9337 results for "cancer rna-seq"

in 34.97 milliseconds.

• Direct Comparative Analysis of 10X Genomics Chromium and Smart-seq2

  Single cell RNA sequencing (scRNA-seq) is widely used for profiling transcriptomes of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently-used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data by the two platforms from the same samples of CD45- cells, we systematically evaluated their features using a wide spectrum of analysis. Smart-seq2 detected more genes in a cell, especially low abundance transcripts as well as alternatively spliced transcripts, but captured higher proportion of mitochondrial genes. The composite of Smart-seq2 data also resembled bulk RNA-seq data better. For 10X-based data, we observed higher noise for mRNA in the low expression level. Despite the poly(A) enrichment, approximately 10-30% of all detected transcripts by both platforms were from non-coding genes, with lncRNA accounting for a higher proportion in 10X. 10X-based data displayed more severe dropout problem, especially for genes with lower expression levels. However, 10X-data can better detect rare cell types given its ability to cover a large number of cells. In addition, each platform detected different sets of differentially expressed genes between cell clusters, indicating the complementary nature of these technologies. Our comprehensive benchmark analysis offers the basis for selecting the optimal scRNA-seq strategy based on the objectives of each study.

  Dataset EGAD00001005448

• CPTAC: Proteogenomic Studies of Ovarian Tumor Responses to Agents Targeting the DNA Damage Response

  There is a critical unmet need for predictors of chemo-refractory High-grade serous ovarian cancer (HGSOC). Despite over 3 decades of research on platinum responses in cancer, no predictive biomarker has been translated into clinical use. Predictors of refractory disease could spare these patients the unnecessary toxicity of a platinum-based regimen and provide a means to triage these patients to clinical trials to identify effective therapies for refractory disease. This study has approached the development of a predictor in a novel way enabling detection of a multi-analyte panel biomarker capable of detecting a subset of refractory HGSOCs with high specificity and implicating potential treatment vulnerabilities for refractory disease. We reported the first proteogenomic analysis of platinum refractoriness in HGSOC and made the novel dataset available as a resource to the community, including a ProTrack portal: http://ptrc.cptac-data-view.org/. Detailed proteogenomic profiles were generated for this study from a retrospective cohort of 242 treatment-naive HGSOCs with enriched representation of "exceptional non-responders" (i.e., patients with chemo-refractory disease). Whole genome sequence (WGS) and RNA seq FASTQ files are available through dbGaP. Proteomic data are available through the NCI Proteomic Data Commons (PDC): https://pdc.cancer.gov/pdc/.

  Study phs003152

• Molecular determinants of response to PD-L1 blockade across tumor types

  Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis lead to durable clinical responses in subsets of cancer patients across multiple indications, including non-small cell lung cancer (NSCLC), urothelial carcinoma (UC) and renal cell carcinoma (RCC). Herein, we complement PD-L1 immunohistochemistry (IHC) and tumor mutation burden (TMB) with RNA-seq in 366 patients to identify unifying and indication-specific molecular profiles that can predict response to checkpoint blockade across these tumor types. Multiple machine learning approaches failed to identify a baseline transcriptional signature highly predictive of response across these indications. Signatures described previously for immune checkpoint inhibitors also failed to validate. At the pathway level, significant heterogeneity was observed between indications, in particular within the PD-L1+ tumors. mUC and NSCLC were molecularly aligned, with cell cycle and DNA damage repair genes associated with response in PD-L1- tumors. At the gene level, the CDK4/6 inhibitor CDKN2A was identified as a significant transcriptional correlate of response, highlighting the association of non-immune pathways to the outcome of checkpoint blockade. This cross-indication analysis revealed molecular heterogeneity between mUC, NSCLC and RCC tumors, suggesting that indication-specific molecular approaches should be prioritized to formulate treatment strategies.

