5246 results for "*oma"

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• WGS dataset of Ewing and Ewing-like-sarcoma tumoroid biobank reveals distinct drug sensitivities: translocation makes the difference

  Creation of living organoid biobank for ewing and ewing-like sarcomas

  Dataset EGAD00001015418

• RNA-seq dataset of Ewing and Ewing-like-sarcoma tumoroid biobank reveals distinct drug sensitivities: translocation makes the difference

  Creation of living organoid biobank for ewing and ewing-like sarcomas

  Dataset EGAD00001015419

• Bulk RNA Seq in FAP Adenoma

  Bulk RNASeq data of Duodenal organoids, sorted ILC3 and duodenal tissue

  Dataset EGAD00001015488

• 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

• 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 EGAD00001015503

• Dataset for glioblastoma panel sequencing

  This dataset contains panel sequencing data of 228 samples from patients with glioblastoma. Sequencing has been performed on Illumina NovaSeq 6000 and NextSeq 500. The sequencing was always paired.

  Dataset EGAD00001015505

• snRNAseq of a hepatoblastoma patient with atypical tetrasomy 18

  snRNAseq was performed on 2 samples from a hepatoblastoma patient with atypical tetrasomy 18. 1 hepatoblastoma sample (resected tumor) and 1 non-tumor liver sample were sequenced.

  Dataset EGAD00001015506

• WGS of a hepatoblastoma patient with atypical tetrasomy 18

  WGS was performed on 4 samples from a hepatoblastoma patient with atypical tetrasomy 18. 2 hepatoblastoma samples (1 diagnostic biospy and 1 resected tumor), 1 blood sample and 1 non-tumor liver sample were sequenced.

  Dataset EGAD00001015507

• 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

• Single-cell RNA-sequencing of a multi-region pleural mesothelioma case

  Single-cell RNA-seq (Smart-Seq2) from 3 distant biopsies (costal, mediastinal, diaphragmatic) of a pleural mesothelioma case.

  Dataset EGAD00001015519