The Pedersen group

Prof. Jakob Skou Pedersen’s group has a dual focus on cancer evolution and gene regulation.
Research projects include statistical method development and Big Data analysis with both basic science and clinical application aims.

Jakob has a background in molecular evolution, comparative genomics, ancient DNA, and analysis of non-coding RNAs from Aarhus University (MSc and PhD), University of Oxford (scientific visit for a year), University of California, Santa Cruz (UCSC; post doc. for three years), and University of Copenhagen (Ass. Prof. for three years).

Prof. Jakob Skou Pedersen at Google Scholar 

Cancer evolution

We seek to understand and quantify the evolutionary process that underlies cancer development and treatment resistance.
Our interests include both the mutational processes, the role of selection, and the overall space and time dynamics of the process.
Our work has primarily been based on large public cancer genomics data sets, in particular those from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). We are currently part of the Pan-Cancer Analysis of Whole Cancer Genomes (PCAWG) Project, where Jakob co-heads the driver discovery working group.

Mutational processes

The mutational process varies between cancer types, cancer patients, over time, and along the genome. We aim to characterize and understand this variation using custom-tailored statistical models and inference. As part of this, we have developed a site-specific predictive model for the mutation rate along the genome and across patients.

Repair pathways

We also seek to characterize the effect of individual repair deficiencies on the mutational landscape. We aim to further characterise the function of individual repair pathways and their region and mutation type specificities. We also evaluate the clinical applicability of the discovered associations.

Non-coding driver mutations

Nearly all known driver mutations, which give cancer cells fitness advantages and are recurrently under positive selection across cancers, directly affect protein-coding regions. The advent of large cohorts of whole cancer genomes allow us to screen for non-coding driver mutations. We have developed and applied several non-coding driver discovery methods. We have also contributed to the non-coding driver discovery efforts of the PCAWG project.

Select references:

  1. Rheinbay, E., Nielsen, M. M., Abascal, F., Tiao, G., Hornshøj, H., Hess, J. M., Pedersen, R. I. I., Feuerbach, L., Sabarinathan, R., Madsen, ..., N., Martincorena, I., Pedersen, J. S. S., Getz, G., PCAWG Drivers and Functional Interpretation Group & ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Net. Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes. bioRxiv 237313 (2017). doi:10.1101/237313
  2. Bertl, J., Guo, Q., Juul, M., Besenbacher, S., Nielsen, M. M., Hornshøj, H., Pedersen, J. S. & Hobolth, A. A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer data. BMC Bioinformatics 19, 147 (2018).
  3. Juul, M., Bertl, J., Guo, Q., Nielsen, M. M., Świtnicki, M., Hornshøj, H., Madsen, T., Hobolth, A. & Pedersen, J. S. Non-coding cancer driver candidates identified with a sample- and position-specific model of the somatic mutation rate. Elife 6, (2017).
  4. Juul, M., Madsen, T., Guo, Q., Bertl, J., Hobolth, A., Kellis, M. & Pedersen, J. S. ncdDetect2: Improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation. Bioinformatics (2018).
  5. Hornshøj H, Nielsen MM, Sinnott-Armstrong NA, Świtnicki MP, Juul M, Madsen T, Sallari, R, Kellis M, Ørntoft T, Hobolth A & Pedersen JS (2018) Pan-cancer screen for mutations in non-coding elements with conservation and cancer specificity reveals correlations with expression and survival. npj Genomic Medicine 3, 1.
  6. Nordentoft I, Lamy P, Birkenkamp-Demtröder K, Villesen P, Shumansky K, Vang S, Hornshøj H, Hedegaard J, Thorsen K, Høyer S, Borre M, Fristrup N, Dyrskjøt L, Shah S, Pedersen JS*, Ørntoft TF* (2014). Mutational context and diverse clonal development in early and late bladder cancer. Cell Reports.

Gene regulation

We seek understand the mechanisms of gene regulation and how they are perturbed in cancer.
Post-transcriptional gene regulation and the role of non-coding RNA has our particular focus.

Circular RNAs

We have several projects on circular RNAs. We are both interested in their biology, their perturbation in cancer, and their potential as biomarkers of outcome. Circular RNAs are known to sponge miRNAs. We are also interested in their ability to sponge RNA binding proteins and thereby affect gene regulation.

Associated motifs

We have recently developed a statistical methods for identifying motifs that associate with changes in gene expression. They can be applied to data from both perturbation experiments and cancer samples. We seek to use these methods to identify motifs and corresponding non-coding RNAs or RNA-binding proteins that appear to regulate gene expression (or rather transcript abundance) post-transcriptionally.

Non-coding mutations

In cancer, non-coding mutations may affect gene expression by perturbing chromatin structure; binding of transcription factor at enhancers and promoters; mRNA splicing and maturation; or post-transcriptional regulation of transcripts. Through integration and statistical analysis of same-sample data on mutation calls and gene expression, we seek to pinpoint mutations with strong driver potential and contribute to our understanding of gene regulation and cancer development.

Select references:

  1. Nielsen, M. M., Tataru, P., Madsen, T., Hobolth, A. & Pedersen, J. S. Regmex, Motif analysis in ranked lists of sequences. (2016). bioRxiv. doi:10.1101/035956
  2. Okholm, T. L. H., Nielsen, M. M., Hamilton, M. P., Christensen, L.-L., Vang, S., Hedegaard, J., Hansen, T. B., Kjems, J., Dyrskjøt, L. & Pedersen, J. S. Circular RNA expression is abundant and correlated to aggressiveness in early-stage bladder cancer. npj Genomic Medicine 2, 36 (2017).
  3. Świtnicki MP, Juul M, Madsen T, Sørensen KD and Pedersen JS (2016). PINCAGE: Probabilistic integration of cancer genomics data for perturbed gene identification and sample classification. Bioinformatics.
  4. Madsen, T., Świtnicki, M. P., Juul, M. & Pedersen, J. S. EBADIMEX: An empirical Bayes approach to detect joint differential expression and methylation and to classify samples. bioRxiv (2018).
  5. Gingold H, Tehler D, Christoffersen NR, Nielsen MM, Asmar  F, Kooistra SM, Christophersen NS., Christensen LL, Borre M, Sørensen KD, Andersen LD, Andersen CL, Hulleman E, Wurdinger T, Ralfkiær E, Helin K, Grønbæk K, Orntoft T, Waszak SM, Dahan, O, Pedersen JS, Lund AH & Pilpel Y. A dual program for translation regulation in cellular proliferation and differentiation. Cell 158, 1281–92 (2014).
  6. Nielsen MM, Tehler D, Vang S, Sudzina F, Hedegaard J, Nordentoft I, Ørntoft TF, Lund AH, Pedersen JS (2013). Identification of expressed and conserved human non-coding RNAs. RNA.