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• T cell reactivity of MHC epitopes

  By using >36,000 immunogenicity assay results, we developed a method to identify peptide-MHC complexes whose structural alignment facilitates T cell reaction. Our method accurately predicted neoepitopes for MHC II as well as MHC I that were responsive to checkpoint blockade when applied to >1,200 samples of various tumor types and on-therapy melanoma samples. To investigate selection by spontaneous immunity at the single epitope level, we analyzed the frequency spectrum of >25 million mutations in >9,000 treatment-naïve tumors in association with >100 immune phenotypes. MHC II immunogenicity specifically lowered variant frequencies in tumors under high immune pressure particularly with high TCR clonality and MHC II expression.

  Study EGAS00001006445

• Single-cell RNA-seq of peripheral blood mononuclear cells in classic Hodgkin lymphoma

  Classic Hodgkin lymphoma (cHL) is a largely MHC class I-negative tumor with recurrent 9p24.1/ PD-L1/ PD-L2 copy gains and the highest reported response rates to PD-1 blockade. We utilized scRNA sequencing to characterize the peripheral immune response to PD-1 blockade and more broadly define non-CD8+ dependent mechanisms of immune evasion in cHL. Peripheral blood mononuclear cells were obtained from 20 patients with relapsed/refractory (R/R) cHL treated with PD-1 blockade (nivolumab) on the CheckMate 205 clinical trial (paired samples from cycle 1 day 1 [C1D1] and cycle 4 day 1 [C4D1]), 11 patients with newly diagnosed, previously untreated cHL and 13 healthy donors.

  Study EGAS00001007569

• Next generation sequencing of diffuse intrinsic pontine glioma samples to identify recurrent mutations, variations, and expression patterns to define novel therapies

  Diffuse intrinsic pontine glioma (DIPG) is an extremely rare (~350 cases/year) and universally fatal childhood brain cancer. Standard clinical strategies such as chemotherapy and radiotherapy show only transient improvement in patient condition and result in negligible change in survival, DIPG remains at below 1% survival after 5 years. Prioritization of panobinostat through previous cooperative work resulted in a phase 1 clinical trial. Nonetheless, new therapies for DIPG must be identified to further dramatically change the statistics for DIPG. To identify novel therapy strategies for DIPG, we performed whole exome(16 new samples, 22 previously published samples, 38 in total with 26 matched normal) and RNA deep sequencing (17 new samples, 11 previously published samples) on a cohort of new patient samples. Sequencing results aid in the identification of recurrent mutations/variations and endotypic expression profiling to identify new therapeutic and treatment strategies for DIPG.

  Study phs001526

• Transcriptomic Analysis of Human Hematopoietic Progenitors from Healthy Donors and Bone Marrow Failure Patients

  The purpose of this study was to define transcriptional changes that occur in the rare and heterogeneous bone marrow progenitors that are affected in Shwachman-Diamond Syndrome. Data are reported for four genetically confirmed SDS patients ranging from 11-26 years old, 3 male and 1 female, and four healthy donors ranging from 25-29 years old, 2 male and 2 female. Subjects provided written, informed consent in accordance with the Declaration of Helsinki's Ethical Principles of Medical Research Involving Human Subjects. Single CD34+ cells were purified from fresh bone marrow and cDNA libraries were prepared using the SMARTer Ultra Low RNA Kit (Clontech; pre-2015) or SMART-Seq v4 Ultra Low RNA Kit (Clontech; post-2015). Libraries were sequenced on a HiSeq 2500 Instrument (Illumina, San Diego, CA) to a read depth of ~3 M paired-end, 25 bp reads per single cell. Paired-end reads were mapped to the hg38 human transcriptome (Gencode v24) using STAR v2.4.2a.

  Study phs001845

• Circulating Tumor DNA as a Biomarker in Patients with Stage III and IV Wilms Tumor: Analysis from a Children's Oncology Group Trial, AREN0533

  We evaluated the utility of circulating tumor DNA (ctDNA) as a biomarker in the serum and urine of 50 patients with Stage III or Stage IV Wilms tumor (WT) that were enrolled on Children's Oncology Group (COG) trial AREN0533. 49 out of 50 patients had matched tumor samples available for comparison. We profiled samples with ultra-low pass whole genome sequencing (ULP-WGS) and detected ctDNA in 82% of serum samples and 26% of urine samples. We also identified copy-number changes and single-nucleotide variants in the serum and urine samples, and compared them to the tumor data. Our study highlights the advantages of using ctDNA as non-invasive prognostic biomarker with the potential to detect tumor heterogeneity in patients with WT. We believe that prospective validation of ctDNA as a prognostic biomarker in patients with WT is warranted. The ULP-WGS and targeted next-generation sequencing (OncoPanel) data are available in this study.

