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• BLUEPRINT release August 2015, Bisulfite-Seq for central memory CD8-positive, alpha-beta T cell, on genome GRCh38

  Bisulfite-Seq data for 1 central memory CD8-positive, alpha-beta T cell sample(s). 13 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001553

• BLUEPRINT release August 2015, RNA-Seq for Acute promyelocytic leukemia, on genome GRCh38

  RNA-Seq data for 7 Acute promyelocytic leukemia sample(s). 7 run(s), 7 experiment(s), 7 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_rnaseq_analysis_crg_20150820

  Dataset EGAD00001001555

• BLUEPRINT release January 2015, ChIP-Seq for mature eosinophil

  ChIP-Seq data for 2 mature eosinophil sample(s). 12 run(s), 12 experiment(s), 12 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001001168

• BLUEPRINT release August 2015, Bisulfite-Seq for effector memory CD4-positive, alpha-beta T cell, on genome GRCh38

  Bisulfite-Seq data for 1 effector memory CD4-positive, alpha-beta T cell sample(s). 15 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001567

• BLUEPRINT release January 2015, RNA-Seq for macrophage

  RNA-Seq data for 18 macrophage sample(s). 19 run(s), 18 experiment(s), 18 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001199

• BLUEPRINT release August 2015, RNA-Seq for neutrophilic myelocyte, on genome GRCh38

  RNA-Seq data for 3 neutrophilic myelocyte sample(s). 3 run(s), 3 experiment(s), 3 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_rnaseq_analysis_crg_20150820

  Dataset EGAD00001001477

• BLUEPRINT release August 2015, ChIP-Seq for Acute Myeloid Leukemia - CTR, on genome GRCh38

  ChIP-Seq data for 4 Acute Myeloid Leukemia - CTR sample(s). 21 run(s), 21 experiment(s), 21 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001533

• BLUEPRINT release August 2015, ChIP-Seq for mature eosinophil, on genome GRCh38

  ChIP-Seq data for 2 mature eosinophil sample(s). 12 run(s), 12 experiment(s), 12 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001539

• BLUEPRINT release August 2015, ChIP-Seq for monocyte, on genome GRCh38

  ChIP-Seq data for 2 monocyte sample(s). 6 run(s), 6 experiment(s), 6 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001580

• BLUEPRINT release August 2015, Bisulfite-Seq for central memory CD4-positive, alpha-beta T cell, on genome GRCh38

  Bisulfite-Seq data for 1 central memory CD4-positive, alpha-beta T cell sample(s). 15 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001563

• BLUEPRINT release January 2015, ChIP-Seq for inflammatory macrophage

  ChIP-Seq data for 6 inflammatory macrophage sample(s). 35 run(s), 35 experiment(s), 35 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001001204

• BLUEPRINT release January 2015, RNA-Seq for conventional dendritic cell

  RNA-Seq data for 1 conventional dendritic cell sample(s). 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001170

• BLUEPRINT release August 2015, Bisulfite-Seq for effector memory CD8-positive, alpha-beta T cell, on genome GRCh38

  Bisulfite-Seq data for 1 effector memory CD8-positive, alpha-beta T cell sample(s). 11 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001583

• Sequencing data on patients with Sezary Syndrome

  This dataset consists of sequencing data on 15 patients with Sezary syndrome. On 12 of these patients, we have exome sequencing data while on 10 patients, we have RNA sequencing data. In total for seven patients, we have both exome as well as RNA sequencing data. We looked for gene mutations and fusion events in these patients to identify genes that could be involved in the pathogenesis of the disease.

  Dataset EGAD00001001998

• BLUEPRINT release August 2016, Bisulfite-Seq for immature conventional dendritic cell - GM-CSF_IL4_T=6_days, on genome GRCh38

  Bisulfite-Seq data for 3 immature conventional dendritic cell - GM-CSF_IL4_T=6_days sample(s). 61 run(s), 4 experiment(s), 6 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002284

• Merged analysis-ready bam files: HiSeq sequencing of matched tumour/normal DNA samples from Pancreatic Ductal Adenocarcinoma cases

  Additional sequencing data for 173 donors in EGAS00001000154, a study of Pancreatic Ductal Adenocarcinoma. WGS libraries were used for high-cellularity cases, WXS sequencing to high depth on low-cellularity cases. HiSeq 2xxx platform was used in all cases. The analysis files associated with this dataset are merged, de-duplicated bams aligned against GRCh37, one tumour and one normal bam per donor.

  Dataset EGAD00001002192

• BLUEPRINT release August 2015, RNA-Seq for inflammatory macrophage, on genome GRCh38

  RNA-Seq data for 3 inflammatory macrophage sample(s). 3 run(s), 3 experiment(s), 3 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_rnaseq_analysis_crg_20150820

  Dataset EGAD00001001480

• BLUEPRINT release August 2015, ChIP-Seq for Acute lymphocytic leukemia - CTR, on genome GRCh38

  ChIP-Seq data for 1 Acute lymphocytic leukemia - CTR sample(s). 7 run(s), 7 experiment(s), 7 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001569

• BLUEPRINT release January 2015, RNA-Seq for alternatively activated macrophage

  RNA-Seq data for 4 alternatively activated macrophage sample(s). 6 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001202

• BLUEPRINT release August 2015, ChIP-Seq for Acute Myeloid Leukemia, on genome GRCh38

  ChIP-Seq data for 15 Acute Myeloid Leukemia sample(s). 75 run(s), 72 experiment(s), 72 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001481

• BLUEPRINT release August 2014, ChIP-Seq for mature neutrophil

  ChIP-Seq data for 7 mature neutrophil sample(s). 68 run(s), 50 experiment(s), 50 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001000930

• BLUEPRINT release August 2016, RNA-Seq for T-cell Acute Lymphocytic Leukemia, on genome GRCh38

