7342 results for "canc*"

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• 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

• Genome and transcriptome sequence data from a metastatic carcinoma of unknown primary patient

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

  Dataset EGAD00001011020

• Genome and transcriptome sequence data from a metastatic breast ductal carcinoma patient

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

  Dataset EGAD00001011021

• Genome and transcriptome sequence data from a melanoma patient

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

  Dataset EGAD00001011022

• Genome and transcriptome sequence data from a follicular lymphoma patient

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

  Dataset EGAD00001011023

• Genome and transcriptome sequence data from a neuroendocrine tumor patient

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

  Dataset EGAD00001011024

• Genome and transcriptome sequence data from a thymic cancer patient

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

  Dataset EGAD00001011025

• Genome and transcriptome sequence data from a small cell lung carcinoma patient

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

  Dataset EGAD00001011026

• Genome and transcriptome sequence data from a pancreatic cancer patient

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

  Dataset EGAD00001011027

• Genome and transcriptome sequence data from a nasopharyngeal carcinoma patient

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

  Dataset EGAD00001011028

• Genome and transcriptome sequence data from a colorectal carcinoma patient

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

  Dataset EGAD00001011029

• Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

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

  Dataset EGAD00001011030

• Genome and transcriptome sequence data from a urachal adenocarcinoma patient

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

  Dataset EGAD00001011031

• Genome and transcriptome sequence data from a liposarcoma patient

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

  Dataset EGAD00001011032

• Genome and transcriptome sequence data from a gastrointestinal stromal tumor patient

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

  Dataset EGAD00001011033

• Genome and transcriptome sequence data from a granulosa cell ovarian tumor patient

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

  Dataset EGAD00001011034

• Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

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

  Dataset EGAD00001011035

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

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

  Dataset EGAD00001011036

• Genome and transcriptome sequence data from a metastatic triple negative breast cancer patient

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

  Dataset EGAD00001011037

• Genome and transcriptome sequence data from a papillary thyroid carcinoma patient

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

  Dataset EGAD00001011038

• Genome and transcriptome sequence data from a thymoma patient

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

  Dataset EGAD00001011039

• Genome and transcriptome sequence data from a rectal adenocarcinoma patient

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

  Dataset EGAD00001011040

• Pediatric Patient-Derived-Xenograft development in MAPPYACTS – international pediatric cancer precision medicine trial in relapsed and refractory tumors

  Pediatric patients with recurrent and refractory cancers are in most need for new treatments. This study developed patient-derived-xenograft (PDX) models within the European MAPPYACTS cancer precision medicine trial (NCT02613962). To date, 131 PDX models were established following heterotopical and/or orthotopical implantation in immunocompromised mice: 76 sarcomas, 25 other solid tumors, 12 central nervous system tumors, 15 acute leukemias, and 3 lymphomas. PDX establishment rate was 43%. Histology, whole exome and RNA sequencing revealed a high concordance with the primary patient’s tumor profile, human leukocyte-antigen characteristics and specific metabolic pathway signatures. A detailed patient molecular characterization, including specific mutations prioritized in the clinical molecular tumor boards are provided. Ninety models were shared with the IMI2 ITCC Paediatric Preclinical Proof-of-concept Platform (IMI2 ITCC-P4) for further exploitation. This new PDX biobank of unique recurrent childhood cancers provides an essential support for basic and translational research and new treatments development in advanced pediatric malignancies.

  Dataset EGAD00001011048

• Bone marrow single cell genomics from blood cancer samples

  Four different types of transcriptomic single-cell sequencing of blood from childhood B acute lymphoblastic leukemia.

  Dataset EGAD00001011055

• Spatially resolved antigen receptor and gene expression data from breast cancer patients

  The dataset includes spatially-resolved and single-cell antigen receptor, as well as gene expression, data from two different HER2+ breast cancer patients. The tumor piece obtained during surgery from each patient was divided into several regions and tissue sections were used for spatial transcriptomics (Visium, 10x genomics). As indicated, some tissue sections were analyzed by a new method (Spatial VDJ) to spatially resolve antigen receptor sequences (target capture), which was developed in our publication. In parallel, tissue pieces from the same tumor were dissociated for single-cell gene expression analysis (10x genomics GEX, VDJ, and feature barcoding/Hash Tag Oligonucleotide). The deposited data is in the form of fastq files. All processed data, metadata, micrographs of the tissue sections (of those used for spatial transcriptomics), and scripts used for the analysis are publicly available at Zenodo (DOI: 10.5281/zenodo.7961605). Final libraries were sequenced on NextSeq2000 (Illumina) or NovaSeq6000 (Illumina) and analyzed with Cell Ranger, Seurat, Space Ranger, and STutility pipelines.