  Study phs002847

• International Age-Related Macular Degeneration Genomics Consortium - Exome Chip Experiment

  Advanced age-related macular degeneration (AMD) is the leading cause of blindness in the elderly, with limited therapeutic options. To further our understanding of AMD genetics, we examined the contribution of common and rare genetic variation in the International AMD Genomics Consortium that included ~50,000 samples of 26 AMD case - control cohorts that were jointly genotyped. Analyzing 16,144 patients with late stage AMD and 17,832 controls, we identified 52 independently associated common and rare variants distributed across 34 loci. Besides these single variant signals, we also observed gene-based enrichment of very rare coding variants (frequency < 0.1%) in cases that implicated causal roles for CFH, CFI and TIMP3 in three of the known AMD risk loci. Our results support the hypothesis that rare coding variants can pinpoint causal genes within known genetic loci and illustrate that applying the approach systematically to detect new loci requires extremely large sample sizes.

  Study phs001039

• Integrative Age-Related Changes in Genome and Epigenome in Human Lung in Relation to Smoking

  The Integrative Age-Related Changes in Genome and Epigenome in Human Lung in Relation to Smoking study was designed to assess DNA methylation and gene expression profiles in cytologically normal bronchial progenitor "basal" cells. The cells were collected from 54 consented bronchoscopy specimen donors at standard airway locations during clinically indicated bronchoscopy, and then outgrown in primary cultures. DNA methylation profiles were assessed using Enzymatic-Methyl sequencing (EM-seq: 96% genomic coverage, 7.5x depth) in all 54 donors' samples. The gene expression profile was evaluated by bulk RNA-Seq in 39 donors' samples. The study participants were at the ages of 33 - 81 years old at enrollment. Of the 54 donors, 31 were newly diagnosed with lung cancer. As for tobacco smoke exposure, there were 19 current, 18, former, and 17 never smokers. There are 26 subjects whose specimens were used for single cell whole-genome sequencing study phs002758.

  Study phs003317

• Functional Variant rs9344 at 11q13.3 Regulates CCND1 Expression in Multiple Myeloma with t(11;14)

  Multiple myeloma (MM) is a plasma cell (PC) malignancy characterized by cytogenetic abnormalities, such as t(11;14)(q13;q32), resulting in CCND1 overexpression. The rs9344 G allele within CCND1 is the most significant susceptibility allele for t(11;14). We have found the G risk allele is enriched in both healthy individuals of African ancestry and African American individuals with MM. The overall aim of this study was to identify the mechanism behind why the G allele is enriched in t(11;14) MM. To reach this goal, we performed ATAC-seq from 100,000 CD138+ tumor cells from 26 patients with MM. In addition, we performed ChIP-seq using antibodies against H3K4me1, H3K27ac and H3K4me3 from 100,000 CD138+ tumor cells from 8 patients with MM. We used these data to evaluate whether the G allele of rs9344 is associated with elevated chromatin accessibility, H3K27ac and H3K4me3 in t(11;14) MM.

  Study phs003997

• SeqControl: Process Control for DNA Sequencing

  As next-generation sequencing (NGS) continues to increase in speed and throughput, routine clinical and industrial application draws steadily closer. These “production” uses of NGS will require enhanced quality-monitoring and quality-control to optimize output and reduce costs. We therefore developed a framework called SeqControl for predicting sequencing quality and coverage using a set of 15 metrics describing overall coverage, coverage distribution, base-wise coverage and base-wise quality. Using whole-genome sequences of 27 prostate cancers and 26 normal references we derive multivariate models that predict sequencing quality and depth. SeqControl robustly predicts how much sequencing is required to reach a given coverage depth (AUC = 0.993), accurately classifies clinically relevant formalin-fixed paraffin-embedded samples and makes predictions from as little as 1/8 of a lane of sequencing data (AUC = 0.967). These techniques can be immediately incorporated into existing NGS pipelines to monitor data quality in real-time. SeqControl represents a first step towards statistical process-control for NGS.

