11979 results for "cancer rna-seq"

in 24.57 milliseconds.

• Reference epigenome DB31_N_Alpha_smRNA-Seq data generated from KEP study

  A DB31_N_Alpha_smRNA-Seq single end data for alpha cells(PSA-NCAM(-), pancreas)

  Dataset EGAD00001003494

• Reference epigenome DB31_N_Alpha_mRNA-Seq data generated from KEP study

  A DB31_N_Alpha_mRNA-Seq paired end data for alpha cells(PSA-NCAM(-), pancreas)

  Dataset EGAD00001003485

• ICGC MMML-seq Data Freeze July 2013 whole genome sequencing

  ICGC MMML-seq Data Freeze July 2013 whole genome sequencing

  Dataset EGAD00001000645

• ICGC MMML-seq Data Freeze July 2013 miRNA sequencing

  ICGC MMML-seq Data Freeze July 2013 miRNA sequencing

  Dataset EGAD00001000650

• Reference epigenome ADMSC01_smRNA-Seq data generated from KEP study

  A ADMSC01_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003867

• BLUEPRINT Bisulfite-seq and Whole Genome Sequencing of mantle cell lymphoma

  BLUEPRINT Bisulfite-seq and Whole Genome Sequencing of mantle cell lymphoma

  Dataset EGAD00001001892

• Reference epigenome IPS-Fibroblast01 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007264

• Reference epigenome Pancreas-Islet03 h3k4me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet03 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007204

• Reference epigenome Fat-Preadipocyte01 h3k4me1 ChIP-Seq data generated from KEP study

  Fat-Preadipocyte01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007224

• Reference epigenome Kidney-Podocyte04 h3k36me3 ChIP-Seq data generated from KEP study

  Kidney-Podocyte04 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007244

• Reference epigenome IPS-Fibroblast01 h3k36me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007265

• Reference epigenome IPS-Fibroblast01 h3k4me1 ChIP-Seq data generated from KEP study

  IPS-Fibroblast01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007266

• Reference epigenome IPS-Fibroblast01 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007267

• Reference epigenome IPS-Fibroblast01 h3k9me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007268

• Reference epigenome IPS-Fibroblast01 input ChIP-Seq data generated from KEP study

  IPS-Fibroblast01 input ChIP-Seq paired end data

  Dataset EGAD00001007269

• Reference epigenome IPS-Fibroblast02 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007271

• Reference epigenome IPS-Fibroblast02 h3k36me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007272

• Reference epigenome IPS-ENeuron01 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007292

• Reference epigenome IPS-ENeuron01 h3k36me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007293

• Reference epigenome IPS-ENeuron01 h3k4me1 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007294

• Reference epigenome IPS-ENeuron01 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007295

• Reference epigenome IPS-ENeuron01 h3k9me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007296

• Reference epigenome IPS-ENeuron01 input ChIP-Seq data generated from KEP study

  IPS-ENeuron01 input ChIP-Seq paired end data

  Dataset EGAD00001007297

• Reference epigenome IPS-ENeuron02 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007299

• Reference epigenome IPS-ENeuron02 h3k36me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007300

• Reference epigenome IPS-ENeuron02 input ChIP-Seq data generated from KEP study

  IPS-ENeuron02 input ChIP-Seq paired end data

  Dataset EGAD00001007304

• Identification of driver oncogenes in scirrhous-type gastric cancer cell lines

  Scirrhous type gastric cancer (SGC) is one of the most intractable cancer subtypes in human, and its therapeutic targets have been rarely identified to date. Exploration of somatic mutations in the SGC genome with the next generation sequencers has been hampered by low tumor contents by the markedly increased fibrous tissues. Thus, SGC cell lines may be useful resources to search for novel oncogenes in. Here we have conducted whole exome sequencing and RNA sequencing on two SGC cell lines, OCUM-8 and OCUM-9. Interestingly, most of the mutations thus identified have not been reported. In OCUM-8 cells, a novel CD44-IGF1R fusion gene is discovered, the protein product of which ligates the amino-terminus of CD44 to the transmembrane and the tyrosine-kinase domains of IGF1R. Further, both of CD44 and IGF1R are markedly amplified in the OCUM-8 genome and abundantly expressed. CD44-IGF1R has a transforming ability, and the suppression of its kinase activity leads to rapid cell death of OCUM-8. On the other hand, OCUM-9 seems to possess multiple oncogenic events in its genome. The MET gene is highly amplified, and the treatment of OCUM-9 with crizotinib, a MET inhibitor, results in cell death. Further, a novel BORCS5-ETV6 fusion gene is identified in the OCUM-9 genome. BORCS5-ETV6 possesses oncogenic activity, and suppression of its message partially inhibits cell growth. Prevalence of these novel fusion genes among SGC awaits further investigation, but we validate the significance of cell lines as appropriate reagents for detailed genomic analyses of SGC.

