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• Genotype data from 'Dense sampling of ethnic groups within African countries reveals fine-scale genetic structure and extensive historical admixture.'

  Previous studies have highlighted how African genomes have been shaped by a complex series of historical events. Despite this, genome-wide data has only been obtained from a small proportion of present-day ethnolinguistic groups. By analysing new autosomal genetic variation data of 1333 individuals from over 150 ethnic groups from Cameroon, Republic of the Congo, Ghana, Nigeria and Sudan, we demonstrate a previously underappreciated fine-scale level of genetic structure within these countries, for example correlating with historical polities in western Cameroon. By comparing genetic variation patterns among populations, we infer that many northern Cameroonian and Sudanese groups share genetic links with multiple geographically disparate populations, likely resulting from long-distance migrations. In Ghana and Nigeria, we infer signatures of intermixing dated to over 2000 years ago, corresponding to reports of environmental transformations possibly related to climate change. We also infer recent intermixing signals in multiple African populations, including Congolese, that likely relates to the expansions of Bantu language-speaking peoples.

  Study EGAS00001006944

• Integrated single cell analysis in transformed follicular lymphoma

  Histological transformation of follicular lymphoma to aggressive form occurs 2-3% per year with poor outcome. Divergent evolution and an altered tumour-microenvironment (TME) have been implicated during transformation. However, phenotypic consequences of this evolution and its implication in TME remain unknown. To address this, we performed single cell whole genome and whole transcriptome sequencing of paired pre/post transformation patient samples. We further performed scWTS of additional samples from patients without transformation. Our analysis revealed evolutionary dynamics of transformation at unprecedented resolution. Integration of scWGS and scWTS identified pathways upregulated during evolution. scWTS analysis revealed a shifting TME landscape, with an exhausted signature emerging during transformation. Using multi-color immunofluorescence we transferred these findings to a TME-based transformation biomarker, subsequently validated in independent pretreatment cohorts. Taken together, our results provide a comprehensive view of the combined genomic and phenotypic evolution of malignant cells during transformation, and shifting cross-talk between malignant cells and TME.

  Study EGAS00001007023

• DERMATLAS__Leiomyoma_WES

  Virtually no other tumour type is associated with so many different forms as skin cancer. Histologically, tumours of the skin may arise from epithelium, including epidermis, hair follicle, sebaceous or sweat gland, melanocytes, dermal-associated mesenchymal structures or tissue resident immune cells, making for a diversity of clinical presentations. Importantly, many skin tumour types have an extremely poor prognosis. Many skin tumour subtypes have never undergone molecular profiling, or if they have, targeted sequencing has been used and the number of cases analyzed has been so limited that deriving firm conclusions about the profile of driver genes, DNA mutational signatures and germline alleles has not been possible For 70 key skin tumour subtypes defined by the World Health Organization, that have not undergone extensive genetic analysis previoulsy, we propose to perform whole exome and transcriptome sequencing, from a range of body sites, to build a genomic atlas of dermatological tumours, including detailed maps of SNVs, copy number alterations, genome-wide methylation and expression profiles. The cases in this cohort are leiomyomas.

  Study EGAS00001007629

• DERMATLAS__Leiomyoma_RNAseq

  Virtually no other tumour type is associated with so many different forms as skin cancer. Histologically, tumours of the skin may arise from epithelium, including epidermis, hair follicle, sebaceous or sweat gland, melanocytes, dermal-associated mesenchymal structures or tissue resident immune cells, making for a diversity of clinical presentations. Importantly, many skin tumour types have an extremely poor prognosis. Many skin tumour subtypes have never undergone molecular profiling, or if they have, targeted sequencing has been used and the number of cases analyzed has been so limited that deriving firm conclusions about the profile of driver genes, DNA mutational signatures and germline alleles has not been possible For 70 key skin tumour subtypes defined by the World Health Organization, that have not undergone extensive genetic analysis previoulsy, we propose to perform whole exome and transcriptome sequencing, from a range of body sites, to build a genomic atlas of dermatological tumours, including detailed maps of SNVs, copy number alterations, genome-wide methylation and expression profiles. The cases in this cohort are leiomyomas.

  Study EGAS00001007630

• DERMATLAS__Leiomyosarcoma_RNAseq

  Virtually no other tumour type is associated with so many different forms as skin cancer. Histologically, tumours of the skin may arise from epithelium, including epidermis, hair follicle, sebaceous or sweat gland, melanocytes, dermal-associated mesenchymal structures or tissue resident immune cells, making for a diversity of clinical presentations. Importantly, many skin tumour types have an extremely poor prognosis. Many skin tumour subtypes have never undergone molecular profiling, or if they have, targeted sequencing has been used and the number of cases analyzed has been so limited that deriving firm conclusions about the profile of driver genes, DNA mutational signatures and germline alleles has not been possible For 70 key skin tumour subtypes defined by the World Health Organization, that have not undergone extensive genetic analysis previoulsy, we propose to perform whole exome and transcriptome sequencing, from a range of body sites, to build a genomic atlas of dermatological tumours, including detailed maps of SNVs, copy number alterations, genome-wide methylation and expression profiles. The cases in this cohort are leiomyosarcomas.

