Harmen van de Werken, Ph.D.

Computational Biology & Bioinformatics in Immunology Group (CBBI) Assistant Professor in Computational Biology

h.vandewerken@erasmusmc.nl

Introduction

The Computational Biology & Bioinformatics in Immunology (CBBI) group is dedicated to understand the immune response to cancer, micro-organisms and identify molecular drivers of human immune disorders. We apply and develop Computational Biology and Artificial Intelligence (AI) methods on big immunogenomics and clinical data sets to gain insights in cancer biology and immunology with the ultimate goal to improve patient treatment and well-being.

Research

The CBBI is interested in the molecular and cellular mechanisms regarding the early onset, progression and drug resistance of cancer and its immune systems. By focusing on hereditary and somatically acquired (epi)genetic aberrations in human cells we wish to gain insight in cancer heterogeneity, immune cell development1 and clonal evolution2. Therefore, we interrogate large scale Next Generation Sequencing, Proteomics and Clinical data sets to reveal distinct patient groups3,4 understand aberrant transcription5,6 in cancer and the properties of the three dimensional structure of the human genome7,8 (Fig. 1-3). To quickly grasp information from our comprehensive molecular and clinical data sets, we apply and develop new computational algorithms9, analysis methods and visualization tools10 in the field of Computer Science, Computational Biology and Artificial Intelligence (AI), including Machine Learning and Deep Learning. In the long run our insights could generate novel biomarkers, potential new therapy targets and improved personalized medicine approaches including immunotherapy.

Facilitate & Support

Next to our research activities, we support and facilitate big data efforts of molecular and clinical data sets for the Department of Immunology, the Erasmus MC in general, and in national and international collaborations. Please email Harmen van de Werken for advice and support of experimental setups, analysis and visualization methods and grant proposals regarding Computational Biology, AI or Bioinformatics. We have dedicated workflows in place for Whole Genome Sequencing3,4 (Fig. 1), Whole Exome Sequencing11, Panel sequencing2, ChIP-seq12, Chromatin Conformation Capture Technologies (such as 4C-seq & T2C)7,8, mRNA-seq6,  small RNA-seq, DNA-methylation, RNA-seq and Proteomics integration13 (Fig. 3) and CRISPR-screens. We support the processing, analyzing, visualization, long and short-term storage of big data sets for life science research groups. Moreover, we can support software engineering of applications in the fields of bioinformatics.

Fig 1. Circosplot of genomic aberrations derived from Whole Genome Sequencing data of 13 CDK12-/-metastatic prostate samples. Inner most circle an extensive number of tandem duplications are shown in red3

Fig 2. 3D-PCA plot of RNA-seq data of Muscle Invasive Bladder Cancer from TCGA with luminal, basal and neuronal subtypes

Fig 3. Hierarchical Clustering of quantitative Platelet proteomics data of 4 WT and 4 PF4 Knock Out (KO) Samples13

Group Members

  • Harmen van de Werken, Group leader
  • Dwin Grashof, Bioinformatician

Selected publications

(See for all publications Harmen van de Werken in PubMed or Scholar Google)

