Cancer develops according to the rules of evolution — mutation and selection. Yet little is known about the timing of this process, the mechanisms by which mutations cause cancer cells to grow and how best to predict and influence the future course of this process.
Understanding these processes requires knowledge of large amounts of information at various scales. A cell’s genome comprises 6 billion base pairs of DNA. Thousands of genetic and epigenetic alterations of the DNA lead to a series of molecular cellular changes in each single cell. Further, tumours are an ecosystem of billions of mutant and normal cells that interact with each other via cellular contacts and signaling molecules.
Charting and modeling this information therefore requires sophisticated machine learning and AI algorithms. The primary focus of the lab is developing such algorithms and carrying out large scale data analyses to understand cancer evolution. The lab is also committed to contribute to generating genomic and molecular data at single and spatial resolution that form the basis of our research.
Specific research projects of the lab involve the establishment of a new spatial genomics platform for data generation and analysis. This will be key for decoding cancer evolution and unlock the secrets of the cancer ecosystem. We are also investigating how our knowledge about the natural history and origins of cancer can be used for early detection and cancer prevention. Lastly, the lab investigates how mutations arise in single cells to understand the very first steps of cancer development.
