11987 results for "cancer rna-seq"

in 16.21 milliseconds.

• Bulk RNA-seq of stromal cells from multiple cancer types

  We sorted CD45-CD44+CD90+ stromal cells from multiple tumor types and performed bulk RNA-seq. The data allows to infer CAF programs active across indications.

  Study EGAS00001006497

• Expressed_fusion_transcripts_in_rare_bone_tumours

  mRNA-Seq on total RNA from primary osteoblastomas and phosphaturic mesenchymal tumours, focussing on fusion transcript expression

  Study EGAS00001000763

• RNA-Seq of tumors

  RNA-Seq on an Ion Torrent Proton of corresponding tumor material of two metastasized breast cancer patients (Breast7, Breast13).

  Dataset EGAD00001002216

• The Transcriptomic Landscape of Oncogenic PI3K Reveals Key Functions in Splicing and Gene Expression Regulation

  This study includes RNA-seq of paired tumor biopsies from PIK3CA-mutated breast cancer patients that underwent treatment with PI3Kα inhibitors.

  Study phs002840

• Targeted pancancer RNA-Seq

  This dataset contains targeted RNA-Seq generated with the Illumina RNA Pan-Cancer panel. It comprises 2200 cancers from children and adults. The cancers represent a wide range of CNS, solid and haematopoeitic tumours. Samples are from pathology specimens including FFPE, fresh frozen, bone marrow aspirate and peripheral blood.The data is used for tumour driver identification. SNVs, indels and fusion detection.

  Study EGAS50000000700

• Single-cell RNA sequencing of metastatic colorectal cancer organoids treated with cetuximab

  Single-cell RNA sequencing was performed on metastatic colorectal cancer (mCRC) organoids derived from a cetuximab-sensitive patient-derived xenograft (PDX) model. The objective of this analysis was to investigate, at single-cell resolution, the transcriptional changes previously identified in bulk RNA-seq analyses of PDX models exposed to anti-EGFR therapeutic pressure.

  Study EGAS50000001460

• Ewings_Sarcoma_RNA_seq_drug_sensitivity

  Illumina RNA-Seq will be performed on four Ewing’s sarcoma cell lines and two control cell lines. RNA was extracted from all the lines using a basic Trizol extraction protocol.

  Study EGAS00001000419

• RNA-seq of Bone Metastasis from breast and prostate cancers

  RNA-seq of Bone Metastasis from breast and prostate cancer (4 breast and 5 prostate samples). Dataset contains BAM files from RNA-seq performed using Illumina HiSeq 2500.

  Dataset EGAD00001006356

• RNA-seq of multi-regional CRC samples

  BAM files of RNA sequencing (RNA-seq) experiment on multi-regional colorectal cancer (CRC) samples. 58 samples corresponding to 16 patients were sequenced and there is one BAM file for each sample.

  Dataset EGAD00001006164

• Characterization of Prostate Cancer Organoids

  Using models from the LuCaP series as well as those generated at the National Cancer Institute, we have established organoids for in vitro mechanistic testing, including drug screening and genetic manipulation. The major findings from these continuing studies is the durability of organoids for maintaining complex subpopulation and intratumoral heterogeneity and the parallels between in vitro testing and patient outcomes. Existing data from prior releases includes whole-genome sequencing, whole-exome sequencing, single-cell and bulk RNA-seq, single-cell ATAC-seq, and SNP array. New data in this release is ChIP-seq data from three models and RNA-seq data from one model.

  Study phs001587

• Whole Exome Sequencing and RNA Sequencing of High Grade Serous Ovarian Cancer in Black and White Patients

  In this study, we generated new RNA and whole exome sequencing data from high-grade serous ovarian cancer tumor samples of self-identified Black and White individuals. Our RNA-Seq analysis revealed that, while the prevalence of subtypes varied across racial groups, the cluster-specific gene expression patterns were consistent. Despite these differences in subtype prevalence, the subtype-specific survival rates were similar across racial groups. Additionally, we used the WES-Seq data to characterize mutational signatures and their underlying causes in Black high-grade serous ovarian cancer (HGSC) cases, and compared the prevalence and distribution of these signatures with those in White TCGA cases.

