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• Molecular analysis of circulating tumour cells identifies distinct profiles in chemosensitive and chemorefractory patients with small cell lung cancer

  In a search for profiles that distinguish chemosensitive versus chemorefractory SCLC disease, we examined copy number aberrations (CNA) in circulating tumour cells (CTCs) from pre-treatment SCLC blood samples. A CNA classifier was generated from 88 CTCs isolated from 13 patients (training set) then verified in 18 additional patients (112 CTC samples) (testing set) and in six SCLC patient-derived CTC explants explant tumours. These data highlight the potential utility of CTCs for molecular profiling and stratification of SCLC patients in future clinical trials.

  Study EGAS00001001951

• Genomic Basis of Phenotypic Variability of Complex Disorders

  The 520-kbp deletion on chromosome 16p12.1 is associated with multiple neurodevelopmental outcomes, including intellectual disability/developmental delay (ID/DD), schizophrenia, autism, and epilepsy. This variant is inherited in >95% of cases from carrier parents manifesting neuropsychiatric features, while affected children with the deletion were more likely to carry another large Copy Number Variation (CNV) or rare deleterious mutation elsewhere in the genome, or "second-hits", compared to carrier parents. Thus, while the 16p12.1 deletion confers significant risk for a range of disorders, the ultimate phenotypic trajectory is determined by "second-hit" variants in the genetic background. However, outstanding questions remain about the genetic background in these disorders, including the functional effects of "second-hit" variants towards gene expression or developmental phenotypes, the full spectrum of "second-hit" variants, and the mechanisms driving accumulation of these variants.In version 1, we performed deep clinical phenotyping, whole-genome sequencing (WGS), SNP microarray analysis, and RNA-sequencing of lymphoblastoid cell lines of 32 individuals in five large families with the 16p12.1 deletion. We found that specific classes of rare single-nucleotide and structural variants in coding and non-coding regions contributed towards expression changes in affected children based on their inheritance patterns, including a subset of variants that acted synergistically with the deletion to alter expression. We further found that rare variant-mediated expression changes contribute to specific developmental phenotypes in the carrier children, suggesting a potential genetic mechanism for variable expressivity of the deletion.In version 2, we additionally analyzed genetic and phenotypic data from individuals with 16p12.1 deletion and their spouses to assess the role of assortative mating modulating phenotypic outcomes of the 16p12.1 deletion. We identified significant within- and cross- disorder phenotypic correlations between spouses, but observed no significant genetic correlations between spouses, in line with expectations.

  Study phs002450

• Prediction of plasma ctDNA fraction and prognostic implications of liquid biopsy in advanced prostate cancer

  Targeted cfDNA and WBC sequencing data from patients profiled in the study "Prediction of plasma ctDNA fraction and prognostic implications of liquid biopsy in advanced prostate cancer". Note that 'Exome sequencing' is listed under Dataset Type since there is no option for targeted panel sequencing.

  Dataset EGAD50000000303

• Molecular_characterization_of_invasive_lobular_carcinoma

  Invasive lobular carcinoma (ILC) is the second most common histological subtype of breast cancer accounting for 10-15% of cases. ILC differs from invasive ductal carcinoma (IDC)with respect to epidemiology, histology, and clinical presentation. Moreover, ILC is lesssensitive to chemotherapy, more frequently bilateral, and more prone to form gastrointestinal, peritoneal, and ovarian metastases than IDCs. In contrast to IDC, the prognostic value ofhistological grade (HG) in ILC is controversial. One of the three major components of histological grading (tubule formation) is missing in ILC which hinders the process of gradingin this histological subtype and results in the classification of approximately two thirds of ILC as HG 2.Over the last decade, a number of gene expression signatures have shed light onto breast cancer classification, allowing breast cancer care to become more personalized. Withrespect to the management of estrogen receptor (ER)-positive breast cancer, several gene expression signatures provide prognostic and/or predictive information beyond what is possible with current classical clinico-pathological parameters alone. Nevertheless, most studies using gene expression signature have not considered different histologic subtypesseparately. Recently, a comprehensive research program has elucidated some of the biological underpinnings of invasive lobular carcinoma. Genetic material extracted from 200 ILC tumor samples were studied using gene expression profiling and identified ILCmolecular subtypes. These proliferation-driven gene signatures of ILC appear to have prognostic significance. In particular, the Genomic Grade (GG) gene signature improved upon HG in ILC and added prognostic value to classic clinico-pathologic factors. In addition this study demonstrated that most ILC are molecularly characterized as luminal-A (~75%)followed by luminal-B (~20%) and HER2-positve tumors (~5%). Moreover, we investigated the prognostic value of known gene signatures/ gene modules in the same cohort of ILC. As a second step within the scope of this project, we aim to investigate the interactionsbetween somatic ILC tumor mutations to observed transcriptome findings. To this end, we aim to perform somatic mutation analysis for the ILC tumors for which Affymetrix gene expression profiling is available. To this end, we will use a gene screen assay, which specifically interrogates the mutational status of a few hundreds of cancer genes. We believe that this pioneering effort will be fundamental for a tailored treatment of ILC withimprovement in patients' outcome.

