4768 results for "*oma"

in 29.54 milliseconds.

• RNA-seq dataset: Single-cell spatial analysis of pediatric high-grade glioma reveals a novel population of immunosuppressive and tumor-promoting SPP1+/GPNMB+ myeloid cells

  Our study utilizes two novel techniques, cyclical immunofluorescence imaging and single-cell spatial transcriptomics, to spatially map the tumor microenvironment of a large sample set of pediatric high-grade gliomas (pHGG). Using these methods, we have identified an abundant immunosuppressive myeloid cell population that has not been described in the context of pediatric high-grade gliomas. We validate our findings using an in vitro assay, spatial analysis on additional pHGG biopsies, independent spatial transcriptomic data, bulk RNA sequencing data of an expanded cohort of in-house patients, and publicly available bulk RNA sequencing data of an even larger cohort of pHGG patients.

  Dataset EGAD00001015450

• 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

• The G2 gene expression signature and MYC overexpression are independent poor prognostic factors in childhood high-grade osteosarcoma

  Key objective Are the prognostic transcriptomic G1/G2 gene expression signature, MYC overexpression, and MYC amplification replicable stratifying biomarkers for future clinical trials in high-grade osteosarcoma? Knowledge gathered In an unselected cohort, the G2 gene expression signature and MYC overexpression, but not MYC amplification, were independently associated with poor event-free and overall survival. Relevance Transcriptomic biomarkers may serve as stratifying factors that guide the management of patients with high-grade osteosarcoma. Current data underlines the importance of prospective validation of the G1/G2 signature and MYC overexpression in an international, multicenter, study.

  Dataset EGAD00001015502

• 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

• Dataset for transcriptome and whole genome bisulfite sequencing of glioblastoma(GBM) tumor cells

  This dataset contains transcriptome and whole genome bisulfite sequencing for 40 samples with glioblastoma(GBM). The sequencing was performed on Illumina NextSeq 2000 and Illumina NextSeq 550, . The sequencing was always paired.

  Dataset EGAD00001015614

• 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

• Dataset for transcriptome sequencing of chordoma cells

  This dataset contains transcriptome sequencing for 15 samples from chordoma cells. The sequencing was performed on Illumina HiSeq 4000 and Illumina NovaSeq 6000. The sequencing was always paired.

  Dataset EGAD00001015642

• 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