5272 results for "*oma"

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• Arcagen – Adrenal and Thyroid cancers

  Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here were are submitting the dataset that contains NGS files in .BAM format from 103 patients with adrenocortical carcinoma, pheochromocytoma, anaplastic thyroid cancer, medullary thyroid cancer and radioiodine refractory thyroid cancer.

  Dataset EGAD50000000886

• Arcagen - Sarcoma

  Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here we are submitting the dataset that contain NGS files in .BAM format from 62 patients with undifferentiated sarcoma of the bone; aggressive angiomyxoma; alveolar soft part sarcoma; small round cell sarcoma without EWSR1 and CIC translocations; undifferentiated pleomorphic sarcoma; undifferentiated sarcoma; myxofibrosarcoma; or sarcoma with an incidence of less than 1 per 10 million.

  Dataset EGAD50000000902

• Transcriptome analysis of 32 pheochromocytoma and paraganglioma samples

  Pheochromocytomas and sympathetic paragangliomas (PPGL) are rare neuroendocrine tumors derived from chromaffin tissue of the adrenal medulla and sympathetic paraganglia, respectively. There is at the moment a lack of accurate biomarkers to predict the biologic behavior of a PPGL. The aim of this study was to investigate the biological behavior of localized and metastatic PPGL by comparing the genomic and transcriptomic landscapes of localized and metastatic PPGL, including PPGL samples with a non-metastatic phenotype at initial diagnosis that developed metachronous metastases during follow-up. The dataset presented here contains the RNA-seq data of 32 tumor samples (n=17 localized, n=15 metastatic).

  Study EGAS50000000988

• RNA-seq of 32 pheochromocytoma and paraganglioma samples

  This dataset contains RNA-seq data of 32 pheochromocytoma and paraganglioma samples (n=17 localized, n=15 metastatic). A NEXTflex Rapid Directional qRNA library preparation kit (Perkin Elmer) was used for RNA quantification. Samples were sequenced on a NextSeq 500 sequencer (Illumina). The files have the FASTQ format (paired; 2 x 32 files).

  Dataset EGAD50000001447

• Germline variants in patients with five cutaneous melanoma

  Skin melanoma arises from a complex interplay between UV radiation exposure and inherited genetic susceptibility. A subset of invidiuals experience multiple independent tumours, suggestingh a stronger underlying genetic component. In this study, we investigate the germline contribution to melanoma development in individuals diagnosed with five or more primary melanomas.

  Study EGAS50000001361

• RASA2 deletion rescues immune synapse dysfunction, enhancing CAR T cell efficacy against DMGs

  Methods: We compared the efficacy of B7-H3 CAR T-cells between SJ-DIPGX7c (DMG) and U87-MG (adult glioblastoma) patient-derived cell lines and showed impaired efficacy both in vitro and in vivo. We performed live-cell imaging and single-cell RNA sequencing to investigate deficiencies in immune synapse (IS) formation between CAR T-cells and DMGs. Lastly, we genetically deleted RASA2, a negative regulator of T cell activation, and evaluated the resulting impact on IS formation and quality, as well as in vitro and in vivo functionality. Sample preparation and sequencing For single cell RNA sequencing, two sets of co-culture arrays were prepared: (1) 2.5×106 Ctrl T cells were co-cultured with 2.5×106 tumor cells (SJ-DIPGX7c, SJ-DIPGX9c, SJ-DIPGX37c, D425, ST1, and U87-MG) and (2) 1×106 CAR T-cells were co-cultured with 4×106 tumor cells (SJ-DIPGX7c, SJ-DIPGX9c, SJ-DIPGX37c, D425, ST1, and U87-MG). Both co-culture arrays were incubated at 37°C 5% CO2 for 16 hours. Co-cultures were enzymatically digested with Accutase (Thermofisher; A1110501) for 10 min at room temperature, and cells were filtered by using Miltenyi dead cell removal kit (Miltenyi; 130-090-101). Highly viable cells were recovered and prepared using the Fixation Kit (PARSE Bioscience) according to manufacturer ‘s protocol. After fixation, the cell count was determined by using a Neubauer hemocytometer. A total of 1.14×105 cells from 12 coculture conditions were used, resulting in approximately 9.5×104 cells per condition. These cells were subjected to the Single Cell Whole Transcriptomic kit v2 (PARSE Bioscience) for library construction. Libraries were subsequently sequenced using the Illumina NovaSeq 6000 platform, generating paired-end 200 bp reads.

