Computational Cancer Genomics and Tumour Evolution

CCGG

Within this line of research, the 'Anatomy and Embryology Research Group' uses experimental and computational approaches to study human carcinogenesis.

Cancer is a disease of the genome. It is caused by the successive accumulation of DNA errors (somatic driver mutations). This carcinogenic process starts in normal cells and is an example of Darwinian evolution, where each driver event results in a fitness advantage, positive selection and clonal expansion of the affected cells.

The CCGG lab aims to understand tumor evolution better using state-of-the-art and newly developed wet lab and computational approaches. By gaining new insights into the key mechanisms underlying tumor evolution, the ultimate goal is to identify novel diagnostic and therapeutic strategies for cancer patients.

The lab's core expertise is in the analysis of somatic mutation patterns, spatial omics applications, machine learning, and interactive data visualization. Analyses are mostly performed on next-generation sequencing data, which are obtained from public repositories or newly sequenced tissues from cancer patients, whole-body donors (post-mortem tissues), or experimental model systems. 

Research projects 

Mutant clones in normal tissues

Recent evidence suggests that somatic mutations in cancer genes lead to microclone formation in multiple histologically normal epithelial tissues, likely forming the foundation of human carcinogenesis.

We aim to determine how these clones and their putative interactions eventually result in malignant tumor formation.  

 

Neuroblastoma 

Neuroblastoma is the most frequent cancer during infancy and accounts for 15% of pediatric cancer-related death.

Based on the analysis of DNA and RNA sequencing data obtained from different experimental model systems (e.g., cell lines, mice models), this multidisciplinary and international project aims to identify novel therapeutic targets.

 

Tumor evolution and immune selection

Tumor evolution is determined by interactions with the immune system, which eventually determine whether and how a patient will respond to cancer immunotherapy.

We develop novel computational approaches to study neoantigens and immune selection signals in large, publicly available genomic and transcriptomic datasets and evaluate those signals as putative biomarkers for immunotherapy responses.

Collaborations

Publications

Questions?

  • Jimmy Van den Eynden, researcher

https://ccgg.ugent.be/

+32 9 332 48 55