Search Results - EGA European Genome-Phenome Archive

Genomic Analysis of Follicular Lymphoma

We included tumors from newly diagnosed or untreated follicular lymphoma in this study. Samples consented to the University of Iowa and Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE) were included for data sharing. RNA was extracted from 1) CD19 sorted B cells from tumor cell suspension frozen in dimethyl sulfoxide (DMSO; RNA Tumor B Cell, n=77), 2) tumor cell suspensions frozen in DMSO (RNA Tumor DMSO, n=78), and 3) tumor tissue sections frozen in optimal cutting temperature compound (OCT; RNA Tumor OCT, n=53). For RNA sequencing, library preparation was done using the Illumina TruSeq RNA Exome Kit and sequenced with 100 nucleotide paired-end reads using the HiSeq 4000. DNA was extracted from 1) tumor cell suspensions frozen in DMSO (DNA Tumor DMSO, n=78), 2) tumor tissue sections frozen in OCT (DNA Tumor OCT, n=33), and 3) germline DNA from peripheral blood (n=102) and from normal tissue adjacent tumor (n=9). For whole exome sequencing (WES), library preparation was done using the Agilent SureSelect XT kit and sequenced with 100 nucleotide paired-end reads using the Illumina HiSeq 4000.

Study phs002989

Pilocytic Astrocytoma RNA sequencing

This dataset contains RNA sequencing samples from the Duesseldorf/LIsbon pilocytic astrocytoma cohort which was profiled using Proteogenomics (RNA Sequencing, proteomics and methylation). There are 48 samples which were sequenced using paired-end sequencing at the University Hopstial Dusseldorf.

Dataset EGAD00001009053

R compatible Seurat's objetc .rds files with count data and metadata from single cell RNA seq analysis to characterize the prostate cancer tumoral microenviroment of 31 fusion biopses.

Pre-processed Seurat objects with cellular and functional annotation information from the scRNAseq study on PC fusion biopsy samples. The data consists of rds files composed of normalised counts matrices, normalised and scaled counts, functional enrichment counts at the cellular level and metadata with patient grouping and cellular annotation variables. There are 3 .rds files, one with the total cells (SCP_data_analysis), with the selection of cells annotated as cancerous (selected_cancer_analysis) and cancer-associated fibroblasts (selected_caf_analysis).

Dataset EGAD50000001298

MutWP4: CRUK Grand Challenge Mutographs of Cancer: Gastric Organoids (2019-08-07)

The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development. . This dataset contains all the data available for this study on 2019-08-07.

Dataset EGAD00001005234

MutWP4: CRUK Grand Challenge Mutographs of Cancer: Pancreatic Organoids (2021-02-02)

The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development. . This dataset contains all the data available for this study on 2021-02-02.

Dataset EGAD00001006933

Coding and non-coding drivers of mantle cell lymphoma identified through exome and genome sequencing

Mantle cell lymphoma (MCL) is an uncommon B-cell non-Hodgkin lymphoma (NHL) that is incurable with standard therapies. The genetic drivers of this cancer have not been firmly established and the features that contribute to differences in clinical course remain limited. To extend our understanding of the biological pathways involved in this malignancy, we performed a large-scale genomic analysis of MCL using data from 51 exomes and 34 genomes alongside previously published exome cohorts. To confirm our findings, we re-sequenced the genes identified in the exome cohort in 191 MCL tumors, each having clinical follow-up data. We confirmed the prognostic association of TP53 and NOTCH1 mutations. Our sequencing revealed novel recurrent non-coding mutations surrounding a single exon of the HNRNPH1 gene. In RNA-seq data from 103 of these cases, MCL tumors with these mutations had a distinct imbalance of HNRNPH1 isoforms. This altered splicing of HNRNPH1 was associated with inferior outcomes in MCL and showed a significant increase in protein expression by immunohistochemistry. We describe a functional role for these recurrent non-coding mutations in disrupting an auto-regulatory feedback mechanism, thereby deregulating HNRNPH1 protein expression. Taken together, these data strongly implicate a role for aberrant regulation of mRNA processing in MCL pathobiology.

Study EGAS00001004289

Long Read sequencing data from 4 tumor/germline samples with non muscle invasive bladder cancer

A better understanding of the molecular landscape of non-muscle-invasive bladder cancer (NMIBC) is essential to improve risk assessment and identify potential therapeutic targets. Here, we perform a comprehensive genomic analysis of patients diagnosed with NMIBC based on whole-exome- (n=438), shallow whole-genome- (n=362), and total RNA-sequencing (n=414). Additionally, tumor/germline samples from 4 patients were sequenced using Oxford Nanopore technology. This dataset contains 8 BAM files corresponding to the Long read sequencing data.

