We are interested in the mutational mechanisms and clonal evolution of cancer, in particular mechanisms of complex structural variants and the impact on 3D chromatin organization. We are collaborating closely with clinicians, to identify the earliest mutational processes in a tumor.
Cancer is a disease of the genome, fueled by the accumulation of genomic alterations that improve the fitness of the cell. We use computational cancer genomics approaches to study clonal evolution and cancer stem cell models, with the overarching goal to understand the mutational processes that allow tumor cells to escape and evolve in time and space to cause treatment-resistance. We have a particular interest in the larger complex structural variants, their presence and impact on cancer stem cells and clonal evolution, and how they impact the chromatin architecture.
Structural variant mediated chromatin alterations and enhancer hijacking mechanisms in cancer genomes
- Rheinbay et al, Nature, 2020
- Weischenfeldt et al, Nature Genetics, 2017
- Northcott et al, Nature, 2017
Evolutionary and mechanistic mutational forces shaping cancer genomes
- Li et al, Nature, 2020
- PCAWG consortium, Nature, 2020
- Hansen et al, Blood, 2019
- Gerhäuser et al, Cancer Cell, 2018
- Gröbner et al, Nature ,2018
- Rausch et al, Cell, 2012
Age-dependent mutational processes in cancer genomes
Clonal evolution analysis to identify recurrent mechanisms of treatment resistance and identify novel therapeutic targets
- Predict clinical trajectories from cancer patients by developing methods to i) reconstruct and model the clonal evolution, ii) analyze clonal dynamics and interplay with tumor microenvironment. We are part of DANSTEM, with the overarching goal to study mechanisms of cancer stem cells.
How complex structural variants emerge and affect the 3D chromatin architecture in cancer
- Integrate epigenetic with somatic alterations and patient-related data using data-driven quantitative genetics-based computational approaches.
- Apply chromatin architecture methodologies such as Hi-C to identify how genomic alterations can impact gene regulation through altered chromatin looping
We use a combination of computational and molecular biology technologies to reach our research aims. We have optimized and integrated a range of different sequencing approaches on clinical material including paired-end sequencing, RNA-seq, ATAC-seq, Hi-C, Capture-Hi-C, mate-pair seq, DNA panel seq, single-cell sequencing, long-read sequencing. We carry out our compute on HPC systems, primarily computerome.dk. We use git for code version control and Docker to deploy our code on other systems.
Analyses of non-coding somatic drivers in 2,658 cancer whole genomes. # Rheinbay, E., Nielsen, M. M., Abascal, F., Wala, J. A., Shapira, O. et al. Nature, 578(7793), 102-111. doi:10.1038/s41586-020-1965-x (joint corresp author).
Patterns of somatic structural variation in human cancer genomes. # Li, Y., Roberts, N. D., Wala, J. A., Shapira, O., et al. Nature, 578(7793), 112-121. doi:10.1038/s41586-019-1913-9 (joint corresp author).
Molecular Evolution of Early-Onset PCa Identifies Molecular Risk Markers and Clinical Trajectories. # Gerhauser, C., Favero, F., Risch, T., Simon, R., Feuerbach, L., Assenov, Y., et al. Cancer Cell, 34(6), 996-1011.e8. doi:10.1016/j.ccell.2018.10.016 (joint first, and corresp author).
Pan-cancer analysis of somatic copy-number alterations implicates IRS4 and IGF2 in enhancer hijacking. * Weischenfeldt, J., Dubash, T., Drainas, A. P. et al. Nat Genet, 49(1), 65-74. doi:10.1038/ng.3722 (joint first author).
Integrative genomic analyses reveal an androgen-driven somatic alteration landscape in early-onset PCa. * Weischenfeldt, J., Simon, R., Feuerbach, L., Schlangen, K., Weichenhan, D., Minner, S., et al. Cancer Cell, 23(2), 159-170. doi:10.1016/j.ccr.2013.01.002 (joint first author).
In the media
05 February 2020 - EurekAlert.org
05 February 2020 - EurekAlert.org
05 February 2020 - University of Copenhagen
05 February 2020 - Videnskab.dk
11 December 2018 - Faculty of Health and Medical Sciences
22 November 2016 - Videnskab.dk.
Article (in Danish) on how 'big data' can be used to understand how cancer genes are activated.