  RNA-Seq data for 1 T-cell Acute Lymphocytic Leukemia sample(s). 1 run(s), 1 experiment(s), 1 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_rnaseq_analysis_crg_20160816

  Dataset EGAD00001002287

• BLUEPRINT release August 2016, RNA-Seq for Acute Promyelocytic Leukemia - ATRA, on genome GRCh38

  RNA-Seq data for 3 Acute Promyelocytic Leukemia - ATRA sample(s). 3 run(s), 3 experiment(s), 3 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_rnaseq_analysis_crg_20160816

  Dataset EGAD00001002356

• BLUEPRINT release August 2015, Bisulfite-Seq for CD4-positive, alpha-beta T cell, on genome GRCh38

  Bisulfite-Seq data for 3 CD4-positive, alpha-beta T cell sample(s). 61 run(s), 3 experiment(s), 3 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001516

• BLUEPRINT release August 2015, ChIP-Seq for neutrophilic myelocyte, on genome GRCh38

  ChIP-Seq data for 4 neutrophilic myelocyte sample(s). 14 run(s), 14 experiment(s), 14 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001517

• BLUEPRINT release August 2015, Bisulfite-Seq for CD8-positive, alpha-beta T cell, on genome GRCh38

  Bisulfite-Seq data for 4 CD8-positive, alpha-beta T cell sample(s). 56 run(s), 4 experiment(s), 4 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001571

• BLUEPRINT release August 2015, Bisulfite-Seq for CD38-negative naive B cell, on genome GRCh38

  Bisulfite-Seq data for 4 CD38-negative naive B cell sample(s). 44 run(s), 4 experiment(s), 4 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001590

• BLUEPRINT release August 2015, Bisulfite-Seq for CD14-positive, CD16-negative classical monocyte, on genome GRCh38

  Bisulfite-Seq data for 7 CD14-positive, CD16-negative classical monocyte sample(s). 101 run(s), 7 experiment(s), 7 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001591

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - 129_cohort

  Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - 129_cohort EAC whole genomic sequencing data - Publication Secrier & Li et al., 2016, Nature Genetics

  Dataset EGAD00001002218

• BLUEPRINT release January 2015, RNA-Seq for inflammatory macrophage

  RNA-Seq data for 3 inflammatory macrophage sample(s). 3 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001132

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - dcc_release_24_dataset

  WGS sequencing for cases from the ICGC ESAD-UK project Tumours 50x Normals 30x HiSeq X BAM files These samples are all available in ICGC release 24

  Dataset EGAD00001003292

• Gene Characterization in Carbohydrate metabolic alterations (neonatel diabetes & congenital hyperinsulinemic) in early childhood (2018-03-14)

  Whole Exome Sequencing of trios (proband + parents) or probands only with Neonatal Diabetes Mellitus (NDM) or Congenital Hyperinsulinism of Infancy (CHI) of unknown genetic origin. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2018-03-14.

  Dataset EGAD00001004040

• BLUEPRINT release August 2014, ChIP-Seq for inflammatory macrophage

  ChIP-Seq data for 3 inflammatory macrophage sample(s). 21 run(s), 21 experiment(s), 21 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001000940

• BLUEPRINT release January 2015, RNA-Seq for class switched memory B cell

  RNA-Seq data for 1 class switched memory B cell sample(s). 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001164

• BLUEPRINT release January 2015, ChIP-Seq for macrophage

  ChIP-Seq data for 13 macrophage sample(s). 55 run(s), 55 experiment(s), 55 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001001196

• BLUEPRINT release August 2015, Bisulfite-Seq for cytotoxic CD56-dim natural killer cell, on genome GRCh38

  Bisulfite-Seq data for 2 cytotoxic CD56-dim natural killer cell sample(s). 24 run(s), 2 experiment(s), 2 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001473

• BLUEPRINT release August 2015, ChIP-Seq for neutrophilic metamyelocyte, on genome GRCh38

  ChIP-Seq data for 4 neutrophilic metamyelocyte sample(s). 18 run(s), 12 experiment(s), 12 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001495

• BLUEPRINT release August 2015, ChIP-Seq for Multiple myeloma, on genome GRCh38

  ChIP-Seq data for 2 Multiple myeloma sample(s). 16 run(s), 14 experiment(s), 14 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001592

• BLUEPRINT release August 2016, RNA-Seq for mature eosinophil, on genome GRCh38

  RNA-Seq data for 2 mature eosinophil sample(s). 3 run(s), 3 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_rnaseq_analysis_crg_20160816

  Dataset EGAD00001002326

• BLUEPRINT release August 2016, ChIP-Seq for Acute Promyelocytic Leukemia - MC2884 (4h), on genome GRCh38

  ChIP-Seq data for 1 Acute Promyelocytic Leukemia - MC2884 (4h) sample(s). 6 run(s), 6 experiment(s), 6 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002372

• BLUEPRINT release August 2016, ChIP-Seq for Chronic Lymphocytic Leukemia, on genome GRCh38

  ChIP-Seq data for 106 Chronic Lymphocytic Leukemia sample(s). 174 run(s), 163 experiment(s), 162 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002384

• BLUEPRINT release August 2016, ChIP-Seq for Mantle Cell Lymphoma, on genome GRCh38

  ChIP-Seq data for 5 Mantle Cell Lymphoma sample(s). 35 run(s), 35 experiment(s), 35 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002397

• BLUEPRINT release August 2016, ChIP-Seq for monocyte - RPMI_BG_T=24hrs, on genome GRCh38

  ChIP-Seq data for 3 monocyte - RPMI_BG_T=24hrs sample(s). 8 run(s), 8 experiment(s), 8 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002408

• BLUEPRINT release August 2016, ChIP-Seq for Acute Promyelocytic Leukemia - MC3324, on genome GRCh38

  ChIP-Seq data for 1 Acute Promyelocytic Leukemia - MC3324 sample(s). 2 run(s), 1 experiment(s), 1 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002431