  Study JGAS000179

• Biallelic tumor suppressor loss and DNA repair defects in de novo small cell prostate cancer

  Small cell prostatic carcinoma (SCPC) is an aggressive pathology that is managed similar to small cell lung cancer. SCPC can evolve from prostatic adenocarcinoma in response to androgen deprivation therapy but in rare cases is present at initial cancer diagnosis. The molecular etiology of de novo SCPC is incompletely understood due to the scarcity of tumor tissue and the brief life expectancy of patients. Through a retrospective search of our regional oncology pharmacy database, we identified 18 patients diagnosed with de novo SCPC between 2004 and 2017. Ten patients had pure SCPC pathology and the remainder had some admixed adenocarcinoma foci, but all were treated with first line platinum-based chemotherapy. Median overall survival was 28 months. We performed targeted DNA sequencing, whole exome sequencing and mRNA profiling on formalin-fixed paraffin embedded archival tumor tissue. We observed frequent biallelic deletion and/or mutation of tumor suppressors TP53, RB1 and PTEN, similar to treatment related SCPC. Indeed, at the RNA level pure de novo SCPC closely resembled treatment-related SCPC. However, five patients had biallelic loss of DNA repair genes including BRCA1, BRCA2, ATM and MSH2/6: potentially underlying the high genomic instability of this rare disease variant. Two patients with pure de novo SCPC harbored ETS gene rearrangements to androgen-driven promoters, consistent with evolution of de novo SCPC from an androgen-driven ancestor. Overall, our results reveal a highly aggressive molecular landscape underlies this unusual pathologic variant, and propose opportunities for targeted therapy strategies in a disease with few treatment options.

  Study EGAS00001003007

• Colorectal cancer organoids expressing BRAF (fusion) genes

  Fusion genes may be oncogenic drivers and potential targets for personalized therapies in a variety of cancer types. The BRAF gene is frequently involved in oncogenic fusions, with fusion frequencies of 0.2-3% throughout different cancers. BRAF can be fused to a wide variety of genes, however, BRAF fusions rarely occur in the exact same gene configuration, making assessment of the fusion relevance and decision for drug treatment challenging. Here, we devised a colorectal cancer organoid-based platform to functionally characterize diverse BRAF fusions, containing various partner genes (AGAP3, DLG1 and TRIM24). We compared these BRAF fusions with respect to cellular behaviour, downstream signaling activation and response to targeted therapies. We found that 5’ partner choice of BRAF affects cellular localization and intracellular signaling capacities of the fusion genes. The DLG1-BRAF fusion gene showed distinct localization to the plasma membrane and exhibited increased levels of MAPK pathway activation under unperturbed conditions. Furthermore, the different BRAF fusions showed varying sensitivities to the targeted inhibition of ERK and BRAF, with the DLG1-BRAF fusion being the most sensitive. Additionally, RNA-sequencing identified distinct subsets of genes affected by the DLG1-BRAF fusion gene. Importantly, all fusion genes conveyed resistance to the clinically relevant EGFR/HER2/HER4-inhibitor afatinib, suggesting that BRAF fusions should be screened alongside other MAPK pathway alterations to identify mCRC patients amenable to cetuximab treatment. In summary, we developed a platform to efficiently assess molecular and cellular effects of fusion genes, and revealed that differential drug responses and distinct gene expression profiles can be induced by different BRAF fusions .

  Study EGAS00001003558

• Transcriptomic intra-tumor heterogeneity of colorectal cancer varies depending on tumor location within the colorectum

  Background; Intra-tumor heterogeneity (ITH) of colorectal cancer (CRC) complicates molecular tumor classification, such as transcriptional subtyping. Differences in cellular states, biopsy cell composition, and tumor microenvironment may all lead to ITH. Here we analyze ITH at the transcriptomic and proteomic levels to ascertain whether subtype discordance between multiregional biopsies reflects relevant biological ITH or lack of classifier robustness. Further, we study the impact of tumor location on ITH.MethodsMultiregional biopsies from stage II and III CRC tumors were analyzed by RNA sequencing (41 biopsies, 14 tumors) and multiplex immune protein analysis (89 biopsies, 29 tumors). CRC subtyping were performed using consensus molecular subtypes (CMS), CRC intrinsic subtypes (CRIS), and TUMOR types. ITH-scores and network maps were defined to determine the origin of heterogeneity. A validation cohort was used with one biopsy per tumor (162 tumors).ResultsOverall, inter-tumor transcriptional variation exceeded ITH, and subtyping calls were frequently concordant between multiregional biopsies. Still, some tumors had high ITH and were classified discordantly. Proximal tumors showed ITH related to differences in the microenvironment, while ITH within distal tumors were cancer-cell related. This subtype discordancy reflected actual molecular ITH within the tumors. The relevance of the subtypes was reflected at protein level where several inflammation markers were significantly increased in immune related transcriptional subtypes, which was verified in an independent cohort (Wilcoxon rank sum test; p<0.05).ConclusionOur transcriptomic and proteomic analyses show that the tumor location along the colorectum influence the ITH of CRC, which again influence the concordance of subtyping.

