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• Federated discovery and sharing of genomic data using Beacons

  The Beacon Project is a Global Alliance for Genomics & Health (GA4GH) initiative that enables genomic and clinical data sharing across federated networks. The project is working toward developing regulatory, ethics and security guidance to ensure proportionate safeguards for distribution of data according to the GA4GH-developed “Framework for Responsible Sharing of Genomic and Health-Related Data”. Within this Nature Biotechnology Correspondence a description of the Beacon protocol and how it can be used as a model for the federated discovery and sharing of genomic data is offered to the community. A Beacon is defined as a web-accessible service that can be queried for information about a specific allele. A user of a Beacon can pose queries of the form “Have you observed this nucleotide (e.g., C) at this genomic location (e.g., position 32,936,732 on chromosome 13)?” to which the Beacon responds with either “yes” or “no.” In this way, a Beacon allows allelic information of interest to be discovered by a remote searcher with no reference to a specific sample or patient, thereby mitigating privacy risks. The Beacon API (represented as a RESTful web application) provides a technical specification that a Beacon server must implement. The specification is open source and available online here. To simplify the process of lighting a Beacon, a free, open-source reference implementation of the latest specification has been developed. GA4GH is promoting different levels of data access (open, registered, and controlled) for convenience and for compatibility across its projects. Each so-called access tier has distinct visibility and requirements for authorization. For example, ‘open access’ Beacons are accessible to anonymous users of the internet, whereas ‘registered access’ Beacons are accessible to registered users (for example, bona fide researchers and clinicians) who have agreed to a set of conditions of data use. Many of the largest genomic archives, such as dbGaP, the European Genome-phenome Archive and the European Variation Archive, have provided access to variation data through Beacons for some or all of their datasets. Beacons can be interconnected through the Beacon Network, a directory and search engine for Beacons. Although individual Beacons answer the question “Have you observed this allele?”, the Beacon Network answers the question “Who has observed this allele?”. Beacons can be freely registered to the Beacon Network and can be searched independently or in aggregate with other connected Beacons.

  Blog federated-discovery-using-beacons

• Whole-genome methylation profiling of menstrual stem cells identifies novel biomarkers for endometriosis

  Endometriosis is a highly prevalent condition that remains underdiagnosed and poorly managed due to the lack of clinically validated biomarkers and limited understanding of its underlying pathophysiology. Although menstrual blood–derived stem cells (MenSCs) have been implicated in disease pathogenesis, their potential as a diagnostic source has not been systematically explored. We conducted a clinical study including 42 women (19 with endometriosis and 23 controls) to evaluate whether DNA methylation profiles from freshly isolated MenSCs could identify disease-specific biomarkers. Whole-genome DNA methylation sequencing identified differentially methylated regions (DMRs) enriched in genes associated with key features of endometriosis, including inflammation, tissue remodelling, and developmental processes. These DMRs robustly discriminated cases from controls, independently of technical and clinical confounders. Machine learning models trained and validated on these regions demonstrated strong diagnostic performance (specificity 83%, sensitivity 79%). Integration with an independent single-cell RNA sequencing dataset suggested that the identified DMRs may influence gene expression, supporting their biological relevance. Collectively, these findings highlight MenSC DNA methylation profiling as a promising, non-invasive strategy for early diagnosis and personalised management of endometriosis. (PubMed IDs will be provided once the manuscript is published.)

  Study EGAS50000001640

• Mutational bias in spermatogonia impacts the anatomy of regulatory sites in the human genome

  Mutation in the germline is the ultimate source of genetic variation, but little is known about the influence of germline chromatin structure on mutational processes. Using ATAC-seq, we profile the open chromatin landscape of human spermatogonia, the most proliferative cell-type of the germline, identifying transcription factor binding sites (TFBSs) and PRDM9-binding sites, a subset of which will initiate meiotic recombination. We observe an increase in rare structural variant (SV) breakpoints at PRDM9-bound sites, implicating meiotic recombination in the generation of structural variation. Many germline TFBSs, such as NRF, are also associated with increased rates of SV breakpoints, apparently independent of recombination. Singleton short insertions (>=5 bp) are highly enriched at TFBSs, particularly at sites bound by testis active TFs, and their rates correlate with those of structural variant breakpoints. Short insertions often duplicate the TFBS motif, leading to clustering of motif sites near regulatory regions in this male-driven evolutionary process. Increased mutation loads at germline TFBSs disproportionately affect neural enhancers with activity in spermatogonia, potentially altering neurodevelopmental regulatory architecture. Local chromatin structure in spermatogonia is thus pervasive in shaping both evolution and disease.

