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!


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

April 25, 2024

Model for transcription-based lung cancer risk prediction published in Genome Medicine

We are glad to announce that our computational model to assess lung cancer risk from non-invasive nasal swabs of healthy volunteers and lung cancer patients was published in Genome Medicine.

By using a transcriptional network approach we were able to demonstrate a causal relationship of de-regulated immune-pathway expression and  lung cancer risk.

Our lung cancer risk classifier based on transcriptomic data derived from nasal swabs provides initial evidence for germline-mediated personalized smoke injury response and risk in the general population, with potential implications for managing long-term lung cancer incidence and mortality.

read more

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.

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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.

read more

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 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.

 

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

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.