4736 results for "*oma"

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• EpS_methylation_850K

  Methylation profile (array data using EPIC_850K) from tumour samples (epithelioid sarcoma)

  Dataset EGAD00010002571

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - pilot normal and pre/post-chemo

  Dataset EGAD00001000704

• Methylation array of upper urinary tract urothelial carcinoma

  Genome-wide methylation analysis of upper urinary tract urothelial carcinoma using Infinium MethylationEPIC BeadChip Kit

  Dataset EGAD00010002096

• Genotype-Multiple-Myeloma-case

  Genome-Wide Human SNP Array 6.0 or the CytoScan HD array, according to the manufacturer’s instructions (Affymetrix, Santa Clara, CA, USA) now part of Thermo Fisher Scientific (Thermo Fisher Scientific, Inc.)

  Dataset EGAD00010002102

• Illumina BeadArray SNP arrays

  SNP measurement. Illumina BeadArray SNP arrays for the study "Molecular characteristics in Burkitt lymphoma over age groups"

  Dataset EGAD00010002137

• The T cell receptor repertoire of tumor infiltrating T cells is predictive and prognostic for cancer survival

  Transcriptomic sequencing on pre-immunotherapy melanoma patients.

  Dataset EGAD00001007710

• Illumina Infinium MethylationEPIC Array IDAT files generated from 11 glioma patient samples

  This dataset includes IDAT files from 6 IDH-mutant, 5 IDH-wild-type glioma patient samples of unmatched initial and recurrent timepoints profiled using the Illumina Infinium MethylationEPIC Array.

  Dataset EGAD00010002157

• Whole Genome Sequencing of HCC

  The present series corresponds to 24 whole genome sequencing (12 Tumoral/Non-tumoral pairs). Hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers, and is a major health problem. It is the 3rd cause of cancer-related mortality. Advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. Thus, the development of next-generation sequencing technologies has made it possible to generate more comprehensive catalogues of somatic alteration events (single nucleotide substitutions, structural variations, and epigenetic changes) in liver cancer genome than ever before.

  Dataset EGAD00001003994

• Genetic landscape of hepatocellular carcinoma

  Genetic landscape of hepatocellular carcinoma

  Dataset EGAD00001000131

• Multi-site tumor sampling highlights molecular intra-tumor heterogeneity in malignant pleural mesothelioma

  Dataset EGAD00001007762

• Pre-neoplastic liver colonization by 11p15.5 altered mosaic cells in young children with hepatoblastoma

  Dataset EGAD00001009669

• OncoScan SNP data set for systemic follicular lymphoma (sFL)

  122 unpaired initial tumor samples (122 FFPE samples) of sFL patients measured with OncoScan SNP microarrays, Affymetrix CEL intensity data file types (Thermo Fisher Scientific, Waltham, Massachusetts, USA)

  Dataset EGAD00010002592

• OncoScan SNP data set for localized follicular lymphoma (lFL)

  149 unpaired initial tumor samples (133/149 FFPE and 16/149 fresh frozen samples) of lFL patients measured with OncoScan SNP microarrays, Affymetrix CEL intensity data file types (Thermo Fisher Scientific, Waltham, Massachusetts, USA)

  Dataset EGAD00010002593

• Study of the microenvironment of angioimmunoblastic T-cell lymphoma

  Dac EGAC00001002756

• Linked-Read Medulloblastoma DAC

  Dac EGAC00001003236

• Efficacy of two different FGFR-inhibitors in a patient with extrahepatic cholangiocarcinoma harboring an FGFR2 mutation

  Cholangiocarcinomas (CCAs) is a type of cancer with few effective systemic therapies. Elucidation of the molecular landscape of the disease from genomic studies based on next generation sequencing (NGS) has contributed to the introduction of new targeted therapies. One of these treatments consists of a class of small molecules that target members of the FGFR family of receptor tyrosine kinases. These drugs are effective and have been approved for cholangiocarcinomas with fusions or rearrangements of FGFR genes. In contrast, the role of these inhibitors in cholangiocarcinomas with mutations in FGFR genes is less well defined. We report here a patient with a cholangiocarcinoma bearing a FGFR2 p.Ser252Trp mutation. The patient was treated with two different FGFR inhibitors, as the first caused ocular toxicity. She obtained clinical benefit from both. This case illustrates the efficacy of FGFR inhibitors on cholangiocarcinoma with specific point mutations. This is the first case to report the clinical benefit of these drugs in FGFR2 p.Ser252Trp mutation. Clinical benefit can be sustained, as seen in our patient. Our case also shows that FGFR inhibitors-induced adverse effects, such as ocular toxicities, may not recur after re-challenge with an alternative drug of the same class.

