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• EGA file encryption types

  EGA file encryption The European Genome-Phenome Archive (EGA) stands as a significant initiative in the field of genomics, facilitating the secure storage and sharing of vast amounts of genomic data. Jointly hosted by the Centre for Genomic Regulation (CRG) and the European Bioinformatics Institute (EMBL-EBI), the EGA offers a robust infrastructure for managing encrypted data, ensuring the privacy and integrity of sensitive genomic information. Both CRG and EMBL-EBI play pivotal roles in the EGA, each institution bringing unique expertise in managing different types of encryptions. EMBL-EBI EGACryptor encryption system, while CRG utilises Crypt4GH, resulting in two distinct encryption file types: cip and c4gh, respectively. How can I check the file encryption? To access your dataset of interest on the European Genome-Phenome Archive (EGA) and determine the corresponding encryption file extension, please follow these step-by-step instructions: Utilise the search or navigation features provided on the EGA website to locate the dataset you are interested in. Once you find the dataset webpage, click on the "Files" tab, typically located on the dataset's webpage. Upon reaching the "Files" tab, you will be presented with a table containing comprehensive information about the files associated with the dataset. This table will include various columns, such as "File Name," "Size," and "Location." The "Location" column is particularly relevant for determining the encryption file extension. Figure 1: File table showing all the information available for the files in a dataset. This table is specific for each dataset. To identify the encryption file extension corresponding to a specific file, locate the "Location" column in the table. If the file is archived in Spain, the file will have the .c4gh extension, indicating that it is encrypted using Crypt4gh. Conversely, if the file is archived in the UK, the encrypted file extension will be .cip, indicating the usage of EGACryptor for encryption. It is worth noting that a file can be available in both locations, meaning it will be accessible with both file extensions (.c4gh and .cip). This allows users to choose the encryption method that aligns with their preferred platform or analysis tools.

  Documentation check-encryption-type

• Liquid Biopsy Versus Tissue Biopsy to Assess Acquired Resistance and Tumor Heterogeneity in Gastrointestinal Cancers

  Tumor heterogeneity can drive the evolution of multiple tumor subclones harboring unique molecular resistance alterations in different metastatic lesions in an individual patient, under the selective pressure of therapy1-3. Studies have suggested that liquid biopsy may better capture the heterogeneity of acquired resistance, but systematic, direct comparisons of post-progression liquid vs. standard single-lesion tumor biopsies are lacking. In a prospective cohort of 44 patients with molecularly-defined gastrointestinal cancers and acquired resistance to targeted therapy, direct comparison of post-progression liquid vs. tumor biopsy revealed that liquid biopsy more frequently identified clinically-relevant resistance alterations and multiple resistance mechanisms, detecting resistance alterations not detected in matched tumor biopsy in 78% of cases. Whole-exome sequencing of serial cell-free DNA, tumor biopsies and rapid autopsy specimens elucidated geographic and evolutionary characteristics of heterogeneity. Our data suggest that acquired resistance is frequently characterized by profound tumor heterogeneity and highlight the potential clinical utility of liquid biopsy. Burrell, R. A. & Swanton, C. Tumour heterogeneity and the evolution of polyclonal drug resistance. Molecular oncology 8, 1095-1111, doi:10.1016/j.molonc.2014.06.005 (2014). Misale, S. et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature 486, 532-536, doi:10.1038/nature11156 (2012). Ahronian, L. G. et al. Clinical Acquired Resistance to RAF Inhibitor Combinations in BRAF-Mutant Colorectal Cancer through MAPK Pathway Alterations. Cancer Disco 5, 358-367, doi:10.1158/2159-8290.Cd-14-1518 (2015).

