Institute for Computational Cancer Biology | Groups


Cancer genomics and evolution

Our lab develops and applies algorithms and computational methods to understand how cellular and intra-tumour heterogeneity (ITH) arises and how it affects tissue and patient phenotypes in space and time. We are particularly interested in chromosomal instability (CIN) and somatic copy-number alterations (SCNA), a key characteristic that separates cancerous from healthy somatic tissue. In our methods we leverage statistical and machine learning approaches as well as classical computer science algorithms and simulations and develop these models in close collaboration with our experimental partners.

Specifically, we are active in three research areas:

  1. Structural evolution of cancer genomes, where we conduct retrospective studies to infer cancer evolution from clinical patient samples and forward simulations that enable us to investigate cancer growth and evolution in-silico
  2. Interpretation of genetic variation, where we leverage machine learning and statistical genetics approaches on large cohorts to understand cancer gene regulation and epigenetics
  3. Early detection and prevention, where we work on population-based molecular biomarkers and patient risk stratification.

Our work bridges theoretical and applied biomedical research and we develop, train, and validate our methods on large clinical datasets as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG), ICGC-ARGO, and the TRACERx Consortium as well as several smaller consortia.


Our main location is at the University Hospital Cologne in the vibrant, international city of Cologne, Germany.

We are also maintaining a branch at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) in the German capital of Berlin.

Keep an eye on our recruitment page for job openings at either location.

join us!

February 24, 2023

SMITH paper published in Bioinformatics

We are thrilled to anounce the release of the SMITH software toolkit for simulating cancer evolution with spatial constraints and the accompanying paper in Bioinformatics.

SMITH is a novel tool and algorithm for simulating cancer evolution including random mutations with varying fitness effects. SMITH is based on the classic branching model of cancer which it extends with local and global "confinement", new mechanisms which simulates spatial constraints by limiting clonal growth based on the size of clones and the overall tumour. 

SMITH is implemented in C# and available on Bitbucket at

It's accompanying visualisation package pyFish is implemented in Python and available at

Read more

January 31, 2023

Stella de Biase successfully defends her PhD

With flying colors Stella de Biase defended her PhD on January 10, 2023 at the Berlin Institute for Molecular Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association.

Her PhD thesis is titled "Exploring the contribution of genetic and environmental factors to cancer risk and development" and she will receive her PhD from the Humboldt University Berlin.

After her Masters degree in Medical Biotechnologies at the Federico II University of Naples, Italy, Stella joined our lab in January 2017 on a MDC NYU PhD Fellowship. Some highlights of her work include the early detection of lung cancer, the effect of SSR mutations on growth rates in yeast, and how smoking affects the expression of SARS-CoV-2 entry genes.

She is soon moving to San Diego as a postdoctoral fellow in the Carter Lab at UCSD! Go Stella! Thanks for all your hard work and kind words. You rock!

November 14, 2022

MEDICC2 manuscript published in Genome Biology!

Eight years after its intial release we are more than proud to announce that the MEDICC2 algorithm and paper for inferring cancer evolution from somatic copy-number alterations is now published online at Genome Biology.

MEDICC2 is the leading algorithm for phylogenetic reconstruction from and quantification of chromosomal instability. It is based on an efficient finite-state transducer framework which, together with integrated parallelisations, allows accurate phylogentic inference even for single-cell data with thousands of cells.

MEDICC2 not only outperforms the original implementation in speed by orders of magnitude, but also, as the first and only algorithm in the field, allows the accurate detection of clonal and subclonal whole-genome doubling events.

Read more



Key publications

Streck A, Kaufmann T, Schwarz RF. SMITH: Spatially Constrained Stochastic Model for Simulation of Intra-Tumour Heterogeneity Bioinformatics (2023).
▶ SMITH is a novel method for simulating cancer evolution to realistic tumour sizes of more than one billion cells that also models spatial constraints. Yay, we found an acronym! That was close.

Kaufmann T, Petkovic M, Watkins TBK, ..., Van Loo P, Haase K, Tarabichi M, Schwarz RF. MEDICC2: whole-genome doubling-aware copy number phylogenies for cancer evolution. Genome Biology (2022).
▶ MEDICC2 is the leading method for inferring cancer evolution from somatic copy-number alterations. It identifies individual evolutionary events and detects whole-genome doubling. Published only eight years after MEDICC! Second best acronym ever.

