Evolutionary and Cancer Genomics
Berlin Institute for Medical Systems Biology (BIMSB)
Max-Delbrück Center for Molecular Medicine (MDC)
Tel (MDC-Main): +49 (0) 30 9406 3200
Tel (MDC-BIMSB): +49 (0) 30 9406 1331
Fax: +49 (0) 30 9406 3201
Our lab develops and applies computational methods for understanding tumour heterogeneity and cancer evolution. We focus on chromosomal instability and somatic copy-number alterations and aim to understand how somatic genetic heterogeneity arises and how it affects cellular and patient phenotypes in space and time. To this end, we develop tailored machine learning (ML) algorithms and statistical methods and work closely with clinical partners on the analysis on large-scale patient cohorts.
The methods we develop help us to overcome three major challenges in cancer genomics: (i) knowledge transfer from dedicated high-resolution experiments on cell lines and model organisms to clinical patient datasets, (ii) integration of heterogenous multi-omics data through consistent, interpretable and robust machine learning models and (iii) dealing with the intrinsic cell-type heterogeneity and noise inherent to clinical patient samples.
We develop and apply these methods on large clinical cohorts and actively pursue interdisciplinary and collaborative endeavours with clinical research groups. We are members of the Pan-Cancer Analysis of Whole Genomes (PCAWG), the German Cancer Consortium (DKTK), and work closely with the TRACERx consortium, in which our methods have found wide acceptance. We further hold close ties to the CRUK Cambridge Institute, the EMBL-EBI and to local clinical research groups at the Charité and ECRC.
In the future, single-cell approaches will play an increasingly important role in the clinic. We are currently setting up multiple collaborations for combining single-cell with bulk sequencing on clinical samples to get a more detailed understanding of tumour heterogeneity and the spatial architecture of tumours. Additionally, we will investigate the role of chromatin conformation and spatial genome architecture on chromosomal instability and somatic cellular evolution.
For information about our current group members please visit:
- PCAWG Transcriptome Core Group, Calabrese C, Davidson NR, Demircioglu D, Fonseca NA, He Y, Kahles A, Lehmann KV, Liu F, Shiraishi Y, Soulette CM, Urban L, Greger L, Li S, Liu D, Perry MD, Xiang Q, Zhang F, Zhang J, Bailey P, Erkek S, Hoadly KA, Hou Y, Huska MR, Kilpinen H, Korbel JO, Marin MG, Markowski J, Nandi T, Pan-Hammarström Q, Pedamallu CS, Siebert R, Stark RG, Su H, Tan P, Waszak SM, Yung C, Zhu S, Awadalla P, Creighton CJ, Meyerson M, Ouellette BF, Wu K, Yang H, PCAWG Transcriptome Working Group, Brazma A, Brooks AN, Göke J, Rätsch G, Schwarz RF, Stegle O, Zhang Z, PCWAG Consortium. Genomic basis for RNA alterations in cancer. Nature. 2020 Feb;578(7793):129-136
- Koche RP, Rodriguez-Fos E, Helmsauer K, Burkert M, MacArthur IC, Maag J, Chamorro R, Munoz-Perez N, Puiggros M, Dorado Garcia H, Bei Y, Röefzaad C, Bardinet V, Szymansky A, Winkler A, Thole T, Timme N, Kasack K, Fuchs S, Klironomos F, Thiessen N, Blanc E, Schmelz K, Künkele A, Hundsdörfer P, Rosswog C, Theissen J, Beule D, Deubzer H, Sauer S, Toedling J, Fischer M, Hertwig F, Schwarz RF, Eggert A, Torrents D, Schulte JH, Henssen AG. Extrachromosomal circular DNA drives oncogenic genome remodeling in neuroblastoma. Nature Genetics. 2020;52(1):29-34
- Turajlic S, Xu H, Litchfield K, Rowan A, Horswell S, Chambers T, O’Brien T et al. Deterministic Evolutionary Trajectories Influence Primary Tumor Growth: TRACERx Renal. Cell, 173:595–610.e11 (2018).
- Abbosh C, Birkbak NJ, Wilson GA, Jamal-Hanjani M, Constantin T, Salari R, Quesne JL et al. Phylogenetic ctDNA analysis depicts early stage lung cancer evolution. Nature (2017).
- Jamal-Hanjani M, Wilson GA, McGranahan N, Birkbak NJ, Watkins TBK, Veeriah S, Shafi S et al. Tracking the Evolution of Non-Small-Cell Lung Cancer. The New England Journal of Medicine (2017).
- Schwarz RF, Ng CKY, Cooke SL, Newman S, Temple J, Piskorz AM, Gale D et al. Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis. PLoS Medicine, 12:e1001789 (2015).
- Schwarz RF, Trinh A, Sipos B, Brenton JD, Goldman N and Markowetz F. Phylogenetic quantification of intratumour heterogeneity. PLoS Computational Biology, 10:e1003535 (2014).