4822 results for "*oma"

in 51.42 milliseconds.

• Single-cell RNA-seq data of bronchoalveolar lavage fluid and extramedullary relapse of a multiple myeloma patient with sarcoidosis-like reactions after anti-BCMA CAR T-cell therapy

  We performed single-cell RNA-sequencing (scRNA-seq) of cells in the bronchoalveolar lavage (BAL) fluid at 3-month follow-up of a multiple myeloma patient experiencing sarcoidosis-like pulmonary reactions after anti-BCMA CAR T-cell therapy (Sample alias: A8_3, A9_3; technical replicates). In addition we performed scRNA-seq of a extramedullary relapse lesion at 6-month follow-up (Sample alias: B10_3).

  Dataset EGAD00001008649

• WES of HCC

  WES of HCC by HiSeq 2000,total 71 samples including Hepatocellular carcinoma cell lines and nornal sample(Peripheral Blood or the adjacent tissues of cancer)

  Dataset EGAD00001001249

• Esophageal Adenocarcinoma Organoid scRNAseq data

  This is 10X single cell RNA-seq data of esophageal adenocarcinoma organoids.

  Dataset EGAD00001007525

• RNA-seq dataset: Single-cell spatial analysis of pediatric high-grade glioma reveals a novel population of immunosuppressive and tumor-promoting SPP1+/GPNMB+ myeloid cells

  Our study utilizes two novel techniques, cyclical immunofluorescence imaging and single-cell spatial transcriptomics, to spatially map the tumor microenvironment of a large sample set of pediatric high-grade gliomas (pHGG). Using these methods, we have identified an abundant immunosuppressive myeloid cell population that has not been described in the context of pediatric high-grade gliomas. We validate our findings using an in vitro assay, spatial analysis on additional pHGG biopsies, independent spatial transcriptomic data, bulk RNA sequencing data of an expanded cohort of in-house patients, and publicly available bulk RNA sequencing data of an even larger cohort of pHGG patients.

  Dataset EGAD00001015450

• Genome and transcriptome sequence data from a metastatic melanoma patient

  Genome and transcriptome sequence data from a metastatic melanoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005851

• Melanoma post mortem analysis

  We have been applying whole genome and transcriptome sequencing across metastases collected during post mortem. Herein we show the findings for the first such patient.

  Dataset EGAD00001005072

• Genome and transcriptome sequence data from a pancreatic ductal adenocarcinoma patient

  Genome and transcriptome sequence data from a pancreatic ductal adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005904

• Genome and transcriptome sequence data from a cecum adenocarcinoma patient

  Genome and transcriptome sequence data from a cecum adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005895

• RNA-seq analysis of transcriptome variation with human ESC subclones

  Many studies over the past 10 years, culminating in the recent report of the International Stem Cell Initiative (ISCI, 2011) have shown that hPSC acquire genetic and epigenetic changes during their time in culture. Many of the genetic changes are non-random and recurrent, probably because they provide a selective growth advantage to the undifferentiated cells. Some are shared by embryonal carcinoma cells, the malignant counterparts of ES cells. The origins of these growth advantages are poorly understood, but may come from altered cell cycle dynamics, resistance to apoptosis or altered patterns of differentiation. Less is known about the nature and consequences of epigenetic changes, but it is likely that these similarly affect hPSC behaviour; e.g., enhanced expression of DLK1, an imprinted gene, is associated with altered hPSC growth (Enver et al 2005). Inevitably, these genetic and epigenetic changes will impact on our ability to use hPSC for regenerative medicine, either because malignant transformation of the undifferentiated cells or their differentiated derivatives to be used for transplantation compromises safety, or because they impede the function of those differentiated derivatives, or because they affect the efficiency with which the undifferentiated cells can be expanded and differentiated into desired cell types. Focusing initially upon the existing clinical grade hESC lines, later moving to iPSC, we will Consolidate and extend knowledge of the rate, type and functional impact of the genetic variations that occur during hPSC culture. We will use whole genome and exome sequencing as well as SNP arrays, together with clonal analysis and other cytogenetics techniques. Common changes will be compared with those found in the normal human population, at low frequency in the original cell population or observed during iPSC generation in the HIPSCI project currently based at the WTSI. These studies will provide a better understanding of the range of genetic changes that occur in hPSC beyond the CNVs already identified. In conjunction with cancer genome resources and expertise at WTSI, bioinformatic analyses of these hPSC data will allow us to assess potential impact on hPSC behaviour pertinent to applications in regenerative medicine, notably the likelihood that specific changes arising in undifferentiated PSC cultures may be associated with potential malignant transformation of differentiated progeny 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/ .

