11980 results for "cancer rna-seq"

in 23.11 milliseconds.

• PRADO trial - Personalized response-directed surgery and adjuvant therapy after neoadjuvant ipilimumab plus nivolumab in stage III melanoma

  The PRADO trial tested a personalized response-directed treatment approach based on the pathologic response after neoadjuvant ipilimumab plus nivolumab in stage III melanoma patients. In patients achieving a major pathologic response, in their index lymph node (the largest lymph node metastasis at baseline), therapeutic lymph node dissection (TLND) and adjuvant therapy were omitted. Patients with partial response underwent TLND only, whereas patients with pathologic non-response underwent TLND and adjuvant systemic therapy ± synchronous radiotherapy. Biomarker analyses based on baseline RNA sequencing data were performed in order to find features that are associated with major pathologic response.

  Study EGAS00001007601

• Understanding Self- Organising Capacity of Stem Cells during Implantation and Early Post-implantation Development in vitro and in vivo: Implications for Human Development (2020-04-20)

  The aim of this project is to differentiate human embryonic stem cells to an extra-embryonic fate, specifically the hypoblast. This is of uttermost importance given the current lack of human hypoblast stem cells. We hypothesized that the pluripotent characteristics of the starting human embryonic stem cell population may dictate the competency for extra-embryonic cell fate specification. Based on this hypothesis and using human embryonic stem cells maintained in different naïve-like culture regimes, we have now developed conditions that allow the differentiation of human embryonic stem cells to a stable GATA6+ SOX2- population. This suggests that these cells may be putative human hypoblast stem cells. To validate this finding here we propose to perform RNA sequencing experiments of the differentiated human embryonic stem cells. By comparing their RNA expression profile to the single cell sequencing data of the human embryo that we are currently generating, we will be able to determine the identity of our GATA6+ SOX2- cells, and establish whether they represent the in vivo human hypoblast. This dataset contains all the data available for this study on 2020-04-20.

  Dataset EGAD00001006056

• Characterisation of the genetic landscape of cutaneous leiomyoma and leiomyosarcoma - Leiomyoma RNA

  Cutaneous leiomyoma (cLM) and leiomyosarcoma (cLMS) are rare benign and malignant soft tissue neoplasms showing smooth muscle differentiation, respectively, that arise from mesenchymal cells in the dermis and subcutis. Through whole exome sequencing of cLM and cLMS cases, we observed distinct differences between the somatic mutational profile of these tumour types. FH was identified as a driver gene in cLM with genetic alterations of FH occurring via somatic point mutation, somatic copy number loss, biallelic inactivation and germline point mutations. TP53 and RB1 were identified as driver genes in the cLMS cohort, with genetic alterations of TP53 occurring via somatic and germline point mutations, copy number loss and biallelic inactivation. Using RNA-sequencing, we identified recurrent gene fusions, including CRTC1/3-MAML2 in cLMS and a novel MYLK-MAP3K2 fusion. Analysis of the cell types present in the tumour microenvironment revealed a significantly increased presence of macrophages and decreased presence of myeloid dendritic cells in the cLMS cohort relative to the cLM cohort. Additionally, we identified common driver genes between cLMS and LMS from other sites. Thus, we provide the first in-depth profile of the genetic landscape of cLM and cLMS.

  Dataset EGAD00001014788

• Characterisation of the genetic landscape of cutaneous leiomyoma and leiomyosarcoma - Leiomyosarcoma RNA

  Cutaneous leiomyoma (cLM) and leiomyosarcoma (cLMS) are rare benign and malignant soft tissue neoplasms showing smooth muscle differentiation, respectively, that arise from mesenchymal cells in the dermis and subcutis. Through whole exome sequencing of cLM and cLMS cases, we observed distinct differences between the somatic mutational profile of these tumour types. FH was identified as a driver gene in cLM with genetic alterations of FH occurring via somatic point mutation, somatic copy number loss, biallelic inactivation and germline point mutations. TP53 and RB1 were identified as driver genes in the cLMS cohort, with genetic alterations of TP53 occurring via somatic and germline point mutations, copy number loss and biallelic inactivation. Using RNA-sequencing, we identified recurrent gene fusions, including CRTC1/3-MAML2 in cLMS and a novel MYLK-MAP3K2 fusion. Analysis of the cell types present in the tumour microenvironment revealed a significantly increased presence of macrophages and decreased presence of myeloid dendritic cells in the cLMS cohort relative to the cLM cohort. Additionally, we identified common driver genes between cLMS and LMS from other sites. Thus, we provide the first in-depth profile of the genetic landscape of cLM and cLMS.

  Dataset EGAD00001014790

• DNA sequening

  The dataset consist of DNA and RNA sequencing results and metadata of the samples. All sample numbers starting with 6716 are tumor samples which has been sequenced using WES (see BAM files). It concerns biopsies of metastatic lesions from patients with BRAFV600 mutated melanoma, obtained before, during and after the study treatment (see samples metadata) and in some cases blood for germline mutation analysis. Sequencing is performed using the Illumina Novaseq 6000 system.

