5528 results for "*oma"

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• Dataset related to ""Ex vivo therapy-driven selection of glioblastoma stem-like cells models resistance evolution"

  RNA-seq and NGS target panel dataset related to "Ex vivo therapy-driven selection of glioblastoma stem-like cells models resistance evolution"

  Dataset EGAD50000002898

• The Precision Medicine in Liver Cancer across an Asia-pacific NETwork (PLANET) – HCC recurrence cohort

  Hepatocellular carcinoma (HCC) is a highly fatal cancer marked by significant intra-tumor heterogeneity (ITH). Given 50% to 70% of HCC patients experience recurrence, understanding the origins and progression of recurrence is crucial. To address this, we assembled a multi-sector, paired HCC recurrence cohort of 24 patients, including comprehensive whole genome sequencing and transcriptome samples. Through this longitudinal cohort, we aimed to trace the origins of recurrence and identify molecular features associated with early recurrence. These insights may improve predictions of early HCC recurrence and inform potential therapeutic strategies.

  Study EGAS50000000869

• Single-nuclei RNAseq of normal human choroid and uveal melanoma

  Single-nuclei RNAseq of normal human choroid and uveal melanoma

  Study EGAS00001004955

• Single-nuclei RNAseq of normal human choroid and uveal melanoma

  Single-nuclei RNAseq of normal human choroid and uveal melanoma. Libraries were prepared using the Chromium Single Cell 3' Reagent Kits v3,. Paired-end libraries were sequenced on a NovaSeq X instrument (Illumina).

  Dataset EGAD50000002941

• 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

• Transcriptional alterations induced by BET inhibitor and Omaveloxolone combination in acute myeloid leukemia cells

  Kasumi-1 and OCI-AML3 cells treated for 24h with BETi and Oma in hypoxia (1% O2) or normoxia (20% O2) were used for RNA extraction and hybridization on microarrays.

  Dataset EGAD00010002852

• Clinical Outcomes of 344 Diffuse Large B-Cell Lymphoma patients

  This study examines clinical outcomes of patients with diffuse large B-cell lymphoma (DLBCL). We analyzed a cohort of 344 patients from the German High-Grade Non-Hodgkin Lymphoma Study Group (DSHNHL). To protect patient confidentiality, all samples were pseudonymized. The dataset includes clinical key characteristics, such as patient age at diagnosis, sex, ECOG performance status, disease stage, serum lactate dehydrogenase levels, bulky disease, bone marrow involvement, extranodal involvement, and B-symptoms. We used these data alongside proteomic and transcriptomic profiles to explore relationships between clinical features and molecular signatures, reconstructing regulatory networks. Insights from this integrative analysis aim to identify key pathological drivers and biomarkers of DLBCL progression, ultimately informing improved risk stratification and targeted therapeutic approaches.

  Study EGAS50000001054

• IMMUcan Upstream SCCHN3 cohort

  The goal of IMMUcan is to understand how the immune system interacts with tumors and how therapeutic interventions influence this interaction. This cohort includes patients from the UPSTREAM trial, with squamous cell carcinoma of the head and neck (SCCHN) cancer which has spread (metastatic) or the tumour has come back after previous treatment (recurrent). Comprehensive profiling data are integrated with longitudinally collected clinical information. Cellular profiling of the same tumor samples was performed using multiplex immunofluorescence imaging, imaging mass cytometry, and H&E staining.

  Study EGAS50000001505

• IMMUcan SCCHN1 cohort

  The goal of IMMUcan is to understand how the immune system interacts with tumors and how therapeutic interventions influence this interaction. This cohort includes patients with confirmed locally recurrent and/or metastatic squamous cell carcinoma of the head and neck (SCCHN) who receive first-line treatment with immune checkpoint inhibitors (ICI) or ICI combined with chemotherapy. Comprehensive profiling data are integrated with longitudinally collected clinical information. Cellular profiling of the same tumor samples was performed using multiplex immunofluorescence imaging, imaging mass cytometry, and H&E staining.

  Study EGAS50000001533

• Whole-genome sequencing study of uveal melanoma

  Uveal melanoma is the most common primary intraocular cancer, characterized by metastasis-related mortality. Here, we explored whole-genome sequences from 40 primary uveal melanomas to illustrate the landscape of genomic mutations and investigate their association with prognosis.

  Study EGAS50000001603