12302 results for "cancer rna-seq"

in 49.20 milliseconds.

• Exome sequencing of patients with Ewings sarcoma

  Cancer is driven by mutations in the genome. We will uncover the mutations that give rise to Ewing's sarcoma, a bone tumour that largely affects children. We will use second generation Illumina massively parallel sequencing, and bespoke software, to characterise the genomes and transcriptomes of Ewing,s sarcoma tumours.

  Dataset EGAD00001000333

• Whole genome analyses of the childhood cancer neuroblastoma

  Neuroblastomas are tumors of peripheral sympathetic neurons and are the most common solid tumor in children. To determine the genetic basis for neuroblastoma we performed whole-genome sequencing (6 cases), exome sequencing (16 cases), genome-wide rearrangement analyses (32 cases), and targeted analyses of specific genomic loci (40 cases) using massively parallel sequencing. On average each tumor had 19 somatic alterations in coding genes (range, 3-70). Among genes not previously known to be involved in neuroblastoma, chromosomal deletions and sequence alterations of chromatin remodeling genes, ARID1A and ARID1B, were identified in 8 of 71 tumors (11%) and were associated with early treatment failure and decreased survival. Using tumor-specific structural alterations, we developed an approach to identify rearranged DNA fragments in sera, providing personalized biomarkers for minimal residual disease detection and monitoring. These results highlight dysregulation of chromatin remodeling in pediatric tumorigenesis and provide new approaches for the management of neuroblastoma patients.

  Dataset EGAD00001000282

• Comprehensive Genomic Profiling of a National Cohort of Pediatric Papillary Thyroid Carcinoma in Hungary

  Pediatric papillary thyroid carcinoma (PTC) is a rare childhood malignancy with increasing incidence and distinct clinicopathological features, including high rate of locoregional metastases and different molecular background compared to adult cases. Traditional treatments (surgery, radioactive iodine) are effective but pose long-term risks, prompting the exploration of targeted therapies. We performed comprehensive genomic profiling on 21 pediatric PTC patients utilising the Illumina TruSight Oncology 500 platform and detected mutations, gene fusions, copy number variations and revealed tumor mutational burden.

  Study EGAS50000001833

• Catalogue Statistics

  EGA Statistics Bibliography Growth Community Archive Distribution Catalogue What is in the EGA? The European Genome-phenome Archive (EGA) overview The EGA archives and distributes the results of several types of studies. Such studies include genome-wide association studies (GWAS), different purpose sequencing and molecular diagnosis assays among others. Moreover, these studies target many different kind of diseases from cancer to neurodegenerative alterations. We summarised all this data in the charts below. The statistics are computed daily based on an updated list of studies. Each pie chart depicts a different aspect of the studies: which disease type was studied, which type of sampling method was used and what was the scope of the genomic analysis. Number of studies per disease type Studies in the EGA by disease barHorizontalChart('disease-graph', 'https://stats.ega-archive.org/catalog/by_disease') Number of studies per technology type Studies in the EGA by technology barHorizontalChart('technology-graph', '/stats/studies/technology', 'Include dbGaP') Number of studies per sample type Studies in the EGA by samples barChart('samples-graph', '/stats/studies/sample-types', 'Include dbGaP')

  Documentation about/statistics/catalogue

• RNAseq___Discovery_of_resistance_mechanisms_to_the_BRAF_inhibitor_vemurafenib_in_metastatic_BRAF_mutant_melanoma

  We propose to biopsy 20 consented BRAF mutant melanoma patients at Addenbrooke's Hospital pre-treatment with vemurafenib and also upon the development of resistant disease, with the aim of using exome sequence and SNP6 data to identify novel sequence variants and copy number alterations that can be used to validate observed resistance mechanisms in our cell line models and also to use these models to inform as to likely candidate small molecule inhibitors to overcome resistance and that could be tested in the clinical trial setting.

  Study EGAS00001000813

• Discovery_of_resistance_mechanisms_to_the_BRAF_inhibitor_vemurafenib_in_metastatic_BRAF_mutant_melanoma

  We propose to biopsy 20 consented BRAF mutant melanoma patients at Addenbrooke's Hospital pre-treatment with vemurafenib and also upon the development of resistant disease, with the aim of using exome sequence and SNP6 data to identify novel sequence variants and copy number alterations that can be used to validate observed resistance mechanisms in our cell line models and also to use these models to inform as to likely candidate small molecule inhibitors to overcome resistance and that could be tested in the clinical trial setting.

  Study EGAS00001000812

• Amplicon_based_sequencing_of_drug_resistant_organoids

  A BRAF V600E colorectal organoid which is sensitive to MAP kinase inhibition was mutagenised with the chemical mutagen ENU and then drug selected using a combination of Trametinib, Dabrafenib and Cetuximab. Single cell derived organoids were then manually picked and expanded in drug. Resistance was confirmed in a 14 day assay and DNA was collected. These then underwent targeted amplicon-based sequencing to confirm candidate resistance effectors from a screen in 2 2D BRAF V600E colorectal cell lines. Pools of resistant clones were also sequenced.