  Dataset EGAD50000000382

• RNAseq from a study of MYC, BCL2 and BCL6 rearrangements in non-Hodgkin lymphoma.

  Paired-end ribodepletion RNAseq performed on 257 samples of Burkitt lymphoma, diffuse large B cell lymphoma, follicular lymphoma, and high-grade B cell lymphoma with MYC and BCL2 or BCL6 rearrangements. RNA was derived from either fresh frozen or formalin fixed/paraffin embedded (FFPE) tissues. Sequencing was performed on Illumina HiSeqX or NovaSeqX instruments.

  Dataset EGAD50000000496

• Bulk RNA-sequencing data from five lines of human iPSC-derived (hiPSC-derived) astrocytes, both alone and in co-culture with neurons, to define the molecular response of astrocytes to misfolded alpha-synuclein.

  Samples were derived from five lines of human iPSC-derived (hiPSC-derived) astrocytes, both alone and in co-culture with neurons +/- alpha-synuclein oligomer treatment. Paired-end FASTQ files for each of the samples are provided.

  Dataset EGAD50000001109

• Differentiation-associated ISG expression of NK cells in chronic viral hepatitis

  Little is known about the effects of ISGs on NK cell responses in chronic viral hepatitis. Here, we determined the expression, regulation and function of ISGs in NK cells with respect to phenotype and function, we performed high-throughput single cell RNA sequencing of circulating CD3-CD56+ NK cells from healthy donors (HD) and patients with chronic HBV or HCV infections.

  Dataset EGAD50000001623

• Transcriptome data of periadrenal and subcutaneous fat in patients with hyperaldosteronism in comparison to patients with non-functional adenomas

  We analyzed subcutaneous adipose tissue (scAT) and periadrenal adipose tissue (paAT) from patients with Aldosterone-Producing Adenomas (APA) and, as controls, non-functional adrenal adenomas (NFA). RNA sequencing and immunohistochemistry of AT was performed (IHC, only on scAT). IHC markers included adipokines (leptin, adiponectin) and transcription factors (eg, c-jun, CaMKII)

  Dataset EGAD50000001590

• Raw sequencing data from low-input chromosome conformation capture in CD4+ T cells

  This dataset contains raw sequencing data from low-input PCHi-C in human primary CD4+ T cells isolated from healthy donors. The data are released as paired-end Illumina FASTQ files. Promoter Capture Hi-C used DpnII digestion and Tn5-based low-input library preparation, followed by targeted capture with custom-designed biotinylated RNA probes targeting most of the annotated gene promoters.

  Dataset EGAD50000001873

• 10X 3' V2 data of single immune cells in hepatocellular carcinoma

  The immune microenvironment of hepatocellular carcinoma (HCC) is poorly characterized. Combining two single-cell RNA sequencing technologies, we produced transcriptomes of CD45+ immune cells for HCC patients from five immune-relevant sites: tumor, adjacent liver, hepatic lymph node (LN), blood, and ascites. This is the droplet data of this study

  Dataset EGAD00001005961

• Genomic analysis of pancreatic neuroendocrine tumour with MEN1, ATRX, or DAXX mutations

  This dataset includes 52 samples from 19 individuals with pancreatic neuroendocrine tumours. These samples were analyzed using RNA-sequencing, whole-exome sequencing, shallow (~0.3x) whole-genome sequencing, and a 21-gene panel targeted capture sequencing. Everything was sequenced using the Illumina HiSeq and is provided in BAM or FASTQ format.

  Dataset EGAD00001006001

• Sequence-based gene expression in uterine and ovarian carcinosarcomas

  Gynecologic carcinosarcomas (CS), including more generally uterine (endometrial) and less frequently ovarian localization, are histologically defined as biphasic neoplasms composed of carcinomatous (C) epithelial and sarcomatous (S) malignant components. We report a comprehensive analysis of 20 patients of macro-dissected samples of C and S components through RNA sequencing.

  Dataset EGAD00001009099

• Sequence Data for Paper: Epigenetic reprogramming during differentiation of human CD4+ T lymphocytes into memory stages

  Sequence Data of total RNA, miRNA, WGB, mRNA, NOMe, Chip (H3K27ac,H3K27me, H3K36me3, H3K4me1, H3K4me3, H3K9me3, Input) Short Desrciption: Epigenetic profiling of human CD4+ memory T cells reveals their proliferative history and argues in favor of a progressive differentiation model driven by epigenetically controlled master regulators.

  Dataset EGAD00001001865

• single-cell transcriptomics data from immune cells

  Smart-seq2 protocol was used to perform single cell RNA-sequencing on 465 immune cells. The immune cells analysed include 215 HLA-DQ2: gluten-(DQ2.5-glia-α1, -α2, -ω1, and -ω2) tetramer-sorted T cells, 247 transglutaminase 2 (TG2)-positive plasma cells from intestinal biopsy or peripheral blood from celiac disease patients, and 3 unassigned cells in 3 batches.