  Study phs003632

• Cancer RNA-seq consisting of FASTQ paired-end reads from melanoma and lung cancer sample

  Cancer RNA-seq consisting of FASTQ paired-end reads from 6 individuals (4 melanoma, 1 lung cancer). RNASEQ was performed on illumina, Truseq capture kit, 40M-80M clusters. 2 Melanoma datasets contain reads from 1 tumor region and from a tumor derived cell line 2 Melanoma, 1 Colon and 1 lung datasets contain each reads from a single region.

  Dataset EGAD00001007951

• UROMOL 2020 - RNA-seq data validation

  For this project about non-muscle invasive bladder cancer (NMIBC), we analysed total RNA-seq data from 47 patients used for validation. Sequencing of total RNA was performed using KAPA RNA HyperPrep Kit with RiboErase HMR (Roche). RNA input was 100 to 500 ng. The dataset is composed of 94 fastq files.

  Dataset EGAD00001006967

• RNA-Seq data from 34 CAF-S3 subset in human breast and ovarian cancers

  This dataset is related to publications Costa et al. Cancer Cell 2018 and Givel et al. Nat. Commun. 2018 which describe the identification of 4 Cancer Associated Fibroblasts (CAF) in breast and ovarian cancer. This dataset contains transcriptomic profiles obtained by RNA-Seq of 34 CAF-S3 samples from breast and ovarian Tumors.

  Dataset EGAD00001004810

• RNA-seq of Liver Cancer

  The French ICGC project on liver tumors is coordinated by Pr Jessica Zucman-Rossi and funded by Inca (French Institute for Cancer). The aim of the present project is to identify the catalog of somatic and germline mutations in liver tumors using whole genome (WGS) and whole exome sequencing (WGS), integrated with DNA methylation and RNA sequencing (RNA-seq) data. The present series corresponds to 161 RNA-seq samples from tumors with matched WES or WGS. Hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers, and is a major health problem. It is the 3rd cause of cancer-related mortality. Advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. Thus, the development of next-generation sequencing technologies has made it possible to generate more comprehensive catalogues of somatic alteration events (single nucleotide substitutions, structural variations, and epigenetic changes) in liver cancer genome than ever before.

  Study EGAS00001002879

• Transcriptome profiling of prostate tissue samples from early onset prostate cancer patients and normal controls by RNA-seq

  The dataset contains RNA-seq data of 96 EOPC patients and 9 controls. For some patients multiple tissue samples were sequenced ("multi-area" samples). The RNA extraction and sequencing protocol was earlier described in Weischenfeldt et al, Cancer Cell, 2013.

  Dataset EGAD00001004791

• BarcUVa-Seq (Biology of Colorectal Cancer Risk Enhancers)

  This study was designed to generate a cohort of normal colon mucosal biopsies from the "average risk" population. Its primary aims included the development of an RNA-seq dataset of normal colon mucosal biopsies across three colon regions (ascending, transverse and descending). Further, we aimed to provide plausible prioritization to genetic components of colorectal cancer risk by employing methods such as expression quantitative trait loci analysis. In doing so, we provide a novel reference dataset for the study of colon cancer risk in the healthy colon epithelium. RNA-seq was performed in batches on an Illumina HiSeq 2500 (batch 1-7) or NovaSeq 6000 (batch 8) on a total of 463 samples. Matched genotyping data was generated on the Illumina OncoArray BeadChip. Phasing was carried out using Minimac 3 and imputation was performed using The Haplotype Reference Consortium panel on the Michigan Imputation Server.Genotype and RNA-seq data will be made available in dbGaP.

