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).
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).
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).
Favero, F, Risch, T, Simon, R, Feuerbach, L, Assenov, Y, Heckmann, D, Sidiropoulos, N et al … Weischenfeldt J. (2018). Molecular Evolution of Early-Onset Prostate Cancer Identifies Molecular Risk Markers and Clinical Trajectories. Cancer Cell, 34, 996-1011.e8.
Gröbner, S. N., Worst, B. C., Weischenfeldt, J., Buchhalter, I., Kleinheinz, K., Rudneva, V. A., et al (2018). The landscape of genomic alterations across childhood cancers. Nature
Wala, J. A., Bandopadhayay, P., Greenwald, N., O’Rourke, R., Sharpe, T., Stewart, C., Schumacher, S., Li, Y., Weischenfeldt, J., Yao, X., Nusbaum, C., Campbell, P., Getz, G., Meyerson, M., Zhang, C. Z., Imielinski, M., and Beroukhim, R. (2018). SvABA: genome-wide detection of structural variants and indels by local assembly. Genome Res
Hopkins, J. F., Sabelnykova, V. Y., Weischenfeldt, J., Simon, R., Aguiar, J. A., Alkallas, R., et al, (2017). Mitochondrial mutations drive prostate cancer aggression. Nat Commun 8, 656.
Northcott PA, Buchhalter I, Morrissy AS, Hovestadt V, Weischenfeldt J, Ehrenberger T, et al. The whole-genome landscape of medulloblastoma subtypes. Nature. 2017;547: 311–317
Weischenfeldt J, Korbel JO. Genomes of early onset prostate cancer. Curr Opin Urol. 2017
Weischenfeldt J, Dubash T, Drainas AP, Mardin BR, Chen Y, Stütz AM, et al. Pan-cancer analysis of somatic copy-number alterations implicates IRS4 and IGF2 in enhancer hijacking. Nat Genet. 2017;49: 65–74
Schlomm T, Weischenfeldt J, Korbel J, Sauter G. The Aging Prostate Is Never “Normal”: Implications from the Genomic Characterization of Multifocal Prostate Cancers. Eur Urol. 2015;68: 348–350
Kluth M, Galal R, Krohn A, Weischenfeldt J, Tsourlakis C, Paustian L, et al. Prevalence of chromosomal rearrangements involving non-ETS genes in prostate cancer. Int J Oncol. 2015;46: 1637–1642
Kovac M, Blattmann C, Ribi S, Smida J, Mueller NS, Engert F, et al. Exome sequencing of osteosarcoma reveals mutation signatures reminiscent of BRCA deficiency. Nat Commun. 2015;6: 8940
Mardin BR, Drainas AP, Waszak SM, Weischenfeldt J, Isokane M, Stütz AM, et al. A cell-based model system links chromothripsis with hyperploidy. Mol Syst Biol. 2015;11: 828
Steurer S, Mayer PS, Adam M, Krohn A, Koop C, Ospina-Klinck D, et al. TMPRSS2-ERG fusions are strongly linked to young patient age in low-grade prostate cancer. Eur Urol. 2014;66: 978–981
Weischenfeldt J, Symmons O, Spitz F, Korbel JO. Phenotypic impact of genomic structural variation: insights from and for human disease. Nat Rev Genet. 2013;14: 125–138
Weischenfeldt J, Simon R, Feuerbach L, Schlangen K, Weichenhan D, Minner S, et al. Integrative genomic analyses reveal an androgen-driven somatic alteration landscape in early-onset prostate cancer. Cancer Cell. 2013;23: 159–170
Weischenfeldt J, Waage J, Tian G, Zhao J, Damgaard I, Jakobsen JS, et al. Mammalian tissues defective in nonsense-mediated mRNA decay display highly aberrant splicing patterns. Genome Biol. 2012;13: R35
Rausch T, Jones DT, Zapatka M, Stutz AM, Zichner T, Weischenfeldt J, et al. Genome sequencing of pediatric medulloblastoma links catastrophic DNA rearrangements with TP53 mutations. Cell. 2012;148: 59–71
Jones DT, Jager N, Kool M, Zichner T, Hutter B, Sultan M, et al. Dissecting the genomic complexity underlying medulloblastoma. Nature. 2012;488: 100–105
Group Leader J. Weischenfeldt
News & media
22 November 2016 - Videnskab.dk
Danskere afslører mekanisme bag aggressive kræftformer
Article (in Danish) on how 'big data' can be used to understand how cancer genes are activated.
Post-doc and PhD positions are available to highly motivated people with a background in computational biology, molecular biology, medical sciences, physics and computer science.