  Study EGAS50000000015

• Efficacy of two different FGFR-inhibitors in a patient with extrahepatic cholangiocarcinoma harboring an FGFR2 mutation

  Cholangiocarcinomas (CCAs) is a type of cancer with few effective systemic therapies. Elucidation of the molecular landscape of the disease from genomic studies based on next generation sequencing (NGS) has contributed to the introduction of new targeted therapies. One of these treatments consists of a class of small molecules that target members of the FGFR family of receptor tyrosine kinases. These drugs are effective and have been approved for cholangiocarcinomas with fusions or rearrangements of FGFR genes. In contrast, the role of these inhibitors in cholangiocarcinomas with mutations in FGFR genes is less well defined. We report here a patient with a cholangiocarcinoma bearing a FGFR2 p.Ser252Trp mutation. The patient was treated with two different FGFR inhibitors, as the first caused ocular toxicity. She obtained clinical benefit from both. This case illustrates the efficacy of FGFR inhibitors on cholangiocarcinoma with specific point mutations. This is the first case to report the clinical benefit of these drugs in FGFR2 p.Ser252Trp mutation. Clinical benefit can be sustained, as seen in our patient. Our case also shows that FGFR inhibitors-induced adverse effects, such as ocular toxicities, may not recur after re-challenge with an alternative drug of the same class.

  Dataset EGAD50000000021

• The Leeds Melanoma Cohort gene expression data access committee

  Dac EGAC00001000893

• Methylome profiling of Solitary fibrous tumor/Hemangiopericytoma (SFT/HPC) and a patient derived cell-line model

  Solitary fibrous tumor/Hemangiopericytoma (SFT/HPC) is a rare subtype of soft tissue sarcoma associated with NAB2-STAT6 gene fusions. This study established and characterized a novel SFT/HPC patient-derived cell line called SFT-S1 using the twist human methylome panel.

  Study EGAS50000000026

• Evaluating potential drug candidates for the treament of Henamgiopericytoma on patient derived cell line models

  Solitary fibrous tumor/Hemangiopericytoma (SFT/HPC) is a rare subtype of soft tissue sarcoma associated with NAB2-STAT6 gene fusions. This study established and characterized a novel SFT/HPC patient-derived cell line called SFT-S1. Potential drug candidates that could be repurposed for the treatment of SFT/HPC were screened.

  Study EGAS50000000027

• Whole genome sequencing of Sinonasal hemangiopericytoma and patient derived cell line model

  Solitary fibrous tumor/Hemangiopericytoma (SFT/HPC) is a rare subtype of soft tissue sarcoma associated with NAB2-STAT6 gene fusions. This study established and characterized a novel SFT/HPC patient-derived cell line called SFT-S1 using whole genome sequencing.

  Study EGAS50000000025

• Methylome profiling of Solitary fibrous tumor/Hemangiopericytoma (SFT/HPC) and a patient derived cell-line model

  Solitary fibrous tumor/Hemangiopericytoma (SFT/HPC) is a rare subtype of soft tissue sarcoma associated with NAB2-STAT6 gene fusions. This study established and characterized a novel SFT/HPC patient-derived cell line called SFT-S1 using the twist human methylome panel.

  Dataset EGAD50000000040

• Evaluating potential drug candidates for the treament of Henamgiopericytoma on patient derived cell line models

  Solitary fibrous tumor/Hemangiopericytoma (SFT/HPC) is a rare subtype of soft tissue sarcoma associated with NAB2-STAT6 gene fusions. This study established and characterized a novel SFT/HPC patient-derived cell line called SFT-S1. Potential drug candidates that could be repurposed for the treatment of SFT/HPC were screened. Screening was performed through RNA-Seq

  Dataset EGAD50000000039

• Whole genome sequencing of Sinonasal hemangiopericytoma and patient derived cell line model

  Solitary fibrous tumor/Hemangiopericytoma (SFT/HPC) is a rare subtype of soft tissue sarcoma associated with NAB2-STAT6 gene fusions. In this study, a novel SFT/HPC was characterized using whole genome sequencing.

