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Molecular Carcinogenesis: Lodewyk Wessels

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Lodewyk Wessels Ph.D. professorGroup leader, Head of Division

About Lodewyk Wessels

Computational Biology

We focus on the development of efficient computational approaches to process a variety of data types originating from cancer research, with the specific goal of generating testable hypotheses such that we can validate our approaches and advance our capacity to comprehend and treat cancer patients. 

Charting the molecular landscape of tumors

Recurrent mutation of single genes, gene sets or even pathways are classical signs of involvement in tumor development and treatment. For example, genes that are either simultaneously targeted (co-occurrent) or never targeted in the same tumor (mutually exclusive) can point to interactions between these genes that are specifically selected for during tumor development and most likely represent tumor dependencies and hence vulnerabilities that can be exploited in treatment strategies. We are specialized  in developing computational approaches to identify such interactions from aCGH and insertional mutagenesis data in a robust fashion. In contrast to classical approaches, our recently published approach, ADMIRE, prioritizes recurrently aberrated genes with highly robust statistics in a fast, permutation free approach.

We develop computational approaches to integrate multiple data data types (gene expression, copy number, mutation and methylation data) to stratify patients into subtypes that are homogeneous from a molecular and disease outcome perspective. Ideally, we strive to link driver mechanisms as outline above to each subtype as this typically sheds light on the biology of the subtype but, more importantly, provides a starting for the development of treatment strategies targeted at a specific subtype. 

Designing personalized treatment strategies

Cell line panels provide a rich data source to match molecular profiles of tumor subtypes to drug response. In the most straight-forward approach a tumor subtying is applied to a cell line panel followed by an analysis to determine, for every subtype, the most effective treatment based on drug screening data available for the cell line panel. In collaboration with Astra Zeneca, we have applied this approach on a colorectal cell line panel, and we are also following this approach in collaboration with the Welcome Trust Sanger Institute. In this collaboration, we have access to 1000 cancer cell lines screened across 400 anti-cancer drugs. On this data set we are also developing predictors of therapy response based on logic modeling and integer programming. These predictors map mutation data to drug response and provide easily interpretable models ('Gene A mutated and Gene B mutated predicts Sensitivity') which are highly amenable to the development of testable biological hypotheses and experimental verification. Such models not only provide an effective way to map specific molecular properties (of subtypes) to treatments, but also allow the design of combination treatments as effect modifiers of existing drugs are identified by the models. Within the Cancer Systens Biology Center (CSBC) we develop more focused, detailed models of cellular responses to treatment with the aim of using these models to tailor (combination) treatment. Our efforts are closely linked to the Dutch Center for Personalized Cancer Treatment (www.cpct.nl/en.aspx).

Positions available

We are recruiting. We have Postdoc and PhD student positions available. If you are interested in joining our lab, please send an enquiring email including your CV and motivation letter to Lodewyk Wessels l.wessels@nki.nl

More information about this vacancy on the NKI Website/working at the NKI

Co-workers

Jinhuyk Bhin

Jinhyuk Bhin

Postdoctoral Fellow

Experience

I completed my Ph.D program in Pohang University of Science and Technology (POSTECH) in South Korea. During my Ph.D program, I studied systems biology based on multi-omics data analysis under supervision of Prof. Daehee Hwang. My research interest is to understand the underlying molecular bases of cancer through integrative analyses of multi-level omics data.

I joined the lab of Lodewyk Wessels at January, 2017

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Tycho Bismeijer

Ph.D. student

Experience

I hold an M.Sc. in Artifical Intelligence from Vrije Universiteit Amsterdam.

My research interests include high throughput cancer data integration and knowledge-driven machine learning.

 

 

 

 

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Bosdriesz, Evert

Evert Bosdriesz

Postdoctoral Fellow

Experience

After I obtained my M.Sc in Theoretical Physics at the University of Amsterdam (cum laude), I decided to switch fields and did a PhD in Systems Biology at the Vrije Universiteit Amsterdam. There, my research focused on understanding cellular physiology from an evolutionary perspective.

