22950 results for "ega"

in 32.62 milliseconds.

• The transition from quiescent to activated states in human hematopoietic stem cells is governed by dynamic 3D genome reorganization

  To elucidate the epigenetic changes which occur when human long-term hematopoietic stem cells (LT-HSC) become activated we performed Bulk ATAC-Seq on 13 sorted bulk hematopoietic populations from cord bloodas well as single-cell ATAC-Seq upon CD34+CD38-CD45RA- cells enriched for HSC as well as CD34+/CD38+ progenitor cells both from cord blood. These studies revealed gains of chromatin accessibility around CTCF binding sites during HSPC activation, as such we additionally performed Low-C to directly profile the 3D conformation of human cord-blood derived LT-HSC and Short-term hematopoietic stem cells (ST-HSC), as well as Hi-C , ATAC-Seq and CTCF ChIP-Seq upon the OCIAML-2 cell line in which CTCF sites gained during LT-HSC activation are enriched. Finally we transduced human cord-blood LT-HSC with an shCTCF vector; in-vitro cultured LT-HSC cells harbouring shCTCF were used to perform RNA-Seq, and scATAC-Seq was performed on CD34+/CD38- human CB cells transduced with shCTCF, four weeks post xeno-transplantation into mice. Collectively these studies have helped us demonstrate the role of 3D chromatin conformation changes during human LT-HSC activation.

  Dataset EGAD00001006447

• Molecular profiling of tissue autopsies and ctDNA

  The dataset contains high-throughput sequencing data derived from a cancer autopsy series of 10 patients. As part of this study, whole-exome sequencing and RNA-seq were performed for spatially distinct tissue biopsies from the patients. In addition, plasma samples from the patients were sequenced using a custom panelt to profile ctDNA. There are 106 files containing whole-exome sequencing data, 107 files containing RNA-seq data, and 9 files containing plasma sequencing data.

  Dataset EGAD00001007040

• Single-cell transcriptome of T-ALL P1

  In this study, we profiled single-cell transcriptome (10X genomics) of Patient-derived xenografts (PDX) T-ALL replase samples from P1 patient. Primary human T-ALL cells were recovered from cryopreserved bone marrow aspirates of patients enrolled in the ALL-BFM 2009 study. Patient-derived xenografts (PDX) were generated as previously described by intrafemoral injection of 1 Million viable primary ALL cells in NSG mice110 PDX-derived (P1)28 cells were frozen until processing. For scRNA-seq library preparation, cryopreserved cells were thawed rapidly at 37 ℃ and resuspended in 10 ml warm Roswell Park Memorial Institute (RPMI) medium with 100 μg/ml Dnase I. Cells were centrifuged for 5 mins at 300 g, and resuspended in ice-cold phosphate buffered saline (PBS) with 2% foetal bovine serum (FBS) and 5mM EDTA. Cells were stained on ice with anti-murine-CD45-PE (mCD45)(clone 30-F11; BioLegend; 1:20) in the dark for 30 mins. 1:100 DAPI was added and incubated in the dark for 5 mins before sorting. Triple negative cells (DAPI-mCD45-GFP-) were sorted (Fig. S27) using a BD FACSAria™ Fusion Cell Sorter into ice cold 0.03% bovine serum albumin (BSA) in PBS. All isolated cells were immediately used for scRNA-seq libraries, which were generated as per the standard 10x Genomics Chromium 3′ (v.3.1 Chemistry) protocol. Completed libraries were sequenced on a NextSeq5000 sequencer (HIGH-mode, 75 bp paired-end).

