16541 results for "sec+and+unspecified+malignant+neoplasm"

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• Introducing Federated EGA Affiliates: a newly defined tier in the Federated EGA Network

  We're happy to announce that we are now accepting Affiliates in the Federated EGA Network! Federated EGA is a global network of repositories enabling secure discovery and access to sensitive human data. Currently, Federated EGA consists of Central EGA (CEGA), which serves the global community, and nine national Nodes that serve respective jurisdictions. Did you know that the Federated EGA (FEGA) has always envisioned a third tier within the network? This brings us to FEGA Affiliates, recently defined in the newly approved "Federated EGA Structure and Organisation" document. This new tier broadens the range of institutions or organisations that can become part of FEGA. Thus begins a new era for the network. What is a FEGA Affiliate? A FEGA Affiliate archives and serves controlled-access data within its own governance scope, while still participating in federated discovery through the EGA catalogue. Affiliates can be individual organisations or institutions, healthcare genomics initiatives, consortia of institutions, as well as project-specific consortia (national or international). In practical terms, a FEGA Affiliate: Hosts and safeguards the data it archives. Submits non-personal metadata to CEGA so studies are discoverable and can receive EGA accessions. Manages permissions and data access workflows for its datasets. Provides data access and/or distribution, along with support, to authorised users. Key takeaways about FEGA Affiliates FEGA is now organised into three tiers: CEGA, FEGA Nodes, and FEGA Affiliates. Affiliates archive data from their own organisation and/or selected partner institutions (for example, an international consortium). Affiliates make studies discoverable through the EGA catalogue by submitting non-personal metadata for accessioning. Access decisions and conditions are managed locally by the Affiliate (e.g. distribution or access in a trusted research environment). Are you considering whether your institution or project might be a good fit as a FEGA Affiliate? Would you like to learn more about this tier? We have published a blog post that addresses potential common questions, providing answers on this topic. You can read it here.

  Blog fega-affiliates

• Genome-wide SNP genotyping of Central African rainforest hunter-gatherers and neighbouring agriculturalists

  The emergence of agriculture in West-Central Africa, ~5,000 years ago, profoundly modified the cultural landscape and mode of subsistence of most sub-Saharan populations. How this major innovation has impacted the genetic history of rainforest hunter-gatherers — historically referred to as “pygmies” — and agriculturalists, however, remains poorly understood. Here, we report genome-wide SNP data from eight of these populations located west-to-east of the equatorial rainforest. We find that hunter-gathering populations present up to 50% of farmer genomic ancestry, and that substantial admixture began only within the last 1,000 years. Furthermore, we show that the historical population sizes characterising these communities already differed before the introduction of agriculture. Our results suggest that the first socio-economic interactions between rainforest hunter-gatherers and farmers introduced by the spread of farming were not accompanied by immediate, extensive genetic exchanges and occurred on a backdrop of two groups already differentiated by their specialisation in two ecotopes with differing carrying capacities.

  Study EGAS00001000605

• Analysis of four key cell types (epithelial, fbroblast, myeloid and T cells)

  Alterations of the normal prostate epithelial cell genome trigger the emergence of carcinoma, however cancer-only analyses of genomic aberrations have resulted in little clinical translation. A deep understanding of the tumor microenvironment is likely to inform on key drivers of disease progression and novel therapeutic opportunities. We investigated the role of key cell types along disease progression, profiling the RNA abundance of 52 samples from 13 patients enriched for one of four key cell types, spanning a wide CAPRA-S risk score range. Using a novel differential transcription Bayes method, TABI, we avoided a priori patient stratification, and mapped transcriptional alteration events to specific risk scores. The developmental and cell-type resolution yielded a landscape of concurrent transcriptional alterations from the tumour microenvironment, including key hallmarks. These transcriptional signatures permitted the identification of a “high survival” patient cluster in an independent prostate cancer dataset. Most importantly, together with our integrated transcriptional analysis, differential tissue composition analysis provided evidence for a key role for monocytes and macrophages in prostate cancer progression and disease recurrence.

  Study EGAS00001003579

• Interactions between the tumor and the systemic response of breast cancer patients

  Breast cancer (BC) research has largely focused on the molecular properties of the tumor proper. However, to understand how cancer progresses and ultimately modulates patient outcome, we also require an understanding of the molecular changes in the patient systemic response (SR). Toward this end, we generated and analyzed RNA profiles from tumor and matched blood samples in 173 BC patients. We designed a system (MIxT) to explore and link tightly co-expressed gene sets (modules) across matched tissues. The gene composition of modules, and their expression, largely vary across tissues. Distinct patterns of expression in the SR are predominantly detected in highly immunogenic BC subtypes. We also find that the expression pattern of a module in one tissue is correlated to the expression pattern of a module in the other tissue in a fashion that depends on subtype. For example, systemic immunosuppression is detected in basalL patients in a fashion that is proportional to the level of expression of several tumor modules highlighting immune evasion mechanisms active in these particular cancers.