• BLUEPRINT release August 2016, ChIP-Seq for neutrophilic metamyelocyte, on genome GRCh38

  ChIP-Seq data for 4 neutrophilic metamyelocyte sample(s). 32 run(s), 23 experiment(s), 23 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002435

• BLUEPRINT release August 2016, RNA-Seq for Acute Myeloid Leukemia - CTR, on genome GRCh38

  RNA-Seq data for 7 Acute Myeloid Leukemia - CTR sample(s). 7 run(s), 7 experiment(s), 7 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_rnaseq_analysis_crg_20160816

  Dataset EGAD00001002443

• BLUEPRINT release August 2016, ChIP-Seq for mature eosinophil, on genome GRCh38

  ChIP-Seq data for 2 mature eosinophil sample(s). 14 run(s), 14 experiment(s), 14 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002477

• BLUEPRINT release August 2016, RNA-Seq for osteoclast, on genome GRCh38

  RNA-Seq data for 2 osteoclast sample(s). 2 run(s), 2 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_rnaseq_analysis_crg_20160816

  Dataset EGAD00001002479

• BLUEPRINT release August 2016, Bisulfite-Seq for CD38-negative naive B cell, on genome GRCh38

  Bisulfite-Seq data for 4 CD38-negative naive B cell sample(s). 51 run(s), 5 experiment(s), 8 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002520

• Single-cell expression of Hodgkin lymphoma

  Single-cell gene expression was profiled for 22 Hodgkin lymphoma tumors and 5 reactive lymph nodes (2 replicates were performed for RLN-1). Library preparation was performed with the 10x Chromium platform (3' version 2 assay). Sequencing was performed on an Illumina NextSeq. The BAM files were generated from the raw sequencing data using Cell Ranger (v2.1.0) mkfastq and count commands.

  Dataset EGAD00001005739

• Investigating the impact of MBD4 on the mutability of the germline (2020-01-15)

  We will be testing the hypothesis that MBD4 PTV germline carriers also show an increased number of C toT germline mutations in their offspring. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005788

• Clinical data

  Clinical data from IMvigor210, POPLAR, IMmotion150: Clinical data include demographics, tumor type, PD-L1 IHC, tumor mutation burden, objective response rate, overall survival and progression free survival for 611 patients across IMvigor210, POPLAR and IMmotion150. Clinical data from PCD4989g: Clinical data include tumor type, PD-L1 expression and objective response rate for 206 patients from PCD4989g.

  Dataset EGAD00001006630

• A108846B

  Whole genome sequencing for single cells for library A108846B 1636 cells; filetype=bam

  Dataset EGAD00001007092

• Mtb infected and uninfected neutrophils after 1 and 6 hrs

  Fastq.gz files for mRNA sequenced from Mtb infected and uninfected neutrophils after 1 and 6 hrs. Samples were sequenced in 2 batched as indicated per experiment. Batch 1 was SE unstranded, 100bp on an Illumina HiSeq4000 sequencer and batch 2 unstranded, 150bp paired-end on an Illumina NovaSeq6000 sequencer. Phenotypic data for the samples is also included.

  Dataset EGAD00001010893

• DATA ACCESS AGREEMENT CGNT- GROUP COMMITTEE for project "Exome sequencing of 22 Pheochromocytoma/paraganglioma tumors" at Gothenburg University.

  Dac EGAC00001000411

• DAC for Greenland Studies of University of Copenhagen and University of Southern Denmark.

  Dac EGAC00001000736

• The data access committee for Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer.

  Dac EGAC00001000757

• Data Access Committee for the study "Somatic chronology of treatment-resistant prostate cancer via deep whole-genome ctDNA sequencing"

  Dac EGAC00001002479

• Access Committee for Separation, characterization, and identification of individuals from multi-person blood mixtures

  Dac EGAC00001002646

• DATA FILES FOR SJRB

  Retinoblastoma whole genome sequencing

  Dataset EGAD00001000261

• WES for Verhaak GBM

  Dataset EGAD00001001109

• WES for verhaak-GBM

  Dataset EGAD00001001110

• TumorSNP

  SNP data for 473 tumor samples

  Dataset EGAD00010002038

• Genotypes_Farmers

  Genotype data for Palanan farmers Philippines

  Dataset EGAD00010002141

• Genotypes_BaYaka

  Genotype data for BaYaka hunter-gatherers Congo

  Dataset EGAD00010002139

• ASTAR Skin Research Laboratory

  Data Access Committee for A*STAR Skin Research Laboratory, A*STAR Research Institutes, Singapore.

  Dac EGAC50000000109

• Data for paper Mold, Weissman et al. 'Clonally heritable gene expression imparts a layer of diversity within cell types'

  Dac EGAC50000000102

• Patient WGS for #198

  Whole genome sequencing of patient tumour and blood

  Dataset EGAD50000000217

• PDX WGS for #264

  WGS of tumour PDX and matched patient blood

  Dataset EGAD50000000033

• PDX WES for #039 and #049

  Exome sequencing of PDX and matched patient blood

  Dataset EGAD50000000034

• DAC - organotypic co-cultures @IEO

  DAC for RNAseq data on patient-derived organotypic models (Battistini et al, CDDis 2024)

  Dac EGAC50000000238

• eQTL-CHiC DAC

  The Data Access Committee for eQTL-CHiC and related data from Ray-Jones et al., 2024.

  Dac EGAC50000000445

• DAC for Transcriptomic and genomic profiling of fragile X syndrome unmethylated full mutation carriers

  Dac EGAC50000000416

• Distinct Phenotypes of Human Intrahepatic and Extrahepatic Bile duct Organoids and their Applications for Biliary Disease Modeling

  Study EGAS00001003792

• DKFZ-HIPO DACO for "Patient-derived tumoroids from CIC::DUX4 rearranged sarcoma identify MCL1 as a therapeutic target"

  Dac EGAC00001003559

• Data Access Committee for Patient-derived tumoroids from CIC::DUX4 rearranged sarcoma identify MCL1 as a therapeutic target

  Dac EGAC00001003558

• DAC_Circadian_Neuroendocrinology

  DAC for projects of the group Circadian Neuroendocrinology at the Department of Endocrinology and Metabolism at Amsterdam UMC.

