Suchergebnisse

For the first time in Europe hundreds of rare disease (RD) experts team up to actively share and jointly analyse existing patient's data. Solve-RD is a Horizon 2020-supported EU flagship project bringing together >300 clinicians, scientists, and patient representatives of 51 sites from 15 countries. Solve-RD is built upon a core group of four European Reference Networks (ERNs; ERN-ITHACA, ERN-RND, ERN-Euro NMD, ERN-GENTURIS) which annually see more than 270,000 RD patients with respective pathologies. The main ambition is to solve unsolved rare diseases for which a molecular cause is not yet known. This is achieved through an innovative clinical research environment that introduces novel ways to organise expertise and data. Two major approaches are being pursued (i) massive data re-analysis of >19,000 unsolved rare disease patients and (ii) novel combined -omics approaches. The minimum requirement to be eligible for the analysis activities is an inconclusive exome that can be shared with controlled access. The first preliminary data re-analysis has already diagnosed 255 cases form 8393 exomes/genome datasets. This unprecedented degree of collaboration focused on sharing of data and expertise shall identify many new disease genes and enable diagnosis of many so far undiagnosed patients from all over Europe. ; The Solve-RD project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 779257. This research is supported (not financially) by four ERNs: (1) The ERN for Intellectual Disability, Telehealth and Congenital Anomalies (ERN-ITHACA)—Project ID No 869189; (2) The ERN on Rare Neurological Diseases (ERN-RND)—Project ID No 739510; (3) The ERN for Neuromuscular Diseases (ERN Euro-NMD)—Project ID No 870177; (4) The ERN on Genetic Tumour Risk Syndromes (ERN GENTURIS)—Project ID No 739547. The ERNs are co-funded by the European Union within the framework of the Third Health Programme.

Background: Rare diseases are individually rare but globally affect around 6% of the population, and in over 70% of cases are genetically determined. Their rarity translates into a delayed diagnosis, with 25% of patients waiting 5 to 30 years for one. It is essential to raise awareness of patients and clinicians of existing gene and variant-specific therapeutics at the time of diagnosis to avoid that treatment delays add up to the diagnostic odyssey of rare diseases' patients and their families. Aims: This paper aims to provide guidance and give detailed instructions on how to write homogeneous systematic reviews of rare diseases' treatments in a manner that allows the capture of the results in a computer-accessible form. The published results need to comply with the FAIR guiding principles for scientific data management and stewardship to facilitate the extraction of datasets that are easily transposable into machine-actionable information. The ultimate purpose is the creation of a database of rare disease treatments ("Treatabolome") at gene and variant levels as part of the H2020 research project Solve-RD. Results: Each systematic review follows a written protocol to address one or more rare diseases in which the authors are experts. The bibliographic search strategy requires detailed documentation to allow its replication. Data capture forms should be built to facilitate the filling of a data capture spreadsheet and to record the application of the inclusion and exclusion criteria to each search result. A PRISMA flowchart is required to provide an overview of the processes of search and selection of papers. A separate table condenses the data collected during the Systematic Review, appraised according to their level of evidence. Conclusions: This paper provides a template that includes the instructions for writing FAIR-compliant systematic reviews of rare diseases' treatments that enables the assembly of a Treatabolome database that complement existing diagnostic and management support tools with treatment awareness data. ; The current paper was written for the Solve-RD project, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 779257.

Almost half of all individuals affected by intellectual disability (ID) remain undiagnosed. In the Solve-RD project, exome sequencing (ES) datasets from unresolved individuals with (syndromic) ID (n = 1,472 probands) are systematically reanalyzed, starting from raw sequencing files, followed by genome-wide variant calling and new data interpretation. This strategy led to the identification of a disease-causing de novo missense variant in TUBB3 in a girl with severe developmental delay, secondary microcephaly, brain imaging abnormalities, high hypermetropia, strabismus and short stature. Interestingly, the TUBB3 variant could only be identified through reanalysis of ES data using a genome-wide variant calling approach, despite being located in protein coding sequence. More detailed analysis revealed that the position of the variant within exon 5 of TUBB3 was not targeted by the enrichment kit, although consistent high-quality coverage was obtained at this position, resulting from nearby targets that provide off-target coverage. In the initial analysis, variant calling was restricted to the exon targets ± 200 bases, allowing the variant to escape detection by the variant calling algorithm. This phenomenon may potentially occur more often, as we determined that 36 established ID genes have robust off-target coverage in coding sequence. Moreover, within these regions, for 17 genes (likely) pathogenic variants have been identified before. Therefore, this clinical report highlights that, although compute-intensive, performing genome-wide variant calling instead of target-based calling may lead to the detection of diagnostically relevant variants that would otherwise remain unnoticed. ; This work was financially supported by Aspasia grants of the Dutch Research Council (015.014.036 to TK and 015.014.066 to LELMV) and Netherlands Organization for Health Research and Development (917.183.10 to TK). The Solve-RD project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 779257.

