7083 results for "canc*"

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• Tumour sample for patient SA285

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA285

  Dataset EGAD00001009543

• Normal sample for patient SA271

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA271

  Dataset EGAD00001009559

• Triple Negative BC RNA Sequencing

  We propose to definitively characterise the somatic genetics of breast cancer through generation of comprehensive catalogues of somatic mutations in breast cancer cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Dataset EGAD00001001339

• RNA-seq data from the tumor samples of a head and neck cancer patient

  This dataset contains paired-end RNA sequencing data (2x50 bp) from the three synchronous primary tumors and the recurrence of a head and neck cancer patient.

  Dataset EGAD00001006654

• Whole-genome sequencing of high-grade serous ovarian cancer (HGSC) tumours and matched normals from long-term survivors.

  Whole-genome sequencing of high-grade serous ovarian cancer (HGSC) tumours and matched normals from long-term survivors performed as part of the Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG) study. The dataset includes fastq files from 58 HGSC tumours (53 primary tumours and 5 recurrent tumours) and 53 matched normals from 53 long-term survivor patients. Sequence libraries were generated from tumour and matched normal genomic DNA using the KAPA HyperPrep PCR-free library preparation kit (Roche) according to manufacturer’s instructions. Sequencing was carried out by the Kinghorn Centre for Clinical Genomics Sequencing Laboratory (Sydney, Australia) on the HiSeq X Ten System (Illumina) to a minimum base coverage of 30-fold for normal DNA and 60-fold for tumour DNA samples.

  Dataset EGAD00001009398

• Strand-seq of hematopoietic stem and progenitor cells along human aging

  Somatic mosaicism (SM), referring to the presence of somatic mutations in sub-populations of cells within healthy individuals, is associated with an increased risk of a variety of diseases, including cancer. Blood is at particularly high risk of SM, given its rapid turnover and functionally- heterogeneous cell-type composition. While the roles of point mutations and large-scale rearrangements in blood SM have been scrutinised in recent years, the functional impact of mosaic structural variants (mSVs) remains poorly understood. Using haplotype-resolved single-cell multi-omics based on Strand-seq technology, we explored the mSV landscape of human hematopoietic stem and progenitor cells (HSPCs).

  Dataset EGAD00001009402

• Recalibrated alignment files of Saudi Thyroid Cancer

  The dataset consists of samples from papillary thyroid cancer patients. A total of 292 DNA samples from blood/normal and cancer tissue are subjected to whole exome sequencing using Illumina. The fastq files generated were aligned with reference genome ‘hg19’, duplicates were marked, realignment around indels and quality recalibration were performed to produce good quality variants. The recalibrated “.bam” files are included with this dataset.​

  Dataset EGAD00001003950

• 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

• Dataset-linking-WGS-via-README-for-EGAS00001004884

  This dataset contains 139 Tumor and Control WGS files for samples for Gerhauser et al.,Cancer Cell, 2018, 34:996-1011. WGS and sequencing protocol was earlier described in Weischenfeldt et al, Cancer Cell, 2013.

  Dataset EGAD00001007669

• Genome and transcriptome sequence data from a lung cancer patient

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

  Dataset EGAD00001001962