  Dataset EGAD00001004266

• Genome and transcriptome sequence data from a gastroesophageal junction adenocarcinoma patient

  Genome and transcriptome sequence data from a gastroesophageal junction adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005910

• Genome and transcriptome sequence data from a peritoneal mesothelioma patient

  Genome and transcriptome sequence data from a peritoneal mesothelioma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001311

• Sequencing data for oesophageal and related samples - Rogerson et al (RNA)

  Data supporting: "Repurposing of KLF5 activates a cell cycle signature during the progression from Barrett’s Oesophagus to Oesophageal Adenocarcinoma." Rogerson et al. RNA-seq data 2 samples

  Dataset EGAD00001005915

• Genome and transcriptome sequence data from a metastatic colorectal carcinoma patient

  Genome and transcriptome sequence data from a metastatic colorectal carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001307

• Genetic analysis of HLA and immune escape genes in Diffuse Large B-cell Lymphoma

  We performed genetic analysis of HLA and immune escape genes in samples from 44 patients sequenced by whole exome sequencing (34 tumor samples, 32 normal samples) and whole genome sequencing (10 tumor samples, 12 normal samples). We also performed HLA targeted sequencing in 26/44 patients (26 tumor samples, 26 normal samples).

  Dataset EGAD00001007565

• ctDNA dataset

  ctDNA guiding adjuvant immunotherapy in urothelial carcinoma (Nature 2021). ABACUS ctDNA data used in 2021 paper: ctDNA data include TMB_(mut/Mb), cfDNA_extracted_ng, Plasma_Volume_Used_ml, Sample_Call, Sample_Number_Positive_Calls, Sample_Mean_VAF_In_Plasma (%), Sample_MTM_per_mL_In_Plasma across 40 patients in ABACUS.

  Dataset EGAD00001007579

• Genome and transcriptome sequence data from a metastatic adenocarcinoma of pancreas patient

  Genome and transcriptome sequence data from a metastatic adenocarcinoma of pancreas patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001010973

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - barretts_1

  Samples from Ross Innes et. al 2015 - doi:10.1038/ng.3357

  Dataset EGAD00001001394

• DNA whole-exome sequencing data from patients with metastatic basal cell carcinoma

  Whole-exome sequencing data in fastq format of matched tumour and germline DNA from 8 patients with metastatic basal cell carcinoma. Samples are labeled as Primary, Local or Metastasis: Primary: Sample was obtained from primary tumour. Local: Sample was obtained from local recurrence of primary tumour. Metastasis: Sample was obtained from a metastatic site. Germline: Sample was obtained from normal adjacent tissue. DNA was isolated from FFPE tissue sections of the tumor biopsies using the AllPrep DNA/RNA FFPE Kit (Qiagen) and quality controls conducted using the Qubit fluorometer (Thermo Fisher Scientific). Library preparation was performed using the Agilent SureSelect Human All Exon v7 XTHS2 probes and sequenced on a NovaSeq 6000 S2 2x100bp

  Dataset EGAD00001008675

• Clinical dataset (new)

  ctDNA guiding adjuvant immunotherapy in urothelial carcinoma (Nature 2021). ABACUS clinical data cut (2020) used in 2021 paper: Clinical data include PCR, RFS_months, RFS_event, and OUTCOME for 40 patients in ABACUS with ctDNA data.

  Dataset EGAD00001007578

• WGS of primary mesothelioma cell lines

  Mesothelioma is an aggressive cancer associated with previous exposure to asbestos and dismal prognosis. Since a pemetrexed/cisplatin combination was introduced for treatment of mesothelioma, no new first- or second-line therapies have been discovered. Thus, to better understand what drives mesothelioma carcinogenesis and to identify potential targets for therapy, in this project we aim at performing WGS analysis of a panel of mesothelioma cells lines.

  Dataset EGAD00001009641

• 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

• Whole-exome sequencing of adult T-cell leukemia/lymphoma

  Whole-exome sequencing of 81 tumor/normal pairs of adult T-cell leukemia/lymphoma

  Dataset EGAD00001001410

• Whole transcriptome sequencing of 38 follicular lymphoma tumours.

  This data set includes RNAseq from 38 follicular lymphoma tumours. All tumours were fresh frozen. Libraries were constructed by enriching for poly-A transcripts and sequenced as 75bp paired end reads on an Illumina HiSeq 2500 instrument.

  Dataset EGAD00001009650

• Whole genome data for study EGAS00001000824 (Diverse modes of genomic alterations in hepatocellular carcinoma)

  Whole genome data (Complete genomics platform) for the study EGAS00001000824

  Dataset EGAD00001001034

• ChIP sequencing in Cancer Cell Lines

  The project is to evaluate the genomic binding sites of the histone demethylase JARID1C. This gene was recently identified in CGP as a novel recessive cancer gene in human renal cell carcinoma.

