7406 results for "canc*"

in 37.63 milliseconds.

• Intra-prostatic tumour evolution, steps in metastatic spread and histogenomic associations revealed by integration of multi-region whole genome sequencing with histopathological features

  We performed whole genome sequencing on 42 prostate cancer samples from the prostate, seminal vesicles and regional lymph nodes of five treatment-naive patients with locally advanced disease who underwent radical prostatectomy. Whole genome sequencing was performed as 150bp paired end reads on the Illumina NovaSeq 6000 platform.

  Dataset EGAD00001011174

• cfDNA methylation profiling in Metastatic Cancer and Cancer of Unknown Primary

  This study contains methyl-binding domain sequencing and shallow whole genome sequencing from circulating cell-free DNA (cfDNA) for 143 patients with metastatic cancer of known type, 41 patients with Cancer of Unknown Primary (CUP) and 27 non-cancer controls.

  Dataset EGAD00001011178

• Cancer treatment re-shapes the somatic mutational landscape of normal esophagus - wgs

  Ageing epithelial tissues, including the esophagus, are progressively colonized by somatic mutant clones, some carrying mutations linked to cancer. However, whether normal epithelial mutational landscapes that evolve over decades can be altered by short term interventions such as cancer treatment is not known. Here we sequenced normal esophageal epithelium removed from patients after 5-8 weeks of therapy for esophageal cancer. Some patients had no prior treatment (NT), while others had 8 weeks of 5 Fluorouracil (5-FU) containing chemotherapy (FLOT) or 4 weeks of chemotherapy and radiation directed at the tumor (CROSS). Despite the brief duration of therapy, we observed treatment specific differences in the selection of mutant clones. The median size of mutant TP53 and PPM1D clones in the CROSS group was double that in the other groups. Mutants of the tumor suppressor SPOP were selected in CROSS but not FLOT or NT patients. In FLOT patients we found positive selection of activating MTOR mutations, consistent with the hypothesis that these mutants confer 5-FU resistance. Sequencing densely mutated normal epithelia after cancer reveals treatment specific selection of mutations with potential therapeutic impact.

  Dataset EGAD00001011181

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 9 part 1)

  Whole genome sequencing data of 6 high-grade serous carcinoma (HGSC) patients (11 samples) sequenced with MGISEQ-2000

  Dataset EGAD00001011353

• neoALTTO

  RNAseq fastq files for 254 samples for the neoALTTO study of lapatinib, trastuzumab or combination in HER2+ breast cancer patients. Those are pre-treatment baseline samples.

  Dataset EGAD00001011354

• 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

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

  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 EGAD00001011646

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

  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 EGAD00001011647

• RNA-sequencing of Human Thyroid cancer cell lines

  RNA-sequencing data of 5 human thyroid cancer cell lines cultured in control conditions

  Dataset EGAD00001011677

• Low-coverage whole-genome sequencing of cancer and healthy plasma circulating DNA

  Plasma samples from patients with melanoma (stage II/III/IV) and breast cancer (stage IV) as well as healthy individuals were subjected to low-coverage whole-genome sequencing (less than 10x average depth). This dataset contains raw fastq files from 39 breast cancer, 127 melanoma and 42 healthy control plasma samples.

  Dataset EGAD00001011817

• 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

• Elucidating the heterogeneity of immunotherapy response and immune-related toxicities by longitudinal ctDNA and immune cell compartment tracking in lung cancer

  The dataset for the study Elucidating the heterogeneity of immunotherapy response and immune-related toxicities by longitudinal ctDNA and immune cell compartment tracking in lung cancer includes 207 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 30 individuals with non-small cell lung cancer, alongside 46 bam files from whole exome sequencing of tumor and matched normal DNA for 21 individuals with non-small cell lung cancer who received immunotherapy-containing regimens.

  Dataset EGAD00001011991

• Shallow-whole genome sequencing for copy numbers in resectable gastric cancer treated with surgery alone

  Shallow-whole genome sequencing for copy numbers in resectable gastric cancer treated with surgery alone

  Dataset EGAD00001011994

• 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_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0206_002 for Triple negative breast cancer patient-derived xenograft SA535X10XB03696

  Dataset EGAD00001011235

• 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_CHIP0076_000

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0076_000 for Triple negative breast cancer sample SA1035X5XB03021

  Dataset EGAD00001011240

• 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_CHIP0162_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0162_002 for Triple negative breast cancer sample SA1035X7XB03502

  Dataset EGAD00001011246

• 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_CHIP0014_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0014_001 for Triple negative breast cancer patient-derived xenograft SA604X6XB01979

  Dataset EGAD00001011251

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0015_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0015_001 for Triple negative breast cancer patient-derived xenograft SA604X6XB01979

  Dataset EGAD00001011252

• 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

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0020_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0020_002 for Triple negative breast cancer patient-derived xenograft SA604X8XB02164

  Dataset EGAD00001011254

• 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

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

  This dataset contains paired-end RNA sequencing data (2x50 bp) from the primary tumors and the recurrences/metastases of 6 head and neck cancer patients.