  Study EGAS00001004343

• Chromatin accessibility maps of chronic lymphocytic leukemia identify subtype-specific epigenome signatures and transcription regulatory networks

  Chronic lymphocytic leukemia (CLL) is characterized by substantial clinical heterogeneity, despite relatively few genetic alterations. To provide a basis for studying epigenome deregulation in CLL, we established genome-wide chromatin accessibility maps for 88 CLL samples from 55 patients using the ATAC-seq assay, and we also performed ChIPmentation and RNA-seq profiling for ten representative samples. Based on the resulting dataset, we devised and applied a bioinformatic method that links chromatin profiles to clinical annotations. Our analysis identified sample-specific variation on top of a shared core of CLL regulatory regions. IGHV mutation status – which distinguishes the two major subtypes of CLL – was accurately predicted by the chromatin profiles, and gene regulatory networks inferred for IGHV-mutated vs. IGHV-unmutated samples identified characteristic differences between these two disease subtypes. In summary, we discovered widespread heterogeneity in the chromatin landscape of CLL, established a community resource for studying epigenome deregulation in leukemia, and demonstrated the feasibility of chromatin accessibility mapping in cancer cohorts and clinical research.

  Dataset EGAD00001002110

• Comprehensive Genomic Characterization of Acral Melanoma

  In this study, we performed paired tumor/normal long insert whole genome and exome sequencing and tumor RNA sequencing on primary or metastatic acral melanoma tumors collected from 34 patients. Patients were enrolled from either Vanderbilt University or the Memorial Sloan-Kettering Cancer Center. We report an integrated analysis of DNA and RNA sequencing data to describe genomic and transcriptomic characteristics of acral melanoma.

  Study phs001036

• smRNA-Seq assays for reference epigenomes generated by Centre for Epigenome Mapping Technologies at Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, as part of the International Human Epigenome Consortium.

  smRNA-Seq assays for reference epigenomes generated by Centre for Epigenome Mapping Technologies at Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001001226

• Fastq data for smRNA-Seq assays for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  Fastq data for smRNA-Seq assays assay for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001001401

• (h)MeDIP-Seq of high-risk prostate cancer

  high-throughput sequencing of methylated and hydroxymethylated DNA from tumor and non-tumor tissue of patients with high-risk prostate cancer

  Dataset EGAD00001002010

• CMF_RNA_sequencing

  RNA sequencing will be undertaken to reconstruct rearrangements at level of transcription to determine pathogenomic genomic events in chondromyxoid fibroma

  Study EGAS00001000470

• Multiregion Whole Exome and Smart-Seq3 single cell RNA sequencing of Breast Tumors

  Multiple regions were cut out of each tumor extracted form breast cancer patients and two experiments were run: - Whole exome sequencing on each of the regions plus an adjacent normal tissue sample - Smart-Seq3 Single cell RNA sequencing on EPCAM+ CD45- sorted cells from different tumor regions

  Study EGAS00001006851

• Human lymphoma plasma cfRNA - raw data

  This study contains total RNA sequencing data from plasma of 65 different human donors: 30 diffuse large B-cell lymphoma patients, 13 primary mediastinal large B-cell lymphoma patients and 22 cancer-free controls.

  Study EGAS00001007127

• Validated Single-cell RNA sequencing in early breast cancer

  Single-cell RNA and VDJ sequencing of early breast cancer

  Dataset EGAD00001006608

• Illumina single-cell RNA sequencing of omentum biopsies of ovarian cancer patients

  Illumina Novaseq paired-end single-cell RNA sequencing samples prepared using 10X Genomics platform. 3 ovarian cancer patients, 5 omentum biopsies samples: 3 HGSOC metastasis samples (one per patient), 2 healthy samples (one per patient except Patient2). 4 paired fastq files per sample.

  Dataset EGAD00001009815

• Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification (H021)

  Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity and an in-depth validation process. We detect a broad variety of non-canonical HLA-binding peptides in the majority of patients demonstrating partially immunogenicity. Our validation process allows for the selection of 32 potential neoantigen candidates. The majority of neoantigen candidates originates from variants identified in the RNA data set, illustrating the relevance of RNA as a still understudied source of cancer antigens. This study underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.

