Lodewyk Wessels

Lodewyk Wessels

The Netherlands Cancer Institute, Delft University of Technology

Biography

Lodewyk Wessels is a senior group leader and the head of the Computational Cancer Biology group at the Netherlands Cancer Institute in Amsterdam, The Netherlands. His group focuses on quantifying and understanding treatment response in model systems and human patients. To this end they focus on two themes: Theme 1: Understanding and predicting drug response employing model system (cell lines, organoids, PDX models) and data-driven approaches (AI such as perturbation response modeling) and semi-mechanistic modeling to understand and predict (combination) drug response. Theme 2: Immunology. This theme deals with computational challenges in understanding the immune systems and how we can better predict response to checkpoint blockade.

Dr Wessels received his M.Sc. and Ph.D. both from the Department of Electronic and Computer Engineering, University of Pretoria, South Africa. From 1993 to 1997 he was a member of the Center for Spoken Language Understanding at the Oregon Graduate School of Science and Technology. In 1997 he joined the Faculty of Electrical Engineering, Mathematics and Computer Science at the Delft University of Technology and was appointed assistant professor in 2002. In 2006 he became a faculty member at the Netherlands Cancer Institute in Amsterdam, The Netherlands. He was appointed chair of Computational Cancer Biology at the Delft University of Technology in 2012 and in 2016 as Deputy Director Research of the Netherlands Cancer Institute.

Relevant Publications

  • Reconstructing and comparing signal transduction networks from single-cell protein quantification data
    Stohn T., van Eijl R. A. P. M., Mulder K. W., Wessels L. F. A., Bosdriesz E., Bioinformatics (2026).
  • Microbial metabolic pathways guide response to immune checkpoint blockade therapy
    Mimpen I. L., Battaglia T. W., Parra Martinez M., Toner-Bartelds C., Zeverijn L. J., Geurts B. S., Verkerk K., Hoes L. R., van Renterghem A. W. J., Noe M., Hofland I., Broeks A., van der Noort V., Stigter E. C. A., Gulersonmez C. M. C., Burgering B. M. T., van Gogh M., de Zoete M. R., Gelderblom H., Dijkstra K. K., Wessels L. F. A., Voest E. E., Cancer Discovery (2026).
  • Neoadjuvant immunotherapy in mismatch-repair-proficient colon cancers
    Tan P. B., Verschoor Y. L., van den Berg J. G., Balduzzi S., Kok N. F. M., Ijsselsteijn M. E., Moore K., Jurdi A., Tin A., Kaptein P., van Leerdam M. E., Haanen J. B. A. G., Voest E. E., de Miranda N. F. C. C., Schumacher T. N., Wessels L. F. A., Chalabi M., Nature (2025).
  • A pan-cancer screen identifies drug combination benefit in cancer cell lines at the individual and population level
    Vis D. J., Jaaks P., Aben N., Coker E. A., Barthorpe S., Beck A., Hall C., Hall J., Lightfoot H., Lleshi E., Mironenko T., Richardson L., Tolley C., Garnett M. J., Wessels L. F. A., Cell Reports Medicine (2024).
  • Spatial relationships in the urothelial and head and neck tumor microenvironment predict response to combination immune checkpoint inhibitors
    Gil-Jimenez A., van Dijk N., Vos J. L., Lubeck Y., van Montfoort M. L., Peters D., Hooijberg E., Broeks A., Zuur C. L., van Rhijn B. W. G., Vis D. J., van der Heijden M. S., Wessels L. F. A., Nature Communications (2024).
  • Distinct spatiotemporal dynamics of CD8+ T cell-derived cytokines in the tumor microenvironment
    Hoekstra M. E., Slagter M., Urbanus J., Toebes M., Slingerland N., de Rink I., Kluin R. J. C., Nieuwland M., Kerkhoven R., Wessels L. F. A., Schumacher T. N., Cancer Cell (2024).
  • MYC is a clinically significant driver of mTOR inhibitor resistance in breast cancer
    Bhin J., Yemelyanenko J., Chao X., Klarenbeek S., Opdam M., Malka Y., Hoekman L., Kruger D., Bleijerveld O., Brambillasca C. S., Sprengers J., Siteur B., Annunziato S., van Haren M. J., Martin N. I., van de Ven M., Peters D., Agami R., Linn S. C., Boven E., Altelaar M., Jonkers J., Zingg D., Wessels L. F. A., Journal of Experimental Medicine (2023).
  • Truncated FGFR2 is a clinically actionable oncogene in multiple cancers
    Zingg D., Bhin J., Yemelyanenko J., Kas S. M., Rolfs F., Lutz C., Lee J. K., Klarenbeek S., Silverman I. M., Annunziato S., Chan C. S., Piersma S. R., Eijkman T., Badoux M., Gogola E., Siteur B., Sprengers J., de Klein B., de Goeij-de Haas R. R., Riedlinger G. M., Ke H., Madison R., Drenth A. P., van der Burg E., Schut E., Henneman L., van Miltenburg M. H., Proost N., Zhen H., Wientjens E., de Bruijn R., de Ruiter J. R., Boon U., de Korte-Grimmerink R., van Gerwen B., Féliz L., Abou-Alfa G. K., Ross J. S., van de Ven M., Rottenberg S., Cuppen E., Vaslin Chessex A., Ali S. M., Burn T. C., Jimenez C. R., Ganesan S., Wessels L. F. A., Jonkers J., Nature (2022).
  • Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning
    Mourragui S. M. C., Loog M., Vis D. J., Moore K., Manjon A. G., van de Wiel M. A., Reinders M. J. T., Wessels L. F. A., Proceedings of the National Academy of Sciences (2021).
  • Limited evolution of the actionable metastatic cancer genome under therapeutic pressure
    van de Haar J., Hoes L. R., Roepman P., Lolkema M. P., Verheul H. M. W., Gelderblom H., de Langen A. J., Smit E. F., Cuppen E., Wessels L. F. A., Voest E. E., Nature Medicine (2021).

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