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• Polygenic Risk Score for the Prediction of Breast Cancer Is Related to Lesser Terminal Duct Lobular Unit Involution of the Breast

  The Susan G. Komen Tissue Bank (KTB) at the Indiana University Simon Cancer Center is an annotated biobank, which has recruited healthy women who provided demographic, lifestyle, and cancer-related information via self-administered questionnaire, blood samples and normal breast tissues. H&E slides of the normal breast tissues were scanned for image analysis and a pathologist measured the number of terminal duct lobular units (TDLUs) ("TDLU count"). The acini count per TDLU was quantified using the TDLU analyzer software. In this analysis, we computed the recently developed polygenic risk score (PRS) based on 313 common variants for the prediction of breast cancer and related this PRS to TDLU count and acini count per TDLU. We did this for the overall PRS, the ER-positive PRS and the ER-negative PRS.

  Study phs002062

• Beacon v2 and Federated EGA, part of the GDI project Starter Kit to enable access to genomic and phenotypic data across borders

  The European Genomic Data Infrastructure project has released a ‘Starter Kit’ based on the rare disease and cancer proof-of-concepts from the Beyond 1 Million Genomes (B1MG) project. The Starter Kit, freely available to any interested parties, contains a set of software applications and components co-developed by the 20 GDI nodes and based on open community standards from the Global Alliance for Genomics and Health (GA4GH). Products in the Starter Kit give all countries the technical capability to access synthetic genomic and phenotypic data across borders. The image above shows the full workflow of the GDI Starter Kit user journey. The dotted circles are not included in the Starter Kit yet The EGA’s work is represented in the Starter Kit with Beacon v2 and Federated EGA among the software applications and components. About Beacon v2 Beacon is a protocol for data discovery. This API allows the search of genomic variants and associated information without breaching the datasets privacy. The European Genome-phenome Archive (EGA) is a key contributor to the Beacon v2 Protocol, the Beacon v2 Reference Implementation (B2RI) and the EGA Beacon(s). About Federated EGA The Federated EGA network offers a tool for data storage and sharing. Officially launched in 2022, this network of connected resources enables transnational discovery of and access to human omics data for research, while also respecting jurisdictional data protection regulations. The Federated EGA Onboarding Website contains all the information related to the establishment of a Federated Node.

  Blog beacon-v2-and-federated-ega-part-of-the-gdi-project-starter-kit

• European Genome-phenome Archive 15th Anniversary Celebration

  2023 marks the 15th Anniversary of the EGA, jointly managed by the European Bioinformatics Institute (EMBL-EBI) and the Centre for Genomic Regulation (CRG). To mark the occasion, a simultaneous celebration was held in both institutions on the 13th of December 2023. The teams gathered online to play a quiz game and celebrate all the achievements and milestones with two wonderful anniversary cakes. In 2008, the European Genome-phenome Archive was created at the EBI-EMBL in Cambridge to guarantee that human genome and phenome data were available to the international scientific community while data privacy was preserved. The six-person staff at the beginning of the project is now far behind us, with a team that reaches the number of 35 members. Since 2013, the European Bioinformatics Institute and the Centre for Genomic Regulation share responsibility for The European Genome-phenome Archive (EGA). At that time, the EGA had data for about 0,5 petabytes. Currently, the Archive contains more than 12 petabytes. 2023 comes to an end with several good news. The EGA has been renewed as an ELIXIR Core Data Resource. This was announced during the GA4GH 11th Plenary held in San Francisco last September when ELIXIR-Beacon was also confirmed to be a GA4GH Driver Project. Thus, the Beacon API has maintained this title since 2018. What’s more, the first Federated EGA dataset is now live on our website. This year we also launched new services for EGA users in September. By the numbers, in 2023 the EGA counts 2.5 PB archived, 371 studies published, 208 new submitters and 19 active projects in which the team is participating, among others. We look forward to continuing to support your research in 2024!

  Blog 15-anniversary

• Single cell characterization of T-cell lymphoma: RNA (2025-07-31)

  Lymphomas are types of blood cancer derived from lymphocytes, and can be classified into two main categories, Hodgkin's lymphomas (HL) and non-Hodgkin lymphomas (NHL). T-cell lymphoma is a type of NHL that develops from T lymphocytes and accounts for 10-15% of total NHL cases. As a rare disease, T-cell lymphoma is functionally, pathologically, and clinically complex and heterogeneous. The 2016 revision of the WHO classification has listed 29 subtypes of mature T/NK-cell neoplasms, among which over 25 are T lymphocyte in origin. These subtypes of T-cell lymphomas have different biological behaviours and clinical prognosis. Clinically, T-cell lymphoma ranges from indolent (slow-growing) to aggressive (fast-growing and spreading) disease. According to the guideline booklet provided by the Lymphoma Research Foundation (https://lymphoma.org/aboutlymphoma/nhl/), indolent T-cell lymphoma subtypes include adult T-cell leukaemia/lymphoma (ATLL), cutaneous T-cell lymphoma (CTCL), and mycosis fungoides (MF), while the aggressive subtypes include anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), peripheral T-cell lymphoma (PTCL), and T lymphoblastic lymphoma (TLBL). PTCL and ALCL represent the most common subtypes of aggressive T-cell lymphoma whereas TLBL is a rare and aggressive subtype mostly diagnosed in children. In this study, we will use single cell RNA sequencing and single cell multiomic methods three aggressive T-cell lymphoma subtypes (ALCL, PTCL and TLBL) and try to gain a deeper insight into the genotypic and phenotypic features of these T-cell lymphomas. . This dataset contains all the data available for this study on 2025-07-31.

