381 results for "cancer rna-seq"

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• Single-cell RNA-seq data from metastatic ovarian cancer for quality control study

  This study includes treatment-naïve fresh tissue sample from 4 HGSOC patients.

  Dataset EGAD00001006984

• Smart-seq3 scRNA-seq of cells from primary (OV2295) and metastatic (OV2295R2) high-grade serous ovarian cancer cell-line

  In this project, single cells extracted from two high-grade serous ovarian cancer cell-lines, one primary (OV2295) and one metastatic (OV2295R2), where split into two 384 well plates and libraries were prepared using Smart-seq3 single cell RNA sequencing (Hagemann-Jensen et al, 2020).

  Study EGAS00001006868

• Transcriptome profiling for Korean Early Onset Gastric Cancer

  To identify transcriptome profile in this unique subset of gastric cancers, RNA-seq analyses were performed using frozen cancer tissue. Adjacent normal tissue of the same patients were used in differently expressed gene selection and fusion gene prediction.

  Dataset EGAD00001002187

• RNA-seq of high-grade serous ovarian cancer in long-term survivors (MOCOG study)

  RNA-seq dataset of high-grade serous ovarian cancer (HGSC) tumours from long-term survivors performed as part of the Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG) study. The dataset includes fastq files from 56 HGSC tumours (53 primary tumours and 3 recurrent tumours) from 53 long-term survivor patients. Libraries were generated using the Illumina Stranded mRNA Prep and 150 bp paired-end sequencing was performed to a minimum of 100 million reads on Illumina NovaSeq 6000 instruments.

  Dataset EGAD00001008537

• High-grade serous ovarian cancer refined with single-cell RNA-sequencing

  We performed single-cell RNA-sequencing of 18,403 cells unbiasedly collected from 7 treatment-naive HGSOC tumours. For each phenotypic cluster of tumour or stromal cells, we identified specific transcriptomic markers. Next, we explored which phenotypic clusters correlate with overall survival based on expression of these transcriptomic markers in 1467 bulk RNA-seq tumours. By evaluating molecular subtype signatures in single cells, we assessed to what extent a phenotypic cluster of tumour or stromal cells contributes to each molecular subtype.

  Study EGAS00001004987

• Chordoma_Sequencing_Project_Whole_Genome

  This is a continuation of the Chordoma Sequencing Project, prelim #G0175-122-CF. All cancers arise due to somatically acquired abnormalities in DNA sequence. Systematic sequencing of cancer genomes allows acquisition of complete catalogues of all classes of somatic mutation present in cancer. These mutation catalogues will allow identification of the somatically mutated cancer genes that are operative and characterise patterns of somatic mutation that may reflect previous exogenous and endogenous mutagenic exposures. In this application, we aim to perform whole genome sequencing on 10 chordoma matched genome pairs. The Chordoma Foundation will fund the RNA Sequencing/Methylation and SNP6 costs for this project while the whole genome and library sequencing will be covered under the CGP, 2012-2013 core WTSI internal funding. An additional sequencing of three cancer cell lines will be added to this work, financed by the Chordoma Foundation. In this project, the Chordoma foundation will pay for the additional costs of methylation, RNA Seq,

  Study EGAS00001000409

• Identifying and Understanding Drivers of Chemoresistance in Small Cell Lung Cancer

  Using patient derived models of small cell lung cancer (SCLC), either the KEAP1 gene or the USP22 gene was deleted using lentiviral expression of a vector expressing CAS9 and single guide RNA (sgRNA) targeting KEAP1 or USP22. Control vector expressing models were also generated. RNA-Seq analysis was performed to compare gene expression in tumors growing in vivo, comparing KEAP1 deleted or USP22 deleted to control tumors under saline-treated and cisplatin/etoposide-treated conditions. Similar experiments were performed overexpressing USP22 or a control vector in a USP22-deleted patient derived xenograft model of SCLC. For USP22-deleted models, we also performed CUT&TAG analyses to examine the impact of USP22 loss on histone modifications.

  Study phs004087

• Comparison of capture-based method for transcriptome profiling of formalin-fixed paraffin embedded tumor samples

  Background: The need for fresh frozen (FF) tissue limits implementing RNA sequencing (RNA-seq) in the clinic. The majority of clinical samples are stored as formalin-fixed, paraffin-embedded (FFPE) tissues. Exome capture platforms have been developed for RNA-seq from FFPE samples. However, these methods have not been systematically compared. Methods: Transcriptomic analysis of 32 FFPE tumor samples from 11 patients was performed using three exome capture-based methods: Agilent SureSelect V6, TWIST NGS Exome, and IDT XGen Exome Research Panel. We compared these methods to TruSeq RNA-seq of fresh frozen (FF-TruSeq) tumor samples from the same patients. We assessed the recovery of clinically relevant biological features. Results: The Spearman's correlation coefficients between the global expression profiles of the three capture-based methods from FFPE and matched FF-TruSeq were high (rho = 0.72-0.9, p < 0.05). A significant correlation between the expression of key immune genes between individual capture-based methods and FF-TruSeq (rho = 0.76-0.88, p < 0.05) was observed. All exome capture-based methods reliably detected outlier expression of actionable genes, including ERBB2, MET, NTRK1, and PPARG. In urothelial cancer samples, the Agilent assay was associated with the highest molecular subtype concordance with FF-TruSeq (Cohen's k = 0.7, p < 0.01). Both Agilent and IDT detected all the clinically relevant fusions that were initially identified in FF-TruSeq. Conclusion: All exome capture-based methods had comparable performance and concordance with FF-TruSeq. By enabling the interrogation of FFPE tumor samples, our findings will enable the implementation of RNA-seq in the clinic to guide precision oncology approaches.

  Study EGAS00001005255

• Comprehensive RNA repository of tissue and plasma from patients with esophageal cancer or precursor lesions

  A comprehensive RNA repository (both coding and non-coding) from 17 patients diagnosed with esophageal adenocarcinoma, high-grade dysplastic or non-dysplastic Barrett’s esophagus. Per patient, a blood plasma sample, and a healthy esophageal and disease tissue sample were collected. This dataset includes both mRNA and small RNA sequencing data (fastq.gz files) of all tissue and plasma samples. In total,102 RNA-seq libraries from 51 samples (17 plasma and 34 tissue samples) were sequenced (plasma mRNA libraries were sequenced twice).

  Dataset EGAD00001006857

• Leiomyosarcoma Cancer Transcriptome Sequencing

  RNA-Seq data (fastq raw read sequences) for French ICGC leiomyosarcoma cancer sequencing project, 78 samples representing 67 donors. Sequencing was performed on Illumina HiSeq. The libraries were then sequenced with a 2 x 75bp paired-end protocol to a minimum mean reads of 50 million.

  Dataset EGAD00001003192