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in 28.89 milliseconds.

• CBD-RAW-CLINVAR: Clinical metadata

  Anonymised patient metadata and associated data dictionary. For further information regarding this dataset, please contact Alexander Mentzer at contact@combat.ox.ac.uk.

  Dataset EGAD00001007931

• NICHE - DNA-seq of MMR proficient and MMR deficient early stage colon cancers

  The dataset includes 174 FASTQ files from paired-end WXS sequencing on Illumina HiSeq2500 for 39 patients.

  Dataset EGAD00001006041

• HV31 - MGI standard short-read sequencing

  MGI standard short-read sequencing data for individual HV31 generated using DNA from peripheral blood mononuclear cells, to a sequencing depth of ~57×.

  Dataset EGAD00001007044

• Identifying new diagnostic and treatment pathways for patients with unclassifiable sarcomas

  Whole genome sequencing of tumour normal pairs of human undifferentiated sarcomas.

  Dataset EGAD00001003216

• Colon cancer ctDNA

  Samples prepared using Safe-SeqS technology. All samples ran on an Illumina MiSeq instrument. Fastq files for read 1 and the index read present (R and I respectively).

  Dataset EGAD00001003579

• TGS - Comprehensive Molecular Characterization of Colorectal Cancer Metastases [MOSAIC] (2017-05-04)

  This dataset contains all the data available for this study on 2017-05-04.

  Dataset EGAD00001003321

• TPM matrix

  TPM matrices of counts from RNAseq data for IMvigor010 from bulk pre-treatment tumors.

  Dataset EGAD00001007653

• ORCADES 15x (2019-07-23)

  15x whole genome sequencing in samples from the isolated population of Orkney. This dataset contains all the data available for this study on 2019-07-23.

  Dataset EGAD00001005194

• RNA-seq data for de-methylation of FOXP3-TSDR study

  22 RNA-seq samples of ex-vivo (TN and Treg), cultured Treg, TET1 and untreated mCherry-MOCK

  Dataset EGAD00001006865

• HV31 - MGI CoolMPS short-read sequencing

  MGI CoolMPS short-read sequencing data for individual HV31 generated using DNA from peripheral blood mononuclear cells, to a sequencing depth of ~57×.

  Dataset EGAD00001007048

• RnaSeq samples for 25 UPS samples

  Whole Transcriptiome Rnaseq of 25 UPS samples - raw FastQ sequences, 125x2 nc Paired End Reads, min 30M PE, HiSEq technology

  Dataset EGAD00001006355

• RNA-Seq Illumina GAII dataset

  RNA-Seq Illumina GAII dataset for the TraIT cell-line use case (added reverse and forward reads).

  Dataset EGAD00001001626

• HIV exome pilot, exome data part 1 GRCh37_53

  exome sequence data for 49 HIV elite long term non-progressors and rapid progressors. Partial dataset (overlap with EGAD00001000087) of raw BAMs mapped to GRCh37_53.

  Dataset EGAD00001000047

• MiSeq-High Coverage

  Seven files of patients 3, 21, 29, 30, 31, 32 and 33 with WGS done on Illumina MiSeq with high coverage. For research purpose and authorised user only.

  Dataset EGAD00001003436

• Copy number profiling of pleura samples and respective tumors

  Low-coverage whole genome sequencing for the establishment of genomewide copy number alterations in pleura effusions and respective primary tumors

  Dataset EGAD00001003273

• Exome sequencing of tumor samples

  We utilized exome sequencing for DNA obtained from saliva (germline DNA) and the four spatially separated tumor foci and 3 corresponding lymph node metastases

  Dataset EGAD00001000762

• Processed RNAseq data

  This dataset contains log2(TPM + 1) transformed counts for the 823 tumor samples profiled by RNAseq.

  Dataset EGAD00001006618

• Raw FASTq files

  This dataset contains FASTq files for tumors from the 823 patients profiled by RNAseq.

  Dataset EGAD00001006619

• DNA sequencing for gastric cancer ascites

  Whole-exome sequencing (WES) was performed on a total of 34 PC specimens, with 15 cases having matched gDNAs extracted from blood.

  Dataset EGAD00001004364

• Amplicon–Based Metagenomic Analysis.

  Amplicon-based fungal metagenomic sequencing for the identification of fungal species in brain tissue from Alzheimer's disease. The study consists in 14 samples, sequenced using Illumina's paired-end technology.

  Dataset EGAD00001003196

• Genome - MBD4-deficient AML

  Genome sequencing at diagnosis and post induction for WEHI-AML-1 and WEHI-AML-2. Whole genome sequencing was performed on an Illumina HiSeq X Ten.

  Dataset EGAD00001003568

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0169_001

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0169_001 for Follicular lymphoma patient sample TFRIPAIR4_FL

  Dataset EGAD00001010217

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0163_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0163_002 for Triple negative breast cancer sample SA609X7XB03510

  Dataset EGAD00001011218

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0172_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0172_001 for Triple negative breast cancer sample SA609X6XB03447

  Dataset EGAD00001011217

• 10x Single Cell Gene Expression Library TENX048

  10x Single Cell Gene Expression library TENX048 for Triple negative breast cancer sample SA535X5XB02895

  Dataset EGAD00001011222

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0184_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0184_001 for Triple negative breast cancer sample SA535X7XB03305

  Dataset EGAD00001011227

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_004

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_004 for Triple negative breast cancer sample SA535X7XB03305

  Dataset EGAD00001011228

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0206_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0206_001 for Triple negative breast cancer sample SA535X10XB03693

  Dataset EGAD00001011236

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0142_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0142_002 for Triple negative breast cancer sample SA1035X4XB02879

