16452 results for "sec+and+unspecified+malignant+neoplasm"

in 42.22 milliseconds.

• Lung Adenocarcinoma Promotion by Air Pollutants

  A complete understanding of how environmental carcinogenic exposures promote cancer formation is lacking. Over 70 years ago, tumour formation was proposed to occur in a two step process: an initiating step which induces mutations in normal tissue, followed by a promoter step which triggers cancer development. Recent evidence has revealed healthy human tissue contains a patchwork of clones harbouring oncogenic mutations.This led us to hypothesise that environmental particulate matter measuring PM2.5, known to be associated with lung cancer risk, might promote lung cancer by acting on pre-existing cells harbouring oncogenic mutations in normal lung tissue. Here we use a combination of WGS and RNA-seq of mouse tumours from pollution-exposed mice to examine the impact of particulate matter on mutagenesis and gene expression respectively.

  Study EGAS00001006951

• Memory-like HCV-specific CD8+ T cells retain a chronic scar after cure of chronic HCV infection

  Chronic hepatitis C virus (HCV) infection is associated with CD8+ T-cell exhaustion characterized by limited effector functions and thus compromised anti-viral activity. Exhausted HCV-specific CD8+ T cells are comprised of memory-like and terminally exhausted CD8+ T-cell subsets. So far, little is not known about the molecular profile and fate of these cells after elimination of chronic antigen stimulation by direct acting antiviral therapy (DAA). Here, we report an antigen-driven molecular core signature underlying exhausted CD8+ T-cell subset heterogeneity in chronic viral infection with a progenitor/progeny relationship of memory-like and terminally exhausted HCV-specific CD8+ T cells via an intermediate stage. Furthermore, transcriptional profiling reveals that the memory-like cells remain after DAA-mediated cure while terminally exhausted HCV-specific CD8+ T-cell subsets are lost. Thus, the memory polarization of the overall HCV-specific CD8+ T-cell response after cure does not result from re-differentiation of exhausted T cells. Consequently, antigen elimination has little impact on the exhausted core signature of memory-like CD8+ T cells that remains clearly different from bona fide T-cell memory. These results identify a molecular signature of T-cell exhaustion that is imprinted like a chronic scar in HCV-specific CD8+ T cells even after HCV cure, highlighting the requirement of re-programming to elicit full effector potential of exhausted T cells.

  Dataset EGAD00001006259

• Direct Comparative Analysis of 10X Genomics Chromium and Smart-seq2

  Single cell RNA sequencing (scRNA-seq) is widely used for profiling transcriptomes of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently-used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data by the two platforms from the same samples of CD45- cells, we systematically evaluated their features using a wide spectrum of analysis. Smart-seq2 detected more genes in a cell, especially low abundance transcripts as well as alternatively spliced transcripts, but captured higher proportion of mitochondrial genes. The composite of Smart-seq2 data also resembled bulk RNA-seq data better. For 10X-based data, we observed higher noise for mRNA in the low expression level. Despite the poly(A) enrichment, approximately 10-30% of all detected transcripts by both platforms were from non-coding genes, with lncRNA accounting for a higher proportion in 10X. 10X-based data displayed more severe dropout problem, especially for genes with lower expression levels. However, 10X-data can better detect rare cell types given its ability to cover a large number of cells. In addition, each platform detected different sets of differentially expressed genes between cell clusters, indicating the complementary nature of these technologies. Our comprehensive benchmark analysis offers the basis for selecting the optimal scRNA-seq strategy based on the objectives of each study.