Markowski J, Kempfer R, ... , Kehr B, Pombo A, Rahmann S, Schwarz RF. GAMIBHEAR: whole-genome haplotype reconstruction from Genome Architecture Mapping data. Bioinformatics (2021).
▶ GAMIBHEAR is a novel algorithm for inferring chromosome-spanning haplotypes from Genome Architecture Mapping data. It provides the basis for accurate haplotype-specific chromatin contact maps in human. Best acronym ever.

PCAWG Transcriptome Core Group, Calabrese C, Davidson NR, Demircioğlu D, Fonseca NA, He Y, Kahles A, ...,
Brazma A*, Brooks A*, Göke J*, Rätsch G*, Schwarz RF*, Stegle O*, Zhang Z*. Genomic basis for RNA alterations in cancer. Nature (2020).
▶ In the PCAWG consortium we investigated the allele-specific effects of somatic mutations on gene expression as part of PCAWG Working Group 3. Another interesting acronym story.

Watkins TBK, Lim EL, Petkovic M, Elizalde S, Birkbak NJ, Wilson GA, Moore DA, ..., Schwarz RF*, McGranahan N*,
Swanton C*. Pervasive chromosomal instability and karyotype order in tumour evolution. Nature (2020).
▶ In this seminal paper we used the reference phasing algorithm we developed to detect parallel evolution across human cancers in the largest multi-region sequencing dataset to date. Go refphase!

Jamal-Hanjani M, Wilson GA, McGranahan N, Birkbak NJ, Watkins TBK, Veeriah S, Shafi S, ..., Schwarz RF, et al.
Tracking the Evolution of Non–Small-Cell Lung Cancer. N. Engl. J. Med., 376(22):2109–2121 (2017).
▶ In this work, we developed and contributed the reference phasing algorithm to the TRACERx consortium, which lead to the detection of mirrored subclonal allelic imbalance events (MSAI). #notmyacronym

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Meet the Team

Group Leader

Prof. Dr. Roland F. Schwarz

Computer scientist by training. Lover of formal grammars, Markov models and phylogenetic trees.

Quote: "What if it's a Markov chain?"

PhD Student

Maja-Celine Stöber

Biomathematician. Fond of single-cell data analysis and extrachromosomal DNA. Master of Journal Club.

Quote: "No, Roland, you cannot skip Journal Club."

PhD Student

Tom L. Kaufmann

Physicist by training. Fan of deep learning and mutational processes shaping copy number. Developer of MEDICC2 and refphase. Master of Lab Meeting.

Quotes: "Oups.", "This is funny..."

Postdoc / Scientific Programmer

Dr. Adam Streck

Computer scientist by training. Modelling the world through cellular automata and stochastic processes. Gamer at heart, living the VR hype. Author of SMITH. Master of Technology.

Clinician Scientist

Dr. Daniel Schütte

Medical doctor and computer scientist. Improving patient care through early detection and better stratification.

MD Student

Felix Schifferdecker

Medical student and computer scientist. Simulating cancer evolution and structural alterations.


MD Student

Selina Wächter

Medical student. Investigating how selectional constraints shape cancer evolution.


Research Associate

Teodora Bucaciuc

Biologist and Bioinformatician by training. Interested in genomics and the co-evolution of genome and epigenome. Master of Events.


Dr. Cody Duncan

Physicist by training. Interested in simulation-building and stochastic processes. In-house Rugby League expert. Master of Technology Cologne.


Dr. Nathan Lee

Applied mathematician & computational biologist. Interested in cancer evolution, stochastic processes, and simulations of carcinogenesis.

PhD Student

Claudia Robens

Biotechnologist by training. Investigating chromosomal instability and chromatin architecture in cancer.

PhD Student

Katyayni Ganesan

Biologist by training. Interested in single-cell cancer evolution and transcriptomics.

Postdoc / Scientific Coordinator

Dr. Laura Godfrey

Biologist by training. Interested in epigenetics and -genomics.

Additionally, scientific coordination, third party funding, web editor.



We are part of the CRUK funded TRACERx consortium lead by Charles Swanton at the Francis Crick Institute in London where we contribute algorithms for phasing of copy-number alterations and phylogenetic tree inference.



In ICGC-ARGO we are part of the data coordination and management group and are leading a project that contributes pipelines for allele-specific expression analysis.