  Dac EGAC50000000692

• Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis

  Study EGAS00001005115

• Nimblegen SeqCap Custom Panel Sequencing

  Nimblegen SeqCap Custom Panel Sequencing for pNet

  Dataset EGAD00001005494

• Genomic Sequencing of Triple Negative Breast Cancer - Exome data

  Exome data for study EGAS00001008261

  Dataset EGAD00001015687

• WGS dataset

  4 WGS bam files for 4 cases with fusion

  Dataset EGAD00001004359

• PDAC

  ctDNA and protein markers for earlier detection of pancreatic cancers

  Dataset EGAD00001004399

• AML WGS bam

  WGS bam files for paired diagnostic and remission samples

  Dataset EGAD00001015515

• A proof-of-concept study of sequential treatment with the HDAC inhibitor vorinostat following BRAF and MEK inhibitors in BRAFV600 mutated melanoma

  "Purpose: Development of resistance limits the clinical benefit of BRAF and MEK inhibitors (BRAFi/MEKi) in BRAFV600 mutated melanoma. It has been shown that short-term treatment (14 days) with vorinostat was able to initiate apoptosis of the resistant tumor cells. We aimed to assess the anti-tumor activity of sequential treatment with vorinostat following BRAFi/MEKi in patients with BRAFV600 melanoma who progressed after initial response to BRAFi/MEKi. Patients and Methods: Patients with BRAFi/MEKi resistant BRAFV600 melanoma were treated with vorinostat 360 mg QD for 14 days followed by BRAFi/MEKi. The primary endpoint was an objective response rate of progressive lesions of at least 30% according to RECIST 1.1. Secondary endpoints included progression-free survival (PFS), overall survival (OS), safety, pharmacokinetics of vorinostat and translational molecular analyses using ctDNA and tumor biopsies. Results: Twenty-six patients with progressive BRAFi/MEKi resistant BRAFV600 mutated melanoma received treatment with vorinostat. Twenty-two patients were evaluable for response. The ORR was 9% (one complete response for 31.2 months and one partial response for 14.9 months. Median PFS and OS were 1.4 and 5.4 months, respectively. Common adverse events were fatigue (23%) and nausea (19%). ctDNA analysis showed emerging secondary mutations in NRAS and MEK in eight patients at time of BRAFi/MEKi resistance. Elimination of these mutations by vorinostat treatment was observed in three patients. Conclusions: Intermittent treatment with vorinostat in patients with resistant BRAFV600 mutated melanoma is well tolerated. Although the primary endpoint of this study was not met, durable anti-tumor responses were observed in a minority of patients (9%). "

  Study EGAS00001007709

• Germ Cell Tumor Molecular Heterogeneity Revealed through Analysis of Primary and Metastasis Pairs

  Purpose: While primary germ cell tumors (GCTs) have been extensively characterized, molecular analysis of metastatic sites has been limited. We performed whole-exome sequencing and targeted next-generation sequencing on paired primary and metastatic GCT samples in a patient cohort enriched for cisplatin-resistant disease. Experimental Design: Tissue sequencing was performed on 100 tumor specimens from 50 patients with metastatic GCT, and sequencing of plasma cell-free DNA (cfDNA) was performed for a subset of patients. Data is being provided for a subset of 10 patients whose samples were analyzed using whole-exome sequencing. Results: The mutational landscape of primary and metastatic pairs from GCT patients was highly discordant (68% of all somatic mutations were discordant). Whereas genome duplication was common and highly concordant between primary and metastatic samples, only 25% of primary-metastasis pairs had =50% concordance at the level of DNA copy number alterations (CNAs). Evolutionary-based analyses revealed that most mutations arose after CNAs at the respective loci in both primary and metastatic samples, with oncogenic mutations enriched in the set of early occurring mutations versus variants of unknown significance (VUS). TP53 pathway alterations were identified in nine cisplatin-resistant cases and had the highest degree of concordance in primary and metastatic specimens, consistent with their association with this treatment-resistant phenotype. Conclusions: Analysis of paired primary and metastatic GCT specimens revealed significant molecular heterogeneity for both CNAs and somatic mutations. Among loci demonstrating serial genetic evolution, most somatic mutations arose after CNAs but oncogenic mutations were enriched in the set of early occurring mutations as compared to VUS. Alterations in TP53 were clonal when present and shared among primary-metastasis pairs. Reprinted from pending publication, with permission from JCO Precision Oncology.

  Study phs002229

• Genetic Progression of Head and Neck Squamous Cell Carcinoma

  These data are tumor/normal-matched exome sequencing results on 45 participants (median age of 58.6 years) treated for squamous cell carcinoma of the oropharynx at Mass Eye and Ear (MEE; Boston, MA, USA). Some tumors had two portions sequenced separately. All research complied with all relevant ethical regulations. Human subject research on patients at MEE was approved by the MEE Human Studies Committee under protocol HSC 11-024H, with informed consent obtained from participants. Participants were not compensated. Clinical data, including HPV status, were abstracted from patient records. Sequencing and clinical data were combined with TCGA data on 486 cases of head and neck squamous cell carcinoma (HNSCC). The PhylogicNDT suite of genomic analysis tools was used to infer the relative timing of genetic events during tumor progression for major molecularly defined classes of HNSCC: HNSCC negative for human papillomavirus (HPV), HPV-associated HNSCC, and HPV-negative HNSCC involving mutations in CASP8, NSD1, and NOTCH1. The timing of the development of whole-genome somatic events leading to tetraploidy or triploidy and to increased measures of intratumor genetic heterogeneity was determined. Associations of heterogeneity measures with clinical outcomes were assessed. The analysis identified genes with early mutations that likely play roles in HPV-tumor initiation (FBXW7, NOTCH1, CASP8, FAT1, NSD1, HRAS, EP300, CREBBP, KMT2D). Genes with mutations at later median relative timing (PK3CA, NFE2L2, HLA-A, SPEN, and KMT2C) were more likely to be important for progressing to later stages of the disease. Gain of chromosome arm 3q and loss of arm 11q were found to occur more frequent in HPV+ HNSCC cases. A high prevalence of 3p and 17p loss were also observed in HPV+ HNSCC cases.