  Study phs001853

• Stand Up To Cancer East Coast Prostate Cancer Research Group

  Most prostate cancer deaths are caused by metastatic, castration resistant disease (mCRPC). To develop a precision medicine framework for mCRPC, we established a multi-institutional, international clinical sequencing infrastructure to enroll and carry out prospective whole exome and transcriptome sequencing of tumors from a cohort of mCRPC patients. We obtained high quality DNA and RNA sequence data from 150 bone or soft tissue biopsies. Central pathology revealed high-grade adenocarcinoma with only four cases (3.6%) showing neuroendocrine differentiation. Aberrations of AR, ETS genes, TP53 and PTEN were frequent (40-60% of cases), with TP53 and AR alterations being the most enriched in mCRPC compared to primary prostate cancer. We identified novel genomic alterations in PIK3CA/B (fusions and mutations); R-spondin, BRAF and RAF1 (fusions); APC (inactivating mutations); delta-catenin (missense mutations); and ZBTB16/PLZF (homozygous deletions). Aberrations of BRCA2, BRCA1 and ATM were observed at substantially higher frequencies (19.3% overall) than seen in primary prostate cancers, with 56% of these being exclusively somatic. Putative driver gene alterations were identified in nearly all patients, and over half also harbored driver gene fusions, homozygous deletions and/or amplifications. Moreover, 89% of patients harbored a clinically actionable aberration including 62.7% with aberrations in AR, 65% in other cancer-related genes, and 8% with actionable pathogenic germline alterations. Overall, integrative clinical sequencing analysis can be safely and efficiently performed in mCRPC, yields findings that may be actionable for enrolling patients in clinical trials of targeted therapies, and may inform the basis of individual clinical responses.

  Study phs000915

• High-Resolution Clonal Mapping of Multi-Organ Metastasis, and Resistance to Neoadjuvant Chemotherapy, in Triple Negative Breast Cancer

  Clonal dynamics during metastasis were studied in two patient-derived xenograft (PDX) models established from treatment-naive primary breast tumors from TNBC patients diagnosed with concurrent metastatic disease. Genomic sequencing and barcode-mediated clonal tracking revealed robust alterations in clonal architecture between primary tumors and metastases. Polyclonal seeding and maintenance of low-abundance subclones was observed in each metastatic lesion examined. Lung, liver, and brain metastases were enriched for an identical population of subclones. Clones dominating multi-organ metastases shared a genomic lineage. Thus, properties of rare mammary tumor subclones enabled the seeding and colonization of metastases in multiple secondary organ sites. In this study, patient-derived xenograft (PDX) models of treatment-naive TNBC and serial biopsies from TNBC patients undergoing NACT were used to elucidate mechanisms of chemoresistance in the neoadjuvant setting. Barcode-mediated clonal tracking and genomic sequencing of PDX tumors revealed that residual tumors remaining after chemotherapy maintained the subclonal architecture of untreated tumors yet their transcriptomes, proteomes, and histologic features were distinct from those of untreated tumors. Once treatment was halted, residual tumors gave rise to chemotherapy-sensitive tumors with similar transcriptomes, proteomes, and histological features to those of untreated tumors. Taken together, these results demonstrated that tumors can adopt a reversible drug-tolerant state that does not involve clonal selection as a chemotherapy resistance mechanism. Serial biopsies obtained from patients with TNBC undergoing NACT revealed similar histologic changes as well as maintenance of stable subclonal architecture, demonstrating that PDXs capture molecular features characteristic of human TNBC chemoresistance.

  Study phs001742

• Analysis of mutational and proteomic heterogeneity of gastric cancer to monitor post-treatment tumor burden using circulating tumor DNA

  It remains unclear whether complete spectrum of tumor heterogeneity is reflected in circulating tumor DNA (ctDNA). Here, we performed multi-regional target sequencing of primary tumors from 10 patients who underwent surgical resection for gastric cancer. A total of 126 non-synonymous mutations were identified from 30 samples from 10 tumors. Of these, 16 (12.7%) were likely mutations that occurred early in tumor development (i.e., present in all three regions) and were designated founder mutations. Among those validated with highly sensitive digital PCR (dPCR), 93% of non-founder mutations were found in all three regions. Variant allele frequencies (VAFs) of founder mutations were significantly higher than those of non-founder mutations. Phylogenetic analysis demonstrated that five of eight tumors (62.5%, excluding two hypermutator) had at least one identifiable truncal/founder mutation. These findings led us to prioritize founder mutations for ctDNA detection with individually-designed primer/probe sets for dPCR. Marked proteomic diversity was present across the tumor regions both within and between patients independent of mutational status. In preoperative plasma, the average ctDNA VAF of founder mutations was significantly higher than that of non-founder mutations (p = 0.039). Our present data demonstrated that high VAF mutations in the tumor likely represent founder or truncal mutations. However, mutation patterns alone appear insufficient to predict protein heterogeneity. These results suggest that, in practice, mutations having high VAF identified without multi-regional sequencing may be useful for ctDNA monitoring but are inadequate to fully predict proteomic heterogeneity of the tumor.

