7189 results for "({!join from=dataset_studies to=id v=$q1}) OR ("transcriptome" OR "RNA-seq" OR "rna sequencing" OR "rna-sequencing")"

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• Characterization of HCV-specific CD4 T cells during DAA-Therapy

  To be added...

  Dataset EGAD00001005432

• IRF5 HL RNASeq dataset

  bam files associated with the study EGAS00001001205

  Dataset EGAD00001001417

• Pipeline study - RNAseq dataset

  Fernandez-Cuesta et al., RNAseq data Pipline

  Dataset EGAD00001000746

• PTA_thyroid__RNA_

  Exploring the somatic mutations in single cells derived from the thyroid

  Study EGAS00001007946

• RNAseq dataset for MALT1 in MCL

  Manuscript Title: Co-targeting of BTK and MALT1 overcomes resistance to BTK inhibitors in mantle cell lymphoma Journal: Journal of Clinical Investigation Authors Vivian Changying Jiang1, Yang Liu1, Junwei Lian1, Shengjian Huang1, Alexa Jordan1, Qingsong Cai1, Fangfang Yan3, Joseph Mitchell McIntosh1, Yijing Li1, Yuxuan Che1, Zhihong Chen1, Jovanny Vargas1, Maria Badillo1, JohnNelson Bigcal1, Heng-Huan Lee1, Wei Wang1, Yixin Yao1, Lei Nie1, Christopher Flowers1, and Michael Wang1, 2* Abstract Bruton’s tyrosine kinase (BTK) is a proven target in mantle cell lymphoma (MCL), an aggressive subtype of non-Hodgkin lymphoma. However, resistance to BTK inhibitors is a major clinical challenge. We here report that MALT1 is one of the top overexpressed genes in ibrutinib-resistant MCL cells, while expression of CARD11, which is upstream of MALT1, is decreased. MALT1 genetic knockout or inhibition produced dramatic defects in MCL cell growth regardless of ibrutinib sensitivity. Conversely, CARD11 knockout cells showed anti-tumor effects only in ibrutinib-sensitive cells, suggesting that MALT1 overexpression could drive ibrutinib resistance via bypassing BTK-CARD11 signaling. Additionally, BTK knockdown and MALT1 knockout markedly impaired MCL tumor migration and dissemination, and MALT1 pharmacological inhibition decreased MCL cell viability, adhesion, and migration by suppressing NF-κB, PI3K-ATK-mTOR, and integrin signaling. Importantly, co-targeting MALT1 with safimaltib and BTK with pirtobrutinib induced potent anti-MCL activity in ibrutinib-resistant MCL cell lines and patient-derived xenografts. Therefore, we conclude that MALT1 overexpression associates with resistance to BTK inhibitors in MCL, targeting abnormal MALT1 activity could be a promising therapeutic strategy to overcome BTK inhibitor resistance, and co-targeting of MALT1 and BTK should improve MCL treatment efficacy and durability as well as patient outcomes. Dataset description: The bulk RNA-seq dataset was generated for the cell lines below and used for two major purposes: 1. DEG analysis and GSEA analysis comparing IBN-R and IBN-S cells 2. DEG analysis and GSEA analysis comparing MCL cells with/without MI-2 treatment. sample Cell MI-2 Ibrutinib (IBN) Venetoclax (VEN) Used for IBN-R vs IBN-S comparison Used for MI-2 vs untreated (DMSO) H9 Granta519 - R S yes H21 Granta519 - R S yes H33 Granta519 - R S yes H10 Granta519-VEN-R - R R yes H22 Granta519-VEN-R - R R yes H34 Granta519-VEN-R - R R yes H3 JeKo BTK KD_1 - R R yes yes H15 JeKo BTK KD_1 - R R yes yes H27 JeKo BTK KD_1 - R R yes yes H5 JeKo BTK KD_2 - R R yes yes H17 JeKo BTK KD_2 - R R yes yes H29 JeKo BTK KD_2 - R R yes yes H1 JeKo-1 - S R yes yes H13 JeKo-1 - S R yes yes H25 JeKo-1 - S R yes yes H7 Mino - S S yes H19 Mino - S S yes H31 Mino - S S yes H8 Mino-VEN-R - S R yes H20 Mino-VEN-R - S R yes H32 Mino-VEN-R - S R yes H11 Rec-1 - S S yes H23 Rec-1 - S S yes H12 Rec-VEN-R - S S yes H24 Rec-VEN-R - S R yes H36 Rec-VEN-R - S R yes H35 Rec-1 -- S R yes H4 JeKo BTK KD_1 + MI-2 + yes H16 JeKo BTK KD_1 + MI-2 + yes H28 JeKo BTK KD_1 + MI-2 + yes H6 JeKo BTK KD_2 + MI-2 + yes H18 JeKo BTK KD_2 + MI-2 + yes H30 JeKo BTK KD_2 + MI-2 + yes H2 JeKo-1 + MI-2 + yes H14 JeKo-1 + MI-2 + yes H26 JeKo-1 + MI-2 + yes

