7147 results for "canc*"

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• Normal sample for patient SA495

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

  Dataset EGAD00001009372

• Single Cell Genome Sequence for DLP+ library A96180A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 6 on DLP+ library A96180A

  Dataset EGAD00001009463

• A developmental cell atlas of the human thyroid gland - RNA

  The thyroid gland produces hormones essential for health from embryogenesis to adulthood. Thyroid disorders, including congenital hypothyroidism and thyroid carcinoma, are prevalent and pose significant health challenges. Congenital hypothyroidism often results from thyroid dysgenesis or impaired hormone synthesis, is particularly prevalent in trisomy 21 (T21), while thyroid carcinoma is the most frequent endocrine malignancy, affecting both paediatric and adult populations. Understanding the molecular basis of these conditions requires deeper insights into fetal thyroid development. We generated a spatiotemporal atlas of the human thyroid during early pregnancy, revealing key cell types, including hormone-producing thyrocytes. Thyroid follicular cells are heterogeneous, with two functional cell states (fTFC1, fTFC2) that persist into adulthood, with fTFC2 characterised by elevated PAX8 expression. We demonstrated that T21 thyroids displayed dysgenesis with disrupted follicular morphology, and altered extracellular matrix interactions, and that the fTFC2 signature was enriched in paediatric papillary thyroid cancer compared to adults. These findings uncover thyrocyte heterogeneity across the lifespan, advancing understanding of thyroid development and disease.

  Dataset EGAD00001015448

• Organoid Derivation Project: WGS (2023-06-22)

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients. . This dataset contains all the data available for this study on 2023-06-22.

  Dataset EGAD00001011090

• Tumour sample for patient SA668

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA668

  Dataset EGAD00001009211

• Somatic mutation in edited cholangiocyte organoids

  Primary human cells cultured in organoid format have great promise as potential regenerative cellular therapies. However, their immunogenicity and mutagenic profile remain unresolved, impeding effective long-term translation to the clinic. In this study we report, for the first time, the generation of human leukocyte antigen (HLA)-I and HLA-II knock-out human expandable primary cholangiocyte organoids (PCOs) using CRISPR-Cas9 as a potential ‘universal’ low-immunogenic therapy for bile duct disorders. HLA-edited PCOs (ePCOs) displayed the same phenotypic and functional characteristics as parental unedited PCOs. Despite minimal off-target edits, duplex sequencing approaches demonstrated that ePCOs and PCOs acquire mutations in culture at similar rates, but without evident selection for cancer-driver mutations. ePCOs induced reduced T cell-mediated immunity and donor-dependent NK cell cytotoxicity in vitro and evaded cytotoxic responses with increased graft survival in humanized mice in vivo. Our findings have important implications for assessment of safety and immunogenicity of primary cell-derived organoid cellular therapies.

  Dataset EGAD00001015455

• Dataset for Ewing_sarcoma_PNET-RNA

  Rare cancer sequencing data of 28 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008845

• RNA Seq of 25 spheres derived from lymph nodes of lung cancer patients

  To further decipher the cell type composition within single spheres we performed RNA-Seq of 25 selected spheres of 4 different categories (including 8 signature-/balanced, 9 signature+/balanced, 1 signature-/aberrant, 7 signature+/aberrant). Samples were sequenced using Illumina NextSeq 2000.Fastq reads were processed using inhouse RNA-Seq workflow.

  Dataset EGAD00001011099

• Transcriptomic Profile of Colorectal Cancer Samples

  Samples encompass primary colorectal tumors or metastasis of 75 patients, collected by Medical Pathologists from surgically removed specimens. Tissues were embedded in optimal cutting temperature (OCT) medium, snapshot frozen in liquid nitrogen within 40 minutes of collection and preserved at -80ºC. Samples were collected between June 2010 and October 2017 as part of a prospective biobanking project.

