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• The EGA joins EUCAIM, the European project powering AI and imaging in cancer research

  EUCAIM (EUropean Federation for CAncer IMages) is the cornerstone of the European Cancer Imaging Initiative, one of the flagships of the Europe’s Beating Cancer Plan (EBCP). The European Genome-Phenome Archive (EGA), as member of the EUCAIM consortium, attended the kick-off meeting organized this week in Brussels for the launch of the Initiative. The EGA will contribute to the project with the experience gained in the deployment of our federated network, the Federated EGA (FEGA), and the experience gained as partners of EuCanImage (AI4HI initiative) contributing to the definition of common data models, ontologies and data standards. The ultimate goal of the project is to unlock the power of imaging and Artificial Intelligence for the benefit of cancer patients, clinicians and researchers. The consortium will deploy a pan-European digital federated infrastructure hosting cancer-related images, and associated pathology, molecular and laboratory data from Real-World. This endeavour will provide a comprehensive dashboard for imaging data discovery, federated search, metadata harvesting, annotation and distributed data processing. EUCAIM joins 76 partners, gathering the experience and previous effort made by the different repositories of the AI4HI initiative, European Research Infrastructures and national/regional repositories. Find out more information about the EUCAIM launch here

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• Upcycling and merging data to challenge the dogma and identify new therapeutic targets for Glioblastoma

  Glioblastoma is the most common brain tumour. Characterised by a poor prognosis and its recurrence after multimodal treatments, the search for preventable risk factors has been mainly inconclusive up to date. Recently, the data merge from datasetsdeposited at the EGA allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. Challenges in glioblastoma research As for virtually all cancer types, many efforts have been made to unveil the molecular features responsible of the disease, and several cellular pathways have indeed been identified as being frequently mutated in glioblastoma. Nonetheless, targeted therapies based on identified genes have so far failed to improve outcome, thus survival mostly relies on a standard treatment unchanged since 2005. This is a frustrating situation for both the scientific and medical communities, and above all for the patients, still facing a dreadful path. Some researchers hypothesised that this may be due to the inability to efficiently target cancer stem cells, the originators of the other cell types, thus inducing cancer relapse. In 2019 Charles P. Couturier and colleagues sequenced RNA from single cells of freshly excised glioblastomas of 16 patients, and demonstrated that glioblastoma cells replicate normal brain cell development with a conserved neural cancer cell hierarchy centered around glial progenitor-like cells. In this way, they helped identify the possible target cells to improve efficacy and durability of treatment. Data upcycling at the EGA: the glioblastoma case study Single cell RNA sequencing is generated with a laborious and expensive protocol. The quality of the starting material is crucial (cells sample freshly extracted from the patients) and often several attempts are needed before producing reliable quality sequencing results. Collecting big numbers of patients is also challenging. The sequencing data produced by Kevin Petrecca’s group in Montreal, Canada and deposited at the EGA (EGAS00001004422) was recently upcycled by Lin Wang in San Francisco, California and pooled with their freshly produced ones, and then deposited at the EGA as Dataset EGAS00001004909 The data merge allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. They profiled 86 primary-recurrent patient-matched paired glioblastoma samples with single-nucleus RNA, among other techniques. These very comprehensive results lead the team to challenge the findings from several other cancer fields where chemotherapy standard chemo-radiation therapy selection pressure at the level of genomic alterations; this is indeed not the case for glioblastoma, where the pressure results in phenotypic transition between cellular states. Several technical controls were made to ensure that the merge of the data was not introducing a bias in the results, and an Inter-table analysis demonstrated nearly equal contribution to overall variance from each of the studies included, indicating that the findings were not due to inter-laboratory technical effect. A novel principal-component analysis showed that the largest contribution to variation in primary glioblastoma neoplastic cells was an axis between MES (mesenchymal) and proneural expression programs. In summary, treating glioblastoma often makes a MES, as commented by Lucy Stead about this remarkable work. Check Aaron Diaz’s team Nature Cancer paper, with strong technical tools, state of the art analysis and data from different sources converging to the same outcomes that made possible a significant step toward a better handling of a frightful cancer. References Couturier, C.P., Ayyadhury, S., Le, P.U. et al. Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy. Nat Commun 11, 3406 (2020). Stead, L.F. Treating glioblastoma often makes a MES. Nat Cancer 3, 1446–1448 (2022). Wang, L., Jung, J., Babikir, H. et al. A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets. Nat Cancer 3, 1534–1552 (2022). Related links: Kevin Petrecca’s group dataset deposited at the EGA: EGAS00001004422 Lin Wang dataset deposited at the EGA: EGAS00001004909

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• Providing safe access to sensitive human data across borders: Federated EGA becomes a reality

  Enabling discovery and access to sensitive data across national boundaries is vital for improving human health. It enables more powerful and efficient research by increasing the volume and diversity of data available for analysis. It allows us to better understand the causes of diseases – cancer, rare diseases, infectious diseases like COVID-19 – and develop new medicines and treatments. Sensitive human omics data are typically generated by research initiatives and shared using specialist repositories which provide services for data submission, discovery, and access. The EGA is one such repository. Established in 2008 at EMBL’s European Bioinformatics Institute (EMBL-EBI) in the UK, since 2012 the EGA (“Central EGA”) has been jointly managed by EMBL-EBI and the Centre for Genomic Regulation (CRG) in Spain. Many countries have emerging personalised medicine programmes which generate data from national initiatives. These programmes are driving a transition in human genomics from being research-driven to receiving funding through healthcare. Data generated in a clinical context are subject to stricter governance than research data and must follow national data protection legislation. To solve these challenges, the Federated EGA provides a network of connected resources to enable transnational discovery of and access to human omics data for research, while also respecting jurisdictional data protection regulations. In this way, the Federated EGA infrastructure supports the goals of European initiatives such as the 1+ Million Genomes initiative (1+MG), the European Health Data Space, and a number of EU-funded 1+MG implementation projects including Beyond 1 Million Genomes. The Federated EGA is made up of “Nodes” – typically nationally funded and operated – which store and manage data locally while allowing global discovery within the Federated EGA network. Since 2016, multiple parallel efforts, supported by ELIXIR and other transnational and national initiatives, have created technical and legal frameworks for establishing the Federated EGA. In 2022, the first five Nodes – Finland, Germany, Norway, Spain, and Sweden – officially joined the Federated EGA by signing Federated EGA Collaboration Agreements with Central EGA. The Finnish FEGA Node is operated by CSC - IT Center for Science and provides data management services according to national laws and the requirements of the EU General Data Protection Regulation (GDPR). These services provide tools and support for the whole life-cycle of sensitive research data from collating to analysis, publication, and authorised re-use. The development of the services has been a joint effort with the other Nordic nodes within NeIC's Tryggve and Heilsa projects, and funded by Finnish Ministry of Education and Culture and projects coordinated by ELIXIR Finland. Read more about Finnish FEGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. In Norway, a key component of the infrastructure is the services of The Sensitive Data Service (TSD) offered by USIT at University of Oslo. The Federated EGA Norway Node is developed by ELIXIR Norway and operated by the University of Oslo as the responsible legal entity. Core software modules are developed jointly with the other Nordic nodes in the NeIC Tryggve and Heilsa projects. Read more about FEGA Norway signing. The Spanish FEGA (es-FEGA) is a national service for storing sensitive biomedical data in Spain. Supported by the Spanish Institute of Bioinformatics (INB) in collaboration with Central EGA, sensitive research datasets are primarily hosted at the Barcelona Supercomputing Centre facilities. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. By providing a solution for secure and efficient management of human omics data, the Federated EGA aims to foster data reuse, enable reproducibility, accelerate biomedical research, and improve human health. Find out more Interested in setting up your own Federated EGA Node? Check out the FEGA Onboarding Knowledge Base for more information. The ELIXIR Federated Human Data Community is a great entry point for anyone interested in learning more about the Federated EGA. You can: Join the ELIXIR Federated Human Data Community mailing list (select “Human Data”) Attend the ELIXIR Federated Human Data Community calls

