5524 results for "*oma"

in 33.20 milliseconds.

• Bladder cancer subtyping study across 4 atezo clinical trials

  Checkpoint inhibitors targeting the PD-1/PD-L1 interaction have revolutionized cancer therapy across many indications. In urothelial carcinoma (UC), several PD-1/PD-L1 inhibitors have been approved for treatment in localized and metastatic disease. While these agents provide durable clinical benefit in a number of patients, the majority do not respond. A better understanding of the molecular mechanisms underlying response and resistance is needed to tailor existing treatments and develop new therapeutics in UC. We profiled tumors from 2803 patients from four late-stage randomized clinical trials evaluating the PD-L1 inhibitor atezolizumab in metastatic UC and muscle-invasive bladder cancer by RNA-seq, a targeted DNA panel, immunohistochemistry, and digital pathology. A machine learning algorithm identified four UC transcriptional subtypes, representing luminal desert, stromal, immune, and basal tumors. Overall survival benefit from atezolizumab over standard-of-care was observed in immune and basal tumors (45% of patients). Digital pathology showed enrichment of human interpretable features in specific molecular subtypes, and a self-supervised approach could classify molecular subtypes from H&E slides with high accuracy, suggesting that machine-based image interpretation could accelerate tumor molecular profiling. Somatic alterations showed limited capacity to identify responders. Finally, mechanisms of benefit with PD-L1 blockade appeared different in immune and basal tumors, with immune tumors benefiting from B/plasma cell enrichment, while basal tumors benefited from granulocyte enrichment. This study represents the largest integration of UC molecular and curated clinical data in randomized clinical trials to date, paving the way for prospective clinical studies tailoring treatment regimens to specific tumor molecular subtypes in UC and other indications.

  Study EGAS50000000497

• Nanopore Amplicon Sequencing data of 33 patient tumor and one primary cell line samples - all uveal melanoma

  Nanopore Amplicon Sequencing data of 4 amplicons in the GNA11, GNAQ, SF3B1 and SRSF2 genes in 33 uveal melanoma patient tumor samples and one primary cell line sample. Sequencing was performed on a MinION platform (Mk1B) using the SQK-RBK114.24 kit multiplexing 24 samples per flow cell. Basecalling was performed using the dna_r10.4.1_e8.2_400bps_sup@v4.3.0 model and dorado v.0.6.2.

  Study EGAS50000002137

• Cell-free DNA methylation profiling in classic Hodgkin lymphoma using cfEM-seq

  Genome-wide DNA methylation profiling using cfEM-seq and Ultima Genomics sequencing in classic Hodgkin lymphoma. The study includes plasma-derived cell-free DNA from patients with classic Hodgkin lymphoma together with classic Hodgkin lymphoma and anaplastic large cell lymphoma cell lines. The deposited data comprise original sequencing CRAM files and associated sequencing-provider QC metadata.

  Study EGAS50000002038

• Single-cell characterization of anti-LAG3+anti-PD1 treatment in melanoma patients

  Study EGAS00001005580

• Orthotopic Patient-Derived Organoid Model Reveals Tumor Microenvironment-Dependent Phenotypes in Lung Squamous Cell Carcinoma

  This dataset contain NGS raw fastq data from 27 samples, including 5 WES, 4 WGS, 1 shallow WGS(sWGS) and 17 RNAseq.

  Dataset EGAD50000002915

• Multi-modal single-cell sequencing dataset for large B cell lymphoma samples

  This dataset contains raw FASTQ sequencing data from single-cell transcriptome, BCR, TCR, and HTO profiling of 72 hashtag-multiplexed large B-cell lymphoma samples, generated using the 10x Genomics Chromium Single Cell 5′ workflow and sequenced on an Illumina NextSeq 2000.

  Dataset EGAD50000002966

• scRNA-seq data from HNSCC samples before and after BA treatment

  Tumor biopsies from 6 head and neck small cell carcinoma (HNSCC) berfore and 3-weeks after bintrafusp-alfa treatment were analyzed using single-cell RNA sequencing (droplet-based 3' 10x genomics). Library were sequenced using the Chromium plateform. Generated fastq files were processed using cellranger to generat the count matrices.

