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• Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor infiltrating T cells.

  Despite extensive studies on the chromatin landscape of exhausted T cells, the transcriptional wiring underlying functional and dysfunctional states of human tumor infiltrating lymphocytes (TILs) is incompletely understood. Here, we identify tissue-specific and general gene-regulatory landscapes in the wide breadth of CD8+ TIL functional states covering four cancer entities using single-cell chromatin profiling. We map enhancer-promoter interactions in human TILs by integrating single-cell chromatin accessibility with single-cell RNA-seq data from tumor entity-matching samples and prioritize key elements by super enhancer analysis. Our analysis reveals a human core chromatin trajectory to TIL dysfunction and identifies key enhancers, transcriptional regulators, and deregulated target genes involved in this process. Finally, we validate enhancer regulation at loci encoding PD1, TCF1, and TIM3 by targeting non-coding regulatory elements with potent CRISPR activators and repressors. In summary, our study advances the understanding of molecular regulation of TIL (dys-)function and provides a framework for modulating immunotherapeutic relevant TIL genes via their enhancers.

  Dataset EGAD00001008662

• Whole exome sequencing of virus-associated HCC

  To understand comprehensive combinations of molecular alterations and seek for potential therapeutic targets/pathways in virus-associated HCC, we have conducted a whole exome sequencing (in-solution capturing all human coding exons) of 150 pairs of HCC cases.

  Study EGAS00001000389

• Chromatin accessibility of clear cell renal cell carcinoma

  To investigate chromatin accessibility in ccRCC, we performed ATAC-seq in 3 ccRCC cell lines (786-O, A498, UMRC-2), and 1 normal kidney epithelium cell line (HK-2), cultured under normoxic (20% O2) and hypoxic (0.05% O2) conditions

  Study EGAS50000001325

• APOBEC mutagenesis is a common process in normal human small intestine

  The single base substitution mutational signatures SBS2 and SBS13, likely caused by APOBEC cytosine deaminases, are common in many human cancer types. However, the stimulus activating APOBEC mutagenesis is unknown and understanding of when it occurs in the progression from normal to cancer cell is limited. Here, as part of a wider survey of human tissues, we whole genome sequenced 342 microdissected normal epithelial crypts from the small intestines of 39 individuals. SBS2/13 mutations were present in 17% normal small intestine crypts and were likely due to APOBEC3A activity. Localised clusters of SBS2/13 mutations (kataegis) were also commonly found. APOBEC mutation burdens were variable between individuals and between crypts from the same individual. Crypts with SBS2/13 often had immediate crypt neighbours without SBS2/13, suggesting that the underlying cause of SBS2/13 is cell-intrinsic rather than a widely distributed microenvironmental exposure, or needs to be permitted by cell-intrinsic conditions. APOBEC mutagenesis occurred throughout the human lifespan, including in young children, and was episodic with a small number of episodes occurring during the life history of a single cell. The results indicate that APOBEC mutagenesis is more common in the small intestine epithelium than in many other cell types, and is an episodic process in vivo initiated or permitted by cell intrinsic factors.

  Dataset EGAD00001008764

• Gene fusion and transcriptomic landscapes of sarcomas

  Sarcoma represents a highly heterogeneous group of tumors. We report here the first unbiased and systematic search for gene fusions and analysis of transcriptomic profiles in 100 monomorphic sarcomas cases using RNA-sequencing profiling. Fusion genes were detected in two thirds of samples. Similarly to fusion genes such as PAX3/7-FOXO1, EWSR1/FUS-ETS, SS18-SSX, BRD3/4-NUTM1 or EWSR1/FUS/TAF15-NR4A3 characterizing well-defined entities, we show that all sarcomas displaying CIC fusions- whatever their fusion partner (being DUX4, NUTM1 or FOXO4) - form a transcriptionally homogeneous group of tumors. Likewise, tumors with either a BCOR fusion (with CCNB3, MAML3 or ZC3H7B partners) or with a BCOR internal duplication form a single biological entity. We also found that EML4-ALK fusions also characterize a homogeneous infantile fibrosarcoma subgroup. In contrast, fusions like VGLL2-NCOA2/CITED2, TMP3/TRP-NTRK1 or ETV6-NTRK3 are observed in more heterogeneous tumors. Finally, we also describe a new group of bone sarcomas characterized by EWSR1- or FUS-TFCP2 fusions.

