4736 results for "*oma"

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• Proteome data

  Proteome data of neuroblastoma patients

  Dataset EGAD00001006737

• Exploring the heterogeneity of sarcoma using single cell sequencing

  Multi region samples are collected from patients, with consent, immediately after resection of the tumour. Samples are digested and sorted using FACS as single cells into lysis buffer. Cells are then stored until further processing for G&T-seq. After sequencing, we will explore intra-tumour heterogeneity using computational approaches to integrate RNA and DNA data onto the tumour phylogeny This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ .

  Dataset EGAD00001006786

• Somatic whole exome sequencing of a hypermutated gliosarcoma case

  The dataset includes the whole-exome sequencing (WES) of an extramedullary tumor anterior to the spinal cord at T4, which was resected and diagnosed as gliosarcoma. The patient initially diagnosed with a low-grade brain glioma via biopsy, followed by adjuvant radiation and temozolomide treatment. WES was performed using Illumina NovaSeq6000 with 2x100 bp reads. Mean coverage of 152.4x and 230.6x was achieved for normal and tumor, respectively.

  Dataset EGAD00001006816

• WGS of Glioblastoma stem cells (Sachamitr et al)

  WGS of 17 GSC populations derived from patient tumours

  Dataset EGAD00001006848

• Mutational signatures in esophageal squamous cell carcinoma from eight countries of varying incidence – sequence data (Mutographs)

  Mutational signatures in esophageal squamous cell carcinoma from eight countries of varying incidence – sequence data (Mutographs)

  Dataset EGAD00001006868

• Dedifferentiated Melanoma (2021-02-02)

  De- and transdifferentiation of melanoma is a rare histopathological phenomenon that has not be characterised genetically. In this project we plan to sequence the genomes of de and transdifferentiated cases so as to define their genetic make-up. . This dataset contains all the data available for this study on 2021-02-02.

  Dataset EGAD00001006931

• Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis

  Single-cell RNA-Sequencing of three primary breast cancers, two primary prostate cancers, and a metastatic melanoma sample from Wu et al. (2021) Genome Medicine study. Each tumour was sequenced across different cryopreservation conditions including Fresh Tissue (FT), cryopreserved single-cell suspensions (CCS), cryopreserved solid tissue fragments (CT) and a cryopreserved after overnight cold storage (CO). Data was generated using the Chromium controller (10X Genomics) and sequenced on the NextSeq platform.

  Dataset EGAD00001007030

• Dataset of whole-exome sequencing of clonally related neuroblastoma and teratoma

  This dataset includes bam files of WES of clonally related neuroblastoma and teratoma as well as peripheral blood samples as a control. Neuroblastoma and teratoma samples were formalin-fixed paraffin embedded.

  Dataset EGAD00001007039

• UTUC RNAseq data

  RNA sequencing of upper urinary tract urothelial carcinoma

  Dataset EGAD00001007667

• Genomic and transcriptomic landscape of T-cell lymphoblastic lymphoma compared to T-cell acute lymphoblastic leukemia: similar subtypes and different fusions

  Due to the lower incidence of T-LBL and difficulties in obtaining diagnostic T-LBL material, extensive research on T-LBL has been hampered whereas genetic aberrations in T-ALL are thoroughly characterized. Given the similarities and differences between T-LBL and T-ALL, the question has been raised whether T-LBL and T-ALL represent two different diseases or different manifestations of the same disease. This study aims to identify the genomic and transcriptomic landscape of T-LBL and compare the findings to what is found T-ALL. Comparison of the molecular aberrations between T-LBL and T-ALL can provide insights into the overlap and differences in malignant development between the two entities, which could lead to improved risk stratification in T-LBL in order to eventually adapt T-LBL treatment protocols based on molecular-genetic prognostic factors.

  Dataset EGAD00001015357