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• MutWP1: CRUK Grand Challenge Mutographs of Cancer: Kidney (1)

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute. Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Dataset EGAD00001006427

• Elevated somatic mutation burdens in normal human cells due to defective DNA polymerases

  Mutation accumulation over time in normal somatic cells contributes to cancer development and is proposed as a cause of ageing. DNA polymerases POLE and POLD1 replicate DNA with high fidelity during normal cell divisions. However, in some cancers defective proofreading due to acquired mutations in the exonuclease domains of POLE or POLD1 causes markedly elevated somatic mutation burdens with distinctive mutational signatures. POLE and POLD1 exonuclease domain mutations also cause familial cancer predisposition when inherited through the germline. Here, we sequenced normal tissue DNA from individuals with germline POLE or POLD1 exonuclease domain mutations. Increased mutation burdens with characteristic mutational signatures were found to varying extents in all normal adult somatic cell types examined, during early embryogenesis and in sperm. Mutation burdens were further markedly elevated in neoplasms from these individuals. Thus human physiology is able to tolerate ubiquitously elevated mutation burdens. Indeed, with the exception of early onset cancer, individuals with germline POLE and POLD1 exonuclease domain mutations are not reported to show abnormal phenotypic features, including those of premature ageing. The results, therefore, do not support a simple model in which all features of ageing are attributable to widespread cell malfunction directly resulting from somatic mutation burdens accrued during life.

  Dataset EGAD00001006212

• The mission of the BIOS Consortium is to create a large-scale data infrastructure and to bring together BBMRI researchers focusing on integrative omics studies in Dutch Biobanks.

  The advent of the genome-wide association study (GWAS) led to the successful identification of thousands of variants that are robustly associated with complex disease phenotypes. Dutch biobanks played a substantial role in these discoveries. For most of these variants, however, the mechanisms through which they contribute to these phenotypes remain unknown. The BIOS Consortium applies a functional genomics approach that integrates genome-wide genetic data with data on the epigenome and transcriptome to elucidate these mechanisms. Over 4000 samples from BBMRI-NL biobanks with in-depth information on disease phenotypes and GWAS data are being enriched with RNA-sequencing and genome-wide DNA methylation data. The same is true for samples with whole-genome sequencing data from GoNL. This unique data infrastructure provides a powerful platform to evaluate key questions in integrative omics from establishing comprehensive eQTL and meQTL catalogues to linking molecular pathways across omics levels to phenotypic outcomes.

  Study EGAS00001001077

• Drug development for pediatric cancers: the importance of patient' s genomic data re-use

