Welcome!
We are a computational research group at 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. We infer cancer evolution from clinical patient samples and develop forward simulations that enable us to investigate cancer growth and evolution in-silico. These advances will help us one day to predict cancer evolution and provide treatment suggestions that avoid resistance. #medicc #refphase #smith
- Haplotyping and allele-specific effects of genetic variation. We develop algorithms to reconstruct haplotypes from sequencing data and apply machine learning and statistical genetics approaches on large patient cohorts to understand cancer gene regulation and epigenetics. #gamibhear #refphase
- Cancer early detection and prevention. We develop machine learning methods to identify and exploit population-based molecular biomarkers for risk screening and stratification, as well as for prognosis of outcome and relapse. #mop-c
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.
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.
July 15, 2024
New Cologne offices at the TRIO building
At the beginning of April, the Schwarzlab moved to the ground floor of the new TRIO building on the campus of the University Hospital Cologne. The TRIO building provides modern offices and an inspiring atmosphere for research. We are looking forward to lots of new opportunities for collaboration with our colleagues from oncology and immunology! From now on, our new street and mail address for the ICCB is:
Institute for Computational Cancer Biology
Gebäude 66 (TRIO)
Robert-Koch-Straße 21
50931 Cologne
Germany
September 12, 2024
ecDNA paper published in Cell Reports
In this collaborative project with Anton Henssen and Kerstin Haase from the Charite Berlin we analyze the role of extrachromosomal DNA (ecDNA) copy number heterogeneity in neuroblastoma.
By using DNA and RNA sequencing from the same single cells in cell lines and neuroblastoma patients we compare intercellular ecDNA copy-number heterogeneity with that obtained from linear genomic amplifications. We show how ecDNA driven oncogene amplifications influence gene expression and the overall transcriptional cell state.
Our results highlight the importance of ecDNA copy-number alterations in neuroblastoma and emphasize the need to develop ecDNA-specific treatment strategies.
April 2, 2024
News & Views in Nature Genetics
Roland and Tom were invited to give their views on the Nature Genetics paper of Jin et al. presenting MuSiCal, a set of novel algorithms for improved identification of cancer mutational processes by mutational signatures.
November 9, 2023
MOP-C paper published in Blood
As part of a multidisciplinary team led by the Borchmann Lab the Schwarzlab implemented a publicly available shiny application for MOP-C (Molecular prognostic index for central nervous system lymphomas).
MOP-C provides risk assessment for central nervous system lymphomas based on clinical risk factors, radiographic response and peripheral residual disease measured by circulating tumor DNA (Heger et al. 2023, Blood).
By integrating these clinical and molecular features MOP-C was proven to be highly predictive of outcomes a CNSL cohort with a failure-free survival hazard ratio (HR) per risk group of 6.60.
Research
Publications
Key publications
De Biase MS, Massip F, ..., Ponder B*, Rintoul R*, Schwarz RF*. Smoking-associated gene expression alterations in nasal epithelium reveal immune impairment linked to lung cancer risk. Genome Medicine (2024).
▶ In this collaboration with the Royal Papworth Hospital, Cambridge, and Cancer Research UK we develop classifiers and risk scores for early detection of lung cancer from nasal swaps.
TBK Watkins, EC Colliver, MR Huska, TL Kaufmann, ..., McGranahan N, Schwarz RF. Refphase: Multi-sample phasing reveals haplotype-specific copy number heterogeneity. PLOS Computational Biology (2023).
▶ Refphase uses genomic regions of allelic imbalance across multiple samples from the same patient to phase germline variants and somatic copy-number alterations.
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. 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!
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?"
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."
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.
Postdoc
Dr. Cody Duncan
Physicist by training. Interested in simulation-building and stochastic processes. In-house Rugby League expert. Master of Technology Cologne.
Postdoc
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.
Scientific coordinator, third party funding, web editor.
Master Student
Giuseppe Barranco
Biologist and bioinformatician by training. Interested in single-cell genomics.
PhD Student
Chenxi Nie
Bioinformatician by training. Interested in stochastic processes, stochastic sampling and Formula 1.
Administrative Assistant
Stefanie Fleer
Lab coordination and administrative organisation
Master Student
Jeremiah Santoso
Computational scientist by training. Interested in computational methods for cancer treatment and detection.
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.