  Study EGAS00001002189

• Sequencing data from a phase II study of nivolumab and ipilimumab in recurrent or refractory cancer of unknown primary (CheCUP trial)

  Sequencing data from a phase II study of nivolumab and ipilimumab in recurrent or refractory cancer of unknown primary (CheCUP trial). Panel sequencing data from baseline FFPE biopsies were used to perform a comprehensive genomic profiling of CUP metastases. Combined targeted next-generation sequencing of patient-specific hotsport mutations and shallow whole genome sequencing of baseline and follow-up liquid biopsy samples were used to analyze ctDNA and to evaluate response to immunei checkpoint inhibitor treatment. In some cases, whole exome sequencing of peripheral blood mononuclear cells was performed to screen for potential CHIP and germline mutations.

  Dataset EGAD00001011130

• INCLUDE: Human Trisome Project

  The INCLUDE (INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE) Project is an NIH-wide collaboration that seeks to improve health and quality-of-life for people with Down syndrome. The INCLUDE Project Data Coordinating Center and partners created the INCLUDE Data Hub, a centralized data resource that allows access to large-scale clinical and multi-omics datasets specific to Down syndrome and supports collaborative, cloud-based analysis to accelerate scientific discoveries related to Down syndrome and its co-occurring conditions.

  Study phs002981

• Papuan Genomes: whole genome sequencing

  The genomes of 25 individuals from Papua New Guinea was sequenced to high depth (30x) to address population genetic questions relating to the spread of humans outside of Africa and the peopling of Sahul (the continent of Greater Australia). The study design of this project was to sequence small numbers of genomes from several geographically distinct Papuan groups, focusing on the highland regions that are thought to have been more isolated, to enable comparisons between populations within Papua New Guinea and with Australian Aboriginal populations. Data from this project feed directly into on-going projects on the genetic history of Australia and also serve as a first look at the population genomics of the Papuan highlands.

  Study EGAS00001001247

• microRNA sequencing data from pediatric obesity lifestyle intervention study

  This dataset contains small RNA sequencing data generated from peripheral blood samples collected from children and adolescents with obesity enrolled in a 12-month multicenter lifestyle intervention study. Libraries were prepared using the QIAseq miRNA Library Kit and sequenced on the Illumina NextSeq platform using single-end 75 bp sequencing. The dataset includes fastq files and associated metadata for the identification of circulating microRNA biomarkers associated with response to lifestyle modification and obesity-related metabolic traits.

  Dataset EGAD50000002758

• DAC for Early detection of ovarian cancer using cell-free DNA fragmentomes and protein biomarkers

  Ovarian cancer is a leading cause of death from gynecological malignancies worldwide. No effective screening methods exist resulting in tumor detection at advanced stages where treatment is less effective. In this study, we used whole-genome cell-free DNA (cfDNA) fragmentome and protein biomarker (CA-125 and HE4) analyses to evaluate 591 women, mostly post-menopausal, from the European Union or the United States with ovarian cancer, benign adnexal masses, or without ovarian lesions. Using a machine learning model that incorporated multi-analyte fragmentome data and protein measurements, we evaluated the detection of ovarian cancer in two clinical settings, detection of ovarian cancers among a healthy population, and classification of adnexal masses as benign or malignant. The combined approach detected ovarian cancer with specificity >99% and sensitivity of 72%, 69%, 87%, and 100% for stages I–IV, respectively (AUC=0.96, 95% CI: 0.94-0.99), including 90% of high grade serous ovarian cancers. At the same specificity, CA-125 alone detected 34%, 62%, 63%, and 100% of ovarian cancers for stages I–IV (p=0.001, two-sided test of equal proportions). Additionally, our approach differentiated benign masses from ovarian cancers with high accuracy (AUC of 0.88, 95% CI=0.83-0.92). These results were validated in an independent population. These findings show that integrated cfDNA fragmentome and protein analyses detect ovarian cancers with high performance, enabling a new accessible approach for noninvasive ovarian cancer screening and diagnostic evaluation.