  Dataset EGAD00001008325

• minION fastq files of 10 different tumor samples from the Master program (H021)

  Nanopore RNA Sequencing was done for 10 tumor samples. Direct cDNA sequencing was performed using the SQK-DCS109 kit (Oxford Nanopore Technologies). For analysis of a single sample on a MinION flow cell (version R9.4.1), 5 μg RNA was used as input. For multiplexing on a MinION flow cell, 2.5 μg RNA per sample was used as input, and the native barcoding expansion kit EXP-NBD104 was employed in conjunction with SQK-DCS109. After reverse transcription with Maxima H Minus Reverse Transcriptase (Thermo Scientific), second-strand synthesis was performed using the 2x LongAmp Taq Master Mix (New England Biolabs). The resulting double-stranded cDNA was subjected to end-repair and dA-tailing using the NEBNext Ultra End Repair/dA-Tailing Module (New England Biolabs). For multiplexed libraries, this step was followed by barcode ligation and library pooling. Next, libraries were quantified with a Qubit Fluorometer 3.0 (Life Technologies). Finally, sequencing adapters were added to the library preparations and ligated with Blunt/TA Ligase Master Mix (New England Biolabs), followed by further quality control using a Qubit. Samples ACC1 and ACC2 were analyzed on individual MinION flow cells, while the remaining eight samples were sequenced as multiplexed libraries on two MinION flow cells by pooling four samples for each run. Five ACC samples were also analyzed individually on Flongle flow cells

  Dataset EGAD00001008970

• WES dataset of 20 pre-post paired neoadjuvant chemotherapy treated breast cancer samples

  This dataset contains whole exome sequencing data (WES) of 20 paired pre- and post neoadjuvant chemotherapy breast cancer samples. From every patient a pre-treatment biopsy (B) and a post-treatment surgery (S) specimen has been sequenced. From most patients a paired normal blood sample (N) has been sequenced as a reference control.

  Dataset EGAD00001008442

• RNA-seq data

  RNAseq FASTq files of 181 bulk pre-treatment and 14 post-treatment tumors from GO30140 Ph1b group A and F and 177 bulk pre-treatment tumors of IMbrave150 PhIII

  Dataset EGAD00001008128

• Adult B-precursor acute lymphoblastic leukemia transcriptomes

  Bone marrow or peripheral blood samples were collected of adult patients at first diagnosis of B-precursor acute lymphoblastic leukemia. RNA was isolated from mononuclear cells and subjected to mRNA library prep using Poly-A selection and sequencing on a NovaSeq 6000 system. Obtained gene expression profiles and gene fusion calls were used to allocate samples to molecular disease subtypes.

  Dataset EGAD00001008633

• Identifying transcriptional programs underlying anti-EGFR small molecule response and resistance with TraCe-seq

  To QC the TraCe-seq strategy, single-cell RNA-seq libraries were generated from a variety of human cancer cell lines transduced with the TraCe-seq library to validate the TraCe-seq strategy. Specifically, 5 different cell lines (PC9, MCF-10A, MDA-MB-231, NCI-H358, and NCI-H1373) were each transduced with a unique TraCe-seq barcode. The transduced cells were selected with puromycin only, dissociated to single cell suspensions, and then mixed together. The complex mixture of the 5 cell lines was profiled by 10X scRNA-seq. Furthermore, transduced NCI-H1373 cells were sorted by FACS to enrich for the top 50% of eGFP positive cells, and sorted cells were cultured briefly and used to construct scRNA-seq libraries and profiled by 10x scRNA-seq. To carry out the full TraCe-seq experiment, ~600 PC9 cells carrying unique TraCe-seq barcodes were expanded over 12 doublings to establish the barcoded population. A subset of the barcoded PC9 population was used to generate scRNA-seq libraries and profiled by 10x scRNA-seq prior to treatment to establish a baseline transcription profile for each barcoded clone. The rest of the cells were then treated for four days with 1 µM erlotinib, 1 µM GNE-069, or 1 µM GNE-104 respectively. scRNA-seq libraries were then generated form the treated cells and profiled by 10x scRNA-seq.

