Welcome!
We are computational research group in the Institute for Computational Cancer Biology Cologne (ICCB) at the University of Cologne and the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at the TU Berlin.
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:
- 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
- Interpretation of genetic variation, where we leverage machine learning and statistical genetics approaches on large cohorts to understand cancer gene regulation and epigenetics
- Early detection and prevention, where we combine results from (i) and (ii) to enable population-based screenings and patient risk stratifications.
Our work bridges theoretical and applied biomedical research and we develop, train, and validate our methods on large clinical datasets. We are therefore part of the Pan-Cancer Analysis of Whole Genomes (PCAWG), ICGC-ARGO, the TRACERx Consortium and the Human Cell Atlas, in which our methods are widely applied.
Locations
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.
Research
Key publications
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. bioRxiv, (2021).
▶ MEDICC is the leading method for inferring cancer evolution from somatic copy-number alterations. It identifies individual evolutionary events and detects whole-genome doubling. Published soon(TM).
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
People
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?"
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.
Quote: "First, we remove all the R dependencies..."
Clinician Scientist
Dr. Daniel Schütte
Medical doctor and computer scientist. Improving patient care through early detection and better stratification.
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..."
PhD Student
Victoria M. Dombrowe
Knower of Molecular Medicine. Now mapping the epigenome and its evolution. Master of Events.
MD Student
Felix Schifferdecker
Medical student and computer scientist. Simulating cancer evolution and structural alterations.
Visiting Scientist
Dr. Tom Watkins
Physician by training. Chromosomal instability and somatic copy-number alterations.
From the Swanton lab, Francis Crick Institute.
Lab mascot
Quark
Fierce winged unicorn. Destroyer of bugs. Defender of students. Do not mess with Quark.
Quote: "No, I'm not a pegacorn."
Network
Consortium
CRUK - TRACERx
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.
Consortium
ICGC - ARGO
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.
