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• TUM Experimental Neuroimmunology

  This DAC will decide about all data requests to human data deposited at EGA that was primarily produced at the Institute for Experimental Neuroimmunology at the Technical University Munich, Germany.

  Dac EGAC50000000093

• University College London Great Ormond Street Institute of Child Health DAC

  This DAC oversees access to de-identified datasets generated by the University College London (UCL) Great Ormond Street Institute of Child Health.

  Dac EGAC50000000985

• The Jerusalem Perinatal Study

  The Jerusalem Perinatal Study (JPS) aimed to examine the developmental origins of cardiometabolic risk.

  Dataset EGAD00010001816

• EXPR_COUNT_VALUES

  Gene expression gene-level count values from Stringtie processing including alignment to GRCh37 or GRCh38 genome version. Software: HISAT2 v2.1.0;StringTie v1.3.4d.

  Dataset EGAD00010001924

• Targeted sequencing analysis for MDS with HSCT

  Targeted capture sequencing for cases with MDS who were subjected to unrelated bone marrow transplantation via Japan marrow donor program

  Dataset EGAD00001003118

• Study on the Genetics of Alcoholism (COGA): African American Family GWAS

  COGA is a family study of alcoholism, in which the subjects have been drawn from the Collaborative Study on the Genetics of Alcoholism (COGA), a large, ongoing family-based study that includes subjects from seven sites around the US. COGA has gathered detailed, standardized data on study participants, including diagnostic and neurophysiological assessments. This project has already proved successful in identifying several genes that influence the risk for alcoholism and neurophysiological endophenotypes, which have been independently replicated. COGA data were included as part of two Genetic Analysis Workshops, and the phenotypes are familiar to the genetics community. Alcoholic probands were recruited from treatment facilities, assessed by personal interview, and after securing permission, other family members were also assessed. A set of comparison families was drawn from the same communities as the families recruited through an alcoholic proband. Assessment involved a detailed personal interview developed for this project, the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA), which gathers detailed information on alcoholism related symptoms along with other drugs and psychiatric symptoms. Many participants also came to the laboratories for electroencephalographic studies. Neurophysiological features that have been shown to be useful endophenotypes for which we have linkage and in some cases association results are included on a subset of the case-control sample: the beta power of the resting electroencephalogram (EEG), the P3(00) amplitude of the visual event-related potential (ERP), and the theta and delta event-related oscillations (EROs) underlying the P3. As part of COGA, a set of informative African American families were selected to have Genome-Wide Association data obtained within families. Genotyping was performed using the Illumina Omni2.5_080814_1 chip to genotype 3,438 subjects selected from densely affected families. Genotyping was performed at CIDR. This sample complements a set of densely affected European American families previously made available under dbGaP study accession phs000763. In addition, exome sequencing data on a subset of individuals with GWAS were added in version 2.For version 3, a subset had 30X Whole Genome Sequencing (WGS) as part of the NIDA Sequencing Initiative. The subset contained two distinct sets: Sibling pairs where one sibling had at least two dependence diagnoses in the set (alcohol, cannabis, cocaine, and opioid), and the other had none, and non-related Case-Control pairs matched for age and ethnicity where the cases had alcohol and at least 2 other dependence diagnoses and controls had none. After sequencing, some sibling pairs are re-classified as half siblings, Three VCF files (small variants, structural variants, and copy number variations) are provided. Additional substance abuse variables are made available in version 3. We note that the full sample data are deposited in four dbGaP submissions and the sequenced samples are split across all four: CIDR: Collaborative Study on the Genetics of Alcoholism Case Control Study (phs000125). GWAS data on cases (primarily probands) and controls drawn from the families. Families with highest density of alcohol dependence and/or extreme event-related oscillation data (phs000763). GWAS data on 119 extended families of European descent are available here, along with extensive documentation. Study on the Genetics of Alcoholism (COGA): African American Family GWAS (phs000976). GWAS data on all available COGA families of African descent are available. COGA: Smokescreen GWAS (phs001208). GWAS data on all remaining COGA DNA samples, primarily of other racial background, were genotyped on the Smoke Screen array. A listing of all sequenced pairs is provided in the documentation to facilitate the merging of these samples.

