16461 results for "sec+and+unspecified+malignant+neoplasm"

in 43.52 milliseconds.

• Non-Mendalian inheritance of extrachromosal DNA elements can drive disease evolution in glioblastoma

  Genomic heterogeneity of glioblastoma (GBM) is suspected to contribute to the poor response to therapy of this disease. We compared molecular characteristics between primary GBM, neurospheres and orthotopic xenograft models derived from the same parental tumor. Driver alterations were in majority propagated from tumor to model systems. Extrachromosomal amplifications of MET, a proto-oncogene coding for a receptor tyrosine kinase, were detected in three primary GBM, largely discarded in neurospheres cultures, but resurfaced in xenografts. The clonal dynamics inferred by somatic single nucleotide variants (sSNVs) in MET-amplified samples diverged from the pattern delineated by the MET amplification event suggesting that the MET event and sSNVs were inherited in different manners. Our analysis shows that extrachromosomal elements are able to drive tumor progression.

  Study EGAS00001001878

• The Precision Medicine in Liver Cancer across an Asia-pacific NETwork (PLANET)

  Hepatocellular carcinoma (HCC) is the 2nd most important cause of cancer deaths in the world. The outcomes of treatment in HCC are poor compared to other common cancers because there are no good systemic therapy drugs. We designed a prospective study that will involve 5 different sites within the established Asia-Pacific Hepatocellular Carcinoma Trials Group to study both the genetics as well as immune profiles of HCC in different ethnicities. In this study, patients will be followed-up and at recurrence the tissues to understand the evolution of disease progression. Here, we presented both the genomic (DNA) and phenotypic (RNA) evolution for the first batch of 56 patients. The detailed findings will be listed in the corresponding publication soon.

  Study EGAS00001003813

• Multiple migrations to the Philippines during the last 50,000 years

  A key link to understand human history in Island Southeast Asia is the Philippine archipelago and its poorly investigated genetic diversity. We collected and analyzed the most comprehensive set of population-genomic data for the Philippines, 1028 individuals covering 115 indigenous communities. We demonstrate that the Philippines were populated by at least five waves of human migration. The Cordillerans migrated into the Philippines prior to the arrival of rice agriculture, where some remain to be the least admixed East Asians carrying an ancestry shared by all Austronesian-speaking populations; thereby challenging an exclusive Out-of-Taiwan model of joint farming-language-people dispersal. Altogether, our findings portray Philippines as a crucial gateway, with a multilayered history, that ultimately changed the genetic landscape of the Asia-Pacific region.

  Study EGAS00001005083

• Germline SDHB-inactivating mutation in gastric spindle cell sarcoma (HIPO-021)

  Gastrointestinal stromal tumors (GISTs) are the most frequent mesenchymal tumors of the gastrointestinal tract. Inactivating mutations or epigenetic deregulation of succinate dehydrogenase complex (SDH) genes are considered defining features of a subset of GIST occurring in the stomach. Based on comprehensive molecular profiling and biochemical analysis within a precision oncology program, we identified hallmarks of SDH deficiency (germline SDHB-inactivating mutation accompanied by somatic loss of heterozygosity, lack of SDHB expression, global DNA hypermethylation, and elevated succinate/fumarate ratio) in a 40-year-old woman with undifferentiated gastric spindle cell sarcoma that did not meet the diagnostic criteria for other mesenchymal tumors of the stomach, including GIST. These data reveal that the loss of SDH function can be involved in the pathogenesis of non-GIST sarcoma of the gastrointestinal tract.

  Study EGAS00001004277

• Peripheral immunoprofiling of stratifies COVID-19 patients based on disease-specific neutrophil signatures

  The SARS-CoV-2 pandemic has led to increasing numbers of COVID-19 patients all over the world. Aetiopathologies range from no symptoms, mild flu-like to severe cases succumbing to respiratory failure. Reports on a dysregulated immune system in the severe cases, showing similarities to cytokine release syndrome, calls for better characterization and understanding of the changes in the immune system as well as their variance across COVID-19 patients in order to be able to design according to host-directed therapies. Here, we profiled blood transcriptomes of 39 COVID-19 patients and 10 control donors. Enriched granulocyte signatures in whole blood samples were verified in granulocyte samples from 49 COVID-19 patients in a second cohort.

