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• Identification of Somatic Changes in Tumors from Fanconi Anemia Patients

  This study focused on the identification of genomic signatures present in tumors from Fanconi anemia patients. Fanconi anemia is a rare hereditary disease caused by defects in DNA interstrand crosslink repair. The primary patient phenotypes are bone marrow failure and cancer predisposition. We concentrated on squamous cell carcinomas and used whole genome Illumina sequencing (22 tumor samples and 12 normal samples) , whole exome sequencing (37 tumor samples and 33 normal samples), PacBio long-read whole-genome (nine tumor and four normal samples),10X linked-read whole-genome sequencing, 10x single-cell/nuclei 3’ RNA sequencing (three tumor samples), 10x Visium spatial transcriptomics (one tumor sample), EPIC 850K methylation array (six samples), and bulk RNAseq (six tumor samples) to identify somatic changes present in tumors. The study aimed to identify changes that drive tumorigenesis in Fanconi anemia, give insight into the early development and aggressive nature of these tumors, and suggest therapeutic avenues for these patients. Another goal was to compare the genomic changes identified in tumors from Fanconi anemia patients with sporadic head and neck cancers. This study demonstrated that the tumors from Fanconi anemia individuals are generally not driven by HPV. Instead, they acquire mutations in the p53 tumor suppressor. The main characteristic of the tumors is the presence of a high number of structural variants in the form of small deletions, unbalanced translocations, and fold-back inversions, which often form complex rearrangements. These rearrangements result in amplifications and deletions of a multitude of oncogenes and tumor suppressors, respectively. The study also shows that lack of the Fanconi anemia DNA repair pathway leads to an epithelial-to-mesenchymal transition in a mouse model and in human samples that may influence the behavior of tumors in Fanconi anemia. Finally, comparing genomic changes in Fanconi anemia and sporadic tumors of the head and neck, we propose that the structural variants present in sporadic cancer result in a functional deficiency of the Fanconi anemia DNA repair pathway.

  Study phs002652

• Contextual and Health Behavior Effects on Epigenetic Aging Among Africans in the Family and Community Health Studies

  The Family and Community Health Studies (FACHS) is a longitudinal examination of health and health related behaviors in African American families. The original study cohort consisted of 867 African American parent-child dyads from Georgia and Iowa who were ascertained in cooperation with local school districts. The key inclusion criteria for participating in the study were the presence of an adolescent in the 5th grade (approximately 10-11 years old) who self-identified as African American (i.e., the Target) and the participation of a primary caretaker (PC) for the Target. The clinical and biological data included in this collection are from the Target (i.e., FACHS-T) portion of these studies of these African American parent-child dyads. The initial wave of behavioral assessments of the FACHS-T subjects was conducted in 1997-98 (Wave 1), with seven subsequent ascertainments, Wave 2 (1999–2000), Wave 3 (2002–2003), Wave 4 (2005–2006), Wave 5 (2007–2009), Wave 6 (2010–2011), Wave 7 (2014–2016) and Wave 8 (2019-2021). Full interviews were obtained at each wave of the study with the exact content of the wave varying as a function of developmental age. These interviews of the FACHS-T subjects collected a wide variety of data including the following: perceived discrimination; socioeconomic status; substance use exposure; common internalizing and externalizing disorders such as depression, substance use, and conduct disorder; school efficacy; peer support and perceived parenting practices. Biological sampling of the FACHS-T subjects was conducted at three time points. Saliva DNA was collected at Wave 5 while full phlebotomy was conducted at Waves 7 and 8. The genome wide methylation data listed below is derived from the whole blood DNA samples collected at Wave 8 and Wave 9. The genome wide genotyping data was determined using DNA from the Wave 7 samples. The clinical and biological data available herein are those from 309 FACHS-T subjects who participated in both Wave 7 and Wave 8, and who consented for deposition of their data in dbGAP.