  Dataset EGAD00001011279

• 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

• 10x Single Cell Gene Expression Library TENX063

  10x Single Cell Gene Expression library TENX063 for Triple negative breast cancer patient-derived xenograft SA609X3XB01584

  Dataset EGAD00001011268

• Whole-exome sequencing data from head and neck cancer patients

  This dataset contains paired-end whole-exome sequencing data (2x50 bp) from the normal samples, the primary tumors and the recurrences/metastases of 8 head and neck cancer patients.

  Dataset EGAD00001011278

• The ALT pathway generates telomere fusions that can be detected in the blood of cancer patients

  Telomere fusions (TFs) can trigger the accumulation of oncogenic alterations leading to malignant transformation and drug resistance. Despite their relevance in tumour evolution, our understanding of the patterns and consequences of TFs in human cancers remains limited. Here, we characterize the rates and spectrum of somatic TFs across >30 cancer types using whole-genome sequencing data. TFs are pervasive in human tumours with rates varying markedly across and within cancer types. In addition to end-to-end fusions, we find novel patterns of TFs that we mechanistically link to the activity of the alternative lengthening of telomeres (ALT) pathway. We show that TFs can be detected in the blood of cancer patients, which enables cancer detection with high specificity and sensitivity even for early-stage tumours and cancers of high unmet clinical need. Overall, we report a novel genomic footprint that enables characterization of the telomere maintenance mechanism of tumours and liquid biopsy analysis.

  Dataset EGAD00001012101

• Geographic variation of mutagenic exposures in kidney cancer genomes – sequence data (Mutographs)

  Geographic variation of mutagenic exposures in kidney cancer genomes – sequence data (Mutographs)

  Dataset EGAD00001012102

• Geographic variation of mutagenic exposures in kidney cancer genomes – filtered vcf files (Mutographs)

  Geographic variation of mutagenic exposures in kidney cancer genomes – filtered vcf files (Mutographs)

  Dataset EGAD00001012222

• Geographic variation of mutagenic exposures in kidney cancer genomes – patient metadata files (Mutographs)

  Geographic variation of mutagenic exposures in kidney cancer genomes – patient metadata files (Mutographs)

  Dataset EGAD00001012223

• Data from Representation of genomic intratumor heterogeneity in multi-region non-small cell lung cancer patient-derived xenograft models

  Data from Representation of genomic intratumor heterogeneity in multi-region non-small cell lung cancer patient-derived xenograft models

  Dataset EGAD00001012228

• Geographic variation of mutagenic exposures in kidney cancer genomes – structural variation vcf files (Mutographs)

  Geographic variation of mutagenic exposures in kidney cancer genomes – structural variation vcf files ( Mutographs )

  Dataset EGAD00001013726

• 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

• Geographic variation of mutagenic exposures in kidney cancer genomes – copy number variants (Mutographs)

  Geographic variation of mutagenic exposures in kidney cancer genomes – copy number variants (Mutographs)

  Dataset EGAD00001013727

• Targeted and shallow whole genome sequencing identifies therapeutic opportunities in p53abn endometrial cancers

  Shallow whole genome sequencing of 196 formalin-fixed paraffin-embedded p53abn endometrial cancers.

  Dataset EGAD00001015241

• The mutation burden of narrowband ultraviolet B phototherapy in human skin - Nanoseq

  Background: Ultraviolet radiation (UV) is used as a treatment for psoriasis, but UV can also induce mutations which may lead to development of skin cancer. Information on the mutagenicity of narrowband UVB (NBUVB) would help inform clinicians and patients who are concerned about the potential risks of this treatment.

  Dataset EGAD00001015249

• The mutation burden of narrowband ultraviolet B phototherapy in human skin - WGS

  Background: Ultraviolet radiation (UV) is used as a treatment for psoriasis, but UV can also induce mutations which may lead to development of skin cancer. Information on the mutagenicity of narrowband UVB (NBUVB) would help inform clinicians and patients who are concerned about the potential risks of this treatment.

  Dataset EGAD00001015250

• 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 EGAD00001015256

• 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 EGAD00001015258

• 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

• 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 infantile fibrosarcoma tumor patient

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

  Dataset EGAD00001015263

• Clinical and genetic factors associated with tumor response to neoadjuvant (chemo)radiotherapy, survival and recurrence risk in rectal cancer

  Dataset consists of 216 samples, with each sample having a BAM, BAI and VCF file.

