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• Spatial concordance of DNA methylation classification in diffuse glioma

  Background: Intratumoral heterogeneity is a hallmark of diffuse gliomas. DNA methylation profiling is an emerging approach in the clinical classification of brain tumors. The goal of this study is to investigate the effects of intratumoral heterogeneity on classification confidence. Methods: We used neuronavigation to acquire 133 image-guided and spatially-separated stereotactic biopsy samples from 16 adult patients with a diffuse glioma (7 IDH-wildtype and 2 IDH-mutant glioblastoma, 6 diffuse astrocytoma, IDH-mutant and 1 oligodendroglioma, IDH-mutant and 1p19q codeleted), which we characterized using DNA methylation arrays. Samples were obtained from regions with and without abnormalities on contrast enhanced T1 weighted and fluid-attenuated inversion recovery MRI. Methylation profiles were analyzed to devise a three-dimensional reconstruction of (epi)genetic heterogeneity. Tumor purity was assessed from clonal methylation sites. Results: Molecular aberrations indicated that tumor was found outside imaging abnormalities, underlining the infiltrative nature of this tumor and the limitations of current routine imaging modalities. We demonstrate that tumor purity is highly variable between samples and explains a substantial part of apparent epigenetic spatial heterogeneity. We observed that DNA methylation subtypes are often, but not always, conserved in space taking tumor purity and prediction accuracy into account. Conclusion: Our results underscore the infiltrative nature of diffuse gliomas and suggest that DNA methylation subtypes are relatively concordant in this tumor type, although some heterogeneity exists.

  Study EGAS00001005434

• Cancer Genetic Markers of Susceptibility (CGEMS) Breast Cancer Genome-wide Association Study (GWAS) - Primary Scan: Nurses' Health Study - Additional Cases: Nurses' Health Study 2

  The initial stage of the Cancer Genetic Markers of Susceptibility (CGEMS) breast cancer genome-wide association study (GWAS) included genotyping 528,173 SNPs (Illumina HumanHap550) in 1,145 postmenopausal women of European ancestry with invasive breast cancer and 1,142 controls from the Nurses' Health Study (NHS). Subsequently, incident invasive breast cancer cases from the Nurses' Health Study 2 (NHS2) cohort were genotyped using the Illumina HumanHap 610 quad. The NHS2 cases are younger than the cases in the first stage of the CGEMS breast cancer GWAS, which only included postmenopausal women. The NHS2 cases are a mix of pre- and postmenopausal women

  Study phs000147

• Ameloblastoma Cell Line Resource

  Ameloblastoma (AB) is an odontogenic tumor that is locally destructive to the jaw bone. Treatment entails surgical resection, but AB often recurs locally. Recent driver mutations have been identified in the MAPK and Hedgehog pathways. However, translation to molecularly-targeted therapies has been hampered by the lack of AB cell line models. The goal of our study has been to create a new AB cell line resource that reflects the spectrum of AB driver mutations. We created 6 new AB cell lines, and by targeted exome sequencing, defined their driver mutations. We showed the utility of these cell lines in preclinical studies of oncogene dependence and drug sensitivity (which includes whole transcriptome assays).

  Study phs002753

• National Institute of Mental Health (NIMH) Amish Mennonite Bipolar Genetics Study (AmBiGen)

  Bipolar affective disorder is a severe, heritable condition affecting about one percent of the population. The mode of inheritance is poorly understood and probably involves multiple loci of small to moderate effect. In this project, we use genetic mapping and sequencing methods to identify genetic markers and variations that contribute to the risk of bipolar disorder. Individuals diagnosed with bipolar disorder are studied, along with their relatives. Phenotypic information obtained from clinical interviews and family history is correlated with genotypic information obtained from genetic marker and sequencing methods. The goal is to identify genes involved in bipolar disorder and related conditions so that better methods of diagnosis, treatment, and prevention can be developed.

  Study phs000899

• Metagenomic characterization of tracheal aspirates from non-pulmonary sepsis patients

  Our previous studies in mechanically ventilated Intensive Care Unit (ICU) patients with extrapulmonary sepsis revealed that pulmonary dysbiosis within 8 h from sepsis diagnosis was associated with ICU mortality. These observations offer promising options that could be technologically adapted to rapidly identify those patients at higher risk of death. However, they relied on 16S rRNA sequencing experiments which impedes deep characterization of the bacterial communities that could help to further substantiate the association. To further discern this association, here we assessed the pulmonary bacterial communities in these patients by leveraging metagenomics and evaluated if the presence of antibiotic resistance genes could explain the higher mortality of the patients.

  Study EGAS50000000384

• Whole blood RNAseq from a large ALS case-control study at Univ of Michigan

  Patients with amyotrophic lateral sclerosis (ALS), a rare fatal neurodegenerative disease, face a lengthy diagnostic process, and, although most survive only 2 to 4 years from diagnosis, lack information regarding their specific anticipated disease course due to a lack of prognostic tools. Although ALS is a heterogeneous disease of varied etiology, peripheral immune system dysfunction is ubiquitous, reflected in altered whole blood transcriptome. Herein, we profiled whole blood gene expression by RNA sequencing in a large cohort of ALS cases versus controls. Several machine learning classifiers trained on our gene expression dataset predicted case-control status and survival, and integration analysis with external cohorts led to the identification of drug candidates.

  Study EGAS50000001019

• Genome-to-genome analysis highlights the impact of the human innate and adaptive immune systems on the hepatitis C virus

  Outcomes of hepatitis C virus (HCV) infection and treatment depend on viral and host genetic factors. We use human genome-wide genotyping arrays and new whole-genome HCV viral sequencing technologies to perform a systematic genome-to-genome study of 542 individuals chronically infected with HCV, predominately genotype 3. We show that both HLA alleles and interferon lambda innate immune system genes drive viral genome polymorphism, and that IFNL4 genotypes determine HCV viral load through a mechanism that is dependent on a specific polymorphism in the HCV polyprotein. We highlight the interplay between innate immune responses and the viral genome in HCV control.

  Study EGAS00001002324

• Understanding_the_multicellular_dynamics_of_clear_cell_renal_cell_carcinoma___visium

  This study will reconstruct the single cellular phylogeny of tumour and immune cells in relation to both genomic, functional, and spatial location. 20 tumours in total will be studied, including non-progressing small renal masses, progressing small renal masses, relatively indolent larger tumours, and high risk tumour with metastatic sampling. We aim to: Generate a focused, comprehensive phylogenetic characterisation of tumour cells Understand the dependencies of the cancer genome, transcriptome, tissue architecture and the single cellular micro-environment Quantify the single cellular composition and functional intra- and inter- tumoural heterogeneity Determine the phylogeny of tumour associated lymphocytes and their relationship to neo-epitopes and the immune microenvironment Investigate the mechanisms by which the tumour-normal interface constrains tumoural growth.

  Study EGAS00001006045

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data

  We present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture model to simultaneously cluster sparse single-cell DNA methylation data and infer their corresponding hidden methylation profiles. Using synthetic and published single-cell CpG datasets we show that Epiclomal outperforms non-probabilistic methods and is able to handle the inherent missing data feature which dominates single-cell CpG genome sequences. Using a recently published single-cell 5mCpG sequencing method (PBAL), we show that Epiclomal discovers sub-clonal patterns of methylation in aneuploid tumour genomes, thus defining epiclones. We show that epiclones may transcend copy number determined clonal lineages, thus opening this important form of clonal analysis in cancer.

  Study EGAS00001003504

• Functional Genomics Laboratory, Kolling Institute of Medical Research DAC - TP53 mutation data in ovarian cancer

  Dac EGAC00001000589