My general research interest is in using theoretical and computational approaches to elucidate complex  biological systems.  At the Netherlands Cancer Institute, in collaboration with others, I work on signaling crosstalk in colon cancer, Our aim is to elucidate how signaling networks are rewired in response to targeted drug treatment, in order to rationally design combination therapies.

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De Bruijn, Roebi

Roebi de Bruijn

Bioinformatician

Personal details

Groups

Experience

I completed the bachelor Biomedical Sciences at the UvA and subsequently finished the master Life Sciences track Bioinformatics at the UvA/VU. I did my internships at AMC, NKI (Computational Cancer Biology - Wessels) and CWI (Life Sciences - Klau). During these internships I analyzed breast cancer RNA-Seq data by using bioinformatics tools, programming (R/Python/websites) and machine learning.

At the NKI I am currently working in the groups of Jos Jonkers and Lodewyk Wessels on the topic of tumor evolution and therapy resistance in triple negative breast cancer.

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Sander Canisius

Associate Staff Scientist

Experience

I received my PhD degree from Tilburg University in 2009 for my thesis on machine learning approaches to processing and understanding of natural language. Since 2009, I have been employed by the Netherlands Cancer Institute. From 2009 to 2015,

I had a postdoc position in the Computational Cancer Biology group of Lodewyk Wessels, where I worked on a wide range of statistical analyses of cancer genomics data.

Late 2015, I joined the group of Marjanka Schmidt at the NKI. My work in this group focuses on dissecting the role of inherited genetic variation in tumour biology and patient survival.
 

 

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Corradi

Marie Corradi

Bioinformatician

Experience

I obtained a Master's degree in Food Science from AgroParisTech (Paris) in 2012. I have worked in the flavors and fragrances industry for 4 years as a sensory scientist.

I then did a Master's degree in bioinformatics and systems biology at the Vrije Universiteit (Amsterdam). During my master's thesis, I focused on the use of deep learning for Named Entity Recognition in biomedical literature.

I joined the Wessels group in March 2018, where I currently work on the identification of biomarkers for antibody therapy in various cancer indications.

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Dyk van

Ewald van Dyk

Postdoctoral Fellow

Experience

I obtained a bachelor's degree in electrical engineering and a master's in computer engineering, focusing on theoretical pattern recognition. I did my PhD in computational biology at the Delft University of Technology specializing in DNA copy number analysis in diverse cancer types with Lodewyk Wessels as my main promotor. Afterwards, I did a postdoc in adaptive immunity (computational prediction of T-cell receptor ligands) at the University of Utrecht under Can Kesmir.

In October 2018 I joined the group of Karin de Visser, where we seek to gain a mechanistic understanding on how breast cancer effects the systemic immune milieu.

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Gil Jiminez

Alberto Gil Jimenez

PhD student

Experience

I hold a BSc in Physics and Chemistry from the Autonomous University of Barcelona since 2016. Subsequently, I obtained a MSc in Bioinformatics and Systems Biology (UvA-VU) in 2018.

After graduating, I joined The Hyve (IT consultancy) as a Bioinformatician, in which I worked with an open-source community based on healthcare data focused towards clinical applications.

In April 2019, I started a PhD in Computational Biology at the group of Michiel van der Heijden under a close collaboration with Lodewyk Wessels' group. In the project, I will use bioinformatics approaches to better understand the effect and treatment response of chemo- and immunotherapies in bladder cancer. The ultimate goal is to translate the gained knowledge into novel therapeutic strategies to treat bladder cancer patients. 

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Joana Goncalves

Postdoctoral Fellow

Experience

I am a computer scientist with a PhD from the Technical University of Lisbon, Portugal (2013). In my thesis, I developed computational methods enabling integrated multi-omics data analysis to study transcriptional gene regulation and link disruptions to disease. I worked with Sara Madeira (TULisbon,PT) and Yves Moreau (KULeuven,BE), funded by FCT (PT).

