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• Normal sample for patient SA1026

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1026

  Dataset EGAD00001009587

• Normal sample for patient SA1027

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1027

  Dataset EGAD00001009588

• Normal sample for patient SA1028

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1028

  Dataset EGAD00001009589

• Normal sample for patient SA1040

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1040

  Dataset EGAD00001009590

• Normal sample for patient SA1064

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1064

  Dataset EGAD00001009591

• Normal sample for patient SA1065

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1065

  Dataset EGAD00001009592

• Normal sample for patient SA1069

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1069

  Dataset EGAD00001009593

• Normal sample for patient SA1073

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1073

  Dataset EGAD00001009594

• Normal sample for patient SA1074

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1074

  Dataset EGAD00001009595

• Normal sample for patient SA576

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA576

  Dataset EGAD00001009596

• Normal sample for patient SA610

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA610

  Dataset EGAD00001009597

• Normal sample for patient SA992

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA992

  Dataset EGAD00001009598

• Normal sample for patient SA994

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA994

  Dataset EGAD00001009599

• Normal sample for patient SA288

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA288

  Dataset EGAD00001009601

• Normal sample for patient SA299

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA299

  Dataset EGAD00001009602

• Normal sample for patient SA415

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA415

  Dataset EGAD00001009603

• Normal sample for patient SA666

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA666

  Dataset EGAD00001009604

• Tumour sample for patient SA1065

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1065

  Dataset EGAD00001009605

• Tumour sample for patient SA1064

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1064

  Dataset EGAD00001009606

• Tumour sample for patient SA1026

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1026

  Dataset EGAD00001009607

• Tumour sample for patient SA1069

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1069

  Dataset EGAD00001009608

• Tumour sample for patient SA1017

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1017

  Dataset EGAD00001009609

• Tumour sample for patient SA1027

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1027

  Dataset EGAD00001009610

• Tumour sample for patient SA1073

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1073

  Dataset EGAD00001009611

• Tumour sample for patient SA1074

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1074

  Dataset EGAD00001009612

• Tumour sample for patient SA576

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA576

  Dataset EGAD00001009613

• Tumour sample for patient SA1028

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1028

  Dataset EGAD00001009614

• Tumour sample for patient SA1040

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1040

  Dataset EGAD00001009615

• Tumour sample for patient SA992

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA992

  Dataset EGAD00001009616

• Tumour sample for patient SA610

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA610

  Dataset EGAD00001009617

• Normal sample for patient SA416

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA416

  Dataset EGAD00001009618

• Normal sample for patient SA1070

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1070

  Dataset EGAD00001009619

• Tumour sample for patient SA1070

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1070

  Dataset EGAD00001009620

• Tumour sample for patient SA997

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA997

  Dataset EGAD00001009621

• Multiple Myeloma treated with anti-BCMA T-cell-redirecting therapy (1st Batch)

  Bone marrow samples were collected from patients treated with either anti-BCMA CART and/or anti-BCMA TCE. All samples were sorted using the StemCell EasySep Human CD138 positive selection kit II (StemCell, Cambridge, MA). The positive CD138+ were submitted for WGS, using peripheral blood mononuclear cells as normal matched. WGS was carried out by the Molecular Genomics Core Facility at Moffitt Cancer Center contingent on the availability of high-quality pre-treatment samples. Germline DNA was obtained from peripheral blood mononuclear cells, while tumor DNA was extracted from CD138-enriched bone marrow aspirate specimens using magnetic bead-based selection. Genomic DNA was isolated using the AllPrep DNA/RNA/miRNA Universal Kit (Qiagen). For library preparation, 100 ng of genomic DNA was fragmented using the Covaris ML230 focused ultrasonicator (Covaris, Inc.), followed by library construction with the TruSeq DNA Nano Library Prep Kit (Illumina) in accordance with manufacturer instructions. Library quality and fragment size distribution were assessed via Agilent TapeStation, and quantification was performed using the Kapa Library Quantification Kit (Roche). Sequencing was conducted on the Illumina NovaSeq 6000 system using S4-300 reagent kits with a 2×150 bp paired-end configuration. Tumor samples were sequenced to a median coverage of 60×, and matched germline controls to 30×. Reference Genome: GRCh38.

