The Netherlands Cancer Institute
Plesmanlaan 121 1066CX Amsterdam
Prof. dr. L.F.A.Wessels
0031 (0)20 5127987
The NWO funded Cancer Systems Biology Center (CSBC) is a centre of excellence within the Netherlands Cancer Institute-Antoni van Leeuwenhoek hospital. The CSBC employs computational modelling and experimental validation cycles spanning multiple levels of complexity including cell lines, mouse models and patients. Within the CSBC several research groups from the NKI-AVL work together to develop a strategy to tackle the complexity of molecular networks that govern breast tumorigenesis with the goal to deliver improved diagnostic tools to enable tailored cancer therapy.
The Computational Cancer Biology group develops novel computational approaches and performs state-of-the-art analyses of a wide array of data types to further our basic understanding of cancer and to translate these findings to the clinic. We follow a three-pronged approach. First, we focus on employing computational approaches to chart the cancer landscape by integrating heterogeneous molecular data sets. Second, we model transcriptional, signalling and metabolic networks to better understand the determinants of the cancer landscape. Finally, we employ patient response data, where available, and drug response data from model systems to map the cancer landscape to (combination) treatments most likely to effectively combat the cancer. Our philosophy is to form strong collaborative ties with experimental and clinical groups such that our computational approaches are firmly embedded in biology and clinical practice.
Theme 1: Charting the molecular landscape of tumors
Recurrent copy number aberration of a single gene is a classical sign of its involvement in tumor development. In many cases tumors become dependent on such driver genes and targeting these genes has proven to be a fruitful strategy in combatting cancer. Similarly, recurrent patterns of copy number aberration involving multiple genes also provide valuable clues regarding ways in which genes collaborate during oncogenesis. For example, genes that are simultaneously inactivated can point to redundant pathways that both need to be inactivated during tumor development. As with single genes, frequent recurrence of such patterns is strong evidence that these patterns were selected for during tumor development and most likely represent dependencies that can be exploited in treatment strategies. Reliably identifying recurrently aberrated (groups of) genes is therefore an essential step in charting the cancer landscape. As a first step, reliable estimates of copy number profiles are required. To this end we have developed an approach to capitalize on the growing amount of (capture-based) DNA sequencing data to accurately estimate copy number profiles of tumors (PropSeg). For the subsequent steps required to chart the landscape, we have developed algorithms to identify recurrently aberrated single genes (ADMIRE) and co-occurrently aberrated gene pairs from copy number data.
Theme 2: Understanding molecular regulatory mechanisms
Understanding the basic molecular regulatory mechanisms that underlie the cancer landscape is important for several reasons. First, it allows more accurate intervention to reach the desired therapeutic effect. Second, it is essential for understanding the functional oncogenic role of spontaneously occurring somatic and germline variants as well as artificially induced perturbations routinely used in genetic screens such as insertional mutagenesis screens. Gene regulatory elements and local chromatin context are indeed important determinants of the effects of mutations on neighboring genes. Similarly, miRNAs are important regulators of target genes, thus impacting the larger genetic network and subsequently oncogenesis and therapy response. We develop Bayesian models of signaling pathways that integrate multiple data types and use measurable variables to estimate unobservable, but informative, variables such as pathway activation. We have applied these approaches on a breast cancer cell line panel exposed to an array of targeted drugs. The models allow the identification of effective (combination) therapies to kill cancer cells.
Theme 3: Mapping the molecular landscape to optimal treatments
In this theme we developed a number of approaches to map molecular profiles of tumors to optimal treatments. 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).
- Biomedical & health
- Computational biology
- Gene regulation
- Data integration
- Systems biology
- Signaling pathway modeling
Expertise and Track Record
We offer extensive computational biology expertise with a specific focus on oncology. Our strongest areas of expertise are genomic data integration for prediction of outcome and drug response in cell lines, mouse models and patient. We also perform genome scale metabolic modeling as well as integrative Bayesian modelling od signaling pathways.
Farazi TA, Ten Hoeve JJ, Brown M, Mihailovic A, Horlings HM, van de Vijver MJ, Tuschl T, Wessels LFA. Identification of distinct miRNA target regulation between breast cancer molecular subtypes using AGO2-PAR-CLIP and patient datasets. Genome Biol. 2014 Jan 7;15(1):R9.
We performed the integration of miRNA and mRNA data.
