Predictive models for biological aggressiveness of pancreatobiliary cancers by integrative molecular analysis of their precursor lesions

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Pancreatobiliary (PB) cancers are a group of biologically aggressive neoplasms with a poor prognosis. In recent years, molecular subgroups have been defined and work has begun to characterize the genomic and transcriptomic landscape of PB carcinomas. Due to the limited number of known predisposing factors, early detection seems to be the most effective approach to improve prognosis, whereas primary prevention measures show limited relevance. Therefore, a comprehensive characterization of the genetic landscape of PB precursors is of great importance in order to uncover their molecular evolution to cancer on the one hand and to identify predictors of biological behavior in the early stages of disease development on the other hand. In this project, our goal is to decipher the molecular basis of the heterogeneous biological behavior of pancreatic and biliary duct cancer by isolating their known and putative precursor lesions and characterizing them from their development to invasion. Using established and newly developed methods based on human tissue, pancreatic and bile duct cancer will be used as a model to develop a concept of tumorigenesis that will allow better early detection and targeted therapeutic approaches. For this purpose, clinically and morphologically well characterized human tissue collectives will be used to generate pure cell populations from different normal tissue compartments and precursor lesions (preinvasive lesions), which will then be subjected to genomic and transcriptomic characterization. The results obtained from precursor lesions from different patients will be compared with each other and as matched pairs with the accompanying invasive tumors and their lymph nodes and blood-borne metastases to identify risk profiles of cancer precursors that predict the biological behavior of a given neoplasm using a bioinformatics approach. Organoids from human tissue are used for genomediting and gene knock-down experiments to assess the functional significance of the predicted driver events. Finally, tissue and fluid based approaches are used to determine the value of identified relevant molecular markers in vivo in retrospective and prospective series.