  Dataset EGAD50000002632

• Development of CMT Peds Scale for Children with CMT

  Information about how different forms of CMT affect children is not readily available because there are not good methods to measure impairment of children with CMT. The purpose of this project is to develop and test such a method and to then test this scale to ensure that it is an effective measurement of impairment of CMT in children, that the children tolerate it well, that different investigators using this method obtain similar results and that changes over time with the scale allow measurement of progression of CMT in the children. Primary Objective: To develop and test the CMT Peds scale (CMTPedS) in children with CMT in order to refine the scoring for future natural history and therapeutic trials. Secondary Objective: We propose to test the sensitivity of the CMT Peds Scale in a longitudinal study and compare the CMT Peds Scale with the Children's Quality of Life scale CHQ-PF50. 2000 patients <21 years of age with various forms of CMT will be evaluated at baseline and at subsequent annual follow up visits when possible. This first study release makes available data of n=208 study participants.

  Study phs001553

• Mapping Genes for Mammographic Density

  We recruited 1,521 women from the Old Order Amish (OOA) population of Lancaster County, Pennsylvania, with the primary goal of identifying genetic factors that influence mammographic density. All women were between the ages of 40 and 88 years, with a mean of 54 years. Additional study design details, including eligibility criteria, are described elsewhere (Douglas et al., 2008). The present-day OOA of Lancaster County are the descendants of approximately 550 individuals from central Western Europe who immigrated to the U.S. in the early eighteenth century. All living individuals derive from a single, 14-generation pedigree (Lee et al., 2010). Although the OOA are a genetically closed founder population, allele frequencies and LD profiles are remarkably similar between the OOA and U.S. participants in the International HapMap project (HapMap CEU) for common SNPs (MAF>/=5%) (Van Hout et al., 2010). We recently completed genome-wide linkage and association analyses of mammographic density, including dense and non-dense areas of the breast and the ratio of dense to total area of the breast. Summary-level data from our GWAS are available here and through the Marker of DEnsity (MODE) Consortium.

  Study phs000604

• Establishing stable brain tumor stem cell lines and translational research for new treatments

  Choroid plexus carcinomas (CPCs) have dismal prognoses with limited clinical options. A lack of characterized cell lines has hindered the development of new treatment strategies. Here, we perform whole genome sequencing analyses of two cell lines established from CPCs. Whole genome sequencing (WGS),was performed in CCHE-45 and NGT131 cell lines. Both cell lines harbored pathogenic TP53 point mutations; CCHE-45 additionally displayed TP53 loss. Furthermore, alterations of the NOTCH and WNT pathways were also detected in both cell lines. Two protein-coding gene fusions, BZW2-URGCP, and CTTNBP2-ERBB4, mutations of two oncodrivers, GBP-4 and KRTAP-12-2, and several copy number alterations were observed in CCHE-45, but not NGT131. The discovered difference's importance and implications highlight the possible diversity of choroid plexus carcinoma and call for additional research to fully understand disease pathogenesis. Whole genome sequencing revealed that the two choroid plexus carcinoma cell lines shared TP53 mutations and other common pathway alterations and activation of NOTCH and WNT pathways. Noticeable differences were also observed. These cell lines can serve as valuable models to propose new treatments in these rare but deadly brain tumors.

  Study JGAS000657

• Investigation of molecular diagnosis by molecular biological analysis using next-generation sequencer for actionable endocrine diseases (including neoplastic diseases)

  Fructose-1,6-bisphosphatase (FBPase) deficiency, caused by an FBP1 mutation, is an autosomal recessive disorder characterized by hypoglycemic lactic acidosis. Due to the rarity of FBPase deficiency, the mechanism by which the mutations cause enzyme activity loss still remains unclear. Here we identify compound heterozygous missense mutations of FBP1, c.491G>A (p.G164D) and c.581T>C (p.F194S), in an adult patient with hypoglycemic lactic acidosis. The G164D and F194S FBP1 mutants exhibit decreased FBP1 protein expression and a loss of FBPase enzyme activity. The biochemical phenotypes of all previously reported FBP1 missense mutations in addition to G164D and F194S are classified into three functional categories. Type 1 mutations are located at pivotal residues in enzyme activity motifs and have no effects on protein expression. Type 2 mutations structurally cluster around the substrate binding pocket and are associated with decreased protein expression due to protein misfolding. Type 3 mutations are likely nonpathogenic. These findings demonstrate a key role of protein misfolding in mediating the pathogenesis of FBPase deficiency, particularly for Type 2 mutations. This study provides important insights that certain patients with Type 2 mutations may respond to chaperone molecules.

