4058 results for "*oma"

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• A single-cell multi-omic atlas of nodal B-cell non-Hodgkin lymphomas

  CITE-Seq (cellular indexing of transcriptomes and epitopes) of 51 lymph node (LN) samples from including mantle cell lymphoma (MCL, n = 8), follicular lymphoma (FL, n = 12), germinal center (GCB, n = 5) or activated B-cell (non-GCB/ABC, n = 7) diffuse large B-cell lymphoma (DLBCL), and marginal zone lymphomas (MZL, n = 11), in addition to non-malignant reactive lymph nodes (rLN, n = 8). Of the malignant LN samples, 20 were collected at the time of initial diagnosis and 23 were from patients who had previously undergone one or more lines of systemic treatment. Relapse samples were collected at least 3 months after cessation of systemic treatment. This dataset also includes 5 prime RNA sequencing and immune receptor sequencing (10X Genomics) for 11 of these samples (2 rLN, 2 MCL, 3 FL, 2 DLBCL (GCB) and 2 MZL).

  Dataset EGAD50000000497

• Dataset for manuscript titled: Spatial Intra-Tumour Heterogeneity and Treatment-Induced Genomic Evolution in Oesophageal Adenocarcinoma: Implications for Prognosis and Therapy

  Dataset for manuscript titled: Spatial Intra-Tumour Heterogeneity and Treatment-Induced Genomic Evolution in Oesophageal Adenocarcinoma: Implications for Prognosis and Therapy

  Dataset EGAD00001015373

• ISA Nutrition 2015

  Comprehensive data on lifestyle-related modifiable factors, sociodemographic, anthropometric, economic, biochemical, and genetic markers related to the occurrence of cardiometabolic diseases as part of the observational cross-sectional survey (2015 Health Survey of Sao Paulo with Focus on Nutrition (2015 ISA-Nutrition), a population-based study. Data of 805218 SNPs for 841 individuals was genotyped using the Axioma 2.0 Precision Medicine Research Array in the Thermo Fisher Scientific laboratory (Affymetrix Inc, Santa Clara, CA).

  Dataset EGAD00010002678

• Whole genome sequencing of patients with or at risk for HCC

  Liver cancer is a major cause of cancer mortality worldwide. Screening individuals at high risk, including those with cirrhosis and viral hepatitis, provides an avenue for improved survival, but current screening methods are inadequate. In this study, we used whole-genome cell-free DNA (cfDNA) fragmentome analyses to evaluate 724 individuals from the United States, the European Union, or Hong Kong with hepatocellular carcinoma (HCC) or who were at average or high-risk for HCC. Using a machine learning model that incorporated multifeature fragmentome data, the sensitivity for detecting cancer was 88% in an average-risk population at 98% specificity and 85% among high-risk individuals at 80% specificity. We validated these results in an independent population. cfDNA fragmentation changes reflected genomic and chromatin changes in liver cancer, including from transcription factor binding sites. These findings provide a biological basis for changes in cfDNA fragmentation in patients with liver cancer and provide an accessible approach for noninvasive cancer detection.

  Study EGAS00001007249

• Failure of Differentiation of the Rhombic Lip Constitutes Medulloblastoma

  We used single-cell transcriptomics to study cells from the developing human cerebellum, and show that different molecular subgroups of medulloblastoma resemble distinct glutamatergic progenitors.

  Study EGAS00001005826

• Stratifying and Targeting Pediatric Medulloblastoma through Genomics

  In this project, genomic analyses of pediatric medulloblastoma samples, obtained through the international medulloblastoma consortium, will be performed. RNA and miRNA expression profiles of 1000 samples, representing all four subgroups (Wnt, Shh, Group C, and D), will be studied to identify novel subtypes within each subgroup. The resulting subtype-specific expression profiles will support the development of reliable and robust biomarkers to more accurately and reliably classify medulloblastomas for treatment in clinical trials. For that purpose, two assays will be developed: an antibody-based immunohistochemical assay and an orthogonal nucleic acid-based hybridization assay. Additional genomic DNA analysis of the 300 high risk subgroup cases will support the discovery of subgroup specific somatic mutations in order to inform current clinical trials of targeted therapies, and to identify genes and pathways already targeted in other diseases. Such therapies could be rapidly transitioned to Phase II trials in medulloblastoma. Furthermore, the discovery of somatic mutations could be used for developing as well as validating specific biomarkers. The project team will also try to identify risk factors that predispose children to this type of cancer.