  Dataset EGAD00001009506

• Normal sample for patient SA1026

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1026

  Dataset EGAD00001009587

• Tumour sample for patient SA101

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA101

  Dataset EGAD00001009527

• 10x Genomics Single Cell Gene Expression for SA1035X8XB03425

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 8

  Dataset EGAD00001009148

• 10x Genomics Single Cell Gene Expression for SA1035X6XB03211

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 6

  Dataset EGAD00001009150

• 10x Genomics Single Cell Gene Expression for SA535X6XB03101

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 6

  Dataset EGAD00001009149

• Normal sample for patient SA576

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA576

  Dataset EGAD00001009596

• 10x Genomics Single Cell Gene Expression for SA535X5XB02891

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 5

  Dataset EGAD00001009155

• 10x Genomics Single Cell Gene Expression for SA535X5XB02895

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 5

  Dataset EGAD00001009156

• A spatiotemporal cancer cell trajectory underlies glioblastoma heterogeneity

  Abstract: Cancer cells display highly heterogeneous and plastic states in glioblastoma, an incurable brain tumour. However, how these malignant states arise and whether they follow a tractable cellular trajectory across tumours is poorly understood. Here, we generated a deep single cell and spatial multi-omic atlas of human glioblastoma that pairs transcriptomic, epigenomic and genomic profiling of 12 tumours across multiple regions. We identify that glioblastoma heterogeneity is driven by spatially-patterned transitions of cancer cells from developmental-like states towards those defined by a glial injury response and hypoxia. This cancer cell trajectory regionalizes tumours into distinct tissue niches and manifests in a conserved manner across tumours as well as genetically distinct tumour subclones. Moreover, using a new deep learning framework to jointly map cancer cell states and clones in situ, we show that tumour subclones are finely spatially intermixed through glioblastoma tissue niches. Finally, we show that this cancer cell trajectory is intimately linked to myeloid heterogeneity and unfolds across regionalised myeloid signalling environments. Our findings define a stereotyped trajectory of cancer cells in glioblastoma and unify glioblastoma tumour heterogeneity into a tractable cellular and tissue framework.

  Dataset EGAD00001015516

• WES and RNAseq of Simultaneous Bilateral Breast Cancer

  This dataset includes NGS profiling of 13 women with simultaneous bilateral breast cancer. Seven women have WES of untreated surgical resections and matched healthy tissue. The six other women have WES of healthy tissue, WES+RNAseq of pre-neoadjuvant tumor biopsies, and when residual disease was present (6 tumors in 4 patients), WES+RNAseq of residual disease from post-neoadjuvant therapy surgery. One patient from the WES+RNAseq cohort had multifocal bilateral disease at diagnosis so there are 2 pre-neoadjuvant biopsy samples from each breast.

  Dataset EGAD00001009987

• 10x Genomics Single Cell Gene Expression for SA604X9XB02425

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA604 passage 9

  Dataset EGAD00001009159

• A standardised framework for robust fragmentomic feature extraction from cell-free DNA sequencing data

  Fragmentomic features of cell-free DNA represent promising non-invasive biomarkers for cancer diagnosis. However, a lack of systematic evaluation of biases in feature quantification has hindered the adoption of such applications. We compared features derived from whole-genome sequencing of ten healthy donors using nine library kits and ten data-processing routes, and validated them in 1,182 plasma samples from published studies. Our results clarify the variations resulting from library preparation and feature quantification methods. We designed the Trim Align Pipeline and the cfDNAPro R package as unified interfaces for data pre-processing, feature extraction, and visualisation, aiming to standardise multimodal feature engineering and integration for machine learning.

