Vývoj ex-vivo buněčných modelů pro studium heterogenity adenokarcinomu pankreatu
| Title in English | Development of ex vivo cell models for investigation of heterogeneity of pancreatic adenocarcinoma |
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| Authors | |
| Year of publication | 2023 |
| Type | Conference abstract |
| MU Faculty or unit | |
| Citation | |
| Description | Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer and ranks 11th among cancers worldwide. The severity of the disease is underlined by the fact that the mortality curve almost follows the incidence curve. A major complication of pancreatic cancer is the enormous capacity for dissemination, the high resistance to treatment and the high clinical heterogeneity of patients. Despite advances in therapeutic options, long-term survival of patients is minor and does not exceed one percent. Therefore, we are looking for new individualized disease models and alternative molecular targets that may influence the efficacy of chemotherapy or contribute to the prediction of disease progression. One way to appropriately model and study cancer at the individual level is to isolate and expand tumor and tumor-associated cells from a tumor sample, and culture them under conditions that appropriately mimic the tumor microenvironment. We have now established over twenty patient cell lines in which we have analyzed the cellular response to stress associated with defects in protein synthesis and endoplasmic reticulum (ER) integrity. The molecular mechanism responding to ER stress in various cancers correlates significantly with clinical parameters, and our pilot data indicate a similar association in pancreatic cancer. At the same time, advanced bioanalytical methods, such as intact cell mass spectrometry, have been used to incorporate a large amount of complex information about the biological background of tumors, and mathematical analyses have been performed on large datasets to process these data. Moreover, the multidimensional datasets thus obtained contain specific patterns that can be processed and analyzed using machine learning and artificial intelligence. The data obtained so far confirm that cell lines of PDAC patients differ molecularly and biologically, and the bionanalytic (spectral) data contain sufficient information to distinguish between different pathological types of PDAC. The use of individual ex vivo cell models, understanding specific mechanisms of cellular stress response and identification of specific spectral patterns can help in predicting disease progression, but also possible resistance to selected therapies. |
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