker panels for type 2 diabetes and cardiovascular disease are analyzed using existing MS analysis algorithms. Finally, a support vector machine based multi-classification algorithm is developed for evaluating the biomarkers' effectiveness in discriminating type 2 diabetes and cardiovascular diseases and is shown to perform better than a linear discriminant analysis based classifier. / Dissertation/Thesis / Ph.D. Electrical Engineering 2012