  Dataset EGAD00001001453

• Single Cell Genome Sequence for DLP+ library A95650A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1052J passage 1 on DLP+ library A95650A

  Dataset EGAD00001009325

• Somatic mutations in facial skin from countries of contrasting skin cancer risk

  The incidence of non-melanoma skin cancer is 17-fold lower in Singapore compared to the UK1, despite Singapore receiving 2-3 times more year-round ultraviolet radiation (UV)2,3. The ageing epidermis of the skin comprises competing somatic mutant clones4,5, from which such cancers develop. We question if differences in keratinocyte skin cancer incidence are reflected in the mutational landscape by comparing ageing facial epidermis from donors of Singapore and the UK. We find UK skin to be a highly competitive, densely mutated landscape with 4-fold greater mutation burden compared to Singaporean skin and differences in clonal selection by country. We disproportionately observe multiple features common to keratinocyte skin cancers6,7,8 in UK skin, such as UV mutagenesis, copy number aberration and hotspot mutations (in particular TP53 R248W). We conclude that keratinocyte skin cancer incidence is reflected in the somatic clones of non-cancerous epidermis. Finally, we re-analyse squamous cell carcinoma exomes from Korea9 to show, even in low incidence populations, carcinogenesis is driven by UV damage.

  Dataset EGAD00001009666

• Single Cell Genome Sequence for DLP+ library A95652B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1049A passage 1 on DLP+ library A95652B

  Dataset EGAD00001009327

• Hospital for Sick Children 2020 Pediatric Low-Grade Glioma RNA and Targeted DNA Sequencing

  Whole transcriptome and targeted dna sequencing (Ampliseq) of pediatric low-grade glioma samples at the Hospital for Sick Children

  Dataset EGAD00001005987

• McGill EMC Community projects Release 7 for cell line "SaOS-2"

  Complete reference epigenome (as defined by IHEC) of a SaOS-2 cell line with osteosarcoma.

  Dataset EGAD00001007678

• High-resolution lung adenocarcinoma expression subtypes

  High-resolution lung adenocarcinoma expression subtypes identify tumors with dependencies on MET, CDK4, CDK6, and PD-L1

  Dataset EGAD00001009339

• Transcriptome: chondrosarcoma

  Transcriptome sequencing of nine patients diagnosed with chondrosarcoma. cDNA was generated using the NuGEN Ovation RNA-Seq FFPE system. Total RNA was randomly primed and thermally sheared and the resulting cDNA fragment was amplified. The cDNA was then mechanically sheared using sonication to generate ~250 bp fragments. Sequencing libraries were generated using the NuGEN Ovation Ultralow System V2 library prep kit and sequenced on HiSeq2500 TruSeq v3.

  Dataset EGAD00001008699

• DERMATLAS: Hidradenoma papilliferum_RNAseq

  Virtually no other tumour type is associated with so many different forms as skin cancer. Histologically, tumours of the skin may arise from epithelium, including epidermis, hair follicle, sebaceous or sweat gland, melanocytes, dermal-associated mesenchymal structures or tissue resident immune cells, making for a diversity of clinical presentations. Importantly, many skin tumour types have an extremely poor prognosis. Many skin tumour subtypes have never undergone molecular profiling, or if they have, targeted sequencing has been used and the number of cases analyzed has been so limited that deriving firm conclusions about the profile of driver genes, DNA mutational signatures and germline alleles has not been possible.For 70 key skin tumour subtypes defined by the World Health Organization, that have not undergone extensive genetic analysis previously, we propose to perform whole exome and transcriptome sequencing, from a range of body sites, to build a genomic atlas of dermatological tumours, including detailed maps of SNVs, copy number alterations, genome-wide methylation and expression profiles.

  Dataset EGAD00001015480

• Paired WGS data of 45 samples and 2 paired WES sample of RRMM (multiple myeloma)

  Here is mostly paired WGS data of RRMM, 45 samples (tumors and controls) in 86 runs. This data was produced by using Illumina TruSeq Nano DNA and NovaSeq6000 or HiSeq X Ten for sequencing. One tumor/control pair is WES data using Agilent SureSelect V5+UTRs and NovaSeq6000 for sequencing.

  Dataset EGAD00001009682

• RRBS profiling for a cohort including 88 precancer specimens from 62 resected lung nodules from 39 patients including atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (ADC) and 39 matched normal lung tissues.

  To study the evolution of DNA methylation at genome level and methylation intra-tumor heterogeneity (ITH) during early lung carcinogenesis, we performed multiregional reduced representation bisulfite sequencing (RRBS) of 127 resected lung samples from 39 patients using single end library Hiseq3000.

  Dataset EGAD00001006367

• RNA-seq of human glioblastoma and matched gliomasphere cell lines (BLN panel)

  This dataset contains all RNA-seq runs for the BLN panel of cell lines and matched parental tumors. Tumor/cell line pairs have been authenticated using SNP profiles and all pairs were confirmed. Please note: The dataset also contains raw data from an early primary culture (BLN-1) where no stable cell line could be generated. Please also note different reference genomes.

