12405 results for ""non-traditional+communal+storytelling+approach"+novel+segment+noted+in+critical+commentary"

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• Influence of the Microbiome on Epigenetic Mechanisms in IBD

  Live CD4 T cells were sorted from inflamed and non-inflamed tissue samples of IBD patients or from healthy and IBD blood samples. ATAC-Seq libraries were generated from live CD4 T cells sorted from i) inflamed and non-inflamed tissue samples, ii) healthy and IBD blood samples, or from iii) CD4 T cell subsets polarised from healthy blood samples. After isolating crude nuclei, live CD4+ T cells were treated with Tagment DNA buffer and Tagment DNA Enzyme (Nextera DNA Library Prep Kit, Illumina), and then the DNA was purified by MinElute PCR Purification Kit (Qiagen). Transposed DNA fragments were amplified using specific adapters followed by purification with MinElute PCR Purification Kit (Qiagen). Fragments from 240-360pb were selected in the PippinHT system (Sage Science). The quality of the library and its DNA concentration were assessed by Bioanalyzer instruments (Agilent Technologies) and ultimately submitted for sequencing using Illumina HiSeq 2500 sequencer, V4 chemistry. On the other hand, single cell RNA-Seq libraries were generated exclusively from inflamed and non-inflamed tissue samples of Crohn’s disease patients. Briefly, live CD4 T cells were captured and encapsulated before cDNA amplification using the 10X Genomics Chromium Platform. Samples were prepared as outlined by 10x genomics Single Cell 3’ Reagent Kits v2 user guide. Samples were sequenced on a HiSeq 2500 with the following run parameters: Read 1 – 26 cycles, read 2 – 98 cycles, index 1 – 8 cycles.

  Dataset EGAD00001011066

• 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

• Characterization of genomic landscape of Peripheral T-cell Lymphomas, not otherwise specified (PTCL-NOS) (2018-10-30)

  Peripheral T-cell lymphomas not otherwise specified (PTCL-NOS) represent a heterogeneous group of nodal and extra-nodal mature T-cell lymphomas, with a low prevalence in Western countries. PTCL-NOSs account for about 25% of all PTCLs and are currently diagnosed based on exclusion criteria, as this lymphomas lack unifying morphological, phenotypic and genomic features. Cytogenetic and FISH analysis of PTCL-NOS samples have not revealed recurrent pathogenetic abnormalities, while gene expression profiling has shown only partial ability to segregate cases representing homogeneous clinic-pathological entities. This underscores the need to look at PTCL-NOS with innovative and high-throughput approaches to identify recurrent genetic lesions that could further our understanding of the biology of this heterogeneous group of diseases, provide better diagnostic tools and perhaps new targets for innovative treatments. Our aim is to study ~15 patients affected by PTCL-NOS. Out study will be funded by a private, non-profit Italian cancer research fund (Associazione Italiana per la Ricerca sul Cancro, www.airc.it) based on a grant owned by Anna Dodero and Cristiana Carniti, hematologists at INT. Samples will be analysed by whole genome sequencing using Illumina X10 machines, on a 150bp-PE protocol. Data will be analysed using the pipeline available in Team 78, under the supervision of Peter Campbell, the WTSI faculty who will oversee the project, and by Francesco Maura, visiting scientist at the WTSI. . This dataset contains all the data available for this study on 2018-10-30.

  Dataset EGAD00001004428

• Facial Skin Biophysical Multi-Parameter and Microbiome-Based Korean Skin Cutoype (KSC) Determination

  In this study, we conducted an integrated analysis of skin measurements, clinical BSTI surveys, and the skin microbiome of 950 Korean subjects to examine the ideal skin microbiome-biophysical association. By utilizing four skin biophysical parameters, we identified four distinct Korean Skin Cutotypes (KSCs) and categorized the subjects into three aging groups based on their age distribution. We established strong connections between 15 core genera and the four KSC types within the three aging groups, revealing three prominent clusters of the facial skin microbiome. Together with skin microbiome variations, skin tone/elasticity distinguishes aging groups while oiliness/hydration distinguishes individual differences within aging groups. Our study provides prospective reality data for customized skin care based on the microbiome environment of each skin type.