Data de publicació elecrònica: 10-05-2021 ; Rare genetic neurological disorders (RND; ORPHA:71859) are a heterogeneous group of disorders comprising >1700 distinct genetic disease entities. However, genetic discoveries have not yet translated into dramatic increases of diagnostic yield and indeed rates of molecular genetic diagnoses have been stuck at about 30–50% across NGS modalities and RND phenotypes [1, 2]. Existence of yet unknown disease genes as well as shortcomings of commonly employed NGS technologies and analysis pipelines in detecting certain variant types are typically cited to explain the low diagnosis rates. To increase the diagnostic yield in RNDs - one of the four focus disease groups in Solve-RD - we follow two major approaches, that we will here present and exemplify: (i) systematic state-of the art re-analysis of large cohorts of unsolved whole-exome/genome sequencing (WES/WGS) RND datasets; and (ii) novel-omics approaches. Based on the way Solve-RD systematically organizes researchers' expertise to channel this approach [3], the European Reference Network for Rare Neurological Diseases (ERN-RND) has established its own Data Interpretation Task Force (DITF) within SOLVE-RD, which is currently composed of clinical and genetic experts from 29 sites in 15 European countries. ; The Solve-RD project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 779257. Data were analysed using the RD‐Connect Genome‐Phenome Analysis Platform, which received funding from EU projects RD‐Connect, Solve-RD and EJP-RD (Grant Numbers FP7 305444, H2020 779257, H2020 825575), Instituto de Salud Carlos III (Grant Numbers PT13/0001/0044, PT17/0009/0019; Instituto Nacional de Bioinformática, INB) and ELIXIR Implementation Studies. The study was further funded by the Federal Ministry of Education and Research, Germany, through the TreatHSP network (01GM1905 to RS and LS), the National Institute of Neurological Diseases and Stroke (R01NS072248 to SZ and RS), the European Joint Program on Rare Diseases-EJP-RD COFUND-EJP N° 825575 through funding for the PROSPAX consortium (441409627 to MS, RS and BvW). CW was supported by the PATE program of the Medical Faculty, University of Tübingen. CEE received support from the Dutch Princess Beatrix Muscle Fund and the Dutch Spieren voor Spieren Muscle fund. Authors on this paper are members of the European Reference Network for Rare Neurological Diseases (ERN-RND, Project ID 739510)

Reanalysis of inconclusive exome/genome sequencing data increases the diagnosis yield of patients with rare diseases. However, the cost and efforts required for reanalysis prevent its routine implementation in research and clinical environments. The Solve-RD project aims to reveal the molecular causes underlying undiagnosed rare diseases. One of the goals is to implement innovative approaches to reanalyse the exomes and genomes from thousands of well-studied undiagnosed cases. The raw genomic data is submitted to Solve-RD through the RD-Connect Genome-Phenome Analysis Platform (GPAP) together with standardised phenotypic and pedigree data. We have developed a programmatic workflow to reanalyse genome-phenome data. It uses the RD-Connect GPAP's Application Programming Interface (API) and relies on the big-data technologies upon which the system is built. We have applied the workflow to prioritise rare known pathogenic variants from 4411 undiagnosed cases. The queries returned an average of 1.45 variants per case, which first were evaluated in bulk by a panel of disease experts and afterwards specifically by the submitter of each case. A total of 120 index cases (21.2% of prioritised cases, 2.7% of all exome/genome-negative samples) have already been solved, with others being under investigation. The implementation of solutions as the one described here provide the technical framework to enable periodic case-level data re-evaluation in clinical settings, as recommended by the American College of Medical Genetics. ; The Solve-RD project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 779257. Data were analysed using the RD‐Connect Genome‐Phenome Analysis Platform, which received funding from EU projects RD‐Connect, Solve-RD and EJP-RD (grant numbers FP7 305444, H2020 779257, H2020 825575), Instituto de Salud Carlos III (grant numbers PT13/0001/0044, PT17/0009/0019; Instituto Nacional de Bioinformática, INB) and ELIXIR Implementation Studies.

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes. ; We acknowledge the support of the developers of PhenoTips, which was used in the past by RD-Connect and NeurOmics as the primary tool to collate phenotypic data. We would also like to thank the leaders and members of the Instituto Nacional de Bioinformática (INB) and ELIXIR for their support and collaboration throughout the years. RD-Connect (RD-Connect, an integrated platform connecting registries, