9338 results for "cancer rna-seq"

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• Characterizing Advanced Breast Cancer Heterogeneity and Treatment Resistance through Serial Biopsies and Comprehensive Analytics

  This dataset accompanies the manuscript "Characterizing Advanced Breast Cancer Heterogeneity and Treatment Resistance Through Serial Biopsies and Comprehensive Analytics". In this study we discuss the clinical implications and consequences of molecular heterogeneity in a metastatic breast cancer patient identified through serial biopsies, multi-omic analysis, and longitudinal patient monitoring, performed through the precision oncology program, Serial Measurements of Molecular and Architectural Responses to Therapy (SMMART). This case study describes a metastatic breast cancer patient with a highly heterogenous tumor that began as ER positive/HER2 negative on diagnosis, then was found to be ER negative/HER2 positive in a metastatic liver lesion (Study biopsy #1), and finally triple negative (TNBC) in another metastatic liver biopsy (Study biopsy #2). Multi-omic assays included clinical genomic panel sequencing, clinical immunohistochemistry, a clinical protein profiling assay (Intracellular Signaling Protein Profiling), RNA sequencing, and RPPA. Here we provide the expression data from RNAseq and RPPA data on Study biopsy #2 for the patient discussed in this case report. Note: The first liver biopsy did not yield sufficient material for RNAseq or RPPA.Raw RNA FASTQ files from Study biopsy #2 of the liver Gene expression (RNAseq) files from Study biopsy #2Protein expression (RPPA) files (level 2, 3, 4) for Study biopsy #2

  Study phs002321

• 'KOREAN' never-smoker female adenocarcinoma RNA-seq

  Multi-layered genomic studies such as the TCGA project have greatly advanced our understanding on molecular pathogenesis of lung cancer. For Asian nonsmoker patients, however, majority of studies have been limited to mutational analyses, emphasizing ethnic differences in driver mutations (e.g. more frequent EGFR mutations and ALK fusions). In essence, a comprehensive multi-layered characterization that could lead to molecular etiology and patient stratification scheme to be translated into clinical applications for this patient group is still missing. Here we report molecular profiling of tumor and matched normal tissues from 114 non-small-cell lung adenocarcinoma patients using whole exome sequencing, transcriptome sequencing, and array comparative genomic hybridization (CGH).

  Study EGAS00001003789

• Molecular risk stratification in patients with T1 colorectal cancer_WES

  The introduction of bowel cancer screening has led to a significant increase in the proportion of patients being diagnosed with asymptomatic, early-stage colorectal cancer (CRC). Although the majority of these patients are successfully treated with surgery alone, a small proportion of patients have 'born-to-be-bad' aggressive lesions with early dissemination leading to distant metastases. Current standard of care histological assessment is unable to distinguish between these aggressive versus non-aggressive early lesions which is essential to provide appropriate clinical management decisions. This study aims to carry out molecular and histological profiling of approximately 300 T1 CRCs in order to develop a molecular stratifier based on the risk of relapse in early-invasive CRC. This novel T1 cohort will represent the world's largest molecularly characterised T1 cohort of samples, with digital pathology assessment alongside whole exome sequencing, copy number variation analysis and 3' RNA-seq. This data will be used to generate a robust panel of molecular and/or histological markers applicable to formalin-fixed paraffin embedded (FFPE) archival tissue which discriminates between T1 lesions based on risk of relapse, which will ultimately be used to inform clinical management of CRC at the earliest stages of the disease.

  Dataset EGAD00001010890

• Transcriptome sequencing of Gingivo-buccal Cancer - ICGC India Project YR03

  This dataset pertains to transcriptome sequencing of paired RNA samples.RNA was isolated from the tumor and adjacent normal tissues of 12 patients (24 samples). We have performed rRNA removal followed by total RNA sequencing in Illumina HiSeq platform.We have uploaded TopHat2 aligned BAM files.

  Dataset EGAD00001003981

• RNA sequencing

  RNAseq of 45 high-grade serous ovarian cancer tumour samples. Libraries were generated using the NEB Ultra II Directional RNA library Prep kit with polyA enrichment. Libraries were sequenced as paired-end 50 or 100bp on an Illumina NextSeq or NovaSeq.

  Dataset EGAD00001009844

• CCMA-RNA

  This dataset has the raw RNA sequencing data for the cancer models in CCMA.

  Dataset EGAD00001010034

• Finding structural variation from the human skin fibroblast at the single-cell level

  Somatic structural variants (SVs) are widespread in cancer, but their impact on disease evolution is understudied due to a lack of methods to directly characterize their molecular consequences. We present a computational method, scNOVA, that integrates haplotype-resolved SV discovery with nucleosome occupancy analysis using Strand-seq, to functionally characterize SVs in single cells.