  Study EGAS00001006706

• The Effect of the Menstrual Cycle on DNA Expression in the Normal Human Breast Epithelium

  The Susan G. Komen for the Cure® Tissue Bank at the IU Simon Cancer Center [www.komentissuebank.iu.edu] (KTB) was established expressly for the prospective collection of normal, healthy breast tissue from volunteer donors. Blood is also obtained from donors at the time of donation and is processed for serum, plasma and peripheral blood leukocyte DNA. Specimens are annotated and data includes age, race, ethnicity, personal health history, family cancer history, medication usage at the time of donation, and breast cancer risk factors. Premenopausal donors to the KTB were identified by a query of the Bank's database. Hematoxylin and eosin stained sections of the formalin-fixed paraffin-embedded tissue of the identified donors were reviewed and tissue was graded on the basis of the abundance of epithelium within the section. Only tissue containing abundant epithelium was considered for this study. Based on dates, the specimens of nine women in the follicular phase of the menstrual cycle and five in the luteal phase were chosen. Six donors using hormonal contraception at the time of donation were also included. Whole blood obtained from 19 of the 20 donors at the time of tissue donation was available and it was processed for serum. Estradiol, estriol, luteinizing hormone and progesterone concentrations were determined by the Indiana University Health Pathology Laboratory using a Beckman Unicel DxI 800 Immunoassay System. The phase of the menstrual cycle was verified by serum progesterone concentration. The epithelium of these 20 specimens was microdissected from multiple 8 micron frozen tissue sections. Total RNA extracted from the tissue was subsequently depleted of rRNA via locked nucleic acid probes. This enabled profiling of both poly-A and non-poly-A RNA species. Barcoded cDNA libraries from the 20 normal breast epithelia were prepared and sequenced on an Applied Biosystems (AB) SOLiD3 or SOLiD 4 platform. RNA-seq reads for each sample were then mapped to the human genome (hg19) using the LifeScope software version 2.5.1 (Life Technologies, Foster City, CA) and BAM (Binary Alignment/Map) files generated. Read counts for each gene were derived from the output BAM files using the RefSeq database (UCSC Genome Brower) as the gene model.

  Study phs000644

• Single-cell RNA-sequencing reveals that glioblastoma recapitulates a normal neurodevetlopmental hierarchy

  We discovered a conserved neural tri- lineage cancer hierarchy centered around glial progenitor-like cells. We also found that this progenitor population contains the majority of the cancer’s cycling cells, and, using RNA velocity, is often the originator of the other cell types. Finally, we show that this hierarchal map can be used to identify therapeutic targets specific to progenitor cancer stem cells. Our analyses show that normal brain development reconciles glioblastoma development, suggests a possible origin for glioblastoma hierarchy, and helps to identify cancer stem cell-specific targets.

  Study EGAS00001004422

• High-Throughput RNA Isoform Sequencing using Programmable cDNA Concatenation

  MAS-ISO-seq is a novel technique for programmably concatenating cDNAs into single molecules for optimal long-read sequencing. In practice, this boosts throughput >15 fold to nearly 40 million cDNA reads per run on the PacBio Sequel IIe sequencer. We have validated this method via application to synthetic RNA isoforms and single-cell sequencing of tumor-infiltrating T cells. Results demonstrated a >30 fold boosted discovery of differentially spliced genes and robust cell clustering, as well as canonical PTPRC splicing patterns across T cell subpopulations and the concerted expression of the associated hnRNPLL splicing factor.

  Study phs003200

• Neuroblastoma patient Total RNA Seq data

  Tumor Total RNA Seq data of primary neuroblastomas. This is an update of the „Berlin Neuroblastoma Dataset” (EGAS00001004022). This data was used for the analysis of circular RNA expression and regulation in neuroblastoma.

  Dataset EGAD00001008124

• Stromal and dendritic cells from lymph nodes: single-cell RNA-seq

  Although cross-species transcriptional analysis has been generated for DCs, transcriptomic conservation between mouse and human FRCs at single-cell resolution has been unclear. To test whether GREM1+ FRCs might also play a role in DC homeostasis in humans, we performed scRNA-seq of CD45–PDPN+ stromal cells, as well as CD45+CD11c+ immune cells from healthy human LNs of three human donors. Data was generated using the 10x platform.

  Dataset EGAD00001008731

• RNA sequencing data of 66 matched primary and recurrent high grade serous ovarian cancer

  Dac EGAC00001000741

• Whole Exome and RNA sequencing of synchronous female bilateral breast cancers

  Study EGAS00001006910

• Transcriptomic profiles of neuroblastoma PDXs and primary tumors

  This dataset contains bam files for RNA-seq experiments for 6 neuroblastoma PDXs (Patient Derived Xenograft) and 3 pairs of neuroblastoma tumors at diagnosis and at relapse.

  Dataset EGAD00001003393

• ICGC PCAWG Dataset: PACA-AU_PCAWG_RNA-Seq_Star

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using Star. Project: PACA-AU.