  Study phs003338

• RNAseq data to study PRPF6 regulated splice forms in colon cancer cell lines

  Alternative splicing plays critical roles in differentiation, development, and cancer (Pettigrew et al., 2008; Chen and Manley, 2009). The recent identification of specific spliceosome inhibitors has generated interest in the therapeutic potential of targeting this cellular process (van Alphen et al., 2009). Using an integrated genomic approach, we have identified PRPF6, an RNA binding component of the pre-mRNA spliceosome, as an essential driver of oncogenesis in colon cancer. Importantly, PRPF6 is both amplified and overexpressed in colon cancer, and only colon cancer cells with high PRPF6 levels are sensitive to its loss. Our data clearly point to an important role for PRPF6 in colon cancer growth and suggest that a better understanding of its role in alternative splicing in colon cancer is warranted. To determine the specific alternative splice forms that PRPF6 regulates in colon cancer, we plan three experiments: 1. The first involves knocking down expression of PRPF6 in two different cancer cell lines with 3 different siRNAs, and then completing RNA-seq to determine the gene expression changes that occur relative to a non-targeting control siRNA. Because of the role for PRPF6 in pre-mRNA splicing, we especially want to quantify the changes in splice-specific forms of all genes genome-wide to identify genes whose splicing is altered upon PRPF6 knockdown. 2. The second involves immunoprecipitating PRPF6 from two different cancer cell lines and isolating any RNA that is bound to PRPF6, since PRPF6 is an RNA-binding protein. We then want to carry out RNA-seq to identify which RNA molecules co-immunoprecipitated with PRPF6. This will help us determine possible functions for PRPF6 in regulating colon cancer growth. 3. The third involves overexpressing PRPF6 in cell lines and then carrying out RNA-seq to identify any changes in splice-specific gene expression. This will allow us to determine whether increased PRPF6 expression is sufficient to drive alternative splicing changes.

  Study EGAS00001000761

• RNA Seq of 25 spheres derived from lymph nodes of lung cancer patients

  To further decipher the cell type composition within single spheres we performed RNA-Seq of 25 selected spheres of 4 different categories (including 8 signature-/balanced, 9 signature+/balanced, 1 signature-/aberrant, 7 signature+/aberrant). Samples were sequenced using Illumina NextSeq 2000.Fastq reads were processed using inhouse RNA-Seq workflow.

  Dataset EGAD00001011099

• Lung cancer RNA Seq Data Access Committee

  Dac EGAC00001003317

• bulk RNA-Seq of colorectal cancer patient samples

  Study EGAS00001006666

• HIPSD&R-seq enables scalable genomic copy number and transcriptome profiling

  Single-cell DNA-sequencing (scDNA-seq) enables decoding somatic cancer variation. Existing methods are hampered by low throughput or cannot be combined with transcriptome sequencing in the same cell. We propose HIPSD&R-seq (HIgh-throughPut Single-cell Dna and Rna-seq), a scalable yet simple and accessible assay to profile low-coverage DNA and RNA in thousands of cells in parallel. Our approach builds on a modification of the 10X Genomics platform for scATAC and multiome profiling. In applications to human cell models and primary tissue, we demonstrate the feasibility to detect rare clones and we combine the assay with combinatorial indexing to profile over 17,000 cells.

  Study EGAS50000000691

• Profiling RNA Translation in Pediatric Medulloblastoma

  This study provides RNA-sequencing and ribosome profiling data for patient-derived cell lines and patient tissue samples for children with medulloblastoma. Ribosome profiling is a variant protocol of RNA-sequencing that directly sequences ribosome-bound RNA fragments only. Associated RNA-seq and Ribo-seq data obtained separately for some cancer cell lines can be found on the NCBI SRA as PRJNA957428. Samples were processed for poly-A mRNA sequencing using the Roche Kapa Kit. Ribosome profiling was performed as described in the manuscript referenced below (Hofman et al.) and based on the article by McGlincy et al., Methods (2017). Ribo-seq data were analyzed for sample quality using RiboseQC (Calviello, Nat Struct Mol Biol, 2020). These data were used to quantify P-sites of open reading frames. RNAseq and ribo-seq data were integrated and compared to determine translational efficiency values using TPM (Ribo-seq) over TPM (RNA-seq) as the metric. Ribo-seq data on 14 samples and RNAseq data on 21 samples derived from 16 fresh-frozen surgical samples for medulloblastoma and 5 autopsy samples are available through dbGaP. A second cohort of 4 medulloblastoma samples (RNAseq, Ribo-seq) related to this study can be found on the EGA at EGAS00001007426.