  Dataset EGAD00001011061

• 47 urothelial cancer patients WES and 38 RNAseq

  We performed a global mutational landscape analysis using tumor samples from the 47 urothelial cancer patients included in MATCH-R or MOSCATO studies, with advanced metastatic disease and WES available (RNAseq is included for 38 patient samples).

  Dataset EGAD00001011063

• Total RNA sequencing data from the Triple Negative Trial (TNT)

  BAM files from total RNA sequencing of samples from breast cancer patients in the TNT trial. Data includes 186 primary tumour samples and 13 matched recurrence samples.

  Dataset EGAD00001011141

• Exome sequencing data from two small cell prostate cancer patients

  Exome sequencing data from two small cell prostate cancer patients - 4 cancer samples (FFPE) from Patient 1 collected at 3 different time points and 2 cancer samples (FFPE) from Patient 2 collected at 1 time point. Exonic DNA was enriched using the TruSeq Exome Kit (Illumina) and sequenced on the Illumina NextSeq 500 as 75bp paired end reads (total read length 150bp).

  Dataset EGAD00001011149

• Cell-free DNA sequencing data of healthy control, atrophic gastritis, and gastric cancer patients’ blood

  Sample cohort (n=48) is consisted of healthy, atrophic gastritis and gastric cancer patients. Some of the gastric cancer patients samples are collected at the separate time points: -1 before the operation; -2 after the operation; -3 during the control visit. For the hybridisation capture of the genes unique 15 gastric cancer-related gene panel was developed and very deep sequencing using TruSight Oncology Unique Molecular Identifier (UMI) Reagents (Illumina) was used.

  Dataset EGAD00001011153

• RNASeq data from one small cell prostate cancer patient (4 samples from 3 time points)

  RNASeq data from one small cell prostate cancer patient - 4 cancer samples (FFPE) from Patient 1 collected at 3 different time points. mRNA was selected using the Magnetic mRNA Isolation Module (NEB) and sequenced on the Illumina NextSeq 500 as 75bp paired end reads (total read length 150bp).

  Dataset EGAD00001011154

• Colorectal cancer organoid-stroma biobank cohort

  In colorectal cancers (CRC) the tumor microenvironment plays a key role for prognosis and therapy efficacy. Patient-derived tumor organoids (PDTOs) show enormous potential for preclinical testing, however, purely epithelial cultures features including the ‘consensus molecular subtypes’ (CMS) are largely eradicated. To better reflect the cell type heterogeneity, we established the CRC organoid-stroma biobank of matched PDTOs and cancer-associated fibroblasts (CAFs) from 30 patients. Whole exome sequencing and transcriptome analysis in various in vitro and in vivo contexts was performed to study the influence of the TME on the CRC phenotype.

  Dataset EGAD00001011173

• 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

• 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_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

• 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

• 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

• 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

• Geographic and age-related variations in mutational processes in colorectal cancer - filtered vcf files (Mutographs)

  Geographic and age-related variations in mutational processes in colorectal cancer - filtered vcf files (Mutographs)

  Dataset EGAD00001015486

• Geographic and age-related variations in mutational processes in colorectal cancer - copy number variants (Mutographs)

  Geographic and age-related variations in mutational processes in colorectal cancer - copy number variants (Mutographs)

  Dataset EGAD00001015487

• SGCC GASCAD-II dataset

  The GASCAD-II dataset from the Singapore Gastric Cancer Consortium includes paired tumor-blood whole exome sequencing data for 209 gastric cancer (GC) patients, along with whole transcriptome sequencing data for 125 GC samples. Whole exome sequencing was conducted using Agilent SureSelect Human All Exon V6 kits. For RNA sequencing, total RNA was isolated using the RNeasy Mini Kit, and libraries were prepared with the TruSeq Stranded Total RNA with Ribo-Zero Gold protocol (Illumina). Aligned BAM files for both exome and RNAseq data are included in this dataset

