9958 results for "cancer rna-seq"

in 37.17 milliseconds.

• Tumour evolvability metrics predict recurrence in advanced localised prostate cancer (tumour data)

  There is a need for quantitative measurements of evolutionary metrics in controlled clinical trials with long term follow-up information. This is particularly true in advanced localised prostate cancer, which can recur more than a decade after diagnosis. Here we mapped genomic intra-tumour heterogeneity in 642 tumour samples from 114 patients who took part in the IMRT and DELINEATE clinical trials, for which full clinical information and 12y median follow-up was available. We concomitantly assessed phenotypic (morphological) heterogeneity using Deep Learning in 1,923 histological sections from 250 IMRT patients (fully overlapping with the genetic set). This study shows that combining genomics with AI-aided histopathology in clinical trials leads to novel clinical biomarkers. This EGA repository contains data produced from tumour samples using low coverage whole genome sequencing and a prostate cancer specific gene panel data following compression of unique molecular identifiers.

  Dataset EGAD00001010274

• Tumour evolvability metrics predict recurrence in advanced localised prostate cancer (normal data)

  There is a need for quantitative measurements of evolutionary metrics in controlled clinical trials with long term follow-up information. This is particularly true in advanced localised prostate cancer, which can recur more than a decade after diagnosis. Here we mapped genomic intra-tumour heterogeneity in 642 tumour samples from 114 patients who took part in the IMRT and DELINEATE clinical trials, for which full clinical information and 12y median follow-up was available. We concomitantly assessed phenotypic (morphological) heterogeneity using Deep Learning in 1,923 histological sections from 250 IMRT patients (fully overlapping with the genetic set). This study shows that combining genomics with AI-aided histopathology in clinical trials leads to novel clinical biomarkers. This EGA repository contains data produced from tumour samples using low coverage whole genome sequencing and a prostate cancer specific gene panel data following compression of unique molecular identifiers.

  Dataset EGAD00001010295

• Whole Exome Sequencing for Colorectal Cancer

  This project was designed to use next generation sequencing technology to screen the protein coding regions of the genome for low frequency variants in a panel of high-risk colorectal adenocarcinoma cases. Blood and cell-line DNA for colorectal cancer patients and a subset of quality control samples that had existing whole exome sequence data were analyzed using Illumina HiSeq sequencers. Samples from this project were from participants in the Women's Health Initiative (WHI) and the Diet, Activity, and Lifestyle Study (DALS).

  Study phs000410

• Comprehensive Analysis of Structural Variants in Breast Cancer Genomes Using Single Molecule Sequencing

  This study used the SKBR3 breast cell line and tumor/normal organoids derived from breast tissue to explore the utility of long read sequencing for detecting structural variants in complex samples. Three technologies were used to characterize the samples: Illumina/10X, Pacific Biosciences, and Oxford Nanopore. Methylation results were derived from the Oxford Nanopore data. It is hoped that this resource will better help researchers better understand the utility of long read sequencing in cancer samples.

  Study phs002210

• A National Translational Science Network of Precision-Based Immunotherapy for Primary Liver Cancer (PLC)

  The primary objective of the NCI-CLARITY study is to establish a biospecimen repository for genomic, genetic and epigenetic analysis to study the biology of primary liver cancer (PLC) development and progression. Secondary objectives include predicting overall and progression-free survival following immunotherapy. In the retrospective phase, anonymous archival tissue from PLC patients was obtained from five participating sites. Whole exome and transcriptome sequencing was generated and used to predict overall survival in response to immunotherapy.