  Dataset EGAD50000000038

• Single-cell multi-omic analysis of control and glioblastoma samples from the brain and border regions.

  Single-cell RNA-sequencing. Some samples were analyzed using CITE-Seq and samples were multiplexed and run on the same 10X reaction

  Dataset EGAD50000000045

• Origination of Ovarian Cancer is Dependent on Specific Aneuploidy Landscape

  Ovarian high-grade serous carcinoma (HGSC), a highly chromosomally instable malignancy, develops through sequential stages of precursors in fallopian tubes including histologically unremarkable but TP53 mutated clones (p53 signature) and serous tubal intraepithelial carcinoma (STIC). Here, we applied RealSeqS to assess aneuploidy in 127 microdissected tubal regions. We found nearly all p53 signatures lost the entire Chr17 chromosome, offering a “two-hit” mechanism of both TP53 and BRCA1 in BRCA1 germline mutation carriers. Proliferatively active STICs harbor gains of 19q12 (CCNE1), 19q13.2, 8q24 (MYCyc), or 8q arm, while proliferatively dormant STICs show 22q loss. Based on aneuploidy patterns in a training set (n= 67), we developed an algorithm, REAL-FAST, to classify NFTE and STICs into 5 clusters. In an independent validation set (n= 72), REAL-FAST was able to detect STIC/HGSC with high sensitivity and specificity. Taken together, REAL-FAST promises a molecular test for diagnosing HGSC precursor lesions based on specific aneuploidy patterns.

  Study EGAS00001007220

• Biallelic HMBS Inactivation Defines a Homogenous Clinico-Molecular Subtype of Hepatocellular Carcinoma

  Dataset EGAD00001009745

• Methylation profiling of sarcoma and TFCP2-rearranged rhadomyosarcoma samples

  Methylation profiling of 345 sarcoma and TFCP2-rearranged rhadomyosarcoma samples, using the approach described "Genomic, transcriptomic, functional, and mechanistic characterization of rhabdomyosarcoma with FUS-TFCP2 or EWSR1-TFCP2 fusions"

  Dataset EGAD00010002451

• CGMH-OCCC-WES

  CGMH-OCCC-WES data (tumor-normal paired) from 104 patients with ovarian clear cell carcinoma.

  Dataset EGAD50000000048

• Dataset for RNA sequencing of Glioblastoma samples

  This dataset contains 10x scRNA sequencing of glioblastoma samples. Sequencing was performed on a Illumina HiSeq 4000. The sequencing was always paired.

  Dataset EGAD50000000081

• Berlin Pediatric Sarcoma PDX Whole Exome Sequencing dataset

  Fastq files of 18 pediatric sarcoma PDX WXS samples. Each sample was sequenced on 2 Lanes.

  Dataset EGAD50000000065

• Pediatric Sarcoma PDX whole exome sequencing dataset

  Data access policy to access Pediatric Sarcoma PDX whole exome sequencing dataset

  Dac EGAC50000000051

• RNAseq and ATACseq data, derived from HGSC cell lines pre- and post- treatment with an epigenetic compound

  These data consist of transcriptome and chromatin accessibility data (RNAseq and ATACseq respectively) derived from five High-Grade Serous Ovarian Carcinoma (HGSC) cell lines. HGSC lines included PEO1, PEO4, PEA2, OVCAR5 and OVCAR8. Samples were taken pre- and post-treatment with a novel epigenetic compound, HKMTi-1-005, which targets the activity of two histone methyltransferases, EZH2 and G9a.

  Dataset EGAD50000000064

• HGSC lines: ATACseq and RNAseq, pre- vs post-treatment with HKMTi-1-005

  Formation of a DAC to support the deposition of RNAseq and ATACseq data, from pre- vs post-treatment high-grade serous ovarian carcinoma cell lines. The lines include PEO1, PEO4, PEA2, OVCAR5, OVCAR8, and the treatment includes use of a novel epigenetic drug called HKMTi-1-005.

  Dac EGAC50000000034

• DAC for "Integrated genetic analysis of primary CNS lymphoma"

  Dac EGAC00001003233

• DAC for glioblastoma studies

  Dac EGAC00001003461

• Whole genome bisulfite sequencing of prostate cancer samples upon oral pimonidazole administration

  Whole genome bisulfite sequencing was performed using laser-capture of microdissected specimen sections comparing PIMO positive and negative carcinoma areas. A hypoxia related methylation molecular signature was generated.

