Typically in patients with ovarian cancer, parts of their tumors respond well to chemotherapy, whereas the cancer cell sub-populations that do not respond grow to lethal recurrences. Our study will use a new algorithm to find similarities between groups of cancer cells across patients' tumors in order to identify distinct types of chemoresistant sub-populations. We will predict drug responses for these sub-populations and test the drugs in patient derived organoid cultures and xenograft models. To address the arising ethical conflicts, we will study personalised medicine also from the perspective of human dignity. Our novel approach enables the design of effective personalised treatments of HGSOC to complement chemotherapy by targeting the specific chemoresistance mechanisms active in each patient's tumour cell subpopulations. The overall approach can be extended to other diseases where subpopulations provide treatment resistance, towards a paradigm shift in personalised oncology.

2021

2024