  Dataset EGAD00001005448

• FOXM1 is a biomarker of resistance to PI3Kα inhibition in ER+ breast cancer that is detectable using metabolic imaging (RNA-seq data)

  PIK3CA, encoding the PI3Kα isoform, is the most frequently mutated oncogene in estrogen receptor-positive breast cancer. Isoform-selective PI3K inhibitors are used clinically but intrinsic and acquired resistance limits their utility. Improved selection of patients that will benefit from these drugs requires novel predictive biomarkers. We show here that persistent FOXM1 expression following drug treatment is a biomarker of resistance to PI3Kα inhibition in ER+ breast cancer. Since the transcription factor drives expression of lactate dehydrogenase (LDH) but not hexokinase 2 (HK-II), changes in FOXM1 expression can be detected using magnetic resonance imaging of LDH-catalyzed hyperpolarized 13C label exchange between pyruvate and lactate but not by PET measurements of HK-II-mediated trapping of the glucose analog 2-deoxy-2-[18F]fluorodeoxyglucose.

  Study EGAS00001004452

• Correction of a Factor VIII genomic inversion with designer recombinases

  Despite advances in nuclease-based genome editing technologies, correcting human disease-causing genomic inversions remains a challenge. Here, we describe the potential use of a recombinase-based system to correct the 140 kb inversion of the F8 gene frequently found in patients diagnosed with severe Hemophilia A. Employing substrate-linked directed molecular evolution, we developed a coupled heterodimeric recombinase system (RecF8) achieving 30% inversion of the target sequence in human tissue culture cells. Transient RecF8 treatment of endothelial cells, differentiated from patient derived induced pluripotent stem cells (iPSCs) of a hemophilic donor, resulted in 12% correction of the inversion and restored Factor VIII mRNA expression. Our data suggests that designer-recombinases may represent efficient and specific means towards treatment of monogenic diseases caused by large gene inversions.

  Study EGAS00001005496

• Data Access Committee of divisions B060 and B062, German Cancer Research Center (DKFZ)

  Dac EGAC00001001626

• EGAD00010000538

  28 unlinked autosomal microsatellite loci for 20 African and 4 philippine populations

  Dataset EGAD00010000538

• RNA Editing Exome

  Exome sequencing data for tumor and matched normal samples of the EGAS00001000495 project.

  Dataset EGAD00001000626

• RHD_NC_OMNI_Controls

  Healthy volunteers and missing phenotype individuals recruited in New Caledonia with higher density genotyping

  Dataset EGAD00010000962

• Transcriptome of (monocytes and dendritic cells ), from donor S8, replicate 1 patient

  Dataset EGAD00001009020

• Transcriptome of (monocytes and dendritic cells ), from donor S6, replicate 1 control

  Dataset EGAD00001009016

• Transcriptome of (monocytes and dendritic cells ), from donor S9, replicate 1 control

  Dataset EGAD00001009022

• Transcriptome of (monocytes and dendritic cells ), from donor S14, replicate 1 control

  Dataset EGAD00001009032

• Transcriptome of (monocytes and dendritic cells ), from donor S9, replicate 2 control

  Dataset EGAD00001009023

• Transcriptome of (monocytes and dendritic cells ), from donor S5, replicate 2 control

  Dataset EGAD00001009015

• Transcriptome of (monocytes and dendritic cells ), from donor S6, replicate 2 control

  Dataset EGAD00001009017

• Transcriptome of (monocytes and dendritic cells ), from donor S14, replicate 2 control

  Dataset EGAD00001009033

• Transcriptome of (monocytes and dendritic cells ), from donor S7, replicate 1 patient

  Dataset EGAD00001009018

• Transcriptome of (monocytes and dendritic cells ), from donor S7, replicate 2 patient

  Dataset EGAD00001009019

• Transcriptome of (monocytes and dendritic cells ), from donor S8, replicate 2 patient

  Dataset EGAD00001009021

• Transcriptome of (monocytes and dendritic cells ), from donor S15, replicate 1 patient

  Dataset EGAD00001009034

• Transcriptome of (monocytes and dendritic cells ), from donor S15, replicate 2 patient

  Dataset EGAD00001009035

• Transcriptome of (monocytes and dendritic cells ), from donor S16, replicate 1 patient

  Dataset EGAD00001009036

• Transcriptome of (monocytes and dendritic cells ), from donor S16, replicate 2 patient