  Study phs003723

• Profiling Genome-Wide Circulating ncRNAs for the Early Detection of Lung Cancer

  Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of all cases. Although imaging techniques such as low-dose computed tomography (LDCT), combined with tissue biopsies, have improved early detection rates, current diagnostic approaches are often limited by high false-positive rates, invasiveness, and radiation exposure, which lead to misdiagnosis and increased patient burden. Therefore, there is an urgent need for minimally invasive, accurate, and safe diagnostic biomarkers, particularly those derived from liquid biopsies such as blood-based exosomal RNA.This dataset specifically focuses on plasma-derived exosomal small non-coding RNAs (sncRNAs).A total of 233 de-identified plasma samples were obtained from Rush University Medical Center, including 116 NSCLC patients and 117 non-cancer controls. Exosomes were isolated from plasma, RNA was extracted, and small RNA sequencing was performed using the Illumina NextSeq 500 platform. Data available through dbGaP include individual-level raw sequencing files (FASTQ) and basic phenotype annotations of the plasma cohort.A diagnostic model was initially developed using small RNA-seq data from 1,446 tissue samples obtained from TCGA and GEO, identifying a robust sncRNA signature that distinguished NSCLC from non-cancer samples with an AUC of 0.90 in hold-out tissue validation. This signature was subsequently validated in the plasma exosome cohort, achieving an AUC of 0.84 in independent validation. The model demonstrated consistent diagnostic performance across subgroups stratified by age, sex, smoking history, cancer stage, and NSCLC subtype. Survival and functional analyses further supported the clinical and biological relevance of the identified sncRNAs. It is hoped that this resource will contribute to a better understanding of the sncRNA expression landscape in NSCLC and promote the development of non-invasive biomarkers for early cancer detection. These data may be applied to the investigation of transcriptomic and other risk factors, provide insight into cancer screening, tumor progression mechanisms, and prognosis prediction, and ultimately support precision medicine for NSCLC.

  Study phs004166

• METSIM (METabolic Syndrome In Men) Study

  The METSIM Study includes 10,197 men, aged from 45 to 73 years, randomly selected from the population register of the town of Kuopio, Eastern Finland, and examined in 2005-2010. The aim of the study is to investigate genetic and non-genetic factors associated with the risk of type 2 diabetes (T2D), cardiovascular disease (CVD), and insulin resistance-related traits in a cross-sectional and longitudinal setting. Study protocol includes collection of data on CVD risk factors (smoking, exercise, diet, history of chronic diseases including coronary heart disease, stroke, cardiac failure, medication, history of diabetes or early onset coronary heart disease in the family), questionnaire on the FINDRISC Score, measurement of height, weight, waist, hip, blood pressure, and bioimpedance for the evaluation of fat percentage. July 2016 - The first study release (v1) included phenotype and whole exome sequencing (WES) data of n=982 participants in substudy: Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Multi-Ethnic Samples (T2D-GENES) Project 1: Metabolic Syndrome in Men Study (METSIM) - phs001100.v2.p1. The second study release (v2) included phenotype data for the entire METSIM cohort and made two additional substudies available, phs000919.v1.p1 and phs000752.v1.p1. The third study release (v3) made three subcutaneous adipose biopsy substudies available, phs003385.v1.p1, phs003386.v1.p1, and phs003387.v1.p1.The fourth study release (v4) added phenotype data for the subcutaneous adipose biopsies, made two additional substudies available, phs01579.v1.p1 and phs004033.v1.p1, and is utilized in the following dbGaP sub-studies. To view genotypes, other molecular data, and derived variables collected in these sub-studies, please click on the following sub-studies below or in the "Sub-studies" section of this top-level study page phs000743 METSIM Study. phs000752 METSIM FinMetSeq Exome Sequencing phs000919 METSIM GWAS and Exome Chip phs001100 METSIM T2D-GENES Exome Sequencingphs001579 METSIM CCDG Whole Genome Sequencingphs003385 METSIM Subcutaneous Adipose Needle Biopsy RNA-Seq phs003386 METSIM Subcutaneous Adipose Needle Biopsy ATAC-Seq phs003387 METSIM Subcutaneous Adipose Surgical Biopsy RNA-Seqphs004033 METSIM Metabolomics

  Study phs000743

• Research of factors related to diagnosis, progression, prognosis and treatment of hepato-biliary-pancreatic malignancies

