7073 results for "canc*"

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• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0175_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0175_001 for Triple negative breast cancer patient-derived xenograft SA535X6XB03101

  Dataset EGAD00001011224

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0182_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0182_001 for Triple negative breast cancer patient-derived xenograft SA535X7XB03304

  Dataset EGAD00001011225

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0189_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0189_001 for Triple negative breast cancer patient-derived xenograft SA535X7XB03448

  Dataset EGAD00001011226

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0184_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0184_001 for Triple negative breast cancer sample SA535X7XB03305

  Dataset EGAD00001011227

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_004

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_004 for Triple negative breast cancer sample SA535X7XB03305

  Dataset EGAD00001011228

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0189_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0189_002 for Triple negative breast cancer patient-derived xenograft SA535X8XB03663

  Dataset EGAD00001011229

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0182_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0182_002 for Triple negative breast cancer sample SA535X8XB03431

  Dataset EGAD00001011230

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0184_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0184_002 for Triple negative breast cancer patient-derived xenograft SA535X8XB03434

  Dataset EGAD00001011231

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0173_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0173_002 for Triple negative breast cancer patient-derived xenograft SA535X9XB03617

  Dataset EGAD00001011232

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0173_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0173_001 for Triple negative breast cancer patient-derived xenograft SA535X9XB03616

  Dataset EGAD00001011233

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0195_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0195_001 for Triple negative breast cancer patient-derived xenograft SA535X8XB03664

  Dataset EGAD00001011234

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0206_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0206_001 for Triple negative breast cancer sample SA535X10XB03693

  Dataset EGAD00001011236

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0208_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0208_002 for Triple negative breast cancer patient-derived xenograft SA535X9XB03776

  Dataset EGAD00001011237

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0142_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0142_002 for Triple negative breast cancer sample SA1035X4XB02879

  Dataset EGAD00001011238

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0071_000

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0071_000 for Triple negative breast cancer sample SA1035X5XB03015

  Dataset EGAD00001011239

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0079_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0079_001 for Triple negative breast cancer sample SA1035X6XB03216

  Dataset EGAD00001011241

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0079_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0079_002 for Triple negative breast cancer patient-derived xenograft SA1035X6XB03211

  Dataset EGAD00001011242

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0142_004

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0142_004 for Triple negative breast cancer sample SA1035X6XB03209

  Dataset EGAD00001011243

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0145_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0145_001 for Triple negative breast cancer patient-derived xenograft SA1035X7XB03338

  Dataset EGAD00001011244

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0145_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0145_002 for Triple negative breast cancer patient-derived xenograft SA1035X7XB03340

  Dataset EGAD00001011245

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0151_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0151_001 for Triple negative breast cancer patient-derived xenograft SA1035X8XB03425

  Dataset EGAD00001011247

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0162_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0162_001 for Triple negative breast cancer sample SA1035X8XB03420

  Dataset EGAD00001011248

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0175_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0175_002 for Triple negative breast cancer patient-derived xenograft SA1035X8XB03631

  Dataset EGAD00001011249

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0164_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0164_002 for Triple negative breast cancer sample SA530X3XB03295

  Dataset EGAD00001011250

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0019_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0019_001 for Triple negative breast cancer patient-derived xenograft SA604X7XB02089

  Dataset EGAD00001011253

• WGS of a Li-Fraumeni patient's HSPCs

  WGS data of clonally expanded HSPCs from a Li-Fraumeni patient at the time of second cancer (Burkitt lymphoma and <5% t-MN) after primary osteosarcoma diagnosis and a reference MSC bulk.

  Dataset EGAD00001011257

• Germline genome sequencing samples from the Hereditary Cancer Syndromes (ICCon) Cancer Flagship

  A prospective study of individuals with suspicion of a hereditary cancer syndrome for whom previous clinical targeted genetic testing was either not informative or was not available. To identify pathogenic disease-causing variants explaining participant presentation, germline whole-genome sequencing and a comprehensive cancer virtual gene panel analysis were undertaken.