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• How upcycled prostate cancer sequences enabled key findings on telomeres length

  The length of telomeres, the chromosomes’ end tails, as depicted in the figure below, physiologically shorten with aging. This means that younger cells have longer telomeres compared to their aged counterparts; one could say that the length of cells’ telomeres is a measure of their replicative power. Cancer cells are known to be able to turn this into their favour, and expand their replicative immortality through telomere lengthening and maintenance. Despite being a well-studied pan-cancer hallmark, the relation of the telomeres length to specific cancer genomic features is poorly understood. Julia Livingstone and her colleagues from P.C. Boutros' team in Los Angeles focused their work on prostate cancer. To investigate the relations between telomere length and the clinico-genomics of prostate tumours, they used whole genome sequencing (WGS) of 392 published tumour–reference pairs previously deposited at the EGA under accession numbers EGAS00001000400 and EGAS00001000900. Their work was published on Nature Communications in November 2021. They determined the telomere length of the sequencing sample, from both tumour and non-tumour adjacent tissues, and subsequently ran statistical analysis to investigate possible correlation between the telomeres length with several cancer features. The team discovered that telomeres length does not correlate with any specific driver gene mutation, but it does with the overall frequency of single nucleotides variants in a sample. This finding suggests that tumours with shorter telomeres accumulate more mutations without strong selective pressures for specific ones. While proliferation rate did not correlate directly with telomere length, methylation of almost half of all genes strongly did. The team’s results are massive and reveal a complicated and fascinating relation of different clinical and genomic features of prostate cancer with telomere length. In the paper discussion, they smartly consider different possibilities. As noted above, all these amazing results have been obtained analysing previously sequenced samples. Remarkably, the researchers who generated the samples did not focus at all on telomeres length. They indeed studied specific genes mutations and the features of early and late-onset prostate cancer. This proves, once again, the power of sequencing data re-use. This data is tremendously rich in information, and can hardly be exhausted with any number of analyses. Generating sequencing data, especially Whole Genome Sequencing, even better if tumor-reference paired, is like shopping every last article at the grocery store. You can then cook several recipes, or prepare a seven-course dinner, and still have ingredients left for someone else to borrow and cook something you have not thought about, a recipe that is just not in your cookbook. Noteworthy, the data retrieved from the EGA for this paper is associated with rich and complete metadata, meaning pheno-clinical information regarding the sample donors. As Julia Livingstone and her team wrote, using rich clinical annotations enabled them to assess the relationship between telomere length and patients outcome. This highlights once again the importance of reporting and annotating metadata correctly; It might feel like extra work for the researchers depositing their data, but it really has a huge value. Our team at the EGA is currently working to improve the metadata annotation process, with the aim to maximize the benefits for the scientific community.

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• EuCANCan: EUropean-CANadian Cancer network

  The EUropean-CANadian Cancer network (EuCANCan) is a project aiming to develop a federated network of aligned and interoperable infrastructures for the homogeneous analysis, management and sharing of genomic oncology data for Personalized Medicine. This initiative, funded by the European Commission and the Canadian Institutes of Health and coordinated by the Barcelona Supercomputing Center (BSC), is expected to process and provide the scientific community with around 30-35 thousand patient samples from different types of cancer, coming from different nodes involved in this project, during the four years of the project. Data generated as part of primary studies, often in small hospitals or research centers, is hardly reused by other projects or groups. EuCANCan is proposed to foster the homogenisation and discoverability of cancer samples from very different sources for the whole community. These efforts will help paving the way to future standardised and global genomics projects, specially for those participant countries that eventually integrate the methodology and infrastructure generated during the project. “The more data we have access to the more and better discoveries. We attempt to overcome the legal and technical limitations in order to generate, gather and exploit bigger datasets as well as answer more ambitious questions”. These were some of the words Dr. David Torrents, main coordinator of the project, shared with a regional radio during the kick-off meeting of the project, on February 11th, 2019. The EUCANCan network will be composed of reference nodes in Amsterdam, Barcelona, Berlin, Heidelberg, Paris and Toronto which have established strong research and clinical programs in the field of genomic oncology. The Centre for Genomic Regulation (CRG), specifically the European Genome-phenome Archive (EGA), is among the leading institutions. Its exhaustive experience in biomedical data archival and secure distribution is considered key for crucial developments within the project. It is also notable the recent inclusion of EUCANCan in the list of GA4GH driver projects. Further information and details can be found in the following links: InsideHPC BSC official description Techweek (Spanish)

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• The last addition to the list of clonal evolution studies in the EGA

  Tumours of different types may arise from a single cell with at least one malignant mutation. Accumulation of subsequent hits generates lineages of cells with different cancer transformation power; thus, every clone follows an autonomous evolution, and several clones may co-exist into the same solid or liquid tumour. Clonal evolution studies are not new: the conceptual power of this approach has been recognized since the early ’80, especially in leukaemia patients. Nonetheless, these studies greatly benefit from recent technological improvements -machineries and protocols-, which enable ever-increasing sequencing depth, allowing the detection of low frequency variants and consequently more tumour clones. Note: the figure belongs to the original paper and is owned by the authors. Researchers at the University of Kyoto in Japan, have indeed used state of the art knowledge to investigate the clonal evolution of chronic myeloid leukemia (CML) in a large panel of patients. CML progresses from a majoritarian benign chronic phase into an often fatal blast crisis phase. It is nowadays known that blast crisis are triggered by genetic instability and disrupted DNA repair, which lead to accumulation of DNA mutations. The use of thyrosine kinase inhibitors targeting the Philadelphia chromosome have enormously improved the treatment of blast crisis, nonetheless resistant patients are still a medical challenge. Yotaro Ochi and collegues used Whole Exome Sequencing of paired chronic phase and blast crisis samples to depict the genetic landscape of progression from the first to the second stage of the disease (as illustrated in their figure). Their results were published in Nature Communication on this May 2021. They used a dataset previously deposited at the EGA (EGAS00001003071) and their new generated data have been made available under accession number EGAS00001005075. Their analysis of the occurring mutations contribute to understanding the mechanisms of CML evolution and therefore develop a protocol for early management of patients with a TKI resistance signature. Up to date, the EGA hosts 99 clonal evolution studies, developed in several cancer types, including myeloma, renal carcinomas and ovarian cancer, in addition to various leukaemia subtypes, where this approach has been more widely applied and produced important results and seminal papers. From a pan-cancer point of view, the definition of tumour architecture through clonal evolution studies is instrumental to unravel the mechanisms of disease progression and treatment-resistance insource. At the EGA, we are honoured to contribute to exciting and important science advances with our service!

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• Data Use Ontology (DUO) at EGA

  For those of you that follow the updates of GA4GH you will have seen the unanimous approval of the steering committee for DUO to be included in its suite of technical standards for the sharing of genomic and health related data. DUO has three main features: Each term has been generated by the community and includes a human readable definition that can be expanded where necessary. It is a machine-readable file that encodes how that data can be used and how a researcher intends to use the data. Can be implemented alongside an advanced search algorithm which would allow authenticated users to query and gain access to datasets pertaining to their research. e.g., an industry researcher working on cancer could potentially be matched to any dataset that is allowed for commercial use and for cancer research and offered the opportunity to fetch them automatically. So, what does this mean for EGA? EGA as a driver project has adopted this standard and will be utilizing it for two main purposes in the first instance. i) It will allow EGA users to instantly identify what terms they will need to agree to as part of any Data Access Agreements. This will save valuable time for both applicants and Data Access Committees alike - as you will be able to see if you are likely to be able to access the data based on your research intentions and working background. ii) In the future it will allow users to be able to search for data based on Data Use Ontology terms e.g., you could search for all data that can be used for General Research Use (GRU). As EGA moves forwards submitters will be asked to align their data access policies with DUO so that their dataset(s) can be tagged appropriately on our web pages. If you would like to add DUO to any of your existing datasets, or to any future datasets, do please just let us know.

