Browsing by study line "Systems Biology and Medicine"

Ovarian cancer is the most lethal gynecological cancer and high-grade serous ovarian cancer (HGSOC) is the most common type of it. HGSOC is often diagnosed in advanced stages and most patients will relapse after optimal first-line treatment. One reason for the lack of successful treatment in HGSOC is high tumor heterogeneity including differences across the tumors in distinct patients, and even within each tumor. This heterogeneity is the result of genetic and non genetic factors. Phenotypical variabilty exists also within cancer cells that have the same genetic background. This is due to the fact that a cell can exist in more than one stable state where its genome is in a specific configuration and it expresses certain genes. Diverse cell states and transitions between them initially offer a path for tumor development, and later enable essential tumor behavior, such as metastasis and survival in variable environmental pressures, such as those posed by anti-cancer therapies. Generally, phenotypic heterogeneity is acquired from the cell of origin for a tumor. This thesis studies cell states in HGSOC cancer cells and their normal counterparts, fallopian tube epithelial cells. Exploration of cell states is based on gene expression data of individual cells. Gene expression data was analyzed with state-of-the-art tools and computational methods. Gene modules representing cell states were constructed using genes found in differential gene expression analysis of cancer cells, normal cells and tumor microenvironment. Differentially expressed gene (DEG) groups of cancer, normal FTE and shared epithelial genes were grouped separately into gene modules based on gene-gene associations and community detection. Potential dynamical relationships between cell states were addressed by pseudo-temporal ordering using RNA velocity modeling approach. We are able to capture biologically meaningful cell states which are relevant in the development of HGSOC with chosen research strategy. Found cell states represent processes such as epithelial-mesenchymal transition, inflammation and stress response which are known to have a role in cancer development. The transition patterns showed consistent tendencies across the samples, and the trajectories for normal samples presented more directionality than those of cancer specimens. The results indicate existence of shared epithelial states which stay in fixed positions in the developmental trajectory of normal and cancer cells. For example, both epithelial stem cells and stem-like cancer cells seem to utilize oxidative phosphorylation (OXPHOS) for their metabolic needs. On the other hand, cell states that are more terminal showed higher activities of tumor necrosis factor alpha and Wnt/beta-catenin pathways that were both mutually exclusive with OXPHOS. Overall, this thesis presents a novel approach to study cell states the characterization of which is essential in understanding tumorigenesis and cancer cell plasticity.

One of the biggest hurdles in cancer patient care is the lack of response to treatment. With the support of high-throughput drug screening, it is nowadays feasible to conduct vast amounts of drug sensitivity assays, aiding in the identification of sensitive and resistant samples to chemical perturbations. In an oncology setting, drug screening is the process by which patient cells are examined experimentally for response and activity to distinct drugs and analysed via dose-response curve fitting. However, the ability to reproduce and replicate with high confidence drug screening outcomes proved to be a challenge that needs to be addressed. Inefficient experimental designs, lack of standard protocols to control both biological and technical factors in such cell-based assays are at the core of a steep influx of experimental biases. Hence, additional endeavour has to be carried out to provide less biased estimations of drug effects. This thesis work focuses on reducing erroneous inferences (i.e., bias) from dose-response data in the curve fitting step, thereby improving the reproducibility of drug sensitivity screening through efficient dose selection. A novel two-step experimental design is introduced which significantly improves the estimation of dose-response curves while keeping the amount of cellular and chemical materials feasible.