14977 results for "cancer studies using rna-seq"

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• Short-read (RNA-seq)

  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.

  Dataset EGAD00001006596

• Analyses of RNAseq data from NEN patient derived tumor organoids (PDTOs) and matched parental tumors

  The dataset includes RNA-seq expression R data, RNA-seq gene counts matrix, and RNA-seq gene FPKM matrix from 21 NEN tumors or metastases and matched PDTOs. The sequencing was done with either a Nextseq 2000 or a NovaSeq 6000 instrument.

  Dataset EGAD00001009994

• Whole genome sequencing of multiple myeloma patient samples.

  Tumor (CD138+ plasma cells) and non-tumor (peripheral blood white cells or stem cell harvest) DNA from patients with a plasma cell dyscrasias were sequenced. Whole genome sequencing using high molecular weight DNA was performed using the 10X Genomics Chromium platform on either a HiSeq4000 or NovaSeq (Illumina) using 100 to 150 bp paired-end reads. The dataset consists of 111 patients, and 223 samples in total (matched tumor and control per patient; one patient had 2 tumor samples sequenced). Diseases consisted of 2 MGUS patients, 8 SMM patients, 91 newly diagnosed myeloma patients, 1 previously treated patient, 4 relapsed MM patients, and 5 PCL patients. Paired RNA-seq data are available for 81 of the samples under study EGAS00001003411.

  Dataset EGAD00001004452

• HipSci_Illumina 450K Methylation analysis_Healthy volunteers

  The HipSci project brings together diverse constituents in genomics, proteomics, cell biology and clinical genetics to create a UK national iPS cell resource and use it to carry out cellular genetic studies. In this sub-study we performed Methylation analysis using the Infinium HumanMethylation450 BeadChip on iPS cells generated from skin biopsies from healthy volunteers.

  Study EGAS00001000865

• Genetic coupling of enhancer activity and connectivity in gene expression control

  In this study, we investigated whether the genetic effects of eQTLs on enhancer activity and enhancer-promoter contacts (connectivity) are inherently coupled or distinct. To address this, we performed Capture Hi-C targeting ~1100 eQTL loci, along with ATAC-seq and RNA-seq, in monocytes from 34 healthy male donors. Using single-modality and multi-modality Bayesian statistical approaches, we demonstrated that eQTL effects are predominantly shared across enhancer activity, connectivity, and gene expression. However, we also identified a locus where an eQTL disrupts a CTCF binding site at a putative insulator, facilitating contacts between the PCK2 gene promoter and downstream enhancers. To validate these findings, we used 4C-seq to confirm the allelic effects of eQTLs on enhancer-promoter connectivity at the PCK2 locus and another eQTL region associated with THBS1 expression, both of which showed consistent eQTL effects across all three modalities. Additionally, we conducted Hi-C on monocytes from two donors to map reference TADs and performed ChIP-seq to confirm the allelic effects of the PCK2 eQTL on CTCF binding. Together, these data provide comprehensive insights into the genetic coupling of enhancer activity, connectivity, and gene expression.

  Study EGAS50000000756

• Prognostic whole-genome and transcriptome signatures in colorectal cancers

  This dataset consists of RNA-seq CRAM raw-data files of 1063 primary colorectal cancer and 120 adjacent normal tissue samples. The expression profiles for these samples can be found at the ArrayExpress with accession number E-MTAB-12862.

  Dataset EGAD50000000169

• RNA-Seq Dataset of Organoid drug profiling identifies methotrexate as a therapy for SCCOHT, a rare pediatric cancer

  We generated and characterized tumoroids from small cell carcinoma of the ovary, hypercalcemic type. Furthermore, we identified a drug that is selective and effective against SCCOHT tumoroids.

  Dataset EGAD00001015441

• Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma

  Background: Cancer patients with advanced disease exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates sequencing data with functional assay data to develop patient-specific combination cancer treatments. Methods: Tissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient’s epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient’s primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay. Results Our computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model). Conclusions These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.