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• Advancing fast in the analysis of circulating tumour DNA

  In cancer patients, solid tumours also release DNA fragments into the blood, and they are called Circulating Tumours DNA (ctDNA). Nowadays, the standard procedure for cancer monitoring is analysis of surgically obtained biopsies. They generate a big amount of material, but the operation is highly invasive and provide medical doctors with a single-time snapshot of the tumours, which are often highly changeable. Analysis of ctDNA, on the other hand, can provide continuous information regarding the tumour itself without the need of invasive procedures. This is why interest in this method is growing, and with it, the need for its technological optimization. Guanhua Zhu and colleagues, at the Genome Institute of Singapore, developed a methodology to quantify ctDNA burden, which means to estimate the amount of tumours derived circulating DNA among all cell-free DNA in the blood of individuals. Their work, published in Nature Communications in april 2021 and summarized in a sketch below, uses state-of-the art methods to analyse cfDNA degradation patterns at promoters and exon regions, to discern it from the DNA coming from the tumours. The bright point of their approach is that simultaneously enable for profiling of actionable cancer mutations and quantitative estimation of ctDNA burden. Their sequencing data of ctDNA has been deposited in the EGA under the accession number EGAS00001004657. This study sums up to a total of 37 of cfDNA and 36 about ctDNA present up to date in the EGA archive. The existence of cfDNA was firstly described back in 2002, while the use of ctDNA analysis in the oncological field exploded only after 2016. The approach is rapidly evolving, moving in parallel with the ever-improving sequencing technologies. Especially, the possibility to obtain high quality sequences from minimal amount of material is a crucial factor. Being able to alleviate cancer patients of disturbing and often slow-recovery procedures, while at the same time improving the quality of diagnosis and disease monitoring seems like a lot to ask. Thankfully, we are blessed with brilliant and dedicated scientists from all around the world who collaborate to make this a reality, sharing their results, knowledge and data.

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• Finding the way towards the eradication of therapy-related myeloid neoplasms

  Therapy-related neoplasms are directly caused by the drugs used to eradicate a previous cancer. It is well known that a subset of drugs designed to kill cancer cells through DNA-damage-induced apoptosis can intrinsically, as secondary effect, cause DNA-damage in other cells, thus inducing a neoplasm. Those neoplasms have especially bad prognosis. Why are these drugs still in use? Because for many diseases we do not have anything better, at the moment. Approximately 0.5-1% of the kids treated for various types of cancers develop a therapy-related myeloid neoplasm (tMN) as a result of the treatment. Despite having the same origin, their prognostic and molecular features are different from the adult ones, and a complete understanding of the pathogenesis of tMN in children is still missing. A total of 84 paediatrics neoplasms have been profiled by either Whole Genome or Whole Exome sequencing by Jason R. Schwartz and his colleagues, in a collaborative effort guided at the St. Jude Children's hospital, in Memphis. They identified a mean of 28 somatic mutations per patient, a number which is significantly bigger than the number of mutations found in paediatrics primary neoplasms, as depicted in the original paper's figure 1b, shown here. Their sequencing data identified that Ras/MAPK pathway mutations, alterations in RUNX1 or TP53, and KMT2A rearrangements as the most frequent drivers of the disease. Clonal evolution analysis (the comparative study of the behaviour of cells harbouring different mutations) highlighted that, unlike in adults’ tMN, there were not pre-existing leukemic clones, and virtually all mutated cells were found arising as a direct consequence of cytotoxic therapy. This represents yet another important difference between adult and paediatric tMN. This important study have been published in Nature communications in February 2021. The sequencing data have been deposited at EGA under the study number EGAS00001004850. Thanks to this, other researchers around the world could re-use it to uncover more information, integrate and validate their studies. Revealing the molecular features underlying the development of cancers is the only way towards the development of new generation targeted therapies, which will allow us to finally drop the cytotoxic drugs eliciting such unwanted and catastrophic secondary effects as tMN. It is not an overstatement to say that, while improving the treatment of primary tumours, we could entirely eradicate therapy-related neoplasms right from their origin. Looking forward.

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• Data upcycling, powered by EGA

  In 2012 a collaborative effort headed by the teams of Carlos Caldas in Cambridge, UK and Samuel Aparicio, Canada, produced a pivotal study aimed at defining the mutational landscape of breast cancer. They gathered a collection of over 2000 breast cancer samples (METABRIC) associated with long-term clinical follow-up. They delineated an array of inherited and somatically acquired genetic variants and associated them to gene expression. This led to the identification of putative cancer-driving events and improved stratification of breast cancer patients. The study elicited huge attention and was tackled as herculean effort by fellow researchers. Cristina Curtis and her colleagues deposited the data in the EGA archive (EGAS00000000083). What they probably could not foresee is that their data would be reused more than 100 times, becoming one of the most downloaded datasets at EGA. In 2015, Frederik S. Varn and colleagues accessed the above-mentioned data from the New Hampshire, USA, to correlate it to transcriptional programs of hematopoietic cells infiltrated in breast tumours and found that those can influence patients' prognosis. Their findings, published in Nature Communications, improved the understanding of the interplay between immune system and cancer. Again, in 2017 the dataset was downloaded by an Italian/Polish collaboration and used to corroborate their mechanistic study that demonstrate how a long non-coding RNA (lncRNA) is aberrantly localized into the chromatin, where it modulates oncogenic isoforms expression, thus contributing to breast cancer. To date, the METABRIC dataset has been reused to contribute to 147 publications. Its impact on breast cancer research is incalculable, and it even spread beyond that, with recent contributions to the understanding of pan-cancer mechanisms and features. Some of the publications are translating into improvements in diagnosis and treatment of different types of breast cancer. This is just an example of how data upcycling can amplify the potential of any dataset, well past the scope of its creation, the imagination of its owners and any geographical border. At EGA, we are proud to empower such fruitful worldwide cycles of knowledge, providing a platform that enables safe sharing of sensitive genetic data.

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• Gut microbiome dynamics unravelled with metagenomics sequencing

  It has been already several years since we discovered that microbial therapeutics like Faecal Microbiota Transplantation can be used to treat Clostridium difficile infection (CDI) and Inflammatory bowel disease (IBD) with great success. The concept has challenged us to test the limits of our knowledge with the perspective to improve health in the most diverse conditions like Parkinson disease, depression, autism and obesity, just to mention a few. Note: the figure belongs to the original paper and is owned by the authors. Allyson L.Bird and colleagues from Genentech Inc. (San Francisco) recently published an interesting study building up on the key concept that “As microbial therapeutics are increasingly being tested in diverse patient populations, it is essential to understand the host and environmental factors influencing the microbiome.” The authors of this work analysed 1359 gut microbiome samples using shotgun metagenomics sequencing.  The results of the comparison done between microbiota composition across the different groups showed that biological sex is the strongest driver of diversity, while aging (age range studied: 20–69) appears to be the second factor. As smartly outlined in the authors’ own graphical abstract (figure to the left), Bacteroidota species consistently increased with donors' age while Actinobacteriota species, including Bifidobacterium, decreased. This study also integrated data from oncological patients to draw cancer-related evidences. After correcting the biases for technical and geographic differences, they found that cancer patients show significantly altered gut bacterial communities compared with their healthy counterparts. Eg. E.coli was found to be increased in cancer patients together with Bacteroidota/Firmicutes ratios. By comparing sequential samples taken from the same donors (413) within short-distance time points, the authors found that in the short term, intra-individual differences were less than the inter-individual ones, indicating a certain community stability being the norm. One of the pivotal points from this interesting study is that the data proceeds from extremely well-defined and selected donors. Specifically, the data for 949 selected healthy donors came from the Milieu Intérieur (MI) Consortium and extensive metadata, including demographic variables, serological measures, dietary information, and systemic immune profiles, are available. This precise stratification allowed for the robust detection of smaller changes, compared to former works. All sequencing data has been deposited in the EGA archive under the accession number EGAS00001004437. Enjoy this amazing open access paper published in the Experimental Journal of Medicine on January 2021.

