4991 results for "*oma"

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• Whole genome sequencing of 78 follicular lymphoma tumours and matched normals

  Whole genome sequencing for tumour/normal matched pairs from 78 samples

  Dataset EGAD00001011343

• scRNA-seq, WES, and bulk RNA-seq on longitudinal samples from 7 Lymphoma patients treated with CD20xCD3 bispecific antibodies

  We performed scRNA-seq on 15 fresh lymph node core biopsies from 7 Lymphoma patients treated with CD20xCD3 bispecific antibodies. We also performed whole-exome sequencing (WES) and bulk RNA-seq on formalin-fixed paraffin embedded (FFPE) tumor samples from these patients. WES was additionally performed on matched germline samples (blood) for each patient.

  Dataset EGAD00001011350

• 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

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 9 part 1)

  Whole genome sequencing data of 6 high-grade serous carcinoma (HGSC) patients (11 samples) sequenced with MGISEQ-2000

  Dataset EGAD00001011353

• Single-cell RNA-seq of peripheral blood mononuclear cells in classic Hodgkin lymphoma

  Abstract: Classic Hodgkin lymphoma (cHL) is a largely MHC class I-negative tumor with recurrent 9p24.1/ PD-L1/ PD-L2 copy gains and the highest reported response rates to PD-1 blockade. We utilized scRNA sequencing to characterize the peripheral immune response to PD-1 blockade and more broadly define non-CD8+ dependent mechanisms of immune evasion in cHL. Peripheral blood mononuclear cells were obtained from 20 patients with relapsed/refractory (R/R) cHL treated with PD-1 blockade (nivolumab) on the CheckMate 205 clinical trial (paired samples from cycle 1 day 1 [C1D1] and cycle 4 day 1 [C4D1]), 11 patients with newly diagnosed, previously untreated cHL and 13 healthy donors.

  Dataset EGAD00001011360

• PRADO trial - Personalized response-directed surgery and adjuvant therapy after neoadjuvant ipilimumab plus nivolumab in stage III melanoma

  The dataset includes RNA sequencing data on PRE- treatment biopsies of lymph node metastasis (n=80) The technology used for sequencing is llumina HiSeq 2500

  Dataset EGAD00001012116

• Dataset for RNA and Exome sequencing of Glioblastoma samples

  This dataset contains RNA Seq, 10x scRNA Seq and Exome sequencing of glioblastoma samples. Sequencing was performed on a Illumina NovaSeq 6000 and Illumina HiSeq 4000. The sequencing was always paired.

  Dataset EGAD00001012235

• RNA sequencing of in vitro generated suppressive myeloid cells using parental and Sialidase expressing A549 cancer cell lines

  Fresh peripheral blood mononuclear cells of four human donors were cultured together with either lung adenocarcinoma A549 cancer cells or A549-expressing H1N1 Sialidase cancer cells. These treatments induced the differentiation of donor cells into immunosuppressive MDSC-like cells, which were further subjected to bulk RNA sequencing. RNA-seq TruSeq libraries were generated from polyA-enriched mRNA isolated from the samples, and sequenced in paired-end mode on 4 lanes of an Illumina NextSeq 500 flow-cell

  Dataset EGAD00001012437

• Characterisation of the genetic landscape of cutaneous leiomyoma and leiomyosarcoma - Leiomyoma WES

  Cutaneous leiomyoma (cLM) and leiomyosarcoma (cLMS) are rare benign and malignant soft tissue neoplasms showing smooth muscle differentiation, respectively, that arise from mesenchymal cells in the dermis and subcutis. Through whole exome sequencing of cLM and cLMS cases, we observed distinct differences between the somatic mutational profile of these tumour types. FH was identified as a driver gene in cLM with genetic alterations of FH occurring via somatic point mutation, somatic copy number loss, biallelic inactivation and germline point mutations. TP53 and RB1 were identified as driver genes in the cLMS cohort, with genetic alterations of TP53 occurring via somatic and germline point mutations, copy number loss and biallelic inactivation. Using RNA-sequencing, we identified recurrent gene fusions, including CRTC1/3-MAML2 in cLMS and a novel MYLK-MAP3K2 fusion. Analysis of the cell types present in the tumour microenvironment revealed a significantly increased presence of macrophages and decreased presence of myeloid dendritic cells in the cLMS cohort relative to the cLM cohort. Additionally, we identified common driver genes between cLMS and LMS from other sites. Thus, we provide the first in-depth profile of the genetic landscape of cLM and cLMS.

  Dataset EGAD00001014787

• Characterisation of the genetic landscape of cutaneous leiomyoma and leiomyosarcoma - Leiomyoma RNA

  Cutaneous leiomyoma (cLM) and leiomyosarcoma (cLMS) are rare benign and malignant soft tissue neoplasms showing smooth muscle differentiation, respectively, that arise from mesenchymal cells in the dermis and subcutis. Through whole exome sequencing of cLM and cLMS cases, we observed distinct differences between the somatic mutational profile of these tumour types. FH was identified as a driver gene in cLM with genetic alterations of FH occurring via somatic point mutation, somatic copy number loss, biallelic inactivation and germline point mutations. TP53 and RB1 were identified as driver genes in the cLMS cohort, with genetic alterations of TP53 occurring via somatic and germline point mutations, copy number loss and biallelic inactivation. Using RNA-sequencing, we identified recurrent gene fusions, including CRTC1/3-MAML2 in cLMS and a novel MYLK-MAP3K2 fusion. Analysis of the cell types present in the tumour microenvironment revealed a significantly increased presence of macrophages and decreased presence of myeloid dendritic cells in the cLMS cohort relative to the cLM cohort. Additionally, we identified common driver genes between cLMS and LMS from other sites. Thus, we provide the first in-depth profile of the genetic landscape of cLM and cLMS.

  Dataset EGAD00001014788