5495 results for "*oma"

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• Blood sample of serious ovarian carcinoma patient

  Blood sample of serious ovarian carcinoma patient

  Dataset EGAD00001000140

• Osteosarcoma Whole Genome

  Osteosarcoma Whole Genome

  Dataset EGAD00001000147

• Subgroup-specific structural variation across 1,000 medulloblastoma genomes

  Subgroup-specific structural variation across 1,000 medulloblastoma genomes

  Dataset EGAD00001000158

• Whole Genome Sequencing accompanying Genetic landscape of pediatric Rhabdomyosarcoma

  Whole Genome Sequencing accompanying Genetic landscape of pediatric Rhabdomyosarcoma.

  Dataset EGAD00001000164

• Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma

  Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma

  Dataset EGAD00001000001

• Osteosarcoma Sequencing

  Osteosarcoma Sequencing

  Dataset EGAD00001000007

• Familial Melanoma Sequencing

  Familial Melanoma Sequencing

  Dataset EGAD00001000016

• ICGC Germany PedBrain Medulloblastoma Pilot_2_LM

  ICGC Germany PedBrain Medulloblastoma Pilot_2_LM

  Dataset EGAD00001000027

• Pancreatic adenocarcinoma QCMG 20110901

  Pancreatic adenocarcinoma QCMG 20110901

  Dataset EGAD00001000049

• UK10K_RARE_COLOBOMA REL-2012-11-27

  UK10K_RARE_COLOBOMA REL-2012-11-27

  Dataset EGAD00001000307

• Whole genome sequencing of matched germline-tumour samples in follicular lymphoma

  Whole genome sequencing analysis was performed on 6 patients within matched germline, follicular lymphoma and transformed follicular lymphoma.

  Dataset EGAD00001000292

• Molecular characterization of invasive lobular carcinoma

  Invasive lobular carcinoma (ILC) is the second most common histological subtype of breast cancer accounting for 10-15% of cases. ILC differs from invasive ductal carcinoma (IDC)with respect to epidemiology, histology, and clinical presentation. Moreover, ILC is lesssensitive to chemotherapy, more frequently bilateral, and more prone to form gastrointestinal, peritoneal, and ovarian metastases than IDCs. In contrast to IDC, the prognostic value ofhistological grade (HG) in ILC is controversial. One of the three major components of histological grading (tubule formation) is missing in ILC which hinders the process of gradingin this histological subtype and results in the classification of approximately two thirds of ILC as HG 2.Over the last decade, a number of gene expression signatures have shed light onto breast cancer classification, allowing breast cancer care to become more personalized. Withrespect to the management of estrogen receptor (ER)-positive breast cancer, several gene expression signatures provide prognostic and/or predictive information beyond what is possible with current classical clinico-pathological parameters alone. Nevertheless, most studies using gene expression signature have not considered different histologic subtypesseparately. Recently, a comprehensive research program has elucidated some of the biological underpinnings of invasive lobular carcinoma. Genetic material extracted from 200 ILC tumor samples were studied using gene expression profiling and identified ILCmolecular subtypes. These proliferation-driven gene signatures of ILC appear to have prognostic significance. In particular, the Genomic Grade (GG) gene signature improved upon HG in ILC and added prognostic value to classic clinico-pathologic factors. In addition this study demonstrated that most ILC are molecularly characterized as luminal-A (~75%)followed by luminal-B (~20%) and HER2-positve tumors (~5%). Moreover, we investigated the prognostic value of known gene signatures/ gene modules in the same cohort of ILC. As a second step within the scope of this project, we aim to investigate the interactionsbetween somatic ILC tumor mutations to observed transcriptome findings. To this end, we aim to perform somatic mutation analysis for the ILC tumors for which Affymetrix gene expression profiling is available. To this end, we will use a gene screen assay, which specifically interrogates the mutational status of a few hundreds of cancer genes. We believe that this pioneering effort will be fundamental for a tailored treatment of ILC withimprovement in patients' outcome.

  Dataset EGAD00001000288

• Whole genome analyses of the childhood cancer neuroblastoma

  Neuroblastomas are tumors of peripheral sympathetic neurons and are the most common solid tumor in children. To determine the genetic basis for neuroblastoma we performed whole-genome sequencing (6 cases), exome sequencing (16 cases), genome-wide rearrangement analyses (32 cases), and targeted analyses of specific genomic loci (40 cases) using massively parallel sequencing. On average each tumor had 19 somatic alterations in coding genes (range, 3-70). Among genes not previously known to be involved in neuroblastoma, chromosomal deletions and sequence alterations of chromatin remodeling genes, ARID1A and ARID1B, were identified in 8 of 71 tumors (11%) and were associated with early treatment failure and decreased survival. Using tumor-specific structural alterations, we developed an approach to identify rearranged DNA fragments in sera, providing personalized biomarkers for minimal residual disease detection and monitoring. These results highlight dysregulation of chromatin remodeling in pediatric tumorigenesis and provide new approaches for the management of neuroblastoma patients.

