10117 results for "cancer rna-seq"

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• RNA-seq data for genomic determinants of response and resistance to inotuzumab in B-ALL

  RNA-seq data for 101 samples with B-cell acute lymphoblastic leukemia

  Dataset EGAD50000000095

• 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

• Comparison Between qPCR and RNA-Seq Reveals Challenges of Quantifying HLA Expression

  Human leukocyte antigen (HLA) class I and HLA class II loci are essential elements of innate and acquired immunity. Their exceptional influence on disease outcome is well documented by GWAS and candidate gene studies. The impact of HLA allelic variation on human disease through allele-specific presentation of antigenic peptides to T cells has been the main focus to determine HLA effects on disease susceptibility/pathogenesis. However, HLA expression levels have also been implicated in disease, adding another dimension to the extreme diversity of HLA that impacts variability in immune responses across individuals. HLA expression levels routinely rely on quantitative PCR (qPCR). An alternative is adoption of high throughput technologies such as RNA-Seq. This provides the opportunity to quantify HLA expression in large datasets, but also allows comparison between RNA-Seq and qPCR. In this study, we analyze expression data for HLA Class I genes for a matched set of individuals (N = 96) by RNA-Seq, qPCR and cell surface expression. Samples were obtained from the Research Donor Program (NCI-Frederick). We observed a moderate correlation between RNA-Seq and qPCR. We suggest technical and biological factors that might contribute to differences in the techniques.

  Study phs003177

• RNA-seq from islet differentiation model

  Whole transcriptome RNA sequencing (RNA-seq) of human induced pluripotent stem cell lines from three independent donors at seven islet developmental stages: definitive endoderm (DE), primitive gut tube (GT), posterior foregut (PF), pancreatic endoderm (PE), endocrine progenitors (EP), endocrine-like cells (EN), and beta-like cells (BLC).

  Dataset EGAD00001003807

• RNA-Seq data of S24 cells for the publication Recording physiological history of cells with chemical labeling.

  Recordings of elevated calcium levels allowed selections of cells from heterogenous populations for transcriptomic analysis. Paired RNA-Seq from S24 cells using Takara SMARTer Ultra Low Input RNA v4 kit and sequencing on a Illumina NovaSeq 6000.

  Dataset EGAD50000000082

• RNA-seq as a tool for evaluating human embryo competence

  The majority of embryos that are created through IVF do not implant. It seems plausible that rates of implantation would improve if we had a better understanding of molecular factors affecting embryo competence. Currently, the process of selecting an embryo for uterine transfer utilizes an ad-hoc combination of morphological criteria, the kinetics of development, and genetic testing for aneuploidy. However, no single criterion can ensure selection of a viable embryo. In contrast, RNA-sequencing of embryos could yield highly dimensional data, which may provide additional insight and illuminate the discrepancies among current selection criteria. Indeed, recent advances enabling the production of RNA-sequencing (RNA-seq) libraries from single cells have facilitated the application of this technique to the study of some transcriptional events in early human development. However, these studies have not assessed the quality of their constituent embryos relative to commonly used embryological criteria. Here, we perform proof-of-principle advancement to clinical selection procedures by generating high quality RNA-seq libraries from a trophectoderm biopsy as well as the remaining whole embryo. We combine state-of-the-art embryological methods with low-input RNA-seq to develop the first transcriptome-wide approach for use in future predictive embryology studies. Specifically, we demonstrate the capacity of RNA-seq as a promising tool in preimplantation screening by showing that biopsies of an embryo can capture valuable information content available in the whole embryo from which they are derived. Furthermore, we show that this technique can be used to generate a RNA-based digital karyotype, and to identify candidate competence-associated genes. Together, these data establish the foundation for a future RNA-based diagnostic in IVF.

  Dataset EGAD00001005044

• RNA sequencing of tumor samples from patients with BPLL

  RNA-Seq was performed on 12 samples of sorted CD19+ tumor cells. RNA-Seq libraries were prepared using the SureSelect Automated Strand Specific RNA Library Preparation Kit as per manufacturer’s instructions (Agilent technologies) and subjected to paired-end (2 x 100 bp) sequencing on HiSeq2000 (Illumina). The files are in FASTQ format.

