Academic Awards 2025 booklet

43 Understanding CXCL9 regulation in pancreatic cancer cell lines Pancreatic cancer is a deadly type of cancer with limited response to treatment. Even immunotherapy, which aims at reactivating the immune response, is often not effective because immune cells are commonly excluded from the tumor. The usage of targeted anticancer drugs, which can inhibit specific proteins in tumor cells, is a promising strategy to improve treatment responses. This therapy could not only directly kill tumor cells but also stimulate the immune system. Promoting chemokine expressions like CXCL9 can for instance attract immune cells to the tumor. However, underlying mechanisms of CXCL9 regulation remain unclear. This thesis presents a systematic workflow combining wet lab and computational modeling to investigate CXCL9 regulation. We treated two pancreatic cancer cell lines with various inhibitors (e.g., PI3K, MEK) and stimuli (IFNγ, TNFα), and the resulting data were used to build and personalize a dynamic mathematical model for each cell line. Model simulations had a good agreement with the experiments (Pearson correlation score of 0.98 for both cell lines). The model highlighted differences in response dynamics and predicted effects of new drug combinations to guide future experiments. Altogether, resulting insights can support development of more effective anti-cancer treatments for precision oncology. Figure 1: Thesisworkflow Overview of thedifferent stepsof the thesis, incorporatingpriorknowledge, wet laband computationalmodels Figure 2: Proteinnetwork CXCL9-specificproteinnetwork, curatedwithdifferentsignalingpathways. Thenetwork was used for selectionof perturbation targets (stimuli, inhibitors) andwas necessary for constraining themodel Figure 3: Comparison of cell lines Comparison of model parameters to highlight differences between cell lines AsPC1 and BxPC3. Interactions are colored by their corresponding signaling pathway and labeled if the difference between cell lines was significant. Figure 2: Protein network CXCL9-specific protein network, curated with different signaling pathways. The network was used for selection of perturbation targets (stimuli, inhibitors) and was necessary for constraining the model. Figure 1: Thesis workflow Overview of the different steps of the thesis, incorporating prior knowledge, wet lab and computational models. Figure 3: Comparison of cell lin s Comparison of model parameters to highlight differences between cell lines AsPC1 and BxPC3. Interactions are colored by their corresponding signaling pathway and labeled if the difference between cell lines was significant.