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• Discover Cancer Image Europe, the first release of the EUCAIM platform to fuel cancer research and data sharing

  The EUCAIM consortium and the European Commission have announced the release of Cancer Image Europe, a platform to fuel collaborative innovation and data sharing across Europe. This constitutes a major milestone in EUCAIM's development and an exciting step towards achieving the project's vision and goals. The platform aims to accelerate the pace of AI development and other data-intensive cancer research activities, constituting the basis for a fully-fledged infrastructure for sharing, reusing, and exploiting cancer imaging data, especially using AI techniques. A platform paving the way for the future of cancer diagnosis and treatment Cancer Image Europe brings benefits to researchers, clinicians, and AI innovators across Europe as it addresses the fragmentation of existing cancer image repositories. The platform gathers a public catalogue of cancer imaging datasets from the repositories of the EU-funded AI for Health Imaging projects. A distributed Atlas of Cancer Imaging with over 60 million anonymised cancer image data from over 100,000 patients will be established as part of future updates. The first version of Cancer Image Europe also includes a public catalogue of cancer imaging datasets, a federated searching tool to understand the information available in the federated providers, and full integration with Life Science Login. Moreover, it reuses and adds value to key components of EU-funded research projects and infrastructure in the field of cancer. The Public Catalogue When we talk about data re-use, data discovery is key. The EUCAIM public catalogue is the result of a systematic and collaborative process to which the EGA-CRG has actively contributed. This browsing interface gathers the metadata of the available cancer imaging datasets. This descriptive information will help researchers in their data retrieval. Our contribution to the Public Catalogue comes from our experience gained in the deployment of our federated network, the Federated EGA (FEGA), and as partners in projects such as EuCanImage (AI4HI initiative), in which we worked on the definition of common data models, ontologies and data standards. The EUCAIM Project EUCAIM is a cornerstone of the European Commission-initiated European Cancer Imaging Initiative, a flagship of the Europe's Beating Cancer Plan, which aims to foster innovation and deployment of digital technologies in cancer treatment and care, to achieve more precise and faster clinical decision-making, diagnostics, treatments, and predictive medicine for cancer patients. For more information about the first release of the Cancer Image Europe platform and the EUCAIM project, please visit cancerimage.eu.

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• First datasets available in the Federated EGA Network

  In February 2024, a great milestone was reached for the human omics data community: the first datasets are now available in the Federated European Genome-phenome Archive (FEGA). These datasets - hosted by national nodes in Poland, Norway, and Sweden - are discoverable in the EGA portal and accessible according to national laws. These datasets include: From FEGA Poland - The Genomic Map Of Poland From FEGA Norway - CRCbiome: Gut metagenome of Norwegian screening participants using FIT sampling Also from FEGA Norway - Single cell transcriptomics in expanded Tregs of APS-1 patients From FEGA Sweden - A Combined Omics and Tissue Biobank for Paediatric Cancers The establishment of these datasets represents a significant step towards achieving the broader objectives of the FEGA; to facilitate the sharing of high-quality human genomic and phenotypic data across borders. “The value for users is twofold: it becomes easier to make data available securely compared to storing data locally, and data becomes easier to find for other researchers. The possibility of reuse improves the chances of both verifying scientific discoveries and reaching groundbreaking conclusions, of benefit to the research community and later also patients,” said Anna Hagwall, project manager and head of human data at FEGA Sweden, NBIS. About Federated EGA FEGA was launched in 2022 as a network to store and lawfully share sensitive human omics data safely. It relies on the technology originally developed by the EGA and support from ongoing collaborations with ELIXIR - a European life sciences infrastructure. FEGA has contributed to and benefitted from the implementation of community standards for sensitive data management including those developed by the Global Alliance for Genomics and Health (GA4GH). “The FEGA concept and the development of the Nordic FEGA nodes have been made possible thanks to several projects coordinated by NeIC,” said Kjell Petersen, FEGA Norway technical lead. Managing human omics data is challenging due to its sheer size and privacy concerns, often resulting in restrictions on how and where sensitive data can be shared. The FEGA network follows a federated data access model whereby sensitive data is stored securely within a local or national jurisdiction, while discovery across the network for data of interest is enabled via a central portal. As of February 2024, the FEGA network comprises seven European national nodes with tens of additional national partners working towards establishing a FEGA node. As the FEGA network continues to onboard nodes from around the world, populated with more and more data, we move closer to a vision of a truly global resource for discovery of and access to sensitive human omics data to accelerate disease research and improve human health. "The Federated EGA network is entering a key phase where the first research datasets are now available across the network. In coming years, we look forward to expanding these datasets to cover key areas such as cancer and rare diseases for the benefit of patients" said Thomas Keane, Group Leader at EMBL-EBI.

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• How mitochondrial DNA research can benefit from data reuse through EGA?

  Mitochondria are the only cellular organelles harbouring their own DNA, which encodes for proteins essentials in its respiration process. Despite its discovery in 1960, Mitochondrial DNA is often overlooked compared to nuclear DNA. Indeed, not all sequencing technologies capture a good representation of mtDNA (mitochondrial DNA). Recently, Ed Reznik's team (The Ed Reznik Lab | Memorial Sloan Kettering Cancer Center) carried out a massive analysis and reanalysis of single cells WGS (whole genome sequencing) produced with an amplification-free protocol (Direct library prep, DLP). As they demonstrated in the paper Single-cell mtDNA dynamics in tumors is driven by coregulation of nuclear and mitochondrial genomes - PMC, DLP is more effective in catching mtDNA compared to classical single-cell sequencing. These data allowed the team to unravel mtDNA dynamics in tumoral and normal samples, highlighting how mtDNA and nuclear DNA are co-regulated and their ratio is critical for phenotype. Data reuse to achieve the greatest results For this project, Reznik's team re-used data from 3 studies stored at the European Genome-phenome Archive (EGAS00001006343, EGAS00001004448 and EGAS00001003190) that comprised a total of 602 datasets. All of them were initially produced by the teams of Sam Aparicio and Sohrab P. Shah at the Memorial Sloan Kettering Cancer Centre, where Ed Reznik is a promising group leader. This demonstrates, once again, that networking, collaboration and data sharing can achieve the greatest results when coupled with innovative and smart ideas. At the EGA we are proud to provide the scientific community with an infrastructure to enable all those very smart ideas out there to find the right data to be tested.

