11925 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

• Drug-induced epigenomic plasticity reprograms circadian rhythm regulation to drive prostate cancer towards androgen-independence

  ChIP-seq for AR, FOXA1 and H3K27ac in primary prostate tumors before and after 3 months of neoadjuvant enzalutamide treatment. RNA-seq expression data of primary prostate tumors before and after 3 months of neoadjuvant enzalutamide treatment.

  Dataset EGAD00001008562

• 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

• RNA-seq of PDAC patient-derived xenograft tumors

  This dataset contains single cell RNA-seq data of stromal cells derived from two PDX models (N = 2 in total) and bulk RNA-seq data of two PDX models treated with gemcitabine and our novel antibody-drug conjugate, C6-EBET (N = 59 in total). Bulk RNA-seq experiments were performed with Agilent SureSelect Strand Specific RNA Library Prep Kit (Agilent). Single cell RNA-seq experiments were performed with Chromium Single Cell 3' Reagent Kits v2 Chemistry (10x Genomics).

  Dataset EGAD00001010130

• bulk RNA-seq of the 5 HCC patients which also have the single cell T cell RNA-seq data

  bulk RNA-seq data of the 5 HCC patinets. Single cell RNA seq data of these patients was under the accession number EGAD00001003337

  Dataset EGAD00001004148

• 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

• Response and Resistance to ER-Directed Therapy in Metastatic Breast Cancer

  Despite the reduction in cancer recurrence and mortality provided by hormonal therapy, estrogen receptor positive (ER+) metastatic breast cancer (MBC) remains the most common cause of breast cancer death, resulting in more than 20,000 deaths in the U.S each year. Although several therapeutic options exist to target the estrogen receptor, resistance to these therapies invariably occurs. Genetic mechanisms of acquired resistance to targeted therapies in ER+ MBC are not well understood. To address this, we developed a prospective biopsy protocol where multiple research biopsies are collected from metastatic samples and used for pathology, ER/PR/HER2, OncoPanel (a CLIA 300 gene sequencing panel), whole exome sequencing (WES), transcriptome sequencing (RNA-Seq), and single-cell RNA-seq (scRNA-seq). In some cases, tissue is used for cell line and xenograft generation. Archival primary biopsies are also obtained when possible for parallel genomic studies. Patients are followed and serial metastatic biopsies are obtained. This data will be used to determine the landscape of genomic alterations in treatment-resistant ER+ MBC, and compared to data generated by TCGA for treatment-naive ER+ primary breast cancer. Comparison of genomic studies from resistant, metastatic specimens with corresponding pre-treatment primary samples will allow us to identify genomic association with resistance to specific therapies. Analysis of the serial metastatic biopsies collected over the course of multiple treatments will also shed light on tumor evolution under selective therapeutic pressure.NoteA subset of the MBC blood biopsies that are included in our study can be accessed and downloaded from the phs001977 accession.Another subset of patients in this study participated in a clinical trial (NCT02871791) and have their data deposited in the current study as well.

  Study phs001285

• CASCADE tumour transcriptome data

  RNA-Seq data from 20 fresh-frozen postmortem samples from three patients who participated in the CASCADE rapid autopsy program and died of metastatic castration resistant prostate cancer.

  Dataset EGAD00001009492

• Expressed Pseudogenes in the Transcriptional Landscape of Human Cancers

  In this study, we describe a systematic analysis of pseudogene 'transcription' from an RNA-Seq resource of 293 samples, from 13 cancer and normal tissue types. We observed a highly prevalent, genome-wide expression of pseudogenes that could be categorized as universally expressed or lineage- and/or cancer-specific. We also explored disease subtype specificity and functions of selected expressed pseudogenes. We provide evidence that transcribed pseudogenes are a significant contributor to the transcriptional landscape of cells and are positioned to play significant roles in cellular differentiation and cancer progression. Our work provides a transcriptome resource that enables high-throughput analyses of pseudogene expression.

  Study phs000525

• BLUEPRINT EpiVar RNA-seq for Naive naive CD4+ T-cells

  BLUEPRINT EpiVar RNA-seq for Naive naive CD4+ T-cells

  Study EGAS00001000752

• 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

• Single Cell Transcriptomic Analysis of Cellular Heterogeneity in Human Colorectal Tumours

  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.

  Dataset EGAD00001002727

• In this study single cell RNA-Seq data was used to train a deconvolution algorithm. The algorithm was validated on paired bulk RNA-Seq profiles.

  In this study single cell RNA-Seq data was used to train a deconvolution algorithm. The algorithm was validated on paired bulk RNA-Seq profiles.

  Dataset EGAD00001009688

• Hereditary Gastric Cancer Syndromes: An Integrated Genomic and Clinicopathologic Study of the Predisposition to Gastric Cancer

  Gastric cancers are cancers of the stomach. Hereditary cancers are passed from parent to child. Researchers want to gather data about hereditary gastric cancers. Bulk RNA sequencing (RNA-seq) of signet ring cells (SRCs) and adjacent non-SRC epithelium (NEP) was performed on laser-capture microdissected (LCM) regions of interest found in risk-reducing total gastrectomy specimens from hereditary diffuse gastric cancer (HDGC) patients (Clinicaltrials.gov ID: NCT03030404). Twenty patients (6 male, 14 female) with confirmed HDGC were identified. Analysis of differentially expressed genes (DEGs) demonstrated upregulation of certain individual epithelial–mesenchymal transition (EMT) and proliferation genes. SRC regions had significant enrichment in pathways involved in T cell signaling. CIBERSORTx predicted significant increases in the presence of regulatory T cells (Tregs) specific to SRC regions.