  Dataset EGAD00001011238

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0071_000

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0071_000 for Triple negative breast cancer sample SA1035X5XB03015

  Dataset EGAD00001011239

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0076_000

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0076_000 for Triple negative breast cancer sample SA1035X5XB03021

  Dataset EGAD00001011240

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0168_003

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0168_003 for Follicular lymphoma patient sample TFRI_Cont_3

  Dataset EGAD00001010205

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0164_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0164_002 for Triple negative breast cancer sample SA530X3XB03295

  Dataset EGAD00001011250

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0152_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0152_002 for Triple negative breast cancer sample SA609X6XB03404

  Dataset EGAD00001011216

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0172_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0172_002 for Triple negative breast cancer sample SA609X7XB03554

  Dataset EGAD00001011220

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0182_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0182_002 for Triple negative breast cancer sample SA535X8XB03431

  Dataset EGAD00001011230

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0162_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0162_001 for Triple negative breast cancer sample SA1035X8XB03420

  Dataset EGAD00001011248

• RNA-seq data for HCC patients from Y90+Nivolumab trial

  This dataset includes RNA-seq fastq files of pre-treatment and on-treatment samples from 28 patients

  Dataset EGAD00001010132

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0170_003

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0170_003 for Follicular lymphoma patient sample TFRI_Cont_5

  Dataset EGAD00001010207

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0197_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0197_003A for Follicular lymphoma patient sample TFRI_Cont_12

  Dataset EGAD00001010213

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0183_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0183_001A for Follicular lymphoma patient sample TFRIPAIR9_FL

  Dataset EGAD00001010221

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0187_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0187_001A for Follicular lymphoma patient sample TFRIPAIR10_FL

  Dataset EGAD00001010222

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0197_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0197_001A for Follicular lymphoma patient sample TFRIPAIR12_FL

  Dataset EGAD00001010224

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0150_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0150_001 for Triple negative breast cancer sample SA609X5XB03230

  Dataset EGAD00001011212

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0162_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0162_002 for Triple negative breast cancer sample SA1035X7XB03502

  Dataset EGAD00001011246

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0169_003

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0169_003 for Follicular lymphoma patient sample TFRI_Cont_4

  Dataset EGAD00001010206

• DLP+ Single Cell Genomic Library A98208

  DLP+ Single Cell Genomic Library A98208 for Diffuse large B-cell lymphoma patient sample TFRIPAIR10_DLBCL

  Dataset EGAD00001010244

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0171_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0171_003A for Follicular lymphoma patient sample TFRI_Cont_6

  Dataset EGAD00001010208

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0178_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0178_003A for Follicular lymphoma patient sample TFRI_Cont_8

  Dataset EGAD00001010209

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0183_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0183_003A for Follicular lymphoma patient sample TFRI_Cont_9

  Dataset EGAD00001010210

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0187_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0187_003A for Follicular lymphoma patient sample TFRI_Cont_10

  Dataset EGAD00001010211

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0188_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0188_003A for Follicular lymphoma patient sample TFRI_Cont_11

  Dataset EGAD00001010212

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0167_001

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0167_001 for Follicular lymphoma patient sample TFRIPAIR2_FL

  Dataset EGAD00001010215

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0168_001

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0168_001 for Follicular lymphoma patient sample TFRIPAIR3_FL

  Dataset EGAD00001010216

• Tumor from individual with germline POLD1 L474P

  This dataset includes WXS sequencing for 1 tumor FFPE sample and adjacent normal tissue from the individual from one family member.

  Dataset EGAD00001009281

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0198_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0198_003A for Follicular lymphoma patient sample TFRI_Cont_13

  Dataset EGAD00001010214

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0171_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0171_001A for Follicular lymphoma patient sample TFRI_Pair_6_FL

  Dataset EGAD00001010219

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0178_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0178_001A for Follicular lymphoma patient sample TFRIPAIR8_FL

  Dataset EGAD00001010220

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0188_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0188_001A for Follicular lymphoma patient sample TFRIPAIR11_FL

  Dataset EGAD00001010223

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0198_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0198_001A for Follicular lymphoma patient sample TFRI_Pair_13_FL

  Dataset EGAD00001010225

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0167_003

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0167_003 for Follicular lymphoma patient sample TFRI_Cont_2

  Dataset EGAD00001010204

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0170_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0170_001A for Follicular lymphoma patient sample TFRIPAIR5_FL

  Dataset EGAD00001010218

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_006

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_006 for Triple negative breast cancer sample SA535X6XB03099

  Dataset EGAD00001011223

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0142_004

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0142_004 for Triple negative breast cancer sample SA1035X6XB03209

  Dataset EGAD00001011243

• 1_fibroblasts_HD_vs_Ctrl_DMSO_Branaplam

  n=4 Ctrl and n=4 HD fibroblasts lines were treated with DMSO or 10nM Branaplam for 72h and RNA-seq was performed.

  Dataset EGAD00001008807

• BGI study for primary and metastatic Chinese lung adenocarcinomas

  We sequenced 292 patients who were suffering NSCLC with Whole genome sequencing or Exome sequencing method.

  Dataset EGAD00001001397

• ICGC Benchmark 2 (Medulloblastoma)

  Raw fastq files for sequence data generated at 5 sequencing centers from a Medulloblastoma sample and matching blood normal control.

  Dataset EGAD00001001859

• SCNA-Seq of tumor DNA samples

  Low coverage whole genome sequencing for the identification of somatic copy number alterations (SCNA) and focal amplification mapping of corresponding tumor material

  Dataset EGAD00001002150

• PacBio data of de novo assembly individual EGYPT

  This is the PacBio long read data used for performing de novo assembly of the EGYPT individual (mapped against GRCh38).