  Study EGAS00001000899

• Feasibility and safety of a multi-cancer blood test for screening and intervention

  We evaluated the feasibility and safety of blood testing for cancer in an interventional study of 10,006 women. Positive tests for DNA or protein were independently confirmed and diagnostic PET-CT imaging was used to localize disease. During the study, 26 cancers were first detected by blood testing, 24 more by standard-of-care screening, and 46 by neither approach. Surgicalexcision of the primary tumors was performed on 12 cancers first detected by blood testing.1.0% of participants underwent PET-CT imaging based on false positive blood tests, and 0.22% underwent a futile invasive diagnostic procedure. These data demonstrate that multi-cancer blood testing, when followed by PET-CT localization, can be safely incorporated into routineclinical care, in some cases leading to surgery with intent to cure. Further studies will be required to determine the clinical validity and utility of multi-cancer blood testing.

  Study EGAS00001004372

• Whole exome and Transcriptome sequencing of treatment-naïve esophageal adenocarcinoma biopsies and matched peripheral blood mononuclear cells

  Exome sequencing was performed on n=28 treatment-naïve esophageal adenocarcinoma (EACs). Three to four biopsies sampling different areas of each tumor were pooled before nucleic acid extractions to mitigate the elevated heterogeneity described for EAC. WES was performed on EAC biopsies at 120X average coverage, with autologous PBMCs used as germline controls at 80X average coverage. Libraries were prepared from 30 ng of input DNA using the SureSelect QXT Human All Exon V7 kit (Agilent Technologies) and sequenced on the NextSeq 550 (Illumina), 2x150 bp. BCL files were demultiplexed to FastQ files using bcl2fastq2 software (Illumina). Three paired end sequencing batches were analyzed independently (Batch1: samples 8, 10, 11, 12, 15, 17, 18; Batch2: samples 20, 24, 25, 26, 27, 29, 30, 31, 33, 34 ; Batch3: samples 35, 37, 39, 40, 41, 43, 45, 48, 54, 55, 57). RNA sequencing was performed on n=26 treatment-naïve esophageal adenocarcinoma (EACs). Three to four biopsies sampling different areas of each tumor were pooled before nucleic acid extractions to mitigate the elevated heterogeneity described for EAC. RNAseq libraries were prepared on 50 ng of total RNA (with RNA integrity index RIN >=7) with the TruSeq Stranded mRNA library preparation kit (Illumina) in accordance with low-throughput protocol. After PCR enrichment (15 cycles) and purification of adapter-ligated fragments, the concentration and length of DNA fragments were measured using D1000 Screen Tape System (Agilent), obtaining a median insert size of 311 nucleotides. Then, RNAseq libraries were sequenced using the Illumina NovaSeq platform, 1x100 bp, obtaining on average 100 million single reads per sample.

  Dataset EGAD00001010876

• Matching of actionable mutations with therapies in cancer patients: comparison of three commercial decision support platforms

  Purpose: In oncology, precision medicine centers on tailoring the most appropriate therapy to a patient according to their tumor molecular profile. We addressed the question whether there is a consensus in the classification of actionability of somatic variants and their alignment to treatment recommendations. To this end, we evaluated three commercial clinical decision support tools to assess their variant classification and drug matching strategies.Methods: In this two centers study 48 patients with metastatic breast (n=12), colorectal (n=17) or non-small cell lung cancer (n=19) were recruited based on circulating tumor DNA levels in peripheral blood. In individual patients’ plasma samples, we established somatic copy number alterations and somatic mutations across a 77 cancer-associated gene panel and exposed the obtained molecular profiles to the decision support packages NAVIFY Mutation Profiler (NMP; Roche), Qiagen Clinical Insight (QCI) Interpret (Qiagen) and CureMatch Bionov (CureMatch).Results: In the plasma samples, altogether 492 somatic alterations were assessed. Each decision support platform varied in their format of data input, mode of variant classification and strategies for identifying druggable targets and clinical trials, which resulted in discrepancies in tier classification as well as designations of actionability. The frequency of concordant actionable events for tier I-A or tier I-B classifications, i.e. those with the strongest clinical evidence, was only 4.3%, 9.5% and 28.4% when comparing NMP with QCI, NMP with CureMatch, and CureMatch with QCI, respectively. As a consequence, the obtained treatment recommendations differed drastically, and alignment of treatment recommendations was limited to established predictive markers.Conclusions: The lack of standards for the extraction of treatment recommendations from comprehensive tumor molecular profiling data challenge the promising concepts of precision oncology.

