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Artificial Intelligence in Oncology

Division of Artificial Intelligence in Oncology

Prof. Dr. Moritz Gerstung

Maps of distinct breast cancer clones detected by Base-Specific In Situ Sequencing (

The Gerstung lab investigates how tumours grow and change over time using AI and big data.

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.


Prof. Dr. Moritz Gerstung
Artificial Intelligence in Oncology (B450)
Deutsches Krebsforschungszentrum
Im Neuenheimer Feld 280
69120 Heidelberg

Selected Publications

  • Vöhringer, Harald S., Theo Sanderson, Matthew Sinnott, Nicola De Maio, Thuy Nguyen, Richard Goater, Frank Schwach, et al. 2021. “Genomic Reconstruction of the SARS-CoV-2 Epidemic in England.” Nature, October.
  • Killcoyne, Sarah, Eleanor Gregson, David C. Wedge, Dan J. Woodcock, Matthew D. Eldridge, Rachel de la Rue, Ahmad Miremadi, et al. 2020. “Genomic Copy Number Predicts Esophageal Cancer Years before Transformation.” Nature Medicine 26 (11): 1726–32.
  • Vöhringer, Harald, Arne Van Hoeck, Edwin Cuppen, and Moritz Gerstung. 2021. “Learning Mutational Signatures and Their Multidimensional Genomic Properties with TensorSignatures.” Nature Communications 12 (1): 3628.
  • Fu, Yu, Alexander W. Jung, Ramon Viñas Torne, Santiago Gonzalez, Harald Vöhringer, Artem Shmatko, Lucy R. Yates, Mercedes Jimenez-Linan, Luiza Moore, and Moritz Gerstung. 2020. “Pan-Cancer Computational Histopathology Reveals Mutations, Tumor Composition and Prognosis.” Nature Cancer 1 (8): 800–810.
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