Edwin Cuppen Group
Cancer genetics
Our Focus
The Cuppen group investigates the causes and consequences of genetic variation under normal and disease conditions. We are using the latest next-generation sequencing based technologies for studying the basis and molecular mechanisms of specific human diseases with a specific focus on cancer. Interpretation of alterations at the single base letter and structural level in both protein-coding and regulatory domains is one of the main challenges that we address using systematic integrative experimental and computational approaches. Furthermore, we are studying the origin and mechanisms by which de novo genetic variation is induced.
Our research is performed in a highly collaborative fashion both within the group and with other research groups and clinical laboratories and researchers on a local, national and international level. These collaborations are important for translating fundamental knowledge into clinical practice and routine diagnostics.
Our main fundamental research questions are:
- What are the mutation rates and processes in healthy and pre-malignant cells in different tissues during the process of aging?
- How do these mutations contribute to disease and aging and what are the mechanisms that protect normal cells from disfunctioning?
- How do mutations contribute to tumor characteristics including response to treatment?
- What are the causes of complex structural variation (SVs)
- What are the consequences of complex SVs on genome/gene function in congenital disease
We address these questions utilizing primary patient material, stem cell-based technologies, including organoids and patient-derived induced pluripotent stem cells. We apply whole genome sequencing technologies to obtain a comprehensive overview of all types of genetic variants. The effects of single nucleotide variants and structural variants are determined by sequencing-based technologies such as RNA-seq, Chip-seq et cetera. Such data sets are integrated and analyzed in the context of large bodies of public data by bioinformaticians and computational scientists to dissect putative mechanism and define hypotheses on potential causal relationships. We envision that these approaches will contribute to increasing fundamental biological knowledge, improving diagnostics and the development of personalized medicine.
