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• Bibliography Statistics

  EGA Statistics Bibliography Growth Community Archive Distribution Catalogue Here we expose in a yearly and cumulative manner, the publications, citations, publishers journals and impact factor of EGA related studies. Publications citing data stored at the EGA The studies that use EGA datasets are requested to cite the EGA into their bibliography. This fact allows for a faster spreading of the role and purpose of the EGA, being the availability of their studies for testability and reusability purposes. In order to track the studies we use the unique study accession, provided by the EGA when submitting a new study, and comprised of the keyword EGAS followed by 11 digits such as EGAS00000000001. We use the Europe PMC RESTful Web Service in order to query, and index studies where the EGAS accession was used. Below, you can find a chart showing the journal publishers of the studies citing data stored in the EGA being cited: Publications Citing Data Stored in the EGA pieChart('publishers-citations', 'https://stats.ega-archive.org/publishers/citations', 'Publishers') Number of publications citing data stored in the EGA by year Here, you can find a chart showing the number of publications citing data stored in the EGA by year. These publications could have deposit, or re-used the data. The study acession can be found in the publication. The default values are non-cumulative. You can see cumulative values by clicking "Cumulative". Number of Publications Citing Data Stored in the EGA by Year barChart('publications-by-year', 'https://stats.ega-archive.org/publications/year', ['Number of Publications by Year', 'Publications by Year']) Journal Impact Factor of Publications Citing Data Stored in the EGA Here, you can find a chart showing the journal impact factor of publications citing data stored in the EGA. We have used Scimago Journal Rank for this purpose. Low impact treshold was defined between 0.0 and 5.0 excluding; Medium impact treshold was defined between 5.0 and 10.0 excluding; High impact range was established from values equal to or higher than 10.0; Journal Impact Factor of Publications Citing Data Stored in the EGA pieChart('citations-publication-impact-factor', 'https://stats.ega-archive.org/citations/publication/impact/factor', 'Publication Impact Factor') Publications citing a publication containing a EGA study accession The publications, stating a EGA study accession in the bibliography, can be cited for testibility or reusability purposes. Below, you can find a chart showing the publishers of the publications being cited: Publications Citing a Publication Containing a EGA Study Accession pieChart('publications-citations', 'https://stats.ega-archive.org/publications/citations', 'Publishers') Number of publications citing a publication containing a EGA study accession by year Here, you can find a chart showing the number of publications citing a publication containing a EGA study accession by year by year. The values are non-cumulative: Number of published citations by year barChart('citations-year', 'https://stats.ega-archive.org/citations/year', ['Number of Publications', 'Citations by Year'])

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• Genetic Landscape of Esophageal Squamous Cell Carcinoma Defined by Exome Sequencing on Chinese Patient Cohort and Cell Lines

  Esophageal Squamous Cell Carcinoma (ESCC) is one of the deadliest cancers worldwide. To define the genetic landscape of ESCC, whole exome sequencing was performed on 113 pairs of tumor and normal DNA samples collected from Chinese individuals with treatment-naive primary ESCC, along with 8 cell lines, including 7 from KYSE series ESCC cell lines and one immortalized esophageal squamous epithelial cell line Het-1A.

  Study EGAS00001000932

• Much ado about nothing? - Off-target amplification can lead to false positive bacterial brain microbiome detection in healthy and Parkinson's disease individuals

  Recent studies suggested the existence of (poly-)microbial infections in human brains. Using 16S rRNA gene amplicon sequencing, we tested the hypothesis that there is a bacterial brain microbiome. We evaluated brain samples from healthy human subjects and individuals suffering from PD (olfactory bulb and pre-frontal cortex). We identified off-target amplification as a major confounding factor in low-bacterial/high-host-DNA scenarios.

  Study EGAS00001004757

• SOFT study - sequencing premenopausal breast cancer (2017-11-22)

  Our project will examine the role of PIK3CA mutations and their sensitivity to endocrine therapies and its role, with the addition of complete ovarian suppression. We plan to test our hypotheses using tumour samples collected from patients enrolled in the SOFT/IBCSG24-02 clinical study (Suppression of Ovarian Function Trial - (NCT00066690). SOFT is a phase III trial that randomised 3066 premenopausal women to evaluate if adding ovarian suppression to adjuvant endocrine therapy will improve clinical outcomes. This dataset contains all the data available for this study on 2017-11-22.

  Dataset EGAD00001003811

• Kids First: Whole Genome Sequencing Studies of Multiplex Nonsyndromic Cleft Lip/Palate Families

  Nonsyndromic cleft lip with or without cleft palate (NSCLP) is a common birth defect that affects approximately 135,000 newborns worldwide, and imposes significant medical, psychosocial and financial burdens to affected individuals and their families. Genetic variation in numerous genes has been associated with NSCLP, however the exact mechanisms by which they contribute to the condition remain largely unknown. Here, we will use whole genome sequencing and robust analytical approaches to systematically evaluate the causes of NSCLP in 923 individuals from large multigenerational families.