  Advanced cancers with metastasis and/or recurrence are usually resistant and refractory to treatment. In our studies, whole exome sequencing analysis, comprehensive epigenetic analysis and genome-wide expression analysis were performed using cancerous and non-cancerous tissues derived from multicentric cohort study of hepatocellular carcinoma (HCC). The aim of the studies is identification of the refractoriness-related deriver genes for application to novel molecular targeted therapy and preventive medicine. The studies are intended for refractory HCC. High frequency of the tumor recurrence even after curative resection is one of the major difficulties in the treatment of HCC. According to our previous studies, not the recurrence itself, but the recurrence pattern has critical effects on prognosis of the patient with HCC. Then, we analyzed the cancerous and non-cancerous tissues of primary HCC on the point of the recurrence patterns in accordance with or without Milan criteria. The materials were obtained from thirty three patients underwent curative hepatectomy for HCC at Department of Hepato-Biliary-Pancreatic Surgery, Tokyo Medical and Dental University Hospital. Written informed consent from these patients, as well as the institutional review board approved was obtained. The determination of the refractory HCC group (16 cases) was based on the point of the recurrence patterns in accordance with Milan criteria for the recurrent tumor (solitary <5cm, or up to 3 nodules <3 cm, without major vascular invasion or distant metastasis) that has been proposed as the useful criteria of the liver transplantation for HCC. The cellular DNA was extracted from the cancerous and non-cancerous tissues of primary HCC by the phenol-chloroform method. After SNP certification using Genome-Wide Human SNP Array 6.0 (Affymetrix), whole exome sequencing was performed at the Cancer Institute of JFCR, as the support infrastructure. Exome sequencing was performed using HiSeq 2000 (Illumina), and the obtained reads were mapped to the human reference genome (hg19) using BWA software. GATK software was used for local realignment base call recalibration. Single nucleotide variants (SNVs) and indels were analyzed by MuTect and SomaticIndelDetector, as well as visual confirmation by IGV software.

  Study JGAS000052

• Personalized Medicine Strategy for MPNSTs: Using Precision Oncology on PDOX Models to Inform Tumor Boards

  Background: Malignant peripheral nerve sheath tumors (MPNSTs) are a heterogeneous group of aggressive soft tissue sarcomas with poor prognosis. Currently there is a lack of effective treatments for MPNSTs. Here, we propose a personalized medicine approach that integrates a precision oncology strategy guided by MPNST genomic analysis, with a functional validation of treatment response in an orthotopic xenograft model (PDOX) derived from the same MPNST. Methods: Comprehensive whole genome sequencing analysis was performed in primary MPNSTs, relapses and (in one case) metastases, following disease progression in two independent individuals. Matched MPNST PDOX models were generated by orthotopically implanting tumor fragments near the sciatic nerve of immunodeficient mice. Candidate targeted combination therapies were prioritized based on genomic alterations and tested in vivo in the PDOX models. Results: The feasibility of the developed strategy is illustrated for two MPNST patients, one Neurofibromatosis type 1 (NF1) individual that developed two independent MPNSTs and another sporadic MPNST case with multiple metastatic relapses. Genomic analysis revealed a remarkable degree of genomic stability across primary MPNSTs and their successive relapses in each patient, and even metastases in one individual. While based on a small number of cases requiringing additional analyses, this finding aligns with previous evidence suggesting a fair genomic conservation throughout tumor evolution. This stability supports the identification of consistent therapeutic vulnerabilities throughout disease progression. Among the therapies tested, co-treatment of MEK inhibitor (MEKi) plus bromodomain inhibitor (BETi) elicited the highest antitumor activity, resulting in approximately 60% tumor volume reduction in the sporadic MPNST PDOX model, whose patient has been receiving this therapy for eight months with sustained remission. Conclusions: This study demonstrates the feasibility and clinical utility of integrating genomic-driven precision oncology with PDOX-based functional testing for MPNSTs. This strategy may support molecular tumor boards (MTBs) in their treatment decisions. The observed genomic stability supports the use of longitudinal tumor profiling to guide treatment, and the success of MEKi+BETi highlights its potential as a combination therapy for MPNSTs.

  Study EGAS50000001502

• Histological Transformation and Progression in Follicular Lymphoma: a Clonal Evolution Study

