5495 results for "*oma"

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• 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