  Study EGAS00001000292

• scRNA-Seq & scATAC-Seq Feature-counting Results Derived from 10X Cellrange-arc-count pipeline

  The feature-counting outputs from Cellrange-arc-count pipeline contain information at the single cell level, jointly for both scRNA-Seq and scATAT-Seq on 103 DLBCL samples and 3 normal controls (Please note that sc-40 only has scRNA-Seq data without scATAC-Seq). These files can be used as the input files for further downstream analysis software (e.g. Seurat Package). Moreover, a matrix of single-cell barcodes and associated cell biological types (used in the study) is also provided for users’ reference. This dataset is large and split into two parts to facilitate downloads. This dataset is large and split into two parts to facilitate downloads. The Linux command line of "tar -xvf xxxx_PART1.gz" should be used to extract the individual files. For additional information regarding the genomic data associated with the study, please also see the paper companion website at https://llmpp.ccr.cancer.gov/local/DLBCL_singlecell/

  Dataset EGAD50000002490

• Epilepsy Genetics Initiative

  The Epilepsy Genetics Initiative (EGI) was created in 2014 with the goal of amassing a database of clinically-generated exome sequence data for patients with epilepsy who have had diagnostic whole exome sequencing. Most of the subjects enrolled in the study did not receive a secure genetic diagnosis from their initial sequencing. Following participant/family consent, EGI transfers raw data from the respective clinical lab to the Institute for Genomic Medicine at Columbia University Medical Center. The strategy of the initiative has been to carry out systematic reanalysis of data to identify new diagnoses that were not possible or missed at the time of initial sequencing and to aid in novel gene discovery in epilepsy.

  Study phs001551

• Submitting array based metadata

  Submitting array based metadata For further information please check our Submission FAQs, submission quickguide as well as submission terms! The submission metadata required for Array-based submission must be submitted using EGA programmatic submission and by completing the Array-based format (AF) template. The guidelines for this workflow are described on this page. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA Array-based-Format (AF) template. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA-Array-based-Format (AF) template. Metadata Model Registering Metadata Use the EGA programmatic submission to register your Study, Samples, Data Access Committee (DAC) and Policy. This online interface enables you to create new and edit existing submissions. Do not use the Submitter Portal to register the metadata objects for array submissions. Please, go to the EGA programmatic submission. Registering Study Go to the EGA programmatic submission Submit an study XML Take a note of your study accession number (EGASXXXXXXXXXXX) To use the study accession number in a publication, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found on https://ega-archive.org "The European Genome-phenome Archive of human data consented for biomedical research"(https://doi.org/10.1093/nar/gkab1059 ). Registering Samples Go to the EGA programmatic submission Submit a sample XML Save the list of accession numbers. These will be needed for the AF template. Registering Data Access Committee Further information on the role of your DAC. Go to the EGA programmatic submission Submit a DAC XML Take a note of the DAC accession number (EGACXXXXXXXXXXX) Registering Policy Your Data Access Policy provides the terms and conditions of data use. This is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAA) Go to the EGA programmatic submission Submit a Policy XML Take a note of the Policy accession number (EGAPXXXXXXXXXXX) Complete the Array-based format (AF) spreadsheet Once you have completed the registration of your Study, DAC and Policy using the programmatix submission, you must then complete and return the AF spreadsheet The AF spreadsheet consists on four components: Do not use EGA IDs registered using the Submitter Portal. You can easily identify objects registered in the Submitter Portal by their EGA ID pattern: EGA[A-Z]5{10 more digits} (e.g. for a sample EGAN50000002506). For populating an AF spreadsheet, please exclusively use EGA IDs following this specific pattern: EGA[A-Z]0{10 more digits} (e.g. for a sample EGAN00001691542). Tab1) Webin accessions : Provide the accession numbers for your study, DAC and policy. Please, also add your ega-box number. Tab2) Sample & phenotypes: Please, leave this tab blank. Tab3) Dataset: Describe the dataset to be created Tab4) Data files: Define how your data is going to be organised into datasets and packets for distribution (linkage between samples and files). Should further assistance be required after going through the guide below; please do not hesitate to contact the EGA helpdesk Once the AF spreadsheet is populated, please send it to our EGA helpdesk for further validation. AF spreadsheet Should your submission require multiple DAC's or policies, use ' ; ' to separate the accession numbers. Accessions AF spreadsheet: Samples & phenotypes Samples and phenotypes AF spreadsheet: Datasets We suggest that each dataset consists of a common set of data. The example below consists of two datasets, grouped according to shared data type, technology and by case/control. We also like to capture the number of unique samples that make up the dataset and the Data Access Committee (DAC) responsible for providing the named dataset and their policy (EGAP). Datasets AF spreadsheet: Data files What follows is an example of how to map your samples to the array based files added to your upload account (4th tab). Data Files Please, find below some practical examples on how to register the linkage between samples-files Case 1) 1 sample or list of samples in different datasets: Data Files In case you have a list of samples that belong to different datasets, please, repeat the samples accession number/s in the first column and link the sample to the corresponding dataset each time (each row). Each row is one linkage between sample-file-dataset. Case 2) 1 sample links to several files: Data Files In order to add multiple files to one sample you MUST use “ ; “ between filenames. Example: file1.gpg;file2.gpg;file3.gpg In case that you want to add an extra file to the sample (phenotype or .Rdata), please use “Additional files” column. Important note: You MUST upload the encrypted and unencrypted md5sum values of all files uploaded to your submission account using the filename nomenclature (file.gpg, file.md5,file.md5.gpg). Your submission will not be processed without md5values supplied for all files in the CORRECT format. What happens after the submission of a dataset? All datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitted or DAC contact instructs our EGA helpdesk for the study to be released. Finally, your data is archived within our databases and prepared for encrypted distribution upon the request of permitted EGA account holders. We strongly advise you NOT to delete your data until we confirm that your data has been successfully archived.

