5250 results for "*oma"

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• 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

• Tumoral immune microenvironment profiling in B-cell non-Hodgkin lymphoma stratifies patients in different prognostic groups

  Dac EGAC50000001065

• Tumoral immune microenvironment profiling in B-cell non-Hodgkin lymphoma stratifies patients in different prognostic groups

  Non-Hodgkin lymphomas (NHL) are heterogeneous B-cell malignancies in which the tumor immune microenvironment (TIM) influences disease progression and has emerging potential for prognostic patient stratification. This project investigates the TIM of NHL by combining multiparameter flow cytometry with targeted DNA sequencing of NHL biopsies. The study aimed to characterize the immune cell composition, immune checkpoint expression, and mutational landscape, and to evaluate their association with clinical and prognostic features across NHL subtypes. The classification of TIM profiles by flow cytometry predicts clinical outcomes in patients with follicular lymphoma (FL), highlighting its potential value for risk stratification and the identification of novel immunotherapeutic targets in NHL.

  Study EGAS50000001964

• RNAseq and WES of CHC patients years before HCC development

  We conducted a nested case–control study within a prospective liver biopsy biobank at the University Hospital Basel. Patients with chronic hepatitis C (CHC) who developed hepatocellular carcinoma (HCC) during follow-up (index cases, CHC-HCC) were identified and 1:2 propensity score-matched to CHC controls without HCC (controls, CHC-nonHCC). Matching included clinical, demographic, and virological risk factors for HCC. Liver biopsies were analyzed using RNA and whole-exome sequencing.

  Dataset EGAD50000001591

• Targeted DNA sequencing of 60 non-Hodgkin lymphoma biopsies using a custom gene panel for hematological malignancies: raw (FASTQ) data

  Targeted DNA sequencing was performed on 60 non-Hodgkin lymphoma (NHL) samples using a custom next-generation sequencing panel targeting the coding regions of 190 genes recurrently mutated in hematological malignancies. Libraries were sequenced on an Illumina NovaSeq 6000 platform using 2 × 150 bp paired-end reads. The dataset includes the raw sequencing data (FASTQ files) for all 60 samples.

  Dataset EGAD50000002845

• scRNAseq and spatial transcriptomics data of melanoma samples

  Paired-end 10x single-cell RNA sequencing and Visium spatial transcriptomics data from samples derived from 11 melanoma patients. Sequencing was performed on the Illumina NovaSeq 6000 using 10xGenomics Chromium Next GEM Single-Cell 5 Reagent Kits v2 or 10xGenomics Visium Gene Expression kit.

  Dataset EGAD50000002902

• RNA-seq of adult T-cell leukemia-lymphoma patient-derived xenograft (PDX) models treated with the MALT1 protease inhibitor CRD-1441551

  To elucidate the therapeutic effect of MALT1 protease inhibition in adult T-cell leukemia/lymphoma (ATL), we performed RNA-seq on tumor tissues from five ATL patient-derived xenograft (PDX) models. NOG mice engrafted with each PDX model were treated with the MALT1 protease inhibitor CRD-1441551 or vehicle for 21 days, in biological triplicate (n = 3 per condition; 30 samples in total). Mouse-derived reads were removed and the remaining human reads were aligned to the human reference genome (hg19). These data underlie the gene-expression analyses reported in the associated publication.

  Study JGAS000921

• Impact of Mutated CTCF DNA binding sites of Topologically associating domains on nearby Cancer Gene Regulation: A Multi-Omics Analysis

  Cutaneous squamous cell carcinoma (cSCC) is the most common lethal malignancy world-wide. Due in most part to the extremely high mutational burden (TMB) in cSCC, making sense of it’s non-coding and regulatory regional genomic variation has lagged that of other far less common cancers. This study presents the first investigation of mutations at CCCTC-binding factor binding sites (CTCFbs) of Topologically associating domains (TAD) across defined cSCC progression stages—primary non-metastatic (PNM), primary metastat-ic (PM), and lymph node metastasis (LNM).

  Study EGAS50000001686

• CTCF Binding Site Mutations: Linking Topologically Associated Domains Dysregulation to Cutaneous Squamous Cell Carcinoma Progression

  Fresh tumor tissue from nodal metastases (n = 41) was harvested during surgery and immediately snap-frozen. A total of 72/75 cSCC, WGS samples were included in the analysis (refer to QC section), comprising primary tumors (both metastatic (PM (n = 16)) and non-metastatic (PNM (n = 15)), as well as lymph node metastases (LNM (n = 41)). The metastatic cohort included matched PM and LNM samples for 13 patients. The PNM group had to meet the following criteria: absence of metastases at > 24 months follow-up after resection of the primary or negative sentinel lymph node biopsy at time of resection or histologically negative neck dissection. Somatic variant analysis was performed using DRAGEN pipeline version 4.3.6 on the Illumina Connected Analytics (ICA) v2 environment using in house shell scripting. Tumor-normal paired analyses were carried out in three stages: alignment, variant calling, and integrative somatic analysis. Further information on the somatic calling is available at https://help.dragen.illumina.com/product-guides/dragen-v4.3/dragen-dna-pipeline/small-variant-calling/somatic-mode.

  Dataset EGAD50000002416