7794 results for "canc*"

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• 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

• NGS sequencing data of archival FFPE tumor tissue

  The aim of the study was to identify mutations in key ovarian cancer genes (BRCA1, BRCA2,TP53, PTEN, ATM, ATR, NF1) in archival fresh-frozen paraffin embedded (FFPE) tumor tissue from platinum-resistant ovarian cancer patients enrolled in the phase I/II GANNET53 clinical trial. From the enrolled 133 patients, archival FFPE tissue was available and DNA was extracted thereof. The DNA samples passing quality control (n=118) were NGS sequenced using the SureSeq™ Ovarian Cancer Panel and the NGS Library Preparation kit (Oxford Gene Technology), which covers all exons of 7 key ovarian cancer genes for single nucleotide variants (SNV) and indels, and the Illumina MiSeq platform. For the paired-end runs, one Read 1 (R1) and one Read 2 (R2) FASTQ file were created for each sample. FASTQ files were compressed and created with the extension .fastq.gz.

  Dataset EGAD50000001371

• Tissue architecture and immune niches govern ctDNA release in colorectal cancer

  Circulating tumor DNA (ctDNA) is central to liquid biopsy-based cancer detection, yet its release into the bloodstream varies widely and remains poorly understood. To define the tissue-level determinants of ctDNA shedding in colorectal cancer (CRC), we integrated tumor-informed plasma sequencing with detailed histopathology, immunophenotyping, spatial transcriptomics, and in situ mutation detection in resectable stages (I–III). ctDNA detectability increased with tumor burden, and high ctDNA shedders exhibited a distinct architectural and microenvironmental phenotype characterized by expanded necrotic pseudolumina, frequent epithelial barrier disruption, and dense myeloid infiltration. Spatial profiling revealed stress-associated malignant programs and a myeloid-rich immune-luminal niche. In situ sequencing confirmed plasma-detected mutations within pseudoluminal debris, identifying these structures as focal reservoirs of shed DNA. These findings provide a mechanistic framework linking tissue architecture, immune remodelling, and spatially organized cell death to ctDNA release with implications for refining liquid biopsy applications.

  Study EGAS50000001941

• Type 1 interferon perturbates clonal competition by reshaping human blood development

  We studied hematopoietic stem cells, progenitors and immune cells from patients with myeloproliferative neoplasms (MPN) at baseline and following interferon-⍺ (IFN⍺) treatment, the only therapy to deplete mutated stem cells. We deployed single-cell multi-omics methods that distinguish the IFN⍺ effects on mutated stem cells from the admixed wildtype HSCs, with respect to their differentiation, transcriptomes, immunophenotypes, and chromatin accessibility.

  Study EGAS50000002009

• 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

• Spatial Biology of Cancer of Unknown Primary

  Dac EGAC50000000720

• IMMUcan BC1 cohort

  The goal of IMMUcan is to understand how the immune system interacts with tumors and how therapeutic interventions influence this interaction. This cohort includes metastatic Triple Negative Breast Cancer (mTNBC) patients, naïve of treatment in the metastatic settings, treated in 1st line with chemotherapy or chemo-immunotherapy Comprehensive profiling data are integrated with longitudinally collected clinical information. Cellular profiling of the same tumor samples was performed using multiplex immunofluorescence imaging, imaging mass cytometry, and H&E staining.

  Study EGAS50000001587

• 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

• EGA synthetic data

  Synthetic Data One of the limitations in genomics research is that human genomics data is not openly available; access must be controlled according to participant consent agreements and data protection regulations such as GDPR. Obtaining authorization to access such data can sometimes take a long time, resulting in delays to important research work. In this context, synthetic genomic and phenotype data can be useful resources for researchers to avoid these delays. Synthetic data are artificially generated datasets, often created with algorithms, which can be used without the need for authorization to test new products and tools, build technical demonstrators, validate data models, and train AI models. The EGA provides access to synthetic cohort datasets augmented with rich synthetic metadata that overcomes these real data usage restrictions. Whilst synthetic datasets are not included in the general EGA mandate and services, we can consider such submissions and evaluate their acceptance on the basis of their unique use cases not already covered by existing synthetic datasets. All synthetic data studies are open and permission will be granted automatically, however it is still required to request access. Requests are handled by the responsible DAC at Central EGA or the relevant Federated EGA Node. Study ID Title Located in EGAS00001002472 CINECA synthetic cohort EUROPE UK1 referencing fake samples Central EGA EGAS00001005591 Synthetic data - Genome in a Bottle Central EGA EGAS00001005042 Test Study for EGA using data from 1000 Genomes Project - Phase 3 Central EGA EGAS00001005702 Human genomic and phenotypic synthetic data for the study of rare diseases Central EGA EGAS50000000190 EOSC4Cancer Synthetic Colorectal Cancer Genomic data Central EGA EGAS50000001932 Synthetic longitudinal breast cancer whole-genome sequencing dataset Central EGA EGAS50000000086 Synthetic - FEGA Sweden Heilsa synthetic dataset December 2023 Federated EGA Sweden EGAS50000000678 Synthetic - GDI synthetic data Federated EGA Spain

  Documentation synthetic-data

• What is the cBio Portal

  EGA cBioPortal What is the EGA cBioPortal? EGA hosts a dedicated cBioPortal instance that enables authorized researchers to explore and visualize selected cancer datasets archived at EGA. Data submitters can also contribute cancer datasets to be made available through the portal. The platform provides interactive tools to analyse genomic and clinical data once access has been granted by the relevant Data Access Committee (DAC). How to find and access an EGA cBioPortal Dataset 1. Register as an EGA user 2. Go to the dataset page of interest. You can use the Search function to find datasets available in cBioPortal. If the dataset is available on the EGA cBioPortal Instance, the cBioPortal icon will be displayed on the dataset page, as shown below: On the dataset page, you will find information about access requirements. For more details, please visit how to request access. 3. Once access has been granted Go to the EGA cBioPortal instance and log in with your EGA user accountThe approved dataset will be listed in cBioPortalSelect the dataset to start exploring the data What you can do in cBioPortal The EGA cBioPortal is designed for interactive exploration and visualization of controlled-access cancer datasets, including: Visualising genomic alterationsAnalysing mutation patternsFiltering by clinical attributesComparing molecular profilesGenerating plots and summaries Important Notes Datasets are only visible in cBioPortal after DAC approvalAccess requests must always be submitted through the EGA website If you encounter any issues gaining access to a dataset, please contact EGA Support.If your question is related to the cBioPortal features, please reach out to: cbioportal@googlegroups.com. The EGA Helpdesk does not support this platform. How to link a dataset to the EGA cBioPortal instance during a submission STEP 1 – cBioPortal File Upload to EGA Files must be uploaded to the SFTP INBOX. The supported cBioPortal file types are: Raw data (e.g., VCF files)cBioPortal-specific formats (e.g., MAF files) STEP 2 – Register the cBioPortal dataset in the Submitter Portal Register the cBioPortal files in an analysis objectRegister a dataset with the title “cBioPortal Metadata” and include the analysis and nothing else Please note that cBioPortal is not directly supported by the EGA Helpdesk. For questions about the platform, please visit the cBioPortal website or contact the cBioPortal support team at cbioportal@googlegroups.com. The EGA does not support this platform.

  Documentation submission/metadata/submission/cbioportal