  Blog mitochondrial-dna-research

• 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

• Highlights from 2025: Our top 10 requested studies

  In 2025, as in previous years, EGA has facilitated the reuse of numerous datasets spanning a broad range of research and healthcare topics. To reflect on this activity, we would like to highlight the top 10 studies behind the most requested datasets of last year. Notably, the vast majority of these highly requested datasets are cancer-focused, particularly involving the study of immune checkpoint inhibitor therapies. Immune checkpoint inhibitors (ICIs) have become one of the most transformative advances in modern cancer therapy. These monoclonal antibodies enhance antitumor immune responses by targeting inhibitory immune checkpoint molecules. While ICIs have led to remarkably favorable and durable responses in some patients, treatment outcomes vary widely. This variability has fueled intense research efforts aimed at understanding resistance mechanisms, exploring combination therapies, and discovering predictive biomarkers to better stratify patients and guide treatment decisions. Study Title Disease 1 EGAS00001005503 Integrated genomic analyses reveal molecular correlates of clinical response and resistance to atezolizumab in combination with bevacizumab in advanced hepatocellular carcinoma Liver Cancer 2 EGAS00001005013 Intratumoral plasma cells predict outcomes to PD-L1 blockade in non-small cell lung cancer Lung Cancer 3 EGAS00001004809 BIOKEY: A single-cell catalogue of the dynamic changes underlying Checkpoint Immunotherapy response in Early Breast Cancer Breast Cancer 4 EGAS50000000138 BIOKEY: Immune heterogeneity in small cell lung cancer and vulnerability to immune checkpoint blockade Lung Cancer 5 EGAS50000000497 Bladder cancer subtyping study across 4 atezo clinical trials Bladder Cancer 6 EGAS50000000689 MOSAIC - Multi-Omics Spatial Atlas In Cancer Cancer Atlas 7 EGAS00000000083 METABRIC (Breast cancer - genome and transcriptome) Breast Cancer 8 EGAS00001005702 Human genomic and phenotypic synthetic data for the study of rare diseases Synthetic data 9 EGAS00001002556 TGF-β attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells Bladder Cancer 10 EGAS00001004353 Molecular subsets in renal cancer determine outcome to checkpoint and angiogenesis blockade Renal Cancer The most requested dataset of the year was EGAD00001008128. It belongs to a study that analyzed 358 liver cancer patients enrolled in clinical trials and treated with the combination of atezolizumab (anti–PD-L1) and bevacizumab (anti-VEGF), atezolizumab alone, or sorafenib. The study highlights how bevacizumab enhances anti-PD-L1 effect and identifies candidate biomarkers to predict treatment response. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the third leading cause of cancer-related death worldwide [1]. Because symptoms often appear late, many patients are diagnosed at advanced stages, when curative options are no longer viable. For many years, sorafenib—a multikinase inhibitor approved in 2007—was the only systemic treatment available. Although numerous clinical trials followed, it was not until a decade later that additional therapies showed meaningful clinical benefit. Among these advances, immunotherapy emerged as a promising first-line option [2]. In particular, the combination of atezolizumab and bevacizumab demonstrated improved survival in patients with advanced HCC, reshaping the treatment landscape. However, treatment efficacy remains limited by the complex and diverse hepatic microenvironments that influence immune responses, as well as by high intratumoral heterogeneity. Identifying robust predictors of treatment response is critical to enable effective patient stratification and maximize the benefits of these therapies [2]. The continued demand for these datasets highlights the interest and critical role of data reuse in deepening our understanding of cancer, accelerating the development of more effective therapies, and improving patient outcomes. By providing secure access to high-quality genomic and clinical data, EGA acts as a catalyst for biomedical progress. References Sung, Hyuna, et al. "Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries."CA: a cancer journal for clinicians 71.3 (2021): 209-249. Ladd, Alexandra D, et al.“Mechanisms of drug resistance in HCC.” Hepatology 79(4) (2024): 926-940

  Blog top-10-studies-2025

• PROMETEO

  The SOLTI-1503 PROMETEO trial was a single-arm, preoperative, window-of-opportunity phase II study conducted at four sites in Spain between December 2018 and February 2022. It enrolled female patients with operable HER2-negative breast cancer (triple-negative or Luminal B-like) and residual disease after neoadjuvant chemotherapy. Tumor biopsies were collected at five time points (baseline, post-NAC screening, C2D1, C3D1, and surgery) for translational research. This dataset includes raw paired-end RNA sequencing data (~110M reads per library) generated from the 113 biopy samples, archived in FASTQ format.

  Study EGAS50000001499

• METABRIC

  The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (CNVs, SNPs) and acquired somatic copy number aberrations (CNAs) were associated with expression in 40% of genes, although the landscape was dominated by cis and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP, and MAP2K4. Unsupervised analysis of paired DNA/RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, ER-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration 0152hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the 0152CNA-devoid sub-group and a Basal-specific chromosome 5 deletion-driven mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic copy number aberrations on the transcriptome.

  Study EGAS00000000083

• ICGC PanCancer Analysis of Whole Genomes

  The Pancancer Analysis of Whole Genomes (PCAWG) study is an international collaboration to identify common patterns of mutation in more than 2,800 cancer whole genomes from the International Cancer Genome Consortium. Building upon previous work which examined cancer coding regions (Cancer Genome Atlas Research Network, The Cancer Genome Atlas Pan-Cancer analysis project, Nat. Genet. 2013 45:1113), this project is exploring the nature and consequences of somatic and germline variations in both coding and non-coding regions, with specific emphasis on cis-regulatory sites, non-coding RNAs, and large-scale structural alterations.

  Study EGAS00001001692

• IG-MYC rearrangement defines a high-risk subgroup of B-cell precursor acute lymphoblastic leukaemia

  Study EGAS00001005111

• Prospective Lynch Syndrome Database materials

  The Prospective Lynch Syndrome Database data. These data contain necessary clinical tables to reproduce the previous PLSD papers using the clinical tables for individual level cancer incidence by age, sex and MMR gene affected. The tables contain the demographics, individual cancers at given age, and therapeutic and prophylactic surgeries removing total or partial organs.

  Study EGAS50000001715

• Deregulation of FOXF1/FENDRR from t(14;16)(q32;q24) defines a subtype of high risk lineage ambiguous leukemia

  Despite great progress in understanding the genomic basis of immature T-cell acute lymphoblastic leukemia/lymphoblastic lymphoma (T-ALL) and acute leukemias of ambiguous lineage (ALAL), there are still cases that lack defining genetic markers, complicating risk stratification and limiting targeted therapeutic options. Here, we describe a subtype of leukemia defined by the t(14;16)(q32;q24) translocation, which places the FOXF1 gene and its antisense long noncoding RNA gene FENDRR under the regulatory control of the BCL11B enhancer, leading to their ectopic transcriptional activation. Analysis of different high-throughput sequencing data, including WGS, RNA-seq, scDNA-seq, scRNA-seq, ChIP-seq, ATAC-seq, and HiChIP-seq, identified BCL11B-enhancer mediated deregulation of FOXF1/FENDRR as a hallmark of a subtype of high-risk lineage ambiguous leukemia that is potentially amenable to targeted therapeutic intervention.

  Study EGAS50000001255

• Robust detection of translocations in lymphoma FFPE samples using Targeted Locus Capture-based sequencing

  Chromosomal translocations with immunoglobin (IG) loci are the classic drivers in a large subset of B-cell lymphomas. Detection of these translocations is important for confirmation of diagnosis and for prognosis and therapy decisions. Currently, molecular diagnosis of translocations in lymphomas is not addressed well by Next Generation Sequencing (NGS). The standard method for detection of translocations is Fluorescence In Situ Hybridization (FISH), which is labor-intensive, and can be difficult to interpret. There is a need for a robust technology that can be standardized. Targeted Locus Capture (TLC) selectively enriches and sequences entire genes based on the crosslinking of physically proximal sequences, and thereby enables complete sequencing of genes of interest, including detection of large structural variants. Because the technology is based on the crosslinking and fragmenting of DNA, it has particular advantages in the analysis of Formalin-Fixed, Paraffin-Embedded (FFPE) samples, in which DNA is inherently crosslinked and fragmented. In order to validate the FFPE-TLC technology as a novel approach for translocation detection in lymphoma samples, we have developed a panel assay containing genes with frequent translocations (MYC, BCL2, BCL6, IG loci). With this assay we have analyzed >140 lymphoma and control FFPE samples of variable input amounts and qualities that had previously been analyzed with FISH, and a subset also with standard targeted NGS. Good concordance with FISH results was observed for both translocation positive and negative samples. In 10 cases for which FFPE-TLC detected a relevant fusion and FISH had been called negative, discordance could be explained by higher sensitivity of FFPE-TLC or by inconclusive FISH results. In a specific case, FFPE-TLC detected a small-distance rearrangement on chromosome 3 that caused a BCL6 fusion but led to insufficient and therefore undetectable break-apart with FISH. Secondly, the FFPE-TLC approach was tested on a set of 19 B-cell lymphoma FFPE samples that had previously been analyzed using standard targeted NGS and FISH and was enriched for discordant results between these methods. FFPE-TLC-based NGS enables more robust translocation calling as the detection relies on broad sequencing coverage across the translocation partner rather than on breakpoint sequences only. In 3 cases, FFPE-TLC could proof false negative calls in standard targeted NGS due to breakpoints located in regions difficult to capture or to sequence. In 1 case, standard targeted NGS had made a false positive call on a breakpoint sequence that was shown to be caused by a small insertion rather than a genuine translocation. This study shows that FFPE-TLC promises to be a robust alternative for FISH analysis and standard targeted NGS procedures in lymphoma diagnostics and possibly in other cancers with frequent structural variants. The FFPE-TLC approach enables a single, DNA-based NGS test detecting both small mutations and translocations.

