Open Positions


  1. Bioinformatician
  2. Postdoc
  3. Student projects

Bioinformatician

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

Interested?

Please send CV and motivation letter to Lodewyk Wessels (l.wessels@nki.nl) and Patty Lagerweij (p.lagerweij@nki.nl). 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 http://ccb.nki.nl/

Postdoc

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: https://www.nki.nl/divisions/molecular-oncology-immunology/kvistborg-p/
Computational Cancer Biology: http://ccb.nki.nl/

or contact

Pia Kvistborg, p.kvistborg@nki.nl or
Marleen Kok, m.kok@nki.nl, or
Lodewyk Wessels, l.wessels@nki.nl

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.

Bachelor / Master student project: Assessing tumor heterogeneity in silico

Background

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?

cancer_evolution_heterogeneity

Challenges

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.