Akhtar W, de Jong J, Pindyurin AV, Pagie L, Meuleman W, de Ridder J, Berns A, Wessels LFA, van Lohuizen M, van Steensel B. Chromatin position effects assayed by thousands of reporters integrated in parallel. Cell. 2013 Aug 15;154(4):914-27
We performed the analysis of the TRIP data and the integration thereof with ENCODE data.
Desmet CJ, Gallenne T, Prieur A, Reyal F, Wittner BS, Geigera TR, Smit MA, Visser NL, Laoukilia J, Iskita S, Rodenko B, Zwart W, Evers B, Horlings H, Ajouaou A, Zevenhoven J, van Vliet MJ, Ramaswamy S, Wessels LFA and Peeper DS, Identification of a pharmacologically tractable Fra-1/ADORA2B axis promoting breast cancer metastasis, Proc Natl Acad Sci U S A. 2013 Mar 26;110(13):5139-44
We performed the analysis of the gene expression data from the cell lines and also built the diagnostic predictor for the human samples.
Huang S, Holzel M, Knijnenburg T, Schlicker A, Roepman P, McDermott U, Garnett M, Grernrum W, Sun C, Prahallad A, Groenendijk FH, Mittempergher L, Nijkamp W, Neefjes J, Salazar R, ten Dijke P, Tanaka HUF, Beijersbergen RL, Wessels, LFA, and Bernards R, MED12 controls the response to multiple cancer drugs through regulation of TGFβ receptor signaling. Cell. 2012 Nov 21;151(5):937-50
We analyzed the RNAseq data from the cell line experiments, built RNAseq signatures predictive of EMT and interpreted the results in the context of the Sanger Cell line panel.
Identifying oncogenes and oncogenic pathways high-throughput genomic data To identify cancer genes, we developed a framework for the detection of common insertion sites from insertional mutagenesis screens (de Ridder et al. 2006). The approach has been extended to 1) identify collaborating cancer genes by detecting co-occurrences of insertions (de Ridder et al. 2007); 3) to identify cancer genes by detecting commonly aberrated regions from array comparative genomic hybridization datasets (Klijn et al. 2008) and 4) identify networks of co-occurring copy number alterations (Klijn et al. 2010) which point to interactions between cancer genes. Recently, we have extended this approach to detect combinatorial association logic networks (CALs): simple logic circuits which employ combinations of co-occurring and mutually exclusive insertions to predict the expression pattern of downstream targets (de Ridder et al. 2010). This mutational genomics approach allows the identification of pathways involved in tumorigenesis. We have recently extended KC-SMART (Klijn et al 2008) to a permutation free approach for the detection of recurrent aberrations at multiple levels.
We have recently developed OncoScape, an method that integrates mutiple data types from the The Cancer Genome Atlas (TCGA) and project Achilles to score genes as Oncogenes or Tumor Suppressors. This approach identifies most known cancer genes and most interestingly, points us towards new potential cancer genes.
- van Dyk E, Reinders MJT and Wessels LFA, A scale-space method for detecting recurrent DNA copy number changes with analytical false discovery rate control, Nucleid Acids Research, In press.
- de Ridder J, Gerrits A, Bot J, de Haan G, Reinders M, Wessels L. Inferring combinatorial association logic networks in multimodal genome-wide screens. Bioinformatics. 2010 Jun 15;26(12):i149-57.
- Klijn C, Bot J, Adams DJ, Reinders M, Wessels LFA, Jonkers J. Identification of networks of co-occurring, tumor-related DNA copy number changes using a genome-wide scoring approach. PLoS Comput Biol. 2010 Jan;6(1):e1000631.
- Klijn C, Holstege H, de Ridder J, Liu X, Reinders M, Jonkers J, Wessels LFA. Identification of cancer genes using a statistical framework for multiexperiment analysis of nondiscretized array CGH data. Nucleic Acids Res. 2008 Feb;36(2):e13.
- de Ridder J, Kool J, Uren A, Bot J, Wessels LFA, Reinders M. Co-occurrence analysis of insertional mutagenesis data reveals cooperating oncogenes. Bioinformatics. 2007 Jul 1;23(13):i133-41.
- de Ridder J, Uren A, Kool J, Reinders M, Wessels LFA. Detecting statistically significant common insertion sites in retroviral insertional mutagenesis screens. PLoS Comput Biol. 2006 Dec 8;2(12):e166. Epub 2006 Oct 24.
- 13 Postdocs
- 1 Technician
- 1 Lab manager
- 0 Biostatisticians
- 1 IT-specialist
- High performance computing and storage.