  Dataset EGAD50000003043

• Low-coverage whole-genome sequencing

  Low-coverage whole-genome sequencing from CSF samples collected across timepoints from medulloblastoma patients

  Dataset EGAD50000003058

• Gut microbiota biomarkers associated with Multiple Myeloma: The REMNANT Study

  This dataset contains longitudinal shotgun metagenomic sequencing of stool samples collected across two time points from patients with newly diagnosed multiple myeloma (153 patients , 306 samples total). The suffix "scr" denotes the screening time point, when patients were enrolled in the study and samples were collected before initiation of treatment. Following inclusion, patients received standard multiple myeloma treatment according to Norwegian clinical guidelines. The treatment consisted of four cycles of induction therapy with bortezomib, lenalidomide, and dexamethasone (VRd), followed by autologous stem cell transplantation (ASCT) for eligible patients, and subsequently four cycles of consolidation therapy with VRd. The second stool sample was collected after completion of consolidation therapy. Therefore, samples with the suffix "post_con" represent the post-consolidation time point. This is the time point which MRD was measured. Samples were sequenced on the Illumina NovaSeq X platform. Raw data consists of paired FASTQ files per sample.

  Dataset EGAD50000003072

• Norwegian Sarcoma Consortium Tumor-Normal pairs Whole Exome Sequencing dataset 2026

  The dataset consist of Whole Exome Sequencing results from 287 sarcoma patients in the NoSarC cohort, from which matched sarcoma tumor samples and normal blood samples where pairwise analysed together using the Dragen v.3.7 analysis framework. The dataset contains fastq files as used for input to the analysis and the Dragen 3.7 vcfs outputs for germline mutations, somatic mutations and copy number variants (and their corresponding index files). The phenotype information provided includes: age, sex, times of birth, surgery, death, last followup or last known alive, local recurrence assessment, metastatic-free survival assessment and reccurence-free survival assessment. All times provided in a month-year format. 8 disease and topography fields such as disease status at inclusion, time of WES tumor sampling and final histological diagnosis, as well as tumor lesion sampled and localization of the sarcoma in final diagnosis, are inluced, as well as 5 outcome-related variables incl overall survival and cause of death. Finally 5 treatment-related variables are included. Due to high sequencing depth in several samples, the provided fastq files is the result of a screening process and downsampling if nessearcy, done before the Dragen analysis. The procedure is documented in the included README_fastq_downsampling.txt

  Dataset EGAD50000003102

• 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

• A single-cell multi-omic atlas of nodal B-cell non-Hodgkin lymphomas

  Nodal B-cell non-Hodgkin lymphomas (B-NHL) are a clinically diverse set of cancers thought to originate from distinct stages of B-cell maturation. To characterize intratumor heterogeneity across B-NHL entities, we performed CITE-Seq (cellular indexing of transcriptomes and epitopes) on 51 lymph node samples including mantle cell lymphoma (MCL, n = 8), follicular lymphoma (FL, n = 12), germinal center (GCB, n = 5) or activated B-cell (non-GCB/ABC, n = 7) diffuse large B-cell lymphoma (DLBCL) and marginal zone lymphoma (MZL, n = 11) tumors, in addition to non-malignant reactive lymph nodes (rLN, n = 8). We also performed 5 prime scRNA-seq with immune receptor profiling for 11 of these samples (2 rLN, 2 MCL, 3 FL, 2 DLBCL, and 2 MZL).

  Study EGAS50000000342

• Genomic correlates with survival after treatment of oesophageal adenocarcinoma by surgery alone

  A 451 cancer-associated gene panel, described in McCabe et al. 2019, was used to assess genomic alterations associated with survival in a Swedish cohort of 65 oesophageal adenocarcinoma (OAC) patients who underwent surgical resection without perioperative treatment. Primary tumour tissues and matched normal squamous oesophagus tissues were collected from each individual. Laboratory studies were performed at the Centre for Applied Medical Research (AMR) and sequencing was performed at the Kinghorn Cancer Centre. 64 of 65 cancer samples and 61 matched squamous oesophageal mucosa control tissues were successfully sequenced and passed minimal quality metrics.

  Study EGAS50000002190

• cfEM-seq methylation sequencing in classic Hodgkin lymphoma

  Reference-aligned cfEM-seq sequencing data generated on the Ultima Genomics platform from classic Hodgkin lymphoma plasma samples and lymphoma cell lines, together with associated index and provider QC metadata.

  Dataset EGAD50000002929

• Nanopore Amplicon Sequencing of 34 uveal melanoma samples (33 patient tumors + 1 primary cell line) in 4 mutational hotspot regions for genotyping

  Sequencing of one mutational hotspot region in each of the genes GNA11, GNAQ, SF3B1 and SRSF2. 33 patient tumor samples and a primary cell line were analyzed, all uveal melanoma. Amplicons were generated in a PCR step followed by AMPure XP clean up. Amplicons were pooled equally per sample and sequenced for genotyping.