  Dac EGAC50000000332

• The genomic landscape of cutaneous squamous cell carcinoma from immunosuppressed and immunocompetent patients reveals common drivers and a novel mutational signature associated with chronic azathioprine exposure

  Cutaneous squamous cell carcinoma (cSCC) has a high tumour mutational burden (50 mutations per megabase DNA pair). Combining whole exome analyses from 40 primary cSCC tumours, comprising 20 well differentiated and 20 moderately/poorly differentiated tumours, with accompanying clinical data from a longitudinal study of immunosuppressed and immunocompetent patients, coupled with integration of independent gene expression studies has allowed the identification of commonly mutated genes and altered pathways and processes. Mutational signature analysis revealed the presence of a novel signature (signature 32), which was found exclusively in the tumours from immunosuppressed patients who had been exposed to azathioprine. Characterisation of a panel of 15 keratinocyte cancer cell lines derived from tumours from immunocompetent and immunosuppressed patients reveals that they accurately reflect the mutational signatures and genomic alterations of primary tumours and provide a resource for the validation of tumour drivers and therapeutic targets.

  Study EGAS00001002612

• bulk RNA sequencing, single cell RNA sequencing and nanopore sequencing of T-ALL patients with TCF7-SI1 fusion

  This dataset contains all sequencing data of the publication "Oncogenic cooperation between the TCF7-SPI1 fusion and NRAS(G12D) requires β-catenin activity to drive T-cell acute lymphoblastic leukemia." This is bulk RNA sequencing of 4 T-ALL patients (X09, XB37, XB41 and XB47) of which X09 has a TCF7-SPI1 fusion, single cell RNA sequencing of these 4 patients toghether with a PDX model of the X09 patient and two patients from another cohort (SJTALL030263 and SJTALL031201) which also have a TCF7-SPI1 fusion, and nanopore sequencing of all patients with the TCF7-SPI1 fusion. Moreover these patient samples with the fusion where treated with PKF 118-310, and bulk RNA sequencing was performed in triplicate to determine the differentially expressed genes.

  Dataset EGAD00001007010

• Cell-to-cell variability in Myc dynamics drives transcriptional heterogeneity in cancer cells

  Study EGAS00001007091

• Investigating low frequency variants in CAD/MI cases, controls and pedigrees using whole exome sequencing and custom pulldowns

  The Cardiogenics re-sequencing study will consist of three parts: Eight pools of 25 individuals will be sequenced using a Nimblegen hybrid-capture solution specific to miRNA sequences, 80 pools of 25 individuals will be sequenced using a custom Agilent SureSelect array covering genes associated with coronary artery disease (CAD) and myocardial infarction (MI), 10 individuals from families with a history of CAD/MI will be exome sequenced using the Sanger exome array. The experiment will use the early onset patients from the German MI cohort and the UK BHF CAD/MI cohort both of which have strong family history. For controls we will consider individuals from the UKBS and KORA cohorts.

  Dataset EGAD00001000400

• Exome sequencing of patients with Ewings sarcoma

  Cancer is driven by mutations in the genome. We will uncover the mutations that give rise to Ewing's sarcoma, a bone tumour that largely affects children. We will use second generation Illumina massively parallel sequencing, and bespoke software, to characterise the genomes and transcriptomes of Ewing,s sarcoma tumours.

  Dataset EGAD00001000333

• Y_chromosome_mis_segregation_in_the_DLD_1_cell_line

  Study of the well-established DLD-1 colorectal cancer cell line, engineered with an inactivatable Y centromere that leads to mis-segregation. The goal is to gain insights into the mechanism of chromothripsis due to chromosome mis-segration.

  Study EGAS00001002551

• T-ALL RNA-Seq raw data files

  The data set consists of unprocessed RNA-Seq data from 225 patients diagnosed with T cell acute lymphoblastic leukemia in fastq file format. Samples from bone marrow or peripheral blood were subjected to mRNA library prep using Poly-A selection and sequencing on a NovaSeq 6000 system yielding approximately 30 million reads per sample.

  Dataset EGAD50000000308

• CITE-seq data of primary acute myeloid leukemia patient samples with complex karyotype

  In this study, we aimed to assess RNA expression and surface antigen expression of acute myeloid leukemia with complex karyotype (CK-AML) at the single-cell level. For this purpose, we performed cellular indexing of transcriptomes and epitopes (CITE-seq) of primary leukemia samples from four CK-AML patients.

  Dataset EGAD00001011171

• RNA sequencing of tumor samples from patients with Waldenstrom macroglobulinemia

  RNA was extracted from flow-sorted CD19+. RNA-Seq was performed on 32 samples of 30 patients (2 replicates per samples). RNA-Seq libraries were subjected to non-stranded paired-end (2 x 75 bp) sequencing on HiSeq 2500 (Illumina). The files are in FASTQ format.

