5624 results for "canc*"

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• Organoid_Derivation_Project___GRCh38___WGS

  Generation of normal and cancer organoids for sequencing as part of the WTSI-CRUK international collaboration with NCI (in HCMI) to generate the next generation of cancer cell lines

  Study EGAS00001004712

• Organoid_Derivation_Project___GRCh38___TGS

  Generation of normal and cancer organoids for sequencing as part of the WTSI-CRUK international collaboration with NCI (in HCMI) to generate the next generation of cancer cell lines.

  Study EGAS00001007349

• Organoid Derivation Project WGS: Release 1

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients.

  Dataset EGAD00001005000

• Organoid Derivation Pilot: RNAseq

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients.

  Dataset EGAD00001005001

• Organoid Derivation Project WGS: Release 2

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients.

  Dataset EGAD00001005955

• 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

• Organoid Derivation Pilot: RNAseq (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 EGAD00001011091

• Organoid Derivation Project - GRCh38 - RNAseq (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 EGAD00001011092

• Organoid Derivation Project - GRCh38 - 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 EGAD00001011093

• Organoid Derivation Project - GRCh38 - TGS (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 EGAD00001011094

• Mechanism of action and resistance to Trastuzumab Deruxtecan in patients with metastatic breast cancer: the DAISY trial

  195 exome sequencing samples

  Dataset EGAD00001011110

• Clonal selection after gene therapy in sickle cell disease

  While gene therapy (GT) provides a potentially curative treatment option for patients with sickle cell disease (SCD), the occurrence of myeloid malignancies in clinical trials has prompted concern. To interrogate potential mechanisms underlying increased cancer risk, we used hematopoietic stem cell (HSC) clonal tracking by whole genome sequencing (WGS) to map the somatic mutation and clonal landscape of 2,592 gene modified as well as unmodified single stem and progenitor cells from six SCD patients undergoing gene therapy (7-26 years old, average 12.7× depth). Pre-GT phylogenetic trees in SCD were highly polyclonal and mutation burdens per cell were elevated in some, but not all, patients. Post-GT, no clonal expansions were identified. However, an increased frequency of driver mutations associated with myeloid neoplasms or clonal hematopoiesis (DNMT3A- and EZH2-mutated clones in particular) were seen in both genetically modified and unmodified cells suggested positive selection of mutant clones during gene therapy. This work sheds light on the mutation landscape and HSC clonal dynamics in gene therapy for SCD and highlights enhanced fitness of some HSCs harboring pre-existing driver mutations following gene therapy. Future studies should define the long-term fate of mutant clones including any contribution to expansions associated with myeloid neoplasms.

  Dataset EGAD00001010913

• Clonal selection after gene therapy in sickle cell disease

  While gene therapy (GT) provides a potentially curative treatment option for patients with sickle cell disease (SCD), the occurrence of myeloid malignancies in clinical trials has prompted concern. To interrogate potential mechanisms underlying increased cancer risk, we used hematopoietic stem cell (HSC) clonal tracking by whole genome sequencing (WGS) to map the somatic mutation and clonal landscape of 2,592 gene modified as well as unmodified single stem and progenitor cells from six SCD patients undergoing gene therapy (7-26 years old, average 12.7× depth). Pre-GT phylogenetic trees in SCD were highly polyclonal and mutation burdens per cell were elevated in some, but not all, patients. Post-GT, no clonal expansions were identified. However, an increased frequency of driver mutations associated with myeloid neoplasms or clonal hematopoiesis (DNMT3A- and EZH2-mutated clones in particular) were seen in both genetically modified and unmodified cells suggested positive selection of mutant clones during gene therapy. This work sheds light on the mutation landscape and HSC clonal dynamics in gene therapy for SCD and highlights enhanced fitness of some HSCs harboring pre-existing driver mutations following gene therapy. Future studies should define the long-term fate of mutant clones including any contribution to expansions associated with myeloid neoplasms.

  Dataset EGAD00001010914

• 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

• CRMY-Translational Cancer Biology Research Unit DAC

  Dac EGAC00001003199

• Data Access Commitee FGFR Cancer

  Dac EGAC00001003273

• Bone marrow single cell genomics from blood cancer samples

  The overall goal of this study was to characterize bone marrow cells based on their transcriptome, surface protein expression and BCR- and TCR VDJ-profile for accurate identification of clinically relevant cell states. This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 824110 (EASI-Genomics)

  Study EGAS00001007332

• 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

• Characterization of MCSP+ melanoma DCC and MelDCC lines

  To dissect the sequence of genotypic and phenotypic progression during early metastatic colonization, we searched for the earliest detectable disseminated cancer cells (DCC) and identified MCSP+ melanoma cells as strong candidates for metastasis founder cells (MFC) in lymph nodes. We isolated MCSP+ DCC from various stages of metastatic colony-formation, performed scRNA-seq and unravelled their phenotypic progression. Further we performed bulk sequencing on patient-derived DCC models (MelDCC) and showed that they exhibit an immunosuppressive activity on CD8 T cells.

  Study EGAS00001006702

• Organoid Derivation Project TGS: Release 1

  Targeted gene sequencing of cancer driver genes to determine the driver mutations present in newly-derived cancer organoid models

  Dataset EGAD00001004368

• Organoid Derivation Project TGS: Release 2

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients.

  Dataset EGAD00001004999

• Organoid Derivation Project TGS: Release 3

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients.