  Dataset EGAD00001007872

• Spatially confined sub-tumor microenvironments in pancreatic cancer

  Intratumoral heterogeneity is a critical frontier in understanding how the tumor microenvironment (TME) propels malignant progression. Here, we deconvolute the human pancreatic TME through large-scale integration of histology-guided regional multiOMICs with clinical data and patient-derived preclinical models. We discover subTMEs, histologically definable tissue states anchored in fibroblast plasticity, with regional relationships to tumor immunity, subtypes, differentiation, and treatment response. Reactive subTMEs rich in complex but functionally coordinated fibroblast communities were immune-hot and inhabited by aggressive tumor cell phenotypes. The matrix-rich deserted subTMEs harbored less activated fibroblasts and tumor- suppressive features yet were markedly chemoprotective and enriched upon chemotherapy. SubTMEs originated in fibroblast differentiation trajectories and transitory states were notable both in single cell transcriptomics and in situ. The intratumoral co-occurrence of subTMEs produced patient-specific phenotypic and computationally predictable heterogeneity tightly linked to malignant biology. Therefore, heterogeneity within the plentiful, notorious pancreatic TME is not random but marks fundamental tissue organizational units.

  Dataset EGAD00001008155

• Spatial heterogeneity of follicular lymphoma

  Follicular lymphoma (FL) is an incurable B cell malignancy characterized by advanced stage disease and a heterogeneous clinical course. Recent genomic studies have focused on profiling “single” FL biopsies over several time-points, however, multi-site sampling in solid cancers has demonstrated profound spatial intra-tumor heterogeneity (ITH) with implications for precision medicine based initiatives. This study examined the extent of spatial heterogeneity in FL by whole exome sequencing 22 synchronously removed spatially separated biopsies from 9 patients. We observed significant differences in the extent of ITH across cases, with two distinct patterns of high and low spatial heterogeneity emerging. Site-specific alterations in genes with biological, prognostic or therapeutic relevance included, TNFRSF14, PIK3CD, TNFAIP3, PTEN, EP300 and XBP1. In depth characterization of these variants using deep-sequencing techniques confirmed their discordant nature, suggesting on-going genetic diversification driving evolution after widespread tumor dissemination. There was evidence of tumors comprising multiple competing subclones, with distinct clusters of mutations demonstrating differential expansions within spatially-separated sites. For cases where spatial tumors were examined at two time-points (FL and transformation to diffuse large B cell lymphoma (DLBCL)), the degree of heterogeneity increased with transformation. Collectively, our results demonstrate that spatial ITH is prevalent in FL. The existence of site-specific aberrations suggests that a single biopsy may not be sufficient in all patients to capture the full genomic complexity present and these spatial variations need to be considered in biomarker-led clinical studies.

  Dataset EGAD00001003553

• Multi-region sequencing of RCC with VTT and metastasis using WES and RNAseq

  This dataset contains multi-region sequencing of 16 RCC patients with venous tumor thrombus (VTT), 11 of which were either metastatic on diagnosis or recurred with metastasis. Whole exome sequencing is available for 94 samples across all 16 patients, including 1 matched normal sample per patient, 2-3 primary tumor samples per patient, 1-2 VTT samples per patient, and 0-3 metastasis samples per patient (metastatic lesions were only sampled for 8 of the 11 metastatic patients). RNAseq, generated by exome capture, is available for 67 samples across 12 patients, including 0-1 matched normal samples per patient, 3 primary tumor samples per patient, 0-1 VTT samples per patient, and 0-3 metastasis samples per patient (RNAseq was only available for 4 of the 8 patients from whom metastatic lesions were sampled).

