5032 results for "*oma"

in 32.09 milliseconds.

• GBM-Space: Whole Genome Sequencing of Laser Capture Microdissected Glioblastoma (LCM-WGS)

  Cancer cells display heterogeneous and dynamic states in glioblastoma, but how these malignant states arise and whether they follow a tractable cellular trajectory across tumours is poorly understood. Here, we generate a deep single cell and spatial multi-region atlas of 12 isocitrate dehydrogenase wild-type (IDH-wt) primary glioblastomas that integrates transcriptomic, epigenomic and genomic analysis to comprehensively characterise their tumour heterogeneity. This dataset contains whole genome sequencing (WGS) data generated from regions of interest (ROIs) collected via laser capture microdissection (LCM), towards identifying copy number alterations and subclones directly at the DNA level.

  Dataset EGAD00001016155

• Single cell characterization of T-cell lymphoma: RNA (2025-07-31)

  Lymphomas are types of blood cancer derived from lymphocytes, and can be classified into two main categories, Hodgkin's lymphomas (HL) and non-Hodgkin lymphomas (NHL). T-cell lymphoma is a type of NHL that develops from T lymphocytes and accounts for 10-15% of total NHL cases. As a rare disease, T-cell lymphoma is functionally, pathologically, and clinically complex and heterogeneous. The 2016 revision of the WHO classification has listed 29 subtypes of mature T/NK-cell neoplasms, among which over 25 are T lymphocyte in origin. These subtypes of T-cell lymphomas have different biological behaviours and clinical prognosis. Clinically, T-cell lymphoma ranges from indolent (slow-growing) to aggressive (fast-growing and spreading) disease. According to the guideline booklet provided by the Lymphoma Research Foundation (https://lymphoma.org/aboutlymphoma/nhl/), indolent T-cell lymphoma subtypes include adult T-cell leukaemia/lymphoma (ATLL), cutaneous T-cell lymphoma (CTCL), and mycosis fungoides (MF), while the aggressive subtypes include anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), peripheral T-cell lymphoma (PTCL), and T lymphoblastic lymphoma (TLBL). PTCL and ALCL represent the most common subtypes of aggressive T-cell lymphoma whereas TLBL is a rare and aggressive subtype mostly diagnosed in children. In this study, we will use single cell RNA sequencing and single cell multiomic methods three aggressive T-cell lymphoma subtypes (ALCL, PTCL and TLBL) and try to gain a deeper insight into the genotypic and phenotypic features of these T-cell lymphomas. . This dataset contains all the data available for this study on 2025-07-31.

  Dataset EGAD00001015669

• WGS dataset of Hepatoblastoma tumoroid biobank as a key resource for tumour genetics

  WGS dataset of Hepatoblastoma tumoroid biobank with WGS of tumor and matched tumoroids

  Dataset EGAD00001015671

• Embryonic tumour transmission in monozygotic twins

  We have a case of two siblings (twins) who were born with nodular blue rashes over their body. The first twin had a large periorbital subcutaneous mass which was diagnosed as infantile undifferentiated sarcoma. This baby was too systematically unwell for chemotherapy and died at 12 days of age. The second sibling received two cycles of emergency chemotherapy however died due to disease progression and secondary haemorrhage at 2 months of age. Post-mortem normal and tumour samples have been taken. In addition to placental tissue and parental germline DNA, the goal will be to investigate intra-uterine transfer of sarcoma and the phylogenetic relationship of tumours in both twins.

  Dataset EGAD00001015681

• RNA-seq data of patients with glioblastoma IDH-wt (CNS WHO grade 4) with matched primary and relapse samples.

  Between January 1st, 2016, and December 31th, 2021, patients with glioblastoma IDH-wt (CNS WHO grade 4) were included. RNA-seq was performed in a subset of patients.

  Dataset EGAD00001015683

• Genomic and Functional Profiling of Acral Melanoma from the admixed Brazilian Population Reveals Disease Drivers and Targetable Vulnerabilities

  Acral melanoma, a subtype of melanoma characterized by its occurrence on palms, soles, and beneath the nails, presents unique challenges in diagnosis and treatment due to its distinct genetic and environmental factors. Despite recent advancements in targeted therapies for melanoma, the efficacy of treatments in acral melanoma remains limited, emphasizing the urgent need for a deeper understanding of its genetic landscape. CRISPR-based functional genomics screening has emerged as a powerful tool for uncovering genetic dependencies and potential therapeutic targets in various cancers. In this project, we present the results of CRISPR screening performed on acral melanoma cell lines to identify genes essential for cell survival and proliferation collectively termed “fitness". Through this screening approach, we aim to elucidate the molecular mechanisms underlying the pathogenesis of acral melanoma and identify novel therapeutic vulnerabilities. Our findings reveal key genetic drivers and pathways implicated in acral melanoma progression, offering potential targets for the development of precision therapies tailored to this aggressive subtype of melanoma

  Dataset EGAD00001015690

• Dataset for transcriptomic sequencing of Merkel cell carcinoma(MCC) samples

  This dataset contains transcriptome sequencing for 5 samples of MCC tumors . The sequencing was performed on Illumina NextSeq 6000 . The sequencing was always paired.

  Dataset EGAD00001015702

• 5 scRNA-seq with TCR Enrichment of Tumour-Involved Lymph Nodes, Malignant Seromas and Patient-Derived Xenografts from 18 T-Cell Lymphoma Patients

  This raw data contains 5' scRNA-seq with TCR enrichment data from 18 individual patients analysing cells taken from tumour-involved lymph nodes (LN), malignant seromas and patient-derived xenografts (PDX) including systemic/nodal ALK+ ALCL (n=3 PDX), BIA-ALCL (n=2 seromas), PTCL-NOS (n=5 nodal tumours), nodal T follicular helper lymphoma - angioimmunoblastic type (nTFHL-AI) (n=1), nTFHL, not otherwise specified (nTFHL, NOS, n=1), TLBL (n=5) and 1 case of Sezary Syndrome (SS, cutaneous tumour). Droplet-based 5’ scRNA-seq with TCR enrichment (10x Chromium platform) was performed for all live cells, or the viable CD45+CD3- and CD45+CD3+ fractions following cell sorting by flow cytometry.

  Dataset EGAD00001015703

• Whole-genome sequencing of rhabdomyosarcoma tumouroids (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the whole-genome sequencing of RMS tumoroids data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015707

• Single-cell RNA-sequencing of rhabdomyosarcoma tumouroids (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell RNA sequencing of RMS tumouroids data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015708