Naar een gepersonaliseerde anti-darmkankertherapie door het targeten van de kankercelstofwisseling heterogeniteit

The promise of personalized medicine in cancer is to tailor-treat individual patients based on their predicted drug response (precision medicine). Even though several high-throughput technologies make it possible to perform deep molecular characterization of the tumour, it remains a challenge to confidently predict treatment responses. Recent methodological advances in colorectal cancer (CRC, the 3rd most common cancer worldwide) have made it possible to culture individual patientderived stem cells, both from tumors and adjacent healthy tissue, and to have them form threedimensional structures termed organoids. High-throughput drug screening and sensitivity analyses in these organoids could be an ideal means to predict patient responses. I will perform a deep metabolomic and transcriptomic analysis of control and tumour organoids on a patient-specific basis in order to establish an in silico model of their metabolism, given that metabolism as a target in personalized medicine for CRC is unexplored. I will then use in silico modeling to predict which metabolic genes slow tumour organoid growth but do not affect the normal organoids. Unlike tumour tissue, organoids are amenable to functional validation assays. I will validate my in silico predictions by silencing (combinations) of target metabolic enzymes and determining organoid proliferation and viability. As such, I will provide proof-of-concept for metabolic targets as novel personalized treatment strategies in CRC.