  Study EGAS00001007334

• ATAC-Seq of inflamed and non-inflamed biopsies

  Inflamed and non-inflamed biopsies were digested for 45 minutes at 37ºC with collagenase from Clostridium histolyticum (Sigma). Suspension cells were then stained with an antibody cocktail to sort live CD8-CD4+ T cells. After isolating crude nuclei of two thousand live CD4+ T cells, they were treated with Tagment DNA buffer and Tagment DNA Enzyme (Nextera DNA Library Prep Kit, Illumina), and then the DNA was purified by MinElute PCR Purification Kit (Qiagen). Transposed DNA fragments were amplified using specific adapters followed by purification with MinElute PCR Purification Kit (Qiagen). Fragments from 240-360pb were selected in the PippinHT system (Sage Science). The quality of the library and its DNA concentration were assessed by Bioanalyzer instruments (Agilent Technologies) and ultimately submitted for sequencing using Illumina HiSeq 2500 sequencer, V4 chemistry.

  Study EGAS00001007344

• INCLUDE Data Hub: NDA GUIDs for Down Syndrome Research

  The INCLUDE (INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE) Project is an NIH-wide collaboration that seeks to improve health and quality of life for people with Down syndrome. The INCLUDE Project Data Coordinating Center (DCC) and partners created the INCLUDE Data Hub, a centralized data resource that allows access to large-scale clinical and multi-omics datasets specific to Down syndrome and supports collaborative, cloud-based analysis to accelerate scientific discoveries related to Down syndrome and its co-occurring conditions. The INCLUDE Data Hub includes an innovative researcher data repository, a portal, and a cloud-based platform for data analysis. The portal provides access to de-identified demographics and clinical metadata, as well as multi-omics datasets, including whole genome sequences, transcriptomes, proteomes, and metabolomes. The INCLUDE Data Hub uses a 'registered access' model, whereby users can access the portal and associated data after agreeing to terms of use. From the Data Hub, users can also reach 'controlled access' data (e.g., whole genome sequences) only for those studies for which they have obtained approval by an NIH Data Access Committee through the corresponding dbGaP study. A thoughtful linkage strategy to identify duplicate participants across studies enables researchers to merge multiple datasets and data types to create richer datasets and reduce costly redundancies in large-scale data generation (e.g., -omics) while mitigating potential risks introduced by linkage.Based on recommendations from the NICHD Office of Data Science and Sharing, which completed a comprehensive assessment of existing Privacy Protecting Record Linkage (PPRL) and associated data governance solutions, the INCLUDE DCC and the associated NIH Steering Committee have implemented NIMH Data Archive Global Unique Identifiers (NDA GUIDs) in the INCLUDE Data Hub. The NDA GUID Tool is a government-owned and operated PPRL technology that enables researchers to link data from multiple sources to the same individual without revealing personally identifiable information (PII). With the NDA GUID Tool, the same participant information will return the same GUID whenever or wherever it is entered. The GUID itself is not personally identifiable information or protected health information. Sharing data with NDA GUIDs allows approved researchers to link together data on a single participant, even if the data were collected at different locations or through different studies or are distributed from different data repositories. This dbGaP study enables researchers to access an INCLUDE GUID Mapping File that includes all available NDA GUIDs and their associations to study-specific Participant IDs across studies in the INCLUDE Data Hub. This dbGaP study only distributes NDA GUIDs associated with the INCLUDE Data Hub datasets. To access the associated controlled-access datasets, add the corresponding dbGaP studies to your Data Access Request. A list of these studies is available on the INCLUDE Data Hub Portal: https://portal.includedcc.org/studies.

  Study phs003678

• Foundation Medicine Adult Cancer Clinical Dataset (FM-AD)

  The Foundation Medicine adult cancer clinical dataset consists of 18,004 unique solid tumor samples that underwent genomic profiling on a single uniform platform as part of standard clinical care. The dataset is derived from the FoundationOne® genomic profiling assay version 2 that interrogates exonic regions of 287 cancer-related genes and selected introns from 19 genes known to undergo rearrangements in human cancer. Genomic DNA samples were sequenced to over 500x median coverage, and custom computational analyses identified all classes of genomic alterations (base substitutions, insertions and deletions, copy number alterations, and rearrangements). Since matched normal tissue was unavailable for analysis, these data underwent additional filtering to enrich for cancer-related events. The reported data includes genomic alterations that are known and suspected tumor drivers, as well as variants of unknown significance. To preserve patient anonymity, all known or suspected germline variants were removed from the data unless known to be associated with cancer development. The dataset contains genomic alteration profiles generated by FoundationOne version 2 testing for adult cancer patients (over 18 y.o.), and represents a vast diversity of tumor subtypes, including many rare diseases not profiled as part of large-scale profiling efforts. Cases are grouped into 16 broad disease categories containing tumors from 162 unique disease subtypes. Since specimens were profiled as part of clinical care, limited clinical parameters were available, including age, gender, tissue of origin, % of tumor nuclei, and diagnosis. Publication of this dataset is intended to allow the broad scientific community access to this unique cohort for use in scientific research projects of common and rare types of cancer, both for generating leads regarding causal mechanisms as well as cross-testing and confirming existing hypotheses. A pediatric cancer clinical dataset consisting of data from 1,215 patients under 18 y.o. is available separately at: FOUNDATION MEDICINE

