11931 results for "cancer rna-seq"

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• Evolution of Core Archetypal Phenotypes in High-Grade Serous Ovarian Cancer (HGSOC)

  The evolution of resistance in high-grade serous ovarian cancer (HGSOC) cells in response to chemotherapy is only partially understood. To uncover phenotypic changes associated with chemotherapy resistance, we performed single-cell RNA-sequencing (scRNA-seq) or whole-genome sequencing of 23 total patients, with longitudinal samples collected from 9, treatment-naïve samples from 8 patients, and post-treatment samples from 6 patients. Our study revealed that the HGSOC transcriptome could be described using three core archetypal phenotypes, defined as oncogenic tasks that describe majority of the transcriptional data. We implemented a multi-task learning approach to identify the biological tasks of each archetype and identified metabolism and proliferation, cellular defense response, and DNA repair signaling. The “metabolism and proliferation” archetype evolved during treatment and was enriched in cancer cells from patients that received multiple-lines of treatment and had elevated tumor burden indicated by CA-125 levels. The emergence of archetypes was not consistently linked to specific whole-genome driver mutations. However, archetypes were closely associated with sub-clonal clusters at the single-cell level, indicating that cancer cell sub-populations specialize in particular biological tasks. Our study reveals a defining principle of ovarian tumor evolution using temporal scRNA-seq profiles, identifies the key phenotypic features across patients and links metabolic shifts with a response to chemotherapy.

  Study phs002294

• RNA-Seq

  RNA Sequencing

  Dataset EGAD00001001121

• Paired RNA-Seq for Sarcoma tumors

  Paired RNA-Seq of 199 samples of Sarcoma tumors. The sequencing was done mostly on HiSeq 4000 with Illumina TruSeq Stranded mRNA, ist was also done on NovaSeq 6000 using the same kit. Very few samples were sequenced on HiSeq X Ten with Illumina TruSeq RNA or Illumina TruSeq Stranded mRNA.

  Dataset EGAD00001010256

• RNA-seq analysis of midbrain dopaminergic neurons following ZFHX4 knockdown.

  We performed RNA-seq analysis on midbrain dopaminergic neurons derived from induced pluripotent stem cells following lentiviral-mediated knockdown of the transcription factor ZFHX4. Transcriptomic analysis enabled the identification of potential ZFHX4 target genes. Enrichment analysis of the differentially expressed genes was conducted to assess the biological pathways regulated by ZFHX4.

  Study EGAS50000001111

• RNA-seq analysis of BMP-stimulated glioma initiating cells

  Bone morphogenetic protein (BMP) signaling has been shown to suppress progression of glioblastoma by promoting astrocytic differentiation; however, roles of BMP signaling in cell proliferation and apoptosis of glioblastoma cells remains to be elucidated. We here performed RNA-seq analysis of glioma initiating cells to identify transcriptional target(s) of BMP.

  Study JGAS000077

• 10x Genomics VDJ single cell sequencing and 10x Genomics sc RNA-Seq (5 individuals/17 VDJ runs,19 scRNA runs))

  single cell RNAseq and TCR sequencing data of 5 individuals. 10x Genomics VDJ single cell sequencing (17 runs) and 10x Genomics sc RNA-Seq (19 runs). The VDJ sequencing was done on a Nextseq 550 using the Chromium Single Cell VDJ Reagent Kit. The RNA-Seq was done either on HiSeq4000 or NovaSeq 6000 using the Chromium Single Cell 5 Reagent Kit.

  Dataset EGAD00001009986

• Transcriptional and DNA binding profiling of CEBPE in REH acute lymphoblastic leukaemia cells using shRNA RNA-Seq and ChIP-Seq

  Dac EGAC00001000891

• Tumorigenic role of Musashi-2 in aggressive mantle cell lymphoma

  SOX11 is overexpressed in the highly aggressive conventional mantle cell lymphoma (MCL) but rarely in the indolent non-nodal MCL subtype. We performed RNA-seq of SOX11+ and SOX11- MCLs to search for SOX11-regulated genes that contributed to MCL clinical evolution, identifying Musashi-2 (MSI2) stem cell-related gene. By performing RNA-seq upon MSI2 knockdown or inhibition in MCL, we identified MSI2 as a potential target to inhibit drug resistance in aggressive MCLs.

  Study EGAS00001006613

• Evaluation of protocols for rRNA depletion-based RNA sequencing of nanogram inputs of mammalian total RNA

  Next generation RNA-Sequencing (RNA-seq) is a flexible approach that can be applied to e.g. global quantification of transcript expression, the characterization of RNA structure such as splicing patterns and profiling of expressed mutations. Many RNA-seq protocols require up to microgram levels of total RNA input amounts to generate high quality data, and thus remain impractical for the limited starting material amounts typically obtained from rare cell populations, such as those from early developmental stages or from laser micro-dissected clinical samples. Here, we present an assessment of the contemporary ribosomal RNA depletion-based protocols, and identify those that are suitable for inputs as low as 1-10 ng of intact total RNA and 100-500 ng of partially degraded RNA from formalin-fixed paraffin-embedded tissues.

  Dataset EGAD00001005316

• IBC RNA-seq

  RNA sequencing of IBC patients

  Dataset EGAD00001011649

• Single cell Human Kidney ADPKD

  This ADPKD project has 3 different experiments, 7 different single cell RNA-seq data, 5 different single nuclei RNA-seq data and 6 different bulk ATAC-seq data objects.

