prof. Jimmy Van den Eynden (MD, PhD)
Principal Investigator – Lab of computational cancer genomics and tumour evolution
Assistant professor (Faculty of Medicine and Health Sciences, UGent)
We are using computational approaches to study somatic mutation patterns from different cancer types to gain more insights in tumor evolution. Somatic mutations are small DNA errors that accumulate during lifetime, eventually resulting in the generation of a malignant tumor. Because some of these mutations are under strong evolutionary pressure, studying their patterns has the potential to identify new cancer genes, indicate specific cancer vulnerabilities or unveil fundamental processes that occurred during tumor evolution like tumor-immune interactions.
Jimmy Van den Eynden completed his medical studies in 2003.
He obtained a PhD in biomedical sciences in 2010 and an MSc in bioinformatics in 2013.
Since 2013 he is performing cancer genomics research.
He was a postdoc at Ghent University from 2013-2015 and at the University of Gothenburg (Sweden) from 2015-2018.
During this time, he also worked as an EMBO visiting scientist at the Cancer Research UK Cambridge Institute.
Since 2018 he’s an assistant professor at Ghent University where he’s leading a computational cancer genomics research group focussing on somatic mutation patterns and tumour evolution.
- Lack of detectable neoantigen depletion signals in the untreated cancer genome. Nature Genetics, 2019. (PMID: 31768072)
- Low immunogenicity of common cancer hot spot mutations resulting in false immunogenic selection signals. PLoS Genetics, 2021. (PMID: 33556087)
- ALK ligand ALKAL2 potentiates MYCN-driven neuroblastoma in the absence of ALK mutation. EMBO Journal, 2021 (PMID: 33411331)
- 11q Deletion or ALK Activity Curbs DLG2 Expression to Maintain an Undifferentiated State in Neuroblastoma. Cell Reports, 2020. (PMID: 32966799)
- Phosphoproteome and gene expression profiling of ALK inhibition in neuroblastoma cell lines reveals conserved oncogenic pathways. Sci Signal., 2018. (PMID: 30459281)
- Mutational signatures are critical for proper estimation of selection pressures in somatic mutation data using the dN/dS metric. Front. Genet., 2017. (PMID: 28642787)
- Recurrent promoter mutations in melanoma are defined by an extended context-specific mutational signature. PLoS Genet., 2017. (PMID: 28489852)
- Somatic mutation patterns in hemizygous genomic regions unveil purifying selection during tumour evolution. PLoS Genet., 2016. (PMID: 28027311)
- Pan-cancer transcriptomic analysis associates long non-coding RNAs with key driver mutational events. Nat. Commun., 2016. (PMID: 28959951)
- SomInaClust: detection of cancer genes based on somatic mutation patterns of inactivation and clustering. BMC Bioinformatics, 2015. (PMID: 25903787)