  Study phs003139

• Genomic Studies of Bipolar Disorder in a Large Cohort from The Netherlands

  The transcriptomes of whole blood samples of a large cohort of individuals from the Netherlands were assayed using RNA-seq. These whole blood samples included patients diagnosed with schizophrenia or bipolar disorder, family members of patients, and healthy controls. Within the whole blood dataset, about 600 of the samples were sequenced to an average of 13.9 million mapped reads per sample, while about 2,000 were sequenced to an average of 5.9 million mapped reads per sample, in order to assess changes in power to detect eQTLs based on sample size. We find that expression, defined as the log transcripts per million reads (TPMs), is highly correlated when comparing the moderate (13.9M) and lower (5.9M) coverage datasets. Additionally, a subset of about 150 individuals provided fibroblast cell lines which were assessed for the levels of gene expression using RNA-seq at an average of 50 million mapped reads per sample. We use downsampling techniques to generate synthetic datasets derived from this real fibroblast RNA-Seq data to explore the relationship between coverage and number individuals in an eQTL analysis. Corresponding individual-level genotypes for the samples included in the expression matrix are also made available. The genotypes were derived from multiple other project cohorts, each using different genotyping platforms including OmniExpress Exome, Global Screening Array, Illumina550, and PsychChip. A Plink file set of these cohorts is provided here after merging the separate project file sets and extracting only the individuals who have expression data also provided here; quality control for SNP-missingness is recommended. We demonstrate in this study that given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample to allow for an a greater number of individuals sequenced. 

  Study phs002856

• Gene-Environment Interactions (GxE) and Complex Traits

  Functional variants associated with complex traits tend to fall in non-coding regions and affect regulatory mechanisms that are not yet well characterized. Furthermore, it is generally difficult to determine in which tissues and conditions they may have a functional impact. This is because the effect of a genetic variant on a molecular pathway, and ultimately on the individual's phenotype, may be modulated by "environmental" factors. We denominate such variants "gene-expression environment-specific quantitative trait nucleotides" GxE-QTNs. Achieving a better understanding of the mechanisms underlying GxE is a critical step in understanding the link between genotype and complex phenotype. It is also crucial to develop computationally efficient and statistically sound methods capable to integrate tissue/condition-specific functional genomics data to predict and validate when a sequence variant is functional. In this study we developed novel experimental and computational approaches to screen, analyze and functionally characterize genetic variants for complex traits modulated by environmental exposures. To identify and characterize genes with GxE, we analyzed allele specific gene expression in a panel of five relevant tissues (e.g. the vascular endothelium for cardiovascular diseases) under 50 controlled environmental conditions (e.g. glucocorticoids treatment, as a proxy for stress exposure). These data should be useful to develop computational tools that integrate different sources of evidence including data collected by ENCODE, RoadMap Epigenome and GTEx projects to functionally annotate GWAS variants. The experimental and computational tools developed by this project have widespread applicability, for example, can be used to tackle the functional basis of complex traits in other environmental contexts (e.g. other types of stress and hormonal levels) and genetic backgrounds. This resource represents the first comprehensive catalog of genetic variants that interact with environmental exposure in determining human complex traits.

  Study phs001176

• Human Gene Expression Patterns Associated with Experimental P. falciparum Infection

  Description: Experimental exposure to malaria parasites can lead to development of protective immunity, providing a foothold for the development of a malaria vaccine. The goal of this study is to investigate immune transcriptional profiles associated with malaria protective immune responses induced by experimental Chemo-Prophylaxis and Sporozoites (CPS) immunization of P. falciparum-naive human volunteers. Samples for this study were obtained from a CPS-immunization study conducted in 2011 (ClinicalTrials.gov Identifier: NCT01218893), where healthy volunteers received CPS-immunization with bites from different numbers of P. falciparum-infected mosquitoes (three times 15 (n=5), 10 (n=9) or 5 (n=10)). Five control subjects received uninfected mosquito bites under chloroquine prophylaxis. Fifteen weeks after discontinuation of chloroquine prophylaxis, all volunteers were challenged by exposure to infected mosquito bites. Study Design: A maximum of 30 volunteers were divided into four groups. All volunteers received weekly chloroquine prophylaxis for a period of 13 weeks (91 days). During these 13 weeks, on days 8, 36 and 64 they were exposed to the bites of 15 mosquitoes. Group 1 (n=5, positive control) received three CPS immunizations by 15 mosquitoes infected with P. falciparum sporozoites. Group 2 (n=10) received three times 10 bites from infected mosquitoes and 5 bites from uninfected mosquitoes. Group 3 (n=10) received three times 5 bites from infected mosquitoes and 10 bites from uninfected mosquitoes. Group 4 (n=5), the negative control, received three placebo immunizations with 15 bites of uninfected mosquitoes. Fifteen weeks after discontinuation of chloroquine prophylaxis, all 30 volunteers were challenged at day 196 by the bites of 5 infectious mosquitoes and followed for 21 days. All subjects were treated with chloroquine 21 days after challenge or whenever they had a positive thick smear during that period.

