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• Cancer-Normal chronic myeloid leukaemia sequencing data for "A comparative analysis of algorithms for somatic SNV detection in cancer", Bioinformatics 29 (2013) 2223

  This dataset contains the fastq sequencing data collected from bone marrow DNA of a chronic myeloid leukaemia patient at time of diagnosis.

  Dataset EGAD00001000967

• Phase II trial of targeted immune-depleting chemotherapy and reduced-intensity allogeneic hematopoietic stem cell transplantation using 8/8 and 7/8 HLA-matched unrelated donors and utilizing two graft-versus-host disease prophylaxis regimens for the treatment of leukemias, lymphomas, and pre-malignant blood disorders

  Background: Major problems with stem cell transplantation (SCT) for cancer treatment are a lack of suitable donors for patients without an HLA tissue-matched sibling and graft-versus-host disease (GVHD), a serious side effects of immune-suppressing chemotherapy that is given to bring the cancer under control before SCT. In GVHD, the patient's immune system attacks the transplanted donor cells. This study will try to improve the results of SCT from unrelated HLA-matched donors using targeted immune-depleting chemotherapy to bring the cancer under control before transplantation and to lower the chance of graft rejection, followed by reduced-intensity transplant chemotherapy to make the procedure less toxic. Objectives: To evaluate the safety and effectiveness of targeted immune-depleting chemotherapy followed by reduced-intensity transplant chemotherapy in patients with advanced cancers of the blood and immune system. To evaluate the safety and effectiveness of two different drug combinations to prevent GVHD. Both regimens have been successful in preventing GVHD, but they work by different mechanisms and affect the rebuilding of the immune system after the transplant. Eligibility: People 18 to 74 years of age with advanced or high-risk cancers of the blood and immune system who do not have a suitable HLA-matched sibling. Design: All patients receive chemotherapy before transplant to treat the cancer and suppress immune function. All patients receive a conditioning regimen of cyclophosphamide for 4 days and fludarabine for 4 days before SCT to prepare for the transplant. Patients are randomly assigned to one of two combination drug treatments to prevent GHVD as follows: Group 1: Tacrolimus starting 3 days before SCT and continuing for 6 months, plus methotrexate on days 1, 3, 6, and 11 post-SCT, plus sirolimus starting 3 days before the SCT and continuing through day 14 following SCT. Group 2: Alemtuzumab for 4 days starting 8 days before SCT, plus cyclosporine starting 1 day before SCT and continuing for 6 months. Patients receive the donor's stem cells and immune cells 2 days after the conditioning regimen. Patients are followed at the clinic regularly for the first 6 months after SCT, and then less often for at least 5 years. Some visits may include bone marrow aspirates and biopsies, blood draws, and other tests to monitor disease status. A skin biopsy, oral mucosa biopsy, and saliva collection are done to study chronic GVHD.