  Study EGAS00001004760

• Molecular evolution of metastatic clear cell renal cell carcinoma progressing under tyrosine kinase inhibitor therapy (HIPO-045)

  Background:Acquired resistance to targeted drugs is largely due to intra-tumor molecular heterogeneity and clonal selection. Objective:The objective of this study was to determine molecular alterations and mediating resistant mechanisms and to suggest additional treatment options.Design, Setting, and Participants:Four patients were enrolled into the MORE (Molecular Renal Cancer Evolution) trial. We investigated the mutational spectra of metastatic clear cell renal cell carcinoma (ccRCC) at baseline and compared them to those upon clonal evolution at progression on systemic therapy with tyrosine kinase inhibitors.Outcome Measurements:DNA was isolated from tumor tissues after surgery, metastasis biopsies after progression on sunitinib or axitinib, and subjected to whole-exome sequencing (WES). In addition, the exomes of circulating tumor DNA (ctDNA) from two patients were sequenced at baseline and at progression.Results and Limitations:Our data provide evidence for clonal evolution and various acquired resistance mechanisms of ccRCC with subclonal mutations in FLT4, MTOR, ITGA3/5, SETD2, and VHL on metastatic progression. The limited number of shared mutations in tissue and plasma biopsies indicates that both sources provide complementary information, possibly due to mutations present in metastatic lesions which were not amenable to tissue sampling but are represented in the ctDNA. Conclusion:Acquired resistance to targeted therapies in metastatic ccRCC is due to intra-tumor heterogeneity and clonal evolution. Larger patient cohorts have to be studied to estimate the benefit of WES from biopsies and ctDNA for therapy decision in metastatic ccRCC.Patient Summary:We analyzed the molecular profiles of ccRCC patients in primary tissues and metastatic tissues upon progression on tyrosine kinase inhibitor therapy. Clinically relevant mutations upon progression were detected in the metastatic sites of all of four patients, suggesting that it might be possible to derive therapies based on sequencing the metastases.?HIPO-045

  Study EGAS00001001861

• Mutational signature in colorectal cancer induced by genotoxic pks+ E. coli

  Various species of the intestinal microbiota have been associated with the development of colorectal cancer (CRC), yet a direct role of bacteria in the occurrence of oncogenic mutations has not been established. Escherichia coli can carry the pathogenicity island pks, which encodes a set of enzymes that synthesize colibactin. This compound alkylates DNA on adenine residues and induces double strand breaks in cultured cells. Here, we exposed human intestinal organoids to genotoxic pks+ Escherichia coli by repeated luminal injection over a period of 5 months. Whole genome sequencing (WGS) of clonal organoids before and after this exposure reveals a distinct mutational signature, absent from organoids injected with isogenic pks-mutant bacteria. The same mutational signature is detected in a subset of 3668 human metastatic cancer genomes, predominantly in a subset of CRC cases. Our study describes a distinct mutational signature in CRC and implies that the underlying mutational process directly results from past exposure to bacteria carrying the colibactin-producing pks pathogenicity island.

  Study EGAS00001003934

• Whole Genome Sequencing of Gingivo-buccal Cancer: ICGC-India Project_YR03

  As a part of the ICGC, India has undertaken genomic studies on gingivobuccal cancer of the oral cavity, which is the most prevalent form of cancer among men in India. There are various known environmental (life-style) correlates of this cancer, the most important of which are tobacco chewing and HPV infection. Whole genome sequencing of paired DNA samples – isolated from the tumor tissue and from the blood of twenty five patients – have been performed to catalog germline and somatic mutations. Association between each observed genomic alteration and exposure to environmental risk factors is being explored. Detailed clinical characterization of the patients, collection of data on demographic and environmental exposures, and isolation of DNA samples from blood and tumor tissues collected from each patient are being done at the Advanced Centre for Research, Treatment and Education on Cancer, Mumbai. Whole genome sequencing is being performed at the National Institute of Biomedical Genomics, Kalyani. We have completed whole genome sequencing of paired blood and tumor DNA samples using Illumina HiSeq-2000 and 2500.

  Study EGAS00001001901

• Comprehensive molecular profiling identifies novel genetic drivers and subtypes underlying medulloblastoma

  Current therapies for medulloblastoma (MB), a highly malignant childhood brain tumor, impose debilitating effects on the developing child, warranting the development of molecularly targeted treatments with reduced toxicities. Prior studies have failed to disclose the full spectrum of driver genes and molecular processes operative in MB or adequately explain heterogeneity among molecular subgroups. Herein, we detail the somatic landscape across 491 sequenced MBs and molecular heterogeneity amongst 1,256 epigenetically analyzed cases, identifying subgroup-specific mutational signatures, driver genes, and pathway alterations including previously unappreciated actionable therapeutic targets. Integrative approaches for assigning driver genes to subgroups explained >65-70% of Group 3 and Group 4, doubling previous knowledge. Novel subtypes underlying Group 3 and Group 4 were differentially enriched for specific driver events, including hotspot in-frame indels targeting KBTBD4 and ‘enhancer hijacking’ driving activation of PRDM6. Thus, application of integrative genomics to an unprecedented cohort of clinical samples derived from a single childhood cancer entity has disclosed a series of new cancer genes and biologically relevant subtype diversity that represent attractive therapeutic targets for treating MB.

  Study EGAS00001001953

• ER___HER2___PR__breast_Cancer_genome_sequencing

  Agilent whole exome hybridisation capture will be performed on genomic DNA derived from X triple negative breast cancers and matched normal DNA from the same patients. Three lanes of Illumina GA sequencing will be performed on the resulting X exome libraries and mapped to build 37 of the human reference genome to facilitate the identification of novel cancer genes.

  Study EGAS00001000197

• Mutational dynamics of triple negative breast cancer over neoadjuvant chemotherapy treatment reveal frequent whole genome duplication events

  Background Triple-negative breast cancer (TNBC) is associated with poor survival rate and high genomic instability, generating complex tumour genomes. However, the processes generating this complexity are poorly studied over time. Here, we study the temporal dynamics of TNBC somatic mutations, revealing major transitions in tumour genome evolution, from diagnostic biopsies, through treatment, to cancer remission or recurrence. Methods Deep whole exome sequencing and CUTseq, a reduced representation whole genome sequencing approach, were performed in parallel, to comprehensively identify short nucleotide variants (SNVs), copy number alterations (CNAs) and aneuploidies genome-wide. We profiled (N=74) tumour samples from 22 patients across the course of disease, including spatially diverse samples from many tumours, allowing us to observe the gain and loss of candidate driver variants over time. Results As expected, tumour infiltrating lymphocyte (TIL) levels and residual cancer burden (RCB) score were negatively correlated, with good responses to neoadjuvant chemotherapy (NACT) seen in pre-treatment samples with high infiltration. In general, SNV mutational burden remained stable across disease progression and RCB classes. Recurrent SNVs across the cohort implicated several known TNBC driver genes in disease progression and response to treatment, with TP53, MICA, CYP2D6, BRCA1 and BRCA2 frequently altered. However, the presence of candidate driver SNVs in these genes often varied throughout a patient’s treatment, including the loss of those seen in a given driver gene in pre-treatment samples, but gain of novel variants in the same gene at later time points. Dramatic structural dynamics were seen in all tumours, including abundant CNA including chromosome arm aneuploidies seen in pre-treatment samples, followed by frequent loss of these alterations post-treatment, and their re-emergence at recurrence. Whole genome duplication (WGD) events appear to drive these dynamics, with a higher frequency of pre-treatment WGD seen in patients with the best response to NACT. Conclusions Comprehensive longitudinal profiling demonstrates the complex interplay of SNVs and copy number alterations during TNBC progression, leading to diverse evolutionary trajectories and patient outcomes. Complex mutational patterns emerge as important features during progression, with WGD events emerging as potential candidate biomarkers of response at both tumour establishment and recurrence.