  Study phs003446

• Transcriptome sequencing across a prostate cancer cohort identifies PCAT-1, an unannotated lincRNA implicated in disease progression

  Noncoding RNAs (ncRNAs) are emerging as key molecules in human cancer, with the potential to serve as novel markers of disease and to reveal uncharacterized aspects of tumor biology. Here we discover 121 unannotated prostate cancer-associated ncRNA transcripts (PCATs) by ab initio assembly of high-throughput sequencing of polyA+ RNA (RNA-Seq) from a cohort of 102 prostate tissues and cells lines. We characterized one ncRNA, PCAT-1, as a prostate-specific regulator of cell proliferation and show that it is a target of the polycomb repressive complex 2 (PRC2). We further found that patterns of PCAT-1 and PRC2 expression stratified patient tissues into molecular subtypes distinguished by expression signatures of PCAT-1-repressed target genes. Taken together, our findings suggest that PCAT-1 is a transcriptional repressor implicated in a subset of prostate cancer patients. These findings establish the utility of RNA-Seq to identify disease-associated ncRNAs that may improve the stratification of cancer subtypes.

  Study phs000443

• RNA-seq data of breast cancer HCI011 model from Ros et al (2020)

  RNA-seq data of the HCI011 and HCI011R models, GDC032 treated and control (total of 21 samples), from the paper: FOXM1 is a biomarker of resistance to PI3Kα inhibition in ER+ breast cancer that is detectable using metabolic imaging (Ros et al, 2020)

  Dataset EGAD00001006245

• CRC organoid RNA-seq data access committee

  This DAC is responsible for granting access to RNA-seq data (paired-end FASTQ files) from 10 patient-derived colorectal cancer organoid patients. The data was ad hoc generated as part of the development of NovumRNA, a non-canonical tumor-specific antigen prediction pipeline.

  Dac EGAC50000000406

• NKI-MET HNSCC RNA-Seq

  RNA-Seq data of 8 HNSCC specimens from patients diagnosed with metastatic disease at The Netherlands Cancer Institute, Amsterdam. Primary HNSCC biopsy samples obtained prior to treatment were used for polyA mRNA sequencing.

  Dataset EGAD00001005720

• 133 CRC RNA-seq fastq

  This dataset contain 133 pairs of colorectal tumor and adjacent normal tissue. For each sample paired RNA-seq fastq were generated using an Illuma Myseq-2000. Those colorectal cancer comprise 101 MSI and 32 MSS tumors.

  Dataset EGAD00001006667

• Longitudinal single-cell RNA-seq data from metastatic ovarian cancer

  Longitudinal single-cell RNA-seq data of prospectively collected tumor tissue samples before and after chemotherapy from 11 HGSOC patients.

  Dataset EGAD00001006922

• DESIGN-NKI RNA-seq

  RNA-Seq data of 36 HPV-negative HNSCC specimens from patients treated at The Netherlands Cancer Institute, Amsterdam. HNSCC biopsy samples obtained prior to treatment (chemo-radiotherapy) were used for polyA mRNA sequencing.

  Dataset EGAD00001005715

• Non-invasive prediction of immunotherapy response (NIPIT) project

  This project aims to find transcriptomic biomarkers for immunotherapy response prediction in several cancer types. So far, we have performed poly-A RNA-seq using whloe blood and peripheral blood mononuclear cells from 33 non-small cell lung cancer patients, collected before and after the start of immunotherapy.

