Mission and Goals
Data science and algorithms for cancer research
The Institute for Computational Cancer Biology (ICCB) at the University Hospital Cologne was founded in 2022 to advance cancer research through computational methods and to train and educate the next generation of computational cancer scientists.
We are part of the Cancer Research Center Cologne Essen (CCCE) funded by the Ministry of Culture and Science of the State of North Rhine-Westphalia.
Our mission is to develop bespoke statistical methods, machine learning approaches, algorithms and models to decipher tumour heterogeneity and cancer evolution and improve our understanding of the wealth of genomic, transcriptomic, epigenomic and imaging data collected in cancer research.
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|▶ Research spotlights||▶ Work with us|
October 25, 2022
Multiple job openings in the Schwarzlab
The Schwarzlab at the ICCB has multiple job openings for PhD students, Postdoctoral Fellows and a Scientific Programmer in multiple national and international projects working on chromosomal instability, copy-number evolution and algorithms for tumour heterogeneity.
September 20, 2022
The Lehmann Lab joins the ICCB
We are glad that the Lehmann Lab has joined the ICCB as an associated group.
The Lehmann Lab is interested in the development of approaches that support the molecular characterisation of patient cohorts and in approaches for data integration to gain insights into disease mechanisms. Welcome on board!
September 14, 2022
The ICCB website launches
We are happy to announce the launch of this new ICCB website. The fantastic team at frommo visuelle medien put this together in a record time including custom design and elements. We could not be more thrilled!
Thanks to everyone who contributed design, content and code!
August 2022 - Schwarz Lab
Inferring cancer evolution with whole-genome doublings
Inferring cancer evolution is highly valuable for understanding how tumours develop and progress. Somatic copy-number alterations are a great source of phylogenetic information but difficult to deal with computationally. So far, only few methods could deal with the complexity of copy-number alterations, and none were able to take whole-genome doubling events into account.
The Schwarz lab has developed MEDICC2, a new algorithm for inferring cancer evolution from somatic copy-number alterations from multi-region or single-cell sequencing. MEDICC2 infers the minimum number of copy-number events - gains and losses of arbitrary length as well as whole-genome doublings - to transform one genome into another. It infers the tree topology explaining the data with the smallest number of events, reconstructs ancestral genomes and extracts individual evolutionary events.
Schwarz Lab - Cologne/Berlin
Cancer Genomics and Evolution
Algorithms for inferring and simulating cancer evolution and for understanding tumour heterogeneity, with a special focus on chromosomal instability and somatic copy-number alterations.
Lehmann Lab - Aachen
Molecular Signatures and Data Integration
Approaches that support the molecular characterisation of patient cohorts and reveal disease mechanisms through integration of diverse types of molecular data.
Sponsors and Partners
University Hospital and University of Cologne
Cancer Research Center Cologne Essen (CCCE)
funded by the Ministry of Culture and Science of the State of North Rhine-Westphalia.
Berlin Institute for the Foundations of Learning and Data (BIFOLD)
funded by the German Ministry for Research and Education (BMBF).