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).
The formation of SVs is influenced by inherited, acquired, and environmental factors that together shape the (epi-)genomic architecture. We are using computational classification methods to detect and infer patterns of rearrangements (Gerhaüser et al, Cancer Cell, 2018; Li et al, Nature, 2020), to study genotype-phenotype associations in cancer and to infer drug-sensitivity from mutational signatures. We found an age-dependent hormone-driven pathomechanism, which led to gross differences in the formation and type of SVs in early versus late-onset cancer patients (Weischenfeldt et al. Cancer Cell, 2013) and we are studying the interplay between mutational mechanisms and disease progression in this context (Figure 1 and 2). We develop and apply mathematical models to study how tumors evolve and to predict how mutations can affect the clinical trajectories of the cancer cell. To this end, we have developed PRESCIENT (Gerhaüser et al, Cancer Cell, 2018), a conditional probabilistic framework to predict the molecular changes and associated outcomes (Figure 2) in a clinical context.
We are also using single-cell based methodologies to gain further insight into the clonal evolution and cell-type compositions that influence the tumor cells.
A recurrent observation we made was the importance of chromatin organization and the epigenetic landscape in determining where breakpoints occur (Gerhaüser et al, Cancer Cell, 2018; Rheinbay et al, Nature, 2020). Most SVs occur outside of the protein-coding regions of the genome, but we found that SVs can have a drastic consequence on the 3D chromatin conformation (Figure 3A) and that this can directly impact nearby gene expression, for example, by disrupting or altering hard-wired gene regulatory networks (Weischenfeldt et al, Nat Rev Genet, 2013). We developed a computational method termed CESAM (cis-expression structural alteration mapping) to identify the functional consequences of SVs on nearby gene expression. Using this method, we have performed analyses across multiple tumor types which led us to further uncover mechanisms 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 3B; Weischenfeldt et al, Nat Genet, 2017; Northcott et al, Nature, 2017; Rheinbay et al, Nature, 2020). Our ongoing analyses using chromatin conformation capture methodologies and computational approaches are uncovering novel and unexpected mechanisms by which alterations in the 3D chromatin organization impact gene regulation.
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.
To allow such inferences, we combine data generation from primary tumor samples and model systems together with integrative data analysis.
Current and future research
Clonal evolution analysis to identify recurrent mechanisms of treatment resistance and identify novel therapeutic targets
- Develop and apply methods to reconstruct the clonal evolution and to identify clinical trajectories
- Utilizing the clonal reconstruction to Identify therapeutic targets
- Pursue analysis at the single-cell level to identify clonal dynamics and interplay with the tumor microenvironment
formation mechanism and functional consequences of SVs in cancer
- Associate SVs with functional gene regulatory elements using data-driven quantitative genetics-based methods.
- Identify prognostic and/or predictive signatures of complex SVs
- Integrate chromatin-conformation techniques and genome-editing technologies such as CRISPR-Cas9 to track the consequences of genomic alterations on 3D chromatin
- Li, Y., Roberts ND, Wala, J.A.; Shapira O., Schumacher, S.E., Khurana, E., Waszak, S., Korbel, J., Imielinski, M., Weischenfeldt, J.#; Beroukhim, R.#; Campbell, P.J.,#. Patterns of somatic structural variation in human cancer genomes, Nature, accepted
- Rheinbay, E., Nielsen, M.M., Abascal, F., Wala, J.A., Shapira, O., Tiao, G., Hornshøj, H., Hess, J.M., Istrup, R. …, Weischenfeldt, J.#, Beroukhim, R.#, Martincorena, I.#, Pedersen, J.S.#, Getz, G.#. Analyses of non-coding somatic drivers in 2,658 cancer whole genomes, Nature, accepted
- Hansen, J. W., Pedersen, D. A., Larsen, L. A., Husby, S., Clemmensen, S. B., Hjelmborg, J., Favero, F., Weischenfeldt, J., Christensen, K., and Grønbæk, K. (2019). Clonal hematopoiesis in elderly twins: concordance, discordance and mortality. Blood
- Gerhauser, C., Favero, F., Risch, T., Simon, R., Feuerbach, L., Assenov, Y., Heckmann, D., …(n=47), Weischenfeldt, J.# (2018). Molecular Evolution of Early-Onset PCa Identifies Molecular Risk Markers and Clinical Trajectories. Cancer Cell, 34(6), 996-1011.e8. doi:10.1016/j.ccell.2018.10.016
- Gröbner, S. N., Worst, B. C., Weischenfeldt, J., Buchhalter, I., Kleinheinz, K., Rudneva, V. A., …(n=77), Pfister, S. M. (2018). The landscape of genomic alterations across childhood cancers. Nature. doi:10.1038/nature25480
- Northcott, P. A., Buchhalter, I., Morrissy, A. S., Hovestadt, V., Weischenfeldt, J., Ehrenberger, T., …(n=79), Lichter, P. (2017). The whole-genome landscape of medulloblastoma subtypes. Nature, 547(7663), 311-317. doi:10.1038/nature22973
- Weischenfeldt, J.*, Dubash, T., Drainas, A. P., Mardin, B. R., Chen, Y., Stütz, A. M.,.. (n=24), Glimm, H., & Korbel, J. O. (2017). Pan-cancer analysis of somatic copy-number alterations implicates IRS4 and IGF2 in enhancer hijacking. Nat Genet, 49(1), 65-74. doi:10.1038/ng.3722
- Weischenfeldt, J.*, Symmons, O., Spitz, F., & Korbel, J. O. (2013). Phenotypic impact of genomic structural variation: insights from and for human disease. Nat Rev Genet, 14(2), 125-138. doi:10.1038/nrg3373
- Weischenfeldt, J.*, Simon, R., Feuerbach, L., Schlangen, K., Weichenhan, D., Minner, S., … (n=50), Sultmann, H., Sauter, G., Plass, C., Brors, B., Yaspo, M. L., Korbel, J. O., & Schlomm, T. (2013). Integrative genomic analyses reveal an androgen-driven somatic alteration landscape in early-onset PCa. Cancer Cell, 23(2), 159-170. doi:10.1016/j.ccr.2013.01.002
- Rausch, T., Jones, D. T., Zapatka, M., Stutz, A. M., Zichner, T., Weischenfeldt, J., , S.,…(N=39) , Lichter, P., Pfister, S. M., & Korbel, J. O. (2012). Genome sequencing of pediatric medulloblastoma links catastrophic DNA rearrangements with TP53 mutations. Cell, 148(1-2), 59-71. doi:10.1016/j.cell.2011.12.013
Group Leader J. Weischenfeldt
News & media
11 December 2018 - Faculty of Health and Medical Sciences
Researchers Use Computer Model to Predict Prostate Cancer Progression
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.