  Study phs002021

• The EGA at the International Congress of Human Genetics

  Today is the start of the International Congress of Human Genetics (ICHG). This event, hosted by the African Society of Human Genetics (AfSHG) and the Southern African Society for Human Genetics (SASHG), will reunite international experts to highlight how genomic technologies are being managed to address challenges generated by the current status of Human Health and Genomics. The European Genome-phenome Archive (EGA) will be at the Congress participating in workshops, meetings and talks where topics such as data access and discovery as well as the Federated EGA will be present. Data access and discovery In the context of the “Federated data analysis workshop“ that will be held the 26ᵗʰ, managed in the context of the CINECA project, a hands-on-session on Beacon v2 Discovery will be led by Mauricio Moldes. Another session on authorisation and data access will be presented by Mallory Freeberg and Coline Thomas, with and introduction by Thomas Keane. During the workshop, an end-to-end federated data analysis use case will be presented. Data access and discovery In the context of the “ Federated data analysis workshop“ that will be held the 26ᵗʰ, managed in the context of the CINECA project, a hands-on-session on Beacon v2 Discovery will be led by Mauricio Moldes. Another session on authorisation and data access will be presented by Mallory Freeberg and Coline Thomas, with and introduction by Thomas Keane. During the workshop, an end-to-end federated data analysis use case will be presented. During the last four years, CINECA has been identifying gaps in data sharing between Africa, Canada and Europe in order to build a federated solution for data access. Thomas Keane will present the project within the sessions happening the 24ᵗʰ. The Federated EGA at the ICHG A Federated EGA poster will be shown during the Poster Session 1 that will take place the 23ʳᵈ. The objective is to bring awareness of the European Genome-phenome Archive as a resource for permanent archive and secure data sharing. In this context, the network generated by the Federated EGA is key for cases where research data cannot be shared outside local jurisdictions. The Federated EGA was officially launched on 2022 with research institutes from five countries becoming the first nodes of this network. Nowadays, with more than 20 nodes preparing to join, it aims to become the largest human omics data sharing initiative towards understanding human health and disease. The H3Africa Initiative and the African research represented in the EGA 1.1% of the EGA studies include samples from African populations. Countries such as South Africa, Kenya, Uganda or Egypt, among others, are present in the 72.577 samples from African individuals archived at the EGA. 23 EGA datasets are managed by the Human Heredity and Health in Africa Initiative (H3Africa). This Initiative will celebrate its Consortium Meeting the 27ᵗʰ and 28ᵗʰ, back-to-back with the ICHG. H3ABioNet, the Pan African Bioinformatics Network for the H3Africa consortium, will also take this context to celebrate its 10-Year Symposium during the Congress. Members of the EGA team will attend both of them, as the H3ABioNet works in conjunction with H3Africa projects, to submit data to the European Genome-Phenome Archive.

  Blog the-ega-at-the-international-congress-of-human-genetics

• CINECA_synthetic_cohort_EUROPE_UK1 referencing fake samples

  Please note: This synthetic data set (with cohort “participants” / ”subjects” marked with FAKE) has no identifiable data and cannot be used to make any inference about cohort data or results. The purpose of this dataset is to aid development of technical implementations for cohort data discovery, harmonization, access, and federated analysis. In support of FAIRness in data sharing, this dataset is made freely available under the Creative Commons Licence (CC-BY). Please ensure this preamble is included with this dataset and that the CINECA project (funding: EC H2020 grant 825775) is acknowledged. For any questions please contact isuru@ebi.ac.uk or cthomas@ebi.ac.uk This dataset (CINECA_synthetic_cohort_EUROPE_UK1) consists of 2521 samples which have genetic data based on 1000 Genomes data (https://www.nature.com/articles/nature15393), and synthetic subject attributes and phenotypic data derived from UKBiobank (https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001779). These data were initially derived using the TOFU tool (https://github.com/spiros/tofu), which generates randomly generated values based on the UKBiobank data dictionary. Categorical values were randomly generated based on the data dictionary, continuous variables generated based on the distribution of values reported by the UK Biobank showcase, and date / time values were random. Additionally we split the phenotypes and attributes into 4 main classes - general, cancer, diabetes mellitus, and cardiac. We assigned the general attributes to all the samples, and the cardiac / diabetes mellitus / cancer attributes to a proportion of the total samples. Once the initial set of phenotypes and attributes were generated, the data data was checked for consistency and where possible dependent attributes were calculated from the independent variables generated by TOFU. For example, BMI was calculated from height and weight data, and age at death generated by date of death and date of birth. These data were then loaded to the development instance of Biosamples (https://www.ebi.ac.uk/biosamples/) which accessioned each of the samples. The genetic data are derived from the 1000 Genomes Phase 3 release (https://www.internationalgenome.org/category/phase-3/). The genotype data consists of a single joint call vcf files with call genotypes for all 2504 samples, plus bed, bim, fam, and nosex files generated via plink for these samples and genotypes. The genotype data has had a variety of errors introduced to mimic real data and as a test for quality control pipelines. These include gender mismatches, ethnic background mislabelling and low call rates for a randomly chosen subset of sample data as well as deviations from Hardy Weinberg equilibrium and low call rates for a random selection of variants. Additionally 40 samples have raw genetic data available in the form of both bam and cram files, including unmapped data. The gender of the samples in the 1000 genomes data has been matched to the synthetic phenotypic data generated for these samples. The genetic data was then linked to the synthetic data in BioSamples, and submitted to EGA.

  Dataset EGAD00001006673

• Dataset developed for use with EOSC4Cancer of synthetic colorectal cancer tumor/normal pairs.