  Study EGAS00001004383

• Targeting the DNA Repair Pathway in Ewing Sarcoma

  Ewing’s Sarcoma is a bone and soft tissue tumor that primarily affects adolescents and young adults. With current therapies, 70% of patients with localized disease survive but survival for metastatic and recurrent disease is poor. Whole genome sequencing of 19 Ewing’s Sarcoma tumors showed that STAG2 was mutated in 10% (2/19) of the tumors and STAG2 protein was absent in 14% (13/106) tumors by immunohistochemical staining. Previous studies have shown that glioblastoma cells lacking STAG2 are more sensitive to poly-ADP ribose polymerase (PARP) inhibitors. We found that Ewing’s Sarcoma cell lines are sensitive to PARP inhibitors irrespective of STAG2 protein expression. Ewing Sarcoma cell lines are defective in double strand DNA break repair. PARP inhibitor cytotoxicity in Ewing’s Sarcoma cells was potentiated 10-1,000 fold by DNA damaging agents (irinotecan and temozolomide). To extend these studies in vivo, we developed an orthotopic Ewing’s Sarcoma mouse model and performed pharmacokinetic and pharmacodynamic studies with three different PARP inhibitors (BMN-673, olaparib and veliparib) in clinical development for pediatric cancer. Those data were used to design preclinical phase I studies to identify tolerable drug combinations for pilot efficacy testing (preclinical phase II). Based on the results of the preclinical phase I/II data, we performed a double blind, randomized, placebo controlled preclinical phase III trial with 274 mice in 15 treatment groups. Irinotecan administered in a low-dose protracted schedule optimized for pediatric patients was an effective DNA damaging agent to combine with olaparib and BMN-673 and was better tolerated than combinations with temozolomide. Combining olaparib or BMN-673 with irinotecan and temozolomide gave complete and durable responses in over 80% of the mice.

  Study EGAS00001000839

• Persistence of circulating tumor DNA in breast cancer patients during neoadjuvant treatment is a significant predictor of poor tumor response

  Purpose:Accurate assessment of response during neoadjuvant systemic treatment (NST) poses a major clinical challenge. Therefore, a minimally-invasive assessment of tumor response based on cell-free circulating tumor DNA (ctDNA) may be beneficial to guide treatment decisions.Experimental Design: We profiled 93 genes in tissue from 193 early breast cancer patients. Patient-specific assays were designed to track ctDNA during NST from 145 patients with available plasma. ctDNA presence and levels were correlated with complete pathological response (pCR) and residual cancer burden (RCB) as well as with clinicopathologic characteristics of the tumor to identify potential proxies for ctDNA release. Moreover, we tested the predictive value of driver events identified in the tumor.Results:At baseline, ctDNA could be detected in 63/145 (43.4%) patients and persisted in 25/63 (39.7%) patients at mid-therapy (MT) and 15/63 (23.8%) patients at the end of treatment. ctDNA detection at MT was significantly associated with higher RCB (OR 0.062, 95% CI 0.01-0.48, P=0.0077). Out of 31 patients with detectable ctDNA at MT, 30 patients (96.8%) were non-responders (RCB II, n=8; RCB III, n=22) and only one patient responded to the treatment (RCB I). Considering all 145 patients with baseline (BL) plasma, none of the patients with RCB 0 and only 6.7% of patients with RCB I had ctDNA detectable at MT, while 30.6% and 29.6% of patients with RCB II/III, respectively, had a positive ctDNA result. Conclusion:Overall, our results demonstrate that the detection and persistence of ctDNA at mid-therapy may have the potential to negatively predict response to neoadjuvant treatment and identify patients who will not achieve pCR or be classified with RCB II/III.