Dataset EGAD50000000731

PRO-SPeCT: PROstate cancer heterogeneity deconvolution through Single cell Profiling of Chromatin accessibility and Transcriptomic output

38 prostate cancer specimens derived from five human patients with ISUP ≥ 3 pathology who underwent radical prostatectomy and pelvic lymph node dissection at surgery as part of the International Cancer Genome Consortium (ICGC) late-onset cohort. Using single-cell multiome (expression and chromatin accessibility from RNA and ATAC, respectively) sequencing assay, we characterized 282,956 nuclei/cells, with a median of 6 primary tumor foci from the prostate gland and 3 locoregional lymph node metastases across all patients.

Dataset EGAD50000000745

Using genetics to identify cell types and mechanisms underlying susceptibility to primary sclerosing cholangitis

Primary sclerosing cholangitis (PSC) is a T-cell mediated, chronic inflammatory condition of the biliary tree that is strongly associated with inflammatory bowel disease. Genome-wide association studies have identified 22 non-HLA genetic risk variants associated PSC. Identifying the genes impacted by these variants has proven difficult as the majority lie in non-coding regions of the genome. Knowledge of the genes and biological pathways these non-coding variants are perturbing is vital to understanding the disease biology. One means of assessing the impact of non-coding variants within disease associated loci upon genes is via colocalisation with eQTL. Many eQTL are cell-type specific, requiring the analysis of disease relevant cell types to detect colocalisation. We have collected PSC-relevant T-cell-subtypes from the peripheral blood of PSC patients via fluorescence activated cell sorting in preparation for RNA sequencing and mapping of eQTL. Samples were collected at the Norfolk and Norwich University Hopital, for which local ethical approval has been granted. Lysed cell samples will be transferred to WTSI and DNA/RNA will be extracted from lysed cell samples by T143 before genotyping (DNA) and custom library preparation and sequencing (RNA). 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 EGAD00001011815

Bulk RNA-seq paired fastq files from i3Ns harbouring a SNCA A53T mutation, with or without RSL3 treatment

FASTQ files from bulk RNA sequencing of a iPSC-derived rapid neuronal model (i3Ns) harbouring the SNCA A53T mutation in a WTC11 background as a model of synucleinopathy, with or without treatment of 300nM RSL3 for 6 hours. Libraries were prepared using the Watchmaker rRNA and globin depletion kit and sequenced on a NovaSeq X with an average depth of ~27 million paired-end reads with a fragment length of 100 base pairs, per sample. 19 samples are included in this dataset, each with two paired fastq files (R1 and R2). The dataset includes control neurons (WT: dcas9 parental line and C4 CRISPR control) and A53T clones (B7, H8 and L8) across two neuronal inductions (ind1 vs ind2), with or without treatment of RSL3 (UT vs. RSL3).

Dataset EGAD50000002209

Genome-Wide Association Study of Lung Cancer Susceptibility in Never-Smoking Women in Asia

To learn about the etiology of lung cancer among never-smoking women in Asia, we formed the Female Lung Cancer Consortium in Asia (FLCCA), which includes studies of lung cancer in Eastern Asia and conducted a GWAS. We analyzed a total of 5510 lung cancer cases and 4544 controls and identified 3 novel loci (Lan et al., 2012). Of these study subjects, 4922 lung cancer cases and 3959 controls are available for posting. Lan et al.., Genome-wide association analysis identifies new lung cancer susceptibility loci in never-smoking women in Asia. Nat Genet. 2012 Dec;44(12):1330-5. doi: 10.1038/ng.2456. Epub 2012 Nov 11. PubMed PMID: 23143601.

Study phs000716

Genomics_of_acral_lentiginous_melanoma___RNAseq

Melanoma is the most aggressive type of skin cancer, causing about 75% of dermatological cancer deaths. Acral lentiginous melanoma (ALM) is the most common subtype of melanoma in admixed Latin American populations, but very few tumour genomes and exomes, all from European-descent individuals, have been analysed across several studies. Because of this, the genomic landscape of ALM is mostly unknown. Our aim in this project is to define this landscape and identify driver somatic alterations by whole-exome sequencing a collection of ALM germline/tumour paired FFPE samples from the National Cancer Institute of Mexico.

