The main research directions in the group are Tumor Evolution in HGSC, Multi-modal data analysis and integration and Chemoresistant HGSC patients.
Tumors are not static but evolve before and during therapy. Evolution is one of the most challenging aspects of cancer biology, underpinning the emergence of drug resistance and disease progression. We use the multi-modal data collected in the DECIDER trial to establish genetic and non-genetic tumor evolution models in HGSC before and during therapy. The objective of this research direction is to stratify patient groups that are characterized by molecular or phenotypic level biomarker and identify effective therapy combinations for these groups. We mostly use DNA/RNA/DNA methylation data supplemented by circulating tumor DNA and histopathological images.
Example publications of Tumor Evolution in HGSC: Lahtinen et al. Evolutionary states and trajectories characterized by distinct pathways stratify patients with ovarian high grade serous carcinoma. Cancer Cell, 2023. Oikkonen et al. Prospective longitudinal ctDNA workflow reveals clinically actionable alterations in ovarian cancer. JCO Precision Oncology, 2019
Analysis of data from cancer patients is hindered by various biases, such as tumor cell content of a sample. We develop computational methods to obtain trustworthy data from patient samples. We collect data from various levels, including histopathology, radiology and molecular (DNA,RNA, epigenetics). Accordingly, we establish and use data analysis pipelines for each data layer that are integrated to obtain full-picture of the chemoresistant mechanisms.
Example publications of Multi-model data analysis and integration: Jamalzadeh et al. QuantISH: RNA in situ hybridization image analysis framework for quantifying cell type-specific target RNA expression and variability. Laboratory Investigation, 2022. Häkkinen et al. PRISM: recovering cell-type-specific expression profiles from individual composite RNA-seq samples. Bioinformatics. 2021
Most patients with HGSC respond well to first-line therapy (surgery & platinum-paclitaxel + possible maintenance therapy). However, fraction of patients have tumors that are not affected by chemotherapy. We integrate multi-modal data and take tumor microenvironment into account to discover causes for extremely poor response.
Example publications of Chemoresistant patients: Zhang et al. Longitudinal single-cell RNA-seq analysis reveals stress-promoted chemoresistance in metastatic ovarian cancer. Science Advances, 2022. Kozlowska et al. Mathematical modeling predicts response to chemotherapy and drug combinations in ovarian cancer. Cancer Research, 2018.