  Study phs001346

• Single-cell RNA-Seq of human lymphoma reveals malignant B cell diversity and patterns of T cell immune checkpoint co-expression

  Follicular lymphoma (FL) is a generally incurable B-cell malignancy which has the potential to transform into highly aggressive lymphomas. Genomic studies indicate it is often a small subpopulation rather than the dominant population in the FL that gives rise to the more aggressive subtype. To resolve the underlying transcriptional networks of follicular B-cell lymphomas at single molecule and cell resolution, we leveraged droplet-based barcoding technology for highly parallel single cell RNA-Seq. We analyzed the transcriptomes from tens of thousands of cells derived from five primary FL tumors. Simultaneously, we conducted multi-dimensional flow cell sorting to validate our characterizing of cellular lineages and critical expressed proteins. For each tumor, we identified multiple cellular subpopulations, matching known hematopoietic lineages. Comparison of gene expression by matched malignant and normal B cells from the same patient revealed tumor-specific features. Malignant B cells exhibited restricted immunoglobulin light chain expression (either Ig Kappa or Ig Lambda), as well the expected upregulation of the BCL2 gene, but also down-regulation of the FCER2, CD52 and MHC class II genes. By leveraging the single-cell resolution on large numbers of cells per patient, we were able to examine tumor-resident T cells. We identified pairs of immune checkpoint molecules that were co-expressed, providing a potentially useful strategy for selection of patient-tailored combination immunotherapies. In summary, massively parallel measurement of single-cell expression in thousands of tumor cells and tumor-resident lymphocytes can be used to obtain a systems-level view of the tumor microenvironment and identify new avenues for therapeutic development.

  Study phs001378

• Investigating the Role of Neddylation in the Repair of Topoisomerase I-Mediated DNA Damage in Colorectal Cancer

  Colorectal cancer is one of the most lethal cancers worldwide. First-line chemotherapy for metastatic CRC (mCRC) entails a combination of irinotecan with 5-fluorouracil and leucovorin (FOLFIRI). Irinotecan is a prodrug that is converted within the cell to its active metabolite SN38, a potent TOP1 inhibitor. TOP1 relieves DNA torsional strain arising from DNA metabolism by cleaving one strand of the DNA duplex and forming an enzyme-DNA covalent intermediate that can be trapped by inhibitors. The resulting TOP1 DNA-protein crosslinks (TOP1-DPCs) interfere with replication and transcription and induce cell death if left unrepaired. I have established the role of the ubiquitin-proteasome pathway in TOP1-DPC repair, but it remains unknown how ubiquitylation is activated. One possibility is that collision between advancing replication forks and the DPCs triggers a specific ubiquitin ligase to target DPCs for degradation. The DDB1-CUL4 (Cullin 4)-RBX1 (CRL4) ubiquitin ligase complex performs a salient role in both replication and DNA repair upon its activation by auto-NEDD8 modification (neddylation), but it is unknown whether it plays a role for TOP1-DPC repair. We plan to define the role of neddylation for TOP1-DPC repair during replication and examine irinotecan plus pevonedistat (PEV), a NEDD8-activating enzyme inhibitor in multiple clinical trials, in CRC patient-derived preclinical models including 3D organoids. The three in-house organoids, from which samples have been submitted to dbGaP, were sequenced as a means of verifying their identity as colon cancer organoids, to allow us to investigate the combination of TOP1 inhibitors and PEV and the molecular mechanisms underlying this drug combination in these organoids.

  Study phs003257

• Identifying Novel Small RNA Biomarkers Unique to Patients with Gastric Cancer

  The biomarker development study consisted of two parts: discovery and validation. The first part was the discovery and verification phase of biomarkers using two different platforms: transcriptomic and miRNA. The salivary transcriptomes of 63 GC samples and 31 non-GC controls were profiled using Affymetrix HG U133+2.0 microarrays (Affymetrix, Santa Clara, CA). The identified exRNA candidates were verified by quantitative real-time PCR (RT-qPCR) using all 94 of the original samples. In the discovery phase for the miRNA biomarkers, 10 early-stage GC samples and 10 non-GC controls were selected. The salivary miRNAs of these samples (n=20) were profiled using the TaqMan MicroRNA Array (Applied Biosystems, Foster City, CA). MicroRNA candidates were verified using TaqMan miRNA Assay (Thermo Scientific, Grand Island, NY). The second part of the study was to validate these verified exRNA biomarker candidates with exRNA samples extracted from an independent cohort of 100 GC and 100 non-GC saliva samples. The cohort was not balanced for demographics on gender and smoking history but more accurately reflected the diagnostic setting where our proposed final model could be implemented. Reprinted from "Li F, Yoshizawa MJ, Kim K, Kanjanapangka J, Grogan T, Wang X, Elashoff D, Ishikawa S, Chia D, Liao W, Akin D, Yan X, Lee M, Choi R, Kim S, Kang S, Bae J, Sohn T, Lee J, Choi M, Min B, Lee J, Kim J, Kim Y, Kim S, Wong D. (2018) Development and Validation of Salivary Extracellular RNA Biomarkers for Noninvasive Detection of Gastric Cancer. Clin Chem. PMID: 30097497 DOI: 10.1373/clinchem.2018.290569", with permission from American Association for Clinical Chemistry (United States).