  Dataset EGAD00001002893

• H3K27ac/H3K27me3 landscape of medulloblastoma

  We profiled 111 patient medulloblastoma primary tumor samples by bulk RNA-seq (19 samples), 27ac (98 samples) / 27me3 (61 samples) ChIP-Seq, WGS (4 samples) and 27ac hichip (8 samples). Submitted data consists of data generated from previously unpublished tumors as well as complementary data for data sets already published for identical medulloblastoma tumors (ex: 27me3 ChIP-Seq and RNA-Seq data submitted for a tumor with publicly available WGS data). The raw fastqs and hg19 aligned RNA-Seq bams are provided.

  Dataset EGAD00001009709

• Tracking the evolution of esophageal squamous cell carcinoma under dynamic immune selection by multi-omics sequencing

  Intratumoral heterogeneity (ITH) has been linked to decreased efficacy of clinical treatments. However, although genomic ITH has been characterized in genetic, transcriptomic and epigenetic alterations are hallmarks of esophageal squamous cell carcinoma (ESCC), the extent to which these are heterogeneous in ESCC has not been explored in a unified framework. Further, the extent to which tumor-infiltrated T lymphocytes (TILs) are directed against cancer cells, but how the immune infiltration acts as a selective force to shape the clonal evolution of ESCC is unclear. In this study, we perform multi-omic sequencing on 186 samples from 36 primary ESCC patients. Through multi-omics analyses, it is discovered that genomic, epigenomic, and transcriptomic ITH are underpinned by ongoing chromosomal instability. Based on the RNA-seq data, we observe diverse levels of immune infiltrate across different tumor sites from the same tumor. We reveal genetic mechanisms of neoantigen evasion under distinct selection pressure from the diverse immune microenvironment. Overall, our work offers an avenue of dissecting the complex contribution of the multi-omics level to the ITH in ESCC and thereby enhances the development of clinical therapy.

  Dataset EGAD00001009482

• Single Cell Genome Sequence for DLP+ library A96205B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1049A passage 1 on DLP+ library A96205B

  Dataset EGAD00001009471

• WGS and RNA-Seq data of 2 additional patients of newly diagnosed multiple myeloma (NDMM)

  For WGS DNA of tumor or control samples was prepared for paired sequencing using the Illumina TruSeq DNA Nano Kit and sequenced on NovaSeq 6000. For RNA-Seq the sequencing Kit Illumina TruSeq stranded mRNA was used with the same sequencer. There are 4 samples for WGS (18 runs) and 4 samples for RNA (4 runs) available.

  Dataset EGAD00001009627

• Genome and transcriptome sequence data from an odontogenic ghost cell carcinoma patient

  Genome and transcriptome sequence data from an odontogenic ghost cell carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001658

• Pheochromocytoma paraganglioma single nuclei RNA-seq

  Single nuclei RNA sequencing (snRNA-seq) on tissue samples from 11 patients (SDHB and RET). Files are fastq files of 10x-5'scRNAseq libraries.

  Dataset EGAD00001008739

• Whole Genome Sequencing of low-grade ovarian serous carcinoma

  Whole genome sequencing of 14 cases of low-grade ovarian serous carcinoma with matched normal DNA

  Dataset EGAD00001009626

• Dataset GBM 2022

  The cellular origin and differentiation status of glioblastoma by scRNA-seq

  Dataset EGAD00001008745

• Dataset for transcriptome sequencing of chordoma cells

  This dataset contains transcriptome sequencing for 15 samples from chordoma cells. The sequencing was performed on Illumina HiSeq 4000 and Illumina NovaSeq 6000. The sequencing was always paired.

  Dataset EGAD00001015642

• Paired RNA-Seq of four patients with advanced Parathyroid carcinoma (PC)

  Paired RNA-Seq of four patients with advanced Parathyroid carcinoma (PC). The library was prepared using the Illumina TruSeq stranded mRNA Kit, the sequencing was done either on an Illumina HiSeq 4000 or on Illumina NovaSeq 6000.

  Dataset EGAD00001009730

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

  RNA-Seq data for 14 Multiple Myeloma 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_rnaseq_analysis_crg_20160816

  Dataset EGAD00001002401

• Genomic profiling of patient-derived xenograft models of myxoid liposarcoma either sensitive or resistant to trabectedin

  The dataset consists of two patient-derived xenograft model of myxoid liposarcoma one sensitive and one resistant to trabectedin. Both models underwent the same treatment schedule with trabectedin (tree time points and one treatment with doxorubicin). We performed genomic profiling using Agilent OneSeq assay.

  Dataset EGAD00001005099

• Sequencing files for "A Unifying Paradigm for Transcriptional Heterogeneity and Squamous Features in Pancreatic Ductal Adenocarcinoma."

  Files from DNA and RNA sequencing from primary tumors and metastases from pancreatic cancer patients along with matched normal tissues.

