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• The ICGC-TCGA DREAM Somatic Mutation Calling - Tumour Heterogeneity Challenge

  Tumors are composed of many different cell types, ranging from the normal tissue micro-environment, infiltrating immune cells and tumour cells. This group of tumour cells is itself intrinsically highly heterogeneous, driven by the rapid mutation rate of cancer cells to become a mixture of different sub-populations. These different types of cells, called subclones, have different sets of mutations in their DNA. Because some subclones evolve from others, many subclones share mutations. While it is increasingly easy to determine what mutations are present in the tumor, it remains very difficult to determine which subclones have which mutations. The task of assigning mutations to subclones is called subclonal reconstruction.The ICGC-TCGA DREAM Somatic Mutation Calling - Tumour Heterogeneity Challenge (SMC-Het) is an international effort to improve standard methods for subclonal reconstruction: to quantify and genotype each individual cell population present within a tumor. The goal of this challenge is to identify the best subclonal reconstruction algorithms and to identify the conditions that affect their performance.

  Study EGAS00001002092

• National Cancer Institute (NCI) Non-Hodgkin Lymphoma Genome-wide Association Study (GWAS)

  Within the framework of the International Lymphoma Epidemiology Consortium (InterLymph) Consortium and NCI-sponsored Cohort Consortium, investigators from 22 studies came together to conduct a genome-wide association study (GWAS) of non-Hodgkin lymphoma (NHL). The goal of the study was to identify common genetic variants associated with the risk of NHL. As described previously (Berndt SI et al., Nature Genetics, 2013, PMID: 23770605 ), cases and controls from 22 studies, including nine prospective cohort studies, eight population-based case-control studies, and five clinic or hospital-based case-control studies were included in this effort. Cases of NHL were ascertained from cancer registries, clinics or hospitals, or through self-report verified by medical and pathology reports. The phenotype information for all NHL cases was reviewed centrally at the InterLymph Data Coordinating Center and harmonized according to the hierarchical classification proposed by the InterLymph Pathology Working Group based on the World Health Organization (WHO) classification (2008). Controls were frequency matched to cases on age and gender by study, and the study was limited to subjects of European ancestry. Participants were genotyped using the Illumina Omni Express. The NCI Non-Hodgkin Lymphoma GWAS is utilized in the following dbGaP sub-studies. To view genotypes and derived variables collected in these sub-studies, please click on the following sub-studies below or in the "Sub-studies" box located on the right hand side of this top-level study page phs000801 NCI Non-Hodgkin Lymphoma GWAS. phs000802 NCI Non-Hodgkin Lymphoma GWAS: CLL phs000818 NCI Non-Hodgkin Lymphoma GWAS: Controls phs000889 NCI Non-Hodgkin Lymphoma GWAS: DLBCL phs000890 NCI Non-Hodgkin Lymphoma GWAS: FL phs000891 NCI Non-Hodgkin Lymphoma GWAS: MZL

  Study phs000801

• Spatial and temporal evolution of distal 10q deletion, a prognostically unfavorable event in diffuse low-grade gliomas

  Background: The disease course of patients with diffuse low-grade glioma is notoriously unpredictable. Temporal and spatially distinct samples may provide insight into the evolution of clinically relevant copy number aberrations (CNAs). The purpose of this study is to identify CNAs that are indicative of aggressive tumor behaviour and can thereby complement the prognostically favorable 1p/19q co-deletion. Results: Genome-wide, 50 base pair single-end, sequencing was performed to detect CNAs in a clinically well-characterized cohort of 98 formalin-fixed paraffin-embedded low-grade gliomas. CNAs are correlated with overall survival as an endpoint. Seventy-five additional samples from spatially distinct regions and paired recurrent tumors of the discovery cohort were analysed to interrogate the intratumoral heterogeneity and spatial evolution. Loss of 10q25.2-qter is a frequent subclonal event and significantly correlates with an unfavorable prognosis. A significant correlation is furthermore observed in a validation set of 126 and confirmation set of 184 patients. Loss of 10q25.2-qter arises in a longitudinal manner in paired recurrent tumor specimens, whereas the prognostically favorable 1p/ 19q co-deletion is the only CNA that is stable across spatial regions and recurrent tumors. Conclusions: CNAs in low-grade gliomas display extensive intratumoral heterogeneity. Distal loss of 10q is a late onset event and a marker for reduced overall survival in low-grade glioma patients. Intratumoral heterogeneity and higher frequencies of distal 10q loss in recurrences suggest this event is involved in outgrowth to the recurrent tumor.