  Study EGAS00001001804

• reChIP-seq reveals widespread bivalency of H3K4me3 and H3K27me3 in CD4+ memory T-Cells

  Identification of genomic loci exhibiting the physical co-localization of two distinct DNA-associated proteins remains a technical challenge. To tackle this challenge we have developed a novel reChIP-seq approach that enriches for two distinct DNA-associated proteins in an unbiased and genome-wide manner. We illustrate the utility of our approach by identifying nucleosomes bivalently modified by H3K4me3 and H3K27me3 in primary human CD4+ central memory T-cells (TCMs). We unravel widespread bivalency of many unmethylated CpG-rich regions in TCMs overlapping with transcriptional start sites of developmental regulators. Furthermore, reChIP-seq uncovers two yet unobserved classes of bivalency, where either H3K4me3 or H3K27me3 is mixed with low but detectable bivalency. We provide evidence that bivalency is established by an interplay between genome and epigenome and thus attains stability. Our described reChIP-seq approach augments conventional ChIP-seq and is broadly applicable in the study of co-localization of DNA-associated proteins to unravel the intricate possibilities of combinatorial action.

  Study EGAS00001001568

• Whole genome, transcriptome and methylome profiling enhances actionable target discovery in high-risk paediatric cancer

  Zero Childhood Cancer is a precision medicine program to benefit children with poor-outcome, rare, relapsed or refractory cancer. Utilising tumour and germline whole genome sequencing (WGS) and RNA sequencing (RNAseq) across 252 tumours from high-risk paediatric cancer patients, we identified 968 reportable molecular aberrations (39.9% in WGS and RNAseq, 35.1% WGS-only, 25.0% RNAseq-only). 93.7% of patients had at least one germline or somatic aberration, 71.4% had therapeutic targets and 5.2% had a change in diagnosis. WGS identified pathogenic cancer-predisposing variants in 16.2% of patients. In 76 CNS tumours, methylome analysis confirmed diagnosis in 71.1% and contributed to a change of diagnosis in two cases (2.6%). To date 43 patients have received a recommended therapy, 38 of whom could be evaluated. A small but encouraging proportion of 31% showed some evidence of clinical benefit. Comprehensive molecular profiling resolved the molecular basis of virtually all high-risk cancers, which in some cases has significant clinical impact.

  Study EGAS00001004572

• Sensitive detection of tumor mutations from blood and its application to immunotherapy prognosis

  Cell-free DNA (cfDNA) is attractive for many applications, including cancer detection, locating, and monitoring. A fundamental task underlying these applications is the SNV calling from cfDNA, which, however, faces a new challenge, namely, the generally very low tumor content in cfDNA. Thus all existing callers fail to achieve satisfactory performance. Here we present cfSNV, a method incorporating multi-layer error suppression and hierarchical mutation calling, to address this important challenge. Furthermore, by leveraging cfDNA’s comprehensive coverage of clonal landscape, for the first time cfSNV can profile mutations even in subclones. In both simulated and real patient data, cfSNV vastly outperforms existing tools, showing tens of times increase in sensitivity in detecting mutations with low allele frequency while maintaining high precision. cfSNV can enhance the clinical utilities of cfDNA by dramatically reducing the required sequencing depth and therefore reduce the cost by magnitudes, and further make the Whole-Exome-Sequencing of cfDNA a viable option. As an example, we demonstrate that cfDNA-WES allows a new biomarker to effectively select patients for immunotherapy.

  Study EGAS00001004373

• Title: Divergent levels of CD112 and INKA1 define a distinct subset of human long-term hematopoietic stem cells

  The data provided here was critical in establishing that human long-term hematopoietic stem cells (LT-HSC), previously described as the most primitive HSC population, is actually composed of distinct subsets that can be prospectively isolated. Via mechanistic studies centering around the Rho-GTPase effector kinase PAK4 and its inhibitor INKA1, we identified the immune checkpoint ligand CD112 as a marker for hematopoietic stem and progenitor cells, that is highest expressed on LT-HSC. More importantly, CD112 can be used to stratify functionally distinct subsets within LT-HSC: In response to regeneration-mediated stress, the CD112low subset exhibits a transient restraint (termed latency) before contributing to hematopoietic reconstitution, while the CD112high subset is primed to respond rapidly. High resolution RNA-seq of the CD112 surface expression spectrum within rare LT-HSC subsets (human umbilical cord blood) demonstrated that more genes are differentially upregulated in the deeper quiescent and less metabolic active subset. Genes enriched in this subset centre around cell adhesion and Rho-GTPase signaling. This is in agreement with the scRNAseq data from human G-CSF mobilized peripheral blood (mPB) generated here that was used as an model of in vivo activation/priming revealing via RNA-velocity and pseudo-time analysis that INKA1high versus PAK4high, CDK6high and CD112high enrichment are either detected early or late in diffusion pseudotime indicative of quiescent versus primed cell status, respectively. RNAseq following INKA1 overexpression in LT-HSC and ST-HSC revealed by GSEA an overall stemness preserving phenotype and particularly in LT-HSC, but not in short-term HSC (ST-HSC), suppression of transcriptional programs linked to activation. Collectively, our data decipher the molecular intricacies underlying HSC heterogeneity and self-renewal regulation and point to latency as an orchestrated physiological response that integrates quiescence control with HSC fate choices to preserve a stem cell reservoir.

  Dataset EGAD00001006541

• Co-infection of fungi and bacteria in brain tissue from elderly persons and patients with Alzheimer's disease.

  Dac EGAC00001000797

• iOmics_genomic_data

  iOmics genomic data using 2.5M and Exome array

  Dataset EGAD00010001309