10743 results for "fc coins olx Navštivte Buyfc26coins.com Podpora pro profesionální hráče a týmy..zSiZ"

in 15.05 milliseconds.

• Fragmentation signatures in cancer patients are similar to those in patients with vascular and autoimmune diseases

  The analysis of fragmentation patterns in plasma cell-free DNA (cfDNA) has been shown to distinguish individuals with cancer from healthy controls in multiple case-controlled studies. However, there have been few studies that investigate various types cfDNA fragmentomics patterns in individuals with other diseases. For this purpose, we developed a comprehensive metric, called fragmentation signatures, that integrated the distributions of fragment positioning, fragment length, and fragment end-motifs. Using this metric, we found that individuals with venous thromboembolism, systemic lupus erythematosus, dermatomyositis, or scleroderma had cfDNA fragmentation signatures that closely mimicked those found in individuals with advanced cancers. Furthermore, these signatures were highly correlated with increases in inflammatory markers in the blood. Though these data put substantial limitations on the specificity of fragmentomics-based tests for cancer diagnostics, they also offer ways to improve the interpretability of such tests. Moreover, they should lead to a better understanding of the cells – most likely inflammatory cells - from which plasma cfDNA is derived.

  Study EGAS00001008004

• STimage: Robust and interpretable prediction of gene markers and cell types from spatial transcriptomics data

  Spatial transcriptomics (ST) links tissue morphology with gene expression values, opening new avenues for digital pathology. Deep learning models are used to predict gene expression or classify cell types directly from images, offering significant clinical potential but still requiring improvements in interpretability and robustness. We present STimage as a comprehensive suite of models to predict spatial gene expression and classify cell types directly from standard H\&E images. STimage enhances robustness by estimating gene expression distributions and quantifying both data-driven (aleatoric) and model-based (epistemic) uncertainty using ensemble approach with foundation models. Interpretability is achieved through attribution analysis at single-cell resolution integrated with histopathological annotations, functional genes, and latent representations. We validated STimage across diverse datasets, demonstrating its performance across various platforms. STimage-predicted gene expression can stratify patient survival and predict drug response. By enabling molecular and cellular prediction from routine histology, STimage offers a powerful tool to advance digital pathology.

  Study EGAS50000001503

• Data Use Ontology (DUO) at EGA

  For those of you that follow the updates of GA4GH you will have seen the unanimous approval of the steering committee for DUO to be included in its suite of technical standards for the sharing of genomic and health related data. DUO has three main features: Each term has been generated by the community and includes a human readable definition that can be expanded where necessary. It is a machine-readable file that encodes how that data can be used and how a researcher intends to use the data. Can be implemented alongside an advanced search algorithm which would allow authenticated users to query and gain access to datasets pertaining to their research. e.g., an industry researcher working on cancer could potentially be matched to any dataset that is allowed for commercial use and for cancer research and offered the opportunity to fetch them automatically. So, what does this mean for EGA? EGA as a driver project has adopted this standard and will be utilizing it for two main purposes in the first instance. i) It will allow EGA users to instantly identify what terms they will need to agree to as part of any Data Access Agreements. This will save valuable time for both applicants and Data Access Committees alike - as you will be able to see if you are likely to be able to access the data based on your research intentions and working background. ii) In the future it will allow users to be able to search for data based on Data Use Ontology terms e.g., you could search for all data that can be used for General Research Use (GRU). As EGA moves forwards submitters will be asked to align their data access policies with DUO so that their dataset(s) can be tagged appropriately on our web pages. If you would like to add DUO to any of your existing datasets, or to any future datasets, do please just let us know.

  Blog data-use-ontology

• Somatic mutations in ALS genes in the motor cortex of sporadic ALS patients

  Several lines of evidence suggest that amyotrophic lateral sclerosis (ALS) pathology starts focally and spreads within the central nervous system (CNS). We leveraged DNA and RNA sequencing data from ALS autopsy tissue to explore the presence of somatic mosaic variants in sporadic ALS (sALS) cases. Deep targeted sequencing of known ALS disease genes from motor cortex tissue revealed enrichment of somatic variants in sALS compared to familial ALS (fALS) with a monogenic cause and an increased pathogenicity of mosaic mutations in known ALS mutational hot spots. Experimental validation of the variants identified that the somatic FUS variant p.E516X leads to nucleo-cytoplasmic mislocalization and aggregation. Additionally, somatic variant calling from single-cell RNA-sequencing data of sALS tissue showed an accumulation of somatic variants in excitatory neurons. Collectively, using a multi-modal approach, our findings suggest that somatic mutations within the motor cortex, especially in excitatory neurons, may contribute to the development of sALS.