  Dataset EGAD00001006034

• RNAseq for brainstem glioma

  RNAseq on Illumina HiSeq X Ten of brainstem glioma primary tumor sample

  Dataset EGAD00001006094

• Melanoma exome profiling

  This dataset includes whole exome profiling of 65 early-passage metastatic melanoma cell lines. The data are provided as BAM files for tumor and normal samples.

  Dataset EGAD00001006271

• Submitter Portal

  Submitter Portal The Submitter Portal does not support array submissions! Please refer to the array documentationAny new objects registered in the Submitter Portal will not be available for use in an array submission For further information please check our Submission FAQs, submission quickguide as well as submission terms! The metadata submission process can be difficult and time-consuming. For this reason, the EGA has developed the Submitter Portal, a tool that was created to offer a simplified and user-friendly method of registering metadata. Our portal provides features that are intended to make the input of metadata easier, ensuring that your data is registered correctly and effectively. Our page is divided into logical sections and includes a helpful video instruction to further assist you as you complete the submission process. With the Submitter Portal, you can rest assured that the submission of your data is in good hands. Submitter Portal Index Previous steps Access to Submitter Portal Start a Submission Take The Tour Tutorial Previous steps Create your EGA account To submit data, you first need to create your EGA user. Then, once your account has been verified by the Help Desk team, you will be able to request a submitter role and send the EGA Data Processing Agreement (DPA). In the case you already have an EGA account, or an ega-box (submission account), you can skip this step. Upload your files Please note that all your files must be encrypted using the Crypt4GH tool before upload them.As soon as you are assigned with a submitter role and added your public key to your profile, you will be able to connect to the EGA inbox and upload your files. Understand the EGA metadata schema It's crucial to comprehend the EGA metadata schema, a set of rules that specify how data is organised, described, and shared inside the EGA, in order to get the most out of this resource. Learn all about EGA metadata schema! Register your DAC and policy There are two objects in the EGA metadata schema that are registered in a separate portal. All DACs and policy objects are registered using the DAC Portal, a tool developed by EGA to help data controllers manage their data stored at the EGA. You can find relevant information in the DAC Portal Guide. Access to Submitter Portal 1. Request Submitter Role Once you have created your EGA user, request a Submitter Role and complete relevant information such as submission type and size. 2. Signature of the Data Processing Agreement The Data Processing Agreement (DPA) signed by CRG and EMBL-EBI (Joint Data Processors for the EGA) can be downloaded to the submitter for its signature. Further information related to the DPA can be in the Privacy Notice section. The DPA should be signed by a legal representative of the submitter with power of attorney/appointment duly authorised to sign contracts on behalf of the institution. The submitter will return a fully-executed copy of the DPA to us. Please note that due to the high volume of submissions to EGA the DPA is non-negotiable. Once you have completed all the information and attached the DPA signed, send your Submitter request and the EGA Helpdesk Team will validate it and send a confirmation to start submitting your metadata. Start a Submission The steps below will show you how to start a submission and are accompanied with a tutorial and the Take the tour of Submitter Portal. 1. Login Go to the Submitter Portal interface and log in using your EGA account credentials. 2. Create a submission The Submitter Portal interface will show you any already existing open submission. To start a new submission click on “Create a Submission”.In this phase you have the option to add any collaborator(s) as well as their permissions (“Read only” or “Read & Write”). If you want to invite all the collaborators from a previous submission, you can add them by selecting said submission.In the event that the submission has been closed, you will need to reopen it. To do this, you will need to modify one of the objects in the submission, for example, copy and paste the title. Once the submission is open, you can add a collaborator. Further information can be found in the Submission Tour 3. Create Study Click on add Study and complete the form with the relevant information. The fields with an asterisk (*) are mandatory. Further information can be found in the Submission Tour4. Create Sample Sample registration can be done either individually or in batch by using a template. Batch upload: download the template and complete it with information about the sequencing samples. Upload the samples.csv and the sample will be created.Add single sample: complete the mandatory fields with the metadata information of your sample.  Further information can be found in the Submission Tour5. Create Experiment Register an experiment with ​information about the sequencing methods, protocols and machines. Experiments generate the connection between samples and study. Further information can be found in the Submission Tour6. Create Run Samples, experiments and files are linked through runs. To register a run, you will be asked to select the appropriate experiment and the file format. Run registration can be done either individually or in batch by using a template. Batch upload: download the template and complete it with the sample - file linkage. Upload the run.csv and the runs will register.As the INBOX is a directory, bear in mind to include the file path (/). Example: /filename.c4gh. Add single run: select the file format, sample and experiment from the drop box. For re-using submitted samples, please use the EGA accession ID (EGANXXX). Please note that files must be uploaded in your inbox before linking your files and samples. Further information can be found in the Submission Tour7. Create Analysis Analysis should be only used for BAM/BAI or CRAM/CRAI pair, VCF and phenotype linkage to samples. The analysis is an EGA specific metadata object that links samples to files. This object also stores some metadata about your experiments, such as the experiment type, genome reference, or the platform used. **If only BAM or CRAM alignment files are submitted but not the original unaligned FASTQ files, then please make sure that the BAM or CRAM files also contain the unaligned reads. This is critical to enable primary re-analysis and re-alignment of the dataset using new tools or future genome assemblies.** Start by selecting the samples to be linked to the file, and then populate the required attribute fields. Please be aware of the existence of mandatory fields that must be populated.When populating the chromosome field (mandatory), please select the chromosomes from the checkbox. Please take in consideration that EGA allows for the re-use of any registered metadata. Therefore, a previously registered study or samples can be referenced for the analysis in the current data submission.Further information can be found in the Submission Tour 8. Create Dataset Dataset contains the collection of runs/analysis data files subjected to controlled access by being associated with a Policy. Complete the mandatory fields such as title, description, type and policy. Please, take in consideration that DAC and Policy must be registered in advance in the DAC Portal. Similar to other metadata objects, you can re-use previous runs or analyses, using their EGA accessions IDs (runs - EGAR, analyses - EGAZ). Further information can be found in the Submission Tour 9. Finalise submission Once the files are ingested please delete them from your SFTP INBOX before finalising your submission. This will prevent the files from reappearing in your Submitter Portal Inbox. Once you have completed your submission, click on the Finalise button, and schedule an estimated release date. Afterwards, your submission will be sent to Helpdesk for approval. Please note that once the Helpdesk has approved your submission, you will receive the identifiers for your data. See the Frequently Asked Questions (FAQ) for more information. 10. Update closed submission If you want to append more file to a closed submission, you will need to re-open the submission where the dataset was registered. Find the Submission: go to the Submitter Portal Menu -> Datasets section and find the submission id (EGA0XXX).  Reopen the submission where the dataset is located: to reopen a submission, you must edit any object within the submission. For example, you can copy and paste the title. Take The Tour The Submitter Portal Take The Tour is a visual guidance of all the steps described previously about how to start a submission. Tutorial In this tutorial we will show you how to use the Submitter Portal to register your metadata. Please note the video focuses on the run-based submission (for raw files - fastq - and aligned data - BAM/CRAM) and analysis-based submission (for your BAM/BAI pairs, variation -VCF - and phenotype files).

