15028 results for "cancer studies using rna-seq"

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• Somatic L1 retrotransposition mapping in high-grade serous ovarian carcinoma using LDI-PCR/Nanopore-sequencing (part 2)

  Somatic L1 retrotransposition was mapped in high-grade serous carcinoma (HGSC) tumors using LDI-PCR/Nanopore-seq method. This method identifies somatic L1 insertions originating from two active L1 loci at chr22q12.1 and chrXp22.2. Multiple tumors from the same patient were utilised to understand L1-mediated intrapatient heterogeneity. Long-read data generated by LDI-Nanopore sequencing was aligned to the reference genome (GRCh38). This dataset contains the remaining file of this study.

  Dataset EGAD50000001757

• The data access committee for Detection and characterization of lung cancer using cell-free DNA fragmentomes

  Dac EGAC00001002184

• Cancer-Unknown-Primary-Array-Methylation-H021

  CUP samples using 850k

  Dataset EGAD00010002036

• Human CD4 Memory T Cell Activation Time Course

  We performed a high-resolution time course RNA-seq experiment on CD4+ memory T cells during activation with anti-CD3/CD28 beads. We performed this in 24 adult individuals of European ancestry with no autoimmune diseases. We used 1 million cells as starting material, per time point (up to 8 time points per individual from 0 to 72 hours). We identified widespread dynamic allele-specific expression (dynASE) across the genome, i.e. ASE that changes with time after activation, which reflects cell state-dependent genetic regulatory effects. DynASE events were enriched in risk loci for multiple autoimmune diseases. This highlights the importance of studying multiple cellular states in order to elucidate the mechanisms of autoimmune disease. It also provides a successful framework to do it in other cellular systems relevant for other traits. We also showed an unprecedented amount of regulatory variation for HLA genes. HLA-DQB1 has 3 distinct regulatory haplotypes with expression profiles that could come from 3 different genes. We performed ATAC-seq in replicate for CD4+ memory T cells stimulated for 72 hours from another healthy individual. These data, together with genetic fine-mapping, CRISPR editing, EMSA and luciferase assays, helped us find a SNP in the promoter of HLA-DQB1 that causes at least part of the large cell state-dependent change in expression, at the RNA and protein levels.

  Study phs002259

• Single-cell RNA sequencing of 6 follicular lymphoma tumors

  Follicular lymphoma (FL) is an indolent cancer of mature B-cells but carries increased risk of transformation to a more aggressive histology over time. We present here comprehensive profiling both tumor and immune compartments in 6 diagnostic FL biopsies by single-cell RNA sequencing. This confirmed results from 155 FL tumors characterized by mass cytometry (CyTOF) which revealed two distinct evolutionary trajectories with disparate risk of transformation and alternate biologies.

  Study EGAS00001005257

• DNA from colon cancer samples and matched normal samples was hybridized on Human 660W-Quad SNP arrays and sequenced in Illumina HiScanSQ.

  DNA from colon cancer samples and matched normal samples was hybridized on Human 660W-Quad SNP arrays and sequenced in Illumina HiScanSQ. The objective of the study was to introduce methodology for the detection of genomic copy number alterations (CNAs) using high-throughput sequencing data. The samples in this study allow the comparison of the results from the seqCNA R package with those from other methods that detect copy number alterations (CNAs) in tumoural samples, either using SNP-array data or high-throughput sequencing data.

  Study EGAS00001000558

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: Colorectal_LCM (2020-02-20)

  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. 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 2020-02-20.

  Dataset EGAD00001005994

• 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

• Enhancing OSCC Prediction: The Prognostic Power of WPOI and Tumor Budding, and the Limited Impact of Molecular Resection Margins

  This dataset contains sequences with results of mutations observed in patients with oral squamous cell carcinoma. Mutations were detected by NGS in tumor tissue, tumor margins, and peripheral blood as a healthy control. Study was using a panel of 88 cancer-related genes, which were selected based on a literature search.

  Study EGAS50000000074

• Variant analysis on FFPE specimen from NSCLC patients (FoundationOne CDx)

  This study assessed the presence of somatic variants via the FoundationOne CDx panel of commonly mutated cancer genes and measure tumor mutational burden using next-generation sequencing panels. It was performed on genomic DNA from FFPE tissue of tumor resections. Mutations were used to draw OncoPrints and to assess TMB in relation with response metrics.

  Study EGAS50000001139