Open Positions

  1. Bioinformatician
  2. Postdoc
  3. Student projects


Early detection of ovarian cancer: Integration of tissue spatial maps and gene expression

The research groups of Prof dr Lodewyk Wessels, Prof De Frederic Amant and Dr Christianne Lok are looking for a motivated and talented Computational Biologist to join their teams at the Netherlands Cancer Institute (NKI).

Project descriptions

Early detection of Ovarian cancer. Differentiation between benign growth and ovarian tumors before surgery is currently far from optimal. This leads to a significant fraction of ovarian tumors going undetected and also a large fraction of benign growths being unnecessarily treated in specialized settings. In this project we will be employing innovative non-invasive sampling of peripheral blood to develop a more accurate test for the detection of ovarian tumors. We will employ two strategies. First we will isolate and perform RNAseq profiling on tumor educated platelets (TEP). Second, we will perform DNA sequencing and to detect abnormal DNA copy number profiles from the circulating tumor DNA (ctDNA). For both datasets, the successful candidate will build classifiers to accurately distinguish ovarian cancer from benign growths.

Integration of gene expression and tissue imaging to study post-partum breast cancer. Breast cancers that occur 1 to 2 years after childbirth (postpartum breast cancer) have a particularly poor outcome. It has been hypothesized that (immunological) changes in the breast, occurring after childbirth, are underlying the poor prognosis of postpartum breast cancer. In this project we will investigate postpartum breast cancer by combining two unique data sets across 400 patients. Tumors will be subjected to RNA-sequencing as well as spatial tissue profiling for a panel of immune markers employing the VECTRA technology. The spatial profiling provides the location of cells in a cross section of the tumor as well as the expression of the recorded markers in each cell. This allows a detailed characterization of the tumor and the surrounding stroma, indicating which cells occur in the stroma, their position with respect to each other and with respect to the tumor cells. The successful candidate will perform computational analysis of these datasets separately and in an integrated fashion to study the mechanisms that underlie postpartum breast cancer.

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 image processing is a plus
  • Experience in cancer biology and clinical applications is a plus


Please send CV and motivation letter to Lodewyk Wessels ( and Patty Lagerweij ( Please include the names and contact information of at least two references. For further information on the Wessels group (Computational Cancer Biology) please visit our home page


Computational Tumor Immunologist: Single Cells to Clinical Response

The research groups of Dr Marleen Kok, Pia Kvistborg and Lodewyk Wessels, are looking for a motivated and talented Computational Biologist to join their teams at the Netherlands Cancer Institute (NKI).

The Projects

With your expertise in computational life sciences and a very good understanding of onco-immunology, you will be involved in the following projects:

  • Single T cell analyses: To study the state of T cells we are investigating tumor-specific T cell responses on the population level as well as on the single cell level using transcriptome analysis and high dimensional (30 parameter) flow cytometry. Using these data, we want to understand how T cell populations differ in their gene expression and functional state depending on their antigen specificity and their place in the immunodominance hierarchy.
  • Clinical systems Immunology: Using exome, TCR sequencing data, whole transcriptome and immunohistochemistry/immunofluorescence data of the tumor microenvironment as well comprehensive flow cytometry data of peripheral blood, we aim to better understand how immune checkpoint blockade—alone or in combination with chemotherapy—affects the breast tumor microenvironment. In parallel, systemic immune response characteristics will be studied. You will be a key player in a translational research team with clinicians and immunologists.

Your Profile

You are an ambitious, creative computational biologist, with a strong commitment to translational research. Candidates should hold a degree in bioinformatics, computer science or a related discipline, have experience in statistics and/or machine learning and be proficient in bioinformatics programming languages (e.g. R, Python). We expect candidates to be team-players with strong communication skills. General background knowledge in biology and immunology is essential, and experience with projects involving the use of genomics and immune profiling data to identifying candidate targets and biomarkers is a plus.

The Research Groups

You will join the dynamic, international research groups of Pia Kvistborg, Marleen kok and Lodewyk Wessels. You will collaborate with scientists and clinicians with expertise in different disciplines.

