Utrecht, Netherlands
Internship Explorer
Internship Explorer
Are you excited about applying computational biology and machine learning to advance cancer therapeutics? Are you interested in using large-scale genomics data to address real challenges in antibody drug discovery? This internship offers the opportunity to work on a research project at the intersection of cancer genomics, RNA splicing, and therapeutic target design within Genmab’s Discovery Data Science team. Alternative RNA splicing can generate multiple protein isoforms from a single gene and is increasingly recognized as an important mechanism in cancer biology. In particular, alternative splicing can alter protein structure in ways that affect the accessibility of therapeutic targets on the cell surface, potentially enabling tumors to evade targeted therapies. In this project, you will investigate whether alternative splicing of selected oncology targets can produce isoforms that remove extracellular antibody-binding regions, truncate membrane proteins, or generate soluble decoy proteins. Using large-scale cancer transcriptomic datasets (e.g., TCGA and GTEx), the student will map splicing patterns of therapeutic target genes across tumor types and identify splice isoforms that may impact protein structure, domain composition, or membrane localization. These analyses can reveal whether tumors may evade antibody therapies through alternative splicing, helping guide therapeutic target selection and design. Building on this foundation, you will quantify how frequently such splice isoforms occur across cancers and estimate the potential risk of splice-mediated therapeutic escape across tumor types. If time and the university's guidelines permits, the project can be extended to identify regulatory mechanisms underlying these events by linking target-specific splice changes to broader tumor splicing programs using machine-learning approaches. For example, latent splicing patterns can be learned from cancer transcriptomic data to infer splice-factor activity that may drive escape isoform formation. You will have significant ownership of this fully computational research project and the opportunity to develop novel computational analyses that may contribute to a scientific publication and inform ongoing target discovery efforts!