  Dataset EGAD00001008441

• Dataset for hepatopancreaticobiliary_malignancy-RNA

  Rare cancer sequencing data of 119 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008863

• Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis

  Background: Lung carcinoma-in-situ (CIS) lesions are the pre-invasive precursor to lung squamous cell carcinoma. However, only half progress to invasive cancer in three years, while a third spontaneously regress. Whether modern molecular profiling techniques can identify those pre-invasive lesions that will subsequently progress and distinguish them from those that will regress is unknown. Methods: Progressive and regressive CIS lesions were laser-captured and their genome, epigenome and transcriptome interrogated. We analysed 83 progressive lesions, 41 regressive and 33 normal epithelial control samples. DNA methylation and gene expression profiles were further validated using publicly available lung cancer data. Results: Somatic mutation burden was higher in progressive lesions than regressive CIS lesions, across base substitutions, rearrangements, and copy number changes. Driver mutations were present in both progressive and regressive CIS lesions, but were more numerous in progressive cases. Progressive and regressive CIS lesions had distinct epigenomic and transcriptional profiles, with a strong chromosomal instability signature. Gene expression, methylation and copy number profiles can all predict accurately which CIS lesions will progress to lung cancer. Conclusion: Pre-invasive CIS lesions that will subsequently progress to invasive lung cancer can be distinguished from those that will regress using molecular profiling. Progression is associated with a strong chromosomal instability signature. These findings inform the development of novel therapeutic targets.

  Dataset EGAD00001003883

• Whole transcriptome RNA sequencing on bone marrow and peripheral blood samples from patients with acute myeloid leukemia at diagnosis or relapse.

  One hundred cryopreserved bone marrow and peripheral blood samples from patients with acute myeloid leukemia (AML) with 10-90% blasts were selected from the biobank of the Department of Hematology of Leiden University Medical Center (LUMC). The AML cases cover all subtypes, and specifically include known subtype-defining balanced chromosomal translocations according to the WHO classification. The samples were obtained from 96 patients and include three pairs of de novo and relapsed AML and one pair of de novo and presumed therapy-related AML (tAML). Total RNA was isolated from mononuclear cells without prior enrichment for leukemic blasts. The quality and integrity of total RNA was checked and RNA libraries were prepared using the TruSeq RNA library preparation kit v2 (Illumina, San Diego, CA) in an ISO/IEC 17025-accredited protocol. This workflow started with enrichment of messenger RNA by oligo dT magnetic beads. After fragmentation, cDNA synthesis was performed, followed by adaptor ligation and PCR amplification. Paired-end sequencing with a read length of 126 bp was performed on an Illumina HiSeq 2500 v4 sequencer to at least 12.5 Gbp per sample. Image analysis, base calling, and quality check was performed with Illumina data analysis pipeline RTA v1.18.64 and Bcl2fastq v1.8.4. RNAseq reads are provided in compressed Sanger FASTQ format.

  Dataset EGAD00001004187

• CCOC WGS for the study Genomic consequences of aberrant DNA repair mechanisms stratify ovarian cancer histotypes

  For CCOC cohorts, OvCaRe cases were reviewed, including frozen material, by at least two expert gynecopathologists prior to inclusion in the sequencing cohort who provided the confirmation on final selected cohort. Frozen H&E from Tokyo were also used for evaluation along with representative H&E photos and review done at the Jikei School of Medicine. All CCOC tumours are primary tumour samples. Library construction and sequencing Frozen specimens with >50% tumour cellularity (based on initial slide review) were used for cryosectioning and subsequent nucleic acid extraction. Patient tumour and normal blood samples derived from primary, untreated fresh frozen tumour specimens harvested at diagnosis during standard of care debulking surgery. Germline DNA was provided from peripheral blood buffy coat on all specimens except 13 from Tokyo, where non-cancer frozen tissue was used as a germline source. DNA extraction from both matched normal (blood) and tumour samples (frozen tissue) were performed using the QIAamp Blood and Tissue DNA kit (Qiagen) and quantified using a Qbit fluorometer and reagents (high-sensitivity assay). Three lanes of Illumina HiSeq 2500 v4 chemistry for normal samples and five lanes for tumour samples were obtained. The PCR-free protocol was adopted to eliminate the PCR-induced bias and improve coverage across the genome.

  Dataset EGAD00001003265

• May 2021 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  May 2021 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001008100

• GoNL aligned sequence data in BAM format.