  Study JGAS000231

• Neoantigen responses to Immunotherapy in Prostate Cancer

  To determine whether antigen-specific T cell responses can be elicited following treatment with ICT in cancers that have a low TMB, a clinical trial with ipilimumab (anti-CTLA-4) was conducted in 30 patients with metastatic castration-resistant prostate cancer (CRPC). We identified 2 distinct groups of patients divided by clinical outcomes, which consisted of a cohort with Favorable and Unfavorable outcomes. Compared to the Unfavorable cohort, the patients in the Favorable cohort were more likely to have higher intratumoral CD8 T cell density and/or IFN-γ response gene signature, including five patients who had antigen-specific T cell responses that were only detectable after ipilimumab therapy. Three patients had T cell responses against tumor-associated antigens (PSMA and PAP). Importantly, two patients with a relatively low TMB had T cell responses against unique neoantigens. Collectively, our data indicate that a subset of patients with metastatic prostate cancer benefit from ICTs, particularly those who have immunological biomarkers associated with effector T cell responses. Publication: Sumit K. Subudhi, Luis Vence, Hao Zhao, Jorge Blando, Shalini S. Yadav, Qing Xiong, Alexandre Reuben, Ana Aparicio, Paul G. Corn, Brian F. Chapin, Louis L. Pisters, Patricia Troncoso, Rebecca Slack Tidwell, Peter Thall, Chang-Jiun Wu, Jianhua Zhang, Christopher L. Logothetis, Andrew Futreal, James P. Allison and Padmanee Sharma, 2020, Neoantigen responses, immune correlates, and favorable outcomes after ipilimumab treatment of patients with prostate cancer. Science Translational Medicine, 01 Apr 2020:Vol. 12, Issue 537, eaaz3577DOI: 10.1126/scitranslmed.aaz3577. https://pubmed.ncbi.nlm.nih.gov/32238575/

  Study EGAS00001004050

• Exome-wide mutation analysis of cell-free DNA to simultaneously monitor the full spectrum of cancer treatment outcomes

  Purpose: Cell-free DNA (cfDNA) offers a non-invasive approach to monitor cancer. Here we develop a method using whole-exome sequencing (WES) of cfDNA for simultaneously monitoring the full spectrum of cancer treatment outcomes, including MRD, recurrence, evolution, and second primary cancers. Experimental Design: Three simulation datasets were generated from 26 cancer patients to benchmark the detection performance of MRD/recurrence and second primary cancers. For further validation, cfDNA samples (n=76) from cancer patients (n=35) with six different cancer types were used for performance validation during various treatments.Results: We present a cfDNA-based cancer monitoring method, named cfTrack. Taking advantage of the broad genome coverage of WES data, cfTrack can sensitively detect MRD and cancer recurrence by integrating signals across known clonal tumor mutations of a patient. In addition, cfTrack detects tumor evolution and second primary cancers by de novo identifying emerging tumor mutations. A series of machine learning and statistical denoising techniques are applied to enhance the detection power. On the simulation data, cfTrack achieved an average AUC of 99% on the validation dataset and 100% on the independent dataset in detecting recurrence in samples with tumor fraction ≥0.05%. In addition, cfTrack yielded an average AUC of 88% in detecting second primary cancers in samples with tumor fraction ≥0.2%. On real data, cfTrack accurately monitors tumor evolution during treatment, which cannot be accomplished by previous methods.Conclusion: Our results demonstrated that cfTrack can sensitively and specifically monitor the full spectrum of cancer treatment outcomes using exome-wide mutation analysis of cfDNA.