  Dataset EGAD00001011297

• 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

• Machine Learning Guided Signal Enrichment for Plasma Tumor-burden Monitoring Dataset

  Includes 4 Datasets: 1. 35 Control Plasma WGS samples sequenced on NovaSeq V1.0 Chemistry at the New York Genome Center. Denoted by CTRL-2XX naming scheme. 2. Plasma WGS from 17 patients with Small Cell Lung Cancer. Samples extracted at either Pretreatment or Postoperative at weeks 2 or 3. Denoted by SCLC-XX naming scheme. 3. Plasma WGS from a synthetic mixing study of a high-burden melanoma plasma sample with plasma bag, at estimated tumor concentrations of 10e-3, 10e-4, 10e-5, and 10e-6 for 2 replicates. Denoted by SM-repX naming scheme. 4. Assorted Plasma, Tumor, and Normal WGS from patients with NSCLC expressing high-burden for use in model training. Denoted by NSCLC-2XX naming scheme.

  Dataset EGAD00001011352

• 10x Single Cell Gene Expression Library TENX069

  10x Single Cell Gene Expression library TENX069 for Triple negative breast cancer patient-derived xenograft SA609X4XB03083

  Dataset EGAD00001011211

• Cancer treatment re-shapes the somatic mutational landscape of normal esophagus - 0.25mm punches targeted

  Ageing epithelial tissues, including the esophagus, are progressively colonized by somatic mutant clones, some carrying mutations linked to cancer. However, whether normal epithelial mutational landscapes that evolve over decades can be altered by short term interventions such as cancer treatment is not known. Here we sequenced normal esophageal epithelium removed from patients after 5-8 weeks of therapy for esophageal cancer. Some patients had no prior treatment (NT), while others had 8 weeks of 5 Fluorouracil (5-FU) containing chemotherapy (FLOT) or 4 weeks of chemotherapy and radiation directed at the tumor (CROSS). Despite the brief duration of therapy, we observed treatment specific differences in the selection of mutant clones. The median size of mutant TP53 and PPM1D clones in the CROSS group was double that in the other groups. Mutants of the tumor suppressor SPOP were selected in CROSS but not FLOT or NT patients. In FLOT patients we found positive selection of activating MTOR mutations, consistent with the hypothesis that these mutants confer 5-FU resistance. Sequencing densely mutated normal epithelia after cancer reveals treatment specific selection of mutations with potential therapeutic impact.

  Dataset EGAD00001011182

• Cancer treatment re-shapes the somatic mutational landscape of normal esophagus - nanoseq

  Ageing epithelial tissues, including the esophagus, are progressively colonized by somatic mutant clones, some carrying mutations linked to cancer. However, whether normal epithelial mutational landscapes that evolve over decades can be altered by short term interventions such as cancer treatment is not known. Here we sequenced normal esophageal epithelium removed from patients after 5-8 weeks of therapy for esophageal cancer. Some patients had no prior treatment (NT), while others had 8 weeks of 5 Fluorouracil (5-FU) containing chemotherapy (FLOT) or 4 weeks of chemotherapy and radiation directed at the tumor (CROSS). Despite the brief duration of therapy, we observed treatment specific differences in the selection of mutant clones. The median size of mutant TP53 and PPM1D clones in the CROSS group was double that in the other groups. Mutants of the tumor suppressor SPOP were selected in CROSS but not FLOT or NT patients. In FLOT patients we found positive selection of activating MTOR mutations, consistent with the hypothesis that these mutants confer 5-FU resistance. Sequencing densely mutated normal epithelia after cancer reveals treatment specific selection of mutations with potential therapeutic impact.

  Dataset EGAD00001011183

• Cancer treatment re-shapes the somatic mutational landscape of normal esophagus - 2mm targeted

  Ageing epithelial tissues, including the esophagus, are progressively colonized by somatic mutant clones, some carrying mutations linked to cancer. However, whether normal epithelial mutational landscapes that evolve over decades can be altered by short term interventions such as cancer treatment is not known. Here we sequenced normal esophageal epithelium removed from patients after 5-8 weeks of therapy for esophageal cancer. Some patients had no prior treatment (NT), while others had 8 weeks of 5 Fluorouracil (5-FU) containing chemotherapy (FLOT) or 4 weeks of chemotherapy and radiation directed at the tumor (CROSS). Despite the brief duration of therapy, we observed treatment specific differences in the selection of mutant clones. The median size of mutant TP53 and PPM1D clones in the CROSS group was double that in the other groups. Mutants of the tumor suppressor SPOP were selected in CROSS but not FLOT or NT patients. In FLOT patients we found positive selection of activating MTOR mutations, consistent with the hypothesis that these mutants confer 5-FU resistance. Sequencing densely mutated normal epithelia after cancer reveals treatment specific selection of mutations with potential therapeutic impact.