  Dataset EGAD00001004412

• Single-cell RNA and ATAC sequencing data analysis of human postmenopausal fallopian tube and ovary

  In this study, we used a combination of technologies, including Drop-seq, 10X single-cell RNA-seq (scRNA-seq), and single-cell assay on transposase accessible chromatin (scATAC-seq) to generate  a high-quality cell atlas that consists of four tissue sites from the reproductive tract of postmenopausal women. This atlas, by far the most comprehensive map of the postmenopausal female reproductive system to date, will pave the way to a better understanding of the pathophysiology of many diseases affecting older women.

  Study EGAS00001006780

• Landscape of somatic mutations and DNA copy number alterations and transcriptomic profiling identifies metabolic reprogramming as a hallmark of ibrutinib resistance

  Whole-exome sequencing (WES) was conducted on the clinical specimens of 14 cases that had sufficient isolated tumor DNA, including 7 ibrutinib-sensitive cases and 7 ibrutinib-resistant cases. Whole transcriptome sequencing (RNA-seq) was performed on clinical specimens isolated from 14 ibrutinib-sensitive and 7 ibrutinib-resistant cases. Unsupervised hierarchical clustering of MCL tumors using RNA-seq gene expression data showed a response-specific gene expression signature.

  Study EGAS00001003418

• Reference Epigenomes and Regulatory Landscape of CLL

  Analysis of the reference epigenomes and regulatory landscape of CLL as a whole and its major clinico-biological subtypes (with mutated and unmutated IGHV) in the light of the normal B-cell differentiation. We have extensively characterized the reference epigenomes of seven primary chronic lymphocytic leukemia samples (CLLs) with mutated (n=5) and unmutated IGHV (n=2) as well as several mature B-cell subpopulations (naive B cells from blood and tonsil, germinal center B cells, memory B cells and plasma cells from tonsil) using genome-wide maps of six histone marks (H3K4me3, H3K4me1, H3K27ac, H3K36me3, H3K9me3 and H3K27me3), DNA accessibility (ATAC-seq), DNA methylation (whole-genome bisulfite sequencing) and gene expression (RNA-seq). Furthermore, we have mapped the regulatory chromatin landscape of 100 additional CLL cases using chIP-seq of H3K27ac and ATAC-seq and linked these data to additional layers of information (whole-genome and/or whole-exome sequencing (WGS/WES), RNA-seq and DNA methylation microarrays) studied in the context of the International Cancer Genome Consortium (ICGC).

  Dataset EGAD00001004046

• Meso RNA-seq data (SSA cell line study)

  RNA-seq data for mesothelioma cell lines after spliceostatin (SSA) or control (DMSO) treatment.

  Dataset EGAD00001001914

• WGS and RNA-Seq data from a GBM patient PT-MB9777

  WGS and RNA-Seq data from a GBM patient PT-MB9777

  Dataset EGAD00001004253

• Single Cell RNA Seq GBM

  Single Cell RNA sequencing for 4 high grade glioma samples. NovaSeq6000 was used for snRNA Seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005369

• Single Cell RNA Seq LGG

  Single Cell RNA sequencing for 5 low grade glioma samples. NovaSeq6000 was used for scRNA Seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005368

• WGS and RNA-Seq data from a GBM patient PT-SS3647

  WGS and RNA-Seq data from a GBM patient PT-SS3647

  Dataset EGAD00001008530

• WGS and RNA-Seq data from a GBM patient PT-SJ5453

  WGS and RNA-Seq data from a GBM patient PT-SJ5453

  Dataset EGAD00001008529

• Smart-seq2 single-cell RNA-seq of human liver non-parenchymal cells

  Smart-seq2 single-cell RNA-seq of human liver non-parenchymal cells from lean and obese individuals

  Dataset EGAD00001010301

• WGS/RNA-seq pair of an inflammatory hepatocellular adenoma (IHCA)

  This study contain the WGS and RNA-seq aligned bam files for this particular inflammatory hepatocellular adenoma sample.

  Dataset EGAD00001004141

• Human tumor bulk RNA-seq

  We profiled 7 patient neuroblastoma-FOXR2 tumor samples by bulk RNA-seq. The raw fastqs are provided.

  Dataset EGAD00001010901

• Genomics of Acute Myeloid Leukemia

  Sequence data from the Genomics of AML PPG program at Washington University since 2009 is made available. This repository contains data from tumors (bone marrow or peripheral blood) from Acute Myeloid Leukemia (AML) and several other related disorders - myelodysplastic syndromes (MDS), secondary AML (sAML) and therapy-related AML (tAML). It also contains matched normal data (skin or buccal swab) and healthy donors for comparison. A variety of DNA sequencing assays were used, including whole genome sequencing (WGS), exome sequencing, targeted panels of AML genes, or targeted panels for validation. Other modalities, including RNA-seq, small RNA-seq, whole genome bisulfite sequencing, CHIP-seq, ATAC-seq, and single-cell RNAseq are also represented.