  Dataset EGAD00001009037

• Transcriptome of (monocytes and dendritic cells ), from donor S13, replicate 2 control

  Dataset EGAD00001009031

• Transcriptome of (monocytes and dendritic cells ), from donor S1, replicate 1 control

  Dataset EGAD00001009006

• Transcriptome of (monocytes and dendritic cells ), from donor S1, replicate 2 control

  Dataset EGAD00001009007

• Transcriptome of (monocytes and dendritic cells ), from donor S2, replicate 1 control

  Dataset EGAD00001009008

• Transcriptome of (monocytes and dendritic cells ), from donor S2, replicate 2 control

  Dataset EGAD00001009009

• Transcriptome of (monocytes and dendritic cells ), from donor S3, replicate 1 patient

  Dataset EGAD00001009010

• Transcriptome of (monocytes and dendritic cells ), from donor S3, replicate 2 patient

  Dataset EGAD00001009011

• Transcriptome of (monocytes and dendritic cells ), from donor S4, replicate 1 patient

  Dataset EGAD00001009012

• Transcriptome of (monocytes and dendritic cells ), from donor S4, replicate 2 patient

  Dataset EGAD00001009013

• Transcriptome of (monocytes and dendritic cells ), from donor S5, replicate 1 control

  Dataset EGAD00001009014

• Transcriptome of (monocytes and dendritic cells ), from donor S10, replicate 1 control

  Dataset EGAD00001009024

• Transcriptome of (monocytes and dendritic cells ), from donor S10, replicate 2 control

  Dataset EGAD00001009025

• Transcriptome of (monocytes and dendritic cells ), from donor S11, replicate 1 patient

  Dataset EGAD00001009026

• Transcriptome of (monocytes and dendritic cells ), from donor S11, replicate 2 patient

  Dataset EGAD00001009027

• Transcriptome of (monocytes and dendritic cells ), from donor S12, replicate 1 patient

  Dataset EGAD00001009028

• Transcriptome of (monocytes and dendritic cells ), from donor S12, replicate 2 patient

  Dataset EGAD00001009029

• Transcriptome of (monocytes and dendritic cells ), from donor S13, replicate 1 control

  Dataset EGAD00001009030

• Adaptive single-KIR+NKG2C+ NK cells expanded from superdonors show potent missing-self reactivity and efficiently control HLA-mismatched acute myeloid leukemia

  Study EGAS00001006614

• PDX WES for #87, #95, #32, #217, #86

  Exome sequencing of tumour PDX and matched patient blood

  Dataset EGAD50000000215

• TSO500 on DNA

  Targeted sequencing using the TruSight Oncology 500 DNA probes panel, on samples 162 and 521

  Dataset EGAD50000000270

• Whole Exome Sequencing of Bipolar cases and matched controls performed at the Broad Institute on a cohort from Landen, Stockholm, Sweden.

  Study EGAS00001005838

• Whole Exome Sequencing of Bipolar cases and matched controls performed at the Broad Institute on a cohort from the Netherlands (posthuma)

  Study EGAS00001005857

• Whole Exome Sequencing of Bipolar cases and matched controls performed at the Broad Institute on a cohort from Edinburgh, Scotland, UK.

  Study EGAS00001005843

• Whole Exome Sequencing of Bipolar cases and matched controls performed at the Broad Institute on a cohort from Cardiff, UK (O'Donovan)

  Study EGAS00001005852

• Whole Exome Sequencing of Bipolar cases and matched controls performed at the Broad Institute on a cohort from the Netherlands (Ophoff)

  Study EGAS00001005853

• Whole Exome Sequencing of Bipolar cases and matched controls performed at the Broad Institute on a cohort from Stockholm, Sweden (Pedersen)

  Study EGAS00001005856

• Study of renal cancers and renal cancer metastases

  Study EGAS00001001176

• Integrative genomic analyses of European intrahepatic cholangiocarcinoma: new ROS1 fusion gene and PBX1 as prognostic marker

