Role of CIP2A in carcinogenesis

Worldwide and notably in the developed countries, cancer is an increasing cause of morbidity and mortality, being the second most common cause of death after ischemic heart disease. Now and in the future new cancer cases need to be diagnosed earlier. Prognostic factors may be helpful in recognizing and handling those patients who need more aggressive therapy, and it is also desirable to predict treatment response accurately. Cancerous inhibitor of protein phosphatase 2A (CIP2A) is an oncoprotein predominantly expressed in malignant tissues and inhibiting protein phosphatase 2A (PP2A) activity; it is a promising target for cancer therapy. The aim of this thesis was to evaluate the prognostic role of CIP2A in solid cancers, and for this purpose to explore expression of CIP2A, and investigating regulation of CIP2A in order to gain insight into signalling pathways leading to alteration in prognosis. Patients diagnosed with gastric, serous ovarian, tongue, or colorectal cancer at Helsinki University Central Hospital were included. Tumour tissue microarrays assembled from specimens from these patients were prepared and stained immunohistochemically for CIP2A protein expression. Associations with clinicopathologic parameters and other biomarkers were explored, and survival analyses were done according to the Kaplan-Meier method. Study of the role of CIP2A in intracellular signalling in vitro involved gastric, ovarian, and tongue cancer cell lines. We found CIP2A to be highly expressed in gastric, ovarian, tongue, and colorectal cancer specimens. CIP2A was associated with clinicopathologic parameters characterizing an aggressive disease, namely advanced stage, high grade, p53 immunopositivity, and high proliferation index. CIP2A led to recognition of gastric, ovarian, and tongue cancer patients with poor prognosis, however, with a cancer type-specific cut-off level for prognostic significance. In tongue cancer, it served as an independent prognostic marker. In contrast, in colorectal cancer, CIP2A provided no prognostic value. In cancer cell lines, CIP2A was highly expressed at both protein and mRNA levels, and promoted cell proliferation and anchorage-independent growth. In gastric cancer, we demonstrated with a MYCER construct in mouse embryo fibroblasts that activation of MYC led to increased CIP2A mRNA expression, and hence we suggested that a positive feedback mechanism between CIP2A and MYC may potentiate and prolong the oncogenic activity of these proteins. We demonstrated in ovarian cancer an association between CIP2A and EGFR protein overexpression and EGFR gene amplification. In ovarian and tongue cancer cells we showed that depletion of EGFR downregulates CIP2A expression. In conclusion, high CIP2A expression occurred frequently among patients with aggressive disease. CIP2A may serve as a prognostic marker in gastric, ovarian, and tongue cancer and thus may help in tailoring therapy for cancer patients. The positive feedback mechanism between CIP2A and MYC, as well as the positive regulation of CIP2A by EGFR, are a few signalling pathways regulating and regulated by CIP2A. These and other mechanisms need to be studied further, however. CIP2A is a potential target for therapy, and its potential role as predictive marker and as a tumour marker in serum requires exploration.

Periglasiaalisten ilmiöiden alueellinen mallintaminen subarktisessa ympäristössä

Periglacial processes act on cold, non-glacial regions where the landscape deveploment is mainly controlled by frost activity. Circa 25 percent of Earth's surface can be considered as periglacial. Geographical Information System combined with advanced statistical modeling methods, provides an efficient tool and new theoretical perspective for study of cold environments. The aim of this study was to: 1) model and predict the abundance of periglacial phenomena in subarctic environment with statistical modeling, 2) investigate the most import factors affecting the occurence of these phenomena with hierarchical partitioning, 3) compare two widely used statistical modeling methods: Generalized Linear Models and Generalized Additive Models, 4) study modeling resolution's effect on prediction and 5) study how spatially continous prediction can be obtained from point data. The observational data of this study consist of 369 points that were collected during the summers of 2009 and 2010 at the study area in Kilpisjärvi northern Lapland. The periglacial phenomena of interest were cryoturbations, slope processes, weathering, deflation, nivation and fluvial processes. The features were modeled using Generalized Linear Models (GLM) and Generalized Additive Models (GAM) based on Poisson-errors. The abundance of periglacial features were predicted based on these models to a spatial grid with a resolution of one hectare. The most important environmental factors were examined with hierarchical partitioning. The effect of modeling resolution was investigated with in a small independent study area with a spatial resolution of 0,01 hectare. The models explained 45-70 % of the occurence of periglacial phenomena. When spatial variables were added to the models the amount of explained deviance was considerably higher, which signalled a geographical trend structure. The ability of the models to predict periglacial phenomena were assessed with independent evaluation data. Spearman's correlation varied 0,258 - 0,754 between the observed and predicted values. Based on explained deviance, and the results of hierarchical partitioning, the most important environmental variables were mean altitude, vegetation and mean slope angle. The effect of modeling resolution was clear, too coarse resolution caused a loss of information, while finer resolution brought out more localized variation. The models ability to explain and predict periglacial phenomena in the study area were mostly good and moderate respectively. Differences between modeling methods were small, although the explained deviance was higher with GLM-models than GAMs. In turn, GAMs produced more realistic spatial predictions. The single most important environmental variable controlling the occurence of periglacial phenomena was mean altitude, which had strong correlations with many other explanatory variables. The ongoing global warming will have great impact especially in cold environments on high latitudes, and for this reason, an important research topic in the near future will be the response of periglacial environments to a warming climate.