Evolution of Cancer

Populations of cancer cells are not static, but keep evolving over the different stages of the disease. This complex evolutionary dynamics is shaped by inherently stochastic contributions (such as mutations and reproductive fluctuations) as well as deterministic components (such as selection for faster growth). For this reason, statistical models are key to modelling and analyzing the evolution of tumours. Over the last decades, the sequencing of DNA from cancer biopsies has fundamentally changed our understanding of the evolution of cancer. Genomic data sampled across different patients and over time allows to address fundamental questions on how tumours develop, which genetic changes lead to rapid growth, or how a population of diverse tumour cells evolves under cancer therapy. Answering such questions on the basis of empirical data requires statistical models of cancer evolution, both to infer the past dynamics from current data and to derive predictions, for instance on the response to therapy. This seminar will give an overview over the current state of cancer evolution research and the statistical models used to understand cancer genomic data. It will include tutorials on statistical physics and inference, cancer evolution, and cancer genomics, as well as lectures on the current state of the field by international specialists. The seminar is for MSc and PhD students and young researchers in physics (in particular statistical physics and biophysics), population genetics, and evolutionary biology.

Topics: cancer evolution, cancer genomics, population dynamics modeling and inference, multi-region sequencing, targeted therapy