  Dataset EGAD00001003410

• ICGC PCAWG Dataset: PACA-AU_PCAWG_RNA-Seq_TopHat2

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using TopHat2. Project: PACA-AU.

  Dataset EGAD00001003411

• ICGC PCAWG Dataset: OV-AU_PCAWG_RNA-Seq_Star

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using Star. Project: OV-AU.

  Dataset EGAD00001003415

• ICGC PCAWG Dataset: OV-AU_PCAWG_RNA-Seq_TopHat2

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using TopHat2. Project: OV-AU.

  Dataset EGAD00001003416

• ICGC PCAWG Dataset: RECA-EU_PCAWG_RNA-Seq_Star

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using Star. Project: RECA-EU.

  Dataset EGAD00001003558

• ICGC PCAWG Dataset: LIRI-JP_PCAWG_RNA-Seq_TopHat2

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using TopHat2. Project: LIRI-JP.

  Dataset EGAD00001003546

• ICGC PCAWG Dataset: LIRI-JP_PCAWG_RNA-Seq_Star

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using Star. Project: LIRI-JP.

  Dataset EGAD00001003547

• ICGC PCAWG Dataset: CLLE-ES_PCAWG_RNA-Seq_TopHat2

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using TopHat2. Project: CLLE-ES.

  Dataset EGAD00001003548

• ICGC PCAWG Dataset: CLLE-ES_PCAWG_RNA-Seq_Star

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using Star. Project: CLLE-ES.

  Dataset EGAD00001003549

• ICGC PCAWG Dataset: RECA-EU_PCAWG_RNA-Seq_TopHat2

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using TopHat2. Project: RECA-EU.

  Dataset EGAD00001003559

• ICGC PCAWG Dataset: MALY-DE_PCAWG_RNA-Seq_Star

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using Star. Project: MALY-DE.

  Dataset EGAD00001003560

• ICGC PCAWG Dataset: MALY-DE_PCAWG_RNA-Seq_TopHat2

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using TopHat2. Project: MALY-DE.

  Dataset EGAD00001003561

• Whole-genome sequencing reveals genomic signatures associated with the inflammatory microenvironments in Chinese NSCLC patients

  We integrate genomic (whole-genome sequencing, WGS) and transcriptome (polyA-enriched RNA-Seq) sequencing from 90 NSCLC cases and comprehensively identified the distinct genomic features of Chinese NSCLC patients.

  Dataset EGAD00001004071

• macIDR validation data

  RNA-seq of 24 M-CSF differentiated human peripheral monocyte-derived macrophages (MDMs) activated with short exposure (3hours) to LPS, or long exposure (24 hours) to LPS, LPS with IFNγ, IFNγ, IL-4, IL-10, and dexamethasone.

  Dataset EGAD00001004584

• Human normal brain single-cell

  We profiled two human fetal brainstem specimens at 17 and 19 post-conception weeks by single-cell RNA-seq using 10X Chromium Single Cell 3'. The BAM files are provided.

  Dataset EGAD00001005128

• 17 scRNAseq samples

  17 samples were processed for single cell RNA sequencing (SORT-seq)

  Dataset EGAD00001007523

• Count table for OAK (GO28915)

  This dataset contains counts for 699 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from OAK (GO28915).

  Dataset EGAD00001008628

• CPM table for OAK (GO28915)

  This dataset contains counts per million for 699 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from OAK (GO28915).

  Dataset EGAD00001008629

• Count table for POPLAR (GO28753)

  This dataset contains counts for 192 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from POPLAR (GO28753).

  Dataset EGAD00001008630

• CPM table for POPLAR (GO28953)

  This dataset contains counts per million for 192 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from POPLAR (GO28753).

  Dataset EGAD00001008631

• RNA-Seq of primary pediatric kidney tumor controls for the soft tissue sarcoma tumoroid biobank

  Dataset EGAD00001008709

• Atypical teratoid/rhabdoid tumoroids reveal subgroup-specific drug vulnerabilities - RNA-Seq

  We demonstrate that ATRT tumoroids retain subgroup-specific epigenetic and gene expression profiles

  Dataset EGAD00001009794

• LEF1 knockdown effects on human T cell transcriptome and chromatin accessibility profiles.

