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Cancer is an evolutionary process that typically spans multiple decades before it causes symptoms. The survival probability of patients with a tumor diagnosed early is five to ten times higher than when diagnosed at an advanced stage. The last stage of cancer progression, metastasis, is responsible for 90% of cancer-related deaths. In a tumor with billions of cells virtually any point mutation is expected to be present in a few cells. Hence, at a genetic level, not only is every cancer type different, but also every tumor of the same type and every cell of the same tumor are different. This enormous heterogeneity poses a major barrier to drug development and long-term disease control but also represents a unique opportunity to study the evolutionary principles that govern cancer initiation and progression.My research focuses on the stochastic biological processes underlying cancer evolution, in particular those related to the initiation, progression, spread, and treatment of cancer. The goal of this research is to accurately diagnose aggressive cancers as well as to provide new insights about metastatic spread and the development of resistance against therapies. I develop computational methods and design mathematical models to generate novel hypotheses and explain observations on a mechanistic level in close collaboration with many physician‐scientists.