Study EGAS00001003758

Development and Validation of Patient-Derived Xenografts from Fibrolamellar Carcinoma Human Tissue

We developed and validated Patient-Derived Xenografts (PDX) of Fibrolamellar Carcinoma human tissue from surgical resections. Human tissues from primary liver tumor, recurrent liver, and liver metastases were implanted into NOD-SCID-Gamma (NSG) mice (Jackson laboratories) and monitored for tumor growth. Some of the original tissues were frozen for further analysis including RNA sequencing. Tumors that grew were then harvested and passaged into additional mice. Some tumor tissues from the PDX samples were frozen for RNA-Seq analysis. The PDX was validated by histologic, transcriptomic and proteomic analysis. The RNA-Seq analysis was performed on original tissue samples including primary liver tumor, recurrent liver, liver metastases, normal liver, and the derived PDX.

Study phs002435

Contribution of specific cell types to the development of Barrett’s esophagus and carcinoma via germline genetic risk

This study was designed to elucidate how individual cell types in Barrett’s esophagus (BE) and esophageal adenocarcinoma (EAC) contribute to disease development via germline genetic risk. We performed single cell RNA-sequencing (scRNA-seq) of BE, EAC and paired normal esophagus and gastric fundus tissue samples. After analyzing individual cell types, we integrated the scRNA-seq data and data of a genome-wide association study (GWAS) on BE/EAC to determine cell type-specific genetic risk. Raw data from the RNA-sequencing is provided here.

Study EGAS50000000530

Chromatin accessibility profiling of primary human hepatocytes

The chromatin accessibility profiling of primary human hepatocytes using Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq).

Study EGAS50000001230

DAC_RNA-seq of murine osteosarcoma cell line genetically modified for CYR61

Data access committee for RNA-seq of murine osteosarcoma cell line genetically modified for CYR61

Dac EGAC50000000352

Construction of endoscopic biopsy banking for understanding the intestinal environment in colorectal diseases and exploratory studies using these banking systems.

Colorectal cancer is the most common type of cancer in Japan. Although some colorectal cancers have a genetic background, most colorectal cancers are sporadic. Known risk factors for colorectal cancer include smoking, obesity, lack of exercise, and red and processed meat. Furthermore, although it has been suggested that changes in the intestinal environment due to changes in diet and lifestyle may be involved in the increased incidence of colorectal cancer in Japan, the molecular mechanism has not yet been fully elucidated. The main objective of this study is to elucidate the changes in the intestinal environment that cause colorectal tumors, and also to contribute to the establishment of colorectal mucosal tissue banking in Japan.

Study JGAS000655

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

The Library of Integrated Network-Based Cellular Signatures (LINCS) - NeuroLINCS

The NeuroLINCS Center is part of the NIH Common Fund's Library of Integrated Network-based Cellular Signatures (LINCS) program, which aims to characterize how a variety of human cells, tissues and the entire organism respond to perturbations by drugs and other molecular factors. As Part of the LINCS program, the NeuroLINCS study concentrates on human brain cells, which are far less understood than other cells in the body. Our initial focus is to produce diseased motor neurons from patients by utilizing high-quality induced pluripotent stem cell (iPSC) lines from Amyotrophic Lateral Sclerosis (ALS) and Spinal Muscular Atrophy (SMA) patients in addition to unaffected normal healthy controls. Using state-of-the-art OMICS methods (genomics, epigenomics, transcriptomics, and proteomics), we intend to create a wealth of cellular data that is patient-specific in the context of their baseline genetic perturbations and in the presence of other genetic and environmental perturbagens (e.g. endoplasmic reticulum stress). The primary data will be used to build cell signatures that convey the key features that distinguish the state of a cell and determine its behavior. Ultimately, the analysis of these datasets will lead to the identification of a network of unique signatures relevant to each of these motor neuron diseases. The datasets represented in this study are generated from assays interrogating RNA expression (RNA-seq), chromatin accessibility (ATAC-seq) and whole genome sequencing.

Study phs001231

Brain transcriptome of hereditary cerebral haemorrhage with amyloidosis–Dutch type (HCHWA-D)

HCHWA-D is an early onset hereditary form of Cerebral Amyloid Angiopathy (CAA) caused by a point mutation resulting in an amino acid change (NP_000475.1:p.Glu693Gln) in the Amyloid Precursor Protein (APP). Post-mortem brain tissue (9 patients and 9 age-related controls; frontal and occipital cortex) was used for next generation sequencing of RNA (RNA-Seq with ribosomal RNA depletion).

