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• Prospective high-throughput genome profiling in advanced cancers:

  Genomics-based tumor profiling in advanced cancers (aC) was recently demonstrated as feasible in clinical practice. After the prospective pilot PANDORA study done on advanced breast cancers, we conducted a prospective monocenter clinical trial called PERMED01 to evaluate the number patients with locally advanced or metastatic cancer for whom identification of molecular alterations in tumor samples could lead to the delivery of a targeted therapy

  Study EGAS00001004554

• 46 patients primary malignant glioma cohort in Chinese population

  We recruited 46 Chinese patients with primary malignant glioma. All the patients were analyzed with whole-exome sequencing (WES) and 27 of them were analyzed with RNA-seq. We compared the molecular features between patients in different WHO grades. We used six-month progression-free survival (PFS6) status to group glioblastoma (GBM) patients to a good and a poor survival compared two groups genomic profiles.

  Study EGAS00001005583

• Genomic abnormalities of TP53 define distinct risk groups of paediatric B-cell non-Hodgkin lymphoma

  TP53 alterations are associated with adverse outcome in many malignancies; however, whilst common in paediatric B-NHL, their utility as a risk classifier in this setting is unknown. We evaluated the clinical significance of TP53 abnormalities (mutations, deletion and/or copy number neutral loss of heterozygosity) in a large UK paediatric B-NHL cohort (n=95) and determined their impact on survival.

  Study EGAS00001005617

• Multiomics Characterization of Low-grade Serous Ovarian Carcinoma

  Low-grade serous ovarian carcinoma (LGSOC) is a rare tumor subtype with high case fatality rates. There is a pressing need to develop effective treatments using newly available preclinical models for therapeutic discovery and drug evaluation. In this study, we use multiomics integration of whole exome sequencing, RNA sequencing, and mass spectrometry-based proteomics on fourteen LGSOC patient derived cell lines to elucidate novel biomarkers and therapeutic vulnerabilities.

  Study EGAS00001004724

• ScRNA sequencing and snp-array genotyping of peripheral immune cells in Type 1 diabetes mellitus (T1DM)

  Type 1 diabetes mellitus (T1DM) is a prototypic endocrine autoimmune disease resulting from an immune-mediated destruction of pancreatic insulin-secreting beta-cells. A comprehensive immune cell phenotype evaluation in T1DM has not been performed thus far at the single. In this cross-sectional analysis, we generated a single-cell transcriptomic dataset of peripheral blood mononuclear cells (PBMCs) from 46 manifest T1DM (Stage 3) cases and 31 matched controls.Our study reveals a surprisingly strong systemic dimension at the level of immune cell network in T1DM, defines disease-relevant molecular subtypes and has the potential to guide non-invasive test development and patient stratification.

  Dataset EGAD50000000345

• Brazilian Thyroid WES

  Medullary thyroid cancer (MTC) is a rare malignant tumor that arises from parafollicular cells. Approximately 8% of thyroid cancer cases are MTC, and about 25% of these have a hereditary component. Incorporating molecular parameters into tumor classification is important. Besides, the presence of pathogenic germline variants can impact directly on cancer prevention. Thus, the aim of this study was to perform whole exome sequencing (WES) on a consecutive series of hereditary RET wild-type MTC patients to identify genetic variants that may be involved in the carcinogenesis of this tumor. WES was performed on 28 patients negative for germline RET pathogenic variants using the NovaSeq 6000 platform. Variant classification followed American College of Medical Genetics and Genomics guidelines.Our study represents a significant advancement in gene discovery for MTC genetics.

  Dataset EGAD50000000086

• The Causes of Clonal Blood Cell Disorders Study - SCOR_Custom (2018-04-19)

  We took a bone marrow aspirate and peripheral blood samples from a healthy patient aged around 60, and use flow cytometry to isolate 100 HSCs, 50 MEPs, and 50 GMPs. We grew these up into colonies, then whole genome sequenced each colony. Somatic mutations act as a unique barcode for each clone. We have designed a panel for targeted resequencing of the mutations that we find. We are now looking for these mutations in the peripheral blood, to see the dynamics of how HSCs contribute to the peripheral blood in health. This dataset contains all the data available for this study on 2018-04-19.