  Study EGAS00001008104

• Genetic Basis of Hepatosplenic T Cell Lymphoma (HSTL)

  Hepatosplenic T cell lymphoma (HSTL) is a rare and lethal lymphoma; the genetic drivers of this disease are unknown.  Through whole exome sequencing of 68 HSTLs, we define recurrently mutated driver genes and copy number alterations in the disease. Chromatin modifying genes including SETD2, INO80 and ARID1B were commonly mutated in HSTL, affecting 62% of cases. HSTLs manifest frequent mutations in STAT5B (31%), STAT3 (9%), and PIK3CD (9%) for which there currently exist potential targeted therapies. In addition, we noted less frequent events in EZH2, KRAS and TP53. SETD2 was the most frequently silenced gene in HSTL. We experimentally demonstrated that SETD2 acts as a tumor suppressor gene. In addition, we found that mutations in STAT5B and PIK3CD activate critical signaling pathways important to cell survival in HSTL. Our work thus defines the genetic landscape of HSTL and implicates novel gene mutations linked to HSTL pathogenesis and potential treatment targets.

  Study EGAS00001002182

• Orthotopic Patient-Derived Xenografts of Pediatric Solid Tumors

  Pediatric solid tumors arise from endodermal, ectodermal, or mesodermal lineages. Although the overall survival of children with solid tumors is 75%-80%, that of children with recurrent disease is below 30%. To capture the complexity and diversity of pediatric solid tumors and establish new models of recurrent disease, we developed a protocol to produce orthotopic patient-derived xenografts (O-PDXs) at diagnosis, recurrence, and autopsy. Tumor specimens were received from 168 patients, and 64 O-PDXs were established for 12 types of pediatric solid tumors. The origins of the O-PDX tumors were reflected in their gene-expression profiles and epigenomes. Genomic profiling of the tumors, including detailed clonal analysis, was performed to determine whether the clonal population in the xenograft recapitulated the patient’s tumor. We identified several drug vulnerabilities and showed that the combination of a WEE1 inhibitor (AZD1775), irinotecan, and vincristine can lead to complete response in multiple rhabdomyosarcoma O-PDX tumors in vivo.

  Study EGAS00001002528

• Somatic_mutation_and_clonal_evolution_normal_breast_tissue_WGS

  Cancer is a genetic disease caused by an accumulation of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from breast tissue to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. We will sample from a wide age range of individuals (<20 to >70 years old) to determine whether these processes differ in pre- and post-menopausal women. We will also be comparing the tissue from healthy individuals (samples from breast reduction surgery) to those at elevated risk of breast cancer (mastectomy from BRCA1/2 patients) and those who have breast cancer (adjacent normal, distal normal, and tumour tissue from mastectomy). This will allow us to determine how these processes are different between these groups of individuals, and gain insight into the earliest stages of tumour development.

  Study EGAS00001002857

• MutWP5__CRUK_Mutographs_of_Cancer__Breast__Reduction_Mammoplasty___WG__Novaseq_

  Cancer is a genetic disease caused by an accumulation of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from breast tissue to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. We will sample from a wide age range of individuals (<20 to >60 years old) to determine whether these processes differ in pre- and post-menopausal women. We will also be comparing the tissue from healthy individuals (samples from breast reduction surgery) to those at elevated risk of breast cancer (mastectomy from BRCA1/2 patients) and those who have breast cancer (adjacent normal, distal normal, and tumour tissue from mastectomy). This will allow us to determine how these processes are different between these groups of individuals, and gain insight into the earliest stages of tumor development.

  Study EGAS00001003524

• MutWP5__CRUK_Mutographs_of_Cancer__BRCA_Carriers___Exome__Novaseq_

  Cancer is a genetic disease caused by an accumulation of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from breast tissue to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. We will sample from a wide age range of individuals (<20 to >60 years old) to determine whether these processes differ in pre- and post-menopausal women. We will also be comparing the tissue from healthy individuals (samples from breast reduction surgery) to those at elevated risk of breast cancer (mastectomy from BRCA1/2 patients) and those who have breast cancer (adjacent normal, distal normal, and tumour tissue from mastectomy). This will allow us to determine how these processes are different between these groups of individuals, and gain insight into the earliest stages of tumor development.

  Study EGAS00001003526

• Patient-Derived Lung Cancer Organoid

  Lung cancer shows substantial genetic and phenotypic heterogeneity across individuals, driving a need for personalized medicine. Here we report lung cancer organoids and normal bronchial organoids established from patients tissues comprising five histological subtypes of lung cancer and non-neoplastic bronchial mucosa as in vitro models representing individual patient. The lung cancer organoids recapitulate the tissue architecture of the primary lung tumours and maintain the genomic alterations of the original tumours during long-term expansion in vitro. The normal bronchial organoids maintain cellular components of normal bronchial mucosa. Lung cancer organoids respond to drugs based on their genomic alterations: a BRCA2 mutant organoid to olaparib, an EGFR mutant organoid to erlotinib, and an EGFR mutant/MET amplified organoid to crizotinib. Considering the short length of time from organoid establishment to drug testing, our newly developed model may prove useful for predicting patient-specific drug responses through in vitro patient-specific drug trials.

  Study EGAS00001003786