  Background: Follicular lymphoma (FL) is an indolent, yet incurable B-cell malignancy. A subset of patients experience increased mortality rate driven by two distinct clinical end points: histological transformation and early progression after immuno-chemotherapy. The nature of tumor clonal dynamics leading to these clinical endpoints is poorly understood and previously determined genetic alterations do not explain the majority of transformed cases or accurately predict early progressive disease. We contend that detailed knowledge of the expansion patterns of specific cell populations plus their associated mutations would provide insight into therapeutic strategies and disease biology over the timecourse of FL clinical histories. Methods and Findings: Using a combination of whole genome sequencing, targeted deep sequencing and digital droplet PCR on matched diagnostic and relapse specimens, we deciphered the constituent clonal populations in n=15 transformation patients and n=6 progressors, and measured their change in abundance over time. We observed widely divergent patterns of clonal dynamics in transformed cases relative to progressed cases. Transformation specimens were generally composed of clones that were rare or absent in diagnostic specimens, consistent with dramatic clonal expansions that came to dominate the transformation specimen. This pattern was independent of time to transformation and treatment modality. By contrast, progression specimens were composed of clones that were already present in the diagnostic specimens and exhibited only moderate clonal dynamics, even in the presence of immuno-chemotherapy. Analysis of somatic mutations impacting 94 genes was undertaken in a validation cohort consisting of 395 samples from 277 patients in order to decipher disrupted biology in the two clinical endpoints. We found 12 genes were more commonly mutated in transformed samples than in preceding FL, including TP53, B2M, CCND3, GNA13, S1PR2 and P2RY8. Moreover, 10 genes were more commonly mutated in diagnostic specimens of early progressors including TP53, BTG1, MKI67 and XBP1. Conclusions: Our results illuminate contrasting modes of evolution shaping the clinical histories of transformation and progression. They have implication for interpretation of evolutionary dynamics in the context of treatment-induced selective pressures, and indicate that transformation and progression will require different clinical management strategies.

  Study EGAS00001001709

• Foundation Medicine Genomic Data Used to Identify Prognostic Markers and Fusion Genes in Multiple Myeloma

  The purpose of this study is to identify prognostic markers and treatment targets using a clinically certified sequencing panel in multiple myeloma. Mutational burden was associated with maf and proliferation gene expression groups, and a high-mutational burden was associated with a poor prognosis. We identified homozygous deletions that were present in multiple myeloma within key genes, including CDKN2C, RB1, TRAF3, BIRC3 and TP53, and that bi-allelic inactivation was significantly enriched at relapse. Alterations in CDKN2C, TP53, RB1 and the t(4;14) were associated with poor prognosis. Alterations in RB1 were predominantly homozygous deletions and were associated with relapse and a poor prognosis which was independent of other genetic markers, including t(4;14), after multivariate analysis. Bi-allelic inactivation of key tumor suppressor genes in myeloma was enriched at relapse, especially in RB1, CDKN2C and TP53 where they have prognostic significance. In addition, chromosomal rearrangements that result in oncogenic kinase activation are present in many solid and hematological malignancies, but none have been reported in multiple myeloma (MM). Here we detected fusion genes in 1.5% of patients. These fusion genes were in-frame and the majority of them contained kinase domains from either receptor tyrosine kinases (ALK, ROS1, NTRK3, and FGFR1) or cytoplasmic kinases (BRAF, MAP3K14, and MAPK14) which would result in the activation of MEK/ERK, NF-κB or inflammatory signaling pathways. Fusion genes were present in smoldering MM, newly diagnosed MM and relapse patient samples indicating they are not solely late events. Most fusion genes were subclonal in nature, but one EML4-ALK fusion was clonal indicating it is a driver of disease pathogenesis. Samples with fusions of receptor tyrosine kinases were not found in conjunction with clonal Ras/Raf mutations indicating a parallel mechanism of MEK/ERK pathway activation. Fusion genes involving MAP3K14 (NIK), which regulates the NF-κB pathway, were detected as were t(14;17) rearrangements involving NIK in 2% of MM samples. Activation of kinases in myeloma through rearrangements presents an opportunity to use treatments existing in other cancers.

  Study EGAS00001002874

• High-resolution testing of ctDNA dynamics predicts survival in metastatic NSCLC