  This dataset contains 10 tumor and normal pairs synthetic WGS data of colorectal cancer that were simulated in a standard format of Illumina paired-end reads. The NEAT read simulator (version 3.0, https://github.com/zstephens/neat-genreads) was utilized to synthetize these 10 pairs of tumor and normal WGS data. In the procedure of data generation, simulated parameters (i.e., sequencing error statistics, read fragment length distribution and GC% coverage bias) were learned from data models provided by NEAT. The average sequencing depth for tumor and normal samples aimed to reach around 110X and 60X, respectively. For generation of synthetic normal WGS data per each sample, a germline variant profile from a real patient was down-sampled randomly, representing 50% germline variants of a given patient. These were mixed with the other 50% in silico germline variants that were modelled randomly using an average mutation rate (0.001), finally constituting a full germline profile for normal synthetic WGS data. For generation of synthetic tumor WGS data per each sample, a pre-defined somatic short variant profile (SNVs+Indels) learnt from a real CRC patient was added to the germline variant profile used for creating the normal synthetic WGS data of the same patient, consisting of the variants for tumor sample. Neither copy number profile nor structural variation profile was introduced into the tumor synthetic WGS data. Tumor content and ploidy were assumed to be 100% and 2, respectively. For mapping/variant detection, the Sarek pipeline v3.1.2 (https://nf-co.re/sarek/3.1.2) was used, specifically: 1. BWA v0.7.17-r1188 for read mapping 2. GATK v4.3.0.0 for pre-processing BAM file (including markduplicates and recalibration). 2. Mutect2 (GATK v4.3.0.0) for somatic variant calling 3. Strelka2 v2.9.10 for germline and somatic variant calling Metadata information of 10 CRC patients used for the generation of synthetic normal and tumor WGS data: Patient_id Tumor_barcode Normal_barcode Age Sex Tissue Cancer SIM007 SIM007_T SIM007_N 71 F Rectal Primary CRC SIM008 SIM008_T SIM008_N 45 F Colon Neuroendocrine Metastasis CRC SIM010 SIM010_T SIM010_N 62 M Colon Metastasis CRC SIM011 SIM011_T SIM011_N 55 M Colon Neuroendocrine Metastasis CRC SIM012 SIM012_T SIM012_N 57 M Rectal Metastasis CRC SIM013 SIM013_T SIM013_N 69 M Colon Metastasis CRC SIM014 SIM014_T SIM014_N 68 M Colon Neuroendocrine primary CRC SIM015 SIM015_T SIM015_N 58 F Colon Primary CRC SIM016 SIM016_T SIM016_N 49 M Colon/Rectal Primary CRC SIM017 SIM017_T SIM017_N 78 M Colon Neuroendocrine primary CRC

  Dataset EGAD50000000564

• Immunomodulatory effects and improved outcomes with cisplatin- vs carboplatin-based chemotherapy plus atezolizumab in urothelial cancer

  In metastatic urothelial cancer (mUC), cisplatin versus carboplatin leads to durable disease control in a subset of patients. The IMvigor130 trial reveals more favorable effects with atezolizumab combined with gemcitabine and cisplatin (GemCis) versus gemcitabine and carboplatin (GemCarbo). This study investigates the immunomodulatory effects of cisplatin as a potential explanation for these observations. Our findings indicate that improved outcomes with GemCis versus GemCarbo are primarily observed in patients with pretreatment tumors exhibiting features of restrained adaptive immunity. Additionally, GemCis versus GemCarbo ± atezolizumab induces transcriptional changes in circulating immune cells, including upregulation of antigen presentation and T-cell activation programs. In vitro experiments demonstrate that cisplatin, compared with carboplatin, exerts direct immunomodulatory effects on cancer cells, promoting dendritic cell activation and antigen-specific T cell killing. These results underscore the key role of immune modulation in cisplatin's efficacy in mUC and highlight the importance of specific chemotherapy backbones in immunotherapy combination regimens.

