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• The Innate Immune Response as a Therapeutic Target for Cutaneous Leishmaniasis

  The overall goal of the proposed project is to identify innate host functions that contribute to cure or failure during antileishmanial drug therapy, and their related biomarkers. Results from this project enabled the identification of host-specific predictive and prognostic signatures of therapeutic responsiveness, and the bases for rational selection of host-targeted therapeutics that redirect inflammation and revert pathogenicity. Through sequential transcriptomic profiling of blood monocytes (Mo), neutrophils (Nφ), and eosinophils (Eφ) over the course of systemic treatment with meglumine antimoniate, we discovered that a heightened and sustained Type I interferon (IFN) response signature is a hallmark of treatment failure (TF) in Cutaneous Leishmaniasis (CL) patients. The transcriptomes of pre-treatment, mid-treatment and end-of-treatment samples were interrogated to identify predictive and prognostic biomarkers of TF. Results from this study instigate the evaluation of Type-I IFN responses as new immunological targets for host-directed therapies for treatment of CL, and highlight the feasibility of using transcriptional signatures as predictive biomarkers of outcome for therapeutic decision making.

  Study phs003545

• Mutational Patterns in Metastatic Cutaneous Squamous Cell Carcinoma

  Cutaneous squamous cell carcinoma (cSCC) from the head and neck typically metastasizes to the lymph nodes of the neck and parotid glands. When a primary is not identified, they are difficult to distinguish from metastases of mucosal origin and primary salivary gland SCC. Ultraviolet radiation causes a mutation pattern that predominantly features cytosine to thymine transitions at dipyrimidine sites and has been associated with cSCC. In this study, we used whole genome sequencing data from 15 cSCC metastases and show that a UV signature mutation is pervasive across the cohort and distinct from mucosal SCC. The mutational burden was exceptionally high and concentrated in some regions of the genome, especially insulator elements (mean 162 mutations / Mb). We therefore evaluated the likely impact of UV-induced mutations on the dipyrimidine rich binding site of the main human insulator protein, CCCTC-binding factor (CTCF), and the possible implications on CTCF function and the spatial organization of the genome. Our findings suggest that mutation signature analysis may be useful in determining the origin of metastases in the neck and the parotid gland. Furthermore, UV-induced DNA damage to insulator binding sites may play a role in the carcinogenesis and progression of cSCC.

  Study EGAS00001003370

• 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

• Upper respiratory microbiome of COVID-19 patients

  Understanding the pathology of COVID-19 is a global research priority. Early evidence suggests that the microbiome may be playing a role in disease progression, yet current studies report contradictory results. Here, we examine potential confounders in COVID-19 microbiome studies by analyzing the upper respiratory tract microbiome in well-phenotyped COVID-19 patients and controls combining microbiome sequencing, viral load determination, and immunoprofiling. We found that time in the intensive care unit and the type of oxygen support explained the most variation within the upper respiratory tract microbiome, dwarfing (non-significant) effects from viral load, disease severity, and immune status. Specifically, mechanical ventilation was linked to altered community structure, lower species- and higher strain-level diversity, and significant shifts in oral taxa previously associated with COVID-19.

  Study EGAS00001004951

• Investigating the Role of Neddylation in the Repair of Topoisomerase I-Mediated DNA Damage in Colorectal Cancer

  Colorectal cancer is one of the most lethal cancers worldwide. First-line chemotherapy for metastatic CRC (mCRC) entails a combination of irinotecan with 5-fluorouracil and leucovorin (FOLFIRI). Irinotecan is a prodrug that is converted within the cell to its active metabolite SN38, a potent TOP1 inhibitor. TOP1 relieves DNA torsional strain arising from DNA metabolism by cleaving one strand of the DNA duplex and forming an enzyme-DNA covalent intermediate that can be trapped by inhibitors. The resulting TOP1 DNA-protein crosslinks (TOP1-DPCs) interfere with replication and transcription and induce cell death if left unrepaired. I have established the role of the ubiquitin-proteasome pathway in TOP1-DPC repair, but it remains unknown how ubiquitylation is activated. One possibility is that collision between advancing replication forks and the DPCs triggers a specific ubiquitin ligase to target DPCs for degradation. The DDB1-CUL4 (Cullin 4)-RBX1 (CRL4) ubiquitin ligase complex performs a salient role in both replication and DNA repair upon its activation by auto-NEDD8 modification (neddylation), but it is unknown whether it plays a role for TOP1-DPC repair. We plan to define the role of neddylation for TOP1-DPC repair during replication and examine irinotecan plus pevonedistat (PEV), a NEDD8-activating enzyme inhibitor in multiple clinical trials, in CRC patient-derived preclinical models including 3D organoids. The three in-house organoids, from which samples have been submitted to dbGaP, were sequenced as a means of verifying their identity as colon cancer organoids, to allow us to investigate the combination of TOP1 inhibitors and PEV and the molecular mechanisms underlying this drug combination in these organoids.