  Dataset EGAD00001011260

• Neoantigen Immunogenicity Landscapes and Evolution of Tumor Ecosystems During Immunotherapy with Nivolumab

  We performed Whole Exome (WXS) and RNASeq sequencing on samples obtained from the same site before and during therapy from our prospective clinical trial (CA209-153, NCT02066636) of nivolumab in advanced Non-small cell lung cancer (NSCLC) patients that progressed on chemotherapy. There are 58 pre and 42 on therapy WXS samples and 24 pre and 12 on therapy RNASeq samples. All WXS tumor samples have matching normal samples.

  Dataset EGAD00001011302

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 8a)

  Whole genome sequencing data of 26 high-grade serous carcinoma (HGSC) patients (87 samples) sequenced with MGISEQ-2000 and HiSeq X Ten.

  Dataset EGAD00001011304

• INSPIRE multi-timepoint cfMeDIP dataset

  This dataset contains raw sequencing data from multi-timepoint cell-free methylated DNA immunoprecipitation and sequencing (cfMeDIP-seq) of plasma samples from the INSPIRE study (NCT02644369). Details about the study, including inclusion/exclusion criteria and interventions are available at https://clinicaltrials.gov/study/NCT02644369. Briefly, five cohorts were included, INS-A (head & neck squamous cell carcinoma), INS-B (triple-negative breast cancer), INS-C (high-grade serous ovarian cancer), INS-D (melanoma), and INS-E (mixed solid tumors). All patients in the study were diagnosed with advanced cancer and received treatment with pembrolizumab. Plasma samples were collected at baseline and every 3 cycles until disease progression, death, loss of follow-up, or completion of the study. Plasma samples were first processed for cell-free DNA mutations and remaining samples were then processed by cfMeDIP-seq, prioritizing baseline and post-cycle 3 samples. In total, data from 204 timepoints from 87 distinct patients are deposited.

  Dataset EGAD00001011312

• Sequence data from stage I of the CCTG BR.36 ctDNA-directed, multi-center phase II study of molecular response adaptive immunotherapy in non-small cell lung cancer

  The dataset for the study "Early ctDNA molecular response captures therapeutic response in the first stage of CCTG BR.36 ctDNA-directed, multi-center phase II study of molecular response adaptive immunotherapy in non-small cell lung cancer", includes 134 bam files from hybrid capture targeted error-correction next-generation sequencing (PGDx Elio plasma resolve) from plasma cell-free DNA and matched white blood cell DNA from 35 individuals with non-small cell lung cancer on the BR.36 trial, alongside 11 bam files from targeted next generation sequencing (PGDx Elio tissue complete) of tumor DNA from 11 individuals with non-small cell lung cancer on the BR.36 trial.

  Dataset EGAD00001011359

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 9 part 2)

  Whole genome sequencing data of 35 high-grade serous carcinoma (HGSC) patients (112 samples) sequenced with Illumina Novoseq 6000

  Dataset EGAD00001011368

• An MTOR mutation hitchhikes through renal embryogenesis, driving multifocal, multiphenotypic tumours - WGS

  Embryogenesis is a vulnerable time. Mutations in developmental cells can result in the seeding of cells predisposed to disease within mature organs, creating a field effect. We characterise an embryonic cancer mutation that drives multifocal, multiphenotypic renal tumours in a 14-year-old girl. Their shared MTOR mutation, absent from normal tissues, increases protein flexibility which enables a FAT domain hinge to dramatically increase mTORC1 activity. Developmental mutations, not usually detected in traditional genetic screening, have vital clinical importance in guiding prognosis, targeted treatment, and family screening decisions for paediatric tumours.