  Dataset EGAD00001009328

• Colorectal Microenvironment Spatial Mapping

  In colorectal cancer (CRC), interactions between tumor and immune cells within the tumor microenvironment are critical drivers of tumorigenesis, immune evasion, prognosis, and therapeutic response. Therefore, a deeper understanding of the spatial organization of cancer and immune cells within the CRC microenvironment is urgently needed. In this study, we aimed to characterize distinct cell populations and patterns of spatial gene expression associated with CRC tumorigenesis and immune infiltration. Spatial transcriptomics (ST) was performed on CRC and adjacent normal tissues to investigate the patterns of gene expression. Integrative ST data and publicly available single-cell RNA sequencing (scRNA-seq) data analysis tools were used for data integration, dimension reduction, clustering, and differential expression analysis. This study identified novel immunosuppressive cell populations and immune regulatory factors in CRC. Further functional validation of these findings will be essential to assess their potential as therapeutic targets.

  Study EGAS00001008254

• A Unifying Paradigm for Transcriptional Heterogeneity and Squamous Features in Pancreatic Ductal Adenocarcinoma

  Pancreatic cancer expression profiles largely reflect a classical or basal-like phenotype. The extent to which these profiles vary within a patient is unknown. We integrated evolutionary analysis and expression profiling in multiregion sampled metastatic pancreatic cancers, finding that squamous features are the histologic correlate of an RNA-seq defined basal-like subtype. In patients with coexisting basal/squamous and classical/glandular morphology, phylogenetic studies revealed that squamous morphology represented a subclonal population in an otherwise classical/glandular tumor. Cancers with squamous features were significantly more likely to have clonal mutations in chromatin modifiers, intercellular heterogeneity for MYC amplification, and entosis. These data provide a unifying paradigm for integrating basal-type expression profiles, squamous histology, and somatic mutations in chromatin modifier genes in the context of clonal evolution of pancreatic cancer.  

  Study EGAS00001003974

• Bulk RNA-seq data of isogenic wild-type RPE1

  Deposited here are time series (6, 24 and 48hrs) bulk RNA-seq data generated from isogenic wild-type (WT) RPE1 untreated or treated with Brequinar (and other dNTP-pool disruptor compounds)

  Dataset EGAD50000001544

• Epigentic sequence data of monocytes and macrophages

  Data for paper: Epigenetic dynamics of monocyte to macrophage differentiation with Chip Seq, NOMe, mRNA, total RNA, noncoding RNA, whole genome bisulfite seq,

  Dataset EGAD00001002201

• 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

• Uveal melanoma whole genome sequencing, SNP arrays and RNA-seq

  We conducted whole genome sequencing and DNA SNP array of 12 uveal melanoma genomes and their matched DNA from blood. We also conducted RNA-seq of the 12 tumour samples.

  Dataset EGAD00001000372

• G-SAM: Transcriptional Evolution of Glioblastomas Treated With Standard of Care

  Collection of mostly matching primary and recurrent glioblastoma RNA-seq sample pairs, also matching with an earlier DNA sequencing study

  Study EGAS00001005436

• WGS and RNA-Seq data from a GBM patient PT-PV2594

  WGS and RNA-Seq data from a GBM patient PT-PV2594

  Dataset EGAD00001008526

• RNA-Seq data on multiple myeloma CD138positive bone marrow plasma cells

  RNA-Seq data on multiple myeloma CD138positive bone marrow plasma cells, 11 samples, sequenced on HiSeq 4000 and HiSeq X-Ten, using mostly the TruSeq stranded mRNA Kit.

  Dataset EGAD00001008619

• RNA-Seq data for manuscript titled: A sporadic Alzheimers blood-brain barrier model for developing ultrasound-mediated delivery of Aducanumab and anti-Tau antibodies

  RNA-Seq data for manuscript titled: A sporadic Alzheimers blood-brain barrier model for developing ultrasound-mediated delivery of Aducanumab and anti-Tau antibodies

  Dataset EGAD00001008670

• RNA-seq data of patients with glioblastoma IDH-wt (CNS WHO grade 4) with matched primary and relapse samples.

  Between January 1st, 2016, and December 31th, 2021, patients with glioblastoma IDH-wt (CNS WHO grade 4) were included. RNA-seq was performed in a subset of patients.

  Dataset EGAD00001015683

• Dataset for RNA and Exome sequencing of Glioblastoma samples

  This dataset contains RNA Seq, 10x scRNA Seq and Exome sequencing of glioblastoma samples. Sequencing was performed on a Illumina NovaSeq 6000 and Illumina HiSeq 4000. The sequencing was always paired.

  Dataset EGAD00001012235

• ProstOmics - Spatial Transcriptomics

  Spatial transcriptomics data (ST) from 32 human prostate tissue samples originating from 8 prostate cancer patients (5 patients with post-surgery relapse). The ST data was acquired using the Visium Spatial Gene Expression kit which resulted in over 20 000 spatially defined spots for the 32 tissue samples. The raw transcriptomics data is RNA-seq. The individual samples have information on patient origin and sample type (cancer, cancer-adjacent field-effect normal or normal sample far from cancer). Each ST spot has metadata such as sample origin, histology class (stroma, normal epithelium, cancer of various grading etc), number of cells and estimated cell type fractions. Patient metadata include information of age at surgery, time (months) until reported relapse, total follow-up time, pre-surgery PSA, post-surgery T-stage and metastasis status.

  Dataset EGAD50000000603

• Breast Cancer - immune clusters - RNA-seq

  In this study we use expression data from breast cancer tumors to define immune clusters in breast cancer. Immune clusters have gradual levels of immune infiltration. In the intermediate immune infiltration cluster, we found a worse prognosis which is independent of known clinicopathological features. We also found the immune clusters associated with treatment response. Further using gene expression data and deconvolution algorithms to dissect the immune contexture of the clusters.

  Study EGAS00001003631