  One of the great challenges of therapeutic oncology is determining who might achieve survival benefit from a particular therapy. Circulating tumor DNA (ctDNA) provides real-time assessments of patient prognosis and response to treatment using a simple blood draw. While ctDNA positivity is established as a poor prognostic factor, studies on longitudinal ctDNA dynamics have been small and non-randomized, with ctDNA assessments done at disparate time points. To address this, we performed high-sensitivity longitudinal ctDNA testing in 466 patients across 5 time points (1,954 samples total) in a randomized phase III study comparing different chemotherapy-immunotherapy combinations. We leverage machine learning to jointly model multiple ctDNA metrics to predict overall survival in a training/testing framework. _ . Treatment initiation correlated with reductions in ctDNA levels, and training of our machine learning model suggests that assessment of ctDNA dynamics at C3D1 (cycle 3 day 1) of chemo-IO treatment may be optimal to predict OS. The model performs well in the hold-back test data, enabling stratification of patients with Stable Disease (SD) into high-risk vs low-intermediate-risk (HR = 3.2 [2.0-5.3], p &lt;0.001; median 7.1 versus 22.3 months respectively); similarly, the model stratifies patients with a Partial Response (PR) (HR =3.3 [1.7-6.4], p &lt;0.001; median 8.8 versus 28.6 months). Importantly, the model validates well in an external cohort of patients in a different treatment setting and assayed with a different ctDNA technology, in which model predictions similarly identified high-risk patients (OS HR=3.73 [1.83-7.60], logrank p=0.00012). Simulations of clinical trial scenarios employing our ctDNA model further suggest that early ctDNA testing outperforms early radiographic imaging for predicting trial outcomes (increasing the rate of ‘True Go’ decisions by 5.2 - 22.8% depending on the drug combination). Overall, we show that measuring ctDNA dynamics during the course of therapy dramatically improves patient risk stratification, and may provide a means to differentiate between competing therapies at an early time point during clinical trials.

  Study EGAS00001006703

• TK-EPN862 - Patient-dervied xenograft model of Posterior Fossa A Ependymoma - RNAseq

  Ependymomas (EPN) are the third most common malignant brain cancer in children. Treatment strategies for pediatric EPN have remained unchanged over recent decades, with 10-year survival rates stagnating at just 67% for children aged 0-14 years. Moreover, patients who survive treatment for the most common subtype of pediatric EPN (posterior fossa A (PFA)), often suffer long-term neurological side effects as a result of therapy. It is evident that there is a need for safer, more effective treatments for pediatric EPN patients. There are ten distinct subgroups of EPN, each with their own molecular and prognostic features. To identify and facilitate the testing of new treatments for EPN, in vivo laboratory models representative of the diverse molecular subtypes are required. Here, we describe the establishment of a patient-derived orthotopic xenograft (PDOX) model of PFA EPN, derived from a metastatic cranial lesion. Patient and PDOX tumors were analyzed using immunohistochemistry, DNA methylation profiling, whole genome sequencing (WGS) and RNA sequencing. Both patient and PDOX tumors classified as PFA EPN by methylation profiling, and shared similar histological features consistent with this molecular subgroup. RNA sequencing revealed that gene expression patterns were maintained across the primary and metastatic tumors, as well as the PDOX. Copy number profiling revealed gains of chromosomes 7, 8 and 19, and loss of chromosomes 2q and 6q in the PDOX and matched patient tumor. No clinically significant single nucleotide variants were identified, consistent with the low mutation rates observed in PFA EPN. Overexpression of EZHIP RNA and protein, a common feature of PFA EPN, was also observed. Despite the aggressive nature of the tumor in the patient, this PDOX was unable to be maintained past two passages in vivo. Others who have successfully developed PDOX models report some of the lowest success rates for EPN compared to other pediatric brain cancer types attempted, with loss of tumorigenicity not uncommon, highlighting the challenge in propagating these tumors in the laboratory. Here, we discuss our collective experiences with PFA EPN PDOX model generation and propose potential approaches to improve future success in establishing preclinical EPN models

