14983 results for "cancer studies using rna-seq"

in 18.16 milliseconds.

• Dual spatially resolved transcriptomics for human host-pathogen colocalization studies in FFPE tissue sections

  Technologies to study localized host-pathogen interactions are urgently needed. Here, we present a spatial transcriptomics approach to simultaneously capture host and pathogen transcriptome-wide spatial gene expression information from human formalin-fixed paraffin-embedded (FFPE) tissue sections at a near single-cell resolution. We demonstrate this methodology in lung samples from COVID-19 patients and validate our spatial detection of SARS-CoV-2 against RNAScope and in situ sequencing. Host-pathogen colocalization analysis identified putative modulators of SARS-CoV-2 infection in human lung cells. Our approach provides new insights into host response to pathogen infection through the simultaneous, unbiased detection of two transcriptomes in FFPE samples.

  Study EGAS00001006186

• Genomic and Genetic Characterization of Prostate Tumors Treated with Neoadjuvant Intense Androgen Deprivation Therapy

  The purpose of this study was to develop better ways of detecting prostate cancer before and after pre-operative treatment, and to test the feasibility of multi parametric magnetic resonance imaging (mpMRI) for the localization and detection of focal prostate cancer both before and after pre-operative treatment with ADT and enzalutamide. In this study, patients with intermediate and high risk prostate cancer received six months of neoadjuvant intense androgen deprivation therapy prior to radical prostatectomy. Biopsies were acquired prior to treatment. Tissue from biopsy and radical prostatectomy were subjected to sequencing and analysis for determination of features associated with response or resistance to treatment.

  Study phs001938

• The EGA joins EUCAIM, the European project powering AI and imaging in cancer research

  EUCAIM (EUropean Federation for CAncer IMages) is the cornerstone of the European Cancer Imaging Initiative, one of the flagships of the Europe’s Beating Cancer Plan (EBCP). The European Genome-Phenome Archive (EGA), as member of the EUCAIM consortium, attended the kick-off meeting organized this week in Brussels for the launch of the Initiative. The EGA will contribute to the project with the experience gained in the deployment of our federated network, the Federated EGA (FEGA), and the experience gained as partners of EuCanImage (AI4HI initiative) contributing to the definition of common data models, ontologies and data standards. The ultimate goal of the project is to unlock the power of imaging and Artificial Intelligence for the benefit of cancer patients, clinicians and researchers. The consortium will deploy a pan-European digital federated infrastructure hosting cancer-related images, and associated pathology, molecular and laboratory data from Real-World. This endeavour will provide a comprehensive dashboard for imaging data discovery, federated search, metadata harvesting, annotation and distributed data processing. EUCAIM joins 76 partners, gathering the experience and previous effort made by the different repositories of the AI4HI initiative, European Research Infrastructures and national/regional repositories. Find out more information about the EUCAIM launch here

  Blog the-ega-joins-eucaim

• MutWP5__CRUK_Mutographs_of_Cancer__Lung__PD43291_Novaseq__WG_

  Cancer is a genetic disease caused by an accumulation of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from lung tissue to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. We will deeply sample from individuals with smoking-related lung diseases (COPD, pulmonary fibrosis, lung cancer) and those without smoking history. This will allow us to determine how these processes are different between these groups of individuals, and gain insight into the earliest stages of tumor development.

  Study EGAS00001003502

• 2014_Lung_sq_WES

  Purpose Lung squamous cell carcinoma (SqCC) is the second most prevalent type of lung cancer. Currently, no targeted-therapeutics are approved for treatment of this cancer, largely due to lack of systematic understanding of the molecular parthenogenesis of the disease. To identify therapeutic targets and comparatively analyze them in lung SqCC, we probed somatic alterations of Korean lung SqCC. Patients and Methods We performed massively parallel whole-exome sequencing of 104 Korean lung SqCC patients’ samples with matched normal samples. In addition, copy number analysis and transcriptome analysis were conducted for a subset of these samples. Clinical association with cancer-specific somatic alterations was investigated.

  Study EGAS00001002844

• Microarray_cases

  CASE SAMPLES USING Affymetrix SNP6.0 technology (Thermo Fisher Scientific company): OncoScan FFPE Assay Kit was used for FFPE tissue samples (designed for degraded DNA) and the Cytoscan HD Array Kit was used for the fresh-frozen tissues

  Dataset EGAD00010002034

• Nanopore sequencing of brain tumor tissue obtained by cavitating ultrasonic aspiration

  This dataset contains 21 pediatric brain tumor cases which where obtained during surgery using a cavitating ultrasonic aspiration and submitted to nanopore whole genome sequencing.

  Dataset EGAD50000000269

• OXEL pilot WES data

  This dataset contains aligned BAM files from whole-exome sequencing of 29 patients from the Oxel pilot study. Alignment to GRCh38 reference genome was performed using the BWA aligner.

  Dataset EGAD50000000327

• H3Africa ACEGID H3Africa Array Genotype

  SNP Genotyping for Lassa Fever cases and population controls from Nigeria and Sierra Leone using Illumina H3Africa array version 1

  Dataset EGAD00010002510

• Whole genome sequencing profiling of patient-derived organoids (PDO)

  Whole genome sequencing profiling of 41 PDAC patient-derived organoids (PDO) - WGS unmapped reads, sequenced using NovaSeq 6000, 15x coverage 160 million reads per sample.

  Dataset EGAD50000000280

• N2M2 methylation array data

  Methylation profiling of 228 samples, using the approach described in "Molecularly matched targeted therapies plus radiotherapy in patients with newly diagnosed glioblastoma without MGMT promoter hypermethylation (N2M2/NOA-20 phase I/IIa umbrella trial)"

  Dataset EGAD00010002747

• WES_dataset1

  Fourteen MEITLs and one EATL previously had been sequenced using the SureSelectXT Target Enrichment System (Agilent Technologies, Santa Clara, CA, USA).

  Dataset EGAD50000001620

• LBC1921 gvcfs

  297 members of the LBC1921 were sequenced using the Illumina HiSeq X platform. This dataset contains the gvcfs.

  Dataset EGAD00001006416

• Whole Exome Sequencing of gliomas

  15 whole exome sequencing datasets from five patients. Data is provided as bam files. Libraries were generated using the SeqCap EZ Exome v3.0 kit and sequenced on an Illumina sequencer

  Dataset EGAD00001003763