  Documentation submission/metadata/submission/sequencing-phenotype/submitter-portal

• Ipilimumab plus Decitabine for Patients with MDS or AML in Post-Transplant or Transplant Naïve Settings

  Design and AimsThis study was a phase 1 clinical trial investigating the combination of ipilimumab (IPI), a CTLA-4 checkpoint inhibitor, and decitabine (DEC), a hypomethylating agent, for treating relapsed/refractory (R/R) myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). The goal was to determine whether DEC could enhance the immune response to IPI without causing excessive immune toxicity. This study provides important insights into the use of immune checkpoint inhibitors with hypomethylating agents in treating high-risk MDS/AML and highlights the need for further research on optimizing immune-based therapies in these settings.Population InformationPost-HSCT arm: 25 patients (23 AML, 2 MDS; all relapsed/refractory).Transplant-naïve arm: 23 patients (15 AML, 8 MDS; 20 relapsed/refractory, 3 untreated).Data available through dbGaPDeidentified individual participant data, Multiplex immunofluorescence staining, Olink, RNA-Seq and Whole Exome SequencingPrincipal FindingsSafety & Tolerability: The maximum tolerated dose (MTD) for IPI was determined to be 10 mg/kg, though higher doses were associated with increased immune-related adverse events (irAEs).Efficacy: The overall response rate (ORR) was higher in transplant-naïve patients (52%) compared to post-HSCT patients (20%).Responses were more frequent at higher IPI doses (10 mg/kg). Some patients achieved durable remission (>1 year), often linked to irAE development.Toxicity: The most common grade ≥3 treatment-emergent adverse events included neutropenia (32%-48%), thrombocytopenia (28%-48%), and febrile neutropenia (36%-61%). irAEs were observed in 44%-48% of patients, with GVHD occurring only in the post-HSCT group.Survival Outcomes: Median overall survival and progression-free survival did not significantly differ between the two arms. However, in transplant-naïve patients, 1-year survival was higher in those who developed irAEs (72.7%) versus those who did not (33.3%).

  Study phs003292

• Spatial Transcriptomics of Vaccine Therapy With or Without Cyclophosphamide in Treating Patients Undergoing Chemotherapy and Radiation Therapy for Stage I or Stage II Pancreatic Cancer That Can Be Removed by Surgery

  Pancreatic ductal adenocarcinoma (PDAC) is a rapidly progressing cancer that responds poorly to immunotherapies. Intratumoral tertiary lymphoid structures (TLS) have been associated with rare long-term PDAC survivors, but the role of TLS in PDAC and their spatial relationships within the context of the broader tumor microenvironment remain unknown. Here, spatial transcriptomics data were generated from 16 pancreatic cancer patients treated with GVAX, whose tumors presented with induced intratumoral TLS. Resected tumor specimens were embedded in OCT media and freshly frozen. Sections were cut into 6.5 x 6.5 mm dimensions compatible with Visium spatial transcriptomics slides. TLS-positive tumor sections from 14 patients (14 tumor samples in total) and lymph nodes from remaining patients (2 lymph node samples in total) were selected by an expert pathologist for profiling. We used 4 Visium slides and covered the 16 capture areas with sections cut in a cryostat at 10 µm. The fresh frozen Visium protocol was followed according to manufacturer's recommendations (10x Genomics Spatial 3' v1). Tissue permeabilization and library construction was carried out according to the manufacturer's protocols. Permeabilization time was optimized to 30 minutes. The captured tissue sections were permeabilized to allow RNA to be captured onto the slide where it reverse-transcribed to cDNA. The cDNA was then amplified, and sequencing libraries were prepared using the 10x Genomics Visium Spatial Gene Expression Reagent Kit for Library Preparation. The libraries were then sequenced using an Illumina NovaSeq sequencer. The raw data obtained from the sequencing were processed using the SpaceRanger (spaceranger-1.2.0) pipeline for alignment onto the GRCh38-2020-A reference transcriptome quantification of gene expression. Unsupervised learning generated a TLS-specific spatial gene expression signature that significantly associates with improved survival in PDAC patients. These analyses demonstrate TLS-associated intratumoral B cell maturation.

