11769 results for "cancer rna-seq"

in 13.63 milliseconds.

• Whole-genome sequencing in 14 cases and whole-exome sequencing in 90 cases of Chinese ESCC

  Esophageal cancer is one of the most aggressive cancers and the sixth leading cause of cancer death worldwide1. Approximately 70% of the global esophageal cancers occur in China and over 90% histopathological forms of this disease are esophageal squamous cell carcinoma (ESCC)2-3. Currently, there are limited clinical approaches for early diagnosis and treatment for ESCC, resulting in a 10% 5-year survival rate for the patients. Meanwhile, the full repertoire of genomic events leading to the pathogenesis of ESCC remains unclear. Here we show a comprehensive genomic analysis in 158 ESCC cases, as part of the International Cancer Genome Consortium (ICGC) Research Projects (http://icgc.org/icgc/cgp/72/371/1001734). We conducted whole-genome sequencing in 14 ESCC cases and whole-exome sequencing in 90 cases.

  Dataset EGAD00001001691

• 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 EGAD00001004158

• 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

• 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

• Recurrent Somatic Mutations in CLL

  Study EGAS00001000070

• Human primary and metastatic colorectal cancer (CRC) samples

  Dac EGAC00001002954

• Genetic alterations in metastatic uveal melanoma

  Study EGAS00001003303

• OSCC WES + WGS Boot et al. 2020

  Study EGAS00001004376

• Ovarian Cancer Laboratory, University of Oxford

  Dac EGAC00001000518

• Lung Cancer Single-cell Data Access Committee

  Dac EGAC00001001220

• Breast Cancer Cell Line Data Access Committee

  Dac EGAC00001000134

• DAC for the Prostate Cancer Lymph Node dataset

  Dac EGAC00001000586

• The Ovarian Cancer Association Consortium Data Access Committee

  Dac EGAC00001000616

• Spatial Maps of Prostate Cancer Transcriptomes Data Access

  Dac EGAC00001000913

• Data Access Commitee for gastric cancer ascites

  Dac EGAC00001001026

• DAC for MCO colorectal cancer genomics at UNSW

  Dac EGAC00001001122

• DAC for GAMuT Project - Peter MacCallum Cancer Centre

  Dac EGAC00001001154

• DAC for GAMuT Project - Peter MacCallum Cancer Centre

  Dac EGAC00001001278

• DAC for Angiosarcoma Study. National Cancer Centre Singapore.

  Dac EGAC00001001311

• Hyperpolarized 13C MRI in breast cancer DAC

  Dac EGAC00001001438

• Metastases of a cancer of unknown primary (CUP)

  Dac EGAC00001001484

• Peter MacCallum Cancer Centre Research Data Access Committee

  Dac EGAC00001001500

• Hereditary Cancer Diagnostics with I2HCP Data Access Comitee

  Dac EGAC00001001541

• Inherited Predisposition to Cancer Projects Data Access Committee

  Dac EGAC00001001565