  Dataset EGAD00001005519

• McGill EMC Community projects Release 7 for cell line "lung epithelial"

  Complete reference epigenome (as defined by IHEC) of a lung epithelial cell line with non-small Cell Lung Adenocarcinoma

  Dataset EGAD00001007679

• Genome and transcriptome sequence data from a peripheral T-cell lymphoma patient

  Genome and transcriptome sequence data from a peripheral T-cell lymphoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002038

• Single cell CNV and ATAC profiling of multiple myeloma

  Patient-derived samples were profiled using 10X genomics single-cell CNV and single-cell ATAC kits.

  Dataset EGAD00001007788

• Exome sequencing VCF files for glioma progression

  Exome sequencing VCF files describing mutations during glioma progression.

  Dataset EGAD00001001887

• Sequencing data for oesophageal and related samples - OACs 496 release (WGS)

  Data from 496 OCCAMS (Oesophageal Cancer Clinical And Molecular Stratification) cases. WGS BAM files 496x oesophageal adenocarcinoma samples 496x normal samples

  Dataset EGAD00001007785

• Un-mapped RNA-Seq data

  Un-mapped whole transcriptome RNA-Seq data from 476 human samples of early stage urothelial carcinoma.

  Dataset EGAD00001001940

• Genome and transcriptome sequence data from a glioblastoma tumor patient

  Genome and transcriptome sequence data from a glioblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015288

• Understanding the multicellular dynamics of clear cell renal cell carcinoma - spatial transcriptomics

  The dataset comprises of transcriptomes of tissue sections derived from either the tumour normal interface or tumour core from clear cell renal cell carcinomas. 16 sections are sampled in total using 10x Genomics' Visium technology.

  Dataset EGAD00001008781

• mRNA capture sequencing and RT-qPCR for the detection of pathognomonic, novel and secondary fusion transcripts in formalin-fixed paraffin-embedded tissue: a sarcoma showcase

  mRNA capture sequencing data (FASTQ files) of 28 FFPE sarcoma tumors

  Dataset EGAD00001007696

• Genomic analysis for intrahepatic cholangiocarcinoma

  Intrahepatic cholangiocarcinomas (iCCs) are characterized by their rarity, difficulty in diagnosis, and overall poor prognosis. We performed comprehensive genomic characterization of treatment-naive iCC. This study reports a large-scale genomic analysis of iCC. The findings could assist in patient stratification with iCCs and in developing rational therapeutic strategies.

  Dataset EGAD00001008557

• Acquired RAD51C promoter methylation loss causes PARP inhibitor resistance in high grade serous ovarian carcinoma

  Analysis of RAD51C promoter methylation using targeted bisulfite sequencing (amplicon sequencing) in ovarian cancer pre-clinical models and patient samples.

  Dataset EGAD00001007799

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 0)

  Whole genome sequencing data of 5 High-grade serous carcinoma (HGSC) patients (6 samples) sequenced with BGI.

  Dataset EGAD00001009738

• Genome and transcriptome sequence data from a recurrent glioma patient

  Genome and transcriptome sequence data from a recurrent glioma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002043

• Dataset for transcriptomic sequencing of Merkel cell carcinoma(MCC) samples

  This dataset contains transcriptome sequencing for 5 samples of MCC tumors . The sequencing was performed on Illumina NextSeq 6000 . The sequencing was always paired.

  Dataset EGAD00001015702

• Whole-genome sequencing of rhabdomyosarcoma tumouroids (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the whole-genome sequencing of RMS tumoroids data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015707

• Genomic Analysis of Focal Nodular Hyperplasia with Associated Hepatocellular Carcinoma Unveils its Malignant Potential

  Whole exome sequencing of FNH and two HCC compartments from a single patient, along with matched germline.

  Dataset EGAD00001007702

• 5 scRNA-seq with TCR Enrichment of Tumour-Involved Lymph Nodes, Malignant Seromas and Patient-Derived Xenografts from 18 T-Cell Lymphoma Patients

  This raw data contains 5' scRNA-seq with TCR enrichment data from 18 individual patients analysing cells taken from tumour-involved lymph nodes (LN), malignant seromas and patient-derived xenografts (PDX) including systemic/nodal ALK+ ALCL (n=3 PDX), BIA-ALCL (n=2 seromas), PTCL-NOS (n=5 nodal tumours), nodal T follicular helper lymphoma - angioimmunoblastic type (nTFHL-AI) (n=1), nTFHL, not otherwise specified (nTFHL, NOS, n=1), TLBL (n=5) and 1 case of Sezary Syndrome (SS, cutaneous tumour). Droplet-based 5’ scRNA-seq with TCR enrichment (10x Chromium platform) was performed for all live cells, or the viable CD45+CD3- and CD45+CD3+ fractions following cell sorting by flow cytometry.

  Dataset EGAD00001015703

• Neuroblastoma WGS samples used for detection of seismic amplification

  Paired-end WGS data of 27 neuroblastoma patient samples (10 obtained at diagnosis, 6 at relapse and 11 matched blood samples as controls) used for detection of complex "seismic" amplification. Mean coverage is 24-55x per sample. The remaining patient samples of the dataset can be found under accession number EGAS00001001308.

