7075 results for "canc*"

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• Lung Progression versus Regression Whole Genome Sequencing

  Around 50 samples of pre-invasive lung cancer lesions showing subsequent clinical and pathological progression or regression

  Dataset EGAD00001001271

• Genome and transcriptome sequence data from a metastatic melanoma patient

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

  Dataset EGAD00001005851

• Genome and transcriptome sequence data from a cecum adenocarcinoma patient

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

  Dataset EGAD00001005895

• Genome and transcriptome sequence data from a peritoneal mesothelioma patient

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

  Dataset EGAD00001001311

• Tobacco exposure and somatic mutations in normal human bronchial epithelium

  That tobacco smoking causes lung cancer is well-established, but we lack quantitative understanding of its effects on genomes of normal bronchial epithelium. We sequenced whole genomes of 632 colonies derived from single bronchial epithelial cells across 16 subjects. Tobacco smoking is the major influence on mutation burden, adding 1000-10,000+ mutations/cell, massively increasing both between-subject and within-subject variance, and generating several distinct signatures of substitutions and indels. A population of cells in subjects with smoking history had mutation burdens equivalent to that expected for never-smokers: these cells lacked tobacco-specific mutational signatures, were four-fold more frequent in ex-smokers than current smokers, and had significantly longer telomeres than their more mutated counterparts. Driver mutations increased in frequency with age, affecting 4-14% of cells in middle-aged never-smokers. In current smokers, ≥25% of cells carried driver mutations and 0-6% cells had 2 or even 3 drivers. Thus, tobacco smoking increases mutation burden, cell-to-cell heterogeneity and driver mutations, but quitting promotes replenishment of bronchial epithelium from mitotically quiescent cells that have avoided tobacco mutagenesis.

  Dataset EGAD00001005193

• Genome and transcriptome sequence data from a pancreatic ductal adenocarcinoma patient

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

  Dataset EGAD00001005904

• Genome and transcriptome sequence data from a gastroesophageal junction adenocarcinoma patient

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

  Dataset EGAD00001005910

• Genome and transcriptome sequence data from an unknown tissue unknown histology patient

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

  Dataset EGAD00001005912

• Genome and transcriptome sequence data from a metastatic colorectal carcinoma patient

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

  Dataset EGAD00001001307

• Dataset for gynecologic_cancer-RNA

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

  Dataset EGAD00001008856

• Genome and transcriptome sequence data from a primary unknown cancer patient

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

  Dataset EGAD00001001308

• Triple Negative BC WGS Dataset

  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 EGAD00001001335

• Normal sample for patient SA232

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA232

  Dataset EGAD00001009555

• Cell Line Dataset

  Frequent somatic transfer of mitochondrial DNA into the nuclear genome of human cancer cells

  Dataset EGAD00001001349

• CGP CORE CELL LINES - RNA-seq

  Genomic characterisation of a large series of cancer cell lines.

  Dataset EGAD00001001357

• Somatic mutation and clonal evolution in the human bladder_TGS (2020-05-05)

  In this study, we aim to characterise the landscape of mutation and clonal selection in the human bladder. The study includes targeted sequencing of laser-dissected microbiopsies from the bladder. The samples utilised in this study will include urothelium from transplant donors with no history of bladder cancer and cystectomy specimens from patients with bladder cancer. . This dataset contains all the data available for this study on 2020-05-05.

  Dataset EGAD00001006114

• WES and RNA-seq of triple-negative breast cancers from the MyBrCa cohort

  The dataset contains a genomics characterization of 35 triple-negative Asian breast tumours from the Malaysian Breast Cancer cohort. This includes whole-exome sequencing of tumour tissue at 80X, whole-exome sequencing of matched normal (blood) tissue at 40X, and RNA-seq of tumour tissue at 40X coverage (>15 million reads). Whole-exome libraries were prepared using the Nextera Rapid Capture Exome Kit; exome capture was performed in pools of 3 and subjected to paired end 75 sequencing on a NovaSeq 6000 platform. RNA libraries were prepared using the TruSeq Stranded Total RNA HT kit with Ribo-Zero Gold as per manufacturer’s instructions and also subjected to paired end 75 sequencing on a NovaSeq 6000 platform. Uploaded bam files have been mapped to the hs37d5 human genome and processed using the standard GATK pipelines. Paired clinical, demographic, genotyping, and overall survival data for these patients are available from the associated publications or by request.