  Study EGAS00001007525

• Preoperative ipilimumab plus nivolumab in locoregionally advanced urothelial cancer (NABUCCO Cohorts 1 and 2)

  Study EGAS00001004521

• Whole Exome Sequencing of Bipolar cases and matched controls performed at the Broad Institute on a cohort from Cardiff, UK (Owen)

  Study EGAS00001005855

• Comprehensive Characterization of Early Relapse and End-Stage Relapsed Refractory Multiple Myeloma (HIPO K08K/H067/H021+nonHIPO)

  Study EGAS00001007469

• Genome-wide DNA Methylation Analysis Reveals a Unique Methylation Pattern for Pleural Mesothelioma Compared to Healthy Pleura and Other Lung Diseases

  Study EGAS00001007783

• RNAseq data

  RNAseq data for 10 patients: 10 tumors and 10 cell lines

  Dataset EGAD00001002691

• Biomarker Data Subset

  This dataset contains data for CBC counts and absolute protein abundance measurements from ELISA experiments.

  Dataset EGAD00001011163

• Targeted Sequencing Xenturion

  Targeted sequencing of a biobank of PDOs PDXs and LMHs

  Dataset EGAD00001009653

• Exome sequencing of nine PCC/PGL tumors

  Exome sequencing of nine PCC/PGL tumors, SF and FFPE samples

  Dataset EGAD00001001854

• CHDWB Rare Regulatory Alleles and Gene Expression Study

  In order to evaluate whether rare regulatory variants in the vicinity of promoters are likely to impact gene expression, we conducted a novel burden test for rare variants at the extreme of expression. After targeted sequencing of 2kb promoter regions of 472 genes in 410 healthy adults, burden tests were performed by calculating the summed rare allele counts in ranked expression level bins using Illumina whole blood microarray gene expression data. The results clearly show an enrichment of rare variants at both extremes of gene expression. The rare regulatory variant effects were also partitioned into subsets of genes based on their regulatory functions, positions relative to transcription start sites, disease relatedness, with some intriguing biases. The enrichment of rare regulatory variants in extremely expressed genes was replicated in an independent sample of 75 individuals with RNASeq and whole genome sequence information. Participants were from the Emory-Georgia Tech Center for Health Discovery and Well Being (CHDWB) longitudinal cohort study, but only baseline gene expression data was used.

  Study phs001021

• POISED (Peanut Oral Immunotherapy: Safety, Efficacy, and Discovery)

  Phase 2 POISED (Peanut Oral Immunotherapy: Safety, Efficacy, Discovery) Study In this randomized, double-blind, placebo-controlled (DBPC) clinical trial, blinded placebo group received oat flour, whereas in active participants, dosage was built-up for over ~ 52 weeks and subsequently maintained on 4000 mg peanut protein, daily for next 52 weeks. 80 (98.77%) per-protocol active participants passed the food challenge at week 104. Subsequently, for 12 weeks, peanut ingestion was avoided in a randomized group of 51 blinded active participants (i.e., peanut avoidance group). 21 (41.2%) participants in the peanut avoidance group passed the 4000 mg double-blind, placebo-controlled food challenge (DBPCFC) without any allergic reaction at week 117, thus demonstrating sustained unresponsiveness. These 21 participants continued oral immunotherapy (OIT) for every three months and afterwards were allowed to continue peanut OIT discontinuation if they passed. 8 participants passed the DBPCFCs, 12 months after peanut discontinuation (week 156), i.e. they achieved long-term sustained unresponsiveness with no allergic reaction in response to food challenge. For detailed description, please refer to Chinthrajah et al., PMID: 31522849.