  Study phs000976

• The Spatio-Temporal Evolution of Multiple Myeloma from Baseline to Relapse-Refractory States

  In multiple myeloma, the spatio-temporal evolution patterns of cancer cells in the bone marrow remain largely unknown. Here, we applied spatial-longitudinal sequencing, including a total of 179 samples collected from 25 patients during a follow-up of up to 14 years. Our study highlights focal bone lesions (FL) as hotspots of tumor evolution and provides possible explanations for their negative prognostic impact. We show a close phylogenetic relationship between baseline FL and relapse clones. In patients with ≥3 FDG-PET-positive FL at diagnosis, relapse was driven by multiple distinct sub-clones, whereas in other patients, a single-cell expansion was typically seen (p<0.01). Applying imaging-guided sampling, we identified a novel evolutionary pattern with unique treatment-resistant sub-clones at distinct locations. We observed multiple selective clonal sweeps, resistant clones that can be hidden over years, and epigenetic modifier mutations as a new class of relapse aberrations. Here we show that imaging can enhance tumor evolution studies and response assessment. The sophisticated evolution mechanisms in myeloma and presence of single cells that can drive relapse even after 10 years of remission suggest that a prerequisite for curative therapies would be to overcome not only tumor heterogeneity but also dormancy.

  Study phs002625

• Exploring the Microbiome-Gut-Brain Axis with Respect to Psychoneurological Symptoms for Children with Solid Tumors

  The human body hosts tens of trillions of microbes, a number 10 times greater than the number of human body cells. The collection of these human-associated microbes and their genomes is called the “human microbiome”. More than 90% of these microbes live in the gastrointestinal (GI) tract, representing 500 species on average. With the development of high-throughput sequencing technology and bioinformatics tools (e.g., 16S ribosomal RNA or 16S rRNA sequencing and shotgun metagenomics analysis), recent progress has been made to elaborate the critical role that the gut microbiome plays in human health and disease. A significant disruption (dysbiosis) of the composition and function of the gut microbiome is associated with carcinogenesis, chemotherapeutic metabolism, and treatment-related symptom toxicities. The current state of science on the gut microbiome in cancer has only recently started in animal models and adults, and has not been well investigated in children diagnosed and treated for cancer, nor with respect to associations with cancer treatment-related toxicities. This proposed study will explore the hypothesis that dysbiosis of the gut microbiome is associated with cancer treatment-related GI and psychoneurological symptoms. We will collect both stool samples (for the gut microbiome) and patient-reported outcomes across chemotherapy.

  Study phs002960

• Single-cell profiling reveals mechanisms of response to anti-PD-L1 versus anti-PD-L1 combined with anti-CTLA4 in head and neck squamous cell carcinoma

  Cancer patients often receive a combination of PD-(L)1 and CTLA4 inhibitors, but the mechanisms underlying their concerted actions remain unclear. We conducted a neoadjuvant study in head and neck squamous cell carcinoma (HNSCC) involving anti-PD-L1 monotherapy versus anti-PD-L1/anti-CTLA4 combination. Single-cell profiling of on- versus pre-treatment biopsies confirmed T-cell expansion as response biomarker. We identified a type 1 immune response accompanying T-cell expansion on-treatment across treatment arms, and herein characterized co-localized IgG plasma cell expansion as important contributor. In pre-treatment biopsies, similar features correlated with expansion upon anti-PD-L1, but not anti-PD-L1/anti-CTLA4. By profiling tumor-draining lymph nodes, we found the addition of anti-CTLA4 to trigger activation and subsequent trafficking of CD4+ T-cells via the blood, to become T-helper 1 cells in the tumor. anti-PD-L1/anti-CTLA4 indeed facilitated co-localized expansion of CD4+ and CD8+ T-cells in tumors versus mostly CD8+ T-cells with anti-PD-L1 alone. Hence, we identify complimentary mechanisms underlying anti-PD-L1/anti-CTLA4 therapy in HNSCC.

  Study EGAS50000000037

• Swarm Learning to identify COVID-19, tuberculosis and leukemia patients based on blood transcriptomes

  Identification of patients with life-threatening diseases including leukemias or infections such as tuberculosis or COVID-19 is an important goal of modern precision medicine. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. We have recently illustrated that classical machine learning can identify leukemia patients based on their blood transcriptomes. To facilitate integration of any omics data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, artificial intelligence (AI), blockchain and privacy protection without the need for a central coordinator thereby going beyond federated learning. To illustrate its feasibility, using more than 12,000 transcriptomes from peripheral blood mononuclear cells and more than 2,000 peripheral blood transcriptomes we demonstrate that SL of omics data distributed across different individual sites leads to disease classifiers that outperform those developed at individual sites. Yet, SL completely protects local privacy regulations by design. We propose this approach to noticeably accelerate the introduction of precision medicine.

  Dataset EGAD00001006231