  Study EGAS00001004668

• Functional Multiomics of Cellular Therapy and Immune Checkpoint Blockade Therapy for Solid Tumors

  Tumor samples were collected from a patient with synovial sarcoma, which acquired resistance to ACT targeting NY-ESO-1. Biopsies (n=3; primary, metastasis, and recurrence) were subjected to bulk tumor DNA and RNA sequencing, as well as high-dimensional spatial profiling of RNA and protein targets. Bulk tumor whole exome and RNA sequencing corresponding to all three tumor specimens and whole exome sequencing from patient-matched normal blood are made available through this accession.

  Study phs002762

• Extracellular microRNA Biomarkers for Diagnostic and Prognostic Assessment of Preeclampsia at Triage

  This study is comprised of samples from 123 donors (71 cases and 52 controls) undergoing evaluation for preeclampsia. All samples were collected between 20 and 40 weeks of gestation. Small RNA sequencing was done on serum RNA obtained from donors. We were able to identify 3 bivariate extracellular miRNA biomarkers that can enable early diagnosis of preeclampsia. The raw fastq small-RNA sequencing fastq files will be submitted to dbGaP.

  Study phs003169

• RNA-sequencing on thyroid samples from fetuses with Down syndrome and fetuses with no genetic/developmental abnormality

  The current data pertains RNA-sequencing reads obtained from thyroid samples acquired from fetuses with Down syndrome and fetuses with no genetic/developmental abnormality. Total RNA was isolated from left lobe from thyroid samples using a hand-held homogenizer and the Promega ReliaPrep RNA Miniprep System (Thermo Fisher Scientific). RNA yield was determined with the NanoDrop Microvolume Spectrophotometer (Thermo Fisher Scientific). Fragmentation and mRNA library preparation was performed using the Kapa mRNA Hyperprep Kit (Roche, Basel, Switzerland). Libraries were equimolar pooled and quality was checked on a TapeStation system using the DNA1000 ScreenTape (Agilent Technologies, Santa Clara, CA, USA). Libraries were sequenced with poly(A) selection to sequence all messenger RNA for gene expression analysis on the NovaSeq6000 PE150 (Illumina, San Diego, CA, USA), producing at least 40M 150-bp paired-end reads per library.

  Dataset EGAD50000000387

• RNA sequencing from patient-derived intestinal organoids

  uman duodenal tissues for establishing organoid cultures used in this study were sourced from de-identified surgical specimens (n = 3) of the duodenum obtained from patients who had undergone biopsy procedures unrelated to CeD at Tampere University Hospital. The protocol was approved by the Ethics Committee of Tampere University Hospital, Tampere, Finland (ETL code R18082). RNA from the duodenal organoids was isolated using an RNeasy Kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions. RNA purity and concentration were measured using a NanoDrop One spectrophotometer (NanoDrop Technologies, Wilmington, Delaware, USA). Preparation of the RNA library and transcriptome sequencing was conducted by Novogene Co., LTD (Cambridge, UK). Messenger RNA was purified from total RNA using polyA selection and subjected to library construction. Sequencing was performed on an Illumina platform, and 150 bp paired-end reads were generated.

  Dataset EGAD50000000492

• Dataset for upper_gastrointestinal_tumor-RNA

  Rare cancer sequencing data of 44 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008853

• Tumor-derived cell lines as pharmacogenomic models to predict therapeutic vulnerabilities in hepatocellular carcinoma

  Hepatocellular carcinoma (HCC) is a heterogeneous aggressive malignancy with low efficacy of current therapies at advanced stages. We integrated molecular and pharmacological profiling of a large panel of liver cancer cell lines (LCCL) to assess their clinical relevance as HCC preclinical models and identify new effective therapies and biomarkers of response. Here, we performed multi-omic analysis including whole-exome, RNA and microRNA sequencing in a series 34 LCCL. Molecular profiles of LCCL and primary HCC were compared and we searched for molecular features associated with drug response. Our panel of LCCL faithfully recapitulated the most aggressive molecular “proliferation class” of HCC.