  Study JGAS000625

• Gut microbiome dynamics unravelled with metagenomics sequencing

  It has been already several years since we discovered that microbial therapeutics like Faecal Microbiota Transplantation can be used to treat Clostridium difficile infection (CDI) and Inflammatory bowel disease (IBD) with great success. The concept has challenged us to test the limits of our knowledge with the perspective to improve health in the most diverse conditions like Parkinson disease, depression, autism and obesity, just to mention a few. Note: the figure belongs to the original paper and is owned by the authors. Allyson L.Bird and colleagues from Genentech Inc. (San Francisco) recently published an interesting study building up on the key concept that “As microbial therapeutics are increasingly being tested in diverse patient populations, it is essential to understand the host and environmental factors influencing the microbiome.” The authors of this work analysed 1359 gut microbiome samples using shotgun metagenomics sequencing.  The results of the comparison done between microbiota composition across the different groups showed that biological sex is the strongest driver of diversity, while aging (age range studied: 20–69) appears to be the second factor. As smartly outlined in the authors’ own graphical abstract (figure to the left), Bacteroidota species consistently increased with donors' age while Actinobacteriota species, including Bifidobacterium, decreased. This study also integrated data from oncological patients to draw cancer-related evidences. After correcting the biases for technical and geographic differences, they found that cancer patients show significantly altered gut bacterial communities compared with their healthy counterparts. Eg. E.coli was found to be increased in cancer patients together with Bacteroidota/Firmicutes ratios. By comparing sequential samples taken from the same donors (413) within short-distance time points, the authors found that in the short term, intra-individual differences were less than the inter-individual ones, indicating a certain community stability being the norm. One of the pivotal points from this interesting study is that the data proceeds from extremely well-defined and selected donors. Specifically, the data for 949 selected healthy donors came from the Milieu Intérieur (MI) Consortium and extensive metadata, including demographic variables, serological measures, dietary information, and systemic immune profiles, are available. This precise stratification allowed for the robust detection of smaller changes, compared to former works. All sequencing data has been deposited in the EGA archive under the accession number EGAS00001004437. Enjoy this amazing open access paper published in the Experimental Journal of Medicine on January 2021.

  Blog gut-microbiome-dynamics-unravelled

• Whole-genome methylation profiling of menstrual stem cells identifies novel biomarkers for endometriosis

  Endometriosis is a highly prevalent condition that remains underdiagnosed and poorly managed due to the lack of clinically validated biomarkers and limited understanding of its underlying pathophysiology. Although menstrual blood–derived stem cells (MenSCs) have been implicated in disease pathogenesis, their potential as a diagnostic source has not been systematically explored. We conducted a clinical study including 42 women (19 with endometriosis and 23 controls) to evaluate whether DNA methylation profiles from freshly isolated MenSCs could identify disease-specific biomarkers. Whole-genome DNA methylation sequencing identified differentially methylated regions (DMRs) enriched in genes associated with key features of endometriosis, including inflammation, tissue remodelling, and developmental processes. These DMRs robustly discriminated cases from controls, independently of technical and clinical confounders. Machine learning models trained and validated on these regions demonstrated strong diagnostic performance (specificity 83%, sensitivity 79%). Integration with an independent single-cell RNA sequencing dataset suggested that the identified DMRs may influence gene expression, supporting their biological relevance. Collectively, these findings highlight MenSC DNA methylation profiling as a promising, non-invasive strategy for early diagnosis and personalised management of endometriosis. (PubMed IDs will be provided once the manuscript is published.)

