Background: To describe the scoring methodology and MRI assessments used to evaluate the cross-sectional features observed in cases and controls, to define change over time for different MRI features, and to report the extent of changes over a 24-month period in the Foundation for National Institutes of Health Osteoarthritis Biomarkers Consortium study nested within the larger Osteoarthritis Initiative (OAI) Study. Methods: We conducted a nested case-control study. Cases (n = 406) were knees having both radiographic and pain progression. Controls (n = 194) were knee osteoarthritis subjects who did not meet the case definition. Groups were matched for Kellgren-Lawrence grade and body mass index. MRIs were acquired using 3 T MRI systems and assessed using the semi-quantitative MOAKS system. MRIs were read at baseline and 24 months for cartilage damage, bone marrow lesions (BML), osteophytes, meniscal damage and extrusion, and Hoffa- and effusion-synovitis. We provide the definition and distribution of change in these biomarkers over time. Results: Seventy-three percent of the cases had subregions with BML worsening (vs. 66 % in controls) (p = 0.102). Little change in osteophytes was seen over 24 months. Twenty-eight percent of cases and 10 % of controls had worsening in meniscal scores in at least one subregion (p < 0.001). Seventy-three percent of cases and 53 % of controls had at least one area with worsening in cartilage surface area (p < 0.001). More cases experienced worsening in Hoffa- and effusion synovitis than controls (17 % vs. 6 % (p < 0.001); 41 % vs. 18 % (p < 0.001), respectively). Conclusions: A wide range of MRI-detected structural pathologies was present in the FNIH cohort. More severe changes, especially for BMLs, cartilage and meniscal damage, were detected primarily among the case group suggesting that early changes in multiple structural domains are associated with radiographic worsening and symptomatic progression.

A suite of biomarkers was developed using the crab Carcinus maenas and the mussel Mytilus edulis as test organisms. The ability of the biomarkers to differentiate amongst the major toxic components and to indicate the concentration of chemical mixtures was evaluated in the laboratory. Biomarkers were also applied in a field trial and their potential to monitor environmental water quality in a chemically contaminated estuary investigated. The results from the biomarker assays were compared with and validated against two commonly used toxicity tests (Tisbe battagliai LC-50, and Microtox®). Novel methods for recognising patterns of biomarker responses were developed and assessed. The most sensitive and reliable biomarker assays investigated were neutral red retention time in crabs and mussels and heart rate and glutathione-S-transferase activity in crabs. Effects were observed at environmentally realistic concentrations; for example lysosomal enlargement was observed in mussels exposed to a complex mixture containing chemicals at environmental quality standard concentrations. Exposure concentrations required to illicit biomarker responses were similar to toxicity test EC-50 values. The ease of interpretation and clarity of the results was enhanced when data from suites of biomarkers were pooled and analysed using multivariate statistical techniques (multidimensional scaling and cluster analysis). Multivariate analysis differentiated amongst mixtures containing solely organic chemicals, metals and metal and organic chemical mixtures. Exposure response relationships to complex mixtures were established for some of the individual biomarkers tested (crab heart rate and gill metallothionein) and also for suites of biomarkers when multivariate analysis was carried out. In the field biomarkers, in both transplanted and indigenous animals, were able to differentiate between clean and contaminated sites and indicate a pollution gradient along the Tees Estuary. This was not achieved using toxicity tests. The results were displayed clearly using multivariate analysis, enhancing the power of biomarkers as monitoring tools.