  Study EGAS00001006498

• RNA-seq of high grade serous ovarian tumours

  Somatic RNA for 40 samples matched to the WGS was extracted using the Qiagen Qiasymphony RNA protcol (cat no 931636). The tissue was initially homogenised using a Qiagen Bioruptor, followed by the manufacturers recommended protocol (including DNase digestion). The resulting RNA the 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 measures of RNA purity. A260/280 had to be 2.0 and A260/230 had to be 2.0-2.2. Then RNA was quantified using LifeTechnologies Qubit RNA BR kit (cat no Q10210). RNAseq was carried out by the Edinburgh Clinical Research Facility on an Illumina NExtSeq500. Total RNA samples were assessed on the Agilent Bioanalyser (Agilent Technologies, #G2939AA) with the RNA 6000 Nano Kit (#5067-1512) for quality and integrity of total RNA, and then quantified using the Qubit 2.0 Fluorometer (Thermo Fisher Scientific Inc, #Q32866) and the Qubit RNA HS assay kit (#Q32855). Libraries were prepared from total-RNA sample using the NEBNext Ultra 2 Directional RNA library prep kit for Illumina (#E7760S) with the NEBNext rRNA Depletion kit (#E6310) according to the provided protocol. 400ng of totalRNA was then added to the ribosomal RNA (rRNA) depletion reaction using the NEBNext rRNA depletion kit (Human/mouse/rat) (#E6310). This step uses specific probes that bind to the rRNA in order to cleave it. rRNA-depleted RNA was then DNase treated and purified using Agencourt RNAClean XP beads (Beckman Coulter Inc, #66514). RNA was then fragmented using random primers before undergoing first strand and second strand synthesis to create cDNA. cDNA was end repaired before ligation of sequencing adapters, and libraries were enriched by PCR using the NEBNext Multiplex oligos for Illumina set 1 and 2 (#E7500). Final libraries had an average peak size of 271bp. Libraries were quantified by fluorometry using the Qubit dsDNA HS assay and assessed for quality and fragment size using the Agilent Bioanalyser with the DNA HS Kit (#5067-4626). Sequencing was performed using the NextSeq 500/550 High-Output v2 (150 cycle) Kit (# FC- 404-2002) on the NextSeq 550 platform (Illumina Inc, #SY-415-1002). Libraries were combined in an equimolar pool based on the library quantification results and run across 5 High-Output Flow Cell v2.5.

  Dataset EGAD00001009048

• RNA-seq of high grade serous ovarian tumours

  Somatic RNA for 37 samples was extracted using the Qiagen Qiasymphony RNA protcol (cat no 931636). The tissue was initially homogenised using a Qiagen Bioruptor, followed by the manufacturers recommended protocol (including DNase digestion). The resulting RNA the 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 measures of RNA purity. A260/280 had to be 2.0 and A260/230 had to be 2.0-2.2. Then RNA was quantified using LifeTechnologies Qubit RNA BR kit (cat no Q10210). RNAseq was carried out by the Edinburgh Clinical Research Facility on an Illumina NExtSeq500. Total RNA samples were assessed on the Agilent Bioanalyser (Agilent Technologies, #G2939AA) with the RNA 6000 Nano Kit (#5067-1512) for quality and integrity of total RNA, and then quantified using the Qubit 2.0 Fluorometer (Thermo Fisher Scientific Inc, #Q32866) and the Qubit RNA HS assay kit (#Q32855). Libraries were prepared from total-RNA sample using the NEBNext Ultra 2 Directional RNA library prep kit for Illumina (#E7760S) with the NEBNext rRNA Depletion kit (#E6310) according to the provided protocol. 400ng of totalRNA was then added to the ribosomal RNA (rRNA) depletion reaction using the NEBNext rRNA depletion kit (Human/mouse/rat) (#E6310). This step uses specific probes that bind to the rRNA in order to cleave it. rRNA-depleted RNA was then DNase treated and purified using Agencourt RNAClean XP beads (Beckman Coulter Inc, #66514). RNA was then fragmented using random primers before undergoing first strand and second strand synthesis to create cDNA. cDNA was end repaired before ligation of sequencing adapters, and libraries were enriched by PCR using the NEBNext Multiplex oligos for Illumina set 1 and 2 (#E7500). Final libraries had an average peak size of 271bp. Libraries were quantified by fluorometry using the Qubit dsDNA HS assay and assessed for quality and fragment size using the Agilent Bioanalyser with the DNA HS Kit (#5067-4626). Sequencing was performed using the NextSeq 500/550 High-Output v2 (150 cycle) Kit (# FC- 404-2002) on the NextSeq 550 platform (Illumina Inc, #SY-415-1002). Libraries were combined in an equimolar pool based on the library quantification results and run across 5 High-Output Flow Cell v2.5.

  Dataset EGAD00001010139

• The Library of Integrated Network-Based Cellular Signatures (LINCS) - NeuroLINCS

  The NeuroLINCS Center is part of the NIH Common Fund's Library of Integrated Network-based Cellular Signatures (LINCS) program, which aims to characterize how a variety of human cells, tissues and the entire organism respond to perturbations by drugs and other molecular factors. As Part of the LINCS program, the NeuroLINCS study concentrates on human brain cells, which are far less understood than other cells in the body. Our initial focus is to produce diseased motor neurons from patients by utilizing high-quality induced pluripotent stem cell (iPSC) lines from Amyotrophic Lateral Sclerosis (ALS) and Spinal Muscular Atrophy (SMA) patients in addition to unaffected normal healthy controls. Using state-of-the-art OMICS methods (genomics, epigenomics, transcriptomics, and proteomics), we intend to create a wealth of cellular data that is patient-specific in the context of their baseline genetic perturbations and in the presence of other genetic and environmental perturbagens (e.g. endoplasmic reticulum stress). The primary data will be used to build cell signatures that convey the key features that distinguish the state of a cell and determine its behavior. Ultimately, the analysis of these datasets will lead to the identification of a network of unique signatures relevant to each of these motor neuron diseases. The datasets represented in this study are generated from assays interrogating RNA expression (RNA-seq), chromatin accessibility (ATAC-seq) and whole genome sequencing.

  Study phs001231