 

In 2013-2014, during my tenure as ERCIM-Marie Curie fellow hosted by Gunnar Klau (CWI,NL), I designed algorithms to analyse temporal transcriptomes.

 

At TUDelft / NKI, I am extending these tools and applying them to reveal new insight into transcriptional dynamics in cancer (e.g. androgen response in prostate cancer).

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Haar, van de Joris

Joris van de Haar MD MSc

Ph.D. student

Experience

I am a PhD student in the groups of Emile Voest and Lodewyk Wessels in the fields of bioinformatics and systems biology. I was trained as a medical doctor and biomedical scientist at the Utrecht University/Utrecht Medical Center and the University of California San Diego. I aim to combine biomedical and computational insights to realize personalized medicine. More specifically, I perform bioinformatics analyses on large patient-derived 'omic' datasets to understand why some patients respond to a specific therapy and others do not. In addition, I combine functional and computational approaches to map the molecular pathways that drive cancer.

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Hoogstraat, Marlous

Marlous Hoogstraat

Postdoctoral fellow

Experience

After finishing my bachelor bio-informatics in Leiden, I started a PhD at the Center for Personalized Cancer Treatment at the UMC Utrecht. My main projects involved the analyses of somatic variants, copy number changes and gene expression data. We used these data to compare and characterize multiple tumor samples from single patients, for instance from different metastases or before and after treatment. At the NKI, I will use the same data types, but now my main focus will be on predicting response to therapies in breast cancer patients and characterization of the different breast cancer subtypes.

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Kathy Jastrzebski

Postdoctoral Fellow

Experience

I received my PhD from the University of Melbourne in 2008 and subsequently worked as a postdoctoral fellow at the Peter MacCallum Cancer Centre.

In 2011, I joined the group of Roderick Beijersbergen and my current work involves utilizing high throughput approaches in order to identify predictors of response to therapeutics in breast cancer.

 

 

 

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Kat Moore

Kat Moore

Bioinformatician

Experience

I obtained my PhD from the University of Amsterdam in 2018. I developed an interest in bioinformatics during my PhD project, which focused upon the role of RNA-binding proteins in regulating translation during erythropoiesis. I have experience in analyzing RNA-Seq data with complex experimental design.

I joined the group of Lodewyk Wessels in 2018, where I am currently working on projects related to the molecular characterization of ovarian and (post-partum) breast cancer.

 

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Mourragui

Mourragui, Soufiane

PhD student

Experience

I hold a M.Sc. from Mines ParisTech, PSL Research University, where I majored in statistics. During my master thesis, I worked on a new method to classify Next-Generation-Sequencing data using randomized kernel methods.

In 2017, I joined Lodewyk Wessels' group in NKI and Pattern Recognition lab in TU Delft. My work focuses on domain adaptation methods to integrate diverse biological data.

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Severson, Tesa

Tesa Severson

Postdoctoral Fellow

Experience

I received my doctoral degree from Utrecht University with a focus on characterizing molecular and genetic features of breast cancer to better identify subgroups for specific treatments. In order to study these features and integrate different data streams, I applied computational tools to analyze high-throughput genomics data from patient samples.

Currently, I am working in the lab of Wilbert Zwart emphasizing on prostate cancer using computation applications to interrogate the (epi-) genomic and transcriptomic landscape of prostate cancer patient samples.

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Roos

Silvana Roos

Technician

Experience

asked for input

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Sheinman, Misha

Misha Sheinman

Postdoctoral Fellow

Experience

I am a physicist, interested in data-driven modeling of biological systems.

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Slagter, Maarten

Maarten Slagter

Ph.D. student

Experience

I investigate the interaction between adaptive immunity and developing and established tumors using clinical and -omics data. The end goal is an interpretable, quantitative model of patient response to various immunotherapies.