  Dataset EGAD50000002785

• Carcinoma of Unknown Primary (CUP): A comparison across tissue and liquid biomarkers (CUP-COMP) study

  The Carcinoma of Unknown Primary (CUP): A comparison across tissue and liquid biomarkers (CUP-COMP) study (NCT: NCT04750109) was sponsored by The Christie NHS Foundation Trust and funded by Innovate UK.

  Study EGAS50000001821

• Plasma cell‐free transcriptome profiling in chronic liver disease

  RNAseq data from plasma samples corresponding to a Grand Challenge Programme project to find biomarkers for liver disease, including NAFLD, NASH, Cirrhosis and Hepatocelullarcarcinoma. A template of the DTA and DAA files is available in the following zenodo repository https://doi.org/10.5281/zenodo.19047046.

  Dac EGAC50000000360

• CUP-COMP DAC

  The Carcinoma of Unknown Primary (CUP): A comparison across tissue and liquid biomarkers (CUP-COMP) study (NCT: NCT04750109) was sponsored by The Christie NHS Foundation Trust and funded by Innovate UK. Data generated include: A) Whole genome sequencing* of paired germline blood and tumour tissue B) Whole genome sequencing *of germline blood only C) Next-generation sequencing of circulating tumour DNA (FoundationOne Liquid CDX) D) Next-generation sequencing of tumour tissue (FoudationOne CDX) *Whole genome sequencing - germline sample at 30x coverage and somatic sample at 75x coverage performed by Illumina NovaSeq 6000 instrument in the 150bp PE read mode.

  Dac EGAC50000000610

• DAC policy The Kids Research Institute Australia

  DAC policy related to the study: Visium CytAssist Spatial Gene Expression analysis for FFPE glioblastoma samples. Committee: Raelene Endersby raelene.endersby@thekids.org.au Anya Jones anya.jones@thekids.org.au Emma Stone emma.stone@thekids.org.au

  Dac EGAC50000000734

• Multi-omic signatures in multiple myeloma

  Multiple myeloma (MM) is a genetically and clinically heterogeneous plasma-cell malignancy. Quadruplet therapy, combining anti-CD38 antibodies, immunomodulatory drugs, proteasome inhibitors, and dexamethasone, is now the standard induction treatment for newly diagnosed multiple myeloma (NDMM). To gain a comprehensive view of molecular changes in MM and their association with treatment response in this new standard care, we performed a multi-omic study of 423 NDMM from the MIDAS trial.

  Study EGAS50000001573

• Comprehensive gene expression analysis in renal cell carcinoma

  The goal is to elucidate the mechanisms of tumor initiation and progression, identify gene signatures associated with drug efficacy and adverse effects, and develop more accurate and reliable diagnostic methods and therapeutic targets.

  Study JGAS000913

• Investigating adaptive immune responses in MM patients treated with belantamab mafodotin

  We used single-cell CITE + VDJ sequencing and CapTCRseq to investigate the adaptive immune response, specifically the role of T cells and tumor-reactive gamma delta T cells, in multiple myeloma patients treated with belantamab mafodotin from the on-going Algonquin clinical trial (NCT03715478).

  Study EGAS50000001889

• Identification of broadly tumor-reactive γδ TCRs from multiple myeloma

  We used single-cell CITE + TCR sequencing to identify tumor-reactive gamma delta TCR sequences from multiple myeloma patient bone marrow samples.

  Study EGAS50000001888

• Single-cell sequencing of Belamaf treated MM patients

  Single-cell CITE + VDJ sequencing of bone marrow samples from relapsed/refractory multiple myeloma patients prior to and following one round (28 days) of treatment with belantamab mafodotin from the on-going Algonquin clinical trial (NCT03715478).

  Dataset EGAD50000002742

• Targeted bulk TCR sequencing of Belamaf treated MM patients

  Targeted bulk TCR sequencing from peripheral blood mononuclear cell (PBMC) and plasma cell-free DNA (cfDNA) samples from relapsed/refractory multiple myeloma patients being treated with belantamab mafodotin from the on-going Algonquin clinical trial (NCT03715478).