  Study phs000159

• CPTAC: Molecular Dissection of Chemotherapy Response in Triple Negative Breast Cancer

  Sporadic Triple Negative Breast Cancer (TNBC) represents 10-15% of breast cancers (BC) worldwide. Non-specific chemotherapy remains the standard of care as there are no targeted agents. In 50% of cases, pathological complete response (pCR) is not achieved, and these individuals experience poor survival. Predictive markers for chemotherapy have proved elusive, and patients can receive up to five different chemotherapy agents to optimize outcomes. Microscaled proteogenomics (PMID: 31988290) is a discovery technique that integrates DNA and RNA sequencing with mass spectrometry based proteomics. This approach was applied to snap-frozen biopsies obtained before TNBC treatment on a clinical trial of carboplatin and docetaxel. Proteogenomic profiling included whole exome sequencing (WES), RNA-seq, and tandem mass tag based proteomics and phosphoproteomics.The study was approved by the IRB at both participating sites: Washington University in St. Louis and Baylor College of Medicine (BCM), and followed the Declaration of Helsinki and Good Clinical Practice guidelines. The protocol and informed consent documents were approved by Washington University School of Medicine (WUSM) and BCM.

  Study phs002505

• 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

• Characterization of OCIAML-22 Fractions via ATAC-Seq, RNA-Seq and WGS

  ATAC-Seq on OCIAML-22 CD34+, CD34-, and Bulk Fractions RNA-Seq on OCIAML-22 CD34+/CD38-, CD34+/CD38+, CD34-/CD38+, CD34-/CD38- Fractions WGS on Donor Bulk, OCIAML-22 Bulk, and CD34+ and CD34- Fractions out of OCIAML-22 Xenografts

  Dataset EGAD00001009271

• scRNA-seq, WES, and bulk RNA-seq on longitudinal samples from 7 Lymphoma patients treated with CD20xCD3 bispecific antibodies

  We performed scRNA-seq on 15 fresh lymph node core biopsies from 7 Lymphoma patients treated with CD20xCD3 bispecific antibodies. We also performed whole-exome sequencing (WES) and bulk RNA-seq on formalin-fixed paraffin embedded (FFPE) tumor samples from these patients. WES was additionally performed on matched germline samples (blood) for each patient.

  Dataset EGAD00001011350

• RNA-Seq data from tumor samples collected from 12 UTSW translocation renal cell carcinoma (tRCC) patients.

  This study investigates differentially expressed genes associated with the progression of translocation renal cell carcinoma (tRCC) by analyzing RNA-Seq data from 23 tumor samples obtained from 12 patients. The samples were collected from affiliated hospitals of the University of Texas Southwestern Medical Center (UTSW), including Parkland Hospital and Children’s Medical Center. RNA isolated from tumor samples was sequenced using the Illumina platform.

  Study EGAS50000000127

• RNA-seq following miR-130a knock-down (KD) in Flag-tag AML1-ETO Kasumi-1 cells

  We performed RNA-seq on Flag-tag AML1-ETO Kasumi-1 cells transduced with Control or miR-130a KD lentiviruses to identify differentially expressed genes. Kasumi-1 cells were transduced in 3 independent replicates for control and miR-130a KD samples with lentiviruses containing GFP reporter gene. GFP+ cells were sorted 3 days post-transduction and RNA was harvested for library preparation and sequencing.

  Study EGAS00001005866

• Human melanoma samples with and without resistance to BRAF inhibitor therapy

  Human melanoma samples were collected pre, on, and progression on BRAF inhibitor therapy. RNA was extracted and run on RNA-seq. This has provided insights into different categories of BRAF inhibitor resistance mechanisms.

  Study EGAS00001000992

• Single-cell transcriptomics of neuroblastoma tumors

  Single-cell RNA-sequencing (CITE-seq) and single-nucleus RNA-sequencing data were generated from neuroblastoma patient tumors to uncover transcriptional heterogeneity at a single-cell resolution. In addition, we used the data to validate our findings of the NBAtlas.

  Study EGAS50000000223

• Human Pancreatic Beta Cell lncRNAs Control Cell-Specific Regulatory Networks - Whole Human Islet paired-ended RNA-seq assays

  Whole Human Islet paired-ended RNA-seq of 64 human pancreatic donors.