  Study phs001767

• Type 1 Diabetes Genetics Consortium (T1DGC): Copy Number Variant (CNV) Study

  Type 1 Diabetes Genetics Consortium (T1DGC) was formed to address issues of limited sample size and consistency of phenotyping that had limited genetic investigations on risk of type 1 diabetes (T1D). The T1DGC first collected affected sib pair (ASP) families from four geographic networks (Asia-Pacific, Europe, North America, United Kingdom). In addition, T1D parent-offspring trios as well as cases and controls were ascertained from existing and de novo collections. For T1D, the genome-wide association study (GWAS) design has been successful at detecting ~50 loci that contribute disease risk. However, in the case of T1D as well as almost all other traits, the sum of these loci does not fully explain the heritability estimated from familial studies. One possibility for the undiscovered contribution to familial aggregation is that additional variants exist but have not yet been found because they have not effectively been targeted by the GWAS design. In this study, we focus on a specific class of large deletions/duplications -- copy number variants (CNVs) - and particularly to the subset of these loci that mutate rapidly, are not tagged by SNPs, and are highly polymorphic. The T1DGC assembled 2,601 T1D ASP families and 69 Parent-T1D offspring trios, that were eligible for this study. All samples included in this series have reported or self-declared European ancestry. All DNA samples were collected after approval from relevant institutional research ethics committees. Importantly, the source of DNA for CNV evaluation was uniform within a family (either all from PBMC or from EBV-transformed cell lines). We use a family based design that was optimized to capture these previously untested variants. We then perform a genome-wide scan to assess their contribution to T1D.

  Study phs000910

• The Role of Germline Mutation and Parental Age in Autism Spectrum Disorders

  <p>We will apply whole genome sequencing of trio families to determine how patterns of germline mutation throughout the genome determine risk for Autism Spectrum Disorder (ASD). We will investigate the nature intrinsic hypermutability and the extrinsic forces, such as paternal age, that influence rates of germline mutation. We will accomplish these goals through the following specific aims: Specific Aim 1 will characterize germline de novo mutations (DNMs) by whole genome sequencing in families. These studies will identify and validate ~8,000 de novo point mutations and structural variants in trios and controls to determine the parent of origin of DNMs. Specific Aim 2 will identify hot spots for germline mutation based on the regional density of DNMs in the genome, and determine the effects of DNA sequence features on rates of mutation. We will determine the association of mutation hotspots with ASD in the discovery sample and in genomic datasets from an independent sample of 2700 cases and 2700 controls. Specific Aim 3 will characterize the effects of extrinsic factors, including parental age and environment, on genome-wide rates of mutation. We will quantify the effect of paternal age on pathogenic and neutral alleles in sperm and investigate whether some DNMs confer a germline selective advantage. The findings of this study will provide fundamental insights into the genetic basis of autism risk and the genetic mechanism of the observed parental age effects in ASD. We will identify genes that confer significant risk for autism, and we will determine how intrinsic properties of the genome interact with extrinsic forces to determining risk for disease in offspring.

  Study phs001164

• Genotyping NIGMS CEPH Samples from the United States, Venezuela, and France

  The Human Genetic Cell Repository (HGCR) is sponsored by the National Institute of General Medical Sciences (NIGMS) with the mission of supplying scientists with the materials for accelerating disease gene discovery and functional studies. The resources available include highly-characterized, contaminant-free fibroblast cell lines, transformed lymphoblastoid cell lines (LCLs), and DNA samples derived from these cultures. While the Repository has a major emphasis on inherited diseases, it also contains large collections of cell lines gathered from populations around the world that are intended for use in facilitating the understanding of human variation. Prominent among these resources is the CEPH Reference Family collection contributed by the Centre d'Etude du Polymorphisme Humain (CEPH), Foundation Jean Dausset, Paris, France. The CEPH collection includes families collected by R. White (Utah), J. Dausset (French), J. Gusella (Venezuelan), and J. Egeland (Amish). There are a total of 809 individuals accounting for 832 pedigree positions in the reference families. Family relationships for the 61 reference families were verified at Coriell and approved by CEPH. In an effort to enhance the value of this cell culture resource available from the Repository, the Coriell Genotyping and Microarray Center used the Affymetrix Genome-Wide Human SNP Array 6.0 platform to genotype 181 samples from the NIGMS HGCR CEPH collection. Included are thirteen families from the United States (Amish Pedigree 884 and Utah Pedigrees 1331, 1356, 1400, 1416, 1424, 1427, 1477, and 1582), France (Pedigrees 35 and 66), and Venezuela (Pedigrees 102 and 104). Twelve of the families consist of 3 generations and one family consists of 2 generations. Pedigree charts and sample descriptions are available on the Repository catalog website (see below).

  Study phs000268

• Genome Studies in Hereditary Spastic Paraplegia

  Next-generation sequencing technology is opening up new opportunities to rethink the way we identify disease causing genetic variation. An early application, whole exome sequencing, has now been established by a small number of research labs, including ours. Exome sequencing allows obtaining a near complete set of protein coding genomic variation in single individuals for less than $5,000. Promising targets for exome sequencing studies are Mendelian diseases, such as hereditary spastic paraplegias (HSP). HSP comprise a genetically very heterogeneous set of neurological disorders with currently 39 different HSP chromosomal loci being reported; yet, the identified genes explain only 60% of the genetic effect at best. Traditional methods of gene identification require linkage analysis of large families, but face increasing difficulties to identify such extended pedigrees for rare HSP forms. However, the innovative approach described in this application will overcome some of these limitations and utilize relatively small pedigrees for highly effective gene identification. We will apply exome sequencing, which will characterize all coding changes and flanking exonic variation in two individuals of a family. We have developed a multi-tiered strategy to reduce the number of identified novel variants to the very causative change in an individual family. We propose to study at least 60 HSP families, which are too small to yield conclusive results with linkage analysis. If the developing technology permits we will consider a larger sample or perform whole genome sequencing. Beyond the important benefit to genetics of HSP, this study will allow us to further establish this new method, which will benefit a large range of additional disease studies.

