Our approach is to use systems biology, i.e., integration of large and complex molecular & clinical data (big data) from cancer patients with computational methods and wet lab experiments, to identify efficient patient-specific therapeutic targets.
We are particularly interested in developing and applying machine learning based methods that enable integration of various types of molecular data (DNA, RNA, proteomics, etc.) to clinical information. All our research is done in a cross-disciplinary and collaborative setting with oncologists, pathologists, biochemists and geneticians.
More than half of the patients with high-grade serous ovarian cancer (HGSOC), the most common ovarian cancer subtype, die within five years after diagnosis. An HGSOC tumour is comprised of genetically distinct and unstable cell subpopulations developed via dynamic evolutionary processes throughout the disease course and treatment periods. Some of the subpopulations have mutations that render them resistant to the current treatments. The major aim of this project is to characterize treatment resistant subpopulations and to identify effective means to overcome the resistance mechanisms.
We use sequencing technology to obtain high-resolution and high quality data on DNA, RNA and DNA methylation events. We also use single-cell technologies, such as single-cell RNA-seq and mass cytometry, to account for heterogeneity within a tumor.
Tumor cells, especially dying ones, leak their DNA to the blood stream. With very sensitive sequencing technology it is possible to extract circulating tumor DNA (ctDNA) and measure genetic variations during patient follow-up. In this project we have established liquid biopsy assays to facilitate patient treatment response prediction and monitoring. Our ultimate goal is to faciliate therapy decisions based on genetic variation observed in the cancer patients. Our focus is to develop sensitive and reliable data analysis tools to identify genetic variants, and visualization tools to enable interpretation of the results.
This project is supported by the Academy of Finland key project funding scheme.