14977 results for "cancer studies using rna-seq"

in 14.95 milliseconds.

• Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX026_VOA11083SD_001

  Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 12 samples; filetype=fastq

  Dataset EGAD00001005158

• Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX021_SA854_003

  Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 3 samples; filetype=fastq

  Dataset EGAD00001005143

• Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX023_SA604X9XB02413_001

  Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 12 samples; filetype=fastq

  Dataset EGAD00001005145

• Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX019_SA604X7XB02089_002

  Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 36 samples; filetype=fastq

  Dataset EGAD00001005151

• Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX054_SA604X9XB02417_002

  Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 12 samples; filetype=fastq

  Dataset EGAD00001005173

• Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX057_SA535X5XB02887_001

  Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 12 samples; filetype=fastq

  Dataset EGAD00001005174

• Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX067_VOA12024_003

  Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 12 samples; filetype=fastq

  Dataset EGAD00001005180

• Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX019_SA604X7XB02089_004

  Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 24 samples; filetype=fastq

  Dataset EGAD00001005153

• Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX003_SA605X3XB01966_002

  Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 12 samples; filetype=fastq

  Dataset EGAD00001005160

• Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX018_VOA11019SA_002

  Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 24 samples; filetype=fastq

  Dataset EGAD00001005157

• Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX024_SA604X9XB02417_002

  Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 12 samples; filetype=fastq

  Dataset EGAD00001005148

• Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX024_SA604X9XB02417_001

  Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 12 samples; filetype=fastq

  Dataset EGAD00001005147

• Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX019_SA604X7XB02089_001

  Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 36 samples; filetype=fastq

  Dataset EGAD00001005150

• RNAseq of 76 samples from Uveal Melanoma tumors

  Hotspot mutations in the spliceosome gene SF3B1 are reported in 20% of uveal melanomas. SF3B1 is involved in 3'-splice site (3'ss) recognition during RNA splicing; however, the molecular mechanisms of its mutation have remained unclear. Here we show, using RNA-Seq analyses of uveal melanoma, that the SF3B1 R625/K666 mutation results in deregulated splicing at a subset of junctions, mostly by the use of alternative 3'ss.

  Dataset EGAD00001004398

• Oxidative phosphorylation is a key ontogenetic feature of monocyte immunometabolism promoting myeloid differentiation after birth

  The dataset consists of 12 samples of monocytes, which were analyzed using RNA-sequencing (RNA-seq). These samples were categorized into four distinct groups, each comprising three samples. The dataset was designed to investigate the transcriptomic and metabolic profiles of monocytes across different age groups and stimulation conditions. 3 neonatal controls, 3 neonatal LPS stimulated, 3 adult controls and 3 adult LPS stimulated samples.

  Dataset EGAD00001011340

• Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX_014_SA604X6XB01979_001

  Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 3 samples; filetype=fastq

  Dataset EGAD00001005154

• Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX018_VOA11019SA_001

  Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 24 samples; filetype=fastq

  Dataset EGAD00001005156

• Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX050_PBC04573_001

  Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 12 samples; filetype=fastq

  Dataset EGAD00001005166

• Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX057_SA535X5XB02887_002

  Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 12 samples; filetype=fastq

  Dataset EGAD00001005175

• SAPCS Blood RNA-seq of prostate cancer patients

  The study aims to quantify the gene expression in blood samples in 20 prostate cancer patients under the Southern African Prostate Cancer Study (SAPCS). The objective is to identify a list of differentially expressed genes.

  Study EGAS50000000702

• Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) - Genome-Wide Association Study Meta-Analysis

  Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) was one of five projects funded in 2010 as part of the NCI's Genetic Associations and Mechanisms in Oncology (GAME-ON) initiative (http://epi.grants.cancer.gov/gameon/). GAME-ON's overall goal was to foster an intra-disciplinary and collaborative approach to the translation of promising research leads deriving from the initial wave of cancer GWAS. Specific goals included replication of previous GWAS findings and identification of new susceptibility loci through meta analyses of existing GWAS data and fine mapping of identified loci to better pinpoint causal variants; and identify germline variants that are associated with risk of multiple cancers. The other four funded GAME-ON projects were: the ColoRectal TransdisciplinaryStudy (CORECT), Elucidating Loci Involved in Prostate Cancer Susceptibility (ELLIPSE), Follow-up of Ovarian Cancer Genetic Association and Interaction Study (FOCI), and Transdisciplinary Research in Cancer of the Lung (TRICL). To identify additional cancer risk loci, improve the precision of fine-mapping, and facilitate cross-cancer analyses, DRIVE investigators performed a meta-analysis of eleven genome-wide association studies of breast cancer: The Australian Breast Cancer Family Study (ABCFS), the British Breast Cancer Study (BBCS), the Breast and Prostate Cancer Cohort Consortium (BPC3), the Breast Cancer Family Registries (BCFR), the Dutch Familial Bilateral Breast Cancer Study (DFBBCS), German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC), the Helsinki breast cancer family Study (HEBCS), the Mammary Carcinoma Risk factor Investigation (MARIE), the Singapore and Sweden Breast Cancer Study (SASBAC), the Triple Negative Breast Cancer Study (TNBC), and the UK2 GWAS. These studies comprised a total of 16,062 cases and 46,157 controls. Imputation to the 1,000 Genomes Project Phase 1 v3 ALL reference panel was performed by study, and summary statistics from each study were combined using fixed-effect meta analysis.