  Dataset EGAD00001004087

• Identifying autosomal recessive mutations causing neurological disorders

  The objective of this study is to resequence of targeted intervals containing autosomal recessive variants causing neurological disorders in consanguineous pedigrees. Using homozygosity mapping, three intervals of very different sizes have previously been unambiguously mapped for three different neurological diseases: 2.4Mb, 8Mb and 14.3Mb in size, for Microlissencephaly, Severe Mental Retardation and Complicated hereditary spastic paraplegia respectively. This study is a pilot to assess how well custom targeted resequencing performs across a broad size range of intervals. The study design is to use a different custom capture probe set for each interval, pulldown from a single patient from each family, and sequence 1 lane using Illumina paired-reads for each sample. Candidate variants will be followed up in the families themselves, and in patients with similar phenotypes from outbred populations

  Dataset EGAD00001000340

• A Genome Wide Scan of Lung Cancer and Smoking

  The majority of cases of lung cancer are the culmination of a dynamic process that begins with smoking initiation, proceeds through dependency and smoking persistence, continues with lung cancer development and ends with progression to disseminated disease or response to therapy and survival. We are conducting a whole genome study of lung cancer and smoking to examine critical steps in lung cancer progression. This study is a genome-wide association study (GWAS) to investigate the genetic determinants of lung cancer risk. The study design efficiently allows identification of genes that also contribute to smoking persistence and outcome from lung cancer using a single GWAS of 5,900 subjects using the primary GENEVA dataset, derived from two studies. The first is the Environment and Genetics in Lung Cancer Etiology Study (EAGLE), a population-based, biologically intensive, case-control study from the Lombardy region of Italy including ~2000 newly diagnosed lung cancer cases and ~2000 age-, gender- and region- matched controls. The second is the Prostate, Lung, Colon and Ovary Study (PLCO) Cancer Screening Trial from which we have selected ~850 lung cancer cases and ~850 controls, also matched on age and gender. Understanding the basis for the well-established hereditary component of lung cancer and smoking persistence could provide new insights into etiology, prevention, and treatment, and have an enormous impact on public health. The same GWAS genotyping data in the two studies will be used to investigate the genetic determinants of smoking persistence. Specifically, we will analyze current smokers and former smokers from EAGLE and PLCO for diverse smoking phenotypes, including persistence of smoking as well as ever/never smoking comparisons, quitting attempts, and the Fagerström index of tobacco addiction. PLCO participants are all European-Americans and EAGLE involves subjects from Italy. EAGLE is a case-control study and contains 3937 phenotyped subjects. PLCO is a screening trial with a cohort design and contains 1651 phenotyped subjects. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to lung cancer and smoking through large-scale genome-wide association studies of population-based samples of lung cancer cases and controls. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000093

• Offspring Sex Impacts DNA Methylation and Gene Expression in Placentae from Women with Diabetes during Pregnancy

  Exposure to diabetes in utero is known to increase the offspring's likelihood of developing metabolic disease in adulthood, but the mechanisms involved are unknown. It has been proposed that early exposure to hyperglycemia and elevated insulin levels may lead to malprogramming of the fetus leading to the subsequent development of diabetes and obesity. Epigenetic modifications of the genome including DNA methylation, provide a plausible mechanism that allows for permanent propagation of gene activity states from one generation of cells to the next. The placenta, a fetal tissue easily accessible for study, is a complex organ that is essential in regulating fetal growth. The changes in placental nutrient transport associated with diabetes during pregnancy (DDP) have significant effects on the developing fetus, indicating that the placenta plays a critical role in fetal programming. The aim of our study was to investigate whether exposure to DDP alters genome-wide DNA methylation in the placenta obtained from term pregnancies resulting in differentially methylated loci of metabolically relevant genes and downstream changes in RNA and protein expression.

  Study phs001535