  Dataset EGAD00001009311

• Single Cell Genome Sequence for DLP+ library A95618B

  Single Cell Genome Sequence for Triple negative breast cancer patient-derived xenograft SA609 passage 3 on DLP+ library A95618B

  Dataset EGAD00001009316

• PSCP_mutation analysis in hESCs

  Many studies over the past 10 years, culminating in the recent report of the International Stem Cell Initiative (ISCI, 2011) have shown that hPSC acquire genetic and epigenetic changes during their time in culture. Many of the genetic changes are non-random and recurrent, probably because they provide a selective growth advantage to the undifferentiated cells. Some are shared by embryonal carcinoma cells, the malignant counterparts of ES cells. The origins of these growth advantages are poorly understood, but may come from altered cell cycle dynamics, resistance to apoptosis or altered patterns of differentiation. Less is known about the nature and consequences of epigenetic changes, but it is likely that these similarly affect hPSC behaviour; e.g., enhanced expression of DLK1, an imprinted gene, is associated with altered hPSC growth (Enver et al 2005). Inevitably, these genetic and epigenetic changes will impact on our ability to use hPSC for regenerative medicine, either because malignant transformation of the undifferentiated cells or their differentiated derivatives to be used for transplantation compromises safety, or because they impede the function of those differentiated derivatives, or because they affect the efficiency with which the undifferentiated cells can be expanded and differentiated into desired cell types. Focusing initially upon the existing clinical grade hESC lines, later moving to iPSC, we will Consolidate and extend knowledge of the rate, type and functional impact of the genetic variations that occur during hPSC culture. We will use whole genome and exome sequencing as well as SNP arrays, together with clonal analysis and other cytogenetics techniques. Common changes will be compared with those found in the normal human population, at low frequency in the original cell population or observed during iPSC generation in the HIPSCI project currently based at the WTSI. These studies will provide a better understanding of the range of genetic changes that occur in hPSC beyond the CNVs already identified. In conjunction with cancer genome resources and expertise at WTSI, bioinformatic analyses of these hPSC data will allow us to assess potential impact on hPSC behaviour pertinent to applications in regenerative medicine, notably the likelihood that specific changes arising in undifferentiated PSC cultures may be associated with potential malignant transformation of differentiated progeny. This data is part of a pre-publication release. For information on the proper use of pre-publication data shred by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001002231

• Dataset for hematopoietic_malignancy-EXON

  Rare cancer sequencing data of 91 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008882

• Single Cell Genome Sequence for DLP+ library A95632C

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA609 passage 6 on DLP+ library A95632C

  Dataset EGAD00001009322

• ChIP sequencing in Cancer Cell Lines

  The project is to evaluate the genomic binding sites of the histone demethylase JARID1C. This gene was recently identified in CGP as a novel recessive cancer gene in human renal cell carcinoma.

  Dataset EGAD00001001453

• RNA-seq of multi-regional CRC samples

  BAM files of RNA sequencing (RNA-seq) experiment on multi-regional colorectal cancer (CRC) samples. 58 samples corresponding to 16 patients were sequenced and there is one BAM file for each sample.

  Dataset EGAD00001006164

• Genome and transcriptome sequence data from a metastatic colorectal cancer patient

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

  Dataset EGAD00001002619

• RNA-seq data from 121 tumor samples with muscle invasive bladder cancer.

  In this study, a total of 300 patients with MIBC receiving chemotherapy were included; 62 received NAC before cystectomy and 245 received first-line chemotherapy upon detection of locally-advanced (T4b) or metastatic disease. Treatment response, defined as pathological downstaging (< pTa,CIS,N0) after NAC or complete or partial response after first-line treatment (RECIST criteria). RNA-seq was performed using the QuantSeq kit FWD HT kit (Lexogen) using 500 ng input RNA from 121 tumor samples. Data provided here consist of 780 fastq files for RNA-seq.

