Institute for Computational Cancer Biology | Groups

Schwarzlab

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. 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
     
  2. 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
     
  3. 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.

join us!

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!

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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?"

Postdoc

Dr. 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."

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 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.

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.

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.