  Study phs003422

• Single-cell RNA-seq, single-cell TCR-seq, single-cell ATAC-seq, and CITE-seq of human tonsillar CD4+ T cells

  To describe human tonsillar CD4+ T cells, especially IL10+ Foxp3- follicular CD4+ T cells (Tfr1, T follicular regulatory type 1), human tonsillar CD4+ T cells were analyzed with single-cell RNA-seq, single-cell TCR-seq, single-cell ATAC-seq, and CITE-seq.

  Study JGAS000805

• Single-cell RNA-seq, single-cell TCR-seq, single-cell BCR-seq, and CITE-seq of B and T cells

  To describe human tonsillar CD4+ T cells, especially IL10+ Foxp3- follicular CD4+ T cells (Tfr1, T follicular regulatory type 1), human tonsillar CD4+ T cells were analyzed with single-cell RNA-seq, single-cell TCR-seq, single-cell ATAC-seq, and CITE-seq.

  Study JGAS000827

• RNA-Seq - Characterization of a Breast Patient-derived Tumor Organoid biobank from an underserved population of patients

  RNA-seq datasets of patient and patient-derived organoids

  Study EGAS50000000683

• van Hijfte GBM dataset 2022/A (single-nucleus RNA-seq)

  van Hijfte GBM dataset 2022/A (single-nucleus RNA-seq) 1x GBM

  Study EGAS00001006920

• A Genomic Approach to Improved Diagnosis and Treatment of Neuroendocrine Tumors

  These are RNA-Seq samples from patients undergoing surgery for small bowel and pancreatic neuroendocrine tumors. Normal tissue, primary tumors, lymph nodes, and liver metastases were collected at surgery and stored in RNAlater until RNA extraction was carried out for RNA-Seq.

  Study phs001772

• RNA-seq analysis of human primary keratinocytes and skin

  Bulk and single cell RNA-seq analysis of human keratinocyte and human skin

  Study EGAS00001002981

• Clinical genome sequencing uncovers potentially targetable truncations and fusions of MAP3K8 in spitzoid and other melanomas

  Spitzoid melanoma is a specific morphologic variant of melanoma that most commonly affects children and adolescents and ranges on the spectrum of malignancy from low-grade to overtly malignant. These tumors are generally driven by fusions of ALK, RET, NTRK1/3, MET, ROS1 and BRAF. However, in approximately 50% of cases, no genetic driver has been established. Clinical whole genome and transcriptome sequencing (RNA-Seq) of a spitzoid tumor from an adolescent revealed a novel gene fusion of MAP3K8, encoding a serine−threonine kinase that activates MEK. The patient who had exhausted all other therapeutic options was treated with a MEK inhibitor and underwent a transient clinical response. We subsequently analyzed spitzoid tumors from 49 patients by RNA-Seq and found in-frame fusions or C-terminal truncations in 33% of cases. The fusion transcripts and truncated genes all contained MAP3K8 exons 1-8 but lacked the autoinhibitory final exon. Data mining of RNA-Seq from the Cancer Genome Atlas (TCGA) uncovered analogous MAP3K8 rearrangements in 1.5% of adult melanomas. Thus, MAP3K8 rearrangements - uncovered by comprehensive clinical sequencing of a single case - are the most common genetic event in spitzoid melanoma, are present in adult melanomas, and could be amenable to MEK inhibition.

  Study EGAS00001003430

• The UC San Diego Chronic Lymphocytic Leukemia (CLL) Study

  The UC San Diego Chronic Lymphocytic Leukemia Study is designed to identify genetic, epigenetic, and transcriptional changes important for CLL. This data release consists of samples collected to 1) identify genomic changes associated with CLL progression using serially collected tumor samples; 2) characterize the transcriptional consequences of SF3B1 mutation through RNA-seq; and 3) characterize the expression profiles of ROR1+ vs. ROR1- tumors. The samples were collected from participants at the University of California, San Diego Moores Cancer Center and the data was generated at UC San Diego or in collaboration with the BC Cancer Agency Genome Sciences Centre.

  Study phs000767

• Resistance studies in Lung Cancer

  The purpose of the study was to compare gene expression profiles from a cohort of crizotinib-resistant ALK-rearranged lung tumors and a cohort of treatment-naive ALK-rearranged lung tumors. Expression profiles were generated by RNA-seq. In parallel, gene expression profiles were obtained from ALK-rearranged lung cancer cell lines in the presence or absence of the ALK inhibitor TAE684. Gene expression profiles were also obtained from ALK-rearranged cells ectopically expressing genes associated with ALK inhibitor resistance that were identified from a functional genetic study.

  Study phs000855

• Comprehensive peripheral blood immunoprofiling reveals five immunotypes with immunotherapy response characteristics in cancer patients

  The lack of comprehensive diagnostics and consensus analytical models for evaluating the status of a patient’s immune system has hindered a wider adoption of immunoprofiling for treatment monitoring and response prediction in cancer patients. To address this unmet need, we developed an immunoprofiling platform that uses multiparameter flow cytometry and RNA-seq to characterize immune cell heterogeneity in the peripheral blood of healthy donors and patients with advanced cancers. Using unsupervised clustering, we identified five immunotypes with unique distributions of different cell types, and gene expression profiles.

  Study EGAS50000000286

• Gene signature for predicting homologous recombination deficiency in triple-negative breast cancer

  We developed a gene expression-based classifier for homologous recombination deficiency (HRD) in breast cancer patients using WES and RNA-seq data obtained from 94 TNBC samples from a Malaysian hospital-based cohort (MyBrCa). This classifier was able to predict for high levels of a mutational signature associated with HRD in an additional MyBrCa TNBC validation cohort (this EGA study) as well as in TNBCs from TCGA. We also validated the classifier on a NanoString platform with both fresh frozen and FFPE tissue.

  Study EGAS00001006518