Comprehensive characterization of the prostate tumor microenvironment identifies CXCR4/CXCL12 crosstalk as a novel antiangiogenic therapeutic target in prostate cancer

Background: Crosstalk between neoplastic and stromal cells fosters prostate cancer (PCa) progression and dissemination. Insight in cell-to-cell communication networks provides new therapeutic avenues to mold processes that contribute to PCa tumor microenvironment (TME) alterations. Here we performed a detailed characterization of PCa tumor endothelial cells (TEC) to delineate intercellular crosstalk between TEC and the PCa TME. Methods: TEC isolated from 67 fresh radical prostatectomy (RP) specimens underwent multi-omic ex vivo characterization as well as orthogonal validation of both TEC functions and key markers by immunohistochemistry (IHC) and immunofluorescence (IF). To identify cell-cell interaction targets in TEC, we performed single-cell RNA sequencing (scRNA-seq) in four PCa patients who underwent a RP to catalogue cellular TME composition. Targets were cross-validated using IHC, publicly available datasets, cell culture expriments as well as a PCa xenograft mouse model. Results: Compared to adjacent normal endothelial cells (NEC) bulk RNA-seq analysis revealed upregulation of genes associated with tumor vasculature, collagen modification and extracellular matrix remodeling in TEC. PTGIR, PLAC9, CXCL12 and VDR were identified as TEC markers and confirmed by IF and IHC in an independent patient cohort. By scRNA-seq we identified 27 cell (sub)types, including endothelial cells (EC) with arterial, venous and immature signatures, as well as angiogenic tip EC. A focused molecular analysis revealed that arterial TEC displayed highest CXCL12 mRNA expression levels when compared to all other TME cell (sub)populations and showed a negative prognostic role. Receptor-ligand interaction analysis predicted interactions between arterial TEC derived CXCL12 and its cognate receptor CXCR4 on angiogenic tip EC. CXCL12 was in vitro and in vivo validated as actionable TEC target by highlighting the vessel number- and density- reducing activity of the CXCR4-inhibitor AMD3100 in murine PCa as well as by inhibition of TEC proliferation and migration in vitro. Conclusions: Overall, our comprehensive analysis identified novel PCa TEC targets and highlights CXCR4/CXCL12 interaction as a potential novel target to interfere with tumor angiogenesis in PCa

Exploring the VISTA of glial cells extended data

Extended data of PhD dissertation "Exploring the VISTA of glial cells

HPO Predictions 01-02-2018

HPO prediction Z-score

Exploring the VISTA of glial cells extended data

Extended data of PhD dissertation "Exploring the VISTA of glial cells

Summary statistics for time unadjusted ECG GWAS

This dataset contains 500 summary statistics data-files from GWASes performed ECG, unadjusted for tim

Summary statistics for time adjusted ECG GWAS

This dataset contains 500 summary statistics data-files from GWASes performed ECG, unadjusted for tim

Timepoints for both adjusted and unadjusted ECG datasets

This dataset contains the time offset in ms for iets timepoint in the EC

VIBE benchmark data

This upload contains the following: The benchmark input dataset. The benchmark outputs after post-processing. A README containing information of the used commits for the benchmarking. Additional relevant sources (also referenced in the README.md with the used commit IDs): https://github.com/molgenis/vibe https://github.com/molgenis/vibe-suppl https://github.com/svandenhoek/query_phenomizer

Evaluation datasets and pre-computed scores for: "CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations"

CAPICE is a computational method for predicting the pathogenicity of SNVs and InDels. This new repository added index for CAPICE v1.0 (build37) precomputed files. Repository description: 1) "paper_datasets.tar.gz" contains all datasets used in the CAPICE paper; 2) "capice_v1.0_build37_indels.tsv.gz" contains the precomputed scores for InDels in genome build 37 3) "capice_v1.0_build37_indels.tsv.gz.tbi" contains the index for file"capice_v1.0_build37_indels.tsv.gz" 4) "capice_v1.0_build37_snvs.tsv.gz" contains the precomputed scores for all possible SNVs in genome build 37 5) "capice_v1.0_build37_snvs.tsv.gz.tbi" contains the index for file "capice_v1.0_build37_snvs.tsv.gz"

Evaluation datasets and pre-computed scores for: "CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations"

CAPICE is a computational method for predicting the pathogenicity of SNVs and InDels. It is a gradient boosting tree model trained using a variety of genomic annotations used by CADD score and trained on the clinical significance. CAPICE performs consistently across diverse independent synthetic, and real clinical data sets. It ourperforms the current best method in pathogenicity estimation for variants of different molecular consequences and allele frequency. The dataset contains precomputed scores. "CAPICE_v1.0_GRCh37_whole_genome_SNVs_InDels.tsv.gz" contains the precomputed scores for all possible SNVs and InDels in genome build 37. The gzip file contains two sub-directories, namely, "SNVs" and "InDels". Within each sub-directory, there are gzip files containing precomputed scores per chromosome