  Dataset EGAD00001004081

• Next Generation Sequencing Characterization of Tregs in Human Peripheral Blood during Autoimmunity

  Tregs were sorted as CD4+CD25+CD127- cells from peripheral blood of 14 healthy individuals, 8 patients with systemic lupus erythematosus, 9 patients with rheumatoid arthritis, and 11 patients with multiple sclerosis. RNA was extracted and polyA libraries were prepared using the Illumina Truseq sample preparation kit v.2. Single-end sequencing was performed on NextSeq500.

  Dataset EGAD00001006193

• Spatially resolved cellular and molecular drivers of cardiac remodelling in healthy and failing human hearts: Adult RNA (2025-07-31)

  Cell Atlas of the diseased human heart. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2025-07-31.

  Dataset EGAD00001015668

• WGS of high grade serous ovarian tumours and matched blood normals

  FASTQ reads for 81 matched tumour-normal WGS pairs for high grade serous ovarian cancer patients. Scottish HGSOC samples were collected via local Bioresource facilities at Edinburgh, Glasgow, Dundee and Aberdeen and stored in liquid Nitrogen until required. HGSOC patients were determined from pathology records and were included in the study where there was matched tumour and whole blood samples. Tumour samples were divided into two for DNA and RNA extraction and slivers of tissue were taken, fixed in formalin and embedded in paraffin wax (FFPE). Samples were only included if they were confirmed as HGSOC and there was greater than 40% tumour cellularity throughout the tumour, determined using H&E staining of the FFPE sections and pathology review. Somatic DNA was extracted from the tumour and germline DNA was extracted from whole blood. Somatic DNA was extracted using the Qiagen DNeasy Blood and tissue kit (cat no 69504). The tissue was initially homogenised using a Qiagen Bioruptor, followed by the manufacturers recommended protocol (including RNase digestion step). Germline DNA was extracted from 1-3ml whole blood using the Qiagen FlexiGene kit (cat no 51206) following the manufacturers recommended protocol. The resulting DNA underwent quality control as follows: firstly, A260 and A280nm were measured on a Denovix DS-11 Fx to qualitatively illustrate A260/280nm and A260/230nm ratios as surrogate measures of DNA purity. A260/280 had to be 1.8 or greater and A260/230 had to be 2.0 or greater. Then, DNA was quantified using LifeTechnologies Qubit dsDNA BR kit (cat no Q32850) and we required a minimum of 50ul at 25ng/ul for WGS. Thirdly, DNA was diluted to 25ng/ul and a representative sample was loaded onto a 0.8% TAE gel, ran at 100v for 60mins and then imaged using a BioRad ChemiDoc imaging system to visualise the DNA quality.

  Dataset EGAD00001009049

• WGS of high grade serous ovarian tumours and matched blood normals

  FASTQ reads for 34 matched tumour-normal WGS pairs for high grade serous ovarian cancer patients. Scottish HGSOC samples were collected via local Bioresource facilities at Edinburgh, Glasgow, Dundee and Aberdeen and stored in liquid Nitrogen until required. HGSOC patients were determined from pathology records and were included in the study where there was matched tumour and whole blood samples. Tumour samples were divided into two for DNA and RNA extraction and slivers of tissue were taken, fixed in formalin and embedded in paraffin wax (FFPE). Samples were only included if they were confirmed as HGSOC and there was greater than 40% tumour cellularity throughout the tumour, determined using H&sE staining of the FFPE sections and pathology review. Somatic DNA was extracted from the tumour and germline DNA was extracted from whole blood. Somatic DNA was extracted using the Qiagen DNeasy Blood and tissue kit (cat no 69504). The tissue was initially homogenised using a Qiagen Bioruptor, followed by the manufacturers recommended protocol (including RNase digestion step). Germline DNA was extracted from 1-3ml whole blood using the Qiagen FlexiGene kit (cat no 51206) following the manufacturers recommended protocol. The resulting DNA underwent quality control as follows: firstly, A260 and A280nm were measured on a Denovix DS-11 Fx to qualitatively illustrate A260/280nm and A260/230nm ratios as surrogate measures of DNA purity. A260/280 had to be 1.8 or greater and A260/230 had to be 2.0 or greater. Then, DNA was quantified using LifeTechnologies Qubit dsDNA BR kit (cat no Q32850) and we required a minimum of 50ul at 25ng/ul for WGS. Thirdly, DNA was diluted to 25ng/ul and a representative sample was loaded onto a 0.8% TAE gel, ran at 100v for 60mins and then imaged using a BioRad ChemiDoc imaging system to visualise the DNA quality.

  Dataset EGAD00001010135

• UCL Cancer Institute DAC

  Dac EGAC00001001579

• Treated and control Patient Derived Xenografts of colorectal cancer (CRC) samples

  Dac EGAC00001003073

• Data Access Commitee of Nanjing Medical University Affiliated Jiangsu Cancer Hospital

  Dac EGAC00001003206

• DAC: British Columbia Cancer Agency (BCCA), PHSAs Technology Development Office, Data Access Committee

  Dac EGAC00001000122

• DAC of Synergie Lyon Cancer

  Dac EGAC00001000382

• DAC for study Treg in breast cancer and healthy individuals

  Dac EGAC00001000638