On June 7, 2023 the Tissue Engineering for Drug Delivery platform is organizing a university visit to get an inside look into the UZH's labs and expertise.
The University of Zurich, Department of Pathology and Molecular Pathology supports clinicians to better guide the individual cancer therapy for patients. Discover the activities of Dr. Chantal Pauli and her research group.
10:15 - 10:45 Registration & Coffee
10:45 – 11:00 Welcome by Chantal Pauli and Markus Rimann
11:15 – 11:45 Talk 2: Science Potpourri – Laboratory for Systems Pathology and Functional Tumor Pathology
11:45 – 12:00 Group photo
12:00 - 13:15 Lunch
13:15 – 14:30 Lab Tour (two-three groups of 10 - 15 minutes)
14:30 – 14:45 Closing words, Chantal & Markus
Research Group Chantal Pauli
Our Clinical Activities
Our main focus is the development of high-fidelity patient-derived ex-vivo cancer models for functional precision oncology. With such an approach, we can complement static features by generating dynamic data that may encompass key vulnerabilities, including those conveyed by altered signalling pathways due to, for example, epigenetic changes not necessarily driven by distinct genomic aberrations.
Our functional precision oncology platforms integrate functional testing with comprehensive genomics, transcriptomics and clinical data in order to find druggable targets, redefine the standard of patient care, and improve patient outcomes.
Our Research Activities
Our research activities encompass medium to high throughput drug screening and CRISPR/Cas9 technologies on ex-vivo patient derived cancer models for the discovery and identification of novel drug targets an drug vulnerabilities in especially rare cancers such as soft tissue sarcomas and cancers that are difficult to treat (e.g. pancreatic cancer). We study the environmental influences on drug responses using different hydrogels. With the establishment of resistant patient-derived ex-vivo cancer cell models, we obtain insight into the mechanism of acquired drug resistance. Together with our collaborators, we further pursue projects using cutting-edge machine learning algorithms to better predict drug responses in patient-derived ex-vivo cancer models.