  Study phs003862

• DNA Methylation in Prostate Tumor and Paired Benign Tissue for African and European Ancestry Men

  We recruited prostate cancer (PCa) patients undergoing robotic-assisted laparoscopic radical prostatectomy at the University of Chicago Medical Center Urology Clinic between 2011 and 2017. Our study includes 76 African American (AA) and 75 European American (EA) men with PCa of Gleason Score (GS) ≥7, recruited by the Epidemiology Research Recruitment Core at the University of Chicago. All patients consented to the collection of questionnaire data, prostate tissue, and medical records. The study was approved by the Institutional Review Board of the University of Chicago. Following prostatectomy, prostate tissue was sent to the Human Tissue Research Center (HTRC) at the University of Chicago. Each sample underwent histological examination and Gleason scoring by University of Chicago genitourinary pathologists. The presence of adenocarcinoma was confirmed by overexpression of alpha-methylacyl-coenzyme-A racemase (AMACR) and areas for DNA extraction were marked. Tissue samples used for DNA extraction were obtained using with either a 1mm biopunch or laser microdissection of a 100 µm2 area of tissue. Genome-wide DNA methylation profiles were generated using Illumina's Infinium MethylationEPIC BeadChip array kit. Genome-wide SNP data was generated (from benign tissue DNA) using the Illumina Infinium Multi-Ethnic Global-8 v1.0 array at the University of Chicago Genomics Core Facility. We performed imputation using the Haplotype Reference Consortium (HRC, Version r1.1 2016) panel, which includes all samples from the 1,000 Genomes Project (Phase 3), using the Michigan Imputation Server. The imputed SNP data for mQTL analysis was imputed based on genotype SNP data which was all from benign tissue DNA and the methylation data was identified in both tumor and benign tissue, both AA and EA men, and identified QTLs co-occuring with prostate cancer susceptibility loci.Data including SNP genotypes, DNA methylation, tissue type, and participant characteristics will be available in dbGaP.

  Study phs003516

• A prospective study identifies MisMatch Repair genes as candidate predisposing genes for uveal melanoma.

  Objective: The aim of this study is to identify new predisposing genes for uveal melanoma (UM), the most common intraocular malignancy in adults. Design: Prospective cohort study from Institut Curie. Participants: 381 unselected patients diagnosed with UM between July 2021 and February 2023 at Institut Curie. Methods: All patients received genetic counseling and consented to extended genetic testing. A panel of 122 genes was analyzed by targeted sequencing on germline DNA from these patients. Main outcomes measures: UM was diagnosed by ophthalmologists at Institut Curie, and a senior pathologist confirmed the diagnosis when tumor or biopsy was available. Classification of the germline genetic variants was performed according to the ACMG guidelines. Results: We identified at least one pathogenic variant (PV) in 79 patients. Among those variants, 20 were found in clinically relevant genes for the patient’s follow-up and genetic counseling. We found a significant enrichment of germline pathogenic variants in Mismatch Repair (MMR) MLH1, MSH2 and MSH6 genes in this UM cohort, suggesting that MMR germline alterations could predispose to UM. From this cohort, one tumor was available from a patient carrying a MLH1 germline mutation. The tumor was characterized by a monosomy 3, in line with a loss of heterozygosity (LOH) of MLH1, located on chromosome 3. Loss of expression of MLH1 and PMS2 was observed by immunohistochemistry, and MMR mutational signatures SBS6, ID1 and ID2 were identified from the whole genome sequencing of the tumor. These data suggest that MLH1 is implicated in the oncogenesis of this UM. Conclusions: This study is the largest prospective constitutional study performed on UM patients, and the first demonstrating that MMR germline alterations are significantly enriched among these patients. Further analyses will be needed to estimate the risk of developing UM for patients with Lynch syndrome

  Study EGAS50000000914

• Na?ve Treg-specific genomic DNA hypomethylation for autoimmune disease susceptibility

  Naturally occurring regulatory T (Treg) cells, which are specifically expressing the transcription factor FOXP3, CD25 and CTLA-4, are indispensable for the maintenance of immunological self-tolerance and homeostasis. Mutations of the FOXP3, CD25 and CTLA4 genes compromise the function of natural FOXP3+CD25+CD4+ Treg cells and cause severe autoimmune diseases showing Mendelian inheritance. Also, altered Treg-specific epigenetic changes, such as DNA hypomethylation and enhancer activation, impair the expression of these molecules in developing Treg cells, causing autoimmune diseases. It is obscure, however, whether Treg cells significantly contribute to the development of polygenic common autoimmune diseases (such as type 1 diabetes and rheumatoid arthritis), which afflict ~10% of the population. Recent genome-wide association study (GWAS) of common autoimmune diseases has revealed many autoimmune disease-associated single nucleotide polymorphisms (SNPs) in enhancer regions of immune cells, for example, in non-coding regions of T cell activation-associated genes such as CD25 and CTLA4. These findings, especially the involvement of common T-cell genes in the functions of both Treg cells and effector T cells, prompt one to ask whether autoimmune disease-associated causative SNPs impact on the functions of either disease-causing effector T cells or disease-suppressing Treg cells. Here, we have explored epigenetic profiles of human Treg and conventional T (Tconv) cells in na?ve or activated states, and found that autoimmune disease-associated SNPs are predominantly enriched in Treg-specific CpG demethylated regions (Treg-DRs), which were mostly included in Treg-specific super-enhancers. Treg-DRs are developmentally established in na?ve Treg cells before antigenic stimulation, distinct from activation-induced CpG hypomethylation in Treg or Tconv cells, and highly associated with Treg cell-specific transcriptional and epigenetic changes. Taken together, genetic variants affecting endogenous development of natural Treg cells and their Treg-intrinsic functions are causative of common autoimmune diseases, indicating natural Treg cells as a key target for their treatment and prevention.