  Study EGAS50000001640

• Genomic Profiling of Thyroid Cancer Reveals a Role for Thyroglobulin in Metastasis -Part II

  Papillary thyroid carcinoma (PTC) has a wide geographic variation in incidence, being highest in Saudi Arabia where it is only second to breast cancer as the most common cancer among females. Genomic profiling of PTC from Saudi Arabia has not been attempted previously. We performed whole-exome sequencing of 101 PTC samples and the corresponding genomic DNA to identify genes with recurrent somatic mutations, followed by sequencing of these genes using a next-generation gene panel approach in further 785 samples. In addition to BRAF, N-RAS and H-RAS, which have previously been shown to be recurrently mutated in PTC, our analysis highlights additional genes, including thyroglobulin (TG), which harbored somatic mutations in 3% of the entire cohort. Surprisingly, although TG mutations were not exclusive to mutations in the RAS-MAP kinase pathway, their presence was associated with a significantly worse clinical outcome, which suggests a pathogenic role beyond driving initial oncogenesis. Analysis of metastatic PTC tissue revealed significant enrichment for TG mutations (p<0.001), including events of apparent clonal expansion. Our results suggest a previously unknown role of TG somatic mutations in the pathogenesis of PTC and its malignant evolution.

  Study EGAS00001003357

• Genomic Profiling of Thyroid Cancer Reveals a Role for Thyroglobulin in Metastasis

  Papillary thyroid carcinoma (PTC) has a wide geographic variation in incidence, being highest in Saudi Arabia where it is only second to breast cancer as the most common cancer among females. Genomic profiling of PTC from Saudi Arabia has not been attempted previously. We performed whole-exome sequencing of 101 PTC samples and the corresponding genomic DNA to identify genes with recurrent somatic mutations, followed by sequencing of these genes using a next-generation gene panel approach in further 785 samples. In addition to BRAF, N-RAS and H-RAS, which have previously been shown to be recurrently mutated in PTC, our analysis highlights additional genes, including thyroglobulin (TG), which harbored somatic mutations in 3% of the entire cohort. Surprisingly, although TG mutations were not exclusive to mutations in the RAS-MAP kinase pathway, their presence was associated with a significantly worse clinical outcome, which suggests a pathogenic role beyond driving initial oncogenesis. Analysis of metastatic PTC tissue revealed significant enrichment for TG mutations (p<0.001), including events of apparent clonal expansion. Our results suggest a previously unknown role of TG somatic mutations in the pathogenesis of PTC and its malignant evolution.

  Study EGAS00001001788

• Target gene sequencing for human normal endometrial glands

  To elucidate the timing and mechanism of the clonal expansion of somatic mutations in cancer-associated genes in the normal endometrium, we conducted target sequencing of 112 genes for 1,298 endometrial glands and matched blood samples from 36 women. By collecting endometrial glands from different parts of the endometrium, we showed that multiple glands with the same somatic mutations occupied substantial areas of the endometrium. The 112 genes are as follows: ABCC1, ACRC, ANK3, ARHGAP35, ARID1A, ARID5B, ATCAY, ATM, ATR, BARD1, BCOR, BRCA1, BRCA2, BRD4, BRIP1, CAMTA1, CDC23, CDYL, CFAP54, CHD4, CHEK1, CHEK2, CTCF, CTNNB1, CUX1, DGKA, DISP2, DYNC2H1, EMSY, FAAP24, FAM135B, FAM175A, FAM65C, FANCA, FANCB, FANCC, FANCD2, FANCE, FANCF, FANCG, FANCI, FANCL, FANCM, FAT1, FAT3, FBN2, FBXW7, FGFR2, FRG1, GPR50, HEATR1, HIST1H4B, HNRNPCL1, HOOK3, KIAA1109, KIF26A, KMT2B, KMT2C, KRAS, LAMA2, LRP1B, MLH1, MON2, MRE11A, MSH2, MSH6, MTOR, NBN, PALB2, PHEX, PIK3CA, PIK3R1, PLXNB2, PLXND1, PMS2, POLE, POLR3B, PPP2R1A, PTEN, PTPN13, RAD50, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54B, RAD54L, RICTOR, SACS, SIGLEC9, SLC19A1, SLX4, SPEG, STT3A, TAF1, TAF2, TAS2R31, TFAP2C, TNC, TONSL, TP53, TTC6, UBA7, VNN1, WT1, XIRP2, ZBED6, ZC3H13, ZFHX3, ZFHX4, ZMYM4.