  Study EGAS00001006844

• EGA metadata schema

  EGA Schemas In this page, you will find information on how the European Genome-phenome Archive (EGA) manages its metadata standards using both XML Schema Definition (XSD) and JavaScript Object Notation (JSON) formats. If you are not sure what this means, you may want to explore our brief metadata introduction. This information may be of your interest if you are planning to learn more about how the EGA is built and how to wrap around it for other processes. Nevertheless, if you are a common user (e.g. submitter or requester), you would not have to worry about these schemas nor their format, since they are implemented in user-friendly ways for you. Metadata standards are rules that define how to format and structure data of metadata objects (i.e. entities), like EGA's samples or experiments in a consistent manner. These objects are the nodes of the metadata model of the EGA (Figure 1). Figure 1. Diagram of EGA's metadata model. The model's building blocks are objects (e.g. sample), which can reference each other (e.g. an experiment referencing the used samples). Once your files are uploaded, they can also be referenced by Runs and Analyses. The submission object is an object itself that compiles many others. At EGA, we inherit our metadata schemas from the European Nucleotide Archive (ENA), and we have expanded them to include bespoke objects such as "Policy", "Dataset", and "DAC" (Data Access Committee) for our specific use-case: handling sensitive human data. See below a list of all our metadata objects and some context for each. Metadata Object EGA accession Description Examples of metadata fields Study EGAS… Information about the study Study type, study title, study abstract… Sample EGAN… Information about the used samples in the experiment or analysis Taxon ID, scientific name, biological sex, phenotype… Experiment EGAX… Information about the performed experiment Used libraries, sequencing platform, reference to the used samples… Analysis EGAZ… Contains information about the analysis Type of analysis, used assembly, reference sequence… Run EGAR… The run holds information about the files containing the raw reads generated in a run of sequencing Platform, spot descriptor, raw file references… DAC EGAC… Contains information about the Data Access Committee (DAC) DAC contacts, contact emails… Policy EGAP… Contains the Data Access Agreement (DAA) and policy which its usage complies with Policy text, data use ontologies (DUO) codes… Dataset EGAD… Contains the collection of runs/analysis to be subject to controlled access Dataset type, compilation of Run's and Analysis' IDs There are two different sets of schemas, based on their formats, in which the EGA accepts metadata: XSDs (for XML files) and JSON Schemas (for JSON files). EGA's XML Schema Definition . When programmatic submissions are pushed through the European Bioinformatics Institute (EBI) system, XML format is used. The schemas that are applied for this format are defined in XML Schema Definition (XSD) files, which can be found at ENA's GitHub repository. You can find more information on how to validate and submit your data programmatically in our programmatic submission documentation. Furthermore, see at our GitHub repository some XML examples with either made-up values (similar to what you would submit) or descriptive values for each field (just for documentation). EGA's JSON Metadata Schemas . When programmatic submissions are pushed through the Centre for Genomic Regulation (CRG) system, JSON format specifications are used instead. See the full JSON specifications for further details. In conclusion, the EGA metadata schemas are crucial for maintaining the quality and consistency of submitted data. By understanding and following the rules outlined in these schemas, you can ensure that your submissions comply with the EGA's standards and contribute to a valuable and accessible genomic resource. Sample checklists Besides the standards in our schemas, we have another layer called Sample checklists. These are another system that the EGA inherits from ENA, and specifies what attributes are required or allowed for a sample object. The EGA uses these checklists to enforce that, for example, a sample object has the three mandatory attributes: subject ID, sex and phenotype of the individual the sample was taken from. When you submit to EGA, our checklist is automatically selected by default.

  Documentation submission/metadata/ega-schema

• COVID Response Study 2 (COVRES-2)

  COVID Response Study 2 (COVRES-2) - is a collaborative effort between researchers at Ulster University and the Western Health and Social Care Trust. The study aims to identify temporal immune responses associated with COVID-19 severity. A total of n=20 individuals (10=Hospitalised/10=Non-hospitalised) were recruited following a positive PCR result for COVID-19. Current studies 1. Distinct Omicron longitudinal memory T cell profile and T cell receptor repertoire associated with COVID-19 hospitalisation At 3 months post-infection T Cell Receptor (TCR) sequencing and RNA Sequencing were preformed to investigate immunopathological signatures associated with clinical outcome and recovery. This dataset contains both TCR-seq and RNA-seq data for all participants that took part in this study.

  Dac EGAC50000000594

• Single cell RNAseq and TCRseq data from tumor and blood samples from 4 patients with muscle invasive bladder cancer

  For this dataset we performed single cell RNAseq paired with single cell TCR-seq on tumor and blood samples from 4 patients. This dataset contains 4 tumor samples as well as 4 blood samples. Each sample is made up of 2 sets of paired fastq files. The first pair contains reads corresponding to RNA transcripts (_Transcripts in file name), while the second pair contain reads corresponding to TCRs (_VDJ in file name). Sequenced on the Illumina NovaSeq6000 platform in a paired-end run using an SP flow cell (v1.5, 300 cycles).