  Dataset EGAD00001005958

• Organoid Derivation Project: TGS (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 EGAD00001011089

• Cell-to-cell variability in Myc dynamics drives transcriptional heterogeneity in cancer cells

  Study EGAS00001007091

• Novel method for efficient establishment, expansion and drug response profiling of high-grade serous ovarian cancer organoids

  HGSOC patient-derived organoids and their tissue of origin

  Dataset EGAD00001008753

• Mechanism of action and resistance to Trastuzumab Deruxtecan in patients with metastatic breast cancer: the DAISY trial

  Study EGAS00001006905

• Mechanism of action and resistance to Trastuzumab Deruxtecan in patients with metastatic breast cancer: the DAISY trial

  198 exome sequencing samples

  Dataset EGAD00001009853

• 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

• Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancer

  Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancer

  Dataset EGAD00001011123

• Breast cancer sequencing data

  Dac EGAC00001003293

• Children's Cancer Therapy Institute DAC

  Dac EGAC00001001828

• Cancer Team Data Access Committee

  Dac EGAC00001002762

• Towards standardized whole exome sequencing (WES) for cancer patients: lessons from a multicentric pilot study

  ZPM WES Pilot consisting of 30 samples paired tumor/normal analyzed with WES at four different laboratories in Germany.

  Dataset EGAD00001011087

• 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

• Spectrum of Response to Platinum and PARP Inhibitors in Germline BRCA Associated Pancreatic Cancer in the Clinical and Pre-clinical Setting

  Whole genome sequencing data for germline BRCA pancreatic cancer

  Dataset EGAD00001011129

• Cellular composition of spheres derived from lymph nodes of lung cancer patients

  To 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). To track the individual cell types within single spheres we performed deconvolution analysis.

  Study EGAS00001007369

• Lung cancer RNA Seq Data Access Committee

  Dac EGAC00001003317

• RNAseq of primary mesothelioma cell lines

  Mesothelioma is an aggressive cancer associated with previous exposure to asbestos and dismal prognosis. Since a pemetrexed/cisplatin combination was introduced for treatment of mesothelioma, no new first- or second-line therapies have been discovered. Thus, to better understand what drives mesothelioma carcinogenesis and to identify potential targets for therapy, in this project we aim at performing RNAseq analysis of a panel of mesothelioma cells lines.

  Dataset EGAD00001010924

• Gene expression profile of mesothelial-derived carcinoma-associated fibroblasts

  RNA-sequencing was carried out on ascetic fluid-isolated mesothelial cells from low-grade serous ovarian cancer patients, high-grade serous ovarian cancer patients, chemotherapy-treated high-grade serous ovarian cancer patients and control mesothelial cells obtained from non-oncologic patients to identify differentially expressed genes associated to mesothelial-to-mesenchymal transition process.

  Study EGAS00001003747

• Whole genome, RNA-seq and single-cell Multiome profile of multiple myeloma

  Study EGAS00001007014

• Gene expression profile of mesothelial-derived carcinoma-associated fibroblasts

  RNA-sequencing was carried out on ascetic fluid-isolated mesothelial cells from low-grade serous ovarian cancer patients, high-grade serous ovarian cancer patients, chemotherapy-treated high-grade serous ovarian cancer patients and control mesothelial cells obtained from non-oncologic patients to identify differentially expressed genes associated to mesothelial-to-mesenchymal transition process. The dataset contains 18 samples: - Control mesothelial cells: 4 samples - Group 1, high-grade serous ovarian cancer patients: 3 samples - Group 2, chemotherapy-treated high-grade serous ovarian cancer patients: 5 samples - Group 3, low-grade serous ovarian cancer patients: 6 samples

  Dataset EGAD00001005133

• WGS short read and 10X linked read sequencing of HR Deficient breast cancers

  A set of tumor/normal paired sequencing experiments, performed in short read WGS and 10X linked read whole genomes. BAM files aligned to hg19 are provided. Sample Alias number and Subject ID reflects patient of origin. T/N distinction discriminates between tumor and normal (peripheral blood) tissue.

  Dataset EGAD00001010326

• 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

• Sequencing data from a phase II study of nivolumab and ipilimumab in recurrent or refractory cancer of unknown primary (CheCUP trial)

  Sequencing data from a phase II study of nivolumab and ipilimumab in recurrent or refractory cancer of unknown primary (CheCUP trial). Panel sequencing data from baseline FFPE biopsies were used to perform a comprehensive genomic profiling of CUP metastases. Combined targeted next-generation sequencing of patient-specific hotsport mutations and shallow whole genome sequencing of baseline and follow-up liquid biopsy samples were used to analyze ctDNA and to evaluate response to immunei checkpoint inhibitor treatment. In some cases, whole exome sequencing of peripheral blood mononuclear cells was performed to screen for potential CHIP and germline mutations.

  Dataset EGAD00001011130

• RNA sequencing of high-risk paediatric cancers for identifying T-cell infiltration signatures

  Transcriptome profiling of 121 high-risk paediatric cancer samples for identifying T-cell infiltration signatures using poly-A capture by Truseq and sequenced on NextSeq 500

  Dataset EGAD00001010920

• 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

• Zero Childhood Cancer

  Dac EGAC00001001869

• 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

• Sequencing data for oesophageal and related samples - Mourikis et al (WGS)

  Data supporting: "Patient-specific detection of cancer genes reveals recurrently perturbed processes in esophageal adenocarcinoma." Mourikis et al. WGS (BAM files) 521 samples

  Dataset EGAD00001004775

• Sequencing data for oesophageal and related samples - Mourikis et al (RNA)

  Data supporting: "Patient-specific detection of cancer genes reveals recurrently perturbed processes in esophageal adenocarcinoma." Mourikis et al. RNAseq (BAM files) 137 samples

  Dataset EGAD00001004776