  Dataset EGAD00001011645

• Circulating tumor DNA, pathological and immunologic responses to neoadjuvant nivolumab or nivolumab plus relatlimab and chemoradiotherapy in resectable esophageal/gastroesophageal junction cancer

  The dataset for the study “Circulating tumor DNA, pathological and immunologic responses to neoadjuvant nivolumab or nivolumab plus relatlimab and chemoradiotherapy in resectable esophageal/gastroesophageal junction cancer” includes 173 bam files from hybrid capture targeted error-correction next-generation sequencing (TEC-Seq) from plasma cell-free DNA and matched white blood cell DNA from 32 individuals with esophageal/gastroesophageal junction cancer, who received immunotherapy-containing regimens.

  Dataset EGAD00001011822

• RNA sequencing of in vitro generated suppressive myeloid cells using parental and Sialidase expressing A549 cancer cell lines

  Fresh peripheral blood mononuclear cells of four human donors were cultured together with either lung adenocarcinoma A549 cancer cells or A549-expressing H1N1 Sialidase cancer cells. These treatments induced the differentiation of donor cells into immunosuppressive MDSC-like cells, which were further subjected to bulk RNA sequencing. RNA-seq TruSeq libraries were generated from polyA-enriched mRNA isolated from the samples, and sequenced in paired-end mode on 4 lanes of an Illumina NextSeq 500 flow-cell

  Dataset EGAD00001012437

• Somatic mutant selection is altered by prior NOTCH1 mutation in ageing esophageal epithelium

  Authors: Charlotte King1, Emilie Abbie1, Joanna C. Fowler1, Irina Abnizova1, Roshan K. Sood1, Swee Hoe Ong1, Michael W. J. Hall1,2, Faye Lynch-Williams3, Benjamin A. Hall4, Philip H. Jones1,2,5 Abstract: In cancer evolution, genome alterations often occur in a specific order, implying selection depends on the prior clonal genotype 1-3. It is unknown if similar constraints operate in normal epithelia. Here, we mapped mutations in normal mid-esophagus of aged UK subjects. Mutant NOTCH1 clones colonized most of the epithelium by age 60 and above 70 tissue was saturated with mutants under strong competitive selection. Mutant TP53 was more strongly selected in donors over 60 years of age. Samples predominantly mutant for NOTCH1 showed increased selection of NOTCH2 mutants and weaker selection of mutant TP53 compared with samples that were mostly NOTCH1 wild type. In mouse esophagus lacking Notch1 we observed strong selection of mutant Notch2 and other genes not selected in wild type esophagus. In normal ageing esophagus, the first driver mutation may change the trajectory of subsequent somatic evolution by altering mutant selection.

  Dataset EGAD00001015257

• Somatic mutant selection is altered by prior NOTCH1 mutation in ageing esophageal epithelium

  Authors: Charlotte King1, Emilie Abbie1, Joanna C. Fowler1, Irina Abnizova1, Roshan K. Sood1, Swee Hoe Ong1, Michael W. J. Hall1,2, Faye Lynch-Williams3, Benjamin A. Hall4, Philip H. Jones1,2,5 Abstract: In cancer evolution, genome alterations often occur in a specific order, implying selection depends on the prior clonal genotype 1-3. It is unknown if similar constraints operate in normal epithelia. Here, we mapped mutations in normal mid-esophagus of aged UK subjects. Mutant NOTCH1 clones colonized most of the epithelium by age 60 and above 70 tissue was saturated with mutants under strong competitive selection. Mutant TP53 was more strongly selected in donors over 60 years of age. Samples predominantly mutant for NOTCH1 showed increased selection of NOTCH2 mutants and weaker selection of mutant TP53 compared with samples that were mostly NOTCH1 wild type. In mouse esophagus lacking Notch1 we observed strong selection of mutant Notch2 and other genes not selected in wild type esophagus. In normal ageing esophagus, the first driver mutation may change the trajectory of subsequent somatic evolution by altering mutant selection.