Study EGAS00001002730

Bulk and RIP-seq transcriptome datasets from skin fibroblasts in PTBP1- and PTBP2-related disorders

PTBP1- and PTBP2-related disorders are rare human conditions that cause variable neurodevelopmental phenotypes, including intellectual disability and behavioral problems such as autism spectrum disorder, anxiety, aggressiveness, and attention disorders, with or without hyperactivity. PTBP1-related disorder is also associated with skeletal dysplasia. In this study, we generated bulk RNA sequencing and RNA immunoprecipitation sequencing (RIP-seq) data from skin fibroblasts obtained from patients and healthy controls.

Study EGAS50000001210

Germline variants in patients with rare cancers and their implications for precision cancer medicine: experiences from the Multicenter MASTER Trial by the German Cancer Consortium (HIPO_021)

The era of precision oncology entails an increasing amount of molecular data from tumor sequencing in research and diagnostics. In this setting, germline variant analysis may add clinical benefits for patients and family members. In this study, we evaluated germline variants from genome/exome matched tumor-blood sequencing in 1.485 advanced cancer patients within a prospective observational study carried out by the German Cancer Consortium. Included patients had cancers of rare entities (79.2%) or were under the age of 51 at first diagnosis (76.6%). Ten percent of the patients harbored pathogenic germline variants in 35 tumor predisposition genes with autosomal-dominant inheritance such as BRCA1, BRCA2, TP53, RB1, CHEK2, ATM, PALB2, SDHB etc., while 4.6% of the patients were carriers of pathogenic germline variants in 18 genes with autosomal-recessive inheritance of tumor predisposition. Based on pathogenic germline variants, 14% of the patients received genetic counselling recommendations in the molecular tumor board report. Forty five percent of pathogenic germline variants supported a molecular treatment recommendation, which was implemented for 28 patients. Furthermore, we compared our approach and results to previous studies, broadening the insight into pan cancer germline findings but also revealing differences between studies on various levels, such as cohort demographics, tumor entities, gene nominations, variant detection and variant interpretation. Our data support the importance of germline variant interpretation in precision oncology studies for patients and their families, if applicable. However, guidelines for evaluation and reporting of germline variants in the context of genetic tumor profiling need to be established and further research on the relevance of germline mutations as biomarkers for treatment recommendations is needed.

Study EGAS00001005537

RNA Sequencing of Colorectal Liver Metastases

Reverse-stranded paired-end 75 base-pair RNA sequencing libraries of 93 metastatic FFPE samples were constructed using Illumina Total RNA Stranded Kits. Ribosomal RNAs (rRNAs) were depleted by using the Ribo-Zero rRNA Removal Kit (Illumina). Libraries were sequenced on a HiSEQ2500 machine. Five samples were re-sequenced using paired-end 50 base-pair libraries due to the smaller insert sizes.

Dataset EGAD00001004111

RNA-Seq of non-canonical t(7;12) AML

We performed WGS on AutoMACS-separated BM leukemic blasts (CD34 + CD33+) to characterize the breakpoint and genes involved in the translocation. Additionally, we performed RNA-seq of the bone marrow blast cells to explore the functional consequences of the t(7;12) translocation

Dataset EGAD50000000268

Combined genetic and transcriptome analysis of patients with Systemic Lupus Erythematosus (SLE)

A comprehensive profiling of the genomic architecture of Systemic Lupus Erythematosus (SLE) by combining genetic and transscriptomic analysis by RNA-seq.

Study EGAS00001003662

Patient-derived conditionally reprogrammed cells (CRCs) were established and characterized to assess their biological properties and to apply these to test the efficacies of drugs.

RNA seq data was used for the characterization and profiling of the created patient-derived cells and in vivo tissue.

Study EGAS00001001702

Chromatin 3D interactions mediate genetic effects on gene expression (RNA-seq)

RNA-seq data for 168 Lymphoblastoid Cell Lines (LCL) and 78 Fibroblasts (FIB) from the Gencord Cohort. This dataset was generated as part of the following study: Delaneau et al (2019). Chromatin 3D interactions mediate genetic effects on gene expression.

Dataset EGAD00001004872

Human tumor bulk RNA-seq

We profiled 186 patient tumor samples by bulk RNA-seq. These includes 38 embryonal brain tumors, 101 high-grade gliomas, 24 low-grade gliomas, 10 medulloblastoma and 13 matched normals. The raw fastqs are provided.

Dataset EGAD00001005131

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

RNA-Seq data for 11 Multiple myeloma sample(s). 11 run(s), 11 experiment(s), 11 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 EGAD00001001471

15041 results for "cancer studies using rna-seq"

in 48.13 milliseconds.