  Study phs003257

• Whole Genome Sequencing of 317 individuals from the Pacific region

  Although genetic studies of modern Oceanians have revealed varying levels of admixture with populations of East Asian ancestry, attributed to the Austronesian expansion, the early peopling history of Oceania remains poorly characterized. Additionally, it is currently unknown whether and how the settlement of the Pacific was accompanied by genetic adaptation to island environments, and if archaic introgression facilitated this process in Oceanians, who present the highest worldwide levels of combined Neanderthal and Denisovan ancestry. This study reports the first whole genome-based survey that addresses a wide range of questions related to the demographic and adaptive history of Pacific populations.

  Study EGAS00001004540

• Development and Validation of Organoids from Fibrolamellar Carcinoma Human Cells

  Fibrolamellar hepatocellular carcinoma (FL-HCC) is a rare liver tumor affecting adolescents and young adults with no history of primary liver disease or cirrhosis. We collected fibrolamellar tumor tissue and the adjacent non-tumor liver from patients at the time of surgery. We only used tumor tissue that was validated as fibrolamellar by the presence of the DNAJB1-PRKACA fusion gene. The freshly resected tissue was dissociated and grown as organoids. This yielded 21 patient-derived organoid lines. From the tumor tissue we developed twelve organoids from metastases and three from the liver tumor. Additionally, we generated six organoid lines from the adjacent non-tumor liver. The tumor organoids were all shown to recapitulate the histologic morphology, immunohistochemistry, and transcriptome of the tumor tissue from which they were derived.

  Study phs002439

• Ethiopia Genome Project (high coverage)

  The Ethiopian area stands among the most ancient ones ever occupied by human populations and their ancestors. Particularly, according to archaeological evidences, it is possible to trace back the presence of Hominids up to at least 3 million years ago. Furthermore, the present day human populations show a great cultural, linguistic and historic diversity which makes them essential candidate to investigate a considerable part of the African variability. Following the typing of 300 Ethiopian samples on Illumina Omni 1M (see Human Variability in Ethiopia project, previously approved by the Genotyping committee) we now have a clearer idea on which populations living in the area include the most of the diversity. This project therefore aims to sequence the whole genome of 300 individuals at low (4-8x) depth belonging to the six most representative populations of the Ethiopian area to produce a unique catalogue of variants peculiar of the North East Africa. Furthermore 6 samples (one from each population) will also be sequenced at high (30x) depth to ensure full coverage of the diversity spectrum. The retrieved variants will be of great help in evaluating the demographic dynamics of those populations as well as shedding light on the migrations out of Africa.