  Dataset EGAD00001005324

• Single Cell DNA amplicon sequencing of 12 B-ALL patients (at diagnosis and during treatment)

  This dataset contains single cell DNA amplicon sequencing of 12 B-ALL patients. For all patients a diagnosis sample was processed, while 4 patients were also followed up during treatment, summing up to a total of 23 samples. Mutations were called in the predefined set of amplicons.

  Dataset EGAD00001006955

• Cross-tissue transcriptomic analysis of human secondary lymphoid organ residing ILC3 reveals a default quiescent state in the absence of inflammation

  A cross tissue transcriptional comparison of human ILC3 isolated from non-inflamed lymph nodes (LN) and spleens, inflamed tonsils and the peripheral blood (PB). Using uniform gating and high-purity cell-sorting, ILC3 were sorted as Lineage(CD3,CD19,CD34,CD14,CD94)-CD45intCD127+CRTH2-CD117+. From LN, spleen, tonsils and PB, NKp44- ILC3 were isolated and, in addition, NKp44+ ILC3 were sorted from spleen and tonsils. RNA quality was measured on a RNA 6000 Pico Kit (Agilent Technologies) using a 2100 Bioanalyzer (Agilent Technologies). 100 pg high-quality RNA was isolated and cDNA was generated using the SMART-Seq (v3) Ultra Low Input RNA Kit (Clontech) with 15 cycles of amplification. Amplified and Covaris-fragmented cDNA was quality-checked on a High Sensitivity DNA chip (Agilent Technologies) and further processed according the TruSeq Nano DNA Library Preparation Kit (Illumina). Data was processed as follows: SMARTer adapters were trimmed with cutadapt and the resulting sequences were aligned to the human RefSeq transcriptome using 14 TopHat2 (Kim et al., 2013). Normalization and quantification was performed using Cufflinks (Trapnell et al., 2012). FPKM counts were determined per gene with HTSeq-count.

  Study EGAS00001002636

• Batches 1-3 prostatectomy analysis

  Whole Genome Sequencing Illumina HiSeq data from 95 men with prostate cancer. Samples were taken from primary tissue obtained at prostatectomy (target sequencing depth 50X) with matched blood control (target sequencing depth 30X). This data is from batches 1 to 3 and is the bulk of the data used in Wedge et al, Nature Genetics 2018 (PMID: 29662167). As of September 2020, some of the studies using these data include: Wedge et al, Nature Genetics 2018 (PMID: 29662167) Pan-Cancer Analysis of Whole Genomes, Nature 2020 (PMID: 32025007)

  Dataset EGAD00001001116

• NGS-Based Mutational Analysis of 87 PMBL Patients from the GAINED Cohort (Subset of 382 Sequenced Patients)

  Targeted DNA sequencing was performed on DNA extracted from 87 formalin-fixed paraffin-embedded (FFPE) samples, representing a subset of the 382 sequenced patients from the GAINED cohort. A custom “Lymphopanel” was used to identify mutations in 70 genes associated with lymphomagenesis. Library preparation, exome capture, sequencing, and analysis were conducted by IntegraGen SA (Evry, France). Sequencing was performed on an Illumina HiSeq4000 platform, with variant calling and annotation using established bioinformatics tools such as MuTect and VEP.

  Dataset EGAD50000001359

• RIP-SeqRaw data

  This dataset contains the raw count matrix generated in the PTBP1 project. Sequencing data were obtained from bulk directional RNA-seq and RIP-seq experiments performed on a NovaSeq 6000 platform (2×100 bp paired-end reads). Read counts were computed using HTSeq-count, without normalization. The dataset comprises two groups of samples: controls and patients. For each group, both input and immunoprecipitated samples are available. Patient samples correspond to skin biopsies from individuals affected by a neurodevelopmental disorder associated with variable skeletal dysplasia and disproportionate short-limbed short stature.

  Dataset EGAD50000001722

• Visium CytAssist Spatial Gene Expression analysis for glioblastoma

  The glioblastoma spatial transcriptomics dataset was generated from 4 FFPE fixed adult glioblastoma samples, using the 10x Genomics Visium platform, producing raw FASTQ files, spatial feature matrices (.h5, .mtx, .tsv), and paired histology images (.tif). Spot-level transcript counts were aligned to tissue architecture and processed in Seurat, with results stored as .rds objects. Data visualization was performed using SpatialFeaturePlot, and quantitative analyses were supported by custom Matlab scripts. The dataset provides both raw and processed files, offering reproducible spatially resolved transcriptomic profiles of glioblastoma tissue sections

  Dataset EGAD50000001767