  Dataset EGAD00001004938

• Bulk RNA sequencing of ALS patients

  We performed RNA sequencing to screen for alternative splicing alterations in patients with ALS to identify those linked to ALS pathology.

  Study JGAS000851

• RNA Sequencing of paediatric patients with B lymphoblastic leukemia

  RNA sequencing was performed on 148 Bone marrow of peripheral blood samples of paediatric patients with B-ALL.

  Study EGAS00001004532

• Sequencing_component_for_the_whole_genome_methylation_analysis_in_PBMCs_and_cell_subsets__pilot_study_

  DNA methylation has been shown to play a major role in determining cellular phenotype by regulating gene expression. Moreover, dysregulation of differentially methylated genes has been implicated in disease pathogenesis of various conditions including cancer development as well as autoimmune diseases such as systemic Lupus erythematosus and rheumatoid arthritis. Evidence is rapidly accumulating for a role of DNA methylation in regulating immune responses in health and disease. However, the exact mechanisms remain unknown. The overall aim of the project is to investigate the role of epigenetic mechanisms in regulating immunity and their impact on autoimmune disease pathogenesis.The aim of this pilot study is to perform whole genome methylation analysis in peripheral blood mononuclear cells (PBMCs) and cell subsets (CD4, CD8, CD14, CD19, CD16 and whole PBMCs) obtained from 6 healthy volunteers. Whole genome methylation analysis will be performed using two methodological approaches, the Infinium Methylation Bead Array K450 (Illumina) and MeDIP-seq. mRNA expression arrays will also be performed in order to correlate DNA methylation with gene expression as well as genotyping on the Illumina OmniExpress chip

  Study EGAS00001000490

• single nuclei multiomics (ATAC-RNA) of KOLF2.1J human induced pluripotent stem cells-derived differentiated dopaminergic neurons

  We processed nuclei from hIPSCs differentiated optimized protocols for generating dopaminergic neurons. Cells differentiated with a first iteration were RNA and ATAC-sequenced, to understand our protocol caveats to address for optimization. Cells differentiated with a second protocol and in vivo were RNA-sequenced to validate the identities of our cells.

  Study EGAS50000001418

• Genomic Characterization of Duke Melanoma Brain Metastases

  Previous work has shown that melanoma brain metastases (MBM) have a unique molecular profile compared to extracranial metastases (ECM). Description of the biology of MBM and associations between biological features and clinical outcomes will facilitate the design of rational therapies for patients with MBM. To better characterize the molecular profile of MBM, we conducted whole exome sequencing (WES) and total RNA sequencing on MBM tissues obtained from an independent cohort of 14 patients from Duke University Medical Center. This cohort adds valuable patient samples to the field and enables strengthened investigation of somatic mutations and gene expression profiles within and between cohorts. The mutational landscape and gene expression of MBM from the Duke cohort resembled those previously reported in a previously published dataset of WES and RNA sequencing in MBM and matched ECM tissues from UT MD Anderson Cancer Center (Fischer et al., 2019 30787016). Duke MBM demonstrated similar rates of mutations in genes associated with melanoma, including BRAF, NRAS, and PTEN, as the MD Anderson cohort. Immune populations in MBM were similar between Duke and MD Anderson MBM, though Duke samples demonstrated more resting CD4+ memory T cells. The ratio of M2 to M1 macrophages in the Duke cohort was similar to that of the MD Anderson cohort. In MBM, a higher M2:M1 ratio may contribute to an immune-suppressive tumor microenvironment. Sequencing data and sample attributes from MBM tissues obtained from 14 patients from Duke University Medical Center will be available through dbGaP.

  Study phs003009

• Reference epigenome Islet-derived-MSC04 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007431

• Reference epigenome Islet-derived-MSC02 input ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 input ChIP-Seq paired end data

  Dataset EGAD00001007415

• Reference epigenome Islet-derived-MSC04 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007433

• Reference epigenome Islet-derived-iPSC02 input ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 input ChIP-Seq paired end data

  Dataset EGAD00001007449

• Reference epigenome Islet-derived-MSC04 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007434

• ATAC-seq

  ATAC-seq data for 26 CLL samples (7 controls, 19 tumor) of the CancerEpiSys-PRECiSe project.

  Dataset EGAD00001005967

• Reference epigenome Islet-derived-MSC03 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007416

• Reference epigenome Islet-derived-MSC04 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007430

• Reference epigenome Islet-derived-MSC04 input ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 input ChIP-Seq paired end data

  Dataset EGAD00001007435