  Study EGAS00001003564

• Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 Diabetes susceptibility loci

  Human genetic studies have emphasised the dominant contribution of pancreatic islet dysfunction to development of Type 2 Diabetes (T2D). However, limited annotation of the islet epigenome has constrained efforts to define the molecular mechanisms mediating the, largely regulatory, signals revealed by Genome-Wide Association Studies (GWAS). We characterised patterns of chromatin accessibility (ATAC-seq, n=17) and DNA methylation (whole-genome bisulphite sequencing, n=10) in human islets, generating high-resolution chromatin state maps through integration with established ChIP-seq marks. We found enrichment of GWAS signals for T2D and fasting glucose was concentrated in subsets of islet enhancers characterised by open chromatin and hypomethylation, with the former annotation predominant. At several loci (including CDC123, ADCY5, KLHDC5) the combination of fine-mapping genetic data and chromatin state enrichment maps, supplemented by allelic imbalance in chromatin accessibility pinpointed likely causal variants. The combination of increasingly-precise genetic and islet epigenomic information accelerates definition of causal mechanisms implicated in T2D pathogenesis.

  Study EGAS00001002592

• RNA-seq Phase Ib of olaparib and capivasertib

  RNA-seq Phase Ib of olaparib and capivasertib

  Dataset EGAD00001006840

• RNA-seq from normal human tissues

  RNA-seq from normal human tissues (2 x 75 bp)

  Dataset EGAD00001001057

• RNA-seq from normal human tissues

  RNA-seq from normal human tissues (2 x 250 bp)

  Dataset EGAD00001001922

• RNA-seq from melanoma biopsies

  RNA-seq data for melanoma biopsies at baseline and after treatment

  Dataset EGAD00001009059

• Chip_seq_oesophageal_adenocarcinoma_

  There is complex interplay between underlying genetic sequence and epigenetic factors, however this interplay is still illusive. Massively reconstructed cancer genomes can be used in order to better understand this relationship. Six oesophageal adenocarcinoma organoids will be sequenced using long read sequencing technologies and will be profiled for a range of epigenetic marks. Combination of genetic and epigenetic data will allow the thorough characterisation of reference cancer genomes and will provide novel insights into mechanisms of gene regulation. Description: Chip-seq Sequencing of 5 patient derived organoid cell models. Each model was derived from a piece of patient tumour taken following surgical rescetion of the tumour. All model derivations took place with the CGaP facility in Sanger.

  Study EGAS00001007180

• Immune Determinants of Resistance to PD-1 Blockade in Renal Cell Carcinoma

  We performed an integrated bulk and single-cell transcriptomic analysis of tumors from 102 advanced renal cell carcinoma patients enrolled in a phase II clinical trial of frontline nivolumab (NCT03117309) to investigate determinants of response to anti-PD1 monotherapy. The present study includes 112 archival or pre-treatment tumors of clear cell and non-clear cell histologies from 90 patients that underwent bulk RNA sequencing (RNA-seq). For 76 patients, 74 of whom had matched RNA-seq, whole exome sequencing (WES) was also performed on tumor samples as well as matched peripheral blood. Additionally, 19 fresh tumor samples from 17 patients, including pre and post-treatment samples and mixed histologies, were locally cryopreserved and centrally processed for single cell RNA (scRNA-seq) and TCR (scTCR-seq) sequencing. 7 patients had matched scRNA/scTCRseq, RNAseq, and WES.

  Study phs003618

• RNA-seq from B-ALL patients treated on the ALLG ALL09 study

  Dataset contains RNA-sequencing data (fastq files) from 46 patients treated on the Australasian Leukaemia and Lymphoma Group (ALLG) ALL09 study (ACTRN12618001734257). All patients were diagnosed with CD19 positive B-cell acute lymphoblastic leukaemia and were negative for Philadelphia chromosome positive disease. Patient samples (bone marrow or peripheral blood) were taken at diagnosis. mRNA was extracted from lymphoblasts and underwent paired-end sequencing using either the illumina NextSeq (75 bp PE) or MGI DNABSEQ-G400 (100bp PE) platforms. Transcriptomic data was used for identification of genomic drivers of acute lymphoblastic leukaemia (ALL).

  Dataset EGAD50000001603