  Study EGAS00001005914

• FASTQ files of total RNA-Seq data of POPS control samples

  Placental biopsies (n = 64 female placentas, n = 67 male placentas) were selected from healthy pregnancies from the POPs cohort. These patients had no evidence of hypertension at booking and during pregnancy, did not experience pre-eclampsia, Hemolysis, Elevated Liver enzymes, and Low Platelets (HELLP) syndrome, gestational diabetes, or diabetes mellitus type I or type II and other obstetric complications. They delivered live babies with a birth weight percentile in the normal range (20-80th percentile), with no evidence of slowing in fetal growth trajectory. Chorionic villi from the corresponding placentas (free from decidua, visible infarction, calcification, hematoma, or damage) were collected and processed within 30 minutes of separation from the uterus. After repeated washes in chilled phosphate buffered saline, the samples were placed in RNA later (Applied Biosystems) and stored at -80°C. Total placental RNA was extracted using mirVana Isolation Kit (Ambion). For each placenta, approximately 5 mg of tissue were homogenized in the Lysis/Binding solution for 20 sec at 6 m/s using a bead beater (FastPrep24) and Lysing Matrix D Tubes (MP Biomedicals). The samples were then spun at 13,000 rpm for 5 min at 4°C and the supernatants recovered. Afterwards, the manufacturer's instructions were followed. Immediately after the RNA extraction, placental RNA samples were DNase-treated using DNA-free DNA Removal Kit (Ambion), aliquoted, and stored in -80°C. Quantity and quality of the RNA samples were assessed using the Agilent 2100 Bioanalyzer, the Agilent RNA 6000 Nano Kit (Agilent Technologies), and Qubit fluorometer. Libraries were prepared starting with 300-500 ng of good quality total RNA (RIN ≥7.5) using the TruSeq Stranded Total RNA Library Prep Kit with Ribo-Zero Human/Mouse/Rat (Illumina), according to the manufacturer's instructions. The kit contains 96 uniquely indexed adapter combinations in order to allow pooling of multiple samples prior to sequencing. After determining their size (with the Agilent 2100 Bioanalyzer and the Agilent High Sensitivity DNA Kit by Agilent Technologies) and concentration (by qPCR with the KAPA Illumina ABI Prism Library Quantification Kit, Kapa Biosystems), libraries have been pooled and sequenced (single-end, 125 bp) using a Single End V4 Cluster Kit and an Illumina HiSeq2500 or HiSeq4000 instrument.

  Dataset EGAD00001003457

• Validation of a Haloplex platform for targeted re-sequencing of the exons of 25 genes

  We recently used the Agilent SureSelect platform to re-sequence a set of genes known to be mutated in human AML. The results from 10 AML DNA samples were very satisfactory, but the effort required was significant. Thus, we decided to re-sequence the same genes using the Haloplax system for target enrichment in 48 AML samples. We planned to do this using MiSeq and have data from a pilot of 3 samples. The data is promising but coverage appears pathcy so far. However, in order to get a better understanding of the data we will need deeper sequencing. We will need two lanes of HiSeq to get the same degree coverage as Sureselect. his 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/

