Fibroblast Growth Factor 8 and its Receptors in The Growth and Angiogenesis of Experimental Breast Cancer . With Special Reference to Thrombospondin-1 as a Novel Target Gene for FGF-8

The growth of breast cancer is regulated by hormones and growth factors. Recently, aberrant fibroblast growth factor (FGF) signalling has been strongly implicated in promoting the progression of breast cancer and is thought to have a role in the development of endocrine resistant disease. FGFs mediate their auto- and paracrine signals through binding to FGF receptors 1-4 (FGFR1-4) and their isoforms. Specific targets of FGFs in breast cancer cells and the differential role of FGFRs, however, are poorly described. FGF-8 is expressed at elevated levels in breast cancer, and it has been shown to act as an angiogenic, growth promoting factor in experimental models of breast cancer. Furthermore, it plays an important role in mediating androgen effects in prostate cancer and in some breast cancer cell lines. We aimed to study testosterone (Te) and FGF-8 regulated genes in Shionogi 115 (S115) breast cancer cells, characterise FGF-8 activated intracellular signalling pathways and clarify the role of FGFR1, -2 and -3 in these cells. Thrombospondin-1 (TSP-1), an endogenous inhibitor of angiogenesis, was recognised as a Te and FGF-8 regulated gene. Te repression of TSP-1 was androgen receptor (AR)-dependent. It required de novo protein synthesis, but it was independent of FGF-8 expression. FGF-8, in turn, downregulated TSP-1 transcription by activating the ERK and PI3K pathways, and the effect could be reversed by specific kinase inhibitors. Differential FGFR1-3 action was studied by silencing each receptor by shRNA expression in S115 cells. FGFR1 expression was a prerequisite for the growth of S115 tumours, whereas FGFR2 expression alone was not able to promote tumour growth. High FGFR1 expression led to a growth advantage that was associated with strong ERK activation, increased angiogenesis and reduced apoptosis, and all of these effects could be reversed by an FGFR inhibitor. Taken together, the results of this thesis show that FGF-8 and FGFRs contribute strongly to the regulation of the growth and angiogenesis of experimental breast cancer and support the evidence for FGF-FGFR signalling as one of the major players in breast cancers.

Algorithmic Techniques in Gene Expression Processing. From Imputation to Visualization

The amount of biological data has grown exponentially in recent decades. Modern biotechnologies, such as microarrays and next-generation sequencing, are capable to produce massive amounts of biomedical data in a single experiment. As the amount of the data is rapidly growing there is an urgent need for reliable computational methods for analyzing and visualizing it. This thesis addresses this need by studying how to efficiently and reliably analyze and visualize high-dimensional data, especially that obtained from gene expression microarray experiments. First, we will study the ways to improve the quality of microarray data by replacing (imputing) the missing data entries with the estimated values for these entries. Missing value imputation is a method which is commonly used to make the original incomplete data complete, thus making it easier to be analyzed with statistical and computational methods. Our novel approach was to use curated external biological information as a guide for the missing value imputation. Secondly, we studied the effect of missing value imputation on the downstream data analysis methods like clustering. We compared multiple recent imputation algorithms against 8 publicly available microarray data sets. It was observed that the missing value imputation indeed is a rational way to improve the quality of biological data. The research revealed differences between the clustering results obtained with different imputation methods. On most data sets, the simple and fast k-NN imputation was good enough, but there were also needs for more advanced imputation methods, such as Bayesian Principal Component Algorithm (BPCA). Finally, we studied the visualization of biological network data. Biological interaction networks are examples of the outcome of multiple biological experiments such as using the gene microarray techniques. Such networks are typically very large and highly connected, thus there is a need for fast algorithms for producing visually pleasant layouts. A computationally efficient way to produce layouts of large biological interaction networks was developed. The algorithm uses multilevel optimization within the regular force directed graph layout algorithm.

La Suisse : carte itinéraire projeté sur les messures trigonometriques les plus nouvelles et d'après les meilleurs sources avec le plan des villes de Berne, Bâle, Zurich, Genéve, Lausanne et la Chaux de Fonds = Die Schweiz : besonders für die Reisende nach den neuesten trigonometrischen Vermessungen u. den besten Quellen entworfen mit den Plänen von Bern, Basel, Zürich, Genf, Lausanne, la Chaux de Fonds

Kartta kuuluu A. E. Nordenskiöldin kokoelmaan