Monitoring the epidemic of Human Immunodeficiency Virus (HIV) infection plays a vital role in tracking the leading edge of HIV transmission and designing intervention programs both at individual and population level. At individual level, it is imperative to identify newly infected individuals to reduce onwards transmission. At population level, knowledge on the HIV incidence is essential to monitor the spread of the epidemic and plan/evaluate HIV prevention programs. This dissertation will examine the way in which biomarker data can be used to monitor the HIV epidemic. There are two primary aims of this thesis: a) to investigate the use of biomarkers in quantifying the recency of HIV infection at individual level and b) to estimate quantities such as mean window period and testing rate that are the building blocks for estimating HIV incidence at population level. We apply and further develop existing statistical methods to answer the research questions of interest. At individual level, we investigate the use of one or more biomarkers to quantify the recency of HIV infection. We propose a novel approach to make probabilistic statements on the recency of HIV infection by combining the knowledge on the growth of such biomarkers with observations from a newly diagnosed individual. Univariate and bivariate non-linear mixed-effects models are implemented in a fully Bayesian framework. A simulation study is conducted to investigate the biomarkers’ features that affect the accuracy of the estimation of recency. The research findings suggest that rapidly evolving biomarkers of antibody response, such as LAg Avidity, provide reliable estimates of the probability of recency. The proposed methods are applied to a panel of individuals for whom information on various biomarkers is given along with an estimated date of detectable infection. At population level, we focus on estimating two fundamental ingredients, the mean window period and the HIV testing rate, required for estimating HIV incidence using biomarker data. We compare commonly used statistical methods and explore the use of multi-state models in estimating the mean window period of the fourth generation Architect Avidity. We further investigate the factors that are associated with the probability of having an HIV test and the HIV testing rate using surveillance data. Logistic and count regression models using the Generalized Estimating Equations (GEE) approach are employed to make inference at population level about the probability of testing and HIV testing rate respectively.

Background: Neurodegenerative diseases, like multiple sclerosis, dementia and motor neurone disease, represent one of the major public health threats of our time. There is a clear persistent need for novel, affordable, and patient-acceptable biomarkers of these diseases, to assist with diagnosis, prognosis and impact of interventions. And these biomarkers need to be sensitive, specific and precise. The retina is an attractive site for exploring this potential, as it is easily accessible to non-invasive imaging. Remarkable technology revolutions in retinal imaging are enabling us to see the retina in microscopic level detail, and measure neuronal and vascular integrity. Aims and objectives: I therefore propose that retinal imaging could provide reliable and accurate markers of these neurological diseases. In this project, I aimed to explore the clinical utility of retinal imaging derived measures of retinal neuronal and vessel size and morphology, and determine their candidacy for being reliable biomarkers in these diseases. I also aimed to detail the methods of retinal imaging acquisition, and processing, and the principles underlying all these stages, in relation to understanding of retinal structure and function. This provides an essential foundation to the application of retinal imaging analysis, highlighting both the strengths and potential weaknesses of retinal biomarkers and how they are interpreted. Methods: After performing detailed systematic reviews and meta-analyses of the existing work on retinal biomarkers of neurodegenerative disease, I carried out a prospective, controlled, cross-sectional study of retinal image analysis, in patients with MS, dementia, and ALS. This involved developing new software for vessel analysis, to add value and maximise the data available from patient imaging episodes. Results: From the systematic reviews, I identified key unanswered questions relating to the detailed analysis and utility of neuroretinal markers, and diseases with no studies yet performed of retinal biomarkers, such as non-AD dementias. I recruited and imaged 961 participants over a two-year period, and found clear patterns of significance in the phenotyping of MS, dementia and ALS. Detailed analysis has provided new insights into how the retina may yield important disease information for the individual patient, and also generate new hypotheses with relation to the disease pathophysiology itself. Conclusions: Overall, the results show that retinal imaging derived biomarkers have an important and specific role in the phenotyping of neurodegenerative diseases, and support the hypothesis that the eye is an important window to neurological brain disease.

