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• COVID-19 ACTIV-4 ACUTE: A Multicenter, Adaptive, Randomized Controlled Platform Trial of the Safety and Efficacy of Antithrombotic Strategies in Hospitalized Adults with COVID-19 (ACTIV4A)

  This is a randomized, open label, adaptive platform trial to compare the effectiveness of antithrombotic strategies for prevention of adverse outcomes in COVID-19 positive inpatients.

  Study phs002694

• Disease recurrence after pathologic response

  In this study we aim to gain insight into the mechanisms of immune evasion after initial pathologic response to neoadjuvant immune checkpoint inhibition in macroscopic stage III melanoma by pooling data of the neoadjuvant OpACIN, OpACIN-neo and PRADO trials. Therefore we analyzed in-depth paired baseline(n=10) and recurrent tumor samples of the lymph nodes(n=10) and brain metastasis (n=2) through Whole Exome Sequencing (WES) analyses.

  Dataset EGAD50000000700

• Familial_Thrombocytosis_germline_exome_sequencing

  Agilent whole exome hybridisation capture will be performed on genomic DNA derived from 3 relatives from a family with multiple affected familial thrombocytosis (FT) patients. Germline DNA from 2-4 patients affected with FT will be used to prepare libraries and sequenced in one lane of HiSeq sequencing, mapped to build 37 of the human reference genome to facilitate the identification of novel cancer genes.

  Study EGAS00001000088

• Analysis of transcriptomic profiles from affected and recovered muscle biopsies from children with reversible infantile respiratory chain deficiency.

  All patients carry mitochondrial mutation m.14674T>C but not must also carry mutations in related genes such as TRMU and EARS2. Analysis compares muslce biopsies of affected patients to controls, a patient after recovery to themselves during the the disease phase, and compared patients with additional EARS2 mutations to patients without pathogenic EARS2 mutations.

  Study EGAS00001004647

• NIH Division of Intramural Research Multiomic Monogenic Disease Study

  This cross-sectional, observational study utilized high-dimensional approaches, including transcriptomic analysis, serum protein profiling, and peripheral blood frequency assessments, to compare 228 adult and pediatric patients diagnosed with 22 monogenic immune-mediated conditions impacting various key immunological pathways, with 42 age- and sex-matched healthy controls. Participants were enrolled in clinical protocols at the National Institutes of Health in Bethesda, Maryland, USA. The primary objectives of the study were twofold: first, to identify disease-specific and shared "pan-disease" signatures using the collected data, and second, to develop a Random Forest classifier integrating these datasets to differentiate between age- and sex-matched healthy individuals and monogenic patients. This classifier produced a metric known as the "Immune Health Metric" (IHM). Subsequent analysis demonstrated that the IHM effectively distinguished healthy subjects from individuals with multiple polygenic autoimmune and inflammatory diseases in independent datasets. Moreover, it exhibited correlations with healthy aging and tracked disease activities and treatment responses across both immunological and non-immunological conditions. Additionally, the IHM accurately predicted age-dependent antibody responses to various vaccines, including influenza, Hepatitis A/B, Yellow Fever, Varicella Zoster, and Meningococcal vaccines.The microarray-based transcriptomic data, aptamer-based serum protein concentrations (Somalogic platform), peripheral blood frequency data, and clinical information will be made accessible through the database of Genotypes and Phenotypes (dbGaP).

  Study phs002732

• Chromatin profiles classify castration-resistant prostate cancers suggesting therapeutic targets

  Untreated prostate cancers rely on androgen receptor (AR) signaling for growth and survival, forming the basis for the initial efficacy of androgen deprivation therapy (ADT). Yet, the disease can relapse and progress to a lethal stage termed castration-resistant prostate cancer (CRPC). Reactivation of AR signaling represents the most common driver of CRPC growth and next generation AR signaling inhibitors (ARSIs) are now used in combination with ADT as a first line therapy. However, ARSIs can result in selective pressure generating AR-independent tumors. The transition from AR-dependence frequently accompanies a change in phenotype resembling developmental trans-differentiation or ‘lineage plasticity’. Neuroendocrine prostate cancer, which lacks a defined pathologic classification, is the most studied type of lineage plasticity. However, most AR-null tumors do not exhibit neuroendocrine features and are classified as ‘double-negative prostate cancer’, the drivers of which are poorly defined. Lineage plasticity studies in CRPC are limited by the lack of genetically defined patient-derived models that recapitulate the disease spectrum. To address this, we developed a biobank of organoids generated from patient biopsies to study the landscape of metastatic CRPC and allow for functional validation assays. Proteins called transcription factors (TFs) are drivers of tumor lineage plasticity. To identify the key TFs that drive the growth of AR-independent tumors, we integrated epigenetic and transcriptomic data generated from CRPC models. By presenting a map of the chromatin accessibility and transcriptomic landscape of CRPC using a diverse biobank of organoids, PDXs and cell lines that recapitulate the heterogeneity of metastatic prostate cancer, we validate CRPC-AR and CRPC-NE subtypes and reveal two subtypes of AR-negative/low samples as well as their respective masterTFs. Additional analysis revealed a model in which YAP, TAZ, TEAD and AP-1 function together and drive oncogenic growth in CRPC-SCL samples. Overall, we show here how an approach to stratify CRPC patients into four subtypes using their transcriptomic signatures can potentially inform upon appropriate clinical decisions.