  Dataset EGAD50000002743

• Targeted bulk TCR sequencing of MM patients

  Targeted bulk TCR sequencing of longitudinally collected peripheral blood buffy coat samples from two multiple myeloma patients.

  Dataset EGAD50000002744

• Single-cell sequencing MM patients

  Single-cell CITE + VDJ sequencing of bone marrow samples collected from a mix of diagnosis and relapsed/refractory multiple myeloma patients undergoing standard of care treatment which were used for the development of PreGame.

  Dataset EGAD50000002745

• Single-cell tracking reveals tumor-reactive T cell plasticity during melanoma TIL therapy

  Using 10x genomics 5' Single Cell Immune Profiling kit for single-cell RNA and TCR sequencing, we tracked the transcriptional alterations and fate decisions of tumor-reactive T cells through longitudinal sample obtained from patients undergo-ing TIL therapy. We performed single-cell RNA and single-cell TCR sequencing (scRNA-seq and scTCR-seq) of CD45+ CD3+ tumor-infiltrating lymphocytes from the baseline tumor (pre-ACT), during and after TIL expansion protocol (intermediate product and TIL product, respectively), as well as after cell transfer in blood (PBMC 7dpt) or from metastatic lesions (post-ACT). Samples were derived from patients enrolled in a phase I clinical trial (NCT04165967) that inves-tigated ACT-TIL therapy in combination with nivolumab therapy for metastatic melanoma. Additionally, we established a tumor reactivity assay by co-culturing TIL products with autologous single-cell suspensions of tumor material or short cultured primary melanoma cell lines derived from the tumor material. Reactive T cells were identified by IFNɣ and TNF⍺ expression and sorted to sequence TCRβ genes.

  Study EGAS50000000967

• Single cell RNA and TCR sequencing of adoptive cell transfer using tumor infiltrating lymphocyte.

  This dataset contains two experiments. 1) Single cell RNA and TCR sequencing of tumor infiltrating lymphocites from 7 patients across 5 timepoints: from the baseline tumor (pre-ACT, 7), during and after TIL expansion protocol (intermediate product and TIL product, respectively, 7 each), as well as after cell transfer in blood (PBMC 7dpt, 7) or from metastatic lesions after therapy (post-ACT, 1 patient with one leason and 4 patient with two lesions). Samples were derived from patients with metastatic melanoma which were enrolled in a phase I clinical trial (NCT04165967). The raw reads are available as compressed fastq files. 2) TCRb sequences of the bulk TIL product or TCRb reads derived from an in vitro coculturing assay of TIL product with autologous tumor material and subsequent isolation of IFNɣ and TNF⍺ positive T cells. For some patients multiple autologous tumor material sources could be tested including pre-ACT, post-ACT or tumor lesion derived cell lines. The raw reads are available as compressed fastq files.

  Dataset EGAD50000001408

• MDACC GBM Data Access Committee

  Retrospective global-omics analyses of an anti-PD-L1 treated cohort of newly diagnosed glioblastoma.

  Dac EGAC50000000477

• Transcriptomic characterization of breast cancer cells in a luminal-hybrid state

  Background: We have previously identified a subset of early human breast epithelial progenitors that, despite their luminal affiliation, are enriched for basal markers, including CD146 and keratin 14 (K14). This simultaneous presence of both lineages thus defines a luminal-hybrid state. As luminal progenitors are generally proposed as a cell of origin of breast carcinoma, we examined how cells within this luminal hybrid state relate to breast cancer malignancy. Findings: We first analyzed two patient-derived xenograft (PDX)-derived breast cancer cell lines by dividing cell populations based on CD146-expression by FACS and found that this marker defines a cellular state with increased expression of a selection of luminal hybrid markers, including KRT14. When subjecting the sorted cells to in vitro transwell filter analysis, CD146HI/K14HI luminal hybrid state cells were less invasive. Knocking out either CD146- or K14-expression by CRISPR/Cas9 on a CD146/K14-positive background did not affect the in vivo tumorigenic potential, nor the differentiation state. However, when subjecting the respective sorted populations to a soft agar colony formation assay or as in vivo xenografts, CD146HI/K14HI cells exhibited more growth and metastasis, suggesting that the luminal hybrid state contributes to malignant properties. Conclusions: Our findings demonstrate that multiple components of the luminal hybrid state provide distinct properties to tumor malignancy. The fact that breast cancer can reflect a significant part of the original breast gland differentiation hierarchy may add weight to the argument that the search for novel strategies for treating basal-like breast cancer should include manipulation of cellular differentiation states.