  Study EGAS00001005798

• EGA QuickView

  EGA QuickView Secure and Remote Access to EGA Files What is EGA QuickView? EGA QuickView is a FUSE file system that allows secure and remote access to EGA files. It is a hybrid of sshfs and crypt4ghfs, which means it uses ssh to connect to the EGA distribution servers, downloads data in Crypt4GH format, and transparently decrypts those files. It is compatible with Linux and macOS 12. Why use EGA QuickView? EGA QuickView is a highly useful and convenient tool for anyone who needs to access EGA files remotely and securely. The software offers a quick and convenient way to browse through EGA files without the need for complete downloads, thus saving bandwidth and time. Whether you are a medical professional requiring access to sensitive patient data or a research scientist working on a project that involves EGA files, EGA QuickView provides a highly useful solution. The software boasts advanced security features, ensuring that your data is safe and protected at all times. Get started with EGA QuickView To get started with EGA QuickView, visit the EGA QuickView GitHub repository for more information and installation instructions.

  Documentation access/download/visualisation/ega-quickview

• Submission FAQ

  Submission FAQ Before Submission Is the EGA the right archive for my data? The most suitable archive for your data is dependent on the type of data you are wishing to submit and whether the data require public or controlled access. Public access is defined as complete and open access to all submitted data. On the contrary, controlled access, exerted by the EGA, requires formal applications to be made to access the submitted data files and metadata. EGA only accepts human-derived data subject to controlled access. If your submission contains other types of data, please choose the appropriate repository for it (see image below): ENA, EVA, ArrayExpress, BioSD and GWAS catalog.  Should your submission be subjected to controlled access? Data access conditions are normally defined in the original informed consent agreements signed by the participants involved in your study. All data submitted to the EGA is subject to controlled access. These consents prevent the derived data files, potentially identifiable, from being dispersed by open and public access. Controlled access data often refers to human data derived from medical research and consortium projects. If in doubt, consult the informed consent agreements that apply to your study The EGA enables you to hold a submission before publication. What data types can be submitted to the EGA? Data types accepted by the EGA can be split into three categories: Sequences: both in generic and platform-specific formats. Array-based: from raw signal files to processed matrices. Phenotypes: all possible phenotype formats are accepted. All manufacturer-specific raw data formats derived from major next generation sequencing platforms are accepted. Also generic sequence formats: flat reads in a FASTQ file, aligned sequences (BAM or CRAM files) as well as sequence variation files in VCF format. All array-based technologies are accepted, including raw data, intensity and analysis files, without any restriction on data formats accepted. We also accept and distribute phenotype data (associated to the samples) in almost any format: from an image to a README file. How long does a submission take? Submissions to EGA come in a variety of formats and sizes, thus it is difficult for us to exactly predict how long a submission will take. We, therefore, advise all of our submitters to allow as much time as possible to make a submission. Based on previous records, we anticipate that the submission process may take at least one month. The submitter’s familiarity with the procedures, possible technical issues that may arise during submission and the amount of data that is being submitted are the main factors that will affect the length of the submission process. However, each step of a regular submission should be considered when estimating the time it would take: Encryption of the files Upload of the files Metadata submission Archival of the files Release of the study and datasets to EGA webpage For example, the upload of the files depends on the submission size, while metadata submission mainly relies on each submitter’s expertise. Further, some steps (e.g. answering to inquiries) depend on the EGA Helpdesk team, which may take some days to be processed during busy times Is data deposited in the EGA secure? The EGA set-up consists of a secure computing facility for data processing, a shared EBI set-up for data submissions and distribution of data via data requests made through the EGA website. Data is also copied in the Barcelona Supercomputing Center (BSC) infrastructure, where all stored and distributed data is encrypted Data is encrypted along the submission process and stored securely, granting its access to authorised users exclusively. During the download process, through our Python Client or Aspera, all requested data is downloaded over secure https connections. All data at the EGA is encrypted, and only accessible (for log-in and download) through secure protocols. For further information please, visit our security overview. What documentation do I need to provide? All submissions require policy’s documentation: 'Data Access Agreement (DAA)', 'Data Processing Agreement (DPA)' and 'Authorized Submitters Formulary'. The data processors (EGA) and the data owners will also sign the DPA. Will all metadata be public? Among the submitted metadata we need to make the distinction between identifiable and unidentifiable metadata: (1) the former may allow the identification of the human the sample derived from (e.g. detailed geographical providence, personal name, family ancestry…); (2) while the latter can be used to interpret the data without compromising the anonymity of the patients. The majority of the metadata submitted to the EGA corresponds to the unidentifiable category (e.g. sequencer's model). This type of metadata is publicly available on the EGA website and other EBI resources/partners’ websites. On the other hand, some parts samples’ metadata are subject to being identifiable, and thus only accessible by authorized data requesters, with the exception of: 5 submitter-defined attributes of the sample: alias, title, subject_id, gender and phenotype. It is the submitter’s responsibility not to submit sensitive metadata in these public fields. 