  Study EGAS00001000643

• The_genetics_of_thinness_compared_to_obesity

  The variation in weight within a shared environment is largely attributable to genetic factors. Whilst many genes/loci confer susceptibility to obesity, little is known about the genetic architecture of thinness. In this study we performed a genome-wide association study of 1,622 persistently thin healthy individuals (STILTS), 1,985 severe childhood onset obesity cases (SCOOP) and 10,433 population based individuals (UKHLS) used as a common set of controls. All participants were genotyped on the Illumina Core Exome array, including 551,839 markers and imputed to the combined UK10K and 1000G (phase3) reference panel. We contrast the genetic architecture of thinness with that of severe early onset obesity and explore whether the genetic loci influencing thinness are the same as those influencing obesity pr whether there are important genetic differences between them. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing

  Study EGAS00001002624

• Rapid Whole Genome Sequencing for Genetic Disease Diagnosis in Neonatal Intensive Care Units

  Monogenic diseases are frequent causes of neonatal morbidity and mortality, and disease presentations are often undifferentiated at birth. More than 3,500 monogenic diseases have been characterized, but clinical testing is available for only some of them and many feature clinical and genetic heterogeneity. As such, an immense unmet need exists for improved molecular diagnosis in infants. Because disease progression is extremely rapid, albeit heterogeneous, in newborns, molecular diagnoses must occur quickly to be relevant for clinical decision-making. We describe 50-hour differential diagnosis of genetic disorders by whole-genome sequencing (WGS) that features automated bioinformatic analysis and is intended to be a prototype for use in neonatal intensive care units. Retrospective 50-hour WGS identified known molecular diagnoses in two children. Prospective WGS disclosed potential molecular diagnosis of a severe GJB2-related skin disease in one neonate; BRAT1-related lethal neonatal rigidity and multifocal seizure syndrome in another infant, identified BCL9L as a novel, recessive visceral heterotaxy gene (HTX6) in a pedigree, and ruled out known candidate genes in one infants. Sequencing of parents or affected siblings expedited the identification of disease gene in prospective cases. Thus, rapid WGS can potentially broaden and foreshorten differential diagnosis, resulting in fewer empirical treatments and faster progression to genetic and prognostic counseling. Reprinted from Saunders et. al, Rapid Whole-Genome Sequencing for Genetic Disease Diagnosis in Neonatal Intensive Care Units. Sci. Transl. Med. 4, 154ra135 (2012; PMID: 23035047) with permission from AAAS.

  Study phs000564

• All you need to know about the new Submitter Portal

  In September we launched new services for all EGA users, including a new version of the former Submitter Portal. Our main objective with this Portal is to transform the metadata submission in a simplified and user-friendly method. Although it is possible to find documentation about the Submitter Portal on our website, we wanted to highlight the new features in this article. Enjoy! Credentials: who needs to create a new user to access the Submitter Portal and who doesn't? Users with an old submission account (ega-box) can already access the new Submitter Portal. If it is the first time that you want to access, it is time to request a submitter role linked to the EGA User. In the form, it is mandatory to explain which type of data you want to deposit, the size of your submission, add any comment you’d like us to read, and manage the EGA Data Processing Agreement (DPA) document. This document should be checked by the legal department of your institution and signed by a person with authority to sign contracts on behalf of the institution. Note that we provide a DPA template for new Users; it can be found and downloaded in the Submitter Request section. With the DPA signed and the Submitter Request sent, it is time to wait for our Helpdesk team to validate the request. Metadata submission now can be collaborative It is possible to add collaborator(s) to your submissions. Only registered EGA Users will appear on the search bar. When adding a collaborator, it is mandatory to define the permission granted: Read only: collaborators with this permission can check if the information of the submission is correct but cannot modify anything. Read & write: collaborators with this label can register new objects and modify the registered ones. We wanted to save time for submitters that usually rely on the same collaborators. For that, we have implemented the possibility of adding the same collaborators from previous submissions; all you need to do is to search the submission, and the exact set of EGA users will have access to your submission. You can check the collaborators and their permissions at any point of the submission by going to the collaborators section. Even adding more people to complete the submission is allowed by following the same steps: looking for the EGA User and defining the permission granted (please, note that during the submission it is not possible to add collaborators from previous submissions). Use external links to complement the study information When filling the information about the study we recommend users to enrich the submission with complementary fields such as PubMeds IDs, custom tags and external links. External links are used to connect a study to an external repository. We have implemented an external link to Euro-Bioimaging for a European Project (EuCanImage). Now, we are looking forward to keeping increasing the list of external repositories! Choose the expected release date for your metadata to be searchable on our website Once all the metadata objects are registered it is time to finalise the submission so that the Helpdesk team can approve it. In the new version of the Submitter Portal users can choose the expected release date for the metadata to be searchable on the EGA website. Please, note that the selected date will have a minimum of one week margin from the day of finalisation. This time is necessary for your files to be archived and for the submission to be validated by the Helpdesk Team. Are you looking for the Array submission within the Submitter Portal? Our Submitter Portal does not support array submissions. The submission of Array-based metadata must be done using the EGA programmatic submission. You will find our Array-based format template and all the necessary information on our website, and you can find all the steps in our submission quickguide! More information about the submission process at the EGA Our Team has developed an extended documentation related to metadata submission. A good first step is to take a closer look at the Submission FAQ. To have a general overview of all the metadata submission process we recommend taking a glimpse of our take-the-tour or watching the tutorial available on our YouTube Channel. Don't miss our section on EGA Schemas, to learn more about how the EGA is built. It can be useful to fully ensure that your submissions comply with the EGA's standards and contribute to a valuable and accessible genomic resource.

