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• SUM-seq data for Macrophage polarisation to M1 and M2 phenotypes experiment

  We stimulated iPSC-derived M0 macrophages with LPS and IFN-γ to induce M1 polarization or IL-4 to induce M2 polarization. To discern early and sustained responses at chromatin accessibility and gene expression levels, we collected samples at five time points along the two polarization trajectories; prior to stimulation (M0) and at 1-hour, 6-hour, 10-hour, and 24-hour intervals, each sampled in duplicates totaling 18 samples, and subjected them to SUM-seq library preparation. Sequenced files for both data modalities are provided as demultiplexed fastq files.

  Dataset EGAD50000001206

• Short read sequencing dataset of pathogen-stimulated PBMCs

  QuantSeq 3'-mRNAseq. 5 donors, 4 stimuli, 2 time points

  Dataset EGAD50000000012

• Methylation_PBL_IBD_LTS

  Longitudinal whole-genome DNA methylation profiling of PBL obtained from IBD patients (CD and UC) at two different time points

  Dataset EGAD00010002402

• Integrating molecular imaging and transcriptomic profiling in advanced HER2-positive breast cancer receiving trastuzumab emtansine (T-DM1): an analysis of the ZEPHIR clinical trial

  Purpose: The ZEPHIR clinical trial evaluated the role of [89Zr]trastuzumab-PET/CT (HER2-PET/CT) and 2-[18F]fluoro-2-deoxy-D-glucose PET/CT ([18F]FDG-PET/CT) in predicting outcomes in patients with advanced HER2-positive breast cancer treated with trastuzumab emtansine (T-DM1). Here, we combined molecular/metabolic imaging and transcriptomic data to investigate the biological processes associated with [89Zr]trastuzumab and [18F]FDG uptake, and to dissect the mechanisms involved in T-DM1 resistance. Experimental design: RNA was extracted from metastasis biopsies obtained at study inclusion in the ZEPHIR trial. HER2-PET/CT and [18F]FDG-PET/CT imaging data of biopsied lesions were integrated with RNA sequencing data. Lesions were compared based on the level of [89Zr]trastuzumab uptake (dichotomized into high/low classes) as well as on the presence/absence of metabolic response, defined comparing baseline and on-treatment [18F]FDG-PET/CT. Results: We analyzed matched transcriptomic and molecular/metabolic imaging data for 24 metastases (one lesion/patient). Pathways involved in extracellular matrix (ECM) organization, glycosylphosphatidylinositol synthesis, and collagen formation were enriched in lesions presenting low [89Zr]trastuzumab uptake. [18F]FDG uptake at baseline correlated with proliferation and immune-related processes. Hypoxia and ECM-related processes were enriched in lesions that showed no metabolic response to T-DM1. Interestingly, immune-related processes (e.g., Fc gamma receptor activation, complement cascade) were associated with high [89Zr]trastuzumab uptake and metabolic response. Conclusions: To our knowledge, this study represents the first correlative analysis between [89Zr]trastuzumab tumor uptake and gene expression profiling in humans. Our findings point toward a role of ECM in impairing [89Zr]trastuzumab tumor uptake and T-DM1 metabolic response in advanced HER2-positive breast cancer, and highlight the potential of molecular imaging to depict tumor microenvironment features.

  Study EGAS50000000470

• Whole Exome and Whole Genome Profiling as well Genome-Wide Methylation Profiling from Early to Advanced Chronic Lymphocytic leukemia (CLL), Genome-Wide Methylation Profiling in Monoclonal B Cell Lymphocytosis (MBL)

  Serial whole exome sequencing of samples collected from 21 patients at multiple time points during progression from early to late chronic lymphocytic leukemia (CLL) and one matching non-tumor tissue sample from each patient. Additional whole genome sequencing of samples from 3 selected patients. Genome-wide B cell methylation compared between 5 healthy donors from the same age group as patients, 20 patients with monoclonal B cell lymphocytosis and in serial CLL samples with matching whole exome sequencing. Moreover, genome-wide single-cell RRBS-profiling of B cells from one healthy donor and from one patient with CLL was performed using a protocol newly developed by the A. Meissner group.

  Study phs001431

• Spatial heterogeneity of follicular lymphoma

  Follicular lymphoma (FL) is an incurable B cell malignancy characterized by advanced stage disease and a heterogeneous clinical course. Recent genomic studies have focused on profiling “single” FL biopsies over several time-points, however, multi-site sampling in solid cancers has demonstrated profound spatial intra-tumor heterogeneity (ITH) with implications for precision medicine based initiatives. This study examined the extent of spatial heterogeneity in FL by whole exome sequencing 22 synchronously removed spatially separated biopsies from 9 patients. We observed significant differences in the extent of ITH across cases, with two distinct patterns of high and low spatial heterogeneity emerging. Site-specific alterations in genes with biological, prognostic or therapeutic relevance included, TNFRSF14, PIK3CD, TNFAIP3, PTEN, EP300 and XBP1. In depth characterization of these variants using deep-sequencing techniques confirmed their discordant nature, suggesting on-going genetic diversification driving evolution after widespread tumor dissemination. There was evidence of tumors comprising multiple competing subclones, with distinct clusters of mutations demonstrating differential expansions within spatially-separated sites. For cases where spatial tumors were examined at two time-points (FL and transformation to diffuse large B cell lymphoma (DLBCL)), the degree of heterogeneity increased with transformation. Collectively, our results demonstrate that spatial ITH is prevalent in FL. The existence of site-specific aberrations suggests that a single biopsy may not be sufficient in all patients to capture the full genomic complexity present and these spatial variations need to be considered in biomarker-led clinical studies.

  Dataset EGAD00001003553

• Average_5mC_Roadmap_histoneMarks

  Average methylation levels based on nanopolish calls from Roadmap Epigenomics histone mark annotations using different cell types. Data from 8 individuals at different time points.

  Dataset EGAD00010002422

• Average_5hmC_Roadmap_histoneMarks

  Average hydroxymethylation levels based on megalodon calls from Roadmap Epigenomics histone mark annotations using different cell types. Data from 8 individuals at different time points.

  Dataset EGAD00010002417

• Average_5hmC_Roadmap_chromatinStates

  Average hydroxymethylation levels based on megalodon calls from Roadmap Epigenomics chromatin state annotations using different cell types. Data from 8 individuals at different time points.

  Dataset EGAD00010002414

• Average_5mC_Roadmap_chromatinStates

  Average methylation levels based on nanopolish calls from Roadmap Epigenomics chromatin state annotations using different cell types. Data from 8 individuals at different time points.

  Dataset EGAD00010002413