  Study phs001179

• ImmunAID

  The ImmunAID study is a multi-center research program aimed at improving the diagnosis and understanding of systemic autoinflammatory diseases. The study integrates immunological, and molecular data from patients across several European cohorts. Its objective is to identify biomarkers and biological pathways that characterize disease mechanisms and help differentiate between inflammatory conditions. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 779295. The project has generated genomic datasets, which are available through the European Genome-phenome Archive (EGA). In addition, it has produced several non-genomic datasets, listed below. All datasets have been fully anonymized in accordance with applicable ethical and regulatory frameworks. These non-genomic data are available upon request. To request access to these datasets, please contact: immunaid2024@gmail.com Your request will be studied by the Data Access Committee, and data will be made available upon approval. Non-genomic datasets available: Flow cytometry (general immune phenotype) — Dataset ID: ICKD2021569, size: 23 GB, 342 patients Somascan proteomics data — Dataset ID: ICKD2021570, size: 59 MB, 444 patients ELISA panel (Ferritin, CRP, HO1, IL-1B, IL-6, IL-8, IL-10, IL-12, IL-18, IFN-γ, TNF-α) — Dataset ID: ICKD2021571, size: 295 KB, 439 patients Mass spectrometry (MS/MS) — Dataset ID: ICKD2021572, size: 1.8 TB, 447 patients ELISA (CRP/ SAA) — Dataset ID: ICKD2021573, size: 321 KB, 443 patients Flow cytometry (inflammasome activity) — Dataset ID: ICKD2021574, size: 899 KB, 307 patients Plasma lipidomics — Dataset ID: ICKD2021575, size: 1.1 MB, 427 patients Urine lipidomics — Dataset ID: ICKD2021576, size: 833 KB, 368 patients ELISA (alarmins) — Dataset ID: ICKD2021577, size: 129 KB, 108 patients ELISA (IL-18 / IL-1) — Dataset ID: ICKD2021578, size: 271 KB, 330 patients Flow cytometry (NK cell alterations) — Dataset ID: ICKD2021579, size: 14 GB, 216 patients Chemokine measurements — Dataset ID: ICKD2021580, size: 489 KB, 404 patients Luminex (multiple analyte measurements) — Dataset ID: ICKD2021581, size: 759 KB, 369 patients

  Study EGAS50000001393

• Bulk-RNA Sequencing of high-grade pancreatic and non-pancreatic Neuroendocrine Neoplasms

  Therapeutic decisions in oncology depend on a precise pathological classification of individual neoplasms. Recent years have seen an intensification of research activities aimed at the extraction of clinically relevant information from patient-derived 'omics' data based on Machine-Learning models. However, a comprehensive training of Machine-Learning models requires sufficiently large numbers of training samples, which are usually not available for rare cancer types. The problem is worsened when individual tissues segregate into different cancer subtypes, as their discrimination would require even more training samples. Methods: Here, we report on a new data-augmentation technique to support the training of Machine-Learning models on ‘omics’ data from pancreatic neuroendocrine neoplasms (panNEN). PanNENs display all properties described above: Only about 2-3% of all pancreatic neoplasms are neuroendocrine and they fall into different subtypes with distinctly different prognosis, which makes the precise classification of such samples both difficult and important for therapy decisions. The approach reconstructs a given transcriptome based on healthy pancreatic cell type signatures and creates a Machine-Learning model that integrates the observed reconstruction error and predicted cell type proportions as features. Results: A benchmark of the deconvolution model predictions with the ground-truth found that the model could efficiently predict the sample grading, disease-related patient survival time, and differentiate between different subtypes in four panNEN and one mixed panNEN and non-pancreatic NENs dataset. We compared the predictive performance of the deconvolution-trained model to that of a model trained directly on the transcriptomic data, under inclusion of the Ki-67 classification gold standard biomarker, and found the performances to be comparable. Conclusion: Our approach serves as a data-augmentation technique to facilitate the training of Machine Learning models for rare cancer types via utilization of data from healthy origin. Additionally, the deconvolution results were clinically interpretable and further research can render the deconvolution approach an effective complementary asset for the clinical classification of neoplasms.