  Study EGAS00001008261

• FFPE_whole_genome_pilot

  To assess feasibility of whole genome sequencing

  Study EGAS00001001967

• Whole_genome_sequencing_of_rhabdomyosarcoma_tumouroids

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008270

• Single_cell_RNA_sequencing_of_rhabdomyosarcoma_tumouroids

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008273

• Whole_Genome_sequencing_of_rhabdomyosarcoma_tumour_tissue

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008280

• Molecular_diagnosis_of_albinism

  Albinism is genetically heterogeneous rare genetic condition affecting 1:17000 in the Western world (but more frequent in Africa) whose main feature is a profound visual impairment, characterised by foveal hypoplasia, abnormal chiasmatic connections, nystagmus and photofobia. All these features result in severly altered visual acuity (<0,1), absent depth perception and poor night vision. People with albinism are primarily visually handicapped. In addition, for some types of albinism, the visual phenotype can be presented with partial or total hypopigmentation, hence resulting in a secondary phenotype which can lead to skin cancer if skin is not adequately protected. Recently a new syndrome has been described, FHONDA, with the same visual abnormalities of albinism but without pigment alteration. The traditional classification differentiates Oculoculatenous albinism (OCA), where hypopigmentation involves hair, skin and eyes versus Ocular Albinism (OA), where hypopigmentation only affects the eyes. These are non-sydrimic types of albinism. Some syndromic forms (Hermansky-Pudlak=HPS, Chediak-Higashi=CHS) affect cells beyond pigment cells, present in the lungs, immune system, platelets and intestines, resulting in more severe phenotypes that can be fatal. Mutations in at least 19 genes are assocaited with the corresponding types of albinism. Most hospitals will only diagnose the most frequent cases using traditional Sanger, MLPA approaches. Some will use CGH arrays. We aim to diagnose all cases of albinism through the Albinochip proposal, which combines a Sequenom first step of known mutations combined with subsequent NGS approaches. In some cases we fail to find a second mutation, these are good candidates for further full exome analyses. 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 EGAS00001002068

• Whole genome bisulfite sequencing of hepatitis B virus-associated hepatocellular carcinoma tumor and non-cancerous samples

  To understand comprehensive epigenetic alterations in hepatitis B virus-associated HCC, we have conducted a whole genome bisulfite sequencing of 5 tumor samples and 3 non-cancerous samples (3 pairs and 2 tumors).

  Study EGAS00001002230

• Single_cell_RNA_sequencing_of_rhabdomyosarcoma_tumour_tissue

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008281

• Genomic characterization of retinoblastoma (targeted sequencing)

  Retinoblastoma is the most frequent intraocular malignancy in children, originating from a maturing cone precursor in the developing retina. Little is known on the molecular basis underlying the biological and clinical behavior of this cancer. Here, using multi-omics data, we demonstrate the existence of two retinoblastoma subtypes. Subtype 1, of earlier onset, includes most of the heritable forms. It harbors few genetic alterations other than the initiating RB1 inactivation and corresponds to differentiated tumors expressing mature cone markers. By contrast, subtype 2 tumors harbor frequent recurrent genetic alterations including MYCN-amplification. They express markers of less differentiated cone together with neuronal/ganglion cell markers with marked inter- and intra-tumor heterogeneity. The cone dedifferentiation in subtype 2 is associated with stemness features including low immune and interferon response, E2F and MYC/MYCN activation and a higher propensity for metastasis. The recognition of these two subtypes, one maintaining a cone-differentiated state, and the other, more aggressive, associated with cone dedifferentiation and expression of neuronal markers, opens up important biological and clinical perspectives for retinoblastomas. In this study, we performed targeted sequencing of the exonic regions of RB1, BCOR and ARID1A on 37 samples from the series of 102 retinoblastomas. Among them, 23 samples were not subjected to whole-exome sequencing, and 14 samples have the whole-exome sequencing data (deposited in EGAS00001005248).

  Study EGAS00001005550

• MYC Drives Progression of Small Cell Lung Cancer to a Variant Neuroendocrine Subtype with Vulnerability to Aurora Kinase Inhibition

  Loss of the tumor suppressors RB1 and TP53 and MYC amplification are frequent oncogenic events in small cell lung cancer (SCLC). We show that Myc expression cooperates with Rb1 and Trp53 loss in the mouse lung to promote aggressive, highly metastatic tumors, that are initially sensitive to chemotherapy followed by relapse, similar to human SCLC. Importantly, MYC drives a neuroendocrine-low “variant” subset of SCLC with high NEUROD1 expression corresponding to transcriptional profiles of human SCLC. Targeted drug screening reveals that SCLC with high MYC expression is vulnerable to Aurora kinase inhibition, which combined with chemotherapy strongly suppresses tumor progression and increases survival. These data identify molecular features for patient stratification and uncover a novel targeted treatment approach for MYC-driven SCLC.

  Study EGAS00001002115

• SNU_PROSPECTIVE

  This study includes 5 paired samples of Korean AML paitients. 5 AML tumor samples are paired with each normal(Saliva or BM derived) sequencing data.The results of this WES study were analyzed by Mutect and GATK for ranking germline variants and cancer variants to make mutational profile matrix.

  Study EGAS00001002154

• Somatic pathogenic variants in the normal mammary gland of sporadic breast cancer patients.

  The mammary gland undergoes hormonally stimulated cycles of proliferation, lactation and involution. We hypothesized that these factors increase the mutational burden in glandular tissue and may explain high cancer incidence rate in the general population and recurrent disease. Hence, we investigated the DNA sequence variants in the normal mammary gland, tumor and peripheral blood from 52 reportedly sporadic breast cancer patients, including breast-conserving surgery cases. Targeted resequencing of 542 cancer associated genes revealed mosaic somatic pathogenic variants of: PIK3CA, TP53, AKT1, MAP3K1, CDH1, RB1, NCOR1, MED12, CBFB, TBX3 and TSHR in the normal mammary gland, at considerable allelic frequencies (9x10-2 to 5.2x10-1) indicating clonal expansion. Further evaluation of the frequently damaged PIK3CA and TP53 genes by ultra-sensitive duplex sequencing demonstrated a diversified picture of multiple low level-mosaic (in 10-2 to 10-4 alleles) hotspot pathogenic variants. Our results raise a question about the oncogenic potential in non-tumor mammary gland tissue of breast-conserving surgery patients.

  Study EGAS00001005698

• The role of the mammalian SWI/SNF chromatin remodeling complex in neuroendocrine prostate cancer

  We sought to determine the effects of SMARCA4 and SMARCA2 depletion in prostate cancer cell lines. We performed siRNA-mediated knock-down of SMARCA4 and SMARCA2 in an androgen-sensitive (LNCaP) cell line and in a castration-resistant prostate cancer (CRPC)-adenocarcinoma cell line (22Rv1) and compared global transcriptional alterations using RNA-seq.