  Dataset EGAD00001015433

• Sequencing data for oesophageal and related samples - Linossi et al

  Data supporting: "Utility of ctDNA assessment after six weeks of immunotherapy to predict radiological response in advanced oesophageal cancer" Linossi et al

  Dataset EGAD00001015434

• Geographic and age-related variations in mutational processes in colorectal cancer - patient metadata files (Mutographs)

  Geographic and age-related variations in mutational processes in colorectal cancer - patient metadata files (Mutographs)

  Dataset EGAD00001015437

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

  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 EGAD00001015447

• RNA-Seq Dataset of Organoid drug profiling identifies methotrexate as a therapy for SCCOHT, a rare pediatric cancer

  We generated and characterized tumoroids from small cell carcinoma of the ovary, hypercalcemic type. Furthermore, we identified a drug that is selective and effective against SCCOHT tumoroids.

  Dataset EGAD00001015441

• WGS Dataset of Organoid drug profiling identifies methotrexate as a therapy for SCCOHT, a rare pediatric cancer

  We generated and characterized tumoroids from small cell carcinoma of the ovary, hypercalcemic type. Furthermore, we identified a drug that is selective and effective against SCCOHT tumoroids.

  Dataset EGAD00001015442

• 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

• 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 EGAD00001015457

• 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

• 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 EGAD00001015456

• Combination Immune Checkpoint Inhibition in Australian Rare Cancers_WES

  Combination immune checkpoint inhibition (ICI) with anti-CTLA-4 and anti-PD-1 blockade has demonstrated significant clinical activity across tumour types. Rare cancer patients have limited treatment options due to the scarcity of studies of novel treatment options, so they are often offered chemotherapies untested in their disease. ONJ2016-001/CA209-538 is a prospective, multicentre clinical trial of combination ICI (ipilimumab and nivolumab) in patients with advanced rare cancers, including biliary tract, adrenocortical, rare gynaecological and neuroendocrine cancers (https://clinicaltrials.gov/ct2/show/NCT02923934). The primary endpoint is clinical benefit rate (complete response + partial response + stable disease >3 months) by RECIST 1.1. This study involves generation and analysis of pre-treatment formalin fixed paraffin embedded (FFPE) tumour DNA sequencing data, paired with germline DNA sequencing data, to potentially identify genomic biomarkers that may associate with clinical benefit or other clinical efficacy / toxicity metrics.

  Dataset EGAD00001015465

• A Tumour Organoid Biobank for Mapping Cancer Cell Vulnerabilities - TGS

  Targeted gene sequencing data from cancer patient-derived organoids and their matched tumour tissue samples. Part of a study to include genomic and transcriptomic sequencing data, along with CRISPR screening data from cancer patient-derived organoids and matched tumour tissues. Some organoids were developed as part of the Human Cancer Models Initiative (HCMI) in collaboration with Cancer Research UK (CRUK). The study includes organoids derived from colorectal, oesophageal, ovarian, pancreatic, and stomach cancer samples.

  Dataset EGAD00001015468

• A Tumour Organoid Biobank for Mapping Cancer Cell Vulnerabilities - WGS

  Whole genome sequencing data from cancer patient-derived organoids and their matched tumour tissue samples. Part of a study to include genomic and transcriptomic sequencing data, along with CRISPR screening data from cancer patient-derived organoids and matched tumour tissues. Some organoids were developed as part of the Human Cancer Models Initiative (HCMI) in collaboration with Cancer Research UK (CRUK). The study includes organoids derived from colorectal, oesophageal, ovarian, pancreatic, and stomach cancer samples.

  Dataset EGAD00001015469

• A Tumour Organoid Biobank for Mapping Cancer Cell Vulnerabilities - RNA

  RNA sequencing data from cancer patient-derived organoids. Part of a study to include genomic and transcriptomic sequencing data, along with CRISPR screening data from cancer patient-derived organoids and matched tumour tissues. Some organoids were developed as part of the Human Cancer Models Initiative (HCMI) in collaboration with Cancer Research UK (CRUK). The study includes organoids derived from colorectal, oesophageal, ovarian, pancreatic, and stomach cancer samples.