  Study phs003071

• Gene fusion and transcriptomic landscapes of sarcomas

  Sarcoma represents a highly heterogeneous group of tumors. We report here the first unbiased and systematic search for gene fusions and analysis of transcriptomic profiles in 100 monomorphic sarcomas cases using RNA-sequencing profiling. Fusion genes were detected in two thirds of samples. Similarly to fusion genes such as PAX3/7-FOXO1, EWSR1/FUS-ETS, SS18-SSX, BRD3/4-NUTM1 or EWSR1/FUS/TAF15-NR4A3 characterizing well-defined entities, we show that all sarcomas displaying CIC fusions- whatever their fusion partner (being DUX4, NUTM1 or FOXO4) - form a transcriptionally homogeneous group of tumors. Likewise, tumors with either a BCOR fusion (with CCNB3, MAML3 or ZC3H7B partners) or with a BCOR internal duplication form a single biological entity. We also found that EML4-ALK fusions also characterize a homogeneous infantile fibrosarcoma subgroup. In contrast, fusions like VGLL2-NCOA2/CITED2, TMP3/TRP-NTRK1 or ETV6-NTRK3 are observed in more heterogeneous tumors. Finally, we also describe a new group of bone sarcomas characterized by EWSR1- or FUS-TFCP2 fusions.

  Study EGAS00001002189

• Circulating Tumor DNA Analysis Detects Minimal Residual Disease and Predicts Recurrence in Patients with Stage II Colon Cancer

  Detection of circulating tumor DNA (ctDNA) after resection of stage II colon cancer may identify those at the highest risk of recurrence and help inform adjuvant treatment decisions. We used massively-parallel-sequencing–based assays to evaluate the ability of ctDNA to detect minimal residual disease in 1046 plasma samples from a prospective cohort of 230 patients with resected stage II colon cancer. In patients not treated with adjuvant chemotherapy, ctDNA was detected post-operatively in 14 of 178 (7.9%) patients, 11 (79%) of whom had recurred at a median follow-up of 27 months; recurrence occurred in only 16 (9.8 %) of the 164 patients with negative ctDNA (HR, 18; 95% confidence interval [CI], 7.9 to 36; P<0.001). In patients treated with chemotherapy, the presence of ctDNA following completion of chemotherapy was also associated with an inferior recurrence-free survival (HR, 11; 95% CI, 1.8 to 68; P=0.001). ctDNA detection after stage II colon cancer resection provides direct evidence of residual disease and identifies patients at very high risk of recurrence.

  Study EGAS00001001839

• Clonal dynamics after allogeneic haematopoietic cell transplantation using genome-wide somatic mutations - TGS

  Allogeneic haematopoietic cell transplantation (HCT) replaces the stem cells responsible for blood production with those harvested from a donor, and is received by 40,000 patients worldwide each year. To quantify dynamics of long-term stem cell engraftment, we sequenced whole genomes of 2,824 single-cell-derived haematopoietic colonies from blood samples of 10 donor-recipient pairs taken 9-31 years after HLA-matched sibling HCT. With younger donors, 10,000-50,000 stem cells had engrafted and were still contributing to haematopoiesis at time of sampling, but estimates were 10-fold lower with older donors. Engrafted stem cells made multilineage contributions to myeloid, B-lymphoid and T-lymphoid populations, although individual clones often showed biases towards one or other mature cell type. Recipients had lower clonal diversity than matched donors, equivalent to ~10-15 years of additional ageing, arising from up to 25-fold greater expansion of stem cell clones. An HCT-related population bottleneck alone could not explain these differences: instead, phylogenetic trees evinced two distinct modes of HCT-specific selection. In 'pruning selection', cell divisions underpinning recipient-enriched clonal expansions had occurred in the donor, preceding transplant - their selective advantage derived from preferential mobilisation, harvest, survival ex vivo or initial homing. In 'growth selection', cell divisions underpinning clonal expansion occurred through proliferative advantage in the recipient's marrow after homing - clones with multiple driver mutations especially demonstrated this pattern. Uprooting stem cells from their native environment and transplanting them to foreign soil exaggerates selective pressures, distorting and accelerating the loss of clonal diversity compared to the unperturbed haematopoiesis of donors.

