← Back to list

Cancer Computational Biology Center ErasmusMC

Contact Details

Erasmus MC

Wytemaweg 80, 3015 CN Rotterdam

Harmen van de Werken


Hotel Description

Erasmus MC Cancer Computational Biology Center (CCBC)

The Erasmus MC Cancer Computational Biology Center (CCBC) part of the Cancer Institute and funded by the Daniel den Hoed Foundation, facilitates ICT and bioinformatics both for research as well as clinic. CCBC develops new computational algorithms, analysis methods and visualization tools in the field of computational biology. The CCBC aims to support, stimulate and innovate mostly omics-based cancer research, including genomics, transcriptomics and proteomics. With dedicated employees and well-structured robust infrastructure the CCBC is able to manage big data sources and combine activities of multiple cancer research groups. Involvement of the CCBC is preferentially before the start of a project, but can also be implemented later. Our main goals are four fold.

First, understand the origin and progression of cancer. Cancer (epi)genomes and transcriptomes are considerably different than the normal diploid genomes. We study large data sets of normal and cancer (epi)genomes to gain insights into genome stability, regulation of gene expression, protein function and cellular networks.

Second, identification of cancer biomarkers and therapy targets. Mutations and dysregulation of gene expression can change the quality and quantity of macromolecules, such as DNA, RNA and proteins. The identification of these aberrations and further characterization can provide diagnostic, prognostic and predictive biomarkers, being clinically extremely relevant and, if functionally important, these macromolecules are great candidates as therapy targets. Computational derived hypotheses and predictions are being generated and can be validated in close collaboration with the Erasmus MC Cancer Treatment Screening Facility (CTSF; https://ctcf.erasmusmc.nl/)

Third, implementation of Next Generation Sequencing (NGS)-based molecular diagnostics and personalized oncology. Individualized medicine and molecular diagnostics improve and will improve medical care at the Erasmus MC. We analyze large clinical NGS datasets and develop, build and implement novel computational tools to assess disease-specific DNA molecules, used in a clinical setting.

Fourth, data integration, tool development, data storage and computational biology.We maintain a local high compute and storage system to perform heavy computational analyses. Moreover, we provide storage, develop bioinformatics tools and design new computational algorithms. Therefore the CCBC is able to analyze, locally, normal and cancer-related DNA-seq, RNA-seq, ChIP-seq, 4C-seq, microarray, high-throughput qPCR and proteomics data. Using our implemented work flows, we analyze high-throughput data from alignments to data integration and from mutational calling to pathway analysis and carry out tailor-made cluster and network analysis. In addition, we perform uni- and multivariant statistical analysis, power calculations, survival analysis and advise research groups with their experimental design Understanding cancer and computational biology starts with a good education of BSc-,MSc- and PhD-students. Therefore, the CCBC is sharing its knowledge on cancer computational biology with national and international students.

  • Biomedical & health
  • Cancer Bioinformatics
  • Clustering Analysis
  • Clinical Bioinformatics
  • Omics-data analysis DNA, RNA, Proteomics and Protegenomics
  • Chromosome Conformation Capture Technologies ( 4C-seq, Hi-C )
  • Oncology
  • Gene Expression
  • Nuclear Organization
  • Promoter Enhancer interactions
  • Mutational Profiles and Signatures of Cancer Cells

Expertise and Track Record

Integration of DNA,RNA data with proteomics data (Proteogenomics)

  • Small chromosomal regions position themselves autonomously according to their chromatin class. van de Werken HJ, de Haan JC, et. al. Genome Res. 2017 Mar 24. pii: gr.213751.116. doi: 10.1101/gr.213751.116.
  • A reported 20-gene expression signature to predict lymph node-positive disease at radical cystectomy for muscle-invasive bladder cancer is clinically not applicable. van Kessel KE, van de Werken HJ, et. al. PLoS One. 2017 Mar 20;12(3):e0174039. doi: 10.1371/journal.pone.0174039. eCollection 2017 Mar 20.
  • Decoding the DNA Methylome of Mantle Cell Lymphoma in the Light of the Entire B Cell Lineage. Queirós AC, Beekman R, Vilarrasa-Blasi R, et. al. Cancer Cell. 2016 Nov 14;30(5):806-821. doi: 10.1016/j.ccell.2016.09.014.
  • Correlation of Gene Mutation Status with Copy Number Profile in Uveal Melanoma. Yavuzyigitoglu S, Drabarek W, et. al. Ophthalmology. 2017 Apr;124(4):573-575. doi: 10.1016/j.ophtha.2016.10.039.
  • Identification of a regulatory domain controlling the Nppa-Nppb gene cluster during heart development and stress. Sergeeva IA, Hooijkaas IB, et. al. Development. 2016 Jun 15;143(12):2135-46. doi: 10.1242/dev.132019.
  • Cell-free DNA mutations as biomarkers in breast cancer patients receiving tamoxifen. Jansen MP, Martens JW, et al. Oncotarget. 2016 Jul 12;7(28):43412-43418. doi: 10.18632/oncotarget.9727
  • An autonomous CEBPA enhancer specific for myeloid-lineage priming and neutrophilic differentiation. Avellino R, Havermans M, et. al. Blood. 2016 Jun 16;127(24):2991-3003. doi: 10.1182/blood-2016-01-695759.
  • The lncRNA MIR31HG regulates p16(INK4A) expression to modulate senescence. Montes M, Nielsen MM, et. al. Nat Commun. 2015 Apr 24;6:6967. doi: 10.1038/ncomms7967.
  • Cell-free DNA mutations as biomarkers in breast cancer patients receiving tamoxifen. Jansen MP, Martens JW, et al. Oncotarget. 2016 Jul 12;7(28):43412-43418. doi: 10.18632/oncotarget.9727

Hotel Characteristics

  • 1 Managing Director
  • 3 (Clinical) bioinformaticians


GEO, TCGA, ENCODE,CPCT, Cancer Research UK