  Dataset EGAD00001009771

• Complete sequencing dataset of study EGAS00001002795

  Dataset EGAD00001004572

• Whole transcriptome and 850k methylome profiling of human MBM

  Assessment of methylation status of ~850,000 sites

  Dataset EGAD00010002281

• SCANDARE MACARON 78 bulk-RNAseq HGSOC

  78 bulk-RNAseq from HGSOC patients from SCANDARE MACARON

  Dataset EGAD50000000205

• COVID-19 PBMC single-cell transcriptomics, multiplexed

  COVID-19 PBMC single-cell transcriptomics, multiplexed

  Dataset EGAD50000000188

• RNAsequencing

  RNAsequencing from 271 samples.

  Dataset EGAD50000000312

• RNAseq for 4 pdx and 1 cell-line

  RNAseq for #1049, #111, #1217, #206, COV362

  Dataset EGAD50000000032

• CRISPR_perturbations_IRF4_PRDM1_Bcells

  CRISPR perturbations of IRF4, PRDM1, and AAVS1 (within PPP1R12C gene) in activated B cells

  Dataset EGAD50000002113

• IPF Core Biopsy Study Gene Expression Data

  Matrix of gene x sample RNAseq read count data.

  Dataset EGAD00001007566

• RNAseq samples

  20 RNAseq samples of Human CD4+ cells

  Dataset EGAD00001010182

• Molecular effects of TYK2 inhibition

  Transcriptomic profiling of skin biopsies from psoriasis patients following treatment with deucravacitinib

  Dataset EGAD00001008417

• DSRCT RNAseq

  Desmoplastic small round cell tumor (DSRCT) RNAseq data. 14 tumor samples.

  Dataset EGAD00001003980

• Centroblast Cohort

  miRNA-seq Cohort of 15 Benign Centroblasts

  Dataset EGAD00001001075

• RNA Sequence-Based Analysis Used to Compare Breast Primary and Metastatic Tumor Pairs

  The goal of this study is to capture the transcriptome landscape of luminal and basal-like metastatic breast tumors. We will aim to show the importance of using the most up-to-date cancer biopsy for detecting cancer drivers that were often not present in the primary tumor.

  Study phs001866

• Epigenomic timecourse of brain organoid development

  Single-cell Cut&Tag of three histone modifications and chromatin accessibility over a timecourse of brain organoid development from pluripotency. We profiled 5 time points of brain organoid development and 2 time points of retinal organoid development. At each time point used scCut&Tag to profile histone modifications (H3K27me3, H3K27ac, H3K4me3) as well as multiome-seq (10X) to jointly profile transcriptome and chromatin accessibility from the same cell suspension. Library preparation was carried out with the 10x genomics platform.

  Dataset EGAD50000000222

• Human CMV-specific CD8+ T cells

  In this study we established a comprehensive workflow to collect multi-omics single-cell data using a commercially available micro-well based platform. This included whole transcriptome, cell surface markers (targeted sequencing-based cell surface proteomics), T cell specificities, adaptive immune receptor repertoire (AIRR) profiles and sample multiplexing. With this technique we identified novel paired T cell receptor sequences for three prominent human CMV epitopes. In addition, we review the ability of dCODE dextramers to detect antigenspecific T cells at low frequencies by estimating sensitivities and specificities when used as reagents for single-cell multi-omics.