  Dataset EGAD00001000696

• GA4GH

  Standards The EGA is a long-standing supporter of the Global Alliance for Genomics & Health (GA4GH) to enhance responsible sharing of human genetic data through the development of interoperable global standards for human data access. The EGA is one of the founding GA4GH Driver Projects and has contributed to the development and implementation of several GA4GH standards and APIs. Below is a list of the GA4GH standards and APIs that are currently available or planned for implementation at EGA. TechnicalStandards Purpose SpecificationVersion SupportedVersion Implementation Large Scale Genomics htsget A protocol for secure, efficient, and reliable access to sequencing read and variation data. V1.3.0 V1.0.0 Specification Documentation Endpoint Read File Formats (SAM/BAM/CRAM) Specifications for storing next-generation sequencing read data. V3.0.0 V3.0.0 Implementation Example of Usage Variation File Formats (VCF/BCF) The specifications for Variant Call Format Files (VCF) and its binary counterpart BCF. V4.0.0 V2.0.0 V4.0.0 V2.0.0 Implementation Example of Usage Crypt4GH v1.0 Enables direct byte-level compatible random access to encrypted genetic data stored in community standards (e.g. CRAM, VCF) V1.0 V1.0 Specification Documentation Endpoint refget API Enables access to reference sequences using an identifier derived from the sequence itself. V1.2.6 NA Specification RNAget API v1 Provides a means of retrieving data from several types of RNA experiments including (i) feature-level expression data from RNA-seq type measurements and (ii) coordinate-based signal/intensity data similar to a bigwig representation via a client/server model. V1.0.0 NA Documentation Discovery Beacon v2 Supports discovery of genomic variants, phenotypes, and individuals V1.0.1 V0.3 Web UI API Source Code Service Info API v1 The Service Info API is an endpoint for describing GA4GH service metadata, designed for extension and inclusion in other APIs. Service info is used to describe a single service, while Service Registry is used to describe multiple services. V1.0.0 NA Documentation Service Registry API v1 provides information about other GA4GH services, primarily for the purpose of organizing services into networks or groups and service discovery across organizational boundaries. V1.0.0 NA Documentation Data Use Researcher Identities Data Use Ontology (DUO) Allow users to semantically tag genomic datasets with usage restrictions, allowing them to become automatically discoverable based on a health, clinical, or biomedical researcher’s authorisation level or intended use. 2021-02-23 2021-02-23 Specification Documentation Endpoint Authentication & Authorization Infrastructure (AAI) The GA4GH AAI specification leverages OpenID Connect (OIDC) Servers for use in authenticating the identity of researchers desiring to access clinical and genomic resources from data holders adhering to GA4GH standards, and to enable data holders to obtain security-related attributes of those researchers. V1.2.0 V1.2.0 API URI: ega.ebi.ac.uk:8443 Documentation Repository Researcher IDs (passport, visa) Specify the collection of researchers that may access a dataset at any given time, and the credentials they must supply. V1.0.1 V1.0.1 Specification Documentation Endpoint Cloud Tool Registry Service API TRS is a standard API for exchanging tools and workflows to analyze, read, and manipulate genomic data. V2.0.1 NA Documentation Repostiory Data Repository Service API ​DRS API is a standard for building data repositories and adapting access tools to work with those repositories, works with other approved APIs from the GA4GH Cloud Work Stream to allow researchers to discover algorithms across different cloud environments and send them to datasets they wish to analyse. V1.0.3 NA Documentation Repostiory Workflow Execution Service API This API lets users run a single workflow (defined using CWL or WDL) on multiple different platforms, clouds, and environments, and be confident that it will work the same way. The API provides methods to request that a workflow be run, pass parameters to that workflow, get information about running workflows, and cancel a running workflow. V1.0.1 NA Documentation Repostiory Genomic Knowledge Standards Variation Representation v1 Provides a flexible framework of computational models, schemas, and algorithms to precisely and consistently exchange genetic variation data across communities. V1.3.0 EGA team is contributing to including it in in Beacon v2 Specification and Elixir Reference Implementation Documentation Repostiory Clin/ Pheno Data Capture Phenopackets Provides information models with different levels of complexity to enable high level clinical phenotype information as well as deep clinical phenotype information to be exchanged. V2.0.0 Included in Ongoing Submissions EGA team is contributing to including it in in Beacon v2 Specification and Elixir Reference Implementation Documentation Repostiory Driver Project The EGA, jointly coordinated by the EBI and the CRG, was announced, in 2017, to be one of the 15 Driver Projects for GA4GH. Driver Projects are international genomic data initiatives, focussed on real projects and challenges that will guide the development efforts in order to accelerate and enable completely responsible and standarised data sharing by 2022. All chosen Driver Projects make a cross-sectional effort by playing an important role across the different workstreams. Thomas Keane, Jordi Rambla, Mallory Freeberg, and Aina Jené have been named Driver Project Champions for the EGA. All Driver Project Champions will be leading this ambitious initiative for the following years.

  Documentation about/projects-and-funders/ga4gh

• Discovery and validation of an ancillary genomic test of malignancy for primary melanocytic tumors

  Background:Pathological diagnosis of melanocytic tumors can be difficult and prone to error. More accurate and reliable ancillary investigations than those currently available are required to improve diagnostic accuracy. Goals: (1) to generate a genomic model for distinguishing benign (nevi) from malignant (melanoma) melanocytic skin tumors, developed from a customized gene panel evaluated on a cohort of 250 nevi and melanomas. (2) To evaluate the performance of the genomic model in melanocytic tumors of uncertain diagnosis/behavior. Details: The cohort of 250 melanomas and naevi was split into a discovery set and a validation set. Our genomic model obtained an area under the curve of 95% in the discovery cohort and 96% for the validation cohort. Based on a Youden index threshold, the resulting classifier shows high specificity (>95% in the discovery and validation cohorts), with sensitivities >80% in the discovery and validation cohorts. Evaluation of the genomic model on a cohort of 110 melanocytic tumors identifies key driver mutations in agreement with the likely pathway of origin of the tumors. The probability of melanoma (p=0.02) from the genomic model was significantly associated with pathology-based estimates of malignancy for melanocytic tumors lacking pathway-defining genomic aberrations (conventional tumors). The genomic model identified 8/36 (22%) conventional tumors with unresolved malignancy to have a high probability of melanoma, with 7/8 of these cases showing negative/non-aberrant results with either fluorescent in-situ hybridization, PRAME and/or p16 immunostains. Furthermore, no evidence of malignant behavior based on the genomic model was seen in 4 conventional borderline tumors with more than 1 year follow-up. Conclusion: This genomic model can become a clinically useful ancillary tool that is highly specific for differentiating melanomas from nevi.

  Study EGAS50000000887