3 anonymised fields that pinpoint the sample record in archivals: sample’s EGA stable ID (EGAN…), BioSample ID (SAMEA…) and submitter’s center name. During Submission Are there any sample specific requirements for EGA? All samples submitted to the EGA must include the attributes of biological sex, subject ID (anonymised individual identifier) and phenotype information. These are critical for data findability and its analysis, and we highly recommend using controlled ontology terms where applicable. For example: defining tumour and non-tumour samples and/or defining disease state. The EGA recommends using the Experimental Factor Ontology Database to find ontologized terms that describe your sample phenotypes. How do I get an accession number to use in my publication? You will receive your study accession number (EGAS…) upon complete your submission, either: Programmatically. As soon as the metadata is submitted and validated your study will be assigned an accession number that will be given in the submission’s response. Manually registering your study and relevant metadata using the online metadata submission tool: the EGA submitter Portal. How are files uploaded to the EGA? Data files are uploaded into private submission drop boxes (i.e. environments to which you are granted access and where you can transfer your files) using INBOX or FTP. These spaces are provided as part of the submission procedure. Before uploading any file, you must encrypt your files, . Only encrypted files shall be uploaded to the drop boxes. Why does data need to to encrypted for my submitted files? It is one of the security steps the EGA has implemented. In case of a security breach, people without the proper encryption key will not be able to read or use the information that could have been leaked. This measure is essential when working with sensitive data, such as controlled access human data. All submitters must use crypt4gh to create EGA compliant files prior to uploading them. This encryption is GPG-based, using EGA’s public key. Why are my files not available if I see them in the INBOX? There exists a time window between the data upload and the availability of such files via the Submitter Portal. For this reason, some metadata (run and analysis objects) cannot be registered until at least 24 hours after the files have been uploaded to your box. Why are MD5 sum values generated for my submitted files? We require pre- and post- encryption MD5 (message-digest) checksum values to be provided for all submitted files. These 128-bit values are computed using the content of each file, creating unique sequences that allow us to ensure that file integrity has been maintained during the transfer process. In other words, if the MD5 checksums we generate and those you generated match, we infer that the content of the transferred files is correct (i.e. files are not corrupted or truncated). MD5 checksums are computed automatically using the crypt4gh tool provided. Your submission will not be accepted and may be significantly delayed if you do not provide MD5 checksum values for all data files in the required format. How can I check if my files are correctly uploaded to the inbox? It is important to check the status pf your file so you know whether your files are in the inbox, being processed, or if there is any issue with one of them. In order to check this, you should: Look for the file locally. Drag and drop it to the file table. Then bars will appear on the table, which means that we are processing it. Green: The files checksum are correct and your file will move to “ingested files”. No further actions are needed from your end.. Red: The files checksum does not match and your file needs to be re-uploaded. Please re-upload relevant files to your inbox using the same path. How can I add my dataset to the cBioPortal instance? In order to link your data to the cBioPortal, you must register it as a normal dataset in the Submitter Portal. For more information, please refer to the cBioPortal documentation. After Submission How do I use my accession number in my publication? We suggest the use of the below template, using your study accession ID (EGAS…) : Data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found at https://ega-archive.org and "The European Genome-phenome Archive of human data consented for biomedical research" Your study ID will be the one that groups your whole submission, and thus its usage is recommended as such. Nevertheless, all metadata submitted to EGA hold a unique and persistent identifier (starting with EGA…) that can be used to identify specific records. For example, you could reference a specific dataset (EGAD…) or sample (EGAN…) in your publications (see full list of identifiers). How do I make my data searchable? Once you have finalized your submission, you can schedule the data release. Please take into account that the release process needs time for the files to be archived in our system, and for the Helpdesk team to validate your submission. Can I withdraw (meta)data from the EGA? We have methods in place for the secure removal of deposited (meta)data. Contact EGA-helpdesk for further details. EGA complies with FAIRness of (meta)data, and thus, even when the data is removed we keep an entry for their identifiers in our system. In other words, we execute a soft delete on canceled objects (e.g. a study): metadata is still stored in our systems, but it loses all links, cannot be queried and data files cannot be retrieved anymore. The reason behind this behaviour is so that queries using withdrawn data properly respond back (see example of a canceled study). What happens to the data once it has been submitted to the EGA? When the data is submitted, the submitter can choose either keep their data private or schedule the release of their data.