  Dataset EGAD00001006238

• Molecular Characterization for Nasopharyngeal Carcinoma (NPC)

  Total RNA was isolated from 50 formalin-fixed paraffin-embedded nasopharyngeal cancer (NPC) specimens using the RecoverAll Total Nucleic Acid Isolation kit (Ambion). Tumor RNA libraries were prepared with 200ng RNA using the Illumina TruSeq Stranded Total RNA kit with Ribo-Zero Gold, and sequenced with >80 million 100 bp paired-end reads.

  Dataset EGAD00001009047

• NLG-LBC-05 ctDNA project sequencing data

  Low-pass whole-genome sequencing of pretherapy lymphoma cfDNA and targeted sequencing of cfDNA, tumor tissue and whole-blood samples of NLG-LBC-05 patient samples and cfDNA of nine subjects with no known cancer. Hybrid capture target enrichment; panel target and sequencing information provided in PMID:34932792. FASTQ files provided for targeted sequencing, separate sequencing runs per sample noted with prefix "run" if applicable. Sequences from DTX1 and KLHL6 targets are advised to be excluded from analyses due to PCR/plasmid contaminants of in-house origin.

  Dataset EGAD00001009337

• cell-free DNA Whole Genome Sequencing of 3784 samples on MGI and Illumina platform

  The dataset of Integrative modeling of tumor genomes and epigenomes for enhanced cancer diagnosis by cell-free DNA includes 3784 whole genome sequencing bam files on the MGI and Illumina platform. The analyzed samples include plasma samples from normal individuals and patients with cancer.

  Dataset EGAD00001009335

• Tumour sample for patient SA675

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA675

  Dataset EGAD00001009342

• Tumour sample for patient SA673

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA673

  Dataset EGAD00001009340

• RNA-seq dataset

  HTG EdgeSeq fastq files of bulk baseline tumors from IMbassdor250: a randomised phase 3 trial comparing atezolizumab with enzalutamide vs enzalutamide alone in patients with metastatic castration-resistant prostate cancer.

  Dataset EGAD00001007685

• Tumour sample for patient SA674

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA674

  Dataset EGAD00001009341

• Tumour sample for patient SA677

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA677

  Dataset EGAD00001009344

• Tumour sample for patient SA678

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA678

  Dataset EGAD00001009345

• Tumour sample for patient SA679

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA679

  Dataset EGAD00001009346

• Tumour sample for patient SA683

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

  Dataset EGAD00001009350

• Tumour sample for patient SA238

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

  Dataset EGAD00001009352

• Tumour sample for patient SA239

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

  Dataset EGAD00001009353

• Tumour sample for patient SA425

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

  Dataset EGAD00001009356

• Normal sample for patient SA221

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

  Dataset EGAD00001009362

• Normal sample for patient SA238

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

  Dataset EGAD00001009363

• Normal sample for patient SA280

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

  Dataset EGAD00001009365

• Normal sample for patient SA286

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

  Dataset EGAD00001009366

• The transcription factor GABP selectively binds and activates the mutant TERT promoter in cancer

  Dataset contains Exome-seq and RNA-seq from 2 GBM patients, as well as RNA-seq from the derived cultured cells (GNS).

  Dataset EGAD00001001359

• August 2020 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  August 2020 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001006383

• Normal sample for patient SA291

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

  Dataset EGAD00001009368

• Normal sample for patient SA300

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

  Dataset EGAD00001009369

• Somatic mutation analysis based on Whole exome Sequencing (WES) of multiple tumor biopsies, patient-derived spheroids and leukocyte DNA from colorectal cancer patients (vcf files)

  Somatic mutations were called using whole exome Sequencing (WES) data from colorectal cancer samples (dataset EGAD00001003821) using MuTect2, with matched constitutional WES-data obtained from leukocytes samples as reference.

  Dataset EGAD00001003823

• Normal sample for patient SA495

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

  Dataset EGAD00001009372

• Normal sample for patient SA676

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA676

  Dataset EGAD00001009376