  Study EGAS00001006059

• UK10K NEURO IOP COLLIER

  In the UK10K project we propose a series of complementary genetic approaches to find new low frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will analyse directly quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. The Genetics and Psychosis (GAP) set consists of samples from subjects with schizophrenia, ascertained as a new-onset sample. This set is of UK origin, with data on cognition, brain imaging and other endophenotypes. The Maudsley twin series consists of probands ascertained from the Maudsley Twin Register, defined as patients of multiple birth who had suffered psychotic symptoms. This set is of UK origin, with data on cognition, brain imaging and other endophenotypes, with DNA available from an MZ or DZ affected or unaffected co-twin. The Maudsley family study (MFS) consists of over 250 families who have a history of schizophrenia or bipolar disorder Within the Maudsley Family Study, biological markers of psychosis include neuropsychological tests, Evoked Response Potentials Tests (ERPs), MRI scans, dermatoglyphics and eye tracking. Early risk factors for psychosis and clinical symptoms are also investigated. This set is of UK origin, with DNA available from both affected and unaffected relatives in many of the probands. For further information on this study please contact David Collier (david.collier@kcl.ac.uk).

  Study EGAS00001000121

• Nimblegen

  The aim of this project is to identify rare variants in the 1q region associated with type 2 diabetes. To this end 651 case samples and 651 control samples from six populations have been pooled (pool sizes range from 27-33 individuals), and are being sequenced. The hybridization solution being used captures the exons and UTRs of genes in the 1q region.

  Dataset EGAD00001000424

• Non-invasive human skin transcriptome analysis using mRNA in skin surface lipids

  Non-invasive acquisition of mRNA data from the skin can be extremely useful for understanding skin physiology and diseases. Inspired by the holocrine process, in which the sebaceous glands secrete cell contents into the sebum, we focused on the possible presence of mRNAs in skin surface lipids (SSLs). We found that measurable levels of human mRNAs exist in SSLs, where the sebum protects them from degradation by RNases. The AmpliSeq transcriptome analysis was modified to measure SSL-RNA levels, and our results revealed that the SSL-RNAs predominantly comprised mRNAs derived from sebaceous glands, the epidermis, and hair follicles. Analysis of SSL-RNAs non-invasively collected from patients with atopic dermatitis revealed significantly increased expression of inflammation-related genes and decreased expression of terminal-differentiation���related genes, consistent with the results of previous reports. Further, we found that lipid-synthesis���related genes were downregulated in the sebaceous glands of patients with atopic dermatitis. These results indicate that the analysis of SSL-RNAs is a promising strategy to understand the pathophysiology of skin diseases.

  Study JGAS000416

• Non-invasive human skin transcriptome analysis using mRNA in skin surface lipids

  Non-invasive acquisition of mRNA data from the skin can be extremely useful for understanding skin physiology and diseases. Inspired by the holocrine process, in which the sebaceous glands secrete cell contents into the sebum, we focused on the possible presence of mRNAs in skin surface lipids (SSLs). We found that measurable levels of human mRNAs exist in SSLs, where the sebum protects them from degradation by RNases. The AmpliSeq transcriptome analysis was modified to measure SSL-RNA levels, and our results revealed that the SSL-RNAs predominantly comprised mRNAs derived from sebaceous glands, the epidermis, and hair follicles. Analysis of SSL-RNAs non-invasively collected from patients with atopic dermatitis revealed significantly increased expression of inflammation-related genes and decreased expression of terminal-differentiation���related genes, consistent with the results of previous reports. Further, we found that lipid-synthesis���related genes were downregulated in the sebaceous glands of patients with atopic dermatitis. These results indicate that the analysis of SSL-RNAs is a promising strategy to understand the pathophysiology of skin diseases.

  Study JGAS000418