PI: Ariane Aigelsreiter
The research team investigates histological and molecular changes in liver and pancreatic tumors, particularly hepatocellular carcinoma (HCC) and pancreatic ductal adenocarcinoma. The goal is to gain a deeper understanding of tumor biology by combining classical histopathology with modern molecular biology techniques, in order to improve prognostic assessments and develop new therapeutic strategies.
A key focus is the analysis of histomorphological alterations and their correlation with molecular pathology data. Technologies such as Next Generation Sequencing (NGS), spatial in situ hybridization and proteomic analysis are used to precisely map genetic alterations within the tissue architecture. This approach aims to identify new clinically relevant subgroups that correlate with disease progression and treatment response.
One strategy is the development of digitally annotated tumor cohorts for the application of artificial intelligence (AI). AI-based algorithms are designed to automatically detect morphological features and link them with molecular data. The aim is to improve diagnostic accuracy and to create prognostic models that support personalized therapy decisions.
In addition, the team is establishing tumor-specific organoid and cell culture models from patient-derived tumor tissue obtained during intraoperative frozen section procedures. These 3D culture systems reproduce tumor heterogeneity and histological subtypes in vitro. Organoids and cell models from HCC and pancreatic carcinomas are analyzed in terms of morphology, differentiation, and molecular characteristics, and serve as preclinical platforms for testing novel therapeutic approaches. Genetic, epigenetic and proteomic alterations are thoroughly examined to understand their impact on morphology and treatment response. The ultimate goal is to develop and validate personalized therapies based on organoid and cell culture models.
The research team bridges classical pathology and modern molecular oncology. Through digital analysis, AI-assisted morphological evaluation, and functional modeling, it aims to build a new understanding of the tumor biology of liver and pancreatic cancers—ultimately improving diagnosis, prognostic assessments, and therapeutic decisions.