  Documentation submission/metadata/submission/FAQ

• ctDNA raw data (BAMs)

  Raw data of ctDNA profiling using the PredicineWES+ or PredicineBEACON assay in patients enrolled in divarasib phase I GO42144 study.

  Dataset EGAD50000000530

• RNAseq FASTqs

  RNAseq FASTq files from 817 bulk pre-treatment tumors from three indications (mUC, NSCLC and RCC) across three phase II (IMvigor210, POPLAR, IMmotion150) and a phase I (PCD4989g) clinical trials.

  Dataset EGAD00001006631

• Colorectal cancer samples WES

  The whole exome was sequenced in two cancer-affected members (II:1 and III:1) of the family. The family subject of this study showed an autosomal dominant mode of CRC inheritance, fulfilling the Amsterdam I clinical criteria with three CRCs in two consecutive generations. The exome capture was performed using SureSelectXT Human All Exon V3 (51Mb, Agilent Technologies), and the library was sequenced on an Illumina HiSeq 2000 platform with paired-end reads of 101bp and a 50x average coverage depth.

  Dataset EGAD00001009170

• Methylation profiling of human lung fibroblasts in COPD

  Primary lung fibroblast were isolated from well-matched control donors (no COPD, n=3) and patients with COPD (GOLD stage I-IV, n=8). Genomic DNA of cultured human lung fibroblast was isolated at passage 3, fragmented by tagmentation, and subjected to bisulfite treatment. 100 bp paired-end reads were generated from DNA libraries on Illumina HiSeq2500 platform.

  Dataset EGAD00001009406

• Therapy-related myeloid neoplasms and HSPCs from the International-Berlin-Frankfurt-Münster (I-BFM) Study group

  Dataset contains WGS sequencing data from bulk sorted therapy-related myeloid neoplasms (t-MN) and reference cells (MSCs/B cells/T cells). In addition, from 4 patients, also WGS data from single hematopoietic stem and progenitor cells, obtained from samples of t-MN diagnosis, are included. These are either clonally expanded before WGS, or DNA was directly amplified via the primary template-directed amplification (PTA) protocol (mentioned in the sample name).

  Dataset EGAD00001011256

• Clonal expansion of mutated cell population in bladder urothelium

  1cm biospies of from patients undergoing bladder cystectomy will be collected. The underlying muscle and stroma will be removed and the remaining epithelia dissected into small sequential areas which will be sent for ultra-deep exome sequencing using a panel of known cancer and viral genes. Sequence analysis using similar methods to Martincorena I et al (Science 2015, 348:880) will provide an idea of the somatic mutational landscape in these patient samples. Individual patient muscle samples will also be sequenced as a reference.

  Dataset EGAD00001003250

• Dataset of PGD PGS study in CITIC Xiangya and BGI

  A total of 395 couples were subjected to IVF-PGD treatment, including 129 couples with NGS-based test and 266 couples with SNP array based test for the detection of embryonic chromosomal abnormalities. The NGS test was performed using low coverage whole genome sequencing with HiSeq 2000 platform. And the SNP array test was using Affymetrix Gene Chip Mapping Nsp I 262K. The average age of patients was 32.1 years (age range 20-44 years).

  Dataset EGAD00001001037

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

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

  Dac EGAC50000000332

• NGS-based monitoring of the T cell receptor repertoire in living donor kidney transplant patients undergoing combination cell therapy

  Tolerance induction through mixed chimerism is a promising approach to achieve long term graft patency without or only low dose maintenance immunosuppression. In this phase I/IIa academic trial kidney transplant recipients in the case group were co-transplanted with bone marrow from the same donor and received an autologous cell product. The control group did not receive donor bone marrow or an autologous cell product. Peripheral blood mononuclear cells were collected at multiple timepoints before and after transplantation and the recipients native T-cell receptor (TCR) repertoire at time points 0, 1, 3 and 6 months after transplantation as well as their donor reactive TCR repertoire were analysed using NGS-based TCR sequencing.

  Study EGAS50000000210