  Blog new-submitter-portal

• scRNASeq of human innate lymphoid cells from different compartments

  Group 1 innate lymphoid cells (ILCs) comprise a heterogeneous family of cytotoxic natural killer (NK) cells and ILC1s. We identifiy a population of “liver-type” ILC1s with transcriptional, phenotypic, and functional features distinct from those of conventional and liver-resident NK cells as well as from other previously described human ILC1 subsets. LT-ILC1s were CD49a+CD94+CD200R1+, expressed the transcription factor T-BET, do not express the activating receptor NKp80, or the transcription factor EOMES. Similar to NK cells, liver-type ILC1s produce IFN-γ, TNF-α, and GM-CSF; however, liver-type ILC1s also produce IL-2 and lack perforin and granzyme B. Liver- type ILC1s are expanded in cirrhotic liver tissues, and they can be produced from blood-derived ILC precursors in vitro in the presence of TGF-β1 and liver sinusoidal endothelial cells. Cells with similar signature and function can also be found in tonsil and intestinal tissues. Collectively, our study identifies and classifies a population of human cross-tissue ILC1s.

  Study EGAS00001006847

• The International Consortium for Prostate Cancer Genetics Genome Wide Association Study of Familial Prostate Cancer

  Initially, the ICPCG focused largely on high-risk prostate cancer pedigrees using linkage analysis. However, association studies have also been informative to find prostate cancer susceptibility variants in the genome. We hypothesize that genome-wide (GW) association studies of familial cases of prostate cancer will have the power to identify rarer and perhaps less penetrant risk variants associated with prostate cancer than have been identified to date. Our approach will be to examine evidence for association in selected, independent cases from high-risk pedigrees; we hypothesize that they represent a more homogeneous set of cases with potentially different associations. Use of the 5M plus exome SNP platform will allow us to include in our search more rare variants in our search than have ever been investigated for prostate cancer. Aim 1. Identify a set of independent prostate cancer cases selected from ICPCG high-risk prostate cancer pedigrees that have 3 or more related prostate cancer cases and have an average age at diagnosis ≤ 75 years. Identify appropriate matched controls from participating ICPCG groups/ populations. Aim 2. Perform GW SNP genotyping with the Illumina 5M plus exome SNP set on the set of independent prostate cancer cases and controls described in Aim 1. Aim 3. Perform association analyses appropriately matching controls for different ICPCG populations and groups. There will be a two-stage design for confirmation. Aim 4. Test reported associations using the extensive sample set available to us by collaboration with the PRACTICAL consortium that has access to 50 000 DNA samples from 34 groups internationally (over 7,000 of whom are positive for prostate cancer family history).

  Study phs000733

• 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

• Single Cell RNA-Seq Reveals Malignant and Stromal Programs Associated with Metastasis in Head and Neck Cancer

  This study of head and neck cancer aimed to profile individual malignant, stromal, and immune cells in treatment-naive oral cavity squamous cell carcinoma, with or without matched metastatic cervical lymph nodes. Clinical and pathologic information was collected on all patients, in addition to a fresh biopsy sample from the primary tumor and/or metastatic lymph node at the time of initial surgery. Biopsy specimens were processed for single cell RNA-seq (scRNA-seq) and/or whole exome sequencing (WES). A total of ~6,000 single cells from 18 participants were included in the final dataset. Stromal and immune cells had consistent expression programs across patients. Conversely, malignant cells varied within and between tumors in their expression of signatures related to cell cycle, stress, hypoxia, epithelial differentiation, and partial epithelial-to-mesenchymal transition (p-EMT). One of the top scoring genes in this program was TGFβ-induced (TGFBI), implicating the classic EMT regulator TGFβ. Cells expressing the p-EMT program spatially localized to the leading edge of primary tumors. By integrating single-cell transcriptomes with bulk expression profiles for hundreds of tumors, we refined Head and Neck Squamous Cell Carcinoma (HNSCC) subtypes by their malignant and stromal composition, and established p-EMT as an independent predictor of nodal metastasis, grade, and adverse pathologic features. Our results provide insight into the HNSCC ecosystem and define stromal interactions and a p-EMT program associated with metastasis.

  Study phs001474