  Study EGAS00001005906

• Expression-Based Subtypes Define Pathologic Response to Neoadjuvant Immune-Checkpoint Inhibitors in Muscle-Invasive Bladder Cancer

  Dac EGAC00001002276

• FRIGE IHG Data Access Committee reviews the aim and methodology of the proposed study for which access to anonymised data is requested.

  Dac EGAC00001003629

• resistance to FGFR inhibitor from RNA sequencing

  WTS performed on tissues from resistant patients

  Study EGAS50000000306

• This is a test to check the WEBIN functionality

  Checking WEBIN functionality

  Study EGAS00001008448

• SAFIR02_Agilent

  Genomics to select patients with metastatic breast cancer for targeted therapy (microarray_agilent)

  Dataset EGAD00010002243

• EXCEED Study HRC imputation

  EXCEED samples imputed to HRC reference panel using Michigan Imputation server

  Dataset EGAD00010001685

• OA Functional Genomics

  Functional genomics approaches to understand osteoarthritis

  Dataset EGAD00010001746

• Variant_files_100_ID_trios

  High Quality Variant Call files, generated by bioscope, converted to vcf format. Complete dataset for all 300 samples.

  Dataset EGAD00001000277

• Genomewide Association Study of Alcohol Use and Alcohol Use Disorder in Australian Twin-Families (OZ-ALC GWAS)

  The Australian twin-family study of alcohol use disorder (OZALC study) derives from telephone diagnostic interview studies of two general population volunteer cohorts of Australian twins (cohort 1, mostly born 1940-1964; cohort 2, born 1964-71) and the spouses of the former cohort - a total of over 11,000 families. Three coordinated studies, using a shared assessment protocol and with a shared goal of gene-discovery, were conducted - one funded by the National Institute on Drug Abuse, the others by the National Institute on Alcoholism and Alcohol Abuse - by investigators associated with the Midwest Alcoholism Research Center at Washington University in St. Louis, and investigators at Queensland Institute of Medical Research, Brisbane, Australia (led by Professor Nicholas Martin), using informative families identified from these cohorts. The first of these (NIDA Nicotine Addiction Genetics [NAG] project, PI Pamela Madden) identified index cases from the 3 cohorts with a history of heavy smoking (smoked 20 or more cigarettes daily, or 40 or more cigarettes on 1 or more occasions) and with additional available full siblings who were smokers, and interviewed and obtained blood samples from twins, and cooperative full siblings and parents, in order to identify families that would be informative for linkage analysis of a quantitative heaviness of smoking trait. The second identified additional families with an index case who either reported a history of alcohol dependence (DSM-IV), or scored above the 85th percentile on a quantitative measure of heaviness of alcohol use (alcohol factor score), derived from measures of frequency of heavy drinking, frequency of drinking to intoxication, and typical weekly consumption in standard drinks (all referenced to the respondent's heaviest drinking period) and of lifetime maximum 1-day alcohol consumption and maximum tolerance to alcohol (drinks before getting drunk or before feeling effects of alcohol). Interview and DNA were obtained from index cases and siblings, and DNA only from available parents. The goal of this second study (NIAAA OZ-ALCOHOL EDAC study, PI Andrew Heath) was to identify sibships including pairs who were either extreme concordant for the quantitative consumption measure (both scoring above the 85th percentile) or extreme discordant (one scoring above the 85th percentile and one scoring below the 30th percentile) that would be informative for linkage analysis. The third identified additional sibships solely on the basis of large sibship size, regardless of alcohol or tobacco use phenotypes (NIAAA OZ-BIGSIB study, PIs the late Richard Todd, Andrew Heath). From these coordinated studies a case-control series of alcohol dependent individuals and unaffected controls were constructed for a family-based Genomewide Association Study (OZALC-GWAS) of heaviness of alcohol use and alcohol dependence, funded by the National Institute of Alcoholism and Alcohol Abuse. These data are made available here for all investigators studying outcomes related to alcohol or tobacco use (including major depressive disorder).

