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• Genetic Discovery and Application in a Clinical Setting: Continuing a Partnership (eMERGE Phase II)

  The electronic Medical Records and Genomics (eMERGE) Network is a consortium of ten participating sites funded by the NHGRI to investigate the use of electronic medical record systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 19,000 individuals using EMR-derived phenotypes and DNA from linked Biorepositories. The eMERGE Network brings together researchers with a wide range of expertise in genomics, statistics, ethics, informatics, and clinical medicine from leading medical research institutions across the country. Each center participating in the consortium is uniquely situated to provide critical resources to this highly collaborative and productive network. Each site combines a biobank or study cohort with extensive genomic data and access to clinical data derived from electronic medical records. Sites are geographically dispersed and have diverse patient populations, including two sites focusing specifically on pediatrics. The eMERGE Network is comprised of 9 sites and one coordinating center. Each site maintains its own biorepository where DNA specimens are linked to phenotypic data contained within EMRs. Children's Hospital of Pennsylvania, PI: Hakon Hakonarson, MD, PhD Cincinnati Children's Medical Center with Boston Children's Hospital, PI: John Harley, MD, PhD - CCHMC and Ingrid Holm, MD, MPH - BCH Geisinger Health System, PI: David Carey, PhD and Marc Williams, MD Group Health Cooperative with University of Washington, PI: Eric Larson, MD, MPH - GHC and Gail Jarvik, MD, PhD - UW Marshfield Clinic with Essentia Institute of Rural Health, PI: Catherine McCarty, PhD, MPH - Essentia and Murray Brilliant, PhD - Marshfield Mayo Clinic, PI: Christopher Chute, MD, DrPH and Iftikhar Kullo, MD Icahn School of Medicine at Mount Sinai, PI: Erwin Bottinger, MD Northwestern University, PI: Rex Chisholm, PhD and Maureen Smith, MS Vanderbilt University, PI: Dan Roden, MD eMERGE Coordinating Center - Vanderbilt University, PI: Paul Harris, PhD Using electronic phenotyping methods, the consortium used DNA samples from all participating sites to explore the genetic determinants of red cell indices, white blood count (WBC) differential, diabetic retinopathy, height, serum lipid levels, specifically total cholesterol, HDL (high density lipoprotein), LDL (low density lipoprotein), and triglycerides, and autoimmune hypothyroidism as well as using the phenome-wide association study (PheWAS) paradigm to replicate and discover relationships between targeted genotypes with multiple phenotypes.

  Study phs000948

• Immune landscape of oncohistone-mutant gliomas reveals diverse myeloid populations and tumor-promoting behavior

  Histone H3-mutant gliomas are deadly brain tumors characterized by a dysregulated epigenome and stalled differentiation. In contrast to the extensive datasets available on tumor cells, limited information exists on their tumor microenvironment (TME), including the immune infiltrate. Here, we characterize the TME of H3.3K27M and G34R/V-mutant gliomas, and multiple H3.3K27M mouse models, using transcriptomic and proteomic, including spatial single-cell approaches. Resolution of immune lineages indicates uniform high-infiltration of H3-mutant gliomas with diverse myeloid populations, high-level expression of immune checkpoints, and scarce lymphoid cells, findings uniformly reproduced in all H3.3K27M models tested. We show these myeloid populations communicate with H3-mutant cells mediating immunosuppression and sustaining tumor formation and maintenance. Dual inhibition of myeloid cells and immune checkpoints showed significant therapeutic benefit in pre-clinical syngeneic models. Our findings provide valuable characterization of the TME of oncohistone-mutant gliomas, and insights into means to modulate the myeloid infiltrate for the benefit of patients.

  Study EGAS00001007510

• An ancient DNA perspective on the Russian Conquest of Yakutia

  Yakut communities from northeastern Siberia inhabit some of the coldest environments on Earth preserving an extraordinary archaeological record. Their history was profoundly reshaped by the Russian conquest, which introduced cereals, pathogens and Christianity from 1632 CE. However, the biological impact of these transformations remains unknown. Here, we generated extensive ancient DNA data to elucidate contemporary changes in Yakut genomic diversity and oral microbiomes. We found Yakut origins tracing back to local populations that admixed with trans-Baikal groups migrating as the Great Mongol Empire spread. Despite the Russian conquest, the Yakut gene pool and oral microbiomes appeared largely stable, though smallpox strains distinct from those documented in Europe by ~1650 CE circulated. Marital practices generally maintained low consanguinity, with the exception of one female bearing the latest markers of traditional shamanism, who was the daughter of second-degree relatives.