  Study EGAS00001004503

• Single cell whole genome sequencing of high hyperdiploid acute lymphoblastic leukemia

  This dataset was collected from viable bone marrow cells obtained at diagnosis from nine patients with high hyperdiploid ALL and one normal bone marrow sample. All samples were subjected to low pass single cell whole genome sequencing with the median sequencing coverage of 0.02x. Single nuclei in G0/G1 phase were isolated using a fluorescence-activated cell sorting (FACS) cytometer. DNA libraries were constructed and associated next-generation sequencing was carried out by European Research Institute for the Biology of Ageing (ERIBA), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. Further details regarding the DNA libraries construction are available by Bos et. al., 2019 (https://link.springer.com/protocol/10.1007/978-1-4939-8931-7_15). The dataset has been used for copy number aberrations analysis.

  Study EGAS00001006347

• Single-cell multi-omics of relapse/refractory multiple myeloma patients (Hipo K08K/H067/K43R)

  Therapy resistance in cancers is ingrained in complex clonal dynamics. While the major clonal population in a tumour might respond well to the employed intervention, minor clones will eventually mutate, consequently acquiring a selective advantage and drug resistance. Hence, to improve cancer therapy, it is crucial to understand the mechanisms behind clonal evolution. Here, we applied single-cell transcriptomics and epigenomics, as well as whole-genome sequencing to 15 patients with relapsed/refractory multiple myeloma (RRMM). By profiling longitudinal samples throughout varying treatment regimens, we were able to interrogate patterns of evolution across patients. In particular, by defining clones based on copy number aberrations (CNAs), we found evidence for the majority of clones being diminished but not fully eradicated during treatment.

  Study EGAS00001006538

• Lung Adenocarcinoma Promotion by Air Pollutants

  A complete understanding of how environmental carcinogenic exposures promote cancer formation is lacking. Over 70 years ago, tumour formation was proposed to occur in a two step process: an initiating step which induces mutations in normal tissue, followed by a promoter step which triggers cancer development. Recent evidence has revealed healthy human tissue contains a patchwork of clones harbouring oncogenic mutations.This led us to hypothesise that environmental particulate matter measuring PM2.5, known to be associated with lung cancer risk, might promote lung cancer by acting on pre-existing cells harbouring oncogenic mutations in normal lung tissue. Here we use a combination of WGS and RNA-seq of mouse tumours from pollution-exposed mice to examine the impact of particulate matter on mutagenesis and gene expression respectively.

  Study EGAS00001006951

• Memory-like HCV-specific CD8+ T cells retain a chronic scar after cure of chronic HCV infection

  Chronic hepatitis C virus (HCV) infection is associated with CD8+ T-cell exhaustion characterized by limited effector functions and thus compromised anti-viral activity. Exhausted HCV-specific CD8+ T cells are comprised of memory-like and terminally exhausted CD8+ T-cell subsets. So far, little is not known about the molecular profile and fate of these cells after elimination of chronic antigen stimulation by direct acting antiviral therapy (DAA). Here, we report an antigen-driven molecular core signature underlying exhausted CD8+ T-cell subset heterogeneity in chronic viral infection with a progenitor/progeny relationship of memory-like and terminally exhausted HCV-specific CD8+ T cells via an intermediate stage. Furthermore, transcriptional profiling reveals that the memory-like cells remain after DAA-mediated cure while terminally exhausted HCV-specific CD8+ T-cell subsets are lost. Thus, the memory polarization of the overall HCV-specific CD8+ T-cell response after cure does not result from re-differentiation of exhausted T cells. Consequently, antigen elimination has little impact on the exhausted core signature of memory-like CD8+ T cells that remains clearly different from bona fide T-cell memory. These results identify a molecular signature of T-cell exhaustion that is imprinted like a chronic scar in HCV-specific CD8+ T cells even after HCV cure, highlighting the requirement of re-programming to elicit full effector potential of exhausted T cells.