  1. Holwerda, S. J. B., van de Werken, H. J. G., Ribeiro De Almeida, C., Bergen, I. M., de Bruijn, M. J. W., Verstegen, M. J. A. M., Simonis, M., Splinter, E., Wijchers, P. J., Hendriks, R. W. & De Laat, W. Allelic exclusion of the immunoglobulin heavy chain locus is independent of its nuclear localization in mature B cells. Nucleic Acids Res. 41, 1–12 (2013).
  2. van Doeveren, T., Nakauma‐Gonzalez, J. A., Mason, A. S., van Leenders, G. J. L. H., Zuiverloon, T. C. M. M., Zwarthoff, E. C., Meijssen, I. C., van der Made, A. C., van der Heijden, A. G., Hendricksen, K., van Rhijn, B. W. G., Voskuilen, C. S., van Riet, J., Dinjens, W. N. M., Dubbink, H. J., van de Werken, H. J. G. & Boormans, J. L. The clonal relation of primary upper urinary tract urothelial carcinoma and paired urothelial carcinoma of the bladder. Int. J. Cancer 148, 981–987 (2021).
  3. van Dessel, L. F., van Riet, J., Smits, M., Zhu, Y., Hamberg, P., van der Heijden, M. S., Bergman, A. M., van Oort, I. M., de Wit, R., Voest, E. E., Steeghs, N., Yamaguchi, T. N., Livingstone, J., Boutros, P. C., Martens, J. W. M., Sleijfer, S., Cuppen, E., Zwart, W., van de Werken, H. J. G., Mehra, N. & Lolkema, M. P. The genomic landscape of metastatic castration-resistant prostate cancers reveals multiple distinct genotypes with potential clinical impact. Nat. Commun. 10, 5251 (2019).
  4. Angus, L., Smid, M., Wilting, S. M., van Riet, J., Van Hoeck, A., Nguyen, L., Nik-Zainal, S., Steenbruggen, T. G., Tjan-Heijnen, V. C. G., Labots, M., van Riel, J. M. G. H., Bloemendal, H. J., Steeghs, N., Lolkema, M. P., Voest, E. E., van de Werken, H. J. G., Jager, A., Cuppen, E., Sleijfer, S. & Martens, J. W. M. The genomic landscape of metastatic breast cancer highlights changes in mutation and signature frequencies. Nat. Genet. 41, 1450–1458 (2019).
  5. Guggenberger, F., van de Werken, H. J. G., Erb, H. H. H., Cappellano, G., Trattnig, K., Handle, F., Peer, S., Schäfer, G., Jenster, G., Culig, Z., Skvortsova, I. & Santer, F. R. Fractionated Radiation of Primary Prostate Basal Cells Results in Downplay of Interferon Stem Cell and Cell Cycle Checkpoint Signatures. Eur. Urol. 74, 847–849 (2018).
  6. Schmitt, M., Schewe, M., Sacchetti, A., Feijtel, D., van de Geer, W. S., Teeuwssen, M., Sleddens, H. F., Joosten, R., van Royen, M. E., van de Werken, H. J. G., van Es, J., Clevers, H. & Fodde, R. Paneth Cells Respond to Inflammation and Contribute to Tissue Regeneration by Acquiring Stem-like Features through SCF/c-Kit Signaling. Cell Rep. 24, 2312-2328.e7 (2018).
  7. van de Werken, H. J. G., Haan, J. C., Feodorova, Y., Bijos, D., Weuts, A., Theunis, K., Holwerda, S. J. B., Meuleman, W., Pagie, L., Thanisch, K., Kumar, P., Leonhardt, H., Marynen, P., van Steensel, B., Voet, T., de Laat, W., Solovei, I. & Joffe, B. Small chromosomal regions position themselves autonomously according to their chromatin class. Genome Res. 27, 922–933 (2017).
  8. van de Werken, H. J. G., Landan, G., Holwerda, S. J. B., Hoichman, M., Klous, P., Chachik, R., Splinter, E., Valdes-Quezada, C., Öz, Y., Bouwman, B. A. M., Verstegen, M. J. A. M., de Wit, E., Tanay, A. & de Laat, W. Robust 4C-seq data analysis to screen for regulatory DNA interactions. Nat. Methods 9, 969–972 (2012).
  9. Hoogstrate, Y., Jenster, G. & van de Werken, H. J. G. FASTAFS: file system virtualisation of 1 random access compressed FASTA files. bioRxiv 2020.11.11.377689 (2020). doi:10.1101/2020.11.11.377689
  10. van Riet, J., Krol, N. M. G., Atmodimedjo, P. N., Brosens, E., van IJcken, W. F. J., Jansen, M. P. H. M., Martens, J. W. M., Looijenga, L. H., Jenster, G., Dubbink, H. J., Dinjens, W. N. M. & van de Werken, H. J. G. SNPitty: An Intuitive Web Application for Interactive B-Allele Frequency and Copy Number Visualization of Next-Generation Sequencing Data. J. Mol. Diagnostics 20, 166–176 (2018).
  11. Kenter, A. T., Rentmeester, E., van Riet, J., Boers, R., Boers, J., Ghazvini, M., Xavier, V. J., van Leenders, G. J. L. H., Verhagen, P. C. M. S., van Til, M. E., Eussen, B., Losekoot, M., Klein, A., Peters, D. J. M., van IJcken, W. F. J., van de Werken, H. J. G., Zietse, R., Hoorn, E. J., Jansen, G. & Gribnau, J. H. Cystic renal-epithelial derived induced pluripotent stem cells from polycystic kidney disease patients. Stem Cells Transl. Med. 9, 478–490 (2020).
  12. Mohd-Sarip, A., Teeuwssen, M., Bot, A. G., De Herdt, M. J., Willems, S. M., Baatenburg de Jong, R. J., Looijenga, L. H. J., Zatreanu, D., Bezstarosti, K., van Riet, J., Oole, E., van Ijcken, W. F. J., van de Werken, H. J. G., Demmers, J. A., Fodde, R. & Verrijzer, C. P. DOC1-Dependent Recruitment of NURD Reveals Antagonism with SWI/SNF during Epithelial-Mesenchymal Transition in Oral Cancer Cells. Cell Rep. 20, 61–75 (2017).
  13. Meinders, M., Kulu, D. I., van de Werken, H. J. G., Hoogenboezem, M., Janssen, H., Brouwer, R. W. W., van IJcken, W. F. J., Rijkers, E. J., Demmers, J. A. A., Krüger, I., Van Den Berg, T. K., Suske, G., Gutiérrez, L. & Philipsen, S. Sp1/Sp3 transcription factors regulate hallmarks of megakaryocyte maturation and platelet formation and function. Blood 125, 1957–1967 (2015).