Computational Biology & Bioinformatics in Immunology and Cancer

Department                            Immunology

Principal investigator          Harmen van de Werken

E-mail address                      h.vandewerken@erasmusmc.nl

Website                                     https://www.immunology.nl/research/harmen-van-de-werken/

Multiple projects from Cancer to Immune deficiency and infections

The Erasmus MC Computational Biology & Bioinformatics in Immunology and cancer group (CBBI) is committed to unraveling the intricacies of immune responses to both cancer and microbial infections, while also pinpointing the molecular factors contributing to human immune disorders. To confront these substantial scientific challenges, we employ a multifaceted approach involving the comprehensive examination and integration of extensive genomic and clinical datasets. We pioneer in developing and applying cutting-edge computational algorithms, artificial intelligence, machine learning techniques, and visualization tools to dissect and interpret these datasets within the ever-evolving landscape of Cancer and Immunogenomics. Our ultimate goal is to translate our accrued scientific knowledge and innovative computational algorithms and tools into practical clinical applications, ultimately enhancing patient care and well-being.

The CBBI group’s scientific endeavors are firmly rooted in four fundamental pillars:

Integrating Big Data with Clinical Data: Leveraging advanced statistical learning methods, including Machine and Deep Learning, to enhance clinical decision-making. Our innovative approaches have allowed us to accurately predict the severity of COVID infections and forecast the metastatic spread of solid tumors, such as skin cancer.

Exploring Disease Onset and Development: Using Big Genomic Data to gain insights into the initiation and progression of various diseases, uncovering new avenues for scientific exploration. We’ve successfully unraveled genomic alterations in prostate, bladder, and neuroendocrine cancers, as well as examined gene expression changes in the immune response of Sjögren Syndrome. Additionally, we’ve delved into single-cell genomic analysis in Hepatitis B patients.

Developing Cutting-Edge Algorithms: We frequently design novel algorithms to extract critical biological information from Big Genomic and Cellular Data. Our algorithms can identify clustered mutations on the genome and even predict genome folding in three dimensions.

Empowering Scientific Data Processing: We develop tools and implement algorithms for high-end scientific data processing, enabling the visualization of extensive genomics datasets.

Our student projects are seamlessly integrated into ongoing research initiatives, drawing from one or more of the aforementioned CBBI pillars. For instance, we are currently engaged in a Project to enhance the prognosis of skin carcinoma metastasis by harnessing genomics, imaging, and clinical data through machine learning techniques. We are also dedicated to deepening our understanding of immune diseases in collaboration with 12 countries within the EU ImmunAID consortium. Furthermore, we have access to extensive Whole Genome and Transcriptome datasets from cancer patients through the Hartwig Medical Foundation. These datasets allow us to investigate drug resistance causes, as well as interactions between the immune system and cancer. Moreover, we have numerous other projects related to the immune system, cancer, and Big Data.
If you are interested in our work, please don’t hesitate to contact Harmen van de Werken at h.vandewerken@erasmusmc.nl. We look forward to discussing your research interests and how you can participate in M.Sc. or B.Sc. student projects.

Further reading

Rentroia-Pacheco, B. et al. Personalised decision making to predict absolute metastatic risk in cutaneous squamous cell carcinoma: development and validation of a clinico-pathological model. eClinicalMedicine 63, 102150 (2023).

van Dessel, L. F. et al. The genomic landscape of metastatic castration-resistant prostate cancers reveals multiple distinct genotypes with potential clinical impact. Nat. Commun. 10, 5251 (2019).

Nakauma-González, J. A. et al. Whole-genome mapping of APOBEC mutagenesis in metastatic urothelial carcinoma identifies driver hotspot mutations and a novel mutational signature. Cell Genomics 4, 100528 (2024).

Nakauma-González, J. A. et al. Comprehensive Molecular Characterization Reveals Genomic and Transcriptomic Subtypes of Metastatic Urothelial Carcinoma. Eur. Urol. 81, 331–336 (2022).

van Riet, J. et al. The genomic landscape of 85 advanced neuroendocrine neoplasms reveals subtype-heterogeneity and potential therapeutic targets. Nat. Commun. 12, 4612 (2021).

Huijser, E. et al. Hyperresponsive cytosolic DNA-sensing pathway in monocytes from primary Sjögren’s syndrome. Rheumatology 61, 3491–3496 (2022).

Osmani, Z. et al. HBsAg induces mild activation, but not suppression, of intrahepatic immune cells as shown by single-cell RNA sequencing of fine-needle aspirates in chronic HBV patients. J. Hepatol. 78, S1016 (2023).

Hazelaar, D. M., van Riet, J., Hoogstrate, Y. & van de Werken, H. J. G. Katdetectr: an R/bioconductor package utilizing unsupervised changepoint analysis for robust kataegis detection. Gigascience 12, 1–11 (2023).

van de Werken, H. J. G. et al. Robust 4C-seq data analysis to screen for regulatory DNA interactions. Nat. Methods 9, 969–972 (2012).

van Riet, J. et al. SNPitty: An intuitive web-application for interactive B-allele frequency and copy-number visualization of next-generation sequencing data. J. Mol. Diagnostics 20, 166–176 (2018).

van de Geer, W. S., van Riet, J. & van de Werken, H. J. G. ProteoDisco: A flexible R approach to generate customized protein databases for extended search space of novel and variant proteins in proteogenomic studies. Bioinformatics 38, 1437–1439 (2022).