  Dataset EGAD00001015470

• The earliest stages of neoplastic transformation in Familial Adenomatous Polyposis

  The succession of somatic genetic events associated with the conversion of a normal colorectal epithelial cell into a colorectal carcinoma constitutes a paradigmatic model of cancer development. Familial Adenomatous Polyposis (FAP) is caused by constitutional inactivating mutations in APC, the central gatekeeper gene of colorectal cancer, and is associated with a substantially increased lifetime-risk of colorectal cancer. To investigate the earliest stages of neoplastic change due to APC inactivation, we microdissected and individually whole genome sequenced 279 histologically normal and abnormal colorectal crypts from 15 individuals with FAP. Histologically normal crypts generally exhibited similar mutation burdens and mutational signatures to normal crypts from wild-type individuals of the same age, with 1/110 carrying a somatic inactivating APC mutation. By contrast, 9/18 aberrant crypt foci carried somatic APC mutations and exhibited modestly increased burdens of some mutational signatures found in normal crypts. 12/13 diminutive adenomatous polyps (less than 5mm diameter) showed somatic APC mutations and carried substantially increased mutation loads of most mutational signatures present in normal crypts. Phylogenetic trees of crypts from aberrant crypt foci and adenomatous polyps revealed that some had acquired their initiating somatic APC mutations decades previously during the first few years of life. The results catalogue the changes in somatic mutation rates, mutational processes and “driver” mutations in cancer genes during the earliest stages of colorectal neoplastic transformation initiated by APC inactivation and highlight the long periods of clonal evolution required for a cancer to develop.

  Dataset EGAD00001015471

• The earliest stages of neoplastic transformation in Familial Adenomatous Polyposis

  The succession of somatic genetic events associated with the conversion of a normal colorectal epithelial cell into a colorectal carcinoma constitutes a paradigmatic model of cancer development. Familial Adenomatous Polyposis (FAP) is caused by constitutional inactivating mutations in APC, the central gatekeeper gene of colorectal cancer, and is associated with a substantially increased lifetime-risk of colorectal cancer. To investigate the earliest stages of neoplastic change due to APC inactivation, we microdissected and individually whole genome sequenced 279 histologically normal and abnormal colorectal crypts from 15 individuals with FAP. Histologically normal crypts generally exhibited similar mutation burdens and mutational signatures to normal crypts from wild-type individuals of the same age, with 1/110 carrying a somatic inactivating APC mutation. By contrast, 9/18 aberrant crypt foci carried somatic APC mutations and exhibited modestly increased burdens of some mutational signatures found in normal crypts. 12/13 diminutive adenomatous polyps (less than 5mm diameter) showed somatic APC mutations and carried substantially increased mutation loads of most mutational signatures present in normal crypts. Phylogenetic trees of crypts from aberrant crypt foci and adenomatous polyps revealed that some had acquired their initiating somatic APC mutations decades previously during the first few years of life. The results catalogue the changes in somatic mutation rates, mutational processes and “driver” mutations in cancer genes during the earliest stages of colorectal neoplastic transformation initiated by APC inactivation and highlight the long periods of clonal evolution required for a cancer to develop.

  Dataset EGAD00001015477

• DERMATLAS: Hidradenoma papilliferum_RNAseq

  Virtually no other tumour type is associated with so many different forms as skin cancer. Histologically, tumours of the skin may arise from epithelium, including epidermis, hair follicle, sebaceous or sweat gland, melanocytes, dermal-associated mesenchymal structures or tissue resident immune cells, making for a diversity of clinical presentations. Importantly, many skin tumour types have an extremely poor prognosis. Many skin tumour subtypes have never undergone molecular profiling, or if they have, targeted sequencing has been used and the number of cases analyzed has been so limited that deriving firm conclusions about the profile of driver genes, DNA mutational signatures and germline alleles has not been possible.For 70 key skin tumour subtypes defined by the World Health Organization, that have not undergone extensive genetic analysis previously, we propose to perform whole exome and transcriptome sequencing, from a range of body sites, to build a genomic atlas of dermatological tumours, including detailed maps of SNVs, copy number alterations, genome-wide methylation and expression profiles.