  Study EGAS50000000104

• A Translational, Pharmacodynamic and Pharmacokinetic Phase IB Clinical Study of Everolimus in Resectable Non-Small Cell Lung Cancer

  This phase IB clinical trial studied the pharmacodynamic changes induced by everolimus in patients with previously untreated, resectable non small cell lung cancer. Using a combination of in vivo and ex vivo assessments with FDG PET, immunohistochemistry and genomic assays, evidence of mTOR pathway perturbation was assessed in patients treated with everolimus, an allosteric mTOR inhibitor. Untreated patients served as controls for comparison. Key results from this work include a dose-dependent biologic effect of everolimus, signal of activity in RAS mutant tumors as well as near complete metabolic and pathologic response in a case of sarcomatoid lung cancer. These findings represent novel translational insights that can guide future development of mTOR inhibitors in lung cancer and other tumor types. Additionally, metabolic response and anatomic tumor shrinkage observed in a significant proportion of patients following a short of duration of therapy with everolimus suggests potential clinical utility of this agent in carefully-selected lung cancer patients.

  Study phs000829

• Inactivation of TGFβ receptors in stem cells drives cutaneous squamous cell carcinoma - 30 whole exomes

  Melanoma patients treated with oncogenic BRAF inhibitors can develop cutaneous squamous cell carcinoma (cSCC) within weeks of treatment, driven by paradoxical RAS/RAF/MAPK pathway activation. Here, we identify frequent TGFBR1 and TGFBR2 mutations in human vemurafenib-induced skin lesions and in sporadic cSCC. Functional analysis reveals these mutations ablate canonical TGFβ Smad signaling which is localised to bulge stem cells in both normal human and murine skin. MAPK pathway hyperactivation (through BrafV600E or KrasG12D knockin) and TGFβ signaling ablation (through Tgfbr1 deletion) in LGR5+ve stem cells enables rapid cSCC development in the mouse. Mutation of Tp53 (which is commonly mutated in sporadic cSCC) coupled with Tgfbr1 deletion in LGR5+ve cells also results in cSCC development. These findings indicate that LGR5+ve stem cells can act as cells of origin for cSCC and that RAS/RAF/MAPK pathway hyperactivation or Tp53 mutation, coupled with loss of TGFβ signaling, are driving events of skin tumorigenesis.

  Study EGAS00001001892

• Novel mutational mechanisms and drivers in Pancreatic Neuroendocrine Tumours

  Pancreatic neuroendocrine tumours (PanNETs) are increasing in prevalence due to earlier detection, consequently creating challenges in clinical care, as current classification permits unsatisfactory therapeutic stratification. We performed whole genome sequencing of 102 primary PanNETs and defined the genomic events underlying their pathogenesis. We describe the mutational signatures they harbour, including a novel G:C>T:A Base-Excision-Repair-deficiency signature due to MUTYH inactivation. We uncover a larger than expected proportion of germline mutations in clinically sporadic PanNETs. These include previously unreported mutations in DNA repair genes MUTYH, CHEK2, BRCA2, and PALB2, which together with MEN1 and VHL occur in 16% of patients. Somatic mutations (point mutations and structural rearrangements) commonly occurred in genes involved in 4 main pathways: chromatin remodelling, DNA damage repair, activation of mTOR signalling (including novel EWSR1 gene fusions) and telomere maintenance. Aberrations in these mechanisms are associated with subtypes of PanNET with potential clinical application for prognostic and therapeutic stratification.

  Study EGAS00001001732

• CTCF/cohesin-binding sites are frequently mutated in cancer

  Cohesin is present in almost all active enhancer regions, where it is associated with transcription factors. Cohesin frequently colocalizes with CTCF (CCCTC-binding factor), affecting genomic stability, expression and epigenetic homeostasis. Cohesin subunits are mutated in cancer, but CTCF/cohesin-binding sites (CBSs) in DNA have not been examined for mutations. Here we report frequent mutations at CBSs in cancers displaying a mutational signature where mutations in A•T base pairs predominate. Integration of whole-genome sequencing data from 213 colorectal cancer (CRC) samples and chromatin immunoprecipitation sequencing (ChIP-exo) data identified frequent point mutations at CBSs. In contrast, CRCs showing an ultramutator phenotype caused by defects in the exonuclease domain of DNA polymerase ɛ (POLE) displayed significantly fewer mutations at and adjacent to CBSs. Analysis of public data showed that multiple cancer types accumulate CBS mutations. CBSs are a major mutational hotspot in the noncoding cancer genome.

  Study EGAS00001003010