The field of targeted cancer therapy has progressed in recent years with the approval of new oncology drugs. Coupled with the benefits that these agents provide, has come an appreciation for challenges that occur when attempting to translate successful experiments from the laboratory into effective clinical trials. One such challenge has been predicting the optimal dose and schedule to take into clinical evaluation, given the possibility that certain targeted therapeutics may exhibit maximal anti-tumour efficacy well below maximum tolerated doses. Recent work with a targeted antibody to the mouse vascular endothelial growth factor receptor-2 demonstrated that detection of increased levels of its endogenous ligand in the plasma, namely VEGF, could address this issue, as maximal increases in VEGF paralleled optimal drug activity. The VEGF result has become recognized as a potential class effect for this family of inhibitors. This thesis summarizes experiments designed to build upon this discovery by investigating whether the utility of ligand measurement might also apply to drugs that inhibit the epidermal growth factor receptor (EGFR), which have also received recent regulatory approval, and inhibit angiogenesis as one of their mechanisms of action. In addition, we investigated the potential application of EGFR inhibitors to influence other markers of tumour angiogenesis, specifically their effects on levels of circulating endothelial progenitor cells (CEPs). Finally, we evaluated combination treatment of EGFR inhibition with anti-angiogenic scheduling of chemotherapy. The EGFR ligand TGF-alpha increased in a dose dependent fashion following treatment with cetuximab, and levels in the circulation paralleled anti-tumour activity. This was a host-dependent effect that was not observed with the lower affinity antibody nimotuzumab. Inhibition of host EGFR also reduced plasma CEPs, but at higher doses these drugs increased off target growth factors VEGF and G-CSF, as well as CEPs. In a model of advanced triple negative breast cancer, the combination of nimotuzumab and metronomic cyclophosphamide was efficacious and well tolerated, leading to a potential new treatment strategy for this aggressive disease. Taken together, these studies identify new and useful applications for EGFR-targeted antibodies, and shed further light on their contributions within the field of tumour angiogenesis and antiangiogenic therapy.

Over the past two decades, high-throughput (HTP) technologies such as microarrays and mass spectrometry have fundamentally changed the landscape of aging and disease biology. They have revealed novel molecular markers of aging, disease state, and drug response. Some have been translated into the clinic as tools for early disease diagnosis, prognosis, and individualized treatment and response monitoring. Despite these successes, many challenges remain: HTP platforms are often noisy and suffer from false positives and false negatives; optimal analysis and successful validation require complex workflows; and the underlying biology of aging and disease is heterogeneous and complex. Methods from integrative computational biology can help diminish these challenges by creating new analytical methods and software tools that leverage the large and diverse quantity of publicly available HTP data. In this thesis I report on four projects that develop and apply strategies from integrative computational biology to identify improved biomarkers and therapeutics for aging and disease. In Chapter 2, I proposed a new network analysis method to identify gene expression biomarkers of aging, and applied it to study the pathway-level effects of aging and infer the functions of poorly-characterized longevity genes. In Chapter 4, I adapted gene-level HTP chemogenomic data to study drug response at the systems level; I connected drugs to pathways, phenotypes and networks, and built the NetwoRx web portal to make these data publicly available. And in Chapters 3 and 5, I developed a novel meta-analysis pipeline to identify new drugs that mimic the beneficial gene expression changes seen with calorie restriction (Chapter 3), or that reverse the pathological gene changes associated with lung cancer (Chapter 5). The projects described in this thesis will help provide a systems-level understanding of the causes and consequences of aging and disease, as well as new tools for diagnosis (biomarkers) and treatment (therapeutics).