  Study EGAS50000001329

• CINECA_synthetic_cohort_EUROPE_UK1 referencing fake samples

  Please note: This synthetic data set (with cohort “participants” / ”subjects” marked with FAKE) has no identifiable data and cannot be used to make any inference about cohort data or results. The purpose of this dataset is to aid development of technical implementations for cohort data discovery, harmonization, access, and federated analysis. In support of FAIRness in data sharing, this dataset is made freely available under the Creative Commons Licence (CC-BY). Please ensure this preamble is included with this dataset and that the CINECA project (funding: EC H2020 grant 825775) is acknowledged. For any questions please contact isuru@ebi.ac.uk or cthomas@ebi.ac.uk This dataset (CINECA_synthetic_cohort_EUROPE_UK1) consists of 2521 samples which have genetic data based on 1000 Genomes data (https://www.nature.com/articles/nature15393), and synthetic subject attributes and phenotypic data derived from UKBiobank (https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001779). These data were initially derived using the TOFU tool (https://github.com/spiros/tofu), which generates randomly generated values based on the UKBiobank data dictionary. Categorical values were randomly generated based on the data dictionary, continuous variables generated based on the distribution of values reported by the UK Biobank showcase, and date / time values were random. Additionally we split the phenotypes and attributes into 4 main classes - general, cancer, diabetes mellitus, and cardiac. We assigned the general attributes to all the samples, and the cardiac / diabetes mellitus / cancer attributes to a proportion of the total samples. Once the initial set of phenotypes and attributes were generated, the data data was checked for consistency and where possible dependent attributes were calculated from the independent variables generated by TOFU. For example, BMI was calculated from height and weight data, and age at death generated by date of death and date of birth. These data were then loaded to the development instance of Biosamples (https://www.ebi.ac.uk/biosamples/) which accessioned each of the samples. The genetic data are derived from the 1000 Genomes Phase 3 release (https://www.internationalgenome.org/category/phase-3/). The genotype data consists of a single joint call vcf files with call genotypes for all 2504 samples, plus bed, bim, fam, and nosex files generated via plink for these samples and genotypes. The genotype data has had a variety of errors introduced to mimic real data and as a test for quality control pipelines. These include gender mismatches, ethnic background mislabelling and low call rates for a randomly chosen subset of sample data as well as deviations from Hardy Weinberg equilibrium and low call rates for a random selection of variants. Additionally 40 samples have raw genetic data available in the form of both bam and cram files, including unmapped data. The gender of the samples in the 1000 genomes data has been matched to the synthetic phenotypic data generated for these samples. The genetic data was then linked to the synthetic data in BioSamples, and submitted to EGA.

  Dataset EGAD00001006673

• Epigenetic characterization of glioblastoma stem cells

  Chromatin accessibility discriminates stem from mature cell populations, enabling the identification of primitive stem-like cells in primary tumors, such as glioblastoma (GBM) where self-renewing cells driving cancer progression and recurrence are prime targets for therapeutic intervention. We show, using single-cell chromatin accessibility, that primary human GBMs harbor a heterogeneous self-renewing population whose diversity is captured in patient-derived glioblastoma stem cells (GSCs). In-depth characterization of chromatin accessibility in GSCs identifies three GSC states: Reactive, Constructive, and Invasive, each governed by uniquely essential transcription factors and present within GBMs in varying proportions. Orthotopic xenografts reveal that GSC states associate with survival, and identify an invasive GSC signature predictive of low patient survival, in line with the higher invasive properties of Invasive state GSCs compared to Reactive and Constructive GSCs as shown by in vitro and in vivo assays. Our chromatin-driven characterization of GSC states improves prognostic precision and identifies dependencies to guide combination therapies.

