Group Lodewyk Wessels

Professor Dr. Lodewyk Wessels : Bioinformatics and Statistics Group, Division of Molecular Biology, Netherlands Cancer Institute

Professor Dr. Lodewyk Wessels

Bioinformatics and Statistics Group, Division of Molecular Biology, Netherlands Cancer Institute

"We want to know why a patient does not respond to a certain therapy"
1. What is the leading research theme in your group?
"The two main themes concern diagnostics classifiers for better stratification of cancer patients and unravelling the network of interactions between genes and pathways. Right now, diagnostic classifiers are black boxes, but we want to understand the underlying mechanisms. We want to know why a patient does not respond to a certain therapy. If we better understand how genes and pathways interact, we can hopefully better predict which therapy is the best option for a certain patient. Our work concentrates on the development of new computational algorithms to address these questions."

2. With what type of groups or organisations do you collaborate most and why?
"We mostly collaborate with other groups within the Netherlands Cancer Institute. Clinical groups have access to patient material and data, and together we try to improve clinical practice by building better prognostic and predictive models. With more basic molecular biology groups we collaborate on unravelling molecular pathways. These groups have sophisticated measurement equipment with which they generate the data we need to construct our models. They also provide valuable biological expertise required for crafting the models. We collaborate with external computational or bioinformatics groups as well as with groups that focus on basic mechanisms in biology that are not directly linked to cancer. We also collaborate with companies, both on diagnostics and therapeutics."

3. From your research perspective, what are the main challenges in bioinformatics right now?
"Being able to generate good quantitative predictive models. With that I mean models that can be used to really predict an outcome in a system that was not used to generate the model. For example, to predict the response to therapy in a cell line using a model that was built on data from other, independent cell lines. We are working hard at this, and I hope we have such a model for cell lines in two years from now, 5 years later for response in mice and perhaps 10 years from now in patients. However, predictions like these are usually wrong."

4. What is the most important task of a group leader?
"To formulate a vision and to bring people together, inspire them and give them the confidence to realize that vision."

5. How would you describe the atmosphere in your group?
"They are all creative, enthusiastic, motivated people that share a passion for science. In our group, but also in the Institute in general the atmosphere is very open and critical, but always in a constructive manner."

http://bioinformatics.nki.nl/