4062 results for "*oma"

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• Neuroblastoma sequencing data

  This dataset contains tumor and normal whole exome DNA sequence data for a patient with neuroblastoma

  Dataset EGAD00001008120

• MBD4 targeted sequencing

  We sequenced a gene of interest, MBD4, in both germline (n=1099) and tumor (n=192) sample from Uveal Melanoma patients.

  Study EGAS00001005012

• Integrated genomic, transcriptional and epigenomic analyses in germinal center-cell lymphomas link the mutation landscape with differential DNA methylation in Burkitt lymphoma

  Biologically and clinically diverse B-cell neoplasms, including Burkitt, follicular and diffuse large B-cell lymphoma, show features of germinal center (GC) B-cells. Here we present a comprehensive analysis of the epigenetic landscape of GC-B-cell lymphomas using whole genome bisulfite sequencing paired with genome and transcriptome sequencing from 29 primary tumor samples and four normal GC-B-cell samples. All lymphomas studied showed extensive genome-wide methylation losses as well as intragenic regions where DNA methylation levels were strongly correlated with expression of associated genes. Pathway analysis revealed that these intragenic regions were enriched in signaling networks differentially activated in Burkitt and follicular lymphomas and affected regulatory mechanisms deregulated by recurrent mutations in a large fraction of Burkitt lymphomas (including the ID3/TCF3 complex, the SWI/SNF remodeling complex and the inhibitory GI signaling pathway). Taken together, these results provide a global picture of Burkitt lymphoma pathomechanism and demonstrate a tight connection between epigenetic regulation and other levels of regulatory control in these tumors.

  Study EGAS00001001067

• HYPERMUTATION AND MALIGNANT PROGRESSION IN LOW-GRADE GLIOMA PATIENTS

  While patients with primary glioblastoma (GBM) have benefited from increased overall survival due to treatment with the alkylating chemotherapy temozolomide (TMZ), the benefit ofTMZ for patients with diffuse low-grade gliomas (LGG) remains unknown. To further explore the relationships among TMZ treatment, hypermutation and malignant progression, we studied tumor evolution in an expanded cohort of untreated and TMZ-treated patients by exome-sequencing of paired IDH1/2-mutant LGGs and their post-TMZ recurrences. Together, these findings further our understanding of the molecular features and clinical behavior of hypermutated clones.

  Study EGAS00001002368

• Whole genome sequencing of multiple myeloma identifies novel structural and non-coding mutations.

  The coding region of the myeloma genome is well understood with key mutations having been identified. However, the non-coding regions of the myeloma genome are yet to be analyzed. Here we have performed whole genome sequencing of myeloma patient samples to identify structural abnormalities including chromothripsis and chromoplexy, as well as gene dysregulation through super-enhancer juxtaposition with key oncogenes. We have also identified non-coding region hotspots of mutation, especially in promoter regions, that affect gene expression.

  Study EGAS00001003164

• RNA sequencing of multiple myeloma identifies genes dysregulated by structural variants.

  Multiple myeloma is a disease of structural changes brought about by copy number abnormalities and translocations. Many of these changes result in super-enhancers being placed next to key oncogenes. Here, we have investigated which genes are commonly dysregulated by these changes.

  Study EGAS00001003411

• Whole genome bisulfite sequencing on 10 multiple myeloma cases

  Data quality control and adaptor-trimmed were performed with the Trimomatic tool. Paired-reads were mapped to the hg19 human reference with methylCtools aligner.

  Study EGAS00001004346

• Neuroblastoma sequencing data

  This study shares tumor and normal DNA for a patient with neuroblastoma

  Study EGAS00001005602

• Cerebrospinal fluid cfDNA sequencing for classification of central nervous system glioma

  Primary central nervous system (CNS) gliomas can be classified by characteristic genetic alterations. In addition to solid tissue obtained by surgery or biopsy, cell-free DNA (cfDNA) derived from cerebrospinal fluid (CSF) is an alternative source of material for genomic analyses. Experimental design: We performed targeted next-generation sequencing (NGS) of CSF cfDNA in a representative cohort of 85 patients presenting with with suspicion of primary or recurrent glioma at two neurooncological centers. Copy-number variation (CNV) profiles, single nucleotide variants (SNVs), and small insertions/ deletions (indels) were combined into a molecular-guided tumor classification. Comparison with the solid tumor was performed for 38 cases with matching solid tissue available. Results: Cases were stratified into four groups: glioblastoma (n = 32), other glioma (n = 19), non-malignant (n = 17) and no confirmed diagnosis available (n = 17). We introduce a molecular-guided tumor classification, enabling identification of tumor entities and/ or cancer specific alterations in 75.0 % (n = 24) of glioblastoma and 52.6 % (n = 10) of other glioma cases. The overlap between CSF and matching solid tissue was highest for CNVs (26-48 %) and SNVs at pre-defined gene loci (44 %), followed by SNVs/indels identified via uninformed variant calling (8-14 %). A molecular-guided tumor classification was made 23.5 % (n = 4) of cases with no confirmed diagnosis available. Conclusions: We developed a workflow for targeted sequencing of CSF cfDNA as well as a strategy for interpretation and reporting of sequencing results based on a molecular-guided tumor classification in glioma.

  Study EGAS50000000060

• noninvasive lung cancer subtyping

  Accurate diagnosis of lung cancer is important for treatment decision-making. Currently, the gold standard for diagnosing histological subtypes of lung cancer relies on tumor biopsies. Recently, liquid biopsy, particularly cell-free DNA (cfDNA), has shown promising results in cancer detection and classification. In this study, we investigated the potential of cfDNA methylome for the noninvasive classification of lung cancer histological subtypes. Specifically, we focused on the two most prevalent lung cancer subtypes, lung adenocarcinoma and lung squamous cell carcinoma. Using a fragment-based marker discovery approach, we identified robust subtype-specific methylation markers from tumor samples. These markers were successfully validated in independent cohorts and associated with subtype-specific transcription activity. Leveraging these markers, we constructed a subtype classification model using cfDNA methylation profiles, achieving an AUC of 0.808 in the cross-validation and an AUC of 0.747 in the independent validation. Additionally, tumor copy number variations inferred from cfDNA methylome analysis revealed potential implications for treatment selection. In summary, our study demonstrates the potential of cfDNA methylome analysis for noninvasively lung cancer subtyping, offering insights for cancer monitoring and early detection.

  Study EGAS00001007717