  Study EGAS50000001804

• Multimodal immunogenomic biomarker analysis of tumors from pediatric patients enrolled to a phase 1-2 study of single-agent atezolizumab

  We report herein an extensive exploratory biomarker analysis of refractory tumors taken from pediatric patients prior to receiving atezolizumab monotherapy in the phase 1-2 iMATRIX-atezolizumab trial (NCT02541604). A high percentage of CD8+ T cells and elevated protein levels of programmed cell death ligand 1 (PD-L1) were associated with progression-free survival (PFS). T-cell receptor (TCR) sequencing revealed that diverse infiltrating TCR repertoire at baseline was prognostic. We found no associations between panel-based tumor mutation burden (TMB) or specific genetic aberrations with PFS in this study. Through a pan-cancer gene co-expression network analysis, we developed a novel tumor-agnostic Pediatric Cytotoxicity and Antigen Presentation (PedCAP) signature that was associated with improved PFS in the iMATRIX-atezo study. Our study highlights features of immune response in pediatric cancers when treated with immune checkpoint inhibitors and provides a multi-biomarker pediatric immunogram framework to guide prospective clinical trials in pediatric cancers.

  Study EGAS00001006004

• RB1 Loss Triggers Dependence on ESRRG in Retinoblastoma

  Retinoblastoma (Rb) is a deadly childhood eye cancer that is classically initiated by inactivation of the RB1 tumor suppressor. Clinical management continues to rely on nonspecific chemotherapeutic agents that are associated with treatment resistance and toxicity. Here, we analyzed whole exomes from primary Rb tumors to identify novel Rb dependencies. Several recurrent genomic aberrations implicate estrogen-related receptor gamma (ESRRG) in Rb pathogenesis. ESRRG is an essential mediator of hypoxic adaptation and cell survival in retinoblastoma, representing a promising new therapeutic target.

  Study phs002859

• SweGen whole-genome sequencing from the Swedish Twin Registry

  The dataset contains whole-genome sequencing data (aligned read files) in CRAM-format (lossless compression) for a total of 942 DNA samples, selected to represent a cross-section of the Swedish population. The samples originate from the Swedish Twin Registry (STR) and have been obtained from different geographical regions. For each of the 942 individuals, DNA was extracted from a blood sample and subject to whole genome sequencing (WGS). The WGS was performed using 2x150 bp paired-end chemistry on Illumina HiSeq X Ten instrumentation at the SciLifeLab National Genomics Infrastructure (NGI) in Stockholm and Uppsala. FASTQ files generated by WGS were analyzed using the nf-core pipeline Sarek, which includes pre-processing, alignment to the human GRCh38 reference genome, and germline variant calling. All participants gave their written informed consent and the TwinGene study was approved by the regional ethics committee (Regionala Etikprövningsnämnden, Stockholm, dnr 2007-644-31, dnr 2014/521-32). Access to phenotypic information can be requested from the Swedish Twin Registry (http://ki.se/en/research/the-swedish-twin-registry).

  Dataset EGAD50000001326

• SweGen genetic variation from the Swedish Twin Registry

  The dataset contains files with single nucleotide variants in VCF format for a total of 942 DNA samples, selected to represent a cross-section of the Swedish population. The samples originate from the Swedish Twin Registry (STR) and have been obtained from different geographical regions. For each of the 942 individuals, DNA was extracted from a blood sample and subject to whole genome sequencing (WGS). The WGS was performed using 2x150 bp paired-end chemistry on Illumina HiSeq X Ten instrumentation at the SciLifeLab National Genomics Infrastructure (NGI) in Stockholm and Uppsala. FASTQ files generated by WGS were analyzed using the nf-core pipeline Sarek, which includes pre-processing, alignment to the human GRCh38 reference genome, and germline variant calling. All participants gave their written informed consent and the TwinGene study was approved by the regional ethics committee (Regionala Etikprövningsnämnden, Stockholm, dnr 2007-644-31, dnr 2014/521-32). Access to phenotypic information can be requested from the Swedish Twin Registry (http://ki.se/en/research/the-swedish-twin-registry).