  Study EGAS00001004177

• miR-200-regulated CXCL12β promotes fibroblast heterogeneity and immunosuppression in ovarian cancers

  High-grade serous epithelial ovarian cancer (HGSOC) has been recently subdivided into molecular subgroups, including the mesenchymal HGSOC associated with partial tumor-debulking surgery and poor patient survival. Consistent with stromal-related genes signatures defining mesenchymal HGSOC, we show here that stroma plays a key function in this HGSOC molecular subtype. We first highlight stromal heterogeneity in HGSOC by identifying 4 subsets of Carcinoma-Associated Fibroblast (CAF-S1-4). Mesenchymal HGSOC significantly accumulate the activated CAF-S1 subset, which exhibits immunosuppressive functions by promoting attraction, survival and activation of regulatory T-lymphocytes. The CXCL12β chemokine reliably characterizes mesenchymal HGSOC and specifically accumulates in the CAF-S1 subset through a regulation mediated by the miR-141/200a. CXCL12β promotes attraction of T-lymphocytes and is thus a key actor in CAF-S1-mediated immunosuppressive functions. In conclusion, we highlight here for the first time stromal heterogeneity and immunosuppression in mesenchymal HGSOC that could participate in the poor patient prognosis.

  Study EGAS00001002184

• Single-cell profiling maps the spectrum of crosstalk between glioma cells and tumor associated macrophages

  Tumor associated macrophages (TAMs) are a heterogeneous glioma infiltrate, implicated in cancer progression. We performed single-cell RNA-sequencing, to survey TAM response-patterns in glioma.

  Study EGAS00001002185

• The aim of this project is to identify, on 15 French Caucasian and 10 African-Caribbean men, through an integrative approach of DNA sequencing and transciptomic analyses, relevant genomic events that characterize or allow targeting the various phenotypes of aggressiveness of early stages of prostate cancer.

  Every year about 70,000 new cases of prostate cancer are diagnosed in France and 8,700 men died from this disease. But the incidence and the rate of mortality per 100,000 men in the French West Indies are respectively 1.5x and 2x upper than the rates observed in the metropolitan France. The aim of this project is to identify, on 15 French Caucasian and 10 African-Caribbean men, through an integrative approach of DNA sequencing and transciptomic analyses, relevant genomic events that characterize or allow targeting the various phenotypes of aggressiveness of early stages of prostate cancer.

  Study EGAS00001002176

• Comprehensive analyses of somatic TP53 mutation in tumors with variable mutant allele frequency

  Somatic mutation of the tumor suppressor gene TP53 is reported in at least 50% of human malignancies. Most high-grade serous ovarian cancers (HGSC) have a mutant TP53 allele. Accurate detection of these mutants in heterogeneous tumor tissue is paramount as therapies emerge to target mutant p53. We used a Fluidigm Access Array™ System with Massively Parallel Sequencing (MPS) to analyze DNA extracted from 76 serous ovarian tumors. This dataset has been made available to researchers through the European Genome-phenome Archive (EGA). Herein, we present detailed analyses of this dataset using HaplotypeCaller and MuTect2 through the Broad Institute’s Genome Analysis Toolkit (GATK). We also present comparisons of the ability to detect TP53 mutation by MPS and Sanger sequencing in these tumors. We anticipate that this TP53 mutation dataset will be useful to researchers developing and testing new software to accurately determine high and low frequency variant alleles in heterogeneous aneuploid tumor tissue. Further, the analysis pipeline we present provides a valuable framework for determining somatic variants more broadly in tumor tissue.

  Study EGAS00001002200

• Global Anaplastic Thyroid Cancer Initiative

  Thyroid cancer is the most common endocrine malignancy. Most thyroid cancers are of the well-differentiated (non-aggressive) phenotype, and are almost always cured with standard treatments. In contrast, anaplastic thyroid cancer (ATC) is rare, accounting for only 1% of thyroid cancers, however it is perhaps the most lethal human malignancy with an average survival of 3 to 6 months. ATC presents with dramatic and rapid onset of airway and esophageal blockage with frequent spread to the lungs. Interestingly, 21–79% of ATCs have coexisting areas or a previous history of well-differentiated cancers. Although this suggests a progression from well-differentiated cancers to ATC, to date there is little molecular conformation that such a progression exists versus de novo generation of ATC. As ATC is extremely rare and most cases are inoperable, there are a paucity of tissue samples for study at any one center. The Global Anaplastic Thyroid Cancer Initiative (GATCI) aims to unite international institutions in order to pool samples (ATC with or without a paired well-differentiated component) for a comprehensive analysis of the genomic landscape of this disease.

  Study EGAS00001002234

• Copy number profiling of putative cancer stem from pleural effusion aspirates from breast cancer patients

  A common symptom during late stage breast cancer disease is pleural effusion, which is related to poor prognosis. In pleural effusions malignant cells can be detected, which indicates a metastatic spread from the primary tumor site. Beside the usefulness for investigation of the metastatic process, it has been a source to isolate cells with putative cancer stem cell (CSC) properties. For this study, pleural effusion aspirate from 25 metastatic breast cancer patients were processed to establish in vitro culture. Patient-derived aspirates were cultured under sphere forming conditions and isolated primary cultures were sorted for cancer stem cell populations ALDH1+ and CD44+CD24-/low and sphere forming efficiency of CSC and non-CSC subpopulation was determined. Unsorted cells had a higher potential to form spheres compared to CSC subpopulations. Subsequently, DNA was isolated from pleural effusions before and after cell sorting, to compare their copy number profiles generated by low-coverage whole genome sequencing to DNA from FFPE primary tumors or bone metastasis. Unbalanced profiles could be detected in four pleural samples before sorting, in two ALDH1+ sorted samples, in one CD44+CD24-/low and in all control samples. Additionally, spheroid culture from one pleural sample could be passaged on and was characterized. Therefore, pleural effusions are an appropriate media for enrichment of putative CSC. However, low-coverage sequencing is difficult due to low quantities of abnormal cells compared to normal cells.

  Study EGAS00001002343

• Molecular Subtype-specific Biomarkers Improves Colorectal Cancer Prognostication

  Colorectal cancer (CRC) is characterized by major inter-tumor diversity that complicates the prediction of disease and treatment outcomes. Recent efforts help resolve this by sub-classification of CRC according to natural molecular subtypes, however, this strategy is not yet able to provide clinicians with improved tools for decision-making. We here present an extended framework for CRC stratification that specifically aims to improve patient prognostication. Using transcriptional profiles from 1,100 CRCs, including a novel set of >300 samples, we identify novel cancer cell and tumor archetypes and suggest the tumor microenvironment as a major prognostic determinant that can be influenced by the microbiome. Notably, our subtyping strategy allowed identification of novel archetype-specific prognostic biomarkers that provided information beyond and independent of UICC-TNM staging, MSI-status and consensus molecular subtyping. The results illustrate that our extended subtyping framework, combining subtyping and subtype-specific biomarkers, could contribute to improved patient prognostication and may form a strong basis for future studies.

  Study EGAS00001002376

• Field_effect_of_healthy_and_diseased_livers_WGS

  Recent work in the Campbell group has revealed somatic mutations present in normal, non-cancerous human skin. A subset of the mutations conferred selective advantages to the host cells, leading to clonal expansions and raising the risk for future cancer development. Capturing such somatic mutations in normal tissue is important to advance our understanding about carcinogenesis and could provide prospective medical insights. In this project, our goal is to detect somatic mutations in normal (pre-cancerous) liver tissue. Using Laser Microdissection technology, we will dissect individual liver lobules from patient samples and submit these to sequencing. For each patient sample, we aim to sequence multiple lobules to characterise the mutagenic burden. Samples will be taken from patients with different liver disease aetiologies, including alcoholism and obesity, with a view on distinguishing the prevalent mutation types occurring in each disease context. We will perform both whole genome and targeted sequencing, initially using the WTSI cancer panel. Later we aim to use a novel bait set that captures both cancer genes as well as genes relevant to the non-cancerous samples (ie. genes implicated in hereditary disorders, immune sequences).

  Study EGAS00001002413

• Genentech Small Cell Lung Cancer (SCLC) Screen

  Exome capture, RNA-Seq, whole genome sequencing of set of Small Cell Lung Cancer samples.

  Study EGAS00001000334