  Dataset EGAD00001006259

• Direct Comparative Analysis of 10X Genomics Chromium and Smart-seq2

  Single cell RNA sequencing (scRNA-seq) is widely used for profiling transcriptomes of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently-used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data by the two platforms from the same samples of CD45- cells, we systematically evaluated their features using a wide spectrum of analysis. Smart-seq2 detected more genes in a cell, especially low abundance transcripts as well as alternatively spliced transcripts, but captured higher proportion of mitochondrial genes. The composite of Smart-seq2 data also resembled bulk RNA-seq data better. For 10X-based data, we observed higher noise for mRNA in the low expression level. Despite the poly(A) enrichment, approximately 10-30% of all detected transcripts by both platforms were from non-coding genes, with lncRNA accounting for a higher proportion in 10X. 10X-based data displayed more severe dropout problem, especially for genes with lower expression levels. However, 10X-data can better detect rare cell types given its ability to cover a large number of cells. In addition, each platform detected different sets of differentially expressed genes between cell clusters, indicating the complementary nature of these technologies. Our comprehensive benchmark analysis offers the basis for selecting the optimal scRNA-seq strategy based on the objectives of each study.

  Dataset EGAD00001005448

• FOXM1 is a biomarker of resistance to PI3Kα inhibition in ER+ breast cancer that is detectable using metabolic imaging (RNA-seq data)

  PIK3CA, encoding the PI3Kα isoform, is the most frequently mutated oncogene in estrogen receptor-positive breast cancer. Isoform-selective PI3K inhibitors are used clinically but intrinsic and acquired resistance limits their utility. Improved selection of patients that will benefit from these drugs requires novel predictive biomarkers. We show here that persistent FOXM1 expression following drug treatment is a biomarker of resistance to PI3Kα inhibition in ER+ breast cancer. Since the transcription factor drives expression of lactate dehydrogenase (LDH) but not hexokinase 2 (HK-II), changes in FOXM1 expression can be detected using magnetic resonance imaging of LDH-catalyzed hyperpolarized 13C label exchange between pyruvate and lactate but not by PET measurements of HK-II-mediated trapping of the glucose analog 2-deoxy-2-[18F]fluorodeoxyglucose.

  Study EGAS00001004452

• Correction of a Factor VIII genomic inversion with designer recombinases

  Despite advances in nuclease-based genome editing technologies, correcting human disease-causing genomic inversions remains a challenge. Here, we describe the potential use of a recombinase-based system to correct the 140 kb inversion of the F8 gene frequently found in patients diagnosed with severe Hemophilia A. Employing substrate-linked directed molecular evolution, we developed a coupled heterodimeric recombinase system (RecF8) achieving 30% inversion of the target sequence in human tissue culture cells. Transient RecF8 treatment of endothelial cells, differentiated from patient derived induced pluripotent stem cells (iPSCs) of a hemophilic donor, resulted in 12% correction of the inversion and restored Factor VIII mRNA expression. Our data suggests that designer-recombinases may represent efficient and specific means towards treatment of monogenic diseases caused by large gene inversions.

  Study EGAS00001005496

• Data Access Committee of divisions B060 and B062, German Cancer Research Center (DKFZ)

  Dac EGAC00001001626

• EGAD00010000538

  28 unlinked autosomal microsatellite loci for 20 African and 4 philippine populations

  Dataset EGAD00010000538

• RNA Editing Exome

  Exome sequencing data for tumor and matched normal samples of the EGAS00001000495 project.