  Dataset EGAD50000001323

• scRNA-seq data of BALF and blood cells from COPD and control

  Chronic obstructive pulmonary disease (COPD) is ranked the third leading cause of death worldwide and hence constitutes a significant burden, both medically and financially. To date, the cellular mechanism underlying COPD is poorly understood and thus no effective therapy is currently available. To broaden the current knowledge of the immune landscape in the alveolar space, we performed scRNA-Seq of CD45-enriched bronchoalveolar lavage fluid (BALF) cells and isolated blood leukocytes obtained from COPD patients and healthy controls using both the Seq-Well and the Chromium (10x Genomics) technology.

  Study EGAS00001004369

• MMR (DNA mismatch repair) pathway in human samples

  This data are related to the manuscript by Germano et al. Ms2016-04-06029

  Study EGAS00001002694

• Transcriptional_profiling_of_tauopathies_in_human_IPS_derived_neurons

  This project is a pilot study, in collaboration with Maria Grazia Spillantini and Mariangela Iovino (Cambridge Centre for Brain Repair), to investigate the utility of IPS-derived neurons for the study of neurodegenerative disorders. Our aim is to characterise the transcriptional consequences of tauopathies using neurons derived from differentiated IPSCs as a model system. We will use IPS cells derived from six individuals, four with known mutations in the tau protein, 2 without. RNA will be extracted at Day 0 and Day 65 of differentiation by which time the neuronal tauopathy is apparent. RNA will be extracted and the transcriptome of each line characterised using RNAseq. We will then search for genes that are differentially expressed between the transcriptomes of individuals with tau mutations versus those in controls. My lab will analyse the RNAseq data, comparing both affected and controls and both time-points, to establish candidate genes. Darren Logan’s lab, along with our collaborators, will experimentally verify and further investigate these genes in additional lines and animal models. From this analysis we will generate a list of candidate genes that are differentially expressed between cases and controls. This study will not only help us understand the molecular basis of tauopathies, but also identify gene candidates for biomarkers of neurodegenerative disease. It will serve as a proof of principle for future planned studies into generating and transcriptomically analysing an allelic series of Tau mutations in IPSCs with a controlled genetic background.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001000382

• German early-onset prostate cancer cohort of the Pan-Prostate Cancer Genome (PPCG) project

  Prostate cancer is the most frequent malignant tumor in males and the second most frequent cause of cancer-related death. Currently, in Germany, more than 60,000 prostate cancers are diagnosed every year. Although most of these patients are treated in a curative attempt, more than 10,000 German men die from prostate cancer annually. Owing to the demographic changes of our society, a further doubling of prostate cancer incidences during the next 20 years is expected. Prostate cancer is generally considered a tumor of elderly men. However, a fraction of prostate cancers are diagnosed at the age of 55 years or less. For several reasons, these “early onset prostate cancers” may represent a key entity for the understanding of prostate cancer biology. First, it is likely that early onset prostate cancers represent a distinct molecular subgroup of prostate cancer (PCa), potentially characterized by relatively small numbers of genetic changes, some of which may be particularly strong driver mutations for PCa development. Second, a fraction of prostate cancers in young individuals could represent classical prostate cancers that are detected at a very early stage and might therefore have accumulated molecular changes/mutations occurring that are most instrumental for prostate cancer early detection. Third, PCa with hereditary backgrounds are likely to accumulate in the age group below 55 years. A comparison with other sample sets (e.g. from other ICGC consortia), a systematic genomic analysis of young men with PCa could therefore lead to the detection of mechanisms for hereditary PCa. Fourth, a better understanding of these tumors is particularly relevant as finding optimal treatment regimens is most critical in young cancer patients. We are analyzing the entire genomic DNA sequences of at least 200 PCas (and matched non-tumorous DNA) of young men (≤ 55y), to at least 30 fold coverage, and integrate single-nucleotide variants and genomic structural variations with differential methylation, mRNA and miRNA expression data.Tissues were collected at the University Medical Center Hamburg-Eppendorf. Sequencing was performed at DKFZ and NCT (Heidelberg), EMBL (Heidelberg), and MPIMG (Berlin). Data management and bioinformatic data analyses is being conducted at DKFZ.