  Documentation submission/metadata/submission/array

• KDM6A Loss Triggers an Epigenetic Switch that Disrupts Urothelial Identity and Drives Cell Proliferation in Bladder Cancer

  Disruption of KDM6A, a histone lysine demethylase, is one of the most common somatic alternations in bladder cancer. How mutations of this chromatin modifier impact the epigenetic landscape to promote carcinogenesis is poorly understood. Here, we demonstrate that KDM6A loss triggers an epigenomic switch that disrupts urothelial identity and induces a neoplastic state characterized by increased cell proliferation. In bladder cancer cells with intact KDM6A, FOXA1 binds KDM6A at enhancers to activate genes instructing urothelial differentiation. In KDM6A-deficient cells, we observed simultaneous loss of FOXA1 target binding and genome-wide redistribution of the bZIP transcription factor ATF3, which in turn represses FOXA1 target genes and activates cell-cycle progression genes. Importantly, ATF3 depletion reverses the cell proliferation phenotype. These data establish that KDM6A loss creates an epigenetic state that drives tumor growth in an ATF3-dependent manner, forging a molecular vulnerability that can be targeted by novel therapeutic strategies.

  Study phs002801

• eMERGE Network Study of the Genetic Determinants of Resistant Hypertension

  The electronic Medical Records and Genomics (eMERGE) Network is a consortium of five participating sites (Group Health Seattle, Marshfield Clinic, Mayo Clinic, Northwestern University, and Vanderbilt University) funded by the NHGRI to investigate the use of electronic medical record systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 19,000 individuals using EMR-derived phenotypes and DNA from linked Biorepositories. Using electronic phenotyping methods, the consortium used DNA samples from all participating sites to explore the genetic determinants of resistant hypertension. Treatment resistant hypertension is a common health problem in the clinical setting. A basic definition of resistant hypertension included subjects with uncontrolled blood pressure despite use of three antihypertensive medications or subjects requiring four or more medications to maintain control. Other conditions, such as secondary causes of hypertension, were exclusions. Site and participants include: Vanderbilt University: BioVU, Vanderbilt's DNA databank, is an enabling resource for exploration of the relationships among genetic variation, disease susceptibility, and variable drug responses, and represents a key first step in moving the emerging sciences of genomics and pharmacogenomics from research tools to clinical practice. BioVU acquires DNA from discarded blood samples collected from routine patient care. The biobank is linked to de-identified clinical data extracted from Vanderbilt's EMR, which forms the basis for phenotype definitions used in genotype-phenotype correlations. Marshfield Clinic: The Marshfield Clinic Personalized Medicine Research Project is a population-based biobank in central Wisconsin with more than 20,000 adult subjects who provided written, informed consent to access their medical records and provided a blood sample from which DNA was extracted and plasma and serum stored. In addition to an average of 30 years of medical history data, a questionnaire about environmental exposures, including a detailed food frequency questionnaire, is available to facilitate gene/environment studies. Northwestern University: The NUgene Project is a repository with longitudinal medical information from participating patients at affiliated hospitals and outpatient clinics from the Northwestern University Medical Center. Participants' DNA samples are coupled with data from a self-reported questionnaire and continuously updated data from our Electronic Medical Record (EMR) representing actual clinical care events. Northwestern has a state-of-the art, comprehensive inpatient and outpatient EMR system of over 2 million patients. NUgene has broad access to participant data for all outpatient visits as well as inpatient data via a consolidated data warehouse. NUgene participants consent to distribution and use of their coded DNA samples and data for a broad range of genetic research by third-party investigators. Group Health(GH)/University of Washington (UW): Aging and Dementia eMERGE study biorepository leverages rich population-based longitudinal data from both electronic medical records and in-depth research data to explore genome wide associations. Participants include Seattle-area members of GH (a large integrated health care system in Washington