  Dataset EGAD00001000603

• Precision Medicine for ABCA4 Disease: Modifier Alleles

  Genetic variation affecting the structure and function of the ABCA4 protein gives rise to a broad collection of genetically and clinically heterogeneous diseases, collectively known as ABCA4 disease, which are characterized by both phenotypic overlap and variable penetrance and expressivity. These include autosomal recessive Stargardt disease, cone-rod dystrophy, occult maculopathy, bull's eye macular dystrophy, retinitis pigmentosa-like retinal dystrophy, etc. Some phenotypic variation can be attributed to variability of highly penetrant ABCA4 mutations; however, much of the heterogeneity cannot be explained by ABCA4 mutations alone. The concept of Precision Medicine requires, by definition, identification of all genetic variation contributing to specific disease phenotypes. The main goal of this study is to identify all genetic variation resulting in phenotypes compatible with ABCA4 disease, which, in addition to biallelic disease-causing variants in the ABCA4 locus, include cis- and trans-acting modifiers and mutations in genes other than ABCA4.The study was carried out by performing whole exome sequencing (WES) of affected individuals to identify causal and modifying genetic variation for patients with macular dystrophies compatible with ABCA4 disease. All samples were sequenced with the IDT's Exome Research Panel Version 1 with SeqCap EZ Exome v2 and SeqCap EZ Exome v3 capture kits. Samples were sequenced on Illumina next-generation sequencing machines (San Diego, CA, USA) using DRAGEN Bio-IT Platform v. 2.5.1 (Illumina) to align reads to the Genome Reference Consortium Human Build 37, calling variants in accordance with the Genome Analysis Tool Kit (GATK, v.4.0.2.1) Best Practices Workflow, using ATAV (v.7.0.16) variant-calling pipeline. Variants for analysis were restricted to the consensus coding sequencing public transcripts (CCDS release 20) plus 2 base pair (bp) intronic extensions. Variants were annotated using ClinEff (DnaMiner) with Ensembl-HGRCh37.73 annotations.This set of WES data from unrelated patients of different racial and ethnic backgrounds can be stored and shared through dbGaP. These data provide information on frequencies of disease-causing variants in genes other than ABCA4 in patients with macular dystrophies. These also provide frequencies of selected pathogenic and none-pathogenic variants in different ethnicities.

  Study phs002393

• Security Overview

  Security Overview The European Genome-phenome Archive (EGA) houses consented human data under controlled access. Access decisions are handled by the relevant Data Access Committee (DAC), which autonomously manages requests through the DAC Portal. This document provides an overview of EGA’s practices in ensuring the security of data stored at EGA. As security is a prime concern of the EGA, the EGA is a member of the Global Alliance for Genomics and Health (GA4GH) Data Security work stream. The EGA contributes and helps develop the recommendations outlined the GA4GH Security Technology Infrastructure document, which defines guidelines, best practices, and standards for building and operating an infrastructure that promotes responsible data sharing in accordance with the GA4GH Privacy and Security Policy. Explore the EGA dataflow to gain deeper insights! Accessing data in the EGA involves several steps. First, users need to create an EGA account. Once logged in, they can request access to data controlled by a DAC through the EGA website (see documentation). The DAC oversees these requests through the DAC Portal, and if approved, grants the necessary permissions to the user's EGA account, allowing them to access and download all relevant data and metadata for the requested dataset(s). The key points of EGA security strategy are: 1 Regular Risk Assessment The EGA regularly identifies and assesses risk related to the following: Breach of confidentiality,Breach of privacy or autonomy,Malicious or accidental corruption or destruction of data archived at EGA,Disruption of services provided by the EGA. 2 Risk mitigation The EGA implements and maintains safeguards to minimise the risks identified above in accordance with the 6 control objectives listed in Appendix 1 and outlined in the GA4GH Security and Infrastructure document.If a breach is discovered, the EGA applies a defined protocol to minimise damage. 3 Identity and authorisation management The EGA authenticates the identity of individuals or software accessing controlled access data held at the EGA.The EGA ensures an appropriate level of assurance (LoA) is applied to the identity consistent with the risk associated with that individual, such as multi-factor authentication for DACs.The EGA provides the minimum access rights and privileges consistent with the user’s identity, allowing access consistent with the GA4GH Privacy and Security Policy, as determined by the appropriate DAC. 4 Audit Logs The EGA maintains a set of logs recording: Changes to user access rights,Data access requests,Resource usage. 5 Cryptography, communication security, and data integrity The EGA ensures data transmission integrity using a hash function.All data transmitted to or from the EGA is end-to-end encrypted.All data at EGA is stored using strong encryption.Encryption keys are not stored in the same system as the encrypted data.All data archived at EGA must be accompanied by a signed submission statement ensuring appropriate consent or ethical approval has been obtained, and is in accordance with all applicable laws and regulations. The EGA has a defined protocol defining the response in the event of a security breach, and is continuing to work with the GA4GH Data Security Work Stream to help define best practice and associated standards for breach responses. Appendix 1 GA4GH Control Objectives Control Objective 1: Implement technology safeguards to minimise the risk of unauthorised access, use, or disclosure of confidential and private data.Control Objective 2: Implement technology safeguards to minimise the risk of discovery, access, and use of individuals’ clinical and genomic data, and individual identities, other than as authorised by applicable jurisdictional law, institutional policy, and individual consents.Control Objective 3: Implement technology safeguards to minimise the risk of accidental or malicious corruption or destruction of data.Control Objective 4: Implement technology safeguards to minimise the risk of disruption, degradation, and interruption of services enabling access to data.Control Objective 5: Implement technology safeguards to minimise the risk of potential security attacks and misuse of authorised accesses and privileges.Control Objective 6: Implement technology safeguards to promptly detect the failure to attain the above control objectives and to respond with proper countermeasures.Appendix 2Refer to the document below to learn more about EGA long-term data preservation policy and procedures at EMBL-EBI.Long-term data preservation