  Study EGAS00001003373

• Single molecule molecular inversion probe capture developed using the CIViC database

  This study evaluated the use of the Clinical Interpretations of Variants in Cancer database (CIViC) to develop an Open-sourced CIViC Annotation Pipeline (OpenCAP). OpenCAP is a resource that allows users to develop a clinical capture panel that can be linked back to clinical summaries within the CIViC database. This method for capture panel development and variant annotation was tested using 27 cancer samples from 5 tumor subtypes with original exome or genome sequencing. Specifically, using OpenCAP, 111 variants were identified as relevant for the capture panel and 2,027 smMIPs were designed to target these variants. The smMIPs panel design was employed on all samples to validate the capture panel design. When compared to original sequencing, the CIViC smMIPs capture panel demonstrated a 95% sensitivity for variant detection (n = 61 variants). Additionally, the CIViC smMIPs capture panel identified 182 additional clinically relevant variants that had been missed on original sequencing.

  Study phs001890

• Targeted sequencing of genomics regions of interest in depression and obesity

  Data corresponding to the fastq of the 654 individuals included in the study.

  Dataset EGAD50000000476

• Identification of novel fusion genes in lung cancer using breakpoint assembly of transcriptome sequencing data

  Genomic translocation events frequently underlie cancer development through generation of gene fusions with oncogenic properties. Identification of such fusion transcripts by transcriptome sequencing might help to discover new potential therapeutic targets. We developed TRUP (Tumor-specimen suited RNA-seq Unified Pipeline (https://github.com/ruping/TRUP), a computational approach that combines split-read and read-pair analysis with de-novo assembly for the identification of chimeric transcripts in cancer specimens. We apply TRUP to RNA-seq data of different tumor types, and find it to be more sensitive than alternative tools in detecting chimeric transcripts, such as secondaryrearrangements in EML4-ALK-positive lung tumors, or recurrent inactivating rearrangements affecting RASSF8.

  Study EGAS00001000659

• sn-RNAseq profiling of the impact of a cytokine storm model in human cardiac organoids

  We developed conditions to model conditions of diastolic dysfunction caused by inflammatory factors in human cardiac organoids. Our organoid model is based on conditions promoting maturation (Mills et al., PNAS, 2017 and Voges et al., In Revision) and incorporating human pluripotent stem cell derived cardiomyocytes, epicardial cells, fibroblasts, endothelial cells and pericytes. Following 15 days of differentiation (Voges et al., Development, 2017) we formed human cardiac organoids incorporating both our multicellular differentiation (Mills et al., PNAS, 2017) and additional endothelial cells (Voges et al., In Revision). After another 12 days of maturation in 5 days maturation medium and 5 days weaning medium (Voges et al., In Revision) human cardiac organoids were either treated with weaning medium or “cardiac cytokine storm” comprised of 100 ng/ml IFNg, 10 ng/ml IL-1 β and 10 µg/ml poly(I:C). After 48 hours human cardiac organoids were harvested and snap frozen in -80C before nuclei isolation and single nuclei RNA sequencing.Pooled hCO (~40) were homogenized in 4 mL lysis buffer (300 mM sucrose, 10 mM Tris-HCl (pH = 8), 5 mM CaCl2, 5 mM magnesium acetate, 2 mM EDTA, 0.5 mM EGTA, 1 mM DTT)(all Sigma-Aldrich) with 30 strokes of a dounce tissue grinder (Wheaton). Large pieces of hCO were allowed to settle and homogenate was passed through pre-wetted 40 µm cell strainers (Becton Dickinson). Remaining hCO material in the douncer was resuspended in 4 mL and the douncing and filtering steps were repeated twice. All steps of the homogenization were performed on ice. The filtered homogenate was centrifuged at 1500 x g for 5 min at 4oC. Nuclei pellets were then re-suspended in PBS. A fraction of resuspended nuclei were then stained with hoechst nuclear stain (1:500 dilution) and counted on a haemocytometer under fluorescent microscope.The nuclei were then re-centrifuged (1500 x g for 5 min at 4°C) and resuspended at a density to load ~5,000 nuclei per sample. Cell were loaded into the Chromium Controller (10X Genomics) for gel bead emulsion (GEM) formation. Library preparation was conducted according to the manufacturer’s recommended protocol using the Chromium Next GEM Single Cell 3’ GEM, Library & Gel Bead Kit v3.1. Libraries were sequenced on the NextSeq 500/550 v2 (Illumina) with 150 bp reads.In the snRNA-seq we note the lower than expected percentage of non-myocytes and the loss of endothelial cells (Mills et al., PNAS, 2017 and Voges et al., In Revision), indicating the protocol requires further optimization for hCO samples.