  Study phs002626

• Use of Whole Genome Sequencing for Diagnosis and Discovery in the Cancer Genetics Clinic

  Despite the potential of whole-genome sequencing (WGS) to improve patient diagnosis and care, the empirical value of WGS in the cancer genetics clinic is unknown. We performed WGS on members of two cohorts of cancer genetics patients: those with BRCA1/2 mutations (n = 176) and those without (n = 82). Initial analysis of potentially pathogenic variants (PPVs, defined as nonsynonymous variants with allele frequency < 1% in ESP6500) in 163 clinically-relevant genes suggested that WGS will provide useful clinical results. This is despite the fact that a majority of PPVs were novel missense variants likely to be classified as variants of unknown significance (VUS). Furthermore, previously reported pathogenic missense variants did not always associate with their predicted diseases in our patients. This suggests that the clinical use of WGS will require large-scale efforts to consolidate WGS and patient data to improve accuracy of interpretation of rare variants. While loss-of-function (LoF) variants represented only a small fraction of PPVs, WGS identified additional cancer risk LoF PPVs in patients with known BRCA1/2 mutations and led to cancer risk diagnoses in 21% of non-BRCA cancer genetics patients after expanding our analysis to 3209 ClinVar genes. These data illustrate how WGS can be used to improve our ability to discover patients' cancer genetic risks. "Reprinted from doi:10.1016/j.ebiom.2014.12.003, with permission from EBioMedicine."

  Study phs000942

• Single-cell and spatial transcriptomic profiling of peritoneal metastasis of gastric cancer

  Peritoneal metastasis is associated with disease progression and poor prognosis in gastric cancer. This study uses single-cell RNA sequencing and spatial transcriptomics to characterize the cellular composition, tumor microenvironment heterogeneity, and potential therapeutic vulnerabilities in peritoneal metastasis of gastric cancer. Human peritoneal metastatic samples and humanized mouse models engrafted with human gastric cancer cells were analyzed to investigate tumor and immune microenvironment features.

  Study EGAS50000001875

• Whole Genome Scan for Pancreatic Cancer Risk in the Pancreatic Cancer Cohort Consortium and Pancreatic Cancer Case-Control Consortium (PanScan)

  Within the framework of the NCI-sponsored Cohort Consortium, investigators from 12 prospective epidemiologic cohorts formed the Pancreatic Cancer Cohort Consortium in 2006. This study, also known as "PanScan", is funded by the National Cancer Institute (NCI) and involves conducting a genome-wide association study (GWAS) of common genetic variants to identify markers of susceptibility to pancreatic cancer. In 2007, the study was expanded to include 8 case-control studies. The study team includes scientists from the cohorts comprising the Consortium, the NCI and the Pancreatic Cancer Case Control Consortium (PanC4). PanScan I and II were conducted in 12 cohort studies and 8 case-control studies, leading to the discovery of four novel regions in the genome associated with risk for pancreatic adenocarcinoma. The third phase of PanScan (PanScan III) was conducted using recently identified incident pancreatic cancer cases drawn from fourteen cohorts from the cohort consortium, including nine prospective cohorts who participated in PanScan I, and five newly joined cohorts. The nine cohort studies that participated in PanScan I and had new genotyping of cases in PanScan III include ATBC, CPS-II, EPIC, HPFS, NHS, PHS, PLCO, SMWHS, and WHI; the five newly joined cohort studies include the Agricultural Health Study (AHS), the Multiethnic Cohort Study (MEC), the Melbourne Collaborative Cohort Study (MCCS), the Vitamins and Lifestyle Study (VITAL), and Selenium and Vitamin E Cancer Prevention Trial (SELECT). In addition to the cases from cohorts, we also included cases from the Gastrointestinal Cancer Clinic of Dana-Farber Cancer Institute Study (DFCI-GCC); from the University Hospital in Heidelberg, Germany, which is part of a larger European clinical case-control study (PANDoRA); and from clinic-based cases from eastern Spain (PANKRAS-II). The dbGaP datasets available include all subjects previously made available from PanScan I and II, plus 1,582 new incident pancreatic cancer cases of European descent from prospective cohorts, case-control studies or case series (genotyped as part of PanScan III). Also included are 61 pancreatic cancer cases and 67 control subjects from PanScan I as well as 173 pancreatic cancer cases from PanScan III of Asian ancestry from the Shanghai Men's and Women's Health Study (Supplemental Table 10, Wolpin et al. (Nat Genet, 2014)). The control population used in the analysis for the Wolpin et al. manuscript included cancer-free individuals from the prospective cohorts that contributed pancreatic cancer cases to PanScan III and controls from the Spanish Bladder Cancer SBC/EPICURO study that were previously genotyped using the OmniExpress, Omni 1M or Omni 2.5M SNP arrays. The data from these control subjects were posted to dbGaP under the GWAS in which they were initially genotyped and will not be made available in duplicate under this dbGaP study.The summary statistics for PanScan I-III were generated as detailed in Wolpin BM. et al., Genome-wide association study identifies multiple susceptibility loci for pancreatic cancer, Nature Genetics 2014; 46(9):994-1000 (https://www.nature.com/articles/ng.3052), and Klein, A. et al., Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer, Nature Communications, 2018;9(1):556 (https://www.nature.com/articles/s41467-018-02942-5). The dataset includes results from an association study of 5,117 individuals diagnosed with pancreatic ductal adenocarcinoma (PDAC) and 8,845 control individuals, or a total of 13,962 subjects of European ancestry (as compared to the genotype and phenotype information under this project that includes 9,437 individuals (PanScan I and II PDAC case and control individuals and PanScan III PDAC cases only). This is due to the fact that PanScan III “borrowed” GWAS data from control individuals genotyped separately from the PanScan GWAS project and are therefore not included as raw genotypes in phs000206.v5.p3. Association analysis was performed separately for PanScan I-II and PanScan III, followed by a meta-analysis of the two datasets. Results were filtered based on a minor allele frequency (MAF) < 0.01, an imputation INFO score < 0.3 and a heterogeneity P-value < 1x10-10 leaving a total of 9,758,390 variants. Columns in the summary statistics dataset are as follows: ID: variant rsIDChr: chromosome numberPosition: position in the chromosome, genome build GRCh37/hg19MarkerName: variant identifierAllele1: reference alleleAllele2: alternative alleleFreq1: allele frequency for allele2FreqSE: standard error of the allele frequencyMinFreq: the minimal allele frequency across studiesMaxFreq: the max allele frequency across studiesEffect: effect size for allele2StdErr: standard errorP-value: meta-analysis p-valueDirection: summary of effect direction for each studyHetISq: I^2 statistic which measures heterogeneity on scale of 0-100%HetChiSq: chi-squared statistic in simple test of heterogeneityHetDf: degrees of freedom for heterogeneity statisticHetPVal: P-value for heterogeneity statistic