  Documentation about/security

• Somatic mutation burden and copy-number variation analysis in neurofibromatosis type 1-associated plexiform neurofibromas

  Neurofibromatosis type 1 (NF1) is a common tumor-predisposition disorder due to germline mutations in the tumor suppressor gene NF1. A virtually pathognomonic finding of NF1 is the plexiform neurofibroma (PN), a benign, likely congenital tumor that arises from biallelic inactivation of NF1. PN can undergo transformation to a malignant peripheral nerve sheath tumor, an aggressive soft-tissue sarcoma. To better understand the non-NF1 genetic contributions to PN pathogenesis, we performed whole-exome sequencing and genome-wide copy-number determination for 23 low-passage Schwann cell cultures established from surgical PN material with matching germline DNA. All resected tumors were derived from routine debulking surgeries. None of the tumors were considered at risk for malignant transformation at the time, e.g., there was no pain or rapid growth. Deep (~500X) NF1 exon sequencing was also conducted on tumor DNA. Non-NF1 somatic mutation verification was performed using the Ampliseq/IonTorrent platform. We identified 100% of the germline NF1 mutations and found somatic NF1 inactivation in 74% of the PN. One individual with three PNs had different NF1 somatic mutations in each tumor. The median number of somatic mutations per sample, including NF1, was one (range 0 - 8). NF1 was the only gene that was recurrently somatically inactivated in multiple tumors. We found no recurrent non-NF1 locus copy-number variation in PN. This is the first multi-sample whole-exome sequencing study of NF1-associated PN. Taken together with concurrent copy-number data, our comprehensive genetic analysis reveals the primacy of NF1 loss as the driver of PN tumorigenesis.

  Study phs001403

• Whole Transcriptome Sequencing of Resectable Stage III/IV Melanoma Evaluated After Starting Hu14.18-IL2 Predicts Outcome

  Background: We analyzed whole transcriptome sequencing in tumors from 23 patients with stage III or IV melanoma from a pilot trial of the anti-GD2 immunocytokine, hu14.18-IL2, to identify predictive immune and/or tumor biomarkers in melanoma patients at high risk for recurrence. Methods: Patients were randomized to receive the first of 3 monthly-courses of hu14.18-IL2 immunotherapy either before (Group A) or after (Group B) complete surgical resection of all known disease. Tumors were evaluated by histology and whole transcriptome sequencing. Results: We report here that tumor infiltrating lymphocyte (TIL) levels directly associate with relapse-free survival (RFS) and overall survival (OS) in resected tumors from Group A, where early responses to the immunotherapy agent could be assessed. TIL levels directly associated with a previously reported immune signature which associated with RFS and OS, particularly in Group A tumors. In Group A tumors there were decreased cell cycling gene RNA transcripts, but increased RNA transcripts for repair and growth genes. We found that outcome (RFS and OS) was directly associated with several immune signatures and immune-related RNA transcripts and inversely associated with several tumor growth-associated transcripts, particularly in Group A tumors. Most of these associations were not seen in Group B tumors. Conclusion: We interpret these data to signify that both immunologic and tumoral cell processes, as measured by RNAseq analyses detected shortly after initiation of hu14.18-IL2 therapy are associated with long term survival and could potentially be used as prognostic biomarkers in tumor resection specimens obtained after initiating neoadjuvant immunotherapy. Note: RNA sequences and expression data can be found at NCBI GEO GSE133713

  Study phs001947