  Study phs000181

• Genome-Wide Association of Type 2 Diabetes in Africans: The AADM Study

  The Africa America Diabetes Mellitus (AADM) study is a genetic epidemiological study of type 2 diabetes in Sub-Saharan Africa. Study participants were enrolled through university medical centers in Nigeria, Ghana, and Kenya. Ethical approval for the study was obtained from the Institutional Review Board (IRB) of each participating institution. All subjects provided written informed consent for the collection of samples and subsequent analysis. The case definition of type 2 diabetes was based on the American Diabetes Association (ADA) criteria. After providing informed consent, participants underwent the same enrollment procedures, which included collection of demographic information, medical history, clinical examination and a blood draw. Genome-wide SNP genotyping was done on either the Axiom™ PanAFR SNP array (n=1,808) or the Multi-Ethnic Global Array (MEGA) (n=3,423). After appropriate quality control, in silico imputation was done using the African Genome Resources Haplotype Reference Panel (at the Sanger Imputation Service). Imputed genotypes were filtered for variants with minor allele frequency (MAF)≥ 0.01 and information score (info) ≥ 0.3 for genetic association analysis. Genome-wide association analysis between type 2 diabetes and the imputed genotype dosages was done using a generalized linear mixed model, which adjusted for age, gender, body mass index, the genetic relatedness matrix and the first three principal components (PCs) of the genotypes.Metabolomics profiling of plasma samples of type 2 diabetes (T2D) cases and controls in Nigerians (West Africa) was done in the AADM Study. Plasma metabolites were measured in a total of 580 individuals (N=310 for the discovery phase and N=270 for the replication stage) using the global/untargeted approach on the Metabolon platform and following the manufacturer's standard operation protocols. The analytic methods are described in detail in Doumatey et al. [Genome Med 2024]. The measured metabolites level represented by peak areas are relative values. The peak area data were batch-normalized to remove the instrument batch effects (batch variability) and the batch-normalized data correspond to the median-scaled raw data. For each identified metabolite, the minimum value across all batches in the batch-normalized was imputed for the missing values. The batch-normalized and imputed data are natural log-transformed and consist of 1116 metabolites for the discovery cohort and 1071 metabolites for the replication. Welch's two-sample t-test on the log-transformed data was used to identify metabolites differentially expressed between T2D cases and controls . All other statistical analyses conducted on both the replication and discovery cohorts used the log-transformed data. To merge the discovery and replication, the same quality control samples (bridge samples) were run with each batch of the experimental samples in both cohorts and used to correct for additional variability and uniformize the procedures. The resulting merged data is the QC-normalized and imputed data that contains only metabolites that were common to both cohorts and successfully bridged for all batches (n= 891 metabolites).

  Study phs001844

• This DAC is the list of persons that, on behalf of the UKBEC consortium, manage the access to the UKBEC data.

  Dac EGAC00001000314

• This agreement governs the terms on which access will be granted to the data generated by the United Kingdom Brain Expression Consortium.

  Dac EGAC00001001408

• Azienda Ospedaliero-Universitaria di Parma Data Accessibility

  Regulation on the sharing and accessibility of data belonging to Azienda Ospedaliero-Universitaria di Parma

  Dac EGAC50000000239

• DAC anti-MPO BCR sequences

  DAC for the requests of access to data of anti-MPO BCR sequences in the EGA repository,

  Dac EGAC50000000446

• A single cell view on host immune transcriptional response to in vivo BCG-induced trained immunity

  Study EGAS00001006990

• RNA alignment

  RNAseq aligned to hg38.p14 using nfcore/RNA-seq

  Dataset EGAD50000000428

• EXOME_ARRAY_ANALYSIS

  EXOME ARRAY ANALYSIS OF ADVERSE REACTIONS TO FLUOROPYRIMIDINE-BASED THERAPY FOR GASTROINTESTINAL CANCER

  Dataset EGAD00010001499

• Smoking-dependent expression alterations in nasal epithelium reveal immune impairment linked to germline variation and lung cancer risk

  Study EGAS00001006137