Thesis (Ph. D.)--University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 143-158). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph. D.)--University of Hong Kong, 2008. / Also available in print.

Thesis (M. Med. Sc.)--University of Hong Kong, 2007.

Thesis (Ph. D.)--University of Hong Kong, 2008. / Also available online.

Thesis (M. Phil.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

Thesis (Ph.D.) in Interdisciplinary--University of Maine, 2008. / Includes vita. Includes bibliographical references (leaves 95-114).

It is imperative that the cancer research community has the means with which to effectively locate, access, manage, analyse and interpret the plethora of data values being generated by novel technologies. This thesis addresses this unmet requirement by using pancreatic cancer and breast cancer as prototype malignancies to develop a generic integrative transcriptomic model. The analytical workflow was initially applied to publicly available pancreatic cancer data from multiple experimental types. The transcriptomic landscape of comparative groups was examined both in isolation and relative to each other. The main observations included (i) a clear separation of profiles based on experimental type, (ii) identification of three subgroups within normal tissue samples resected adjacent to pancreatic cancer, each showing disruptions to biofunctions previously associated with pancreatic cancer (iii) and that cell lines and xenograft models are not representative of changes occurring during pancreatic tumourigenesis. Previous studies examined transcriptomic profiles across 306 biological and experimental samples, including breast cancer. The plethora of clinical and survival data readily available for breast cancer, compared to the paucity of publicly available pancreatic cancer data, allowed for expansion of the pipeline’s infrastructure to include functionalities for cross-platform and survival analysis. Application of this enhanced pipeline to multiple cohorts of triple negative and basal-like breast cancers identified differential risk groups within these breast cancer subtypes. All of the main experimental findings of this thesis are being integrated with the Pancreatic Expression Database and the Breast Cancer Campaign Tissue Bank bioinformatics portal, which enhances the sharing capacity of this information and ensures its exposure to a wider audience.

Background: Matrix metalloproteinases (MMPs) have been shown to be involved in cancers and atherosclerosis, the leading causes of present day mortality. The objectives of the cancer element of this project were to investigate single nucleotide polymorphisms (SNPs) in MMP1 and MMP8 regarding breast cancer and malignant melanoma, and a functional characterisation of the genetic variants, including the MMP1 polymorphism rs19799750, previously associated with multiple cancers. The objective of the second part of this project was to investigate whether MMP8 played a role in the development of atherosclerotic lesions and if so, the underlying mechanisms. Methods/Results: Genetic investigations found the MMP8 SNP rs11225395 to be associated with the occurrence of both breast cancer and malignant melanoma; furthermore it was also associated with reduced lymph node metastasis, reduced cancer relapse and greater survival. Functional luciferase assays showed that the minor allele of the polymorphism has higher promoter activity in breast cancer and melanoma cell lines. They also showed haplotypic effects on MMP1 promoter activity in several cancer cell lines by the 2G allele of polymorphism rs1799750 and one or more MMP1 promoter SNPS. The second part of the study found an association between a MMP8 SNP and the extent of coronary atherosclerosis; additionally a relationship among MMP8 gene variation, plasma VCAM-1 level, and atherosclerosis progression was observed in a prospective study. Murine studies showed reduced atherosclerosis in MMP8/ApoE knockout mice compared with ApoE knockout littermate controls. Biochemical studies confirmed that MMP8 can convert angiotensin I to angiotensin II. Conclusions: The data of the first part of this project support the notion that genetic polymorphisms in the MMP1 and MMP8 influence the expression of these genes and the development and progression of cancer. The results of the second part of this project indicate an important role of MMP8 in the pathogenesis of atherosclerosis.

Thesis (M.A.)--Indiana University, 2010. / Title from screen (viewed on February 26, 2010). Department of Sociology, Indiana University-Purdue University Indianapolis (IUPUI). Advisor(s): Lynn Blinn-Pike, Carrie E. Foote, Betsy Fife. Includes vitae. Includes bibliographical references (leaves 100-105).

Prognostic models have clinical appeal to aid therapeutic decision making. In the UK, the Nottingham Prognostic Index (NPI) has been used, for over two decades, to inform patient management. However, it has been commented that NPI is not capable of identifying a subgroup of patients with a prognosis so good that adjuvant therapy with potential harmful side effects can be withheld safely. Tissue Microarray Analysis (TMA) now makes possible measurement of biological tissue microarray features of frozen biopsies from breast cancer tumours. These give an insight to the biology of tumour and hence could have the potential to enhance prognostic modelling. I therefore wished to investigate whether biomarkers can add value to clinical predictors to provide improved prognostic stratification in terms of Recurrence Free Survival (RFS). However, there are very many biomarkers that could be measured, they usually exhibit skewed distribution and missing values are common. The statistical issues raised are thus number of variables being tested, form of the association, imputation of missing data, and assessment of the stability and internal validity of the model. Therefore the specific aim of this study was to develop and to demonstrate performance of statistical modelling techniques that will be useful in circumstances where there is a surfeit of explanatory variables and missing data; in particular to achieve useful and parsimonious models while guarding against instability and overfitting. I also sought to identify a subgroup of patients with a prognosis so good that a decision can be made to avoid adjuvant therapy. I aimed to provide statistically robust answers to a set of clinical question and develop strategies to be used in such data sets that would be useful and acceptable to clinicians. A unique data set of 401 Estrogen Receptor positive (ER+) tamoxifen treated breast cancer patients with measurement for a large panel of biomarkers (72 in total) was available. Taking a statistical approach, I applied a multi-faceted screening process to select a limited set of potentially informative variables and to detect the appropriate form of the association, followed by multiple imputations of missing data and bootstrapping. In comparison with the NPI, the final joint model derived assigned patients into more appropriate risk groups (14% of recurred and 4% of non-recurred cases). The actuarial 7-year RFS rate for patients in the lowest risk quartile was 95% (95% C.I.: 89%, 100%). To evaluate an alternative approach, biological knowledge was incorporated into the process of model development. Model building began with the use of biological expertise to divide the variables into substantive biomarker sets on the basis of presumed role in the pathway to cancer progression. For each biomarker family, an informative and parsimonious index was generated by combining family variables, to be offered to the final model as intermediate predictor. In comparison with NPI, patients into more appropriate risk groups (21% of recurred and 11% of non-recurred patients). This model identified a low-risk group with 7-year RFS rate at 98% (95% C.I.: 96%, 100%).