3773 results for "*oma"

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• Genome and transcriptome sequence data from a NUT midline carcinoma tumor patient

  Genome and transcriptome sequence data from a NUT midline carcinoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015325

• Genome and transcriptome sequence data from a diffuse midline glioma, H3K27 mutant tumor patient

  Genome and transcriptome sequence data from a diffuse midline glioma, H3K27 mutant patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015326

• Genome and transcriptome sequence data from a CNS non-germinoma germ cell tumour tumor patient

  Genome and transcriptome sequence data from a CNS non-germinoma germ cell tumour patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015327

• Genome and transcriptome sequence data from a alveolar rhabdomyosarcoma tumor patient

  Genome and transcriptome sequence data from a alveolar rhabdomyosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015329

• Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma tumor patient

  Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015330

• 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

• Genome and transcriptome sequence data from a pIlomyxoid astrocytoma tumor patient

  Genome and transcriptome sequence data from a pIlomyxoid astrocytoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015333

• Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar tumor patient

  Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015335

• Genome and transcriptome sequence data from a high-grade glioma, glioblastoma tumor patient

  Genome and transcriptome sequence data from a high-grade glioma, glioblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015336

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015337

• 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 EGAD00001015338

• Multiome_HB

  Hepatoblastoma (HB) cells display strong phenotypic heterogeneity with a major impact on drug response, but the underlying mechanisms are poorly understood. Here, we use a single-cell multi-omic strategy to unravel the molecular determinants of this plasticity. HB display a continuum of single-cell states between hepatocytic (scH), liver progenitor (scLP) and mesenchymal (scM) differentiation poles, with an intermediate scH/LP population bordering scLP and scH areas in spatial transcriptomics. Chromatin accessibility landscapes reveal the gene regulatory networks of each differentiation pole, and the sequence of transcription factor activation underlying cell state transitions. Single-cell mapping of somatic alterations reveals the clonal architecture of each tumor, showing that each genetic subclone displays its own range of cellular plasticity across differentiation poles. The most scLP subclones, overexpressing stem cell and DNA repair genes, proliferate faster after neo-adjuvant chemotherapy. These results highlight how the interplay of clonal evolution and epigenetic plasticity shapes the potential of HB subclones to respond to chemotherapy.

  Study EGAS00001006932

• Exome and transcriptome sequencing of Desmoplastic Small Round Cell Sarcoma

  To conduct exome and transcriptome profiling to identify secondary recurrent mutations, multi-site phylogenetic analyse and immune characterization of Desmoplastic Small Round Cell Sarcoma.

  Study EGAS00001004575

• Genomic landscape and PD-1 blockade in Natural-killer/T cell lymphoma

  Natural killer/T-cell lymphoma (NKTL) is an aggressive malignancy with a predilection for Asian, Mexican and South American populations. With the exception of Japan, it is the most common mature T-cell lymphoma in Asia. NKTL presents as extranodal disease and mostly affects the upper aerodigestive tract. Neoplastic cells are invariably infected by the Epstein-Barr virus (EBV) and characterized by a cytotoxic phenotype. The genetic landscape of NKTL has been recently unraveled by discoveries describing recurring mutations altering the JAK-STAT pathway, epigenetic modifiers, the DDX3X gene and genetic predisposition in the HLA-DPB1 gene.The genomic landscape of NKTL has been interrogated by whole-exome sequencing, targeted sequencing and single nucleotide polymorphism arrays, but has yet been studied with whole-genome sequencing (WGS). In this study, we will use a combination of WGS, targeted-capture sequencing (TCS) and RNA sequencing data to explore the genetic landscape and its relevance to immunotherapy in NKTL

  Study EGAS00001003828

• Comprehensive Transcriptional Analysis of Early Stage Urothelial Carcinoma using whole transcriptome sequencing

  The expression profile and sequence variants of 476 early stage urothelial carcinoma were studied using whole transcriptome sequencing. RNA-Seq libraries were prepared by Ribo-Zero treatment of total-RNA followed by library preparation using ScriptSeq (both Epicentre/Illumina). RNA-Seq libraries were paired-end sequenced (2x 101 bp) on Illumina HiSeq 2000 and the resulting fastq files were processed using tools from the Genome Analysis Toolkit (GATK and from the Tuxedo suite. Access to the sequence data (mapped and un-mapped bam and vcf files), containing person identifying information, needs signature on a controlled access form, and can be accessed at The European Genome-phenome Archive (EGA) following request. An expression matrice of FPKM values are available without restriction at ArrayExpress (E-MTAB-4321).

  Study EGAS00001001236

• Machine Learning Guided Signal Enrichment for Plasma Tumor-burden Monitoring Dataset

  Includes 4 Datasets: 1. 35 Control Plasma WGS samples sequenced on NovaSeq V1.0 Chemistry at the New York Genome Center. Denoted by CTRL-2XX naming scheme. 2. Plasma WGS from 17 patients with Small Cell Lung Cancer. Samples extracted at either Pretreatment or Postoperative at weeks 2 or 3. Denoted by SCLC-XX naming scheme. 3. Plasma WGS from a synthetic mixing study of a high-burden melanoma plasma sample with plasma bag, at estimated tumor concentrations of 10e-3, 10e-4, 10e-5, and 10e-6 for 2 replicates. Denoted by SM-repX naming scheme. 4. Assorted Plasma, Tumor, and Normal WGS from patients with NSCLC expressing high-burden for use in model training. Denoted by NSCLC-2XX naming scheme.

  Dataset EGAD00001011352

• Genomic and transcriptomic landscape of T-cell lymphoblastic lymphoma compared to T-cell acute lymphoblastic leukemia: similar subtypes and different fusions

  Due to the lower incidence of T-LBL and difficulties in obtaining diagnostic T-LBL material, extensive research on T-LBL has been hampered whereas genetic aberrations in T-ALL are thoroughly characterized. Given the similarities and differences between T-LBL and T-ALL, the question has been raised whether T-LBL and T-ALL represent two different diseases or different manifestations of the same disease. This study aims to identify the genomic and transcriptomic landscape of T-LBL and compare the findings to what is found T-ALL. Comparison of the molecular aberrations between T-LBL and T-ALL can provide insights into the overlap and differences in malignant development between the two entities, which could lead to improved risk stratification in T-LBL in order to eventually adapt T-LBL treatment protocols based on molecular-genetic prognostic factors.

  Dataset EGAD00001015357