  We mapped the data to the UCSC human reference genome build 37 using BWA 0.5.9-r16. We first mapped each read pair separately using bwa aln. Then we used bwa sampe to map the paired reads together to a BAM9 file. The BAM file was then sorted by genomic position and indexed using PicardTools-1.32 SortSam. To prevent PCR artifacts from influencing the downstream analysis of our data, we used Picard to mark the duplicate reads, which were ignored in downstream analysis. We used GATK IndelRealigner on our data around known indels (from 1KG Pilot). The IndelRealigner creates all possible read alignments using the source and computes the likelihood of the data containing the indel based on the read pileup. Whenever the maximum likelihood contains an indel, the reads are realigned accordingly. Each base is associated with a phred-scaled base quality score. Calibration of Phred scores is crucial as they are used in some of the downstream analysis models. We used GATK to recalibrate the base qualities with respect to (i) the base cycle, (ii) original quality score, and (iii) dinucleotide context. To minimize issues stemming from mapping problems around indels, we decided to undergo a second round of indel realignment using the GATK IndelRealigner by family rather than by individual. For this second round, we considered two sources of possible indels: 1KG Phase 1 indels and indels aligned by BWA in the GoNL data.

  Dataset EGAD00001001038

• Whole-genome sequencing of high-grade serous ovarian cancer (HGSC) tumours and matched normals from long-term survivors.

  Whole-genome sequencing of high-grade serous ovarian cancer (HGSC) tumours and matched normals from long-term survivors performed as part of the Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG) study. The dataset includes fastq files from 58 HGSC tumours (53 primary tumours and 5 recurrent tumours) and 53 matched normals from 53 long-term survivor patients. Sequence libraries were generated from tumour and matched normal genomic DNA using the KAPA HyperPrep PCR-free library preparation kit (Roche) according to manufacturer’s instructions. Sequencing was carried out by the Kinghorn Centre for Clinical Genomics Sequencing Laboratory (Sydney, Australia) on the HiSeq X Ten System (Illumina) to a minimum base coverage of 30-fold for normal DNA and 60-fold for tumour DNA samples.

  Dataset EGAD00001009398

• Prognostic relevance of microenvironmental factors CD163 and CD8 combined with EZH2 and chromosome 18 gain in a validation cohort of follicular lymphoma patients of the Lunenburg Lymphoma Biomarker Consortium

  Background: In follicular lymphoma (FL), studies addressing the prognostic value of microenvironment-related immunohistochemical (IHC) markers and tumor cell-related genetic markers have yielded conflicting results, precluding implementation in practice. Therefore, the Lunenburg Lymphoma Biomarker Consortium (LLBC) performed a validation study for published markers. Methods: To maximize sensitivity, an end-of-spectrum design was applied for 122 uniformly immunochemotherapy-treated FL patients retrieved from international trials and registries; early failure (EF): progression or lymphoma-related death <2 years versus long remission: response duration of >5 years. IHC staining for T-cells and macrophages was performed on tissue microarrays from initial biopsy and scored with a validated computer-assisted protocol. Shallow whole-genome and deep targeted sequencing was performed on the same samples. Results: 96/122 cases with complete molecular and immunohistochemical data were included in the analysis. EZH2 wild-type (p=0.006), gain of chromosome 18 (p=0.002), low percentages of CD8+ cells (p=0.011) and CD163+ areas (p=0.038) were associated with EF. No significant differences in other markers were observed, thereby refuting previous claims on their prognostic significance. Conclusion: Using an optimized study design, this LLBC study validates wild-type EZH2 status, gain of chromosome 18, low percentages of CD8+ cells and CD163+ area as predictors of EF to immunochemotherapy in FL.

  Dataset EGAD00001002738

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: pancreas (2019-04-03)

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute (Illumina HiSeqX, 40X and 20X depth respectively). Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development. . This dataset contains all the data available for this study on 2019-04-03.

  Dataset EGAD00001004893