Weischenfeldt Group – University of Copenhagen

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Weischenfeldt Group

As a newly started group at the Biotech Research & Innovation Centre (BRIC) and the Finsen Laboratory, Copenhagen University Hospital (Rigshospitalet), we combine computational and molecular biology approaches to study genotype-phenotype associations in cancer. A particular focus is to gain a better understanding of the functional consequences of large-scale somatic genomic alterations in cancer and how these changes affect gene regulatory networks. We are collaborating with clinicians with the long-term goal to provide personalized therapeutic intervention options for clinical decision-making.

Keywords: Cancer genomics, structural variants, prostate cancer, precision medicine, bioinformatics

Cancer is a genetic disease and is caused by accumulation of genomic alterations that improve the fitness of the cell. We are particularly interested in the formation mechanisms and functional consequences of large-scale genomic structural variations (SV) (e.g. deletions, duplications and translocations) in cancer.

The formation of SVs is influenced by inherited, acquired, and environmental factors that together shape the (epi-)genomic architecture. For example, we found recently that in prostate cancer, an age-dependent hormone-driven mechanism leads to gross differences in the formation and type of SVs found in early versus late-onset cancer patients (Figure 1; Weischenfeldt et al. Cancer Cell). Moreover, we also found that pediatric brain tumors are frequently caused by a cancer-predisposing germline mutation in TP53 that results in complex whole-chromosome ‘shattering’ events (Rausch et al., Cell).

Figure 1: Click for larger image.

More generally, SVs can have widespread effects on the genome, either by e.g. the homozygous loss or amplification of genes, or by bringing together two distant genes to form fusion gene rearrangements with tumorigenic potential, such as BCR-ABL1, IGH-MYC, TMPRSS2-ERG, EML4-ALK. SVs can also cause effects in cis, for example, by disrupting or altering hard-wired gene regulatory networks (Weischenfeldt et al, Nat Rev Genet). We recently developed a computational method termed CESAM (cis-expression structural alteration mapping) to identify functional consequences of SVs on gene expression in cis. Using this method, we performed analysis across multiple tumor types which led us to further uncover a mechanism by which distant lineage specific enhancers get translocated and positioned in front of normally silenced oncogenes, leading to their high-level upregulation, a mechanism termed Enhancer Hijacking (Figure 2; Weischenfeldt et al, Nat Genet, 2016).

Figure 2: Click for larger image.

Our aim is to both improve the basic understanding of how changes in the genomic architecture impact on gene (de)regulation and to use this knowledge to prospectively identify specific, targeted therapeutic options based on the mutational profile of the tumor. Using existing and repurposed drugs, we are collaborating with cancer clinicians to provide therapeutic alternatives to existing intervention schemes.

To allow such inferences, we combine data generation from primary tumor samples and model systems together with big data analysis. A unique resource is the currently largest international whole-genome cancer genomics effort, namely the Pan-Cancer Analysis of Whole Genomes Project, which aims to analyze and interpret the cancer genomes of more than 2,800 patients, where we actively participate in predicting somatic SVs and in interpreting its downstream effects on gene regulation.

Current and future research


Understand the formation mechanism and functional consequences of SVs in cancer

  • Associate SVs with functional gene regulatory elements using data-driven quantitative genetics-based methods.
  • Utilize chromatin-conformation techniques and genome-editing technologies such CRISPR-Cas9 to reintroduce genomic alterations and track the consequences on chromatin higher-order, to gain important mechanistic insight into the consequences

Personalized drug-target identification

  • Develop and further improve pharmacogenomic methods to identify therapeutic intervention options for high-risk cancers (precision medicine).

Recent references

Weischenfeldt, J., Dubash, T., Drainas, A. P., et al., 2016. Nature Genetics

Schütze, D. M., et al. D. 2016. Oncotarget 

Tsourlakis, M. C., et al. 2016. BMC Cancer 16:641

Schlomm, T., Weischenfeldt, J., et al, 2015. Eur Urol 68:348-350

Kluth, M., et al, et al. 2015. Int J Oncol 46:1637-1642

Kunz, J. B., et al., Nat Commun 6:8940

Mardin, B. R., et al., 2015. Mol Syst Biol 11:828

Steurer, S., et al., 2014. Eur Urol 66:978-981

Brocks, D., et al., 2014. Cell Rep 8:798-806

Weischenfeldt, J., Symmons, O., et al., 2013. Nat Rev Genet 14:125-138

Weischenfeldt, J., et al., 2013. Cancer Cell 23:159-170

Weischenfeldt, J., Waage, J., et al., 2012. Genome Biol 13:R35

Rausch, T., et al., 2012. Cell 148:59-71

Jones, D. T., et al., 2012. Nature 488:100-105