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/

Genomic and transcriptomic characterization of metastases of rare urothelial carcinomas

Bladder cancer is the most common type of urothelial carcinoma (UC), accounting for 90-95% of cases. The remaining 5-10% are considered rare UC for which treatment is suboptimal, leading to worse clinical outcomes. The lack of molecular data hampers the discovery of effective targeted therapies for these patients. In this study, we will analyze whole-genome DNA (WGS) and RNA sequencing data from 223 patients with metastatic bladder cancer (N=164) and metastatic rare UC (N=59). Using bioinformatics tools, we will identify key genomic/transcriptomic differences between metastases of rare UC and bladder cancer, and identify targets from already available therapeutic drugs (e.g., anti-FGFR or immunotherapy) which could benefit these patients in the short term.

Further reading (click to link to article)

https://doi.org/10.1016/j.euo.2025.04.007

CBBI group’s scientific endeavors

1) 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.
2) 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.
3) 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.
4) Empowering Scientific Data Processing: We develop tools and implement algorithms for high-end scientific data processing, enabling the visualization of extensive genomics datasets.

Further reading (click to link to article)

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).

The relation of donor attributes, de novo chromosomal aberrations and IPSC growth

ICELL is a collaboration between Erasmus MC, Erasmus University, and Delft University studying how genetic diversity influences stem cell-based experiments by combining cells from a multitude of donors in the same petri-dish based experiments. For this internship project, we explore whether donor characteristics other than genetics —such as age —affect cell growth, mutation rates, and differentiation. As most data, such as cell growth rates, is already available, the project is mainly focused on literature research and setting up the statistical models to test the relationships. If time permits, the student can obtain missing data for about 20% of the samples. The student will also become familiar with iPSC lab workflows, different forms of genetics, and data analysis practices.

Further reading (click to link to article)

https://convergence.nl/flagship-convergence-in-a-dish/

Long read metagenomics sequencing with Nanopore to gain insights in Depression

In our recent flagship publication, we linked depression symptoms to the gut microbiome1. The microbiome is sequenced using 16S rRNA or metagenomic sequencing, which has been a scientific breakthrough in the field of sequencing in recent years. Within our lab, the Core Facility Genomics at Erasmus MC, we are constantly working to improve the data quality we deliver to both our own researchers and (inter)national clients. In this project, you can be part of this endeavor by developing algorithms and implementing analysis workflows for processing metagenomics data generated by Oxford Nanopore Technologies’ long-read sequencing. During this project, you would conduct literature research on current workflows, assess the ease of implementation, and write and develop software tools to retrieve and benchmark the results across workflows, with the aim of better understanding the interaction of depression with the gut microbiome.

Further reading (click to link to article)

https://doi.org/10.1038/s41467-022-34502-3