  Dataset EGAD50000001381

• sWGS data from 362 tumor with non muscle invasive bladder cancer and 30 blood samples (15 males and 15 females)

  A better understanding of the molecular landscape of non-muscle-invasive bladder cancer (NMIBC) is essential to improve risk assessment and identify potential therapeutic targets. Here, we perform a comprehensive genomic analysis of patients diagnosed with NMIBC based on whole-exome- (n=438), shallow whole-genome- (n=362), and total RNA-sequencing (n=414). This dataset contains 876 BAM files corresponding to the full WES dataset. This dataset contains 392 BAM files corresponding to the full sWGS dataset. Tumor samples were sequenced to median coverage of 2.14x (1.24-9.01).

  Dataset EGAD50000000732

• Raw scRNA-seq data from B-lineage cells in the blood of celiac disease patients

  Raw scRNA-seq data from 355 IgA+ peripheral blood B-lineage cells of two untreated celiac disease patients. The data was generated with the Smart-seq2 protocol and sequenced on a NextSeq500 instrument (Illumina) with 75 bp paired-end reads in high-output mode. The dataset contains R1 and R2 reads for each single cell (fastq.gz files) for cells passing quality control based on number of detected genes, reads, mitochondrial genes, reads mapping to the reference transcriptome and a productively rearranged immunoglobulin heavy chain IgA isotype reconstructed by the computational tool BraCeR. Metadata for the cells is provided in a csv file.

  Dataset EGAD50000000340

• PRADO trial - Personalized response-directed surgery and adjuvant therapy after neoadjuvant ipilimumab plus nivolumab in stage III melanoma

  The dataset includes WES sequencing data on PRE- treatment biopsies of lymph node metastasis (n=79) The technology used for sequencing is llumina HiSeq. The PRADO trial tested a personalized response-directed treatment approach based on the pathologic response after neoadjuvant ipilimumab plus nivolumab in stage III melanoma patients. In patients achieving a major pathologic response, in their index lymph node (the largest lymph node metastasis at baseline), therapeutic lymph node dissection (TLND) and adjuvant therapy were omitted. Patients with partial response underwent TLND only, whereas patients with pathologic non-response underwent TLND and adjuvant systemic therapy ± synchronous radiotherapy.

  Dataset EGAD50000000393

• Mitochondrial DNA mosaicism in human somatic cells

  Genomic alterations accumulate in the somatic cells throughout an individual’s lifetime. Recent sequencing studies have documented widespread mutations in the nuclear genome and the frequent clonal competition of normal cells carrying mutations. However, the landscape of mitochondrial DNA (mtDNA) heteroplasmy in normal human tissues is poorly understood. This study investigated the whole genome sequences (WGSs) of 2,096 clones established from non-neoplastic healthy single cells obtained from 31 donors. In addition, we analyzed 31 WGSs of neoplastic cells, including 12 clones established from adenomatous polyps from one individual with MUTYH-associated polyposis and 19 matched colorectal carcinomas from individuals who donated normal colorectal clones.

  Dataset EGAD50000000373

• Single-nucleus mRNA-Sequencing of prenatal and postnatal samples from the brain and its border regions

  Single-nucleus mRNA Sequencing of prenatal and postnatal samples from the brain and its border regions. Most samples were multiplexed with several samples run in one 10X reaction. A separate immune cell dataset was combined with published data from Braun et al 2023 and Yang et al 2021 integrated using harmony is included.

  Dataset EGAD50000000044

• Summary Statistics GWAS SSNS

  Column 1 rsid: SNP identifier;Column 2 chromosome: name of chromosome on which the SNP is located;Column 3: position: base pair position on the chromosome;Column 4 minor_test_allele: the base that constitutes the minor allele;Column 5 major_allele: the base that constitutes the major allele;Column 6 maf: the frequency of the minor allele, indicated as a fraction of 1;Column 7 allele_freq_cases: the minor allele frequency in cases;Column 8 allele_freq_controls: the minor allele frequency in controls;Column 9 regression_pvalue: the p-value for the difference in allele frequency between cases and controls;Column 10 odds_ratio: the odds ratio, as calculated using logistic regression under an additive model with adjustment for the first ten principal components of ancestry

  Dataset EGAD00010002316

• Genotyping microarray data of molecular tumorboard patients in the context of the HRD-manuscript published in IJC

  Intensity files (.idat) derived from the Infinium CytoSNP-850K BeadChip microarray (Illumina) were used to analzye a possible Homologous-Recombination-Repair Deficiency (Molecular Rationale for a PARP-Inhibitor therapy) of n= 39 tumor patients (each n= 39 .idat files of the green and red chanel respectively) presented in the Molecular Tumorboard at the University Hospital Erlangen. Using these files a Genomic Instability Score was bioinformatically deducted (R-based scripts, e.g. ASCAT, scarHRD) based upon the presence of three parameteres "Loss of Heterozygosity, LOH", "Large Scale Transitions, LST" and "Telomeric Allelic Imbalances, TAIs". The associated manuscript is published in International Journal of Cancer, IJC.