  Dataset EGAD00001015535

• August 2021 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 2021 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 EGAD00001008101

• 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_002

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

  Dataset EGAD00001011213

• Normal sample for patient SA530

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA530

  Dataset EGAD00001009234

• 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_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_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

• Myeloid cell programming in patients with non-medullary thyroid carcinoma

  This dataset contains single-cell RNA sequencing data from patients with thyroid cancer (n=7), multinodal Goiter (n=3) and healthy individuals (n=5). Mononuclear cells were taken from both the peripheral blood and the bone marrow compartments. We used a pooled single-cell design where multiple individuals were pooled in a single sample for sequencing (NextSeq 500-V2) and later demultiplexed using their genotype data. Associated metadata contains information on the phenotypes per individual, the pooling design and the linkage between the supplied files and sequenced pools. Due to limitations from EGA in uploading single-cell data, the raw fastq files were processed as follows: (i) I1/I2/R1/R2 fast files were concatenated over the different lanes. (ii) Concatenated I1 and I2 files were interleaved, as were the concatenated R1 and R2 files to generate two fastq files per pool containing all the information. To interleave the fastq files, the BBmap tool bbmap/reformat.sh was used, which can also be used to de-interleave the files.

  Dataset EGAD00001008108

• 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_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

• MutWP5: CRUK Mutographs of Cancer: Lung: PD43291_Novaseq (WG)

  Sequencing of LCM-derived microbiopsies from explanted lung from COPD patient. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Deep sampling throughout multiple regions of the lung will determine whether there are differences in smoking-related mutaiton burden in different portions of the lung. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this poriton of the study will be compared to other individuals with smoking-related diseases (COPD, pulmonary fibrosis, lung cancer), and normal, non-smoking lungs. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010111

• 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 Genomics Single Cell Gene Expression for SA1035X6XB03216

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 6

  Dataset EGAD00001009154

• MESA colorectal cancer cfDNA EM-seq dataset

  This dataset contains the methylation sequencing data of 60 nonCancer and 70 colorectal cancer cfDNA samples. The methylation library is constructed by using NEBNext Enzymatic-seq Kit.

  Dataset EGAD00001009165

• 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 Genomics Single Cell Gene Expression for SA535X4XB02498

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 4

  Dataset EGAD00001009152

• 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

• 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

• Distinct embryonic phylogenies and driver events of infant Wilms tumor - RNA

  Cancers of adults typically arise through progressive rounds of clonal diversification and intratumoral selective sweeps which generate a long mutational trunk with shorter subclonal branches. Here, we investigated whether tumors of young children exhibit the same phylogenetic configuration. We studied three infants, including two newborns, with the childhood kidney cancer, Wilms tumour, through whole genome sequencing of bulk tissues, of single cell derived organoids, and of microdissections. All three cancers exhibited unusual driver events, with tumours of newborns harbouring FOXR2 rearrangements, delineating a distinct variant of Wilms tumour. Phylogenetic analyses suggest that tumors were seeded in an early, possibly confined window of development. Unusually, following seeding there was extensive polyclonal diversification with little evidence of clonal sweeps, leading to a distinct phylogenetic configuration more reminiscent of normal tissues rather than of adult cancers. These findings indicate that some childhood cancers may diversify via unorthodox phylogenetic pathways.

  Dataset EGAD00001009813

• Sperm sequencing reveals extensive positive selection in the human germline -NanoSeq

  Mutations that occur during human ageing in the cell lineages of sperm or eggs have the potential to be transmitted to offspring. In males, positive selection of driver mutations during spermatogenesis is known to increase the birth prevalence of certain developmental disorders and cancer predisposition syndromes. However, direct observation of the scope of this selection in sperm has been limited by the error rates of sequencing technologies. Using the duplex sequencing method known as NanoSeq, we sequenced bulk sperm samples from individuals aged 24 to 75 years. Our findings revealed a linear accumulation mutations per year and mutational signatures consistent with pedigree studies. Deep targeted and deep exome NanoSeq of sperm samples identified genes subject to significant positive selection in the male germline, which are linked to diverse cellular pathways including RAS/MAPK and BMP signaling. Strikingly, nearly all positively selected genes are associated with developmental or cancer predisposition disorders in children. We find that this effect drives an elevated risk of known likely disease-causing mutations in sperm across a wide age range. This implies that the disorders attributed to these genes likely exhibit elevated birth prevalence in human populations, particularly among older fathers. These findings shed light on the dynamics of germline mutations and highlight an increased disease risk for children born to fathers of advanced age.

  Dataset EGAD00001015590