  Dataset EGAD00001007807

• RNASeq files for Roussel-MBPRG

  RNASeq files for Roussel-MPBRG paper titled "Combination of ribociclib and gemcitabine for the treatment of medulloblastoma"

  Dataset EGAD00001008824

• Comprehensive RNA repository of tissue and plasma from patients with esophageal cancer or precursor lesions

  A comprehensive RNA repository (both coding and non-coding) from 17 patients diagnosed with esophageal adenocarcinoma, high-grade dysplastic or non-dysplastic Barrett’s esophagus. Per patient, a blood plasma sample, and a healthy esophageal and disease tissue sample were collected. This dataset includes both mRNA and small RNA sequencing data (fastq.gz files) of all tissue and plasma samples. In total,102 RNA-seq libraries from 51 samples (17 plasma and 34 tissue samples) were sequenced (plasma mRNA libraries were sequenced twice).

  Dataset EGAD00001006857

• Melanoma microRNA profiling

  This dataset includes microRNA profiling of 61 early-passage metastatic melanoma cell lines. The data are provided as single-end small RNA seq fastq files.

  Dataset EGAD00001006269

• RNAseq dataset for MALT1 in MCL

  Manuscript Title: Co-targeting of BTK and MALT1 overcomes resistance to BTK inhibitors in mantle cell lymphoma Journal: Journal of Clinical Investigation Authors Vivian Changying Jiang1, Yang Liu1, Junwei Lian1, Shengjian Huang1, Alexa Jordan1, Qingsong Cai1, Fangfang Yan3, Joseph Mitchell McIntosh1, Yijing Li1, Yuxuan Che1, Zhihong Chen1, Jovanny Vargas1, Maria Badillo1, JohnNelson Bigcal1, Heng-Huan Lee1, Wei Wang1, Yixin Yao1, Lei Nie1, Christopher Flowers1, and Michael Wang1, 2* Abstract Bruton’s tyrosine kinase (BTK) is a proven target in mantle cell lymphoma (MCL), an aggressive subtype of non-Hodgkin lymphoma. However, resistance to BTK inhibitors is a major clinical challenge. We here report that MALT1 is one of the top overexpressed genes in ibrutinib-resistant MCL cells, while expression of CARD11, which is upstream of MALT1, is decreased. MALT1 genetic knockout or inhibition produced dramatic defects in MCL cell growth regardless of ibrutinib sensitivity. Conversely, CARD11 knockout cells showed anti-tumor effects only in ibrutinib-sensitive cells, suggesting that MALT1 overexpression could drive ibrutinib resistance via bypassing BTK-CARD11 signaling. Additionally, BTK knockdown and MALT1 knockout markedly impaired MCL tumor migration and dissemination, and MALT1 pharmacological inhibition decreased MCL cell viability, adhesion, and migration by suppressing NF-κB, PI3K-ATK-mTOR, and integrin signaling. Importantly, co-targeting MALT1 with safimaltib and BTK with pirtobrutinib induced potent anti-MCL activity in ibrutinib-resistant MCL cell lines and patient-derived xenografts. Therefore, we conclude that MALT1 overexpression associates with resistance to BTK inhibitors in MCL, targeting abnormal MALT1 activity could be a promising therapeutic strategy to overcome BTK inhibitor resistance, and co-targeting of MALT1 and BTK should improve MCL treatment efficacy and durability as well as patient outcomes. Dataset description: The bulk RNA-seq dataset was generated for the cell lines below and used for two major purposes: 1. DEG analysis and GSEA analysis comparing IBN-R and IBN-S cells 2. DEG analysis and GSEA analysis comparing MCL cells with/without MI-2 treatment. sample Cell MI-2 Ibrutinib (IBN) Venetoclax (VEN) Used for IBN-R vs IBN-S comparison Used for MI-2 vs untreated (DMSO) H9 Granta519 - R S yes H21 Granta519 - R S yes H33 Granta519 - R S yes H10 Granta519-VEN-R - R R yes H22 Granta519-VEN-R - R R yes H34 Granta519-VEN-R - R R yes H3 JeKo BTK KD_1 - R R yes yes H15 JeKo BTK KD_1 - R R yes yes H27 JeKo BTK KD_1 - R R yes yes H5 JeKo BTK KD_2 - R R yes yes H17 JeKo BTK KD_2 - R R yes yes H29 JeKo BTK KD_2 - R R yes yes H1 JeKo-1 - S R yes yes H13 JeKo-1 - S R yes yes H25 JeKo-1 - S R yes yes H7 Mino - S S yes H19 Mino - S S yes H31 Mino - S S yes H8 Mino-VEN-R - S R yes H20 Mino-VEN-R - S R yes H32 Mino-VEN-R - S R yes H11 Rec-1 - S S yes H23 Rec-1 - S S yes H12 Rec-VEN-R - S S yes H24 Rec-VEN-R - S R yes H36 Rec-VEN-R - S R yes H35 Rec-1 -- S R yes H4 JeKo BTK KD_1 + MI-2 + yes H16 JeKo BTK KD_1 + MI-2 + yes H28 JeKo BTK KD_1 + MI-2 + yes H6 JeKo BTK KD_2 + MI-2 + yes H18 JeKo BTK KD_2 + MI-2 + yes H30 JeKo BTK KD_2 + MI-2 + yes H2 JeKo-1 + MI-2 + yes H14 JeKo-1 + MI-2 + yes H26 JeKo-1 + MI-2 + yes