  Dataset EGAD00001010874

• Phylogenetic analysis of treatment-naive metastases using whole exome and genome sequencing data

  Metastases are responsible for the majority of cancer related deaths and are often difficult to treat successfully. Even for metastases that occur subsequent to treatment, we do not know whether such relapsing metastases were originally heterogeneous or if the observed heterogeneity is a consequence of therapy. Given that treatment can influence evolutionary dynamics by selecting resistant clones, imposing bottlenecks on cancer cell populations, and even inducing novel somatic mutations, characterizing the prior standing variation among metastases remains an important goal to predict initial therapeutic response. To quantify the heterogeneity at clinical presentation of advanced disease, we surveyed the literature for patients in which at least two treatment-naïve metastases underwent genome/exome-wide sequencing. Across all cancer types surveyed, only 18 subjects were found to fulfill this requirement. Including previously unpublished data from two subjects, we analyzed data of 74 untreated metastases and inferred cancer phylogenies. Putative driver gene mutations were acquired at a 2-fold higher rate on the trunk of all metastases than on branches. Mutations in driver genes along the trunk were strongly enriched for predicted functional consequences. Using a stochastic mathematical model, we find that driver gene heterogeneity among metastases mostly occurs in slowly growing cancers for highly advantageous driver gene mutations. Seeding efficiency only weakly affects the heterogeneity among metastases. These results explain why functional driver gene heterogeneity is uncommon in advanced disease prior to treatment, thus providing optimism for future therapies that depend on mutations across metastases.

  Study EGAS00001002777

• Clonal selection after gene therapy in sickle cell disease

  While gene therapy (GT) provides a potentially curative treatment option for patients with sickle cell disease (SCD), the occurrence of myeloid malignancies in clinical trials has prompted concern. To interrogate potential mechanisms underlying increased cancer risk, we used hematopoietic stem cell (HSC) clonal tracking by whole genome sequencing (WGS) to map the somatic mutation and clonal landscape of 2,592 gene modified as well as unmodified single stem and progenitor cells from six SCD patients undergoing gene therapy (7-26 years old, average 12.7× depth). Pre-GT phylogenetic trees in SCD were highly polyclonal and mutation burdens per cell were elevated in some, but not all, patients. Post-GT, no clonal expansions were identified. However, an increased frequency of driver mutations associated with myeloid neoplasms or clonal hematopoiesis (DNMT3A- and EZH2-mutated clones in particular) were seen in both genetically modified and unmodified cells suggested positive selection of mutant clones during gene therapy. This work sheds light on the mutation landscape and HSC clonal dynamics in gene therapy for SCD and highlights enhanced fitness of some HSCs harboring pre-existing driver mutations following gene therapy. Future studies should define the long-term fate of mutant clones including any contribution to expansions associated with myeloid neoplasms.

  Dataset EGAD00001010913

• MSI-Exome-DAC Team Microsatellites Instability and Cancer, INSERM Hospital Saint-Antoine, Paris 12, France Carte d'identité des tumeurs, French league against cancer, Paris France

  Dac EGAC00001000665

• UCSF Sebaceous Carcinoma DAC for review of sequence data release from manuscript in Nature Communications, based in the UCSF Departments of Dermatology and Pathology

  Dac EGAC00001000850

• Early on-treatment changes in circulating tumor DNA fraction and response to enzalutamide or abiraterone in metastatic castration-resistant prostate cancer - Data Access Committee

  Dac EGAC00001003128

• RNA Editing Transcriptome

  Transcriptome sequencing data of tumor and 10 matched normal samples of the EGAS00001000495 project

  Dataset EGAD00001000627

• RNA-seq data from diagnosis and relapse samples of Down Syndrome acute lymphoblastic leukemia.