  Dataset EGAD50000000894

• Spatio-temporal evolution of the primary glioblastoma genome (newly added after 2015)

  Since our genomic analysis of 38 GBM patients, we recruited more cases to further study spatial and temporal evolution of glioblastoma with international collaboration of multiple institutes. The cases in this study includes the whole exome and transcriptome of GBM patients newly recruited in Samsung Medical Center.

  Study EGAS00001001800

• Molecular profiles and urinary biomarkers of upper tract urothelial carcinomas associated with aristolochic acid exposure

  This study present a molecular analysis of upper urinary tract urothelial tumours (UTUC) and urines of patients with past exposure to carcinogenic aristolochic acid (AA). Tumor exome and transcriptome sequencing revealed the AA-specific mutations in UTUC and identified deleterious mutations at both the gene and transcript levels.

  Study EGAS00001005363

• Growth Hormone (GH) -secreting Pituitary Adenoma

  We profiled the somatic landscape of 21 growth hormone (GH) -secreting pituitary adenomas using somatic copy-number alteration (SCNA), whole-genome sequencing (WGS), bisulfate sequencing, and transcriptome approaches. See details in Valimaki et al. Genetic and epigenetic characterization of growth hormone (GH) - secreting pituitary tumors. Manuscript in preparation, 2019.

  Study EGAS00001003488

• Multi-omics of Richter syndrome

  Richter syndrome (RS) is the aggressive transformation of Chronic Lymphocytic Leukemia, mostly with Diffuse Large B-Cell Lymphoma histology. While histologically similar, RS and de novo DLBCLs present with different clinical courses. We characterized the RS epigenome by combining genome-wide DNA methylation with transcriptome-sequencing on a large RS cohort.

  Study EGAS00001005495

• Development and Validation of a Prognostic and Predictive 32-Gene Signature for Gastric Cancer

  We applied this signature to a 567-patient GC cohort to establish genomic-based molecular subtypes and then used a support vector machine to build a molecular subtype-based risk-scoring model. Both source code and supplementary datasets for risk score prediction are available at https://github.com/hwanglab/Yonsei_gastric_cancer_32genes.

  Dataset EGAD00001008091

• Single cell sequencing of human normal breast myoepithelial cells

  The dataset includes 6 FASTQ files with single cell transcriptome sequencing data of normal breast myoepithelial cells from ducts and TDLUs derived from reduction mammoplasties from three patients. Chromium Single Cell 3’ Reagent Kit v2 or v3 (10x Genomics) were used for processing of cells, whereafter sequencing was performed using the Illumina® NextSeq500/550 High Output Kit v2. Cell Ranger was used for generating FASTQ files and files from different lanes were concatenated prior to uploading the data to EGA.

  Dataset EGAD00001008468

• Molecular and phenotypic data for patients enrolled in the IMmotion150 trial

  Tumor transcriptome and whole exome sequencing data (matched tumor/normal for somatic mutation calling) along with key phenotypic information are provided for patients enrolled in the phase 2 IMmotion150 trial, assessing efficacy of atezolizumab monotherapy or combination of atezolizumab and bevacizumab versus standard of care (sunitinib) in 1L renal cell carcinoma. This data set accompanies the respective Nature Medicine publication (PMID: 29867230).

  Dataset EGAD00001004183

• Genome and transcriptome sequence data from a colorectal adenocarcinoma patient (Response to Angiotensin Blockade with Irbesartan in a Patient with Metastatic Colorectal Cancer)

  Genome and transcriptome sequence data from a colorectal adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study. These data are included in the manuscript entitled, "Response to Angiotensin Blockade with Irbesartan in a Patient with Metastatic Colorectal Cancer".