  Dataset EGAD00001015480

• DERMATLAS: Hidradenoma papilliferum_WES

  Virtually no other tumour type is associated with so many different forms as skin cancer. Histologically, tumours of the skin may arise from epithelium, including epidermis, hair follicle, sebaceous or sweat gland, melanocytes, dermal-associated mesenchymal structures or tissue resident immune cells, making for a diversity of clinical presentations. Importantly, many skin tumour types have an extremely poor prognosis. Many skin tumour subtypes have never undergone molecular profiling, or if they have, targeted sequencing has been used and the number of cases analyzed has been so limited that deriving firm conclusions about the profile of driver genes, DNA mutational signatures and germline alleles has not been possible.For 70 key skin tumour subtypes defined by the World Health Organization, that have not undergone extensive genetic analysis previously, we propose to perform whole exome and transcriptome sequencing, from a range of body sites, to build a genomic atlas of dermatological tumours, including detailed maps of SNVs, copy number alterations, genome-wide methylation and expression profiles.

  Dataset EGAD00001015481

• Geographic and age-related variations in mutational processes in colorectal cancer - sequence data (Mutographs)

  Geographic and age-related variations in mutational processes in colorectal cancer - sequence data (Mutographs)

  Dataset EGAD00001015485

• Genome and transcriptome sequence data from a rosette-forming glioneuronal tumor (RGNT) tumor patient

  Genome and transcriptome sequence data from a rosette-forming glioneuronal tumor (RGNT) patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015320

• Genome and transcriptome sequence data from a choroid plexus carcinoma tumor patient

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

  Dataset EGAD00001015321

• Genome and transcriptome sequence data from a metastatic malignant peripheral nerve sheath tumor tumor patient

  Genome and transcriptome sequence data from a metastatic malignant peripheral nerve sheath tumor patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015322

• Genome and transcriptome sequence data from a malignant rhabdoid tumor tumor patient

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

  Dataset EGAD00001015323

• Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma tumor patient

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

  Dataset EGAD00001015324

• 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 EGAD00001015325

• Genome and transcriptome sequence data from a diffuse midline glioma, H3K27 mutant tumor patient

  Genome and transcriptome sequence data from a diffuse midline glioma, H3K27 mutant patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015326

• Genome and transcriptome sequence data from a CNS non-germinoma germ cell tumour tumor patient

  Genome and transcriptome sequence data from a CNS non-germinoma germ cell tumour patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015327

• Genome and transcriptome sequence data from a GBM (H3 K27M mutant) tumor patient

  Genome and transcriptome sequence data from a GBM (H3 K27M mutant) patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015328

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

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

  Dataset EGAD00001015329

• Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma tumor patient

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

  Dataset EGAD00001015330

• Genome and transcriptome sequence data from a acute myeloid leukemia tumor patient

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

  Dataset EGAD00001015331

• 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 EGAD00001015332

• Genome and transcriptome sequence data from a pIlomyxoid astrocytoma tumor patient

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

  Dataset EGAD00001015333

• Genome and transcriptome sequence data from a wilms tumor tumor patient

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

  Dataset EGAD00001015334

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

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

  Dataset EGAD00001015335

• Genome and transcriptome sequence data from a high-grade glioma, glioblastoma tumor patient

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

  Dataset EGAD00001015336

• 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 EGAD00001015337

• 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 EGAD00001015338

• The long term effects of chemotherapy on normal blood - WGS dataset

  In developed countries, ~10% of individuals are exposed to systemic chemotherapy for cancer and other diseases. Many chemotherapeutic agents act by increasing DNA damage in cancer cells, hence triggering cell death. However, there is limited understanding of the extent and consequences of collateral DNA damage to normal tissues. To investigate the impact of chemotherapy on mutation burdens and cell population structure of a normal tissue we sequenced blood cell genomes from 23 individuals, aged 3-80 years, treated with a range of chemotherapy regimens. Substantial additional mutation loads with characteristic mutational signatures were imposed by some chemotherapeutic agents, but there were differences in burden between different classes of agent, different agents of the same class and different blood cell types. Chemotherapy also induced premature changes in the cell population structure of normal blood, similar to those of normal ageing. The results constitute an initial survey of the long-term biological consequences of cytotoxic agents to which a substantial fraction of the population is exposed during the course of their disease management, raising mechanistic questions and highlighting opportunities for mitigation of adverse effects.

  Dataset EGAD00001015339