Background - The evolution of drug resistance is a frequent cause for cancer treatment failure, impeding cure and prognosis of the patients. This particularly applies to pancreatic ductal adenocarcinoma (PDAC), which is mostly diagnosed at an advanced, often metastasized and hence not curable stage. Thus, even in patients undergoing successful tumor resection followed by adjuvant chemotherapy, the disease commonly progresses. However, current therapies neglect heterogeneity, evolution of tumor cell populations, and also their adaptation to chemotherapy. The concept of adaptive therapy, i.e. usage of lower dosages or intermittent phases without drugs, could be used to control tumor size, possibly improving patient health, and at the same time lowering the spread of resistance. The latter result is obtained when drug-resistant cells pay an evolutionary cost in terms of cell division rate and are then outcompeted by susceptible cells in tissues with low drug concentrations or in drug-free treatment phases. Thus, adaptive chemotherapy might help to counteract the evolution of drug resistance in PDAC cells, control tumor burden, and improve prognosis of PDAC patients. However, transferring these experimental results to patients is challenging and mathematical models can help to overcome this challenge. For example, mathematical models can yield insights into the dynamics of pre-existing and de novo resistance development. Mathematical modelling further allows to assess cancer evolution and therapeutic responses in an abstract, yet comprehensive form, thereby complementing experimental approaches.
- Understand how the concept of adaptive therapy can be used to reduce the evolution of chemoresistance as well as inhibit tumor growth and stem cell features of PDAC cells.
- Model and analyze PDAC evolution in different changing microenvironments in consideration of: Drugs with different modes of action, drug order and concentration, recovery time between drug application, and tumor microenvironment.
- Generate predictions of treatment outcome in an iterative form between the experimental and mathematical projects.
- Analyze the impact of different sequential and adaptive therapy regimens on PDAC cell growth and self-renewal capacity and thereby on evolution of subclones.
- Characterize long-term surviving PDAC cell clones after different treatment regimens. Surviving clones will be characterized at both phenotypic and genomic/transcriptomic level.
- Analyze the impact of the tumor microenvironment on drug response of PDAC cells to different sequential and adaptive therapy regimens.
- Validate predictions from mathematical modeling in sub-project Traulsen.
People working on this project
- Prof. Dr. Susanne Sebens
- MSc Lisa-Marie Philipp
PI's Homepage: https://www.iet.uni-kiel.de/de/arbeitsgruppen/ag-inflammat.-karzinogenese-ss
- Develop a model to describe the dynamics of a cell population under sequentially changing selective pressures and/or adaptive therapy.
- Model resistance evolution based on genetic mutations or phenotypic induction and assess how the arising differences of these are reflected in disease dynamics.
- Infer the theoretically optimal sequential and/or adaptive therapy given certain dynamical assumptions and parameters.
- Extend the model to assess the role of the tumor environment.
People working on this project
- Prof. Dr. Arne Traulsen
- MSc Saumi Shah
PI's Homepage: http://web.evolbio.mpg.de/~traulsen/#home