The translational breast cancer immunology group of Dr Marleen Kok focuses on dissecting breast cancer-immune interactions, to optimize immunotherapy for breast cancer patients using novel combination treatments in clinical trials and the discovery of predictive biomarkers.

The T cell immunology group of Dr Pia Kvistborg focuses on understanding the state of tumor-specific T cells in cancer. We are currently investigating two aspects of the tumor-specific T cell response: 1) does the T cell state depend on which type of antigen the T cell is specific for (e.g. tumor-associated vs tumor-specific); and 2) what is the role of immunodominance in the development of the tumor-specific T cell response.

The Computational cancer biology group of Prof dr Lodewyk Wessels is focused on quantifying and understanding treatment response in model systems and patients. To this end we develop bespoke and novel computational methods focusing on data integration and tailored to new technologies. We actively collaborate with many research groups in the NKI and strongly believe in the power of ‘team science’.

Want More Information?

For further information please visit our home pages
Kvistborg group:
Computational Cancer Biology:

or contact

Pia Kvistborg, or
Marleen Kok,, or
Lodewyk Wessels,

Student Projects

Mining the Tumor Microbiome for Novel Treatment Strategies

Tumors often harbor complex populations of microorganisms, like viruses, bacteria, and eukaryotes, which together constitute the tumor microbiome. These microorganisms can play a central role in the development of cancer, and its response to specific therapies. In this project, we cover three of the hottest cancer research fields of recent years: the microbiome, tumor genomics, and immunotherapy.

Like humans, microorganisms often have a genome of DNA. In theory, this makes these organisms detectable by DNA sequencing. However, the vast majority of cancer DNA sequencing studies make use of whole exome sequencing (WES), in which microbial DNA is filtered out before sequencing is performed. In this project, we analyze whole genome sequencing (WGS) data of ±3,500 patient tumors, which was generated without such filtering. This provides us with a unique opportunity to comprehensively study the tumor microbiome across many different cancers, and link these findings to therapy response.

In this project, we aim to detect and quantify microorganismal DNA in WGS data, in order to address the three themes outlined below.

Theme 1: Comprehensive characterization of the tumor microbiome

Until date, a comprehensive characterization of the tumor microbiome across many cancer types has not been described. However, we know that the tumor microbiome is –at least partly– tumor type-specific, shared across multiple metastases within the same patient, and functional1. The results obtained in this study will provide an atlas of the tumor microbiome in metastatic cancer.

Theme 2: Identification of patients with virus-driven tumors as candidates for immunotherapy treatment

Epstein-Barr Virus (EBV)-driven gastric cancers respond exceptionally well to PD-1 inhibition, because this therapy stimulates the immune system to kill virus-infected tumor cells2. Other EBV-driven cancers, or cancers with high loads of other viruses, might be excellent candidates for immune checkpoint blockade. Here, we aim to explore the abundance of a broad set of viruses across metastatic cancers to identify potential candidates for immune checkpoint blockade. So, your analyses might lead to direct clinical benefit to real patients.

Theme 3: Identification of links between the tumor microbiome and therapy response

Emerging evidence shows that besides viruses, bacteria also affect immunotherapy responses. For example, the gut microbiome affects responses to PD-1 blockade3,4. As a subset of our patients have been treated with PD-1 blockade, we will study whether this is also the case for the tumor microbiome.

Programming language: Python

If you are interested, please send a short CV and a list of grades to Joris van de Haar ( or Lodewyk Wessels (


  1. Bullman, S. et al. Analysis of Fusobacterium persistence and antibiotic response in colorectal cancer. Science 2017 Dec 15. doi: 10.1126/science.aal5240
  2. Kim, S.T. et al. Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer. Nature Medicine 2018 Jul 16. doi: 10.1038/s41591-018-0101-z.
  3. Gopalakrishnan, V. et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 2018 Jan 5. doi: 10.1126/science.aan4236.
  4. Routy, al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 2018 Jan 5. doi: 10.1126/science.aan3706.