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.
Genetic and epigenetic alterations lead to changes in pathways and their effectors. We use multi-modal data to quantify the effect of these alterations to cancer cells' phenotypes and communication with tumor microenvironment. To obtain robust results, we have developed several bioinformatics methods to overcome noise, biases and obstacles in patient-derived data.
Most HGSC tumors respond well to first-line therapy but become resistant to therapy. Our research efforts aim at suggesting effective treatment modalities for patients with chemotherapy resistance disease. The approach is to first use multi-modal data and take tumor microenvironment into account to discover causes for extremely poor response and use preclinical models to test these hypotheses.