  Dataset EGAD00001000282

• SCAT_Osteosarcoma_Validation

  This experiment is to validate putative somatic substitutions and indels identified in an exome screen of ~50 osteosarcoma tumour/normal pairs. It is the first stage in our ICGC commitment to study osteosarcoma. The validation process is an important component of our analysis to clarify the data prior to looking for evidence of new cancer genes, or subverted pathways important in the development of cancer. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001000280

• Data set for Whole-genome-Sequencing of adult medulloblastoma

  Data set for Whole-genome-Sequencing of adult medulloblastoma

  Dataset EGAD00001000275

• UK10K_RARE_COLOBOMA REL-2012-07-05

  UK10K_RARE_COLOBOMA REL-2012-07-05

  Dataset EGAD00001000206

• Neuroblastoma samples (Analyses_vcf files)

  Neuroblastoma samples (Analyses_vcf files)

  Dataset EGAD00001000202

• Neuroblastoma samples

  Neuroblastoma samples

  Dataset EGAD00001000196

• UK10K_RARE_COLOBOMA REL-2012-01-13

  UK10K_RARE_COLOBOMA REL-2012-01-13

  Dataset EGAD00001000179

• Identification of SPEN as a novel cancer gene and FGFR2 as a potential therapeutic target in adenoid cystic carcinoma

  Identification of SPEN as a novel cancer gene and FGFR2 as a potential therapeutic target in adenoid cystic carcinoma

  Dataset EGAD00001000175

• UK10K_RARE_COLOBOMA REL-2012-02-22

  UK10K_RARE_COLOBOMA REL-2012-02-22

  Dataset EGAD00001000185

• SCAT osteosarcoma sequencing

  SCAT osteosarcoma sequencing

  Dataset EGAD00001000107

• Meningioma Exome

  Meningioma Exome

  Dataset EGAD00001000099

• Pancreatic adenocarcinoma QCMG 20120201

  Pancreatic adenocarcinoma QCMG 20120201

  Dataset EGAD00001000096

• Glioma cell lines rearrangement screen

  Glioma cell lines rearrangement screen

  Dataset EGAD00001000090

• Splenic Marginal Zone Lymphoma with villous lymphocytes exome sequencing

  Splenic Marginal Zone Lymphoma with villous lymphocytes exome sequencing

  Dataset EGAD00001000081

• Integrative Oncogenomics of Multiple Myeloma

  Integrative Oncogenomics of Multiple Myeloma

  Dataset EGAD00001000074

• Kaposi sarcoma exome

  Kaposi sarcoma exome

  Dataset EGAD00001000071

• Acral melanoma study whole genomes

  Acral melanoma study whole genomes

  Dataset EGAD00001000060

• Acral melanoma study whole exomes

  Acral melanoma study whole exomes

  Dataset EGAD00001000061

• EGAD00000000119

  Genotypes from cell lines derived from breast carcinoma tissue

  Dataset EGAD00000000119

• EGAD00000000114

  Whole transcriptome sequence data from 18 ovarian clear-cell carcinoma samples and one TOV21G ovarian clear-cell carcinoma cell line

  Dataset EGAD00000000114

• EGAD00000000052

  Sequencing data from natching Pancreatic Carcinoma samples

  Dataset EGAD00000000052

• EGAD00000000051

  Sequencing data from matching Renal Carcinoma samples

  Dataset EGAD00000000051

• Exome data from a high-density melanoma family

  This dataset contains whole-exome sequence data from four individuals in a high-density melanoma family. Sequences were aligned against human reference genome HG19 using BWA.

  Dataset EGAD50000002153

• Large deletion predisposes to familial melanoma

  Mutations in CDKN2A and CDK4 account for ~40% of multi-case melanoma families, with another ~10% attributable to BAP1 and genes associated with telomere maintenance. Thus, a substantial proportion of the genetic risk underlying familial melanoma remains unexplained.

  Study EGAS50000001496

• Whole-genome data from a high-density melanoma family

  This dataset contains whole-genome sequence data from individuals in a high-density melanoma family. Sequences were aligned againt the human reference genome HG19 using BWA.

  Dataset EGAD50000002154

• Predictive value of genomic, transcriptomic and epigenetic biomarkers in patients with recurrent and/or metastatic HNSC treated with PD-1/PD-L1 inhibitors

  Immune checkpoint inhibitors have improved survival in several solid tumors, including head and neck squamous cell carcinoma (HNSCC). However, current use of PD-1/PD-L1 inhibitors relies on PD-L1 expression, which does not reliably predict patient response. Therefore, better predictive biomarkers are needed to identify which patients benefit from immunotherapy. Previous studies suggest that genomic and transcriptomic features may help predict treatment response. This study aims to integrate genomic, transcriptomic, and methylomic data from 31 patients with recurrent/metastatic HNSCC to identify new biomarkers of resistance to anti-PD-1/PD-L1 therapy.

