The genomic landscape of metastatic castration-resistant prostate cancers reveals multiple distinct genotypes with potential clinical impact. Lisanne F. van Dessel, Job van Riet, Minke Smits, Yanyun Zhu, Paul Hamberg, Michiel S. van der Heijden, Andries M. Bergman, Inge M. van Oort, Ronald de Wit, Emile E. Voest, Neeltje Steeghs, Takafumi N. Yamaguchi, Julie Livingstone, Paul C. Boutros, John W. M. Martens, Stefan Sleijfer, Edwin Cuppen, Wilbert Zwart, Harmen J. G. van de Werken, Niven Mehra & Martijn P. Lolkema. (2019). Nat Commun. 2019 Nov 20;10(1):5251.
The genomic landscape of metastatic breast cancer highlights changes in mutation and signature frequencies. Lindsay Angus, Marcel Smid, Saskia M Wilting, Job van Riet, Arne Van Hoeck, Luan Nguyen, Serena Nik-Zainal, Tessa G Steenbruggen, Vivianne C G Tjan-Heijnen, Mariette Labots, Johanna M G H van Riel, Haiko J Bloemendal, Neeltje Steeghs, Martijn P Lolkema, Emile E Voest, Harmen J G van de Werken, Agnes Jager, Edwin Cuppen, Stefan Sleijfer, John W M Martens. (2019).Nat Commun. 2019 Nov 20;10(1):5251.
An intuitive web-application for interactive B-allele frequency and copy-number visualization of next-generation sequencing data. Job van Riet, Niels M.G.Krol, Peggy N.Atmodimedjo, Erwin Brosens, Wilfred F.J.van IJcken, Maurice P.H.M.Jansen, John W.M.Martens, Leendert H.Looijenga, GuidoJenster, Hendrikus J.Dubbink, Winand N.M.Dinjens, Harmen J.G.van de Werken. J. Mol. (2018). J Mol Diagn. 2018. Mar;20(2):166-176
Small chromosomal regions position themselves autonomously according to their chromatin class. Harmen J G van de Werken, Josien C Haan, Yana Feodorova, Dominika Bijos, An Weuts, Koen Theunis, Sjoerd J B Holwerda, Wouter Meuleman, Ludo Pagie, Katharina Thanisch, Parveen Kumar, Heinrich Leonhardt, Peter Marynen, Bas van Steensel, Thierry Voet, Wouter de Laat, Irina Solovei, Boris Joffe. (2017). Genome Res. 2017 Jun;27(6):922-933.
Decoding the DNA Methylome of Mantle Cell Lymphoma in the Light of the Entire B Cell Lineage. Ana C Queirós. Renée Beekman. Roser Vilarrasa-Blasi. Martí Duran-Ferrer. Guillem Clot. Angelika Merkel. Emanuele Raineri. Nuria Russiñol. Giancarlo Castellano. Sílvia Beà. Alba Navarro. Marta Kulis. Núria Verdaguer-Dot. Pedro Jares. Anna Enjuanes. María José Calasanz. Anke Bergmann, Inga Vater, Itziar Salaverría, Harmen J G van de Werken, Wyndham H Wilson, Avik Datta, Paul Flicek, Romina Royo, Joost Martens, Eva Giné, Armando Lopez-Guillermo, Hendrik G Stunnenberg, Wolfram Klapper, Christiane Pott, Simon Heath, Ivo G Gut, Reiner Siebert, Elías Campo, José I Martín-Subero. (2016). Cancer Cell. 2016 Nov 14;30(5):806-821.
Robust 4C-seq data analysis to screen for regulatory DNA interactions. Harmen J G van de Werken, Gilad Landan, Sjoerd J B Holwerda, Michael Hoichman, Petra Klous, Ran Chachik, Erik Splinter, Christian Valdes-Quezada, Yuva Oz, Britta A M Bouwman, Marjon J A M Verstegen, Elzo de Wit, Amos Tanay, Wouter de Laat. (2012). Nat Methods. 2012 Oct;9(10):969-72.
Next to Harmen’s research he runs, as managing director, the Erasmus MC Cancer Computational Biology Center (CCBC). The CCBC facilitates ICT and bioinformatics for research and clinic. The CCBC aims to support, stimulate and innovate mostly omics-based cancer research, including (epi)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. Moreover, the CCBC develops and implement bioinformatics tools to improve and visualize their big data analyses instantly. The synergistic combination of both a facility and research group generates the possibilities to rapidly implement new scientific insights and scientific tools in the facility and support the research at a dynamic and high level.
Cancer onset, progression and drug resistance mechanisms are driven by hereditary and somatically acquired genomic aberrations. The genomic instability of cancer is reflected by mutations in cancer driver genes and their coding changes, but also in somatic structural variants (SV) and mutations in non-coding areas that encompass 98% of our genome. The etiology and importance of SVs as drivers of cancer is relatively under-examined and the contribution of non-coding regions towards cancer cell behavior is still enigmatic. Moreover, insights in cancer heterogeneity and clonal evolution is important to improve our understanding of treatment response and therapy resistance.
This computational biology group interrogate the entire cancer coding and non-coding (epi)genome such as promoters, enhancers, silencers. The analysis and examination of clonal evolution of solid tumors generates insights in somatic genetic changes that contribute to cancer progression and mechanisms of drug-resistance, e.g. mutations in transporters and metabolic enzymes selected by the pressure of the treatment. Moreover, we develop novel tools that visualize our big genomic data results that help improve patient stratification and diagnostics.
We apply and develop algorithms on solid tumors using Whole Genome Sequencing data sets and matched RNA-seq data from breast, prostate cancer patients, among others solid cancers. These comprehensive and in-house data sets will give us the opportunity to unravel novel biology including interaction of DNA elements, regulatory mechanisms, mutation and rearrangement signatures, but also aberrant splicing and fusion gene detection. We apply state-of-the-art bioinformatics, statistical analyses, Machine Learning and Deep Learning methods to process our high-throughput data from alignments to data integration and from mutational calling to pathway analysis and carry out tailor-made cluster and network analysis. Furthermore, we integrate our data with publicly available data sources from ChIP-seq, DNA-methylation and 3D chromosome conformation capture assays, to reveal (non-coding) drivers of cancer initiation and progression and importantly drug-resistance6. With our findings we predict new biomarkers for disease progression and monitoring and discover potential novel therapy targets. Moreover we use the gained knowledge to develop and improve predictive models to stratify patients for the right treatment to the right patient at the right time. Ultimately, our goal is to improve current clinical practice and patient’s well-being.