  Dataset EGAD00001009771

• Single-cell ATAC-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell ATAC sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015715

• RNASeq files for Roussel MBPRP

  RNASeq files for Roussel paper titled "Combination of CDK4/6 with BET-bromodomain and PI3K/mTOR inhibitors in medulloblastoma in vitro and in vivo"

  Dataset EGAD00001008842

• Cohort of NGS lymphoma samples used as control

  Control cohort of lymphoma samples sequenced with a hybrid capture panel designed to be able to detect translocations and mutations in lymphoma samples. used in the paper "Robust detection of translocations in lymphoma FFPE samples using Targeted Locus Capture-based sequencing"

  Dataset EGAD00001007711

• Single Cell Genome Sequence for DLP+ library A118784A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1050B passage 1, patient-derived xenograft SA1050E passage 1 on DLP+ library A118784A

  Dataset EGAD00001009436

• Single Cell Genome Sequence for DLP+ library A118808B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1096C passage 1 on DLP+ library A118808B

  Dataset EGAD00001009441

• Dataset for leiomyosarcoma-RNA

  Rare cancer sequencing data of 87 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008849

• Processed microarray gene expression data from NPC samples_Cohort 1

  We retrospectively collected 150 non-metastatic, pretreatment, formalin-fixed, paraffin-embedded (FFPE) nasopharyngeal carcinoma (NPC) samples as validation cohort 1. Also, we prospectively collected 32 FFPE samples from NPC patients enrolled in a trial evaluating anti-PD-1 antibody as validation cohort 2. Total RNA was extracted and hybridised to an Affymetrix HTA 2.0 microarray. In this study, we investigated the immune status of the tumour microenvironment (TME) based on gene expression profiles to classify NPC into biologically distinct immune subtypes, and clarify their associations with prognosis and immunotherapy response.

  Dataset EGAD00001006275

• Dataset for liposarcoma-RNA

  Rare cancer sequencing data of 40 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008854

• NKI-AvL OpACIN RNA-seq of stage III melanoma patients

  The dataset “NKI-AvL OpACIN RNA-seq of stage III melanoma patients" includes 18 FASTQ files from single-end total RNA sequencing on Illumina HiSeq2500 for 18 stage III melanoma patients.

  Dataset EGAD00001004216

• Dataset for melanoma-RNA

  Rare cancer sequencing data of 49 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008858

• Single cell transcriptomes of of primary tumors and normal endometrial derived organoids treated with DBZ

  Endometrial carcinoma, the most common gynecologic cancer, develops from endometrial epithelium which is composed of secretory and ciliated cells. Pathologic classification is unreliable and there is a need for prognostic tools. We used single cell sequencing to study organoid model systems derived from normal endometrial endometrium to discover novel markers specific for endometrial ciliated or secretory cells. We performed single cell sequencing on endometrial and ovarian tumours, and on organoids both treated with DBZ and normal and found both secretory-like and ciliated-like tumour cells.

  Dataset EGAD00001006280

• Single-cell RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015717

• SF10679

  SF10679 snATAC Seq. Oligodendroglioma, Anaplastic (WHO gr. 3). Tumor Location: Frontal Age: 43. Sex: Male.

  Dataset EGAD00001006312

• BLUEPRINT release August 2016, ChIP-Seq for Activated B-Cell-Like Diffuse Large B-Cell Lymphoma, on genome GRCh38

  ChIP-Seq data for 3 Activated B-Cell-Like Diffuse Large B-Cell Lymphoma sample(s). 12 run(s), 12 experiment(s), 12 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 EGAD00001002307

• Single Cell Genome Sequence for DLP+ library A96142B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient SA1162SA on DLP+ library A96142B

  Dataset EGAD00001009455

• scRNAseq of neuroblastoma PDX and cell lines

  This dataset contains the 22 bam files coresponding to the scRNAseq done in PDX models and cell lines.

