Open Positions

  1. Bioinformatician
  2. Student projects


Biomarker discovery for HexaBody-DR5/DR5 therapy

Genmab BV, Utrecht, The Netherlands
The Netherlands Cancer Institute (NKI), Amsterdam.

Project description
How can we find better treatment strategies for solid cancers? How can we select the patient population or individual patients that will show exquisite response to these treatments? Genmab, an international biotechnology company, is currently developing HexaBody®-DR5/DR5, a therapeutic antibody product targeting the DR5 death receptor protein in solid cancers. This agonistic antibody product mimics ligand-induced apoptosis by clustering of DR5. The general aim of this research collaboration is two-fold. First to find molecular biomarkers to identify HexaBody®-DR5/DR5-responsive patients. Second, to understand the mechanism of action of HexaBody®-DR5/DR5. Our strategy is to employ large panels of tumor cell lines and patient-derived xenograft (PDX) tumor models that have been molecularly profiled and screened for response to HexaBody®-DR5/DR5. Specifically, we have whole exome and RNA sequencing data as well as HexaBody®-DR5/DR5 response measurements across tumor cell lines spanning 14 tumor indications and PDX models covering 2 tumor types. To provide more mechanistic insight we also have access to data from functional genetic screens aimed at identifying modulators of HexaBody®-DR5/DR5 response.

Public-private collaboration
Genmab is an international biotechnology company specializing in the generation and development of differentiated antibody therapeutics for the treatment of cancer. DARZALEX® and Arzerra® are two approved drugs that were developed by Genmab. Genmab has a strong commitment to research and innovation with numerous external research collaborations akin to this one. The Computational Cancer Biology (CCB) group led by Lodewyk Wessels, has a strong track record in developing predictors of drug response from cell line panels, especially in collaboration with the Wellcome Trust Sanger Institute culminating in the second release of the Genomics of Drug Sensitivity on Cancer (GDSC1000) cell line panel (Iorio et al, Cell, 2016). Together with Genmab, the successful candidate will develop machine learning approaches to integrate the molecular data from the cell lines and PDX models in order to find effective HexaBody®-DR5/DR5 biomarkers and shed light on the mechanisms of response and resistance. The successful candidate will be employed at the NKI but regular visits to and meetings with Genmab will ensure sufficient cohesion and momentum.

Candidate profile
We are seeking a highly motivated bioinformatician with:

  • A degree in bioinformatics, computer science or a related discipline
  • Experience with high-throughput data analysis, especially sequencing data
  • Proficiency in bioinformatics programming languages (e.g. R, Python)
  • Good cross-disciplinary collaborative and communication skills
  • Experience in statistics, machine learning and/or pattern recognition is a plus
  • Experience in cancer biology and clinical applications is a plus

Please send CV and motivation letter to Lodewyk Wessels ( and Jan-Hermen Dannenberg ( Please include the names and contact information of at least two references.

Deadline: 15 December 2017

Student projects

If you want to do your master project in the group, you are always welcome to send an open application to Lodewyk Wessels.

Master student project: Understanding drug response via data integration


Most cancer treatments are effective only in a subset of patients. One of the major current challenges in providing effective treatment is to predict which patients will respond to a given treatment and explain the (epi)genomic alterations associated with these responses. State-of-the-art methods for drug response prediction methods employ various data types. Although they can have good predictive value, the results are typically not readily interpretable (Jang et al. 2012). Our goal is to develop a new method to predict drug response while additionally providing novel insights into the determinants of drug response. In this project, we will leverage existing methods and data sets that have not been exploited to their full extent yet.

What needs to be done

The proposed approach will combine 2 existing methods: aNMF (Schlicker et al., in preparation) and ModuleNetworks (Segal et al., 2003). First, we plan to use aNMF to integrate heterogeneous data types, both from tumor samples and cell lines, and produce sample clusters and associated features. Next, ModuleNetworks will bring explanatory value by defining potential regulatory programs leading associated with these clusters. The method will be developed on lung cancer data (TCGA and Sanger cell line panel) and validated using leave-one-out cross-validation on the cell lines. We will perform independent functional validation using the knockdown screens of the Achilles project. Then, the method will be applied on a private melanoma data set. A potential further investigation comprises the extension of aNMF to include additional data types cluster generation.


We are looking for a motivated student with a strong background in computational biology, statistics and the R programming language to work on this project. There will be ample opportunity to bring forward your own ideas. During the project a report has to be written about the work performed and an implementation of the method should be provided. The research group The project will be carried out in Amsterdam at the Netherlands Cancer Institute (NKI-AVL), a dynamic and inter-disciplinary research institute. The Computational Cancer Biology group headed by Prof. Dr. Lodewyk Wessels consists of about 19 scientists including postdocs, PhD students, bioinformaticians and MSc students. The research is focused on the development of novel computational approaches exploiting a wide variety of data sources in order to improve cancer diagnosis and treatment.

Additional information

For further information, you can contact: Lodewyk Wessels (


Bachelor / Master student project: Assessing tumor heterogeneity in silico


Tumors originate from cells that have accumulated genetic mutations. Subsequently, tumors evolve over time, leading to genetically distinct subpopulations of cells within a single tumor. This so-called intra-tumor heterogeneity (ITH) poses several challenges in cancer research and therapy: How to treat such heterogeneous mixtures of cells? How to target at best all of them? The more heterogeneous a cancer, the higher it’s chance to ‘escape’ a treatment? If we know the path of tumor evolution, can we stop it?



Many bioinformatics tools have been developed that try to quantify ITH and/or to reconstruct the tumor’s evolution based on copy number changes, mutations and other information from DNA sequencing data. This project involves a literature search to catalogue applicable tools and their requirements, and subsequently installing and benchmarking a selection of tools on real clinical samples and in silico simulated data. The clinical data comprises multiple sequencing files from a small number of patients, for instance from different tumor locations or before and after treatment. The simulated data would involve mixing samples from different patients in various ratios to mimic cell populations. In addition, TCGA data can be used to validate methods on a large scale.

Project description

The project is in close collaboration with both the department of Pathology and the Computational Cancer Biology group at the Netherlands Cancer Institute (NKI) in Amsterdam. As an end product, we hope to have a pipeline that can be used in multiple research projects and can be run routinely on new clinical samples. Potential findings in our clinical samples can be included in a scientific publication. Experience with working in a Unix environment / command-line programming is a big plus, but not a strict requirement. For more information, please contact Marlous Hoogstraat.