  Dataset EGAD00001003992

• RNA-Seq of human CD4 Treg cells

  This dataset maps gene expression regulation in human primary regulatory CD4+ T cells (Tregs). It includes whole transcriptome data for141 samples. The final quality filtered set included 123 individuals with RNA-seq data.

  Dataset EGAD00001004830

• PCa-LINES: rRNA-minus RNA-seq of PCa cell-lines (VCaP & PC346c) and 5 additional patient samples

  PCa-LINES: rRNA-minus RNA-seq of PCa cell-lines (VCaP & PC346c) and 4 additional patient samples

  Dataset EGAD00001006366

• DIPG WES and RNA-Seq

  We characterised H3K27M-mutant diffuse intrinsic pontine glioma (DIPG, n=21) and RNA-Seq (n=26 DIPG, 12 normal brain)

  Dataset EGAD00001006450

• Human tumor bulk RNA-seq

  We profiled 9 high-grade gliomas patient tumor samples by bulk RNA-seq. The raw fastqs are provided.

  Dataset EGAD00001008348

• Human tumor single-nuclei RNA-seq

  We profiled 4 patient neuroblastoma-FORX2 tumor samples by single-nuclei RNA-seq using 10X Chromium 3'. The raw fastqs are provided.

  Dataset EGAD00001010903

• RNA-seq dataset of patient and healthy donors

  RNA-seq dataset on a patient (P) presenting with defects of immunity and three healthy donors (C1, C5, C6). This dataset contains raw and processed files from RNA-seq transcriptome analysis performed according to the Smart-seq2. There are 24 single-read (50 bp) fastq files, 6 per patient/donor consisting of 2 cell types and 3 replicates per cell type. There is one count matrix file generated using featureCounts against Ensembl v98 gene models.

  Dataset EGAD00001008371

• RNA-Seq data from study: Molecular underpinnings of dedifferentiation and aggressiveness in chromophobe renal cell carcinoma

  The ChRCC study RNA-Seq dataset contains raw whole transcriptome sequencing data of 16 tumor and 6 adjacent normal samples from 7 UTSW patients, who have consented to depositing their genomic data to public repository. RNA-Seq was performed using 50bp single-end on a HiSeq2500 platform (Illumina, San Diego, CA, USA). 50M reads per sample on average. The raw data is in fastq format.

  Dataset EGAD50000000416

• The Institute for Genomic Medicine at Nationwide Children's Hospital Pediatric Cancer and Blood Disorder Project

  The aim of this study is to enable the unification of the clinical and research arms of comprehensive genomic profiling in the setting of cancer and hematologic disease. Increasingly, studies of the genomic etiology of cancer and hematologic diseases are being utilized for patient management, including prognostication, informing diagnosis, and evaluating eligibility for targeted therapeutics and clinical trials. Clinically available testing can be limited in scope and follow-up, and thus assessment of the impact on patient outcomes is difficult to ascertain. N-of-1 studies have the ability to more broadly impact our understanding of the mechanisms of cancer and hematologic disease, and can have benefit for the individual as well as the larger population of patients with these diseases. Thus, we seek to study and characterize the genomic underpinnings of cancer and hematologic disease through genomic profiling studies bridging both clinical and research components. We will rigorously perform and vet research sequencing and genomic studies protocols for the purpose of transitioning these assays to clinically valid testing for use in clinical patient care. We have been enrolling patients onto this comprehensive genomic profiling study since January 2018. As part of the genomic workup for these patients, we have been performing tumor and normal whole genome sequencing, whole exome sequencing and RNA-Seq. The project is largely supported by the Nationwide Children's Foundation, and we anticipate adding another 50-70 cases per year. This protocol is significant in that it expands our understanding of the etiology of cancer and hematologic disease through a comprehensive analysis aimed at detecting a broad scope of genomic aberrations. Additionally, it improves our understanding of the clinical utility of genomic profiling and has the potential to ultimately improve patient management and surveillance at both level of an individual and general populations of patients affected by these diseases. Primary Objectives: Aim 1: Use diagnostic, relapse, and/or germline samples for augmented whole exome sequencing to characterize the spectrum of germline and somatic genomic alterations in pediatric malignancies and hematologic disease. Aim 2: Examine gene expression and fusion events using whole RNA-seq "transcriptome" analysis or targeted RNA-seq analysis such as the Archer FusionPlex technology. Aim 3: Compare the results of our enhanced molecular profiling vs. that which is detectable by traditional methods as related to cytogenetic, molecular, and minimal residual disease analyses performed as part of standard of care. Aim 4: Examine the utility of comprehensive genomic profiling by collection and evaluation of outcomes data as related to enhancement/alteration in diagnosis, prognosis, treatment management, and clinical benefit. Secondary Objective: Aim 1: Use and clinically validate expanded laboratory methodologies and bioinformatic approaches to more broadly assess genomic complexity (e.g. whole genome sequencing, transcriptome sequencing, in vitro analyses, minimal residual disease testing, clonality assays, proteomics) in the setting of hematologic and oncologic disease. In so doing, we aspire to make novel discoveries and associations, as related to disease phenotypes and subtypes, variant causality, therapeutic targeting, and cancer biology.