  Study JGAS000145

• Metadata Public API

  Metadata Rest Endpoints Common Aspects Functionality Identifiers Requests and Responses Request Response Querying Querying by Object Cross-Querying by Object Common Aspects Functionality The Metadata REST API allows EGA users to request metadata from the EGA. Using this api you will be able to obtain the publicly available information from EGA study, sample, experiment, run, analysis, policy, dac and dataset. The API also allows for objects to be cross-referenced in order to obtain for example all the datasets linked to a dac. Identifiers EGA objects can be identified by their unique accession. These are ID's displayed everywhere, shared among all EGA locations and specific for each data type (More information on the list below) EGA Accession ID EGA Object description EGAS EGA Study Accession ID EGAC EGA DAC Accession ID EGAP EGA Policy Accession ID EGAN EGA Sample Accession ID EGAR EGA Run Accession ID EGAX EGA Experiment ID EGAZ EGA Analysis Accession ID EGAD EGA Dataset Accession ID EGAB EGA Submission ID EGAF EGA File Unique Accession ID Server Request and Response Request All the endpoints implement these two HTTP methods, GET and HEAD. Moreover, there is an authentication layer that allows to disclose the private information about each object if the user runs the endpoint with their token and has permissions to the related dataset. HTTP Method Description GET Returns the objects and their public information (including private information if the user has permissions) HEAD Returns the count of objects to be returned Response API responses are diverse but they always return a status call. Below you have a summary on what these codes mean and some tips that can help you troubleshooting them: HTTP Status Code Description Resolution 200 OK No error handling necessary 206 Partial Content Expected status code when paginating the results. No error handling necessary 400 BAD REQUEST Request incorrectly formulated 401 UNAUTHORISED/FORBIDDEN You do not have permissions over the object you are trying to access. / The authorised user is not permitted to make the given request. 404 NOT FOUND The object/resource you are trying to access does not exist. 500 INTERNAL SERVER ERROR This is a server-side error, try later. If the issue persists, contact the Heldpesk. Querying Querying by Object In the below documentation, worked examples can be found on how to use the API, to query about each unique EGA object. https://metadata.ega-archive.org/analyses/{id} https://metadata.ega-archive.org/dacs/{id} https://metadata.ega-archive.org/datasets/{id} https://metadata.ega-archive.org/runs/{id} https://metadata.ega-archive.org/samples/{id} https://metadata.ega-archive.org/studies/{id} Worked example for the EGA data access committee(DAC) EGAC00001000514 https://metadata.ega-archive.org/dacs/EGAC00001000514 Points to notice Please, be advised that all EGA objects can be queried by using the same endpoint. For example: https://metadata.ega-archive.org/datasets Returns all the datasets registered at the EGA. https://metadata.ega-archive.org/datasets?limit=5&offset=15 Returns 5 results skipping the initial 15 results. Cross-Querying by Object The EGA REST API Metadata also allows for the crossquerying of public metadata. For example, should I want to query the datasets included in the ICGC DAC: https://metadata.ega-archive.org/dacs/EGAC00001000010/datasets Below, you can find worked examples on how to perform the call to the API. https://metadata.ega-archive.org/datasets/EGAD00001000645/analyses https://metadata.ega-archive.org/datasetsEGAD00001000645/files https://metadata.ega-archive.org/datasets/EGAD00001000620/runs https://metadata.ega-archive.org/datasets/EGAD00001000620/samples https://metadata.ega-archive.org/samples/EGAN00001092114/datasets https://metadata.ega-archive.org/files/EGAF00000056146/dacs https://metadata.ega-archive.org/files/EGAF00000056146/datasets https://metadata.ega-archive.org/files/EGAF00000056146/studies There are more endpoints included in this category. Please, find all the possibilities (green tick) in the table below. Table The table displays in a ordered fashion the objects that can be crossreferenced using the metadata API. BY Analysis Dac Dataset Experiment Policy Run Sample Study File Analysis ✔ ✔ Dac ✔ ✔ Dataset ✔ ✔ ✔ ✔ Experiment ✔ ✔ ✔ ✔ Policy ✔ Run ✔ ✔ ✔ Sample ✔ ✔* ✔ Study ✔ ✔ ✔ ✔ ✔ ✔* File ✔ *: These cross queries do not include data from Array-File submissions. Check out the SPEC

  Documentation discovery/metadata/public-metadata-api

• Whole_Genome_Sequencing_of_hiPS_cells

  Human induced pluripotent stem (hiPS) cells hold great promise for regenerative medicine. Safety issues of use of hiPS cells however remain to be addressed. One of such issues is mutations derived from somatic donor cells and introduced during genome manipulation. We sequence whole genomes of hiPS cells and analyzed mutations. Our study brings hiPS cell technology one step closer to application to regenerative medicine.

  Study EGAS00001000008

• Inherited T Cell Defects: Diagnosis, Mechanisms and Treatments

  Infants with abnormal newborn screen results for severe combined immunodeficiency (SCID) and their parents were enrolled in UCSF IRB approved protocols with informed consent to have molecular and immunologic investigation to determine the cause of the infant's low T lymphocytes. Studies included exome sequencing of DNA from the infant and both parents. Candidate variants were investigated in mouse and zebrafish model organisms.

  Study phs002968

• Million Veteran Program (MVP) Summary Results from Non-Sensitive Omics Studies

  MVP is an ongoing prospective cohort study and mega‐biobank in the Department of Veterans Affairs Healthcare System designed to study genetic influences on health and disease among veterans. This is the accession to hold publicly available results. Results from sensitive phenotypes or MVP population subsets can be found at accession phs001672 and will require an application for access.