  Study EGAS50000000069

• Dataset for WGS and RNA melanoma samples

  This dataset contains WGS and RNA sequencing data for 4 melanoma samples. Sequencing was performed on Illumina Novaseq 6000 and Illumina HiSeq X. The sequencing was always paired.

  Dataset EGAD50000000093

• Dataset for central nervous system glioma samples

  This dataset contains VCF files for 124 central nervous system glioma samples for the study "Cerebrospinal fluid cfDNA sequencing for classification of central nervous system glioma"

  Dataset EGAD50000000085

• DAC for YCC Sarcoma

  Access can be granted by contacting Hyo Song Kim (hyosong77@yuhs.ac).

  Dac EGAC50000000046

• Tumor reactive γδ T cells contribute to a complete response to PD-1 blockade in a Merkel cell carcinoma patient

  Single cell RNA and TCR sequencing of γδ T cells FACS sorted from peripheral blood of two Merkel cell carcinoma patients

  Dataset EGAD50000000137

• Single-Cell RNA Sequencing of Primary High Grade Serous Carcinoma

  CD45- Single Cell RNA Sequencing data on 8 High Grade Serous Carcinoma Primary Tumors

  Dataset EGAD50000000098

• WES data from tumors and matching controls collected from 11 UTSW translocation renal cell carcinoma (tRCC) patients.

  This study investigates translocation renal cell carcinoma (tRCC) to identify mutations contributing to tRCC progression. The analysis relies on Whole Exome Sequencing (WES) data obtained from 11 patients treated at University of Texas Southwestern Medical Center (UTSW) affiliated hospitals, including Parkland Hospital and Children’s Medical Center. Tumor samples and their corresponding normal DNA were sequenced using the Illumina platform.

  Dataset EGAD50000000171

• RNA-Seq data from tumor samples collected from 12 UTSW translocation renal cell carcinoma (tRCC) patients.

  This study investigates differentially expressed genes associated with the progression of translocation renal cell carcinoma (tRCC) by analyzing RNA-Seq data from 23 tumor samples obtained from 12 patients. The samples were collected from affiliated hospitals of the University of Texas Southwestern Medical Center (UTSW), including Parkland Hospital and Children’s Medical Center. RNA isolated from tumor samples was sequenced using the Illumina platform.

  Dataset EGAD50000000172

• tRCC UTSW 2024 Cohort

  This submission contains the WES and RNA-Seq raw data for the study titled 'Comparative Genomics of a Translocation Renal Cell Carcinoma Mouse Model Reveals Molecular Mechanisms of Tumorigenesis.

  Dac EGAC50000000105

• Transcriptome of CD4+ T cells and CD8+ T cells in glioblastoma multiforme

  Patients included in this study were over 18 years of age and had a histology-confirmed diagnosis of glioblastoma multiforme (GBM). Exclusion criteria were the previous administration of any anti-tumor therapy including radiation therapy. All patients gave written informed consent. The study was approved by the local ethics committee (TUM Medical school) and conducted following the Declaration of Helsinki. During resection of the tumors, tumor tissue and tissue from normal appearing brain within the operative channel was collected. Blood was drawn during the surgical procedure. Single cell suspensions were prepared from the tumor tissue, the normal appearing brain, and the blood. CD4+ T cells and CD8+ T cells were sorted by flow cytometry. Only patients with a complete set of specimens (CD4+ tumor infiltrating lymphocytes (TIL), CD8+ TIL, CD4+ T cells from normal appearing brain, CD8+ T cells from normal appearing brain, blood-derived CD4+ and CD8+ T cells) containing a minimum of 1000 cells in each sorted sample were further analyzed (n=9). Total RNA was isolated from sorted cell populations using the RNAeasy Plus micro kit (Qiagen, 74034). Quality and integrity of total RNA was controlled on a Bioanalyzer 2100 (Agilent Technologies). Library preparation for bulk-sequencing of poly(A)-RNA was done as described previously (Parekh et al., 2016). Briefly, barcoded cDNA of each sample was generated with a Maxima RT polymerase (ThermoFisher Scientific, EP0742) using oligo-dT primer containing barcodes, unique molecular identifiers (UMIs) and an adaptor. Ends of the cDNAs were extended by a template switch oligo (TSO) and full-length cDNA was amplified with primers binding to the TSO-site and the adaptor. NEB UltraII FS kit was used to fragment cDNA. After end repair and A-tailing, a TruSeq adapter was ligated and 3'-end-fragments were finally amplified using primers with Illumina P5 and P7 overhangs. In comparison to previous descriptions (Parekh et al., 2016), the P5 and P7 sites were exchanged to allow sequencing of the cDNA in read 1 and barcodes and UMIs in read 2 to achieve a better cluster recognition. The library was sequenced on a NextSeq 500 (Illumina) with 59 cycles for the cDNA in read 1 and 16 cycles for the barcodes and UMIs in read 2. Data were processed using the published Drop-seq pipeline (v1.0) to generate sample- and gene-wise UMI tables (Macosko et al., 2015). Reference genome (GRCh38) was used for alignment. Transcript and gene definitions were used according to the Genecode Annotation Version 35.