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Bram Thijssen

Postdoctoral Fellow

Experience

Mechanistic computational modeling can allow us to better understand the complexity of cancer and to make predictions for response to therapy. Working together with others, I develop such models within the Cancer Systems Biology Center of the NKI (http://csbc.nki.nl/).

To construct and evaluate these models, I integrate prior biological knowledge with multiple types of data coming from the experimental labs and the clinic. I am a proponent of Bayesian methods.

I obtained a MSc degree in Computational Biology and Bioinformatics from ETH Zurich in 2012 and a BSc degree in Biomedical Sciences from Maastricht University in 2010.

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Daniel Vis

Postdoctoral Fellow

Experience

My academic background is in medical biology and computer science. I have had an IT company for five years that designed software for training purposes. I hold a PhD from the University of Amsterdam in data-analysis studying endocrine dynamics and the detection of events and rhythms. Along with  work on mega-variate model validation and non-linear (mixed) modeling.

In the following post-doc at UMCU I focused on dynamic systems and challenge tests in particular. Currently I work on identifying predictive models for personalized treatments for the Center for Personalized Cancer Treatments (CPCT) using NextGenSequencing, cell line panels, and (in-house) clinical data.

 

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Key publications View All Publications

  • A Landscape of Pharmacogenomic Interactions in Cancer

    Cell. 2016 Jul 5

    Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, Aben N, Gonçalves E, Barthorpe S, Lightfoot H, Cokelaer T, Greninger P, van Dyk E, Chang H, de Silva H, Heyn H, Deng X, Egan RK, Liu Q, Mironenko T, Mitropoulos X, Richardson L, Wang J, Zhang T, Moran S, Sayols S, Soleimani M, Tamborero D, Lopez-Bigas N, Ross-Macdonald P, Esteller M, Gray NS, Haber DA, Stratton MR, Benes CH, Wessels LF, Saez-Rodriguez J, McDermott U, Garnett MJ

    link to PubMed
  • A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence

    Genome Biol. 2016 Dec 16;17(1):261.

    Canisius S, Martens JW, Wessels LF

    link to PubMed
 
 

Recent publications View All Publications

  • Publisher Correction: Immune induction strategies in metastatic triple-negative breast cancer to enhance the sensitivity to PD-1 blockade: the TONIC trial

    Nat Med. 2019 Jul;25(7):1175

    Voorwerk L1, Slagter M1,2,3, Horlings HM4, Sikorska K5, van de Vijver KK4,6, de Maaker M7, Nederlof I7, Kluin RJC8, Warren S9, Ong S9, Wiersma TG10, Russell NS10, Lalezari F11, Schouten PC7, Bakker NAM3,12, Ketelaars SLC1, Peters D13, Lange CAH11, van Werkhoven E5, van Tinteren H5, Mandjes IAM5, Kemper I14, Onderwater S14, Chalabi M1,15, Wilgenhof S14, Haanen JBAG1,14, Salgado R16,17, de Visser KE3,12, Sonke GS14, Wessels LFA2,3, Linn SC7,14, Schumacher TN1,3, Blank CU1,14, Kok M18,19.

    link to PubMed
  • Selective Loss of PARG Restores PARylation and Counteracts PARP Inhibitor-Mediated Synthetic Lethality

    Cancer Cell. 2019 Jun 10;35(6):950-952

    Gogola E, Duarte AA, de Ruiter JR, Wiegant WW, Schmid JA, de Bruijn R, James DI, Llobet SG, Vis DJ, Annunziato S, van den Broek B, Barazas M, Kersbergen A, van de Ven M, Tarsounas M, Ogilvie DJ, van Vugt M, Wessels LFA, Bartkova J, Gromova I, Andújar-Sánchez M, Bartek J, Lopes M, van Attikum H, Borst P, Jonkers J, Rottenberg S.

    link to PubMed
 

Contact

  • Office manager

    Patty Lagerweij

  • E-mail

    p.lagerweij@nki.nl

  • Telephone Number

    +31 20 512 6973

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