prof. Jimmy Van den Eynden (MD, PhD)

CRIG group leader
Jimmy Van Den Eynden


Principal Investigator – Lab of Computational Cancer Genomics and Tumor Evolution (CCGG)
Associate professor (Faculty of Medicine and Health Sciences, UGent)

 

Research focus

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.
My lab aims to better understand this tumor evolution using state-of-the-art as well as newly developed wet-lab and computational approaches. By gaining new insights in the key mechanisms underlying tumor evolution, our ultimate goals is to identify novel diagnostic and therapeutic strategies for cancer patients.
Our 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. These data are obtained from public repositories or from newly sequenced tissues from cancer patients, whole-body donors (post-mortem tissues) or experimental model systems.
The lab has ongoing research projects on skin cancer, head & neck cancer, neuroblastoma and the development of novel spatial omics technologies. Most of these projects are part of multidisciplinary collaborations with other CRIG groups or (inter)national partners. 
 

Biography

Jimmy Van den Eynden completed his medical studies (MD) 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 associate professor at Ghent University where he’s leading the lab of Computational Cancer Genomics and Tumor Evolution (CCGG). In 2022-2023 he was on a 1 year scientific sabbatical in the lab of Ruth Palmer (Sweden) to focus on the set-up and analysis of spatial transcriptomics applications, which is now a key expertise in his lab. 
 

Research team

Key publications

  • MHC class II genotypes are independent predictors of anti-PD1 immunotherapy response in melanoma. Commun Med, 2024. (PMID 39349759)
  • Spatial transcriptomics exploration of the primary neuroblastoma microenvironment unveils novel paracrine interactions. BioRxiv, 2024. 
  • Preclinical exploration of the DNA damage response pathway using the interactive neuroblastoma cell line explorer CLEAN. NAR Cancer, 2024. (PMID: 38213997)
  • Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data. BMC Genomics, 2023. (PMID: 37161318)
  • A clinically annotated post-mortem approach to study multi-organ somatic mutational clonality in normal tissues. Scientific Reports, 2022. (PMID 35725896)
  • ATR inhibition enables complete tumour regression in ALK-driven NB mouse models. Nature Communications, 2021. (PMID: 34819497)
  • Low immunogenicity of common cancer hot spot mutations resulting in false immunogenic selection signals. PLoS Genetics, 2021. (PMID: 33556087)
  • 11q Deletion or ALK Activity Curbs DLG2 Expression to Maintain an Undifferentiated State in Neuroblastoma. Cell Reports, 2020. (PMID: 32966799)
  • Lack of detectable neoantigen depletion signals in the untreated cancer genome. Nature Genetics, 2019. (PMID: 31768072) 
  • Phosphoproteome and gene expression profiling of ALK inhibition in neuroblastoma cell lines reveals conserved oncogenic pathways. Science Signaling, 2018. (PMID: 30459281)
     

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