  Dataset EGAD00001003280

• miRNA profiling of human plucked hair follicle

  miRNA profiling of human plucked hair follicle from frontal and occipital scalp

  Dataset EGAD00010001500

• leukemia_2 SNP6.0

  Over 1.87 million SNP and CNV loci are screened by Affymetrix SNP 6.0 array

  Dataset EGAD00010001814

• germline_2 SNP6.0

  Over 1.87 million SNP and CNV loci are screened by Affymetrix SNP 6.0 array

  Dataset EGAD00010001813

• BLEMD (arrays set)

  Circulating tumor cells for comprehensive and multiregional non-invasive genetic characterization of multiple myeloma (arrays set)

  Dataset EGAD00010001857

• dataset_raw_NativesAmericans_LDGH_august2020

  Genome-wide data for 98 Native American individuals from Andes and Amazon

  Dataset EGAD00010001958

• BreastCancer_Miroarrays

  Genotyping array data for breast cancer and matched normal mammary gland samples

  Dataset EGAD00010002251

• Gentoypes_Lesotho

  Individuals genotyped on the Illumina GSA v2. Autosome and X chromosome.

  Dataset EGAD00010002468

• High throughput profiling of undifferentiated pleomorphic sarcomas identifies two main subgroups with distinct immune contexture, clinical outcome and sensitivity to targeted therapies

  Study EGAS00001004612

• Mutation and Microsatellite Burden Predict Response to PD-1 Inhibition in Children with Germline DNA Replication Repair Deficiency: An Observational Registry Study

  Study EGAS00001005579

• UTSW KCP DAC

  This DAC manages access to WES and RNA-Seq data from studies approved under the IRBs of KCP principal investigators.

  Dac EGAC50000000203

• ORIENT study

  scRNAseq dataset containing 5 healthy donors and 4 asthmatic donors.

  Dataset EGAD00001009687

• RRBS melanoma biopsies

  RRBS data for melanoma biopsies at baseline and after treatment

  Dataset EGAD00001009060

• Melanoma multi site metastases

  These are caveman, pindel, battenberg and brass calls for index patients' metastatic melanoma genomes within this study.

  Dataset EGAD00001005483

• KRAS Mutations in Multiple Myeloma

  Fastq files for 80 Multiple Myeloma Patients and 12 cell-lines

  Dataset EGAD00001005743

• Disorders of growth and insulin action

  Illumina paired-end sequencing of whole- exome pulldown DNA from Severe Insulin Resistant patients.

  Dataset EGAD00001000381

• Multisample2 Amplicon

  Amplicon data of tumor samples generated for validation of WES findings and further sub clonal mapping

  Dataset EGAD00001004020

• Familial CEBPA-mutated AML whole exome sequencing dataset

  Whole exome sequencing data for AML and matched normal samples

  Dataset EGAD00001000996

• Seminoma exome sequencing

  Exome sequencing data for 8 pairs of seminomas and matched normal

  Dataset EGAD00001001002

• Osteosarcoma exome sequencing dataset

  Whole exome sequencing data of primary, secondary and tertiary tumor from a patient.

  Dataset EGAD00001002145

• DAC for the Study EGAS00001006374: Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies. snRNAseq of 79 (61 CM patients + 18 controls).

  Dac EGAC00001002804

• EGAD00010000286

  All cases and controls (Hap550)

  Dataset EGAD00010000286

• EGAD00010000298

  All cases and controls (Hap300)

  Dataset EGAD00010000298

• EGAD00010000450

  Genome Wide Genotype Data

  Dataset EGAD00010000450

• EGAD00010000496

  Genome-wide SNP genotyping of African rainforest hunter-gatherers and neighbouring agriculturalists

  Dataset EGAD00010000496

• Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments

  Dataset EGAD00001001602

• 450K_DKFZ_CLL_NB

  CLL and normal B cell samples using 450K

  Dataset EGAD00010000871

• Second generation noninvasive fetal genome analysis reveals de novo mutations, single-base parental inheritance, and preferred DNA ends

  Dataset EGAD00001003778

• WTCCC2 Pre-eclampsia

  Genotype data from the Affymetrix 6.0 platform for 4,375 Colombian pre-eclampsia cases and controls

  Dataset EGAD00010001647