  Dataset EGAD00001001876

• scRNA-seq of glioblastoma cells alone or after co-culture with cerebral organoid cells

  Dataset EGAD00001005315

• README-for-EGAS00001004349-linking-HIPO-K09R-RNA-files

  Dataset EGAD00001006785

• RNA-sequençing of 21 inflammatory hepatocellular adenomas and 1 non-tumoral sample

  Dataset EGAD00001005284

• RNAseq for 8 PDX

  RNAseq for #111, #1177, #206, #201, #29, #931, WO-19, WO-2

  Dataset EGAD50000000116

• COVID-19 whole blood bulk trasncriptomics multi-center

  COVID-19 whole blood bulk trasncriptomics multi-center

  Dataset EGAD50000000187

• Single cell RNAseq of PBMC in chimeric twins

  scRNAseq data generated with 10x genomics

  Dataset EGAD50000000132

• Pilot data of PDiamond Lung Cancer Data

  RNAseq pilot data of PDiamond Korean lung cancer data

  Dataset EGAD50000000843

• EGAS00001006863

  Targeted DNA-sequencing, whole genome sequencing and whole transcriptome sequencing data on childhood leukaemia patients enrolled on to UK clinical trials (specifically UKALL2003 and UKALL2011).

  Dac EGAC50000000342

• ADARIO Dataset

  RNA editing analyses of BRCA-isogenic cell lines and patient-derived xenografts (PDX)

  Dataset EGAD50000000739

• PBMC_dual_10X_kit

  Dual snRNAseq and ATACseq of replicate PBMC samples for 10X beta testing.

  Study EGAS00001004834

• HCA_Muscle_Adult_Wellcome_RNA_Managed_Access

  Transcriptomic analysis of skeletal muscle, to understand the mechanisms of muscle ageing.

  Study EGAS00001007495

• RNAseq of U251 and two ID1 gene knockouts

  RNAseq of U251 and two ID1 gene knockouts

  Dataset EGAD00001005089

• Dataset of PPB

  Whole exome sequencing and miRNA-seq data of PPB.

  Dataset EGAD00001000822

• DATA FILES FOR GRUBER SJAMLM7 RNASEQ

  DATA FILES FOR GRUBER SJAMLM7 RNASEQ

  Dataset EGAD00001003135

• snRNA-seq of two samples of ATCWGS42

  This dataset contains snRNA-seq data of two samples of ATCWGS42

  Dataset EGAD50000002146

• Single cell sequencing in D425

  Single cell sequencing aligned BAM files for the D425 cell line

  Dataset EGAD50000002304

• Conserved interferon-gamma signaling and decreased immune exclusion programs in responses to immune checkpoint blockade therapy

  We analyzed baseline and on-therapy tumor biopsies from 101 patients with advanced melanoma treated with nivolumab (anti-PD-1) alone or combined with ipilimumab (anti-CTLA-4). Analysis of whole transcriptome data showed that T cell infiltration and interferon-gamma signaling signatures corresponded most highly with clinical response to therapy, with a reciprocal decrease in cell cycle and WNT signaling pathways in responding biopsies. Clinical outcome differences were likely not due to differential melanoma cell responses to interferon-gamma, as 57 human melanoma cell lines exposed in vitro to this cytokine showed a conserved interferon-gamma transcriptome response unless they had mutations that precluded signaling from the interferon-gamma receptor. Therefore, the magnitude of the antitumor T cell response and the corresponding downstream interferon-gamma signaling are the main drivers of clinical response or resistance to immune checkpoint blockade therapy.

  Study EGAS00001004545

• Short read sequencing dataset of pathogen-stimulated PBMCs

  QuantSeq 3'-mRNAseq. 5 donors, 4 stimuli, 2 time points

  Dataset EGAD50000000012

• Normalized read counts for 84 PDAC samples

  Bulk RNAseq analysis of 84 PDAC samples : Normalized read counts

  Dataset EGAD50000000537

• RNAseq dataset

  RNAseq experiment after DIS3 ASO or Control ASO treatment on human cells under plasmablast differentiation.

  Dataset EGAD50000001243

• Neutrophils infected with leishmania donovani at timepoint 6h

  Neutrophils infected with Leishmania donovani at timepoint 6h

  Dataset EGAD00001006808

• Genomic Analysis of Mucinous Tumours (GAMuT) - RNA

  RNASeq for Genomic Analysis of Mucinous Tumours (GAMuT)

  Dataset EGAD00001005190