  Study phs002453

• A prospective pilot study of genome-wide exome and transcriptome profiling in patients with small cell lung cancer progressing after first-line therapy

  In this prospective study, custom genome-wide exome and whole transcriptome sequencing (NGS) was used to identify genomic events and associated expression changes in advanced Small Cell Lung Cancer (SCLC) and attempt to prescribe systemic therapy based on the results. Tumor/normal pairs were sequenced from 12 patients with advanced SCLC for this study.

  Study phs001366

• Genome Wide Association Study of Asthma

  This data forms part of an ongoing genome wide association study into the genetic variants that predispose to pediatric asthma at the Children's Hospital of Philadelphia (CHOP). All cases and controls were recruited at CHOP and genotyped at the Center for Applied Genomics (CAG). The data set includes 793 cases and 1988 controls. The inclusion criteria to this case control study are detailed below.

  Study phs000233

• Family Genomics of Congenital Heart Defects

  The study identified the causal mutation in a five-generation pedigree harboring a cardiac septal defect. The inheritance pattern is consistent with an autosomal dominant mutation with high penetrance. We performed whole-genome sequencing (Complete Genomics) on 21 individuals in the pedigree, of which 11 individuals are affected. We identified a single gene, GATA4, as primarily responsible for this cardiac phenotype in this pedigree.

  Study phs000758

• CALGB/SWOG 80405: Genome-Wide Association Study of Patients with Advanced or Metastatic Colorectal Cancer Treated with First-Line Chemotherapy Combined with Cetuximab and/or Bevacizumab

  CALGB/SWOG 80405 (NCT00265850) was a randomized phase III study of bevacizumab, cetuximab, or the combination of these two monoclonal antibodies, in addition to systemic chemotherapy for patients with untreated metastatic adenocarcinoma of the colon or rectum. We prospectively collected germline DNA and conducted genome-wide association studies using clinical outcomes and molecular features.

  Study phs003428

• BRCA1 secondary splice-site mutations

  BRCA1 splice isoforms d11 and d11q can contribute to PARP inhibitor (PARPi) resistance by splicing-out mutation-containing exons, producing truncated, partially-functional proteins. However, the clinical impact and underlying drivers of BRCA1 exon skipping remain undetermined. We analyzed nine ovarian and breast cancer patient derived xenografts (PDX) with BRCA1 exon 11 frameshift mutations for splice isoform expression and therapy response.

  Study EGAS50000000022

• A Phase Ib/II Study of Regorafenib and Paclitaxel in Beyond First-line Advanced Esophagogastric Carcinoma (REPEAT)

  Regorafenib showed promising results in gastric cancer. We aimed to assess the tolerability of regorafenib and paclitaxel in advanced esophagogastric cancer (EGC) patients refractory to first-line treatment and explore potential biomarkers.. We performed enzyme-linked immunosorbent assay measurements of galectin-1, RNA sequencing, and shallow wholegenome sequencing of metastatic tumor biopsies for biomarker analyses.

  Study EGAS00001006054

• WGS of pre-T cell receptor-alpha immunodeficiency proband and family

  In this study, we describe the role of whole genome sequencing (WGS) in providing a definitive diagnosis for a child with T cell deficiency, where targeted panel sequencing of SCID genes and whole exome sequencing had failed. A novel homozygous 8kb deletion in PTCRA, encoding pTCR, was identified. WGS sequence data from the proband, proband’s mother, and proband’s father are deposited.

  Study EGAS50000000609

• Harnessing the Electronic Health Record to Predict Risk of Cardiovascular Disease: Sangre Por Salud (SPS) Biobank GWAS Data

  These data are from subjects in the Sangre Por Salud (SPS) Biobank, a Latino population from the Southwest United States. Subjects were genotyped in 2 batches on the Illumina Multi-ethnic Global Array. Data from this study were used for testing a new cardiovascular risk score and evaluating polygenic risk scores. SPS biobank GWAS data is available on 3699 subjects.

  Study phs003553

• LuCaP cell line ChIP-seq

  ChIP-seq of 2 LuCaP cell lines with different factors. LuCaP cell line 189.4 was ChIP-sequenced for 6 factors with different conditions as well as the input DNA (n=31 in total). LuCaP 189.4 ChIP-sequence experiments include; 3 H3K27ac/DMSO, 3 H3K27ac/vorinostat, 3 H3K27ac/dexamethasone, 3 H3K27ac/dexamethasone/vorinostat, 3 GR dexamethasone, 3 GR dexamethasone/vorinostat, 3 HDAC3/DMSO, 3 AR, 3 FOXA1, 3 H3K27ac and 1 input sample. LuCaP 35 ChIP-sequence experiments as well as input include (n=10 in total); 3 H3K27ac, 3 AR, 3 FOXA1 and 1 input sample. All 41 samples were sequenced using paired-end reads on Illumina NovaSeq 6000. Each sample has 2 fastq files, one for the forward and reverse read, respectively.

  Dataset EGAD50000001345

• Base modification analysis using single molecule real-time sequencing

  Based on the kinetic characteristics of single-molecule real-time sequencing, we implement a method to directly detect DNA modifications such as 5mC. However, there are room to further improve the accuracy of whole-genome-wide DNA modifications. Therefore, this study aims to utilize deep learning methods for the comprehensive profiling of multiple DNA modifications, including 5mC, 5hmC, and 6mA, using SMRT-seq.