  Study EGAS00001001639

• ENU_LS_411N_TripleTherapy

  BRAF V600E colorectal cancers do not respond to the only currently FDA approved targeted therapy for CRC. There is currently a trial underway in the UK recruiting V600E CRC patients for treatment with a triple therapy combination of Cetuximab, Trametinib and Dabrafenib. We have mutagenized a pool of V600E CRC cell lines and treated with this triple therapy to select out drug resistant clones. We will now sequence these drug resistant clones with the aim of identifying common point mutations engendering resistance to this new therapy.

  Study EGAS00001001777

• Mutational_burden_in_human_hair_follicles

  We would like to dissect human hair follicles and assess the mutations found in diffferent parts of the follicle eg bulge, root etc. We can also compare this with mutations found in the surrounding epidermis of the same patients (this is done under a different study). This may provide insights into the origin of PTCH1 mutants frequently seen in basal cell carcinoma but so far not located in our whole epidermis work as well as defining the clonality of the hair follicle

  Study EGAS00001004462

• Genome and transcriptome sequence data from a metastatic cholangiocarcinoma patient

  Genome and transcriptome sequence data from a metastatic cholangiocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002976

• Genome and transcriptome sequence data from a rectal adenocarcinoma patient

  Genome and transcriptome sequence data from a rectal adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002973

• Genome and transcriptome sequence data from a cholangiocarcinoma patient

  Genome and transcriptome sequence data from a cholangiocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002991

• Genome and transcriptome sequence data from a leiomyosarcoma patient

  Genome and transcriptome sequence data from a leiomyosarcoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002996

• Constit Genome Sequencing

  WGS blood data (fastq raw read sequences) for French ICGC leiomyosarcoma cancer sequencing project, 67 samples representing 67 donors. Sequencing was performed on Illumina HiSeq. The libraries were then sequenced with a 2 x 100bp paired-end protocol to a minimum mean coverage of 30x.

  Dataset EGAD00001003190

• Whole exome and targeted DNA sequencing data for formalin-fixed paraffin embedded tissue from de novo small cell prostatic carcinoma cases

  This dataset consists of 44 compressed paired fastq files, 15 of which are generated from whole exome sequencing, and 29 of which are generated from DNA sequencing using a targeted gene panel capturing the exonic regions of 73 prostate cancer driver genes. Targeted DNA sequencing was performed on an Illumina MiSeq (v3 600 cycle kit), and exome sequencing was done using an Illumina HiSeq 2500 (v4 250 cycle kit) machine. The fastq files are named in accordance with the sample aliases provided, which reflect the pathology of interest to this study (small cell prostatic carcinoma--SCPC), whether it was sequenced using an exome or targeted gene panel, whether the FFPE sample was sourced from tumor or benign tissue (labeled T or B, respectively), and whether there exists multiple samples belonging to a single patient.

  Dataset EGAD00001004139

• The WID-CIN test identifies women with, and at risk of, cervical intraepithelial neoplasia grade 3 and invasive cervical cancer (CIN3+)

  We analysed DNA methylation at >850,000 CpG sites across the genome using the Illumina EPIC array in a total of 1,254 cervical liquid-based cytology samples from cases of screen-detected histologically verified CIN1-3+ (98% hrHPV-positive) and population-based control women free from any cervical disease (100% hrHPV-positive). We developed the WID-CIN-test which is a DNA methylation signature consisting of 5,000 CpG sites.

  Study EGAS00001005078

• Transcriptomic predictors of survival for palbociclib + endocrine therapy vs. capecitabine in aromatase inhibitor-resistant breast cancer from GEICAM/2013-02 PEARL

  We examined transcriptomic tumor features associated with survival from GEICAM/2013-02 PEARL, a phase III trial of palbociclib + ET vs. capecitabine in aromatase inhibitor-resistant HR+/HER2- MBC. PAM50 intrinsic subtype predicted PFS differences between palbociclib + ET and capecitabine, and lower B-cell-associated gene expression predicted longer OS with palbociclib + ET vs. capecitabine. These features may help identify HR+/HER2- tumors resistant to further ET-based treatment with CDK4/6 inhibitors.

  Study EGAS00001008177

• SPECTA: NGS Screening Program for Efficient Clinical Trial Access

  SPECTA comprises a network of participating European clinical sites and NGS screening platforms that can screen individual patients for multiple molecular targets and potentially allow the design of trials that will match the specific biology of the diseases affecting specific patients with cancer.