  Dataset EGAD50000002857

• Transcriptomics analysis of biopsy and resected material from HCC and matched non-tumor

  We hypothesize that pre-analytical handling such as surgery procedure and cold ischemia have significant effects on omics data derived from liver and liver tumor tissue that may confound classification of tumor subtypes of hepatocellular carcinoma. This study is aimed at revealing such effects by comparing resection samples with patient matched biopsy samples in a multi-omics analysis.

  Study EGAS50000001602

• FGFR2b Prevalence in Biliary Tract Cancers

  Patients with intrahepatic cholangiocarcinoma (iCCA) often have limited effective treatment options after first-line therapy. Actionable driver mutations are present in only a small subset of patients with iCCA; therefore, therapeutic strategies such as antibody–drug conjugates (ADCs) that do not exclusively rely on presence of specific oncogenic mutations may be particularly attractive. However, there is a critical lack of knowledge regarding prevalent and actionable cell-surface targets in iCCA. We applied a novel isoform-resolved proteogenomic discovery framework for profiling cell surface targets and identified FGFR2b, a splicing isoform of FGFR2, as a therapeutic target in iCCA that is highly-expressed, tumor-enriched, localized to the membrane, and associated with prognosis. FGFR2b is the dominant isoform in iCCA, and it was present in all patients with FGFR2 fusions. These findings provide a strong rationale for further research and therapeutic development of FGFR2b-targeted biologics, including therapeutic antibodies and ADCs, in iCCA.

  Study EGAS50000002012

• How to use the EGA search box

  Tips on how to search Tip 1: Use simple and clear keywords No matter what you are searching: a study, a dataset or a committee; the search will look recursively in all the fields of all the entities in the database. Thus, there is no need to specify which type of data you are looking for. Try to use single and specific words and avoid long and complex expressions. Try it out: cancer rna-seq instead of cancer studies using RNA-seq. Tip 2: Narrow your results using search operators Search operators give you more control over the results. They manage the way keywords are used to restrict the search. By default, the search engine applies an AND operator when a space is inserted between two words. Include OR (capitalised) between the keywords in your search in order to show pages containing either term or both terms. Try it out: Search all the studies based on transcriptomic data, including those ones that use RNA sequencing to obtain this data. Tip 3: Do not worry about details The search engine automatically checks for the most common spelling mistakes of given words and suggests the use of the correct version. It also ignores capital letters and most punctuation so none of these mistakes will hamper the accuracy of your search. Besides, when using a combination of words, the search engine will suggest similar combinations with a higher number of results. Tip 4: Use words that are likely to appear within the text Unfortunately, our search engine is not able to interpret words yet. Therefore, words which do not appear in the body text will not return any result despite them denoting a group of present terms. For instance, if we are looking for studies focused on neurological diseases, using neurological as a keyword would not be the best option. Instead, you should use the name of each neurological disease such as bipolar disorder, schizophrenia or depression. In the same fashion, incomplete words do not return any result. Please include an asterisk (*) before and/or after the incomplete word in order to allow for partial matching. Try it out: canc* instead of canc(without wildcard). Also you can try out *oma, or even *geneti* Tip 5: Narrow down your search by filtering the results You can filter the results by the following options: general, study, and dataset. Under these categories, you will find more sub-filters to refine your search. Make sure that you click on Apply in order to filter the results. You can clear the filtering anytime. Advanced Searches Additional example queries: Search for DUO:0000005 and word lung example query Or write: DUO:0000005 AND lung Search for an exome sequencing study in dbGaP example query Or write: study_type: "exome sequencing" AND repository:dbGaP Search for an exome sequencing study in EGA example query Or write: study_type: "exome sequencing" AND repository:ega Search for a dataset that is at the EGA beacon and with type "Whole genome sequencing" and with number of samples between 100 and 2000 and with technology "Illumina HiSeq 2000" example query Or write: is_in_beacon:true AND dataset_types:"Whole genome sequencing" AND samples:[100 TO 2000] AND dataset_technologies:"Illumina HiSeq 2000" Search for PMIDS example query Or write: pmids:32025007

  Documentation discovery/metadata/search-box