  Study EGAS50000000156

• Transcriptome of CD4+ T cells and CD8+ T cells in glioblastoma multiforme

  Patients included in this study were over 18 years of age and had a histology-confirmed diagnosis of glioblastoma multiforme (GBM). Exclusion criteria were the previous administration of any anti-tumor therapy including radiation therapy. All patients gave written informed consent. The study was approved by the local ethics committee (TUM Medical school) and conducted following the Declaration of Helsinki. During resection of the tumors, tumor tissue and tissue from normal appearing brain within the operative channel was collected. Blood was drawn during the surgical procedure. Single cell suspensions were prepared from the tumor tissue, the normal appearing brain, and the blood. CD4+ T cells and CD8+ T cells were sorted by flow cytometry. Only patients with a complete set of specimens (CD4+ tumor infiltrating lymphocytes (TIL), CD8+ TIL, CD4+ T cells from normal appearing brain, CD8+ T cells from normal appearing brain, blood-derived CD4+ and CD8+ T cells) containing a minimum of 1000 cells in each sorted sample were further analyzed (n=9). Total RNA was isolated from sorted cell populations using the RNAeasy Plus micro kit (Qiagen, 74034). Quality and integrity of total RNA was controlled on a Bioanalyzer 2100 (Agilent Technologies). Library preparation for bulk-sequencing of poly(A)-RNA was done as described previously (Parekh et al., 2016). Briefly, barcoded cDNA of each sample was generated with a Maxima RT polymerase (ThermoFisher Scientific, EP0742) using oligo-dT primer containing barcodes, unique molecular identifiers (UMIs) and an adaptor. Ends of the cDNAs were extended by a template switch oligo (TSO) and full-length cDNA was amplified with primers binding to the TSO-site and the adaptor. NEB UltraII FS kit was used to fragment cDNA. After end repair and A-tailing, a TruSeq adapter was ligated and 3'-end-fragments were finally amplified using primers with Illumina P5 and P7 overhangs. In comparison to previous descriptions (Parekh et al., 2016), the P5 and P7 sites were exchanged to allow sequencing of the cDNA in read 1 and barcodes and UMIs in read 2 to achieve a better cluster recognition. The library was sequenced on a NextSeq 500 (Illumina) with 59 cycles for the cDNA in read 1 and 16 cycles for the barcodes and UMIs in read 2. Data were processed using the published Drop-seq pipeline (v1.0) to generate sample- and gene-wise UMI tables (Macosko et al., 2015). Reference genome (GRCh38) was used for alignment. Transcript and gene definitions were used according to the Genecode Annotation Version 35.

  Dataset EGAD50000000226

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 8b)

  Whole genome sequencing data of 19 high-grade serous carcinoma (HGSC) patients (48 samples) sequenced with MGISEQ-2000

  Dataset EGAD50000000177

• Single-cell Full Transcriptome RNA sequencing

  Single cell full transcriptome sequencing of CD19 CAR T-cell infusion products used for standard of care treatment for relapsed/refractory large B-cell lymphoma.

  Dataset EGAD50000000201

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 9 part 3)

  Whole genome sequencing data of 25 high-grade serous carcinoma (HGSC) patients (65 samples) sequenced with Illumina Novoseq 6000

  Dataset EGAD50000000211