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.
January 21, 2025
Oncogene paper on DNA methylation heterogeneity and epipolymorphism in kidney cancer
Recently, our insights in the (epi-)genetic heterogeneity of clear cell renal cell carcinoma were published in Oncogene. Together with our partners Sabrina Rossi and Charlie Massie from Cancer Research UK Cambridge Centre we characterized 136 multi-region tumors and normal tissue from 18 ccRCC patients.
Our study revealed a differential epipolymorphism between ccRCC and normal kidney tissue. In addition we found an impact of differential epipolymorphism in gene promoters being an independent predictor of associated gene expression.
Using the cell-type specific patterns of DNA methylation to deconvolute the data we could identify a latent methylation component likely representing tumor infiltrating T-cells. Levels of this component were higher in tumours with positive predictive parameters.
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 with a PhD in Bioinformatics.
Loves formal grammars, Markov models and phylogenetic trees.
Quote: "What if it's a Markov chain?"
PhD Student
Maja-Celine Cwikla
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 with a PhD in Bioinformatics.
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 of cancer through cfDNA and liquid biopsies.
MD Student
Felix Schifferdecker
Medical student and computer scientist.
Simulating cancer evolution and structural alterations and how cancer genome fitness evolves over time.
Postdoc
Dr. Cody Duncan
Physicist with a PhD in Particle Physics.
Interested in simulation-building and stochastic processes for cancer evolution. In-house Rugby League expert and AFL ruckman.
Master of Technology Cologne.
Postdoc
Dr. Nathan Lee
Applied mathematician with a PhD in computational biology.
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 with a PhD in Molecular Biology now turned Bioinformatician.
Interested in epigenetics and -genomics.
Scientific coordinator, third party funding, web editor.
PhD Student
Chenxi Nie
Bioinformatician by training.
Interested in stochastic processes, stochastic sampling and Formula 1.
Working on improving phylogenetic inference from copy number profiles.
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.
Postdoc / Scientific Programmer
Dr. Thomas Kono
Background in plant evolutionary genetics.
Interest in bioinformatics workflow development for robust and reproducible genomics data processing and variant interpretation.
Master Student
Alexander Nicolay
Master student in Biochemistry and Molecular Medicine.
Currently working on benchmarking tree reconstruction software and learning to press the right 0s and 1s along the way.
MD Student
Florian Over
Medical student.
Currently learning data analysis for single cell sequencing.
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.