  Dataset EGAD00001000894

• Guardians of the genome: protecting DNA from endogenous sources of damage

  Mutational signatures are imprints of cell-intrinsic and extrinsic pathophysiological processes that have occurred through tumorigenesis. Experimental efforts to explore signature etiologies have produced a compendium of signatures of exogenous mutagens previously. Here, we unearth major sources of endogenous DNA damage and the genes that are critical to mitigating this innate stream of DNA damage under normal, physiological circumstances. We performed whole genome sequencing of 173 subclones of CRISPR-Cas9 knockouts of 43 genes in a human induced pluripotent stem cell system, in the absence of any added DNA damage. We reveal substitution and indel signatures that arise from those genes which are essential guardians of the genome. By detailed dissection and comparisons to cancer-derived signatures, we demonstrate interminable sources of constitutive DNA damage, and some mechanistic knowledge into how guardian genes preserve the genome. Based on these experimental insights, we develop and benchmark a tool for clinical classification of tumor samples.

  Dataset EGAD00001006055

• Prediction of HLA genotypes using NGS data

  High-precision human leukocyte antigen (HLA) genotyping is crucial for anti-cancer immunotherapy, but existing tools predicting HLA genotypes using next-generation sequencing (NGS) data are insufficiently accurate. We compared the availability and accuracy of eight HLA genotyping tools (OptiType, HLA-HD, PHLAT, seq2HLA, arcasHLA, HLAscan, HLA*LA, and Kourami) using 1,275 cases from the 1000 Genomes Project data and created a new HLA-genotyping algorithm combining tools. Then, we assessed the new algorithm’s performance in 39 in-house samples with normal whole-exome sequencing (WES) data and polymerase chain reaction–sequencing-based typing (PCR-SBT) results.

  Dataset EGAD00001007733

• Spatial transcriptome sequence data from cross section of cancer containing prostates

  Spatial transcriptome sequence data from two tumour containing prostates. Entire cross section of organ divided into cubes to fit spatial transcriptomics arrays. The dataset contains paired-end sequences from 21 sections of 1k array sections and 9 sections of 10x Visium sections for patient 1 as well as 28 sections of 10x Visium sections for patient 2.

  Dataset EGAD00001008644

• Dataset for bone_cancer-WHOLE_GENOME

  Rare cancer sequencing data of 48 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008866

• Dataset for breast_cancer-EXON

  Rare cancer sequencing data of 94 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008868

• Dataset for cancer_of_unknown_primary-EXON

  Rare cancer sequencing data of 62 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008870

• Dataset for colorectal_cancer-EXON

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

  Dataset EGAD00001008871

• Dataset for GIST-EXON

  Rare cancer sequencing data of 64 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008875

• Dataset for GIST-WHOLE_GENOME

  Rare cancer sequencing data of 18 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008876

• Dataset for gynecologic_cancer-WHOLE_GENOME

  Rare cancer sequencing data of 38 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008878

• Dataset for head_and_neck_cancer-WHOLE_GENOME

  Rare cancer sequencing data of 56 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008879

• Dataset for liposarcoma-WHOLE_GENOME

  Rare cancer sequencing data of 48 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008888

• Dataset for melanoma-WHOLE_GENOME

  Rare cancer sequencing data of 22 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008889

• Dataset for melanoma-EXON

  Rare cancer sequencing data of 76 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008890

• Dataset for NSCLC-WHOLE_GENOME

  Rare cancer sequencing data of 34 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008894

• Normal sample for patient SA238

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA238

  Dataset EGAD00001009363

• Normal sample for patient SA221

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA221

  Dataset EGAD00001009362

• Tumour sample for patient SA280

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA280

  Dataset EGAD00001009361

• Tumour sample for patient SA291

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA291

  Dataset EGAD00001009360

• Tumour sample for patient SA289

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA289

  Dataset EGAD00001009359

• Tumour sample for patient SA286

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA286

  Dataset EGAD00001009358

• Tumour sample for patient SA495

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA495

  Dataset EGAD00001009357

• Tumour sample for patient SA425

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA425

  Dataset EGAD00001009356

• Tumour sample for patient SA423

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA423

  Dataset EGAD00001009355

• Tumour sample for patient SA300

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA300

  Dataset EGAD00001009354

• Tumour sample for patient SA239

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA239

  Dataset EGAD00001009353

• Tumour sample for patient SA238

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA238

  Dataset EGAD00001009352

• Tumour sample for patient SA221

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA221

  Dataset EGAD00001009351

• Tumour sample for patient SA683

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA683

  Dataset EGAD00001009350

• Tumour sample for patient SA682

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA682

  Dataset EGAD00001009349

• Tumour sample for patient SA681

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA681

  Dataset EGAD00001009348

• Tumour sample for patient SA680

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA680

  Dataset EGAD00001009347