  Study EGAS50000001746

• A tumor-associated photoreceptor signature unifies distinct central nervous system malignancies

  Pineoblastoma is a clinically aggressive childhood brain tumor composed of distinct molecular subgroups with divergent driver genes, demographics, and clinical outcomes. To identify developmental origins and mechanisms governing disease pathogenesis, we derive single-cell transcriptomes from pineal parenchymal tumors, aligning malignant cells with our atlas of pineal gland development to retrace cellular origins. Integrative computational analyses maps pineoblastoma origins to transient cycling pinealocyte progenitors during development. Lineage-specific perturbation of suspected drivers in the early pineal gland yields preclinical models representative of consensus molecular subgroups. Multi-omic characterization of patient tumors and these models uncovers a tumor-associated photoreceptor signature common to pineoblastoma, retinoblastoma, and Group 3 medulloblastoma. Transcriptional activity of this signature within respective cellular origins establishes a developmental basis for molecular similarities between entities. Photoreceptor signature constituents are selective dependencies across these anatomically distinct central nervous system malignancies, motivating future studies evaluating developmentally encoded programs of malignancy as potential therapeutic liabilities.

  Study EGAS50000001457

• ecDNA copy number heterogeneity

  Neuroblastoma exhibits significant inter- and intra-tumour genetic heterogeneity and varying clinical outcomes. Here, we used DNA and RNA sequencing from the same single cells in cell lines and neuroblastoma patients to investigate these effects. We reveal extensive intercellular ecDNA copy number heterogeneity. We also provide direct evidence of how this heterogeneity influences the expression of cargo genes, including MYCN and its downstream targets, and the overall transcriptional state of neuroblastoma cells.

  Study EGAS50000000509

• Single cell RNA sequencing of relapsed/refractory multiple myeloma

  Study EGAS00001004805

• Whole-exome sequencing of HPV-negative HNSCC to identify pathway alterations

  This study applies whole-exome sequencing (WES) to characterize genomic alterations in HPV-negative head and neck squamous cell carcinoma. Tumors were analyzed to derive pathway-level alteration scores, enabling stratification into Hedgehog-high and Hedgehog-low groups. The genomic data were integrated with imaging, transcriptomic, and functional analyses to investigate tumor-intrinsic Hedgehog signaling. The study aims to identify molecular features associated with biologically distinct tumor subgroups.

  Study EGAS50000001550

• Multi-omics datasets (WES, RNA-seq) of mesothelioma MIST2 study, a phase II CDK4/6 (abemaciclib) clinical trial

  This study comprises 26 mesothelioma patients with whole exome and RNA sequencing, to to assess potential sensitivity to CDK4/6 inhibition in p16ink4A-negative mesothelioma.

  Study EGAS50000001823

• Multi-omics datasets (WES, RNA-seq) of mesothelioma NERO study, a phase II PARP inhibitor clinical trial

  Niraparib Efficacy in Patient With Unresectable Mesothelioma, NERO, this randomised phase II trial is designed to evaluating the efficacy of the PARP inhibitor Niraparib versus active symptom control.

  Study EGAS50000001825

• RNA sequencing dataset of of explants treated with PRX3 inhibitor thiostrepton in mesothelioma

  This dataset includes 66 RNA sequences datasets of mesothelioma explants under three conditions: baseline, untreated control and thiostrepton treatment.

  Dataset EGAD50000002650

• Transcriptome profiling of HNSCC treated with PD1/PDL1

  Immune checkpoint inhibitors have improved survival in several solid tumors, including head and neck squamous cell carcinoma (HNSCC). However, current use of PD-1/PD-L1 inhibitors relies on PD-L1 expression, which does not reliably predict patient response. Therefore, better predictive biomarkers are needed to identify which patients benefit from immunotherapy. Previous studies suggest that genomic and transcriptomic features may help predict treatment response. This study aims to integrate genomic, transcriptomic, and methylomic data from 31 patients with recurrent/metastatic HNSCC to identify new biomarkers of resistance to anti-PD-1/PD-L1 therapy.

  Dataset EGAD50000002506

• Transcriptomic profiling of Hedgehog-high and Hedgehog-low HPV-negative HNSCC

  This study investigates transcriptomic differences between Hedgehog-high and Hedgehog-low HPV-negative head and neck squamous cell carcinoma (HNSCC). Tumors were stratified based on pathway-level genomic alteration scores and imaging-associated Hedgehog activity. RNA sequencing was performed on archival FFPE tumor samples to assess pathway activation and transcriptional programs associated with Hedgehog signaling. The dataset supports integrative radiogenomic and functional analyses of tumor-intrinsic signaling pathways.

  Study EGAS50000001549

• Anaplastic Large Cell Lymphoma Tumor Gene Expression Profiling

  Anaplastic large cell lymphoma tumor RNA-Seq

  Study EGAS50000001011

• Anaplastic Large Cell Lymphoma Gene Expression

  Anaplastic Large Cell Lymphoma Gene Expression Study

  Dataset EGAD50000001478

• Mayo Anaplastic Large Cell Lymphoma Gene Expression

  Dac EGAC50000000633