  Dataset EGAD00001000626

• RHD_NC_OMNI_Controls

  Healthy volunteers and missing phenotype individuals recruited in New Caledonia with higher density genotyping

  Dataset EGAD00010000962

• Transcriptome of (monocytes and dendritic cells ), from donor S8, replicate 1 patient

  Dataset EGAD00001009020

• Transcriptome of (monocytes and dendritic cells ), from donor S6, replicate 1 control

  Dataset EGAD00001009016

• Transcriptome of (monocytes and dendritic cells ), from donor S9, replicate 1 control

  Dataset EGAD00001009022

• Transcriptome of (monocytes and dendritic cells ), from donor S14, replicate 1 control

  Dataset EGAD00001009032

• Transcriptome of (monocytes and dendritic cells ), from donor S9, replicate 2 control

  Dataset EGAD00001009023

• Transcriptome of (monocytes and dendritic cells ), from donor S5, replicate 2 control

  Dataset EGAD00001009015

• Transcriptome of (monocytes and dendritic cells ), from donor S6, replicate 2 control

  Dataset EGAD00001009017

• Transcriptome of (monocytes and dendritic cells ), from donor S14, replicate 2 control

  Dataset EGAD00001009033

• Transcriptome of (monocytes and dendritic cells ), from donor S7, replicate 1 patient

  Dataset EGAD00001009018

• Transcriptome of (monocytes and dendritic cells ), from donor S7, replicate 2 patient

  Dataset EGAD00001009019

• Transcriptome of (monocytes and dendritic cells ), from donor S8, replicate 2 patient

  Dataset EGAD00001009021

• Transcriptome of (monocytes and dendritic cells ), from donor S15, replicate 1 patient

  Dataset EGAD00001009034

• Transcriptome of (monocytes and dendritic cells ), from donor S15, replicate 2 patient

  Dataset EGAD00001009035

• Transcriptome of (monocytes and dendritic cells ), from donor S16, replicate 1 patient

  Dataset EGAD00001009036

• Transcriptome of (monocytes and dendritic cells ), from donor S16, replicate 2 patient

  Dataset EGAD00001009037

• Transcriptome of (monocytes and dendritic cells ), from donor S13, replicate 2 control

  Dataset EGAD00001009031

• Transcriptome of (monocytes and dendritic cells ), from donor S1, replicate 1 control

  Dataset EGAD00001009006

• Transcriptome of (monocytes and dendritic cells ), from donor S1, replicate 2 control

  Dataset EGAD00001009007

• Transcriptome of (monocytes and dendritic cells ), from donor S2, replicate 1 control

  Dataset EGAD00001009008

• Transcriptome of (monocytes and dendritic cells ), from donor S2, replicate 2 control

  Dataset EGAD00001009009

• Transcriptome of (monocytes and dendritic cells ), from donor S3, replicate 1 patient

  Dataset EGAD00001009010

• Transcriptome of (monocytes and dendritic cells ), from donor S3, replicate 2 patient

  Dataset EGAD00001009011

• Transcriptome of (monocytes and dendritic cells ), from donor S4, replicate 1 patient

  Dataset EGAD00001009012

• Transcriptome of (monocytes and dendritic cells ), from donor S4, replicate 2 patient

  Dataset EGAD00001009013

• Transcriptome of (monocytes and dendritic cells ), from donor S5, replicate 1 control

  Dataset EGAD00001009014

• Transcriptome of (monocytes and dendritic cells ), from donor S10, replicate 1 control

  Dataset EGAD00001009024

• Transcriptome of (monocytes and dendritic cells ), from donor S10, replicate 2 control

  Dataset EGAD00001009025

• Transcriptome of (monocytes and dendritic cells ), from donor S11, replicate 1 patient

  Dataset EGAD00001009026

• Transcriptome of (monocytes and dendritic cells ), from donor S11, replicate 2 patient

  Dataset EGAD00001009027

• Transcriptome of (monocytes and dendritic cells ), from donor S12, replicate 1 patient

  Dataset EGAD00001009028

• Transcriptome of (monocytes and dendritic cells ), from donor S12, replicate 2 patient

  Dataset EGAD00001009029

• Transcriptome of (monocytes and dendritic cells ), from donor S13, replicate 1 control

  Dataset EGAD00001009030

• Adaptive single-KIR+NKG2C+ NK cells expanded from superdonors show potent missing-self reactivity and efficiently control HLA-mismatched acute myeloid leukemia

  Study EGAS00001006614

• PDX WES for #87, #95, #32, #217, #86

  Exome sequencing of tumour PDX and matched patient blood

  Dataset EGAD50000000215