  Study phs000206

• Exome sequencing of early and late passage Patient Derived Xenogratf Tumoroids with matched Patient Derived Xenogratfs and matched normal liver

  The breadth and depth at which cancer models are interrogated contribute to successful translation of drug discovery efforts to the clinic. In colorectal cancer (CRC), model availability is limited by a dearth of large-scale collections of patient-derived xenografts (PDXs) and paired tumoroids from metastatic disease, the setting where experimental therapies are typically tested. XENTURION is a unique open-science resource that combines a platform of 129 PDX models and a sister platform of 129 matched PDX-derived tumoroids (PDXTs) from patients with metastatic CRC, with accompanying multidimensional molecular and therapeutic characterization. In this specific dataset we focused our attention on early (passage 3) and late (passage 8-12) PDXTs with their matched PDXs and normal liver

  Dataset EGAD50000000266

• Carboxylesterase 1 mediates a distinctive metabolic profile of dendritic cells to attain an inflammatory phenotype

  The metabolic profile of dendritic cells (DCs) shapes their phenotype and functions. Carboxylestrase 1 (CES1) enzyme is highly expressed in mononuclear myeloid cells however its exact role in DCs is elusive. We used a CES1 inhibitor (WWL113) and genetic overexpression to explore the role of CES1 in DCs differentiation in inflammatory models. CES1 expression was analyzed during CD14+ monocytes differentiation to DCs (MoDCs) using quantitative PCR. CES1 Inhibitor (WWL113) was applied during MoDCs differentiation. Surface markers, secreted cytokines, lactic acid production, phagocytic and T cell polarization capacity were analyzed. Transcriptomic and metabolic profile were assessed with RNA-sequencing and mass spectrometry. Cellular respiration was assessed with seahorse respirometry. Transgenic mice were used to assess CES1 overexpression in DCs in inflammatory models. CES1 expression peaks early during MoDCs differentiation. Pharmacological inhibition of CES1 led to higher expression of CD209, CD86 and MHCII. WWL113 treated MoDCs secreted higher quantities of IL6, IL8, TNF and IL10 and demonstrated stronger phagocytic ability and higher capacity to polarize Th17 differentiation in autologous DCs-T cells co-culture model. Transcriptomic profiling revealed enrichment of multiple inflammatory and metabolic pathways. Functional metabolic analysis shows impaired maximal mitochondrial respiration capacity, increased lactate production and decreased intracellular amino acids and TCA intermediates. Transgenic human CES1 overexpression in murine DCs generated less inflammatory phenotype and increased resistance to T cell mediated colitis. In conclusion, CES1 inhibition directs DCs differentiation towards more inflammatory phenotype, that shows stronger phagocytic capacity and supports Th17 skewing. This is associated with disrupted mitochondrial respiration and amino acids depletion.

  Study EGAS50000000230