Researcher Database
Prof. Dr. Roland Schmid
II. Medizinischen Klinik
Ismaninger Str. 22
81675 München
Program
Exploitation of Oncogenic Mechanisms (EOM)
Summary
Pancreatic cancer is considered as an almost incurable disease due to late diagnosis, rapid spread and negligible response to treatment. Therefore, our aim is to develop concepts to prevent pancreatic cancer development analogous to statins, which prevent coronary heart disease. Acinar-to-ductal metaplasia is the earliest step in pancreatic carcinogenesis, which we are characterizing in a panel of murine in vivo and murine/human in vitro systems. Metabolic requirements and common signaling events as potential vulnerabilities are analyzed using high throughput approaches. In addition, sc-RNA-sequencing is performed to generate a comprehensive atlas of cell populations in early neoplastic lesions to define dynamic cellular, signaling and metabolic changes at a single cell level. Understanding the role of ROS and its interconnection with metabolic rewiring is another aim of the lab. The overall goal is the development of strategies to prevent pancreatic cancer.
DKTK Junior Group Leader for Cancer Systems Biology
Single-cell approaches have not only revealed a wide variety of cell states, characterized by cells exhibiting striking differences in their transcriptional profile, but have also illuminated the mechanisms underlying state transitions in health and disease. Cellular plasticity and adaptive state changes have recently emerged as a basis for therapeutic resistance in cancer, and a better understanding of how cell state transitions are regulated is critical to develop therapeutic approaches that can overcome therapy resistance.
Our research focuses on understanding the mechanisms driving non-genetic cellular heterogeneity and therapy resistance in malignancy. Using novel single-cell sequencing approaches, we seek to develop new experimental and computational strategies to define altered cell states in both, cancer and immune cells. Our aim is to leverage a data driven strategy combined with single cell genomics and systems biology to address the challenges posed by heterogeneity in cancer, and to develop new strategies to overcome it, with the aim of translating laboratory-based findings into the clinic.