7147 results for "canc*"

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• Normal sample for patient SA272

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA272

  Dataset EGAD00001009222

• Tumour sample for patient SA416

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA416

  Dataset EGAD00001009575

• Tumour sample for patient SA400

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA400

  Dataset EGAD00001009576

• Tumour sample for patient SA290

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA290

  Dataset EGAD00001009577

• Tumour sample for patient SA095

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA095

  Dataset EGAD00001009580

• Normal sample for patient SA592

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA592

  Dataset EGAD00001009242

• Normal sample for patient SA596

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA596

  Dataset EGAD00001009245

• Tumour sample for patient SA299

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA299

  Dataset EGAD00001009579

• Normal sample for patient SA720

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA720

  Dataset EGAD00001009583

• Tumour sample for patient SA718

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA718

  Dataset EGAD00001009584

• sWGS of OV cell lines for ACN rascal study

  This dataset contains 60 .bam files of shallow WGS data (~0.1X) from ovarian cancer cell lines. Sequencing reads were aligned to the 1000 Genomes Project GRCh37-derived reference genome using the BWA aligner (v.0.07.17; CRUK-CI alignment pipeline).

  Dataset EGAD00001008118

• Tumour sample for patient SA415

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA415

  Dataset EGAD00001009581

• Dynamics of sequence and structural cell-free DNA landscapes in small-cell lung cancer

  The dataset for the study “Dynamics of sequence and structural cell-free DNA landscapes in small-cell lung cancer” includes 171 bam files from targeted next-generation sequencing (TEC-Seq) from plasma cell-free DNA and matched white blood cell DNA from 33 individuals with small cell lung cancer, alongside 10 bam files from whole exome sequencing of tumor and matched normal DNA for 5 individuals with small cell lung cancer.

  Dataset EGAD00001009860

• Towards standardized whole exome sequencing (WES) for cancer patients: lessons from a multicentric pilot study

  ZPM WES Pilot consisting of 30 samples paired tumor/normal analyzed with WES at four different laboratories in Germany.

  Dataset EGAD00001011087

• Sequencing data from a phase II study of nivolumab and ipilimumab in recurrent or refractory cancer of unknown primary (CheCUP trial)

  Sequencing data from a phase II study of nivolumab and ipilimumab in recurrent or refractory cancer of unknown primary (CheCUP trial). Panel sequencing data from baseline FFPE biopsies were used to perform a comprehensive genomic profiling of CUP metastases. Combined targeted next-generation sequencing of patient-specific hotsport mutations and shallow whole genome sequencing of baseline and follow-up liquid biopsy samples were used to analyze ctDNA and to evaluate response to immunei checkpoint inhibitor treatment. In some cases, whole exome sequencing of peripheral blood mononuclear cells was performed to screen for potential CHIP and germline mutations.

  Dataset EGAD00001011130

• Normal sample for patient SA288

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA288

  Dataset EGAD00001009601

• Somatic mutation and selection at epidemiological scale - TwinsUK_RENanoSeq_Buccal

  Utilising restriction enzyme NanoSeq, we hope to explore the somatic mutation burden and mutational signatures in buccal cells from TwinsUK donors. Some donors have paired blood samples and data from this will help contextualise that work. All donors have paired Targeted NanoSeq data from buccal swabs. As we age, many tissues become colonised by microscopic clones carrying somatic driver mutations. Some of these clones represent a first step towards cancer whereas others may contribute to ageing and other diseases. However, our understanding of the clonal landscapes of human tissues, and their impact on cancer risk, ageing and disease, remains limited due to the challenge of detecting somatic mutations present in small numbers of cells. Here, we introduce a new version of nanorate sequencing (NanoSeq), a duplex sequencing method with error rates of less than 5 per billion base pairs, which is compatible with whole-exome and targeted gene sequencing. Deep sequencing of polyclonal samples with single-molecule sensitivity enables the simultaneous detection of mutations in large numbers of clones, yielding accurate somatic mutation rates, mutational signatures and driver mutation frequencies in any tissue. Applying targeted NanoSeq to 1,042 non-invasive samples of oral epithelium and 371 samples of blood from a twin cohort, we found an unprecedentedly rich landscape of selection, with 46 genes under positive selection driving clonal expansions in the oral epithelium, over 62,000 driver mutations, and evidence of negative selection in some genes. The high number of positively selected mutations in multiple genes provides high-resolution maps of selection across coding and non-coding sites, a form of in vivo saturation mutagenesis. Multivariate regression models enable mutational epidemiology studies on how carcinogenic exposures and cancer risk factors, such as age, tobacco or alcohol, alter the acquisition and selection of somatic mutations. Accurate single-molecule sequencing has the potential to unveil the polyclonal landscape of any tissue, providing a powerful tool to study early carcinogenesis, cancer prevention and the role of somatic mutations in ageing and disease.

  Dataset EGAD00001015621

• Normal sample for patient SA666

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA666

  Dataset EGAD00001009604

• Tumour sample for patient SA1065

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1065

  Dataset EGAD00001009605

• Tumour sample for patient SA1069

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1069

  Dataset EGAD00001009608

• Tumour sample for patient SA1074

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1074

  Dataset EGAD00001009612

• Cancer RNA-seq consisting of FASTQ single-end reads from colon cancer sample

  Cancer RNA-seq consisting of FASTQ single-end reads from 1 colon-cancer individual RNA-seq was performed on illumina This dataset contains reads from a single region.

  Dataset EGAD00001007949

• 10x Single Cell Gene Expression Library TENX068

  10x Single Cell Gene Expression library TENX068 for Triple negative breast cancer patient-derived xenograft SA609X4XB03080

  Dataset EGAD00001011210

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_002 for Triple negative breast cancer patient-derived xenograft SA609X5XB03223

  Dataset EGAD00001011214

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0152_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0152_001 for Triple negative breast cancer patient-derived xenograft SA609X6XB03401

  Dataset EGAD00001011215