  Study EGAS00001000273

• Comprehensive molecular profiling identifies novel genetic drivers and subtypes underlying medulloblastoma

  Current therapies for medulloblastoma (MB), a highly malignant childhood brain tumor, impose debilitating effects on the developing child, warranting the development of molecularly targeted treatments with reduced toxicities. Prior studies have failed to disclose the full spectrum of driver genes and molecular processes operative in MB or adequately explain heterogeneity among molecular subgroups. Herein, we detail the somatic landscape across 491 sequenced MBs and molecular heterogeneity amongst 1,256 epigenetically analyzed cases, identifying subgroup-specific mutational signatures, driver genes, and pathway alterations including previously unappreciated actionable therapeutic targets. Integrative approaches for assigning driver genes to subgroups explained >65-70% of Group 3 and Group 4, doubling previous knowledge. Novel subtypes underlying Group 3 and Group 4 were differentially enriched for specific driver events, including hotspot in-frame indels targeting KBTBD4 and ‘enhancer hijacking’ driving activation of PRDM6. Thus, application of integrative genomics to an unprecedented cohort of clinical samples derived from a single childhood cancer entity has disclosed a series of new cancer genes and biologically relevant subtype diversity that represent attractive therapeutic targets for treating MB.

  Study EGAS00001001953

• Molecular profiling of acinar cell carcinoma of the salivary glands

  Samples of acinar cell carcinoma (AciCC) of the salivary glands and paired normal salivary gland tissue were analyzed by whole transcriptome RNA-seq (n=10), whole genome sequencing (n=6), whole genome bisulfite sequencing (n=3) and ChIP-seq (H3K27ac, H3K4me3, H3K27me3, NR4A3, CTCF).

  Study EGAS00001002795

• Molecular Profiling Reveals Unique Immune and Metabolic Features of Melanoma Brain Metastases

  There is a critical need to improve our understanding of the pathogenesis of -melanoma brain metastases (MBM). Thus, we performed RNA sequencing on 88 resected MBMs and 42 patient-matched extracranial metastases; tumors with sufficient tissue also underwent whole-exome sequencing, T-cell receptor sequencing, and IHC. MBMs demonstrated heterogeneity of immune infiltrates that correlated with prior radiation and post-craniotomy survival. Comparison with patient-matched extracranial metastases identified significant immunosuppression and enrichment of oxidative phosphorylation (OXPHOS) in MBMs. Gene-expression analysis of intracranial and subcutaneous xenografts, and a spontaneous MBM model, confirmed increased OXPHOS gene expression in MBMs, which was also detected by direct metabolite profiling and [U-13C]-glucose tracing in vivo IACS-010759, an OXPHOS inhibitor currently in early-phase clinical trials, improved survival of mice bearing MAPK inhibitor-resistant intracranial melanoma xenografts and inhibited MBM formation in the spontaneous MBM model. The results provide new insights into the pathogenesis and therapeutic resistance of MBMs.

  Study EGAS00001003672

• A Genomics-Driven Artificial Intelligence-Based Model Classifies Breast Invasive Lobular Carcinoma and Discovers CDH1 Inactivating Mechanisms

  We developed an artificial intelligence (AI)-model applied to histological images using CDH1 biallelic mutations, pathognomonic for breast invasive lobular carcinoma (ILC), as ground truth. We evaluated the performance of the AI-model to predict the presence of CDH1 biallelic mutations and to diagnose ILC. Subsequently, we investigated the molecular underpinning cases of predicted by the model to harbor a CDH1 biallelic mutations but lacking these alterations according to targeted sequencing. Among this analyses, we subjected to whole genome sequencing (WGS) one ILC case lacking CDH1 biallelic mutations by targeted sequencing and lacking CDH1 promoter methylation to determine the molecular basis of its lobular phenotype.

  Study EGAS50000000485