  Study EGAS00001005174

• Single-Cell Analysis of Somatic Mutations in Human Bronchial Epithelial Cells in Relation to Aging and Smoking

  While lung cancer risk among smokers is dependent on smoking dose, it remains unknown if this increased risk reflects an increased rate of somatic mutation accumulation in normal lung cells. Here we applied single-cell whole genome sequencing of proximal bronchial basal cells from 33 subjects, aged between 11 and 86 years, with smoking histories varying from never smoking to 116 pack years. We found an increase in the frequency of single-nucleotide variants and small insertions and deletions with chronological age in never-smokers, with mutation frequencies significantly elevated among smokers. However, when plotted against smoking pack-years, mutations were found to follow the linear increase in cancer risk only until about 23 pack years, after which no further increase in mutation frequency was observed, pointing towards individual selection for mutation avoidance. Known lung cancer-defined global mutation signatures tracked with both age and smoking. Within study power limits, no significant enrichment for somatic mutations in lung cancer driver genes was observed.

  Study phs002758

• DNA Methylomic Profiling of Preeclampsia Across Pregnancy

  Preeclampsia (PE) is a hypertensive, multi-system disorder of pregnancy that significantly impacts maternal and infant morbidity/mortality across the globe, as it increases risk of cardiovascular disease and remains a leading killer of women and babies. Despite PE's significant impact on morbidity/mortality, there are no clinically reliable biomarkers that predict PE. DNA methylation, a dynamic regulator of gene expression, represents a mechanism that is known to be impacted by the environment. Because PE stems from a dysfunctional placenta that releases debris into the maternal circulation, we hypothesized that the in-vivo environment created by the dysfunctional placenta will impact DNA methylation in the maternal circulation, and that these blood-based methylation profiles would serve as a systemic biomarker of the maternal response to placental dysfunction. Our overall objective of this pilot study was to longitudinally characterize DNA methylation profiles across the three trimesters of pregnancy in the maternal blood at time points before and after clinically overt PE using a targeted (endoglin and endoglin-related genes) and a discovery-based approach. For this pilot study, 28 normotensive control participants and 28 PE case participants enrolled in the NICHD funded pregnancy study entitled Prenatal Exposures and Preeclampsia Prevention: Mechanisms of Preeclampsia and the Impact of Obesity (PEPP3; P01HD30367) were 1:1 frequency matched on self-reported race, pre-pregnancy BMI, smoking history, and gestational age at sample collection. Methylation data were collected with the Infinium® MethylationEPIC Beadchip. Methylation assay data collection were carried out at Johns Hopkins University Genetic Resources Core Facility, The SNP Center, Baltimore, MD, USA.

  Study phs001937

• A Machine Learning Tool Integrating Circulating Cell-Free DNA Methylation with Clinical Variables for Non-Invasive Diagnosis of Liver Graft Pathology

