495 results for "cancer rna-seq"

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• 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

• 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

• RNA-seq from ASCENT cohort

  Bulk RNA sequencing data generated from primary resected non-small cell lung cancer (NSCLC) tumour tissue collected through the ASCENT study.

  Study EGAS50000001969

• 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

• 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

• 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 for "A preinvasive regulatory T cell axis for lung cancer interception" paper

  Bulk RNA sequencing data generated from primary resected non-small cell lung cancer (NSCLC) tumour tissue collected through the ASCENT study (Analysis of Screen-Detected Lung Cancers’ Genomic Traits; NCT04204499). The dataset comprises tumour RNA-seq from 89 patients with CT screen-detected NSCLC, predominantly early-stage disease, including lung adenocarcinoma and lung squamous cell carcinoma. The RNA-seq data were used to characterise tumour-immune transcriptional programmes and were analysed alongside matched peripheral blood immune profiling and clinical follow-up data.

  Dataset EGAD50000002852

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• Multi-omics analysis of treated cancer samples

  We treated patient-derived cell lines with effective drug inhibitors that we discovered. To understand the mechanisms behind how the inhibitor can effectively inhibit tumor growth, we performed a series of RNA-Seq, Histone ChIP-Seq, TF ChIP-Seq and ATAC-Seq profiling for samples with and without treatment with the inhibitor. There are 18 RNA-Seq, 20 ATAC-Seq and 182 ChIP-Seq experiments performed. This includes 2 biological replicates. Novaseq 6000 was used to sequence the samples. Library preparation was performed in house for ChIP-Seq and ATAC-Seq, using NEB indexes, while libraries for RNA-Seq were constructed by the sequencing company hired. Total RNA was sequenced. Sequencing parameters used are PE150, bi-directional sequencing. For ATAC-Seq, sequencing was performed up to 40M reads, for ChIP-Seq, sequencing was performed up to 20M reads and for RNA-Seq, sequencing was performed up to 20M to 40M reads. Fastq files are uploaded here.

  Dataset EGAD50000000349

• Lung cancer RNA Seq Data Access Committee

  Dac EGAC00001003317

• 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

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

  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 exome seq data.

  Study EGAS00001003306

• ProstOmics: spatial and bulk multi-omics of prostate cancer

  In this ERC-funded ProstOmics project we have used spatial and bulk multi-omics on fresh frozen prostate cancer samples to investigate cancer biology and find for biomarkers to improve patient treatment. All tissue material were collected from prostate cancer patients undergoing radical prostatectomy who had not received any prior cancer-specific treatment.Of the 498 prostate tissue samples (114 patients) included in our project, 176 samples (N=37 patients) have been analyzed with bulk transcriptomics (RNA-seq). Some of these 176 samples were also analyzed with spatial transcriptomics (n=32, N=8, Visium 10x RNA-seq) and DNA methylomics (n=96, N=24, array). All datasets include metadata for histopathological evaluation. Patient metadata include information of age at surgery, time (months) until reported relapse, pre-surgery PSA and post-surgery T-stage.

  Study EGAS50000000413

• QSEA – modelling of genome-wide DNA methylation from sequencing enrichment experiments

  We describe a new method, QSEA, for analyzing methylation enrichment data. We generated a benchmark experiment consisting of MeDIP-seq enrichment data, targeted BS-seq validation data, and RNA-seq data on five pairs of tumor and adjacent normal samples of non-small cell lung cancer patients. Details of the method and its performance on the data have been described in Lienhard et al. (2016).

  Study EGAS00001001822

• 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