  Dataset EGAD00001015259

• Somatic mutant selection is altered by prior NOTCH1 mutation in ageing esophageal epithelium

  Authors: Charlotte King1, Emilie Abbie1, Joanna C. Fowler1, Irina Abnizova1, Roshan K. Sood1, Swee Hoe Ong1, Michael W. J. Hall1,2, Faye Lynch-Williams3, Benjamin A. Hall4, Philip H. Jones1,2,5 Abstract: In cancer evolution, genome alterations often occur in a specific order, implying selection depends on the prior clonal genotype 1-3. It is unknown if similar constraints operate in normal epithelia. Here, we mapped mutations in normal mid-esophagus of aged UK subjects. Mutant NOTCH1 clones colonized most of the epithelium by age 60 and above 70 tissue was saturated with mutants under strong competitive selection. Mutant TP53 was more strongly selected in donors over 60 years of age. Samples predominantly mutant for NOTCH1 showed increased selection of NOTCH2 mutants and weaker selection of mutant TP53 compared with samples that were mostly NOTCH1 wild type. In mouse esophagus lacking Notch1 we observed strong selection of mutant Notch2 and other genes not selected in wild type esophagus. In normal ageing esophagus, the first driver mutation may change the trajectory of subsequent somatic evolution by altering mutant selection.

  Dataset EGAD00001015260

• Genome and transcriptome sequence data from a gliomatosis cerebri anaplastic astrocytoma tumor patient

  Genome and transcriptome sequence data from a gliomatosis cerebri anaplastic astrocytoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015292

• Somatic mutant selection is altered by prior NOTCH1 mutation in ageing esophageal epithelium

  Authors: Charlotte King1, Emilie Abbie1, Joanna C. Fowler1, Irina Abnizova1, Roshan K. Sood1, Swee Hoe Ong1, Michael W. J. Hall1,2, Faye Lynch-Williams3, Benjamin A. Hall4, Philip H. Jones1,2,5 Abstract: In cancer evolution, genome alterations often occur in a specific order, implying selection depends on the prior clonal genotype 1-3. It is unknown if similar constraints operate in normal epithelia. Here, we mapped mutations in normal mid-esophagus of aged UK subjects. Mutant NOTCH1 clones colonized most of the epithelium by age 60 and above 70 tissue was saturated with mutants under strong competitive selection. Mutant TP53 was more strongly selected in donors over 60 years of age. Samples predominantly mutant for NOTCH1 showed increased selection of NOTCH2 mutants and weaker selection of mutant TP53 compared with samples that were mostly NOTCH1 wild type. In mouse esophagus lacking Notch1 we observed strong selection of mutant Notch2 and other genes not selected in wild type esophagus. In normal ageing esophagus, the first driver mutation may change the trajectory of subsequent somatic evolution by altering mutant selection.

  Dataset EGAD00001015261

• Genome and transcriptome sequence data from a plexiform neurofibroma tumor patient

  Genome and transcriptome sequence data from a plexiform neurofibroma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015283

• Genome and transcriptome sequence data from a diffuse intrinsic pontine glioma tumor patient

  Genome and transcriptome sequence data from a diffuse Intrinsic Pontine Glioma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015284

• Genome and transcriptome sequence data from a ewing sarcoma tumor patient

  Genome and transcriptome sequence data from a ewing sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015286

• Genome and transcriptome sequence data from a ependymoma tumor patient

  Genome and transcriptome sequence data from a ependymoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015287

• Genome and transcriptome sequence data from a glioblastoma tumor patient

  Genome and transcriptome sequence data from a glioblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015288

• Genome and transcriptome sequence data from a NUT midline carcinoma tumor patient

  Genome and transcriptome sequence data from a NUT midline carcinoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015289

• Genome and transcriptome sequence data from a pre-B all (2nd relapse in CNS) tumor patient

  Genome and transcriptome sequence data from a pre-B all (2nd relapse in CNS) patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015291

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

  Genome and transcriptome sequence data from a osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015293

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015294

• Genome and transcriptome sequence data from a metastatic alveolar rhabdomyosarcoma tumor patient

  Genome and transcriptome sequence data from a metastatic alveolar rhabdomyosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015295