Weischenfeldt Group

We are interested in the mutational mechanisms and clonal evolution of cancer, in particular mechanisms of structural variants and the impact on 3D chromatin organization. Through close collaborations with clinicians, we are working towards the long-term goal to provide improved therapeutic intervention options for clinical decision-making.

Keywords: Cancer genomics, structural variants, 3D chromatin organization, tumor evolution, precision medicine, bioinformatics, prostate cancer, glioblastoma, medulloblastoma, leukemia

Cancer is a genetic disease and is fueled by the accumulation of genomic alterations that improve the fitness of the cell. A tumor cell is exposed to a plethora of intrinsic and extrinsic stimuli that can act to stimulate or inhibit the progression of the disease. How a tumor cell escapes and evolves in time and space to cause treatment-resistance and lethal disease is a fundamental question in cancer research. We are interested in the mutational mechanisms during tumor evolution (Figure 1), in particular, the mechanisms and functional consequences of large-scale genomic structural variations (SV) (e.g. deletions, duplications, and translocations) in cancer.

Figure 1: Schematic of a clinical trajectory of a cancer patient (top), the clonal evolution (middle panel) and mutational mechanisms (bottom)

Figure 1: Click for larger image.

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; Rheinbay et al, Nature, 2020; 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.

Figure 2: Mutational processes (top), tumor evolution prediction (middle) and molecular subgroup analysis (bottom) in a prostate cancer (Gerhaüser et al, Cancer Cell))

Figure 2: Click for larger image.

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.

Figure 3: A) HI-C based analysis of the 3D chromatin conformation in cells before (top panel) and after SVs (bottom panel), causing disruption of chromatin domain structures. Breakpoints are annotated with arrows. B) SV-mediated enhancer hijacking, whereby SVs juxtapose normally distant, active enhancers (top). An example of enchanter hijacking of the IGF2 gene in colorectal cancer, whereby SVs form a de novo TAD containing an active (super) enchancer and IGF2 leading to >200 fold upregulation of IGF2

Figure 3: 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.

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

Recent references

  • 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.,# (2020). Patterns of somatic structural variation in human cancer genomes, Nature. 10.1038/s41586-019-1913-9
  • 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.# (2020). Analyses of non-coding somatic drivers in 2,658 cancer whole genomes, 10.1038/s41586-020-1965-x
  • ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium (2020). Pan-cancer analysis of whole genomes. 578, 82-93. Nature. 10.1038/s41586-020-1969-6
  • 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. 10.1182/blood.2019001793
  • 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