  Study EGAS00001005366

• Clonal Decomposition and DNA Replication States Defined by Scaled Single-Cell Genome Sequencing

  Accurate measurement of clonal genotypes, mutational processes, and replication states from individual tumor-cell genomes will facilitate improved understanding of tumor evolution. We have developed DLP+, a scalable single-cell whole-genome sequencing platform implemented using commodity instruments, image-based object recognition, and open source computational methods. Using DLP+, we have generated a resource of 51,926 single-cell genomes and matched cell images from diverse cell types including cell lines, xenografts, and diagnostic samples with limited material. From this resource we have defined variation in mitotic mis-segregation rates across tissue types and genotypes. Analysis of matched genomic and image measurements revealed correlations between cellular morphology and genome ploidy states. Aggregation of cells sharing copy number profiles allowed for calculation of single-nucleotide resolution clonal genotypes and inference of clonal phylogenies and avoided the limitations of bulk deconvolution. Finally, joint analysis over the above features defined clone-specific chromosomal aneuploidy in polyclonal populations.

  Study EGAS00001003190

• Changes in alternative splicing and associated neo-antigens due to therapy

  Recent studies have identified alternative splicing (AS) as a novel source of neoantigens for immunotherapy. Surprisingly little is known about the AS milieu in recurrent glioblastoma (GBM), despite this being the venue for most clinical trials. We profiled 29 primary-recurrent paired human GBM specimens via RNA sequencing and re-analyzed RNA-sequencing data from non-malignant human brain tissues. From these data, we reconstructed the landscape of AS in GBM through recurrence and contrasted that to isoforms observed in non-malignant brain. The AS events we identified were cross-referenced with single-cell GBM atlases to determine cell-type specific splicing patterns. From this we identified novel splicing events in cell-surface proteins that are suitable targets for engineered T-cell therapies. We found recurrent-specific isoforms of mitogen-activated kinase pathway genes which are expressed exclusively by GBM stem-like cells that enhance invasiveness. These studies shed light on the effect of therapy on AS and identify novel targets for emerging immunotherapies.

  Study EGAS00001004524

• Accurate detection of tumor-specific fusion genes reveals strongly immunogenic personal neo-antigens

  Fusion genes arising from cancer-associated somatic mutations are a potential rich source for highly immunogenic neo-antigens. However, their exploitation as targets for personalized cancer immunotherapy is currently limited by the lack of computational tools allowing transcriptome-wide identification of unique fusion genes in an accurate and sensitive manner. Here, we present EasyFuse, a computational pipeline, to detect individual and cancer-specific fusion genes in next-generation-sequencing transcriptome data obtained from human cancer samples. Using machine learning, EasyFuse predicts personal fusion genes with high precision and sensitivity and outperforms previously described approaches as qualified by an unprecedented ground-truth dataset of >1500 verification experiments in relevant patient samples. By testing immunogenicity with autologous blood lymphocytes from cancer patients we detected pre-established CD4+ and CD8+ T cell responses for 10 of 21 (48%), and for 1 of 30 (3%) of identified fusion genes, respectively. In conclusion, we demonstrate accurate detection of cancer-specific fusion genes. The high frequency of T cell responses detected in cancer patients support the relevance of private fusion genes as neo-antigens for personalized immunotherapies, especially for tumors with low point mutation burdens.

  Study EGAS00001004877

• Breast Cancer bulk RNA-Seq Dataset

  Bulk RNA-Sequencing of 18 primary breast cancers from Wu et al. (2021) study.

  Dataset EGAD50000000649

• WES

  Whole Exome Sequencing data from Lauss et al Nat Comm 2024. Molecular patterns of resistance to immune checkpoint blockade in melanoma.

  Dataset EGAD50000000380

• TCRseq

  TCRseq data from Lauss et al Nat Comm 2024: Molecular patterns of resistance to immune checkpoint blockade in melanoma.

  Dataset EGAD50000000379

• bam files Targeted BS

  Data from the paper Context-specific Effects of TGFβ/SMAD3 in Cancer Are Modulated by the Epigenome. Tufegdzic et al, Cell Reports 2015

  Dataset EGAD00001001667