State) consented and enrolled in 1) the UW Alzheimer's Disease Patient Registry (ADPR) and 2) the Adult Changes in Thought (ACT) study. The ADPR (PI: Eric B. Larson; NIH/NIA U01 AG 006781) is a population-based registry of incident dementia cases designed to identify all new Alzheimer's Disease cases within GH from 1987 to 1996. Medical history, physical, laboratory testing, and neuropsychological testing were performed on all consenting potential cases for determination of dementia status by a consensus conference. The study base of the ADPR population was stable with an attrition rate of less than 1%/year. The ACT study (PI: Eric B. Larson; NIH/NIA U01 AG 006781) is an ongoing community-based cohort study of aging and dementia. The original cohort of 2,581 randomly selected dementia-free members age 65 and older was enrolled in 1994-1996 and expanded by 811 in 2000-2002. Continuous enrollment to maintain a cohort of 2,000 dementia free persons began in 2005. Participants receive biennial assessment including cognitive status determination. The ACT sub-sample is stable; for the original cohort, median enrollment in GH was 19 years prior to joining the ACT study, and 85% of the cohort has ≥ 10 years of GH enrollment. DNA for the ADPR participants were obtained through a companion study, Genetic Differences in Cases and Controls (PI: Walter Kukull; NIH/NIA R01 AG007584). DNA obtained through both studies were extracted from blood using Gentra Systems Puregene methods. DNA concentration is determined by UV optical density. All samples are checked for quality by 260/280 ratio. For long-term storage, samples are aliquoted and stored at -70°C. Mayo Clinic: The Mayo biobank is a disease-specific biobank for vascular diseases including peripheral arterial disease (PAD). PAD patients were identified from individuals referred to the non-invasive vascular laboratory for lower extremity arterial evaluation. Since 1997, laboratory findings have been recorded into an electronic database employing an in-house software package for data archiving and retrieval; this data becomes part of the Mayo EMR. Patients referred to the center with suspected PAD undergo a comprehensive non-invasive evaluation including the ankle-brachial index (ABI) - the ratio of blood pressure measured in the upper arms divided by blood pressure measured at the ankles. Controls subjects are identified from patients referred to the Cardiovascular Health Clinic for stress ECG. The prevalence of PAD in patients with normal exercise capacity who do not have inducible ischemia on the stress ECG, was <1%. Data regarding risk factors for atherosclerosis such as diabetes, dyslipidemia, hypertension, and smoking are ascertained from the EMR.

  Study phs000297

• Genomic diversity of the African-descent Makranis of Pakistan

  From the eighth century onward, the Indian Ocean was the scene of extensive trade of sub-Saharan African slaves, via sea routes controlled by Muslim Arab and Swahili traders. Several populations in present-day Pakistan and India are thought to be the descendants of such slaves, yet their genetic ancestries and history of admixture and natural selection remain largely undefined. Here we studied the genome-wide diversity of the African-descent Makranis, residing on the Arabian Sea coast of Pakistan, together with that of four neighboring Pakistani populations, to investigate the genetic legacy, population dynamics and tempo of the Indian Ocean slave trade. We show that the Makranis result from an admixture event between local Baluch tribes and Bantu-speaking populations from eastern/southeastern Africa, which we dated to ~300 years ago during the Omani Empire domination. Levels of inbreeding, measured through cumulative runs of homozygosity, were found to be similar across Pakistani populations, suggesting that the admixed Makranis rapidly adopted the traditional practice of endogamous marriages. Finally, we searched for signatures of post-admixture selection at several traits evolving under positive selection, including skin color, lactase persistence and resistance to malaria. We demonstrate that the African-specific Duffy-null blood group — believed to confer resistance against Plasmodium vivax infection — was recently introduced to Pakistan through the slave trade and evolved under positive selection in this vivax malaria-endemic region. Collectively, our study reconstructs the genetic and adaptive history of a neglected episode of the African Diaspora, and illustrates the impact of recent admixture on the diffusion of adaptive traits across human populations.

  Study EGAS00001002558