  Study phs003074

• EOSC4Cancer Longitudinal Synthetic Colorectal Cancer Genomic data developed at BSC

  The synthetic genomes have been created trying to mimic real cancer data of 4 patients (Named 185,186,187 and 188). Mutations are based on real CRC patients from the PCAWG dataset. For each patient, two tumor samples at different time points and one healthy sample have been simulated. The cancer intra-tumor heterogeneity and evolution in the patients is depicted by simulating reads from tumor subclones separately and then mixing them according to their clonal proportions in each sample. For rapid use and transfer only selected chromosomes have been generated for each patient. Chromosomes per patient: -185: chr4, chr5, chr7, chr17 -186: chr1, chr7, chr12, chr17 -187: chr1, chr2, chr5, chr12, chr17 -188: chr2, chr5, chr12, chr13, chr17 Worflows used to create BAM/BAI, VCF and MAF files from FASTQ (Alignment with GRCh38): - https://usegalaxy.eu/published/workflow?id=2c3d05023c02113e - https://usegalaxy.eu/published/workflow?id=1da86d74f8535f4e

  Dataset EGAD50000000276

• The evolving mutational landscape of normal human esophagus

  The extent to which cells in normal tissues accumulate mutations during life is poorly understood. Some mutant cells expand into clones that can be detected by genome sequencing. We mapped mutant clones in normal esophageal epithelium from nine donors aged 20-75. Somatic mutations accumulate with age and are mainly caused by intrinsic mutational processes. We found strong Darwinian selection of clones carrying mutations in 14 cancer genes, with tens to hundreds of such clones per square centimeter. By middle age, clones with cancer-associated mutations cover most of the epithelium, with NOTCH1 and TP53 mutations affecting 40% and 10% of all cells, respectively. Remarkably, the prevalence of NOTCH1 mutations in normal esophagus is several times higher than in esophageal cancers. The esophagus emerges as an evolving patchwork of mutant clones that colonize the majority of the epithelium, with implications for our understanding of cancer and ageing.

  Dataset EGAD00001004159

• Multiscale heterogeneity in gastric adenocarcinoma evolution is an obstacle to precision medicine

  Study EGAS00001004525

• Sequencing_of_rare_human_histiocytic_tumour

  Sequencing of rare human histiocytic tumour

  Study EGAS00001000591

• Low_Coverage_Sequencing_of_rare_human_histiocytic_tumour

  Sequencing of rare human histiocytic tumour

  Study EGAS00001000768

• Primary_angiosarcoma_Whole_Genome_Sequencing

  Whole genome sequencing of primary angiosarcoma

  Study EGAS00001000851

• Induction Failure in Childhood and Young Adult T-cell Acute Lymphoblastic Leukemia

  Study EGAS00001006411

• Genome-wide DNA Copy Number Analysis of high hyperdiploid acute lymphoblastic leukemia

  Study EGAS00001007049

• Targeted next-generation sequencing of 13 chordoid gliomas of the third ventricle

  Study EGAS00001002733

• Organoid Models of Human Liver Cancers Derived from Tumor Needle Biopsies

  Study EGAS00001003115

• Whole-genome and transcriptome sequencing of blastic plasmacytoid dendritic cell neoplasm

  Study EGAS00001003660

• Multi Omics of glioma in the the Chinese Glioma Genome Atlas (CGGA) project

  Study EGAS00001004729

• Detection of Gene Fusions using Targeted Next-Generation Sequencing – a Comparative Evaluation

  Study EGAS00001004934

• Stromal cell diversity associated with immune evasion in human triple‐negative breast cancer

  Study EGAS00001005061

• Mixed Histology Lung Cancers Driven by Transcriptomic Features rather than Genomic Characteristics

  Study EGAS00001005140

• TK-EPN862 - Patient-dervied xenograft model of Posterior Fossa A Ependymoma - WGS

  Study EGAS00001006843

• TRACERx Non-small cell lung cancer

  Dac EGAC00001000632

• Barts Cancer Institute - Follicular Lymphoma Whole Genome Sequencing Project Data Access Committee

  Dac EGAC00001000086

• The University of Hong Kong Gastric Cancer Genomics Study Data Access Committee

  Dac EGAC00001000136

• The University of Hong Kong Gastric Cancer Organoids Genomics Study Data Access Committee

  Dac EGAC00001000981