  Study EGAS50000000266

• Single cell Transcriptomic Analysis of Cellular Heterogeneity in Human Colorectal Tumors

  1,591 single cells from 11 colorectal cancer patients were profiled using Fluidigm based single cell RNA-seq protocol to characterized cellular heterogeneity of colorectal cancer. 630 single cells from 7 cell lines were profiled similarly to benchmark de novo cell type identification algorithms.

  Study EGAS00001001945

• Assessing gene expression profiling from FFPE Patient Samples: A Comparison of Two Library Preparation Approaches and Recommendations

  Next-Generation Sequencing (NGS) has transformed cancer research and clinical practice, with Whole Exome Sequencing (WES) driving advances in mutational profiling and personalized oncology. Yet, transcriptomic signatures remain essential for understanding disease mechanisms, including therapy resistance pathways. RNA sequencing (RNA-seq), however, faces unique challenges when dealing with low-input or degraded RNA, as often found in archival formalin-fixed paraffin-embedded (FFPE) tissues. Although previous studies have compared library preparation protocols, rapidly evolving technologies call for updated evaluations. Here, we present a direct comparison of two FFPE-compatible stranded RNA-seq library preparation kits: TaKaRa™ SMARTer® Stranded Total RNA-Seq Kit v2 (Kit A) and Illumina Stranded Total RNA Prep Ligation with Ribo-Zero Plus (Kit B). Both kits generated high-quality RNA-seq data, yet important differences emerged. Notably, Kit A achieved comparable gene expression quantification to Kit B while requiring 20-fold less RNA input, a crucial advantage for limited samples, albeit with increased sequencing depth. We critically discuss these results in relation to RNA availability, technical performance, cost-effectiveness, processing time, and automation potential, offering practical guidance for selecting optimal RNA-seq strategies in clinical and translational research settings

  Study EGAS50000001066

• Longitudinal single-cell RNA-seq data of metastatic ovarian cancer

  Study EGAS00001005006

• single-cell RNA-Seq of colorectal cancer patient samples

  Study EGAS00001006665

• BPH Tissues for Cell Culture and Analysis - Spatial Transcriptomics Identifies Candidate Stromal Drivers of Benign Prostatic Hyperplasia

  The study goal was to apply spatial transcriptomics to identify candidate stromal drivers of benign prostatic hyperplasia (BPH). Laser microdissection (LCM) with RNA-seq of the stroma adjacent to hyperplastic and budding BPH ducts identified secreted factors including IGF1 and CXCL13. Single‐cell RNA sequencing (scRNA‐seq) revealed their co-expression from BPH fibroblasts. Additional studies sought to define similarities to embryonic prostate development, as well as therapeutic applications. Subjects were those who underwent prostatectomy for prostate or bladder cancer, with concurrent BPH, and who consented to use of surplus tissue for research studies. LCM/RNA-seq studies included 5 patients, and scRNA-seq studies included 1 patient. Phenotypic data submitted to this study include patient age, sex, prostate size, and international prostate symptom score (IPSS). Sequencing was done on an Illumina NovaSeq 6000 (LCM/RNAseq) and Illumina HiSeq X-Ten (scRNA-seq). The data processing pipeline utilized Kallisto (LCM/RNA-seq) and Cell Ranger (scRNA-seq). Data submitted are raw sequencing (Fastq) files.

  Study phs003477

• bladder cancer patient data

  This contains H3K27ac ChIP-seq, RNA-seq and HiC fastq files.

  Dataset EGAD00001007052

• Dataset for "HPV integration induces gene fusions" (RNA)

  RNA-seq data for "HPV integration induces gene fusions" We performed RNA-seq analysis of five HPV+ head and neck cancer samples using Illumina short reads. Sequenced are submitted in bam format. We also sequenced one samples with pacBio long reads, and the reads are submitted in fastq format.

  Dataset EGAD50000001303

• Long-read and short-read isoform sequencing in breast cancer

  The goal of this project was to perform long-read RNA sequencing (LR-seq, PacBio) in combination with short-read RNA-seq for systematic characterization of the isoform diversity in primary breast tumor samples. We sequenced the full-length transcriptomes of 26 breast tumors and 4 normal breast samples.