  Study EGAS00001004861

• SC_DDD-G-4

  Post-QC (pre-imputation) genotype data for N=6,983 DDD probands included in the neurodevelopmental disorder discovery GWAS (Niemi et al., Nature 2018). Consists of filtered set of samples and variants from EGAD00010001598 and EGAD00010001600. Includes patient HPO phenotype terms and GWAS summary statistics (including imputed variants). Samples were genotyped on the Illumina HumanCoreExome BeadChip and Illumina InfiniumCoreExome Beadchip

  Dataset EGAD00010001604

• RNA sequencing of pretreatment, on-treatment and posttreatment gastric and gastroesophageal junction tumors treated with neoadjuvant anti-PD-L1 plus chemotherapy

  This set contains a total of 78 files cram files with RNA sequencing data from 20 patients included in the PANDA study treated with 1 cycle of monotherapy atezolizumab and 4 cycles atezolizumab plus chemotherapy (docetaxel, oxaliplatin and capecitabine). RNA was isolated from fresh frozen material and sequenced at 4 timepoints baseline, after monotherapy atezolizumab, after combination atezolizumab plus chemotherapy and at resection (due to 2 missing samples, there is a total of 78 samples).

  Dataset EGAD50000000241

• NGS-based monitoring of the T cell receptor repertoire in living donor kidney transplant patients undergoing combination cell therapy

  PBMCs from six kidney transplant recipients receiving as part of the Trex001 study autologous Tregs and donor bone marrow and six control patients not receiving either of the two treatments were collected pre-transplant and at one, three and six month post-transplant. Donor reactive T-cells were identified by mixed lymphocyte reactions (MLR) and lineage specific T-cell receptor (TCR) repertoires of native T-cells and proliferating and non-proliferating T-cells from MLRs were determined by next generation sequencing based profiling of the TCR.

  Dataset EGAD50000000302

• NHLBI TOPMed: Early-Onset Atrial Fibrillation in the Estonian Biobank

  The Estonian Biobank is the population-based biobank of the Estonian Genome Centre of University of Tartu. The biobank is conducted according to the Estonian Gene Research Act and all participants have signed broad informed consent. The cohort size is currently 51,535 people from 18 years of age and up.

  Study phs001606

• Transcriptomic Profiles of Neoantigen-Reactive T Cells in Human Gastrointestinal Cancers

  This study explores the transcriptomic profiles of neoantigen-reactive tumor-infiltrating lymphocytes (TILs) from human bile duct and pancreatic cancer. The submitted data are bulk tumor RNA-Seq, tumor and germline whole-exome sequencing from 10 patients, and single cell RNA-Seq data from TIL of 5 of these patients.

  Study phs002765

• Genetic Study on Nephropathy in Type-2 Diabetes

  A collection of patients with type-2 diabetes was collected at the Joslin Diabetes Center. Patients were categorized to a group of cases with diabetic nephropathy, and a group of controls with normoalbuminuria. From these groups, 173 cases and 177 controls were randomly selected for a genome-wide association study.

  Study phs000302

• Analysis of Cell-Free DNA to Predict Outcome to Bevacizumab Combination Therapy in Metastatic Colorectal Cancer Patients

  This dataset includes FASTQ files of low coverage whole genome sequencing of normal tissue (n = 8), tumor tissue (n = 55) and cell free DNA from plasma samples (n=101) from patients with metastatic colorectal cancer treated with bevacizumab. A total of 164 samples are present from two different cohorts. The first batch of samples (n = 139, sample names sAPD302T until sAPD502_P0) were collected from the AC-Angiopredict Phase 2 trial (NCT01822444), the second batch of samples (n = 25m, sample names sAPD503_P0 until sAPD527_P0) were collected from the UMM cohort.

  Dataset EGAD50000000176

• Genomic and Transcriptomic sequencing of neuroblastoma cell lines

  Genomic and Transcriptomic sequencing of neuroblastoma HSR and ecDNA cell lines. Data represents five 96-well plates that were processed with G&T sequencing, resulting in genomic and transcriptomic data from the same single cells. Dataset contains 95 bam files containing the DNA sequencing data and 95 bam files containing the RNA sequencing data of CHP212 cells, 380 bam files (190 DNA and 190 RNA) for TR14 cells, 188 bam files (94 DNA and 94 RNA) for Kelly cells and 192 bam files (96 DNA and 96 RNA) for IMR5/75 cells.