  Liver Transplant Recipients (LTRs) with elevated liver enzymes can develop graft injury due to various etiologies, such as T-cell mediated rejection (TCMR) and NASH (NASH-LT). While liver biopsy is the gold standard method for diagnosis of graft pathology, it is invasive and is associated with risks. The goal of our study was to develop a Machine Learning (ML) tool integrating clinical variables with methylation patterns on circulating DNA in plasma as a non-invasive diagnostic tool of graft injury. We generated methylation profiles of circulating DNA in a pilot study of 43 LTRs (11 NASH-LT, 19 TCMR, and 13 Control-LT), and developed an L2 multinomial logistic regression ML approach across 101 bootstrapped models to distinguish between the graft conditions. NASH was associated with distinctive methylation patterns on genes involved in fatty acid metabolism, while methylation of platelet-derived growth factors was identified in patients with TCMR. Our ML model achieved a mean multi-classification accuracy of 0.91, with mean specificity and sensitivity of 0.94 and 0.91, respectively. The model was found to be particularly adept at detecting TCMR and Control-LT, with true positive rates (TPRs) of 95% and 90%, and areas under the curve (AUROC) of 0.992 and 0.985, respectively. For NASH-LT, the models achieved a performance with a TPR of 82% and an AUROC of 0.991. These preliminary results suggest that our newly developed ML tool, leveraging cell-free DNA methylation and clinical variables, is a promising non-invasive classifier of graft pathology.

  Study EGAS00001007171

• Bladder Chemotherapy Responders

  Cisplatin-based chemotherapy is the standard of care for patients with muscle invasive urothelial carcinoma. Pathologic downstaging to pT0/pTis after neoadjuvant cisplatin-based chemotherapy is associated with improved survival, and some patients with metastatic disease achieve complete responses to these therapies. However, the molecular determinants of cisplatin response are incompletely understood. We performed whole exome sequencing on tumor and germline DNA from patients with muscle invasive or metastatic urothelial carcinoma who received cisplatin-based chemotherapy to identify somatic mutations that occurred preferentially in cisplatin responders.

  Study phs000771

• Whole Exome Sequencing of Waldenström Macroglobulinemia (WM) Precursor Conditions

  In this study we aimed to comprehensively characterize the genomic landscape of WM precursor conditions and identify potential biomarkers of progression to symptomatic WM by means of Whole Exome Sequencing (WES) of CD19-selected samples from 139 patients, primarily with IgM-MGUS and asymptomatic WM. Overall, our study shows that genomic profiling of patients’ tumor at the time of aWM diagnosis might represent an improved strategy for identifying patients at high risk to progression who could benefit from earlier intervention.

  Study EGAS50000001249

• Cold Ischemia Study

  This study aimed to evaluate the impact of cold ischemia time on the expression of cancer-related genes, particularly those involved in hypoxia, apoptosis, and epithelial-to-mesenchymal transition (EMT). We performed RNA sequencing on 54 normal colon mucosa samples from nine patients undergoing colorectal cancer surgeries, freezing samples at predefined intervals ranging from 0 to 60 minutes. A total of 44 differentially expressed genes (DEGs) (p < 0.05) were identified when comparing samples frozen immediately (T0) with those frozen after 60 minutes (T5).

  Study EGAS00001008233

• Whole genome sequencing of pancreatic cystic fluid for early detection and diagnosis of pancreatic cancer

  Pancreatic cancer (PC) is a leading cause of cancer-related deaths globally. Accurate PC detection at early or premalignant stage, when surgery is effective, would increase survival rates and prevent unnecessary surgery or surveillance. Molecular diagnostic attempts using PCy fluid (PCyF), as a liquid biopsy, to analyze common mutations associated with PC development are yet to contribute to early diagnosis. Whole genome sequencing (WGS) is utilized in various cancers, PC inclusive, for detecting genetic changes associated with carcinogenesis, but not in PCyF due to technical limitations, including isolated DNA purity. The goal of the study was to achieve high quality of WGS from PCyF from 9 patients to enable development of signatures of malignancy. The WGS was performed using Illumina sequencing. Experimental design was at a 50x depth.

  Dataset EGAD50000000869

• Test dataset with ligh-weight files

  This is a test dataset derived from public data of the 1000 Genomes Project. Its purpose is not to allow for any inference about cohort data or results, but to aid bioinformaticians in the technical development and testing of tools, as well as data consumers in learning how to access information. This dataset consists of 3 pairs of light-weight (sliced) files: BAM + BAI, CRAM + CRAI and VCF + TBI. These files can be downloaded directly through the EGA-download-client PyEGA3 (https://github.com/EGA-archive/ega-download-client). For any further questions, please contact the DAC (Helpdesk - email: helpdesk [at] ega-archive [dot] org).

  Dataset EGAD00001009826