  Study EGAS00001007631

• Endocrine therapy reprogramming of breast cancer facilitates metastatic escape via upregulation of P-Rex1/Rac1 signalling

  The estrogen receptor (ER) drives growth in ~70% of breast cancers, and endocrine therapies prevent recurrence. However, 30% of patients recur, with many fatal cases occurring late (after >5 years), presenting a challenge due to their often non-proliferative nature and limited options. To study this, we generated slow-growing resistant cells that form small primary tumours but readily metastasize. Single-cell RNA sequencing (scRNAseq) revealed that endocrine therapy reprograms these cells, notably upregulating the Rac1 signalling component P-Rex1. We found in clinical cohorts that P-Rex1 in high in ER+ breast cancer. Intravital imaging demonstrated that Rac1 signalling is active in ER+ cells following endocrine therapy. Targeting the Rac1 pathway with small molecule inhibitors (NSC23766, R-ketorolac) reduced survival and motility in resistant cells, inhibited in vivo Rac1 activity, and reduced tumour burden when combined with tamoxifen in a drug-refractory patient-derived xenograft. This work identifies the P-Rex1/Rac1 axis, as a potential therapeutic target for late recurring ER+ breast cancer.

  Study EGAS00001008353

• Somatic mutation and selection at epidemiological scale - TwinsUK_RENanoSeq_Buccal

  Utilising restriction enzyme NanoSeq, we hope to explore the somatic mutation burden and mutational signatures in buccal cells from TwinsUK donors. Some donors have paired blood samples and data from this will help contextualise that work. All donors have paired Targeted NanoSeq data from buccal swabs. As we age, many tissues become colonised by microscopic clones carrying somatic driver mutations. Some of these clones represent a first step towards cancer whereas others may contribute to ageing and other diseases. However, our understanding of the clonal landscapes of human tissues, and their impact on cancer risk, ageing and disease, remains limited due to the challenge of detecting somatic mutations present in small numbers of cells. Here, we introduce a new version of nanorate sequencing (NanoSeq), a duplex sequencing method with error rates of less than 5 per billion base pairs, which is compatible with whole-exome and targeted gene sequencing. Deep sequencing of polyclonal samples with single-molecule sensitivity enables the simultaneous detection of mutations in large numbers of clones, yielding accurate somatic mutation rates, mutational signatures and driver mutation frequencies in any tissue. Applying targeted NanoSeq to 1,042 non-invasive samples of oral epithelium and 371 samples of blood from a twin cohort, we found an unprecedentedly rich landscape of selection, with 46 genes under positive selection driving clonal expansions in the oral epithelium, over 62,000 driver mutations, and evidence of negative selection in some genes. The high number of positively selected mutations in multiple genes provides high-resolution maps of selection across coding and non-coding sites, a form of in vivo saturation mutagenesis. Multivariate regression models enable mutational epidemiology studies on how carcinogenic exposures and cancer risk factors, such as age, tobacco or alcohol, alter the acquisition and selection of somatic mutations. Accurate single-molecule sequencing has the potential to unveil the polyclonal landscape of any tissue, providing a powerful tool to study early carcinogenesis, cancer prevention and the role of somatic mutations in ageing and disease.

  Dataset EGAD00001015621

• EGA file encryption types

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

  Documentation check-encryption-type

• Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma

  Diffuse large B cell lymphoma (DLBCL) is the most common hematologic malignancy and is characterized by a striking degree of heterogeneity. We enrolled a total of 1001 DLBCL patients and comprehensively defined the landscape of genetic mutations, copy number alterations and expression through whole-exome and transcriptome sequencing. We identified 150 genetic drivers of DLBCL including many novel, clinically relevant genes (e.g. SPEN, SETD1B and KLHL14). Genetic drivers were highly enriched among essential genes identified by CRISPR screening in DLBCL cell lines including those of immediate potential therapeutic relevance such as MTOR, SYK, SF3B1 and XPO1. RHOA emerged as a critical driver gene from our genetic analysis and CRISPR screen; we further delineated its functional role in vitro and in vivo models to define its role as an essential oncogene in DLBCL. Our work thus identifies the functional landscape of genetic drivers and their clinical and therapeutic relevance in DLBCL patients.

  Study EGAS00001002606

• Multi-focal genomic dissection of synchronous primary and metastatic tissue from de novo metastatic prostate cancer

  De novo metastatic prostate cancer is highly aggressive, but the paucity of routinely-collected tissue in this setting has hindered genomic stratification and jeopardizes precision oncology efforts. Here, we leveraged a rare study of surgical intervention in 43 de novo metastatic prostate cancers to assess somatic genotype across 607 synchronous primary and metastatic tissue foci plus circulating tumor DNA. Intra-prostate heterogeneity was pervasive and impacted clinically-relevant genes, resulting in frequent discordance between select primary-restricted foci and synchronous metastases. Additional complexity was driven in several instances by polyclonal metastatic seeding from the reservoir of phylogenetically-related primary populations. When simulating standard clinical practice relying on a single tissue focus, genomic heterogeneity plus highly variable per-sample tumor fraction caused false genotyping of dominant disease. However, in silico modeling demonstrated that analysis of multiple biopsy cores can rescue misassigned somatic genotypes. Our results define the relationship between synchronous treatment-naive primary and metastatic lesions in de novo metastatic prostate cancer and provide a framework for implementing genomics-guided patient management.

  Study EGAS00001006466