  Dataset EGAD00001007870

• Bulk RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the bulk RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015718

• Single-cell ATAC-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell ATAC sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015719

• RNA-Seq samples from the BELOB clinical trial study to find transcriptome associations with response to Bevacizumab and CCNU in glioblastoma patients

  RNA-Seq samples from the BELOB clinical trial study to find transcriptome associations with response to Bevacizumab and CCNU in glioblastoma patients

  Dataset EGAD00001006329

• BLUEPRINT release August 2016, ChIP-Seq for Multiple Myeloma, on genome GRCh38

  ChIP-Seq data for 4 Multiple Myeloma sample(s). 34 run(s), 28 experiment(s), 28 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 EGAD00001002379

• scRNA-seq of total bone marrow and T cells from multiple myeloma long-term survivors

  scRNA This dataset contains 50 scRNA-seq samples from bone marrow aspirates of 11 multiple myeloma patients experiencing long-term survival and 3 healthy donors. For each donor, total bone marrow and CD3+ T cells were sequenced. For multiple myeloma patients, paired samples were collected at initial diagnosis and between 7-17 years after first-line therapy. Bone marrow mononuclear cells were isolated by Ficoll density gradient centrifugation. For sorting of total bone marrow cells singlet, live cells were gated and sorted, for sorting of T cells CD45+, CD3+ cells were gated and sorted on either FACSAria Fusion or FACSAria II. Single-cell RNA sequencing were generated using 10x Genomics single-cell RNAseq technology (Chromium Single Cell 3’ Solution v2) according to the manufacturer’s protocol and sequenced on an Illumina HiSeq4000 (paired end, 26 and 74 bp). Bulk RNA Singlet, live CD3+CD4- CXCR3+CD8+ and CD3+CD4- CXCR3-CD8+ cells were sorted from 7 bone marrow and 3 peripheral blood samples of 7 multiple myeloma patients using a FACSAria Fusion machine. Bulk-RNA sequencing libraries were generated using the SMART Seq Stranded Total RNA-Seq kit (Takara) and sequenced using the Illumina NovaSeq 6000 platform (2 x 100 bp).

  Dataset EGAD00001010025

• Whole Exome PC9 and A375 (2019-04-03)

  Drug resistant population of PC9(human non-small cell lung cancer) or A375 (human melanoma) cell lines were used for this study. By exome sequencing, we will analyse mutations of cells in drug tolerent state and after drug holiday. . This dataset contains all the data available for this study on 2019-04-03.

  Dataset EGAD00001004891

• RNA-seq data from paired tumour and germline samples from mesothelioma patients for study EGAS00001005196

  RNA-seq data from paired tumour and germline samples from mesothelioma patients for study EGAS00001005196

  Dataset EGAD00001007874

• NEC

  This multi-centre, non-randomized, open-label, phase II trial (NCT03016338), assessed niraparib monotherapy (cohort 1, C1), or niraparib and dostarlimab (cohort 2, C2) in patients with recurrent serous or endometrioid endometrial carcinoma. The primary endpoint was clinical benefit rate (CBR). Secondary outcomes were safety and objective response rate (ORR). Translational research was an exploratory outcome. Potential biomarkers were evaluated in archival tissue by immunohistochemistry and next generation sequencing panel. Feasibility of liquid biopsy by ctDNA was assessed.

  Dataset EGAD00001010056

• Dataset for liposarcoma-EXON

  Rare cancer sequencing data of 29 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008886

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

  ChIP-Seq data for 3 Burkitt Lymphoma sample(s). 13 run(s), 13 experiment(s), 13 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 EGAD00001002329

• Single-cell RNA sequencing generated from 11 glioma patient samples

  This dataset includes paired-end fastq files from 6 IDH-mutant, 5 IDH-wild-type glioma patient samples of unmatched initial and recurrent timepoints profiled using single-cell RNA sequencing.

  Dataset EGAD00001007772

• Dataset for leiomyosarcoma-EXON

  Rare cancer sequencing data of 96 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008885

• Targeted bisulfite sequencing

  Raw reads from targeted bisulfite sequencing. The SureSelect Methyl-Seq target enrichment system (Agilent Technologies, Inc., CA, USA) was used to prepare bisulfite sequencing libraries from blood DNA samples of lymphoma patients (Ly1, Ly2), healthy family members (Ly8, Ly9, Ly10, Ly11, Ly12, Ly13 and Ly14), baseline controls (Control1-5), DNMT3A mutation carriers (Id5, Id7, Id9, Id11) and their age-matched controls (Id6, Id8, Id10, Id12). In addition, blood DNA sample of a patient (HLRCC_N7) with germline fumarate hydratase (FH) mutation is included. Illumina paired-end sequencing for targeted libraries from Ly1, Ly2, Ly8, Ly9, Ly10 and Ly11 was done at Karolinska Institutet using 100 base-pair read length and the HiSeq2000 platform. Illumina paired-end sequencing for targeted libraries from Ly12, Ly13, Ly14, and DNMT3A mutation carriers and their age-matched controls was done as a service at BGI (BGI Tech Solutions Co., Ltd., China) using 126 base-pair read length and the HiSeq2500 platform.

  Dataset EGAD00001004785