  Dataset EGAD50000003067

• WES dataset of mesothelioma CONFIRM study

  This dataset comprises 33 whole-exome sequencing (WES) samples from mesothelioma patients, including 18 tumor, 13 matched normal, and 2 relapsed samples.

  Dataset EGAD50000002621

• RNA sequencing dataset of mesothelioma CONFIRM study

  This dataset includes 27 RNA sequencing tumors from mesothelioma, of which two are derived from relapsed patients.

  Dataset EGAD50000002622

• 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

• Cell-free DNA Whole Genome Sequencing for Non-Invasive Minimal Residual Disease Detection in Multiple Myeloma

  Minimal residual disease (MRD) monitoring in multiple myeloma (MM) relies on invasive bone marrow (BM) biopsies, which often yield insufficient tumor material. We performed whole genome sequencing of cell-free DNA from 163 plasma samples from 51 patients to develop a non-invasive MRD classifier. BM WGS identified a median of 2,502 clonal mutations, enabling cfDNA tracking at levels comparable to BM-based MRD testing. The cfDNA classifier achieved a mean AUC of 0.86 against multiparameter flow cytometry and targeted immunoglobulin sequencing (clonoSEQ), and MRD negativity after one year of maintenance was strongly associated with two-year relapse-free survival probability (Hazard Ratio = 24), with cfDNA changes preceding clinical progression by a median of 12.6 months. To establish a BM-agnostic mode, a plasma-only classifier trained on baseline cfDNA established a mean AUC of 0.79 compared to BM clinical testing and stratified relapse risk (HR = 4.18), enabling MRD detection in patients with suboptimal BM samples. Longitudinal cfDNA profiles detected subclonal evolution in half of profiled patients. Cell-free DNA whole genome sequencing provides a sensitive, scalable, and clinically informative platform for non-invasive MRD monitoring in MM.

  Study EGAS50000002055

• Visium Spatial transcriptomic profiling of IOKIN samples

  This study aimed to define the spatial tumor microenvironment features associated with response to immune checkpoint blockade in recurrent/metastatic head and neck squamous cell carcinoma (RM-HNSCC). Spatial transcriptomic profiling was performed on tumor biopsies from ICB-naive patients to characterize malignant, stromal, and immune cell states and their spatial interactions. The study investigated how intratumoral heterogeneity, cancer-associated fibroblast programs, immune contexture, and tumor-microenvironment signaling relate to subsequent clinical response and progression-free survival following immunotherapy.

  Study EGAS50000002196

• Whole-Exome Sequencing of Tumor Biopsies from a Colorectal Cancer Patient with Two Distinct Primary Tumors and Matched Metastases

  Colorectal cancer (CRC) is a heterogeneous disease in which rare genetic alterations, such as neurotrophic receptor tyrosine kinase (NTRK) fusions, have emerged as clinically relevant therapeutic targets. Here, we report the case of a 69-year-old man with an advanced colorectal carcinoma comprising two intermixed but histologically distinct components: a predominant (>90%) poorly differentiated solid carcinoma and a conventional adenocarcinoma with mucinous features. Whole-exome sequencing (WES) of microdissected tumor regions (two primary tumors and two matched metastases) along with germline DNA analysis revealed profound genetic divergence between the two tumor components. This case illustrates the coexistence of two genetically independent yet spatially inseparable CRCs following distinct evolutionary trajectories—one driven by an NTRK1 fusion and microsatellite instability (MSI), and the other by canonical KRAS and SMAD4 alterations.

  Study EGAS50000001385

• Single-cell study of 14 childhood medulloblastoma patients

  Study EGAS00001006392

• Genomic correlates with survival after treatment of oesophageal adenocarcinoma by surgery alone

  A 451 cancer-associated gene panel, described in McCabe et al. 2019, was used to assess genomic alterations associated with survival in a Swedish cohort of 65 oesophageal adenocarcinoma (OAC) patients who underwent surgical resection without perioperative treatment. This DAC controls access to sequencing data.

  Dac EGAC50000001177

• Multi-omics analysis revealed clonal evolution of the sarcoma patient-derived xenograft

  We performed whole-exome sequencing and RNA-seq of sarcoma PDXs, along with their matched original tumors when available.

  Study EGAS50000001860

• Genomic sequencing in an oesophageal adenocarcinoma cohort

  Genome sequencing was used to assess genomic alterations associated with survival in a Swedish cohort of 65 oesophageal adenocarcinoma (OAC) patients. Primary tumour tissues and matched normal squamous oesophagus tissues were sequenced using a 451 cancer-associated gene panel. This dataset consists of sequence data in fastq format.

  Dataset EGAD50000003139