The field of targeted cancer therapy has progressed in recent years with the approval of new oncology drugs. Coupled with the benefits that these agents provide, has come an appreciation for challenges that occur when attempting to translate successful experiments from the laboratory into effective clinical trials. One such challenge has been predicting the optimal dose and schedule to take into clinical evaluation, given the possibility that certain targeted therapeutics may exhibit maximal anti-tumour efficacy well below maximum tolerated doses. Recent work with a targeted antibody to the mouse vascular endothelial growth factor receptor-2 demonstrated that detection of increased levels of its endogenous ligand in the plasma, namely VEGF, could address this issue, as maximal increases in VEGF paralleled optimal drug activity. The VEGF result has become recognized as a potential class effect for this family of inhibitors. This thesis summarizes experiments designed to build upon this discovery by investigating whether the utility of ligand measurement might also apply to drugs that inhibit the epidermal growth factor receptor (EGFR), which have also received recent regulatory approval, and inhibit angiogenesis as one of their mechanisms of action. In addition, we investigated the potential application of EGFR inhibitors to influence other markers of tumour angiogenesis, specifically their effects on levels of circulating endothelial progenitor cells (CEPs). Finally, we evaluated combination treatment of EGFR inhibition with anti-angiogenic scheduling of chemotherapy. The EGFR ligand TGF-alpha increased in a dose dependent fashion following treatment with cetuximab, and levels in the circulation paralleled anti-tumour activity. This was a host-dependent effect that was not observed with the lower affinity antibody nimotuzumab. Inhibition of host EGFR also reduced plasma CEPs, but at higher doses these drugs increased off target growth factors VEGF and G-CSF, as well as CEPs. In a model of advanced triple negative breast cancer, the combination of nimotuzumab and metronomic cyclophosphamide was efficacious and well tolerated, leading to a potential new treatment strategy for this aggressive disease. Taken together, these studies identify new and useful applications for EGFR-targeted antibodies, and shed further light on their contributions within the field of tumour angiogenesis and antiangiogenic therapy.

No description available.

Metastasis initiation is a complex process encompassing numerous steps. To identify molecular alterations associated with early and late stages of metastasis, we used high throughput screening techniques. Early events in metastasis were investigated by differential proteomic analysis of lymph node-negative and positive breast cancer samples. Two candidate biomarkers (DCN and HSP90B1) were discovered and further validated through tissue microarray analyses. To examine late events in metastasis formation, we prospectively evaluated genomic differences between disseminated tumour cells in bone marrow, and metastatic tumour cells obtained from computed tomography guided biopsies of bone metastases. Results indicate that specific subsets of genes are required for breast cancer cells to initiate bone metastases. Discovery of proteomic and genomic alterations specifically associated with metastases may yield biomarkers capable of stratifying patients into different risk categories. Proteins and genes identified in this work may form the foundation of a biomarker panel for metastatic risk assessment.

Over the past two decades, high-throughput (HTP) technologies such as microarrays and mass spectrometry have fundamentally changed the landscape of aging and disease biology. They have revealed novel molecular markers of aging, disease state, and drug response. Some have been translated into the clinic as tools for early disease diagnosis, prognosis, and individualized treatment and response monitoring. Despite these successes, many challenges remain: HTP platforms are often noisy and suffer from false positives and false negatives; optimal analysis and successful validation require complex workflows; and the underlying biology of aging and disease is heterogeneous and complex. Methods from integrative computational biology can help diminish these challenges by creating new analytical methods and software tools that leverage the large and diverse quantity of publicly available HTP data. In this thesis I report on four projects that develop and apply strategies from integrative computational biology to identify improved biomarkers and therapeutics for aging and disease. In Chapter 2, I proposed a new network analysis method to identify gene expression biomarkers of aging, and applied it to study the pathway-level effects of aging and infer the functions of poorly-characterized longevity genes. In Chapter 4, I adapted gene-level HTP chemogenomic data to study drug response at the systems level; I connected drugs to pathways, phenotypes and networks, and built the NetwoRx web portal to make these data publicly available. And in Chapters 3 and 5, I developed a novel meta-analysis pipeline to identify new drugs that mimic the beneficial gene expression changes seen with calorie restriction (Chapter 3), or that reverse the pathological gene changes associated with lung cancer (Chapter 5). The projects described in this thesis will help provide a systems-level understanding of the causes and consequences of aging and disease, as well as new tools for diagnosis (biomarkers) and treatment (therapeutics).