  Dataset EGAD00010002736

• Acral Melanoma PDXs from the admixed Brazilian Population - Tumour and unfiltered PDX sample CRAM files - RNAseq

  Patient Derived Xenograft RNAseq sample CRAM files mapped to build GRCh38. Acral melanoma (AM) is an aggressive melanoma subtype with limited therapeutic options and poor outcomes. In non-European descent and admixed populations, like in Latin America, it can account for a significant proportion of cutaneous melanoma cases. Here, we performed comprehensive genomic and functional profiling of AM from a uniquely diverse Brazilian cohort. Whole-exome and transcriptome sequencing was performed on both tumours and patient-derived xenograft (PDX) samples. The PDX were obtained from a comprehensive collection of patient-derived xenograft (AM-PDX) models that we established which faithfully retained the histopathological and genomic features of the original tumours.

  Dataset EGAD00001015742

• scRNAseq of aplastic anemia and healthy immune cells co-cultured with autologous HSPCs

  This dataset contains 8 batches of bone marrow or peripheral blood cells of patients with aplastic anemia and bone marrow cells of healthy controls. The samples from different experimental conditions have been multiplexed with Totalseq-C antibodies (feature barcoding). To demultiplex the samples, see instructions in 10.5281/zenodo.2590196 and https://github.com/janihuuh/aa_manu. Each sample is analysed with Chromium V(D)J and 5' Gene Expression Platform v2 (10X Genomics). The raw data includes fastq files for Gene expression, feature barcodes and V(D)J Expression. The processed data have been deposited in the ArrayExpress database at EMBL-EBI (www.ebi.ac.uk/arrayexpress).

  Dataset EGAD00001012120

• Spinocerebellar ataxia 15 (SCA15) derived iPSC WGS

  A collection of four induced pluripotent stem cell models (iPSC) derived from patients diagnosed with Spinocerebellar ataxia 15 (SCA15) . Spinocerebellar ataxia 15 (SCA15) is a neurological condition characterised by progressive gait and limb ataxia as well as abnormalities in eye movement and difficulties with balance, speech and swallowing (Synofzik et al., 2011). Whole Genome Sequencing was performed to confirm the presence of a heterozygous deletions in the inositol 1,4,5-triphosphate receptor gene (ITPR1), characteristic of the disease. Cell models names: HPSI0216i-vieg_5 or Vieg_5 (WTSIi472-A), HPSI0216i-vieg_3 or Vieg_3 (WTSIi472-B), HPSI0216i-dacv_6 or Dacv_6 (WTSIi554-A), HPSI0216i-boho_3 or Boho_3 (WTSIi502-A). All iPSC models are available via ECACC-Culture Collections.

  Dataset EGAD00001009851

• Induction Failure in Childhood and Young Adult T-cell Acute Lymphoblastic Leukemia

  We carried out WGS and RNAseq on a cohort of 48 children and young adults with induction failure in T-cell Acute Lymphoblastic Leukemia (T-ALL) to identify genomic drivers of treatment resistance. The study includes WGS for 33 tumour/normal pairs and 15 tumour-only samples. In addition, there is RNAseq data for 37 cases.

  Dataset EGAD00001009058

• Summary Statistics GWAS SSNS

  Column 1 “rsid”: SNP identifier Column 2 “chromosome”: name of chromosome on which the SNP is located Column 3: “position”: base pair position on the chromosome Column 4 “minor_test_allele”: the base that constitutes the minor allele Column 5 “major_allele”: the base that constitutes the major allele Column 6 “maf”: the frequency of the minor allele, indicated as a fraction of 1 Column 7 “allele_freq_cases”: the minor allele frequency in cases Column 8 “allele_freq_controls”: the minor allele frequency in controls Column 9 “regression_pvalue”: the p-value for the difference in allele frequency between cases and controls Column 10 “odds_ratio”: the odds ratio, as calculated using logistic regression under an additive model with adjustment for the first ten principal components of ancestry

  Dataset EGAD00001008782