  Cancer in children is uncommon and the overall prognosis for most pediatric cancers is good. However, while the combined survival rates have improved over the last decades, certain childhood malignancies, such as high-grade gliomas or metastatic sarcomas, still remain incurable in most patients. New strategies for targeting those devastating diseases are imperative, and patient genomic data can become a key asset in the process [1]. The Pediatric Cancer Genome Project’s datasets At EGA, we store over 500 datasets with sequencing data from pediatric cancer patients. A remarkable case is the datasets belonging to the Pediatric Cancer Genome Project (PCGP) from St. Jude Children’s Research Hospital–Washington University (Table 1 - EGAS list). PCGP is an ambitious effort to identify the mutations that drive childhood cancer and find new cures. Those datasets include 600 patients with complete tumor and normal genomes from 15 different tumor types. PCGP datasets have been an important resource for various studies that pooled genomic data from different public and in-house datasets to perform extensive genomic characterizations and publish comprehensive pan-cancer pediatric studies [2],[3]. Other interesting pediatric cancer datasets are from the Pediatric Brain Tumor Consortium, Sickkids or ICGC PedBrain project. How data reuse impacts drug development for pediatric cancers Recently, in October 2024, Nature Communications published a work led by the University of Michigan where authors used data from EGA (PCGP) check the table below as part of their strategy to pursue the identification of new tumor vulnerabilities susceptible to becoming novel therapeutic targets in diffuse midline glioma. Diffuse midline gliomas (DMG) are treatment-resistant and uniformly fatal pediatric brain tumors. The prognosis of this brainstem tumor is dismal with a median overall survival of 9–12 months from diagnosis. In this study, the authors first developed a lab model of the disease and reanalyzed data from the EGA to identify genes potentially linked to tumor severity. This approach revealed involvement of specific metabolic pathways, suggesting new possibilities for therapeutic intervention. In mice, the study shows that the use of statins can improve survival. For a brief overview of the underlying science, diffuse midline gliomas present intratumor heterogeneity with subpopulations of less-differentiated oligodendrocyte precursors and more differentiated astrocytes. Authors established in vitro models to recapitulate both phenotypes and identify metabolic programs in both subpopulations. To determine the clinical relevance, the authors re-used gene expression data from 76 DMG patients identifying a gene signature predicting decreased overall survival. After extensive metabolic characterization of subpopulations, authors defined strategies to target specific metabolic vulnerabilities. Pre-clinical experiments in mice using OXPHOS inhibitors and statins showed a reduction in tumor burden and increased overall survival [4]. In this scenario, the re-use of high-quality patient’s sequencing data appeared as an advantageous strategy to support the clinical relevance of in-vitro and pre-clinical findings. On the other hand, in rare conditions such as childhood cancer, the availability of public datasets and the possibility of pooling data from different sources can be the only way to obtain impactful results that lead to improvements for patients. References Davidoff, A. M. Pediatric Oncology. Seminars in pediatric surgery 19, 225–233 (2010). Gröbner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018). Venu Thatikonda et al. Comprehensive analysis of mutational signatures reveals distinct patterns and molecular processes across 27 pediatric cancers. Nature Cancer 4, 276–289 (2023). Mbah, N. E. et al. Therapeutic targeting of differentiation-state dependent metabolic vulnerabilities in diffuse midline glioma. Nature Communications 15, (2024). Datasets and Studies ID Title Access Policy Year EGAD00001000134 Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma McGill-DKFZ Pediatric Brain Tumour Consortium 2016 EGAS0000100192 Somatic Histone H3 Mutations in Diffuse Intrinsic Pontine Gliomas and Non-Brainstem Paediatric Glioblastomas St. Jude Children's Research Hospital - Washington University Pediatric Cancer Genome Project 2017 EGAS00001000575 Whole genome sequencing and whole exome sequencing of DIPG tumors and matched normal tissue The hospital for sick children ("SickKids") 2016 EGAD00001000792 Exome sequencing reads of paediatric glioblastoma McGill-DKFZ Pediatric Brain Tumour Consortium 2016 EGAD00001002006 Whole genome sequencing of paediatric glioblastoma in the ICGC PedBrain project ICGC PedBrain project 2016

  Blog drug-development-for-pediatric-cancers

• SC_DDD-G-1

  Batch 1 of unfiltered genotype data for DDD Study patients (N=2,997), some of which were used in the neurodevelopmental disorder discovery GWAS (Niemi et al., Nature 2018). Samples were genotyped on the Illumina HumanCoreExome BeadChip. QC'd data is available in release EGAD00010001604

  Dataset EGAD00010001598

• SC_DDD-G-2

  Batch 2 of unfiltered genotype for DDD Study patients (N=8,286), some of which were used in the neurodevelopmental disorder discovery GWAS (Niemi et al., Nature 2018). Samples were genotyped on the Illumina InfiniumCoreExome Beadchip. QC'd data is available in release EGAD00010001604

  Dataset EGAD00010001600

• Expression array

  The whole study comprises of two patient cohorts. Screening cohort: 40 patients of Germany; validation cohort: 40 patients from Asia. Further, bile duct and CCA cell lines have been analyzed.
This dataset contains expression array data for 32 patients of the screening cohort with 8 of them having paired normal tissue plus an additional relapse tumor/normal pair of one of those 8 patients and a patient only with normal tissue. Data was generated on a HumanHT-12 v4 Bead Array (Illumina) and is stored in IDAT file format.

  Dataset EGAD00010002596

• Single-nucleus mRNA-Sequencing of prenatal and postnatal samples from the brain and its border regions

  Single-nucleus mRNA Sequencing of prenatal and postnatal samples from the brain and its border regions. Most samples were multiplexed with several samples run in one 10X reaction. A separate immune cell dataset was combined with published data from Braun et al 2023 and Yang et al 2021 integrated using harmony is included.

  Dataset EGAD50000000044

• Next generation sequencing on cardiac samples in Hungarian patients of dilated cardiomyopathy

  Heterozygous (HET) truncating mutations in the TTN gene (TTNtv) encoding the giant titin protein are the most common genetic cause of dilated cardiomyopathy (DCM). We investigated 127 clinically identified DCM human cardiac samples with targeted sequencing using the TruSight Cardio panel on an Illumina MiSeq system with a special focus on TTNtvs. This dataset belongs to the publication of Kellermayer, D et al. Truncated titin is integrated into the human dilated cardiomyopathic sarcomere

  Dataset EGAD50000000066

• Arcagen – thoracic malignancies

  Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here we are submitting the dataset that contain NGS files from rare thoracic malignancies (n=102)

  Dataset EGAD50000000168