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van Dam, S.

Publications and source records attributed to van Dam, S..

3 recordsLinked to original sources

Inhibitory Nectin-2/TIGIT interactions between CD163+ macrophages and CD8+ T cells in spatially defined immune niches associate with poor outcomes in neuroblastoma

Background: While immunotherapies have transformed outcomes in several adult cancers, more than half of pediatric patients with high-risk neuroblastoma fail to achieve sustained responses, resulting in poor outcomes. Therefore, understanding the phenotypic and spatial organization of immune cells within the neuroblastoma tumor microenvironment is essential to identify mechanisms driving immunotherapy failure. Methods: To identify spatial determinants of the neuroblastoma immune landscape that may contribute to treatment failure, we dissected immunoregulatory mechanisms at single-cell and protein levels in neuroblastoma samples from 28 patients, using imaging mass cytometry (IMC, n=14) and high-dimensional spectral flow cytometry (n=18). Results: We characterized four distinct cellular neighborhoods, ranging from tumor-dominant to stromal and immune niches. Macrophages were broadly distributed across these niches. In contrast, T and B cells were largely confined to immune niches, forming immature tertiary lymphoid structure-like aggregates within the tumor and stromal cores. T cells displayed a CD69+ tissue-resident memory phenotype, with an overall reduced cytotoxic profile (in CD8+ T cells) and increased expression of inhibitory receptors (predominantly in CD4+ T cells), indicative of dysfunctional features. Intriguingly, T cell functional states differed between spatial niches, with a TCF7+/Ki-67+ self-renewing profile within immune niches, and increased granzyme B expression in CD8+ T cells in tumor cores. Interaction analysis pinpointed CD163+ macrophages as the dominant immunosuppressive interaction partner of T cells, particularly within the tumor edge and immune cores. Analysis of inhibitory ligand-receptor interactions identified the TIGIT/PVR/Nectin-2 axis as the main engaged axis between CD163+ macrophages and T cells. Nectin-2 expression on macrophages was particularly high within immune cores, and elevated in macrophages with direct T cell contact, suggesting a high probability of in situ engagement. Consistent with this immunosuppressive role, both CD163 and Nectin-2 expression on macrophages correlated with poor patient survival, substantiating the clinical relevance of inhibitory macrophage-T cell interactions. Conclusions: This study reveals T cell-excluded tumor niches and targetable inhibitory interactions between CD163+ macrophages and T cells. These insights provide a rationale for combinatorial immunotherapeutic strategies that both enhance T cell infiltration into tumor-rich areas and disrupt inhibitory signaling to improve immunotherapy efficacy in neuroblastoma.

cancer biology↗

Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases

Gaining insight into the downstream consequences of non-coding variants is an essential step towards the identification of therapeutic targets from genome-wide association study (GWAS) findings. Here we have harmonized and integrated 8,727 RNA-seq samples with accompanying genotype data from multiple brain-regions from 14 datasets. This sample size enabled us to perform both cis- and trans-expression quantitative locus (eQTL) mapping. Upon comparing the brain cortex cis-eQTLs (for 12,307 unique genes at FDR<0.05) with a large blood cis-eQTL analysis (n=31,684 samples), we observed that brain eQTLs are more tissue specific than previously assumed. We inferred the brain cell type for 1,515 cis-eQTLs by using cell type proportion information. We conducted Mendelian Randomization on 31 brain-related traits using cis-eQTLs as instruments and found 159 significant findings that also passed colocalization. Furthermore, two multiple sclerosis (MS) findings had cell type specific signals, a neuron-specific cis-eQTL for CYP24A1 and a macrophage specific cis-eQTL for CLECL1. To further interpret GWAS hits, we performed trans-eQTL analysis. We identified 2,589 trans-eQTLs (at FDR<0.05) for 373 unique SNPs, affecting 1,263 unique genes, and 21 replicated significantly using single-nucleus RNA-seq data from excitatory neurons. We also generated a brain-specific gene-coregulation network that we used to predict which genes have brain-specific functions, and to perform a novel network analysis of Alzheimers disease (AD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS) and Parkinsons disease (PD) GWAS data. This resulted in the identification of distinct sets of genes that show significantly enriched co-regulation with genes inside the associated GWAS loci, and which might reflect drivers of these diseases.

genomics↗

Improving the diagnostic yield of exome-sequencing, by predicting gene-phenotype associations using large-scale gene expression analysis

Clinical interpretation of exome and genome sequencing data remains challenging and time consuming, with many variants with unknown effects found in genes with unknown functions. Automated prioritization of these variants can improve the speed of current diagnostics and identify previously unknown disease genes. Here, we used 31,499 RNA-seq samples to predict the phenotypic consequences of variants in genes. We developed GeneNetwork Assisted Diagnostic Optimization (GADO), a tool that uses these predictions in combination with a patients phenotype, denoted using HPO terms, to prioritize identified variants and ease interpretation. GADO is unique because it does not rely on existing knowledge of a gene and can therefore prioritize variants missed by tools that rely on existing annotations or pathway membership. In a validation trial on patients with a known genetic diagnosis, GADO prioritized the causative gene within the top 3 for 41% of the cases. Applying GADO to a cohort of 38 patients without genetic diagnosis, yielded new candidate genes for seven cases. Our results highlight the added value of GADO (www.genenetwork.nl) for increasing diagnostic yield and for implicating previously unknown disease-causing genes.

genetics↗