  Study phs001263

• RNA-seq SE

  Transcriptomics of single-end RNA-seq data

  Study EGAS00001006521

• RNA-seq PE

  Transcriptomics of paired-end RNA-seq data

  Study EGAS00001006522

• dbGaP submission of CORECT OncoArray GWAS data

  This analysis represents the genome-wide association study results of several international case-control studies of colorectal cancer.

  Study phs001903

• RNAseq and T cell receptor (TCR) profiling of PBMC cells 3 months post Omicron exposure

  This dataset contains RNA-seq and T cell receptor (TCR) sequencing files from a cohort (n=20) 3 months post positive PCR for SARS-CoV-2. A total of 10 hospitalised (samples H1-10) and 10 non-hospitalised (samples NH1-10) are contained within this dataset. RNA sequencing was performed using the Illumina Stranded mRNA Prep Ligation kit to investigate the protein coding transcriptome of PBMC cells. T cell receptor (TCR) repertoire profiling was performed with the TakaraBio SMART-Seq Human TCR (with UMIs) on PBMC cells to investigate the depth and persistence of SARS-CoV-2 targeting TCRs.

  Dataset EGAD50000001356

• Whole exome sequencing of bladder tumors

  Bladder carcinomas are immunogenic, and patients with bladder cancer benefit from immune checkpoint therapy. This is correlated to a high tumor mutation burden, which provides a higher number of neoepitopes that can be recognized by tumor-specific CD8+ T cells. Intravesical Bacillus Calmette-Guérin (BCG) is used to treat non-muscle invasive bladder cancer (NMIBC), but its mechanism of action remains elusive. Most lymphocytes appearing in the urine of BCG-treated patients are CD4+ T cells though preclinical studies showed that CD8+ T cells are also necessary for BCG treatment efficacy. It is currently unknown which proportion of patients with non-metastatic bladder cancer develop a spontaneous antitumor CD8+ response, and if BCG treatment influences this response. were prepared using the xGen Dual Index UMI Adapters (IDT), and genomic DNA (gDNA) extracted (DNeasy kit, Qiagen) from formalin-fixed paraffin- embedded (FFPE) or snap-frozen tumor tissue, as well as autologous lymphocytes. Libraries were subjected to paired-end sequencing using Illumina HiSeq4000 with a coverage depth of 250X. Library preparation was carried out with the AllType FASTplex NGS 11 Loci Flex Kit (One Lambda), and sequencing was conducted on the Illumina MiSeq platform.

  Study EGAS50000001248

• Enzymatic methylation sequencing of rectal mucus from patients suspected to have colorectal cancer

  This dataset contains comprises enzymatic methylation sequencing (EM-Seq) data generated from 208 rectal mucus samples from patients suspected to have colorectal cancer and healthy controls. Targeted CpG sites were enriched using a panel of genes implicated in colorectal cancer, and libraries were prepared with NEBNext EM-Seq prior to Illumina sequencing. Sequencing was performed on the Illumina NovaSeq6000 platform, producing paired-end FASTQ files. These data support analyses of DNA methylation patterns in CRC detection and progression

  Dataset EGAD50000001871

• RNA-Seq on GeparSixto tissue samples

  RNA-Seq on GeparSixto tissue samples

  Study EGAS00001007124

• Preclinical Pediatric Molecular Analysis for Therapy Choice (MATCH)

  Purpose: Pediatric solid tumors arise from diverse tissues during development and exhibit a wide range of molecular, cellular and genetic features. This diversity, combined with the low incidence of pediatric cancer makes it increasingly difficult to personalize therapy for individual patients based on the unique features of their tumors. Therefore, well-credentialed preclinical models that capture the diversity and heterogeneity of pediatric solid tumors are essential for identifying molecular targeted therapeutics for precision medicine. Experimental Design: Here, we report 281 orthotopic patient derived xenografts (O-PDXs) from 224 patients representing 24 different types of pediatric solid tumors. We have performed genomic characterization of the O-PDXs and compared them to their corresponding patient tumors. To demonstrate the feasibility and utility of using such a diverse collection of O-PDXs in preclinical studies, we performed a preclinical pediatric precision medicine trial based on the NCI-COG Pediatric MATCH trial enrollment criteria. We also tested molecular targeted therapy for a novel oncogenic fusion recently reported in pediatric melanoma and precision drug delivery using nano-liposomal irinotecan. Results: In the preclinical match study, we identified one specific single agent response to LY3023414 in an osteosarcoma model with a TSC2 variant of unknown significance. Combinations of nano-liposomal irinotecan were effective in treating both Ewing sarcoma and rhabdomyosarcoma xenografts. Conclusion: Our studies demonstrate the value of large, well-credentialed preclinical models for future precision medicine in pediatric oncology using single agents, drug combinations and novel drug formulations.

  Study EGAS00001008011

• Japanese liver cancer RNAseq

  RIKEN collection of RNA-seq reads for 458 liver cancer samples and matched normal liver from 247 donors.

  Dataset EGAD00001001880