Lodewyk Wessels

Profile

Lodewyk Wessels is the head of the Computational Cancer Biology group at the Netherlands Cancer Institute in Amsterdam, The Netherlands. His group focuses on developing novel computational approaches to exploit a wide variety of data sources to map out the cancer landscape, unravel its regulation and employ this knowledge the develop strategies for personalized treatment. 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, initially as graduate student and later as post-doctoral fellow. 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 April 2012 and heads the Cancer Systems Biology Center at the Netherlands Cancer Institute. Dr Wessels is deputy director of research at the Netherlands Cancer Institute.

Molecular data integration for response prediction

The exact mechanisms involved in tumor development and therapy response are still largely unclear. Here we report on two computational approaches to systematically unravel these mechanisms and show how these can be employed to predict response to anti-cancer agents.

Clinical response to anti-cancer drugs varies between patients and is modulated bymolecular features. Classic approaches to integrate these data for therapy response prediction almost exclusively employ gene expression data. Such predictors are difficult to interpret. We developed TANDEM, a two-stage approach in which the first stage explains response using upstream features (mutation, copy number and methylation) and the second stage explains the remainder using downstream features (gene expression). Applying TANDEM to 934 cell lines profiled across 265 drugs we show that the resulting models are more interpretable and equally predictive as classic approches.

Second, following a more mechanistic integration approach, we constructed Bayesian models encompassing several of the important driver pathways and resistance mechanisms, and tested how well these models describe drug response data derived from a panel of breast cancer cell lines. The models provide estimates of the relative contribution of each of the drivers and resistance mechanisms and allow estimation of latent variables such as ‘pathway activation’. We identify 4EBP1 protein expression as an important modulator of mTOR inhibitor response.