Predictive models of therapy response in pancreatic neuroendocrine tumors
Pancreatic neuroendocrine tumors (PanNETs) are rare neoplasms of the pancreas and have a mortality rate of 60%. Systemic therapies form the mainstay of treatment, but no molecular markers are currently established that predict an individual patient’s response to any given therapy. Different regimens are thus often administered sequentially until objective response is achieved, placing considerable burden on patients and diminishing therapeutic windows. The clinical partners in this consortium have access to large clinically and molecularly well-defined cohorts of PanNET patients, including samples and data from the most comprehensive molecular analysis of PanNET tumors to date conducted within the International Cancer Genome Consortium (ICGC). Complementary to this, the participating computational biology groups contribute expertise and computational tools for statistical analysis of high-throughput biological data, integration of heterogeneous biological data, and network modeling. Creating unique synergies, this collaborative project will perform systems biology-based analyses of the PanNET data as well as additional publicly available data sets in order to i) identify novel marker signatures in PanNET samples predicting individual patient response to approved therapies; ii) develop new computational methods and construct models of regulatory networks in PanNET to identify novel opportunities for therapeutic intervention with substances used in treatment of other cancers; and iii) validate predictions derived from these analyses in in vitro and in vivo models of PanNET. We expect this project to i) Implement precision biomarker models for a better therapeutic stratification of PanNET patients, ii) improve clinical decision making and medical treatment of PanNET patients, iii) improve our knowledge of fundamental biological mechanisms active in PanNET and iv) generate significant methodological advancements in the field of precision medicine and systems biology.
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This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 964264.