  Study EGAS50000000366

• Targeted DNA sequencing and mRNA sequencing data from patients with peritoneal metastasis from colorectal cancer

  Patients with peritoneal metastasis from colorectal cancer (PM-CRC) have inferior prognosis and respond particularly poorly to chemotherapy. This study aims to identify the molecular explanation for the observed clinical behavior and suggest novel treatment strategies in PM-CRC. Tumor samples (230) from a Norwegian national cohort undergoing surgery and hyperthermic intraperitoneal chemotherapy (HIPEC) with mitomycin C (MMC) for PM-CRC were subjected to targeted DNA sequencing, and associations with clinical data were analyzed. mRNA sequencing was conducted on a subset of 30 samples to compare gene expression in tumors harboring BRAF or KRAS mutations and wild-type tumors.

  Dataset EGAD50000000593

• Human Cancer-Targeted Immunity via Transgenic Hematopoietic Stem Cell Progeny

  Analysis of a patient biopsy from the clinical trial (NCT03240861), a first-in-human study of transgenic NY-ESO-1 TCR- T cells and NY-ESO-1 TCR+ sr39tk autologous hematopoietic stem cells tandem therapy for solid tumors after myeloablative conditioning chemotherapy with busulfan and fludarabine. Single-cell RNAseq and single-cell ATACseq analysis from peripheral blood mononuclear cells obtained from subject NYSCT-03.

  Study phs003898

• Tumor-resident T-cell regulate responses to checkpoint blockade immunotherapies

  In cancer models, CD103+CD8 TRM cells are implicated in the response to anti-PD-1 immunotherapy, and a subset of CD4 TRM cells expressing the integrin CD49a is required for response to anti-CTLA-4 immunotherapy. We performed single-cell RNA sequencing on tumour-infiltrating T lymphocytes (TILs) isolated from colon carcinoma et unsupervised analyses reveal several TRM populations.

  Study EGAS50000000826

• Single-Cell Sequencing of Genomic DNA, RNA, and ADT in CRISPR-Edited Cells

  CRAFTseq data containing multi-modal single-cell genomic DNA amplicon, cDNA and surface-protein from various primary cell types and cell lines, including human CD4 T cells, Jurkat cells, HH cells, Daudi cells and HEK293T cells. Cells were edited with CRISPR HDR or base editing. Also contains bulk RNA-seq data for edited human primary CD4 T cells and Daudi cells.

  Study phs004042

• Collagen Proteostasis in Health and Disease

  This study uses human iPSC-derived cartilage to investigate the effects of the p.Arg719Cys mutation in the COL2A1 gene, which is linked to precocious osteoarthritis. Using a CRISPR-edited isogenic cell line pair, we compared wild-type and mutant cartilage for matrix defects and cellular stress responses. We performed RNA-sequencing and quantitative proteomics to characterize changes in gene expression, protein interactions, and collagen folding.

  Study phs004112

• T cell and Antibody Responses in Rituximab-Treated Lymphoma Patients After SARS-CoV-2 Vaccination

  This dataset contains T cell receptor (TCR) sequencing data from 51 RNA samples, each processed into three independent libraries (tot 153 FASTQ files). Sequencing was performed on an Illumina NextSeq 500 platform using the NextSeq 500/550 Mid Output Kit v2.5 (300 Cycles), generating paired-end FASTQ files with dual 10 bp barcodes and 5% PhiX as internal control. Libraries were quantified with the Qubit HS dsDNA kit and assessed for fragment size distribution using the Agilent Tapestation D1000 system before isochoric pooling to 4 nM and sequencing in two independent runs. The resulting dataset enables analysis of TCR β-chain CDR3 sequences for clonal expansion and diversity studies.

  Dataset EGAD50000001714

• Data access to Small RNA-Seq of MicroRNA's in Tear EV's of Ushers Syndrome patients

  Via this DAC you can get access to Small RNA-Seq dataset of MicroRNA's in Tear EV's of Ushers Syndrome patients. The study involved genetically confirmed USH1B patients and their healthy relatives, with additional fibroblast samples from Spanish collaborators, to ensure a robust comparison of small non-coding RNAs (sncRNAs). Tear and blood samples were collected using non-invasive methods, and extracellular vesicles (EVs) were isolated from both tears and hiPSC-derived retinal pigment epithelium (RPE) cells. RNA was extracted from these EVs and assessed for quality before preparing libraries using the TruSeq Small RNA Library Preparation Kit. Sequencing was performed on the Illumina NextSeq 2000 platform, generating data for comparative analysis of sncRNA profiles between patients and controls.

  Dac EGAC50000000711

• GeoMx DSP of NGS mRNA expression in pre-treatment biopsies from patients with metastatic triple-negative breast cancer treated with PARP inhibitors.

  This dataset contains GeoMx digital spatial profiling (DSP) data from 12 FFPE pre-treatment biopsies of metastatic HR-altered breast cancer patients subsequently treated with PARP inhibitors (Olaparib or Talazoparib). Samples were collected from primary breast tumors (n=7) and metastatic sites including lymph nodes (n=2), bone (n=2), and brain (n=1). Tissue sections were stained for pan-cytokeratin (PANCK) to selectively profile tumor cells, with NGS-based transcriptome data generated for both PANCK+ and PANCK- regions. The dataset includes raw data files (176 fastq files) along with clinical annotations.

  Dataset EGAD50000001932

• Study of wound response like states in glioblastoma

  In order to investigate wound reponse-like states in glioblastoma, we carried out experiments on primary tissue as well as organoids. Single cell RNA sequencing was performed on core and periphery of three human glioblastoma. Bulk RNA sequencing was performed on glioblastoma-derived organoids subject to treatment for 72h with plasma, hypoxia, or both, and analyzed at 0, 24, 72, and 144h.

  Study EGAS50000001442