  Study phs001820

• Single-cell RNA sequencing on 5063 single T cells isolated from peripheral blood, tumour and adjacent normal tissues from six hepatocellular carcinoma patients.

  Single-cell RNA sequencing on 5063 single T cells isolated from peripheral blood, tumour and adjacent normal tissues from six hepatocellular carcinoma patients. The transcriptional properties of these individual cells, coupled with the assembled T cell receptor (TCR) sequences, were used to delineate the molecular subtypes, lineage, and functional properties of each T cell group. For five HCC patient, the exome-seq data of matched normal/tumor and bulk RNA-seq data of tumor are also available.

  Study EGAS00001002072

• single cell RNA-seq and ATAC-seq of human fetal forebrain tissue DAC

  Dac EGAC00001002599

• RNA-sequencing of adult T-cell leukemia/lymphoma samples

  Adult T-cell leukemia/lymphoma (ATL) is a highly aggressive hematological malignancy derived from mature CD4+ T-lymphocytes. In this study, we performed RNA-sequencing (RNA-seq) analysis for ATL samples including 9 primary tumors and 1 ATL cell line to clarify gene expression profiles in this disease.

  Study EGAS00001003575

• RNA-Seq of patient-derived fibroblast cell lines with inborn errors of cobalamin (vitamin B12) metabolism and controls

  RNA sequencing of 31 patient-derived fibroblast cell lines from patients with inborn errors of cobalamin (vitamin B12) metabolism, and 7 control samples. The RNA seq library was prepared using the TruSeq Stranded Total RNA Sample Preparation Kit (Illumina RS-122–2301) including Ribo-Zero Gold depletion to remove ribosomal RNA. Sequencing was done via llumina Hi-Seq2000 sequencer, using 100bp paired end reads.

  Dataset EGAD00001003142

• Analysis of the lung gene expression profile in COPD

  Samples obtained from patients with COPD and from controls by bronchoscopy from the lung were subjected to RNA seq.

  Study EGAS00001001472

• Whole genome and RNAseq analysis of pediatric osteosarcoma

  Integrative analysis whole genome sequencing and RNA-seq in pediatric osteosarcoma primary tumors and patient derived tumor xenografts.

  Study EGAS00001003201

• RNA-Seq files for SJHGG study

  RNA-Seq files accompanying the paper titled "Somatic Histone H3 Mutations in Diffuse Intrinsic Pontine Gliomas and Non-Brainstem Paediatric Glioblastomas".

  Dataset EGAD00001003905

• RNA-seq of small intestinal neuroendocrine tumors.

  RNA-seq of seven small intestinal neuroendocrine tumors, sequenced with illumina Nextseq 500.

  Dataset EGAD00001004491

• RNA-seq from PDAC samples

  Bam and fastq files from RNA-seq of PDAC samples used in the PCSI mismatch repair study

  Dataset EGAD00001006261

• RNA-seq dataset for Mutation-specific non-canonical pathway of PTEN as a distinct therapeutic target for glioblastoma

  RNA-seq dataset for Mutation-specific non-canonical pathway of PTEN as a distinct therapeutic target for glioblastoma

  Dataset EGAD00001006877

• IMpower133 processed RNA-seq data for genes utilized for cluster assignments

  This dataset contains log2(TPM + 1) for 271 tumor samples profiled by RNA-seq for the subset of genes used for validation of the NMF cluster assignments.

  Dataset EGAD00001006928

• STAR outputs from RNA-seq

  STAR outputs from RNA-seq of 84 pancreatic neuroendocrine neoplasms, 10 normal islet samples and 4 cell line samples.

  Dataset EGAD00001006991

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

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

  Dataset EGAD00001008526