  Dataset EGAD00001011184

• HiSeq Colorectal Cancer Dataset

  Set of 19 patients afflicted with colorectal cancer with matching preoperative and postoperative blood plasma, PBMC, and tumor biopsy sequencing data. Originally referenced by Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring. Nat Med. 2020, Zviran et. al.

  Dataset EGAD00001011186

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0150_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0150_001 for Triple negative breast cancer sample SA609X5XB03230

  Dataset EGAD00001011212

• Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma - RNA-Seq

  This study aims to investigate the dysregulation of RNA translation and identify functional non-canonical open reading frames (ORFs) as potential targets for medulloblastoma treatment. The study involves ribosome profiling and RNAseq of medulloblastoma tissues and cell lines to observe the translation of non-canonical ORFs. Multiple CRISPR-Cas9 screens will be used to identify functional non-canonical ORFs implicated in medulloblastoma cell survival.

  Dataset EGAD00001011192

• Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma - Ribo-Seq

  This study aims to investigate the dysregulation of RNA translation and identify functional non-canonical open reading frames (ORFs) as potential targets for medulloblastoma treatment. The study involves ribosome profiling and RNAseq of medulloblastoma tissues and cell lines to observe the translation of non-canonical ORFs. Multiple CRISPR-Cas9 screens will be used to identify functional non-canonical ORFs implicated in medulloblastoma cell survival.

  Dataset EGAD00001011193

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0150_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0150_002 for Triple negative breast cancer sample SA609X5XB03231

  Dataset EGAD00001011213

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0163_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0163_002 for Triple negative breast cancer sample SA609X7XB03510

  Dataset EGAD00001011218

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0163_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0163_001 for Triple negative breast cancer patient-derived xenograft SA609X7XB03505

  Dataset EGAD00001011219

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0063_000

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0063_000 for Triple negative breast cancer patient-derived xenograft SA609X3XB01584

  Dataset EGAD00001011209

• 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

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0152_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0152_002 for Triple negative breast cancer sample SA609X6XB03404

  Dataset EGAD00001011216

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0172_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0172_001 for Triple negative breast cancer sample SA609X6XB03447

  Dataset EGAD00001011217

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0172_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0172_002 for Triple negative breast cancer sample SA609X7XB03554

  Dataset EGAD00001011220

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0148_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0148_001 for Triple negative breast cancer patient-derived xenograft SA535X4XB02498

  Dataset EGAD00001011221

• 10x Single Cell Gene Expression Library TENX048

  10x Single Cell Gene Expression library TENX048 for Triple negative breast cancer sample SA535X5XB02895

  Dataset EGAD00001011222

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_006

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_006 for Triple negative breast cancer sample SA535X6XB03099

  Dataset EGAD00001011223

• 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

• The complexity of tobacco smoke induced mutagenesis in head and neck cancer - structural variation vcf files (Mutographs)

  The complexity of tobacco smoke induced mutagenesis in head and neck cancer - structural variation vcf files (Mutographs)

  Dataset EGAD00001015389

• A non-canonical lymphoblast in refractory childhood T cell leukaemia

  Refractory cancers may arise either through the acquisition of resistance mechanisms by cancer cells or represent distinct diseases. The origin of childhood T-cell acute lymphoblastic leukaemia (T-ALL) that does not respond to initial treatment, i.e. refractory disease, is unknown. Refractory T-ALL carries a poor prognosis and cannot be predicted at diagnosis. Here, we performed single cell mRNA sequencing of T-ALL from children who did or did not respond to initial treatment. We identified a transcriptionally distinctive blast population, exhibiting features of innate-like lymphocytes, as the major source of refractory disease. Evidence of such blasts at diagnosis heralded refractory disease across independent datasets and was associated with survival in a large, contemporary trial cohort. Our findings portray refractory T-ALL as a distinct disease. They may have immediate clinical utility.

  Dataset EGAD00001015381

• The University of Hong Kong Gastric Cancer XClone Study WES Data

  Exome capturing was performed using xGen Exome Research Panel v1.0 based on standard protocols. Paired-end sequencing (2 x 151 bp) was performed using Illumina NovaSeq6000.

  Dataset EGAD00001015382

• The University of Hong Kong Gastric Cancer XClone Study Single Cell CNV Data

  Single cell encapsulation and DNA libraries were prepared by Chromium™ Single Cell DNA Reagent Kits and Chromium™ Single Cell C and D Chip Kits.

  Dataset EGAD00001015383

• The complexity of tobacco smoke induced mutagenesis in head and neck cancer - sequence data (Mutographs)

  The complexity of tobacco smoke induced mutagenesis in head and neck cancer - sequence data (Mutographs)

  Dataset EGAD00001015386