  Dataset EGAD00001015669

• Ancestry and somatic profile predict acral melanoma origin and prognosis – RNA

  Acral melanoma, which is not ultraviolet (UV)-associated, is the most common type of melanoma in several low- and middle-income countries including Mexico. Latin American samples are significantly underrepresented in global cancer genomics studies, which directly affects patients in these regions as it is known that cancer risk and incidence may be influenced by ancestry and environmental exposures. To address this, we characterise the genome and transcriptome of 123 acral melanoma tumours from 92 Mexican patients, a population notable because of its genetic admixture. Compared with other studies of melanoma, we found fewer frequent mutations in classical driver genes such as BRAF, NRAS or NF1. While most patients had predominantly Amerindian genetic ancestry, those with higher European ancestry had increased frequency of BRAF mutations and a lower median number of structural variants. The tumours with activating BRAF mutations have a transcriptional profile more similar to cutaneous non-volar melanocytes, suggesting that acral melanomas in these patients may arise from a distinct cell of origin compared to other tumours arising in these locations. KIT mutations were found in a subset of these tumours, and quadruple wild-type samples (non BRAF/NRAS/NF1/KIT) differed from mutated samples in their structural genomic profile and overall and recurrence-free survival patterns. Transcriptional profiling defined three expression clusters; these characteristics were associated with recurrence-free and overall survival. We highlight potential novel low-frequency drivers, such as PTPRJ, NF2 and RDH5. Our study enhances knowledge of this understudied disease and underscores the importance of including samples from diverse ancestries in cancer genomics studies.

  Dataset EGAD00001015756

• Characterisation of the genomic landscape of CRLF2-d ALL

  To determine the clinical and genetic landscape of CRLF2 deregulated acute lymphoblastic leukaemia (CRLF2-d ALL). We identified 172 patients with a CRLF2 rearrangement treated on either the UKALL2003 trial for children and adolescents (1-24 years) or the UKALLXII trial for adolescents and adults (15-59 years). Genomic technologies from conventional karyotyping, and FISH through to whole genome and exome sequencing were used to characterise the genomes of patients with CRLF2-d ALL. This is the largest study to date to investigate the genomic landscape of CRLF2-d ALL and define CRLF2-d as a unique subgroup of B-other ALL. We have confirmed the high incidence of CRLF2-d in Down syndrome-ALL and demonstrated the co-existence of CRLF2-d with other primary chromosomal rearrangements, suggesting that in these patients CRLF2-d can be a secondary genetic abnormality. Other defining features included enrichment of IKZF1, BTG1 and ADD3 deletions in IGH-CRLF2 patients and specific chromosomal gains seen at much higher frequencies than B-other ALL . We report recurrent established and new co-operating abnormalities and the novel involvement of USP9X and DDX3X in CRLF2-d ALL. It is clear from these data that CRLF2-d ALL is heterogenoeus, requiring a combination of genetic abnormalities in functionally relevent genes, to work alongside the deregulated expression of CRLF2 in order to initiate and drive leukaemogenesis in this subtype. Although the functional relevance of many of the abnormalities presented here are currently unknown, many are likely to activate alternate pathways or sensitize patients to current therapies.

  Dataset EGAD00001002007

• Characterisation of the genomic landscape of CRLF2-d ALL

  To determine the clinical and genetic landscape of CRLF2 deregulated acute lymphoblastic leukaemia (CRLF2-d ALL). We identified 172 patients with a CRLF2 rearrangement treated on either the UKALL2003 trial for children and adolescents (1-24 years) or the UKALLXII trial for adolescents and adults (15-59 years). Genomic technologies from conventional karyotyping, and FISH through to whole genome and exome sequencing were used to characterise the genomes of patients with CRLF2-d ALL. This is the largest study to date to investigate the genomic landscape of CRLF2-d ALL and define CRLF2-d as a unique subgroup of B-other ALL. We have confirmed the high incidence of CRLF2-d in Down syndrome-ALL and demonstrated the co-existence of CRLF2-d with other primary chromosomal rearrangements, suggesting that in these patients CRLF2-d can be a secondary genetic abnormality. Other defining features included enrichment of IKZF1, BTG1 and ADD3 deletions in IGH-CRLF2 patients and specific chromosomal gains seen at much higher frequencies than B-other ALL . We report recurrent established and new co-operating abnormalities and the novel involvement of USP9X and DDX3X in CRLF2-d ALL. It is clear from these data that CRLF2-d ALL is heterogenoeus, requiring a combination of genetic abnormalities in functionally relevent genes, to work alongside the deregulated expression of CRLF2 in order to initiate and drive leukaemogenesis in this subtype. Although the functional relevance of many of the abnormalities presented here are currently unknown, many are likely to activate alternate pathways or sensitize patients to current therapies.

  Dataset EGAD00001002008

• Genotype data for 4607 Greenlandic samples (MEGA array)

  This file fileset has 4607 Greenlanders scored on the Illumina MEGA array (1,622,813 sites), and has been put on the plus strand. The data is in PLINK bed/bim/fam format. The Greenlandic individuals originate from two population surveys, B99 and IHIT.

  Dataset EGAD00010002057

• Whole genome sequencing of Sinonasal hemangiopericytoma and patient derived cell line model

  Solitary fibrous tumor/Hemangiopericytoma (SFT/HPC) is a rare subtype of soft tissue sarcoma associated with NAB2-STAT6 gene fusions. In this study, a novel SFT/HPC was characterized using whole genome sequencing.

  Dataset EGAD50000000038

• Raw ONT R10 data: Rapid brain tumor classification from sparse epigenomic data

  This dataset contains 207 samples sequenced using rapid-multiplexed barcoded library preparation on Nanopore PromethION R10 flow cells on P2 Solo and P24 systems. One unaligned BAM file per sample is provided.

  Dataset EGAD50000000791