  LEF1 (Lymphoid Enhancer-Binding Factor 1) is a key transcription factor in T cell development and function, and its dysregulation has been implicated in various immune-related disorders. This study aimed to comprehensively characterize the transcriptional and epigenetic changes induced by LEF1 knockdown in human T cells. CD3+ T cells isolated from three healthy donors were transfected with either LEF1-specific siRNA or control siRNA, followed by activation with CD3/CD28 Dynabeads and culture in IL-2-containing media. Cells were harvested at 24, 48 and 72 hours post-transfection for multi-omics analysis including bulk RNA-seq, bulk ATAC-seq, single-cell CITE-seq (For CITE-seq, 48 and 72 hours only). This dataset provides insights into LEF1-dependent gene regulatory networks and chromatin accessibility patterns in activated human T cells, contributing to our understanding of T cell biology and potential therapeutic targets. This dataset is part of a comprehensive study investigating functional roles of risk polymorphisms in autoimmune diseases, particularly focusing on the rs58107865 variant at the LEF1 locus.

  Study JGAS000818

• WGS,RNA data of patients with multiple myeloma (MM) refractory to immunomodulatory agents (IMiDs) and proteasome inhibitors (PIs)

  Paired WGS and RNA-Seq data of patients with multiple myeloma (MM) refractory to immunomodulatory agents (IMiDs) and proteasome inhibitors (PIs). We performed whole genome and transcriptome sequencing of 39 heavily pretreated RRMM patients with at least double refractoriness revealing complex structural changes and a high mutational load.

  Dataset EGAD00001006189

• RNAseq on 20 samples of multiple myeloma patients and 3 normal plasma cells

  RNAseq on 20 samples of multiple myeloma patients and 3 normal plasma cells. RNAseq was performed using 200 ng of total RNA by GATC Biotech. Directional libraries were performed after mRNA selection by polyA selection using UTP method. RNA-seq libraries were sequenced on HiSeq2500 Illumina machine using 100bp paired-end reads. Reads alignment was performed using the STAR aligner (version 2.4.0f1) and human genome hg19 as reference.

  Dataset EGAD00001007813

• Whole genome, RNA-seq and single-cell Multiome profile of multiple myeloma

  We analyzed multiple myeloma samples from two patients included in the observational prospective cohort MYRACLE before talquetamab treatment and after relapse. Five other myelomas from the same cohort were included for comparison. Normal plasma cells were also retrieved. All samples were analyzed by whole genome sequencing and single-nucleus Multiome, except one that could only be analyzed by bulk RNA sequencing.

  Dataset EGAD00001010057

• CRUK Accelerator: Non-small cell lung cancer whole exome and RNA sequencing

  Whole exome and RNASeq raw sequencing data for a cohort 24 patients with non-small cell lung cancer, 15 adenocarcinoma (8 female, 7 male) and 9 squamous cell carcinoma(5 female, 4 male). Median age at diagnosis was 69. Tumour tissue and PBMCs were used for whole exome sequencing and RNA sequencing. This data was generated as part of a study funded by a Cancer Research UK Centres Network Accelerator Award Grant (A21998).

  Dataset EGAD00001007934

• Gene Expression Signatures Characterized by Longitudinal Stability and Inter-Individual Variability Delineate Baseline Phenotypic Groups with Distinct Responses to Immune Stimulation

  RNA sequencing (RNAseq) of peripheral blood lymphocytes was used to develop a means to assess immune function in a way that can be used in discovery science and applied to patients individually in clinical settings. The premise is that profiles of RNA present in immune cells is reflective of the combined influence of genetic and environmental variation on immune potential of individuals and that this potential can be tapped to understand human immunity in a variety of biological contexts. CD4+ cells were isolated from fresh whole blood via positive magnetic bead selection and cell lysates were prepared using Qiazol (QIAGEN) and stored at -80ºC for 3 to 8 months. RNA was extracted in batches for cDNA library preparation and RNA-Seq. For this study, we developed standard operating procedures for handling human blood samples and determined: a) the best way to enrich for CD4+ T cells from whole blood and yield high quality RNA, b) the sensitivity of this RNA profiling strategy, and c) the reproducibility of generated immune profiles from healthy subjects. We then developed bioinformatics processes to establish immune response signatures and immune response phenotypes within cohorts of individuals.

  Study phs001512