Jimmy Van den Eynden

This paper introduces SPOT-Mut, a graph convolutional neural network that predicts clonal mutations from spatial transcriptomics data, achieving high precision and recall for key driver genes.

This study identifies RUVBL1 and RUVBL2 as druggable regulators of MYCN in neuroblastoma, showing their inhibition reduces MYC signaling and induces cell cycle arrest and apoptosis.

This paper introduces FibeRtypeR, a tool to estimate muscle fiber type proportions from bulk transcriptomics and proteomics data using single-fiber reference profiles.

The paper uses spatial transcriptomics to analyze neuroblastoma microenvironments, identifying novel paracrine interactions between cancer cells and surrounding cells.

This study shows that combining ATR and ALK inhibitors improves survival in MYCN-amplified neuroblastoma mouse models lacking ALK mutations.

This study uses whole genome sequencing to trace the early origins of small intestine neuroendocrine tumors, showing that major genetic changes and metastases can originate in childhood or adolescence.

The paper benchmarks 13 HLA genotyping tools on three datasets to identify the most accurate methods and provides consensus predictions for TCGA samples.

The paper uses post-mortem tissues to study mutational clonality in healthy tissues, finding smoking-induced clonal changes in oral epithelia and confirming NOTCH1/2-, TP53-, and FAT1-driven clones.

The paper quantifies neoantigen depletion in cancer genomes and finds negligible depletion signals, suggesting weak or absent immunogenic negative selection.

This study identifies an extended context-specific mutational signature in melanoma promoter mutations, showing UV-induced vulnerability and subclonal mutation development in affected positions.