  Study phs001080

• A Missense SNP in the Tumor Suppressor SETD2 Reduces H3K36me3 and Mitotic Spindle Integrity in Drosophila

  While examining DNA from renal cell carcinoma (RCC) patients we observed a single nucleotide polymorphism (SNP; rs58906143,chr3:47163422 C→G (hg19); E902Q*) within SETD2 that was overrepresented in kidney cancer patients compared to control patients. This original study utilized directed sequencing of 14 genes. In an expanded study using a Taqman assay to study the specific SNP, a G allele was found in 72/493 cases (14.6%) and 24/356 (6.7%) controls. Analysis by ancestry revealed that there was there was a G allele in 50/119 (42.0%) cases and 21/71 (29.6%) controls in patients with African Ancestry while there was a G allele in 19/335 cases (5.7%) and 3/269 (1.1%) controls in patients with European Ancestry. These differences did not reach statistical significance. Concurrently, we studied the functional effect of this SNP in a Drosophila model. The homologous amino acid substitution at E741Q reduces H3K36me3 levels which could be rescued by wild type Set2. Additionally, the mutation resulted in significant defects in spindle morphogenesis, consistent with a role for SETD2 in alpha-tubulin methylation during mitosis. Data available through dbGAP include the 14 gene sequence files of 52 control and 128 RCC patients, the results of Taqman SNP (rs58906143) analysis in text files for 221 controls and 357 RCC patients, and compiled phenotype data including age and assigned race. Due to data use limitations, genotyping data for 8 RCC patients and 83 controls as well as GWAS data for ancestry determination will not be available on dbGaP, but will be made available on request.

  Study phs003474

• Clinical and Molecular Features of Acquired Resistance to Immunotherapy in Non-Small Cell Lung Cancer

  PD-(L)1 blockade has been shown to promote durable responses in non-small cell lung cancer (NSCLC) patients and has been rapidly incorporated into the treatment regimens for many with advanced disease. Despite this, mechanisms of acquired resistance (AR) to PD-(L)1 have not been well understood. To address the clinical and molecular landscape of AR to PD-(L)1 blockade in NSCLC patients, a large clinical cohort of AR to PD-(L)1 blockade in lung cancer was examined. In conjunction, a systematic genomic and transcriptomic analysis in a subset of patients (n = 29) with available tissue samples was conducted. To investigate the molecular mechanisms of AR to PD-1 blockade from pre- and/or post-treatment tumors, microarray-based whole transcriptome expression data for 29 tumor samples, and whole exome sequencing (WES) data from 34 tumor samples was generated from this subset. Microarray data was uploaded to the Gene Expression Omnibus (GEO) database. Most AR tumors showed retained or increased inflammatory characteristics with significant upregulation of IFNγ which was suggestive of persistent albeit insufficient anti-tumor immune response. This finding expounds upon the prior pre-clinical and translational data supporting the intricate role of IFNγ in sensitivity and resistance to immunotherapy. Whereas initial IFNγ exposure may be fundamental to T cell activation (a hallmark of immune response), persistent IFNγ related effects and upregulation may potentially signal immune dysfunction and IFNγ insensitivity in contrast. The inflammatory phenotypes identified have implications for future rational development of new immunotherapy strategies for patients with AR.

  Study phs002834

• Response and Resistance to ER-Directed Therapy in Metastatic Breast Cancer

  Despite the reduction in cancer recurrence and mortality provided by hormonal therapy, estrogen receptor positive (ER+) metastatic breast cancer (MBC) remains the most common cause of breast cancer death, resulting in more than 20,000 deaths in the U.S each year. Although several therapeutic options exist to target the estrogen receptor, resistance to these therapies invariably occurs. Genetic mechanisms of acquired resistance to targeted therapies in ER+ MBC are not well understood. To address this, we developed a prospective biopsy protocol where multiple research biopsies are collected from metastatic samples and used for pathology, ER/PR/HER2, OncoPanel (a CLIA 300 gene sequencing panel), whole exome sequencing (WES), transcriptome sequencing (RNA-Seq), and single-cell RNA-seq (scRNA-seq). In some cases, tissue is used for cell line and xenograft generation. Archival primary biopsies are also obtained when possible for parallel genomic studies. Patients are followed and serial metastatic biopsies are obtained. This data will be used to determine the landscape of genomic alterations in treatment-resistant ER+ MBC, and compared to data generated by TCGA for treatment-naive ER+ primary breast cancer. Comparison of genomic studies from resistant, metastatic specimens with corresponding pre-treatment primary samples will allow us to identify genomic association with resistance to specific therapies. Analysis of the serial metastatic biopsies collected over the course of multiple treatments will also shed light on tumor evolution under selective therapeutic pressure.NoteA subset of the MBC blood biopsies that are included in our study can be accessed and downloaded from the phs001977 accession.Another subset of patients in this study participated in a clinical trial (NCT02871791) and have their data deposited in the current study as well.

  Study phs001285

• MUSIC: Long-TerM OUtcomes after the Multisystem Inflammatory Syndrome In Children

  This study is an observational cohort study that will use routinely-collected clinical and cardiac (EKG, echocardiogram, CMR, exercise testing) data to assess the association between MIS-C (multisystem inflammatory syndrome in children) and cardiac outcomes within the first year after hospital discharge. Research funding will be available for EKGs, echocardiograms and MRIs in protocol windows that are not ordered by primary caregivers. Our principal goal is to determine the spectrum and early time course of coronary artery involvement, LV systolic function, and arrhythmias or conduction system abnormalities, and, using these data, to define associated clinical and laboratory factors. We will include all eligible patients, including retrospective cases beginning January 1, 2020, with follow-up (in-person or telehealth) up to one year and annual medical history forms until up to 5 years have elapsed since illness onset. Because many patients will have been identified by retrospective review, we will obtain consent at different times in their illness course. For this reason, it may be hard to reach some patients and their families. Waiver of consent will be obtained after three attempts have been made to locate the patient and family without success, as well as for the rare child who dies before informed consent can be obtained. We will include a HIPAA-compliant cryptographic algorithm to create a sharable “hashed” identifier from patient information. If blood work for research purposes is added to usual clinically-indicated blood work during follow-up visits, this will be covered by other informed consent forms. Our primary outcomes are the largest proximal Left Anterior Descending Coronary Artery (LAD) or Right Coronary Artery (RCA) z score and lowest Left Ventricular Ejection Fraction (LVEF).

  Study phs002770