  Dataset EGAD50000000729

• Multiple paralogues and recombination mechanisms contribute to the high incidence of 22q11.2 Deletion Syndrome

  The 22q11.2 deletion syndrome (22q11.2DS) is the most common microdeletion disorder. Why the incidence of 22q11.2DS is much greater than that of other genomic disorders remains unknown. Short read sequencing cannot resolve the complex segmental duplications (SDs) to provide direct confirmation of the hypothesis that the rearrangements are caused by non-allelic homologous recombination between the low copy repeats on chromosome 22 (LCR22s). To enable haplotype-specific assembly and rearrangement mapping in LCR22 clusters, we used whole genome (ultra-)long read sequencing of 9 duos (patients and parent of origin).

  Dataset EGAD50000000855

• Single Cell Sequencing of Medulloblastoma Samples

  This dataset contains the results of sequencing 9 samples (7 human/PDX, 2 cell lines) of medulloblastoma tumours (or 2 Neural Stem Cell lines). The data was collected to characterize the intratumour heterogeneity of chromothripsis in this tumour type. The 7 tumour samples underwent single cell DNA and RNA sequencing using the 10x CNVkit and 10xRNA protocols, and 1 of the tumour samples as well as the Neural Stem Cell lines underwent StrandSeq sequencing. All experiments were sequenced on Illumina sequencers. The provided files are paired-end fastq files from the sequencing experiments.

  Dataset EGAD50000000909

• Comprehensive molecular phenotyping of ARID1A-deficient gastric cancer reveals pervasive epigenomic reprogramming and therapeutic opportunities

  This dataset includes paired tumor-blood whole exome sequencing data for 209 gastric cancer (GC) patients, along with whole transcriptome sequencing data for 125 GC samples. Whole exome sequencing was conducted using Agilent SureSelect Human All Exon V6 kits. For RNA sequencing, total RNA was isolated using the RNeasy Mini Kit, and libraries were prepared with the TruSeq Stranded Total RNA with Ribo-Zero Gold protocol (Illumina). Aligned BAM files for both exome and RNAseq data are included in this dataset.

  Dataset EGAD50000000660

• Arcagen - NET / NEC G3

  Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here were are submitting the dataset that contains NGS files in .BAM format from 85 patients with extra-pulmonary neuroendocrine tumour or cancer grade 3 (NET / NEC G3).

  Dataset EGAD50000000913

• Long-term Survival Update and Extended RAS Mutational Analysis of the CAIRO2 Trial: Addition of Cetuximab to CAPOX/Bevacizumab in Metastatic Colorectal Cancer

  Here we present updated survival of the CAIRO2 trial and assessed whether the addition of anti-EGFR to anti-VEGF therapy could still be an effective treatment option for patients with extended RAS/BRAF wildtype and left-sided metastatic colorectal cancer (mCRC).

  Study EGAS50000000775

• Ex vivo modeling of precision immuno-oncology responses in lung cancer

  Single-cell RNA-sequencing (scRNA-seq) data from paired lung cancer organoids and immune cells. The experiment was performed using the Single Cell 5' solution of 10X Genomics. The dataset includes 15 samples from 4 multiplexed experiments. The multiplexing was performed using the Feature Barcoding technology of 10X Genomics.

  Study EGAS50000000593

• Identifying rare genetic variants in 21 highly multiplex autism families

  Study EGAS00001006928

• PanelSeq IDC samples

  PDX samples of breast cancer IDC mouse models created from patient tumor material of the NKI. Genomescan prepared the samples according to the procedure for Hybridization Capture using an Agilent SureSelect custom 0.5-2.9Mb kit. The prepared libraries were sequenced with Illumina sequencing technology. The samples are in fastq format and consist of the following PDX samples: IDC025, IDC026, IDC029, IDC031, IDC032, IDC038, IDC057, IDC062, IDC065, IDC069, IDC072, IDC090B, IDC092, IDC097, IDC099, IDC107, IDC113, IDC117, IDC143, IDC152, IDC159A, IDC159B, IDC180, IDC186, IDC192, IDC197, IDC198, IDC207, IDC209, IDC216, IDC218, IDC222, IDC229, IDC232, IDC274, IDC282, IDC290, IDC299, IDC307, IDC338, IDC344, IDC346, ILC006, ILC012, ILC083 and ILC248.

  Dataset EGAD50000001149