  Study EGAS00001004819

• Oesophageal adenocarcinoma whole exome and RNA-seq raw sequencing data

  Study EGAS00001005957

• Identification of novel fusion genes in lung cancer using breakpoint assembly of transcriptome sequencing data

  Genomic translocation events frequently underlie cancer development through generation of gene fusions with oncogenic properties. Identification of such fusion transcripts by transcriptome sequencing might help to discover new potential therapeutic targets. We developed TRUP (Tumor-specimen suited RNA-seq Unified Pipeline (https://github.com/ruping/TRUP), a computational approach that combines split-read and read-pair analysis with de-novo assembly for the identification of chimeric transcripts in cancer specimens. We apply TRUP to RNA-seq data of different tumor types, and find it to be more sensitive than alternative tools in detecting chimeric transcripts, such as secondaryrearrangements in EML4-ALK-positive lung tumors, or recurrent inactivating rearrangements affecting RASSF8.

  Study EGAS00001000659

• RNA-seq dataset of Oncogenic and immunological targets for matched therapy of pediatric blood cancer patients: Dutch iTHER study experience

  RNA-seq dataset of Oncogenic and immunological targets for matched therapy of pediatric blood cancer patients: Dutch iTHER study experience

  Dataset EGAD00001015639

• Genomic Profiling of Advanced Pancreatic Cancer to inform therapy - RNA-Seq unmapped reads

  Genomics-Driven Precision Medicine for Advanced Pancreatic Cancer - Early Results from the COMPASS Trial - RNA-Seq unmapped reads

  Dataset EGAD00001003582

• Genomic Profiling of Advanced Pancreatic Cancer to inform therapy - RNA-Seq mapped reads

  Genomics-Driven Precision Medicine for Advanced Pancreatic Cancer - Early Results from the COMPASS Trial - RNA-Seq mapped reads

  Dataset EGAD00001003584

• Identification of fusion transcripts by RNA-sequencing and Whole genome sequencing of a METABRIC patient sample

  Fusion transcripts and their exact breakpoints were identified in RNA-seq data (30X) of an ER-,HER2+ breast cancer. DNA-level breakpoints associated with the fusion events were determined by analyzing whole genome sequencing (WGS) data (30X) from the same donor. The RNA-seq and WGS data for this case were generated on the Illumina platform using standard protocols. The patient (MB-0152) derives from the METABRIC cohort for which array based copy number and gene expression data were previously described (EGAS00000000083).

  Study EGAS00001002475

• Cancer Alliance RNA-Seq PolyA

  PolyA selection transcriptome profiling by high-throughput for individualized cancer interpretation

  Dataset EGAD00001006234

• Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer RNA-Seq

  The goal of the sequencing study was to determine the neoantigen profile of melanoma patients. We performed whole exome sequening of the tumor and respective patien matched PBMC samples (2x50nt) and RNA-sqeuencing of the tumor samples. The was analyzed for somatic point mutaions, using the RNA-Seq data to determine mutation expression. This study contains the patient matched RNA-Sequencing data.

  Study EGAS00001003307

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

  Study EGAS00001007014

• Human colorectal cancer single-cell RNA sequencing

  The aim was to investigate changes in transcriptomics following irradiation. This was studied in colorectal cancer-patient derived organoids. Analysis was performed with scRNA-seq.

  Study EGAS50000001348

• Transcriptional and Epigenetic Profiles of Male Breast Cancer at Single-Cell Resolution Nominate Salient Cancer Specific Enhancers

  We performed matched single-cell RNA sequencing (scRNA-seq) and single-cell chromatin accessibility sequencing (scATAC-seq) for two fresh human tumors collected from male breast cancer patients. The integration of these datasets revealed the intratumoral heterogeneity in both transcription and chromatin accessibility for each male breast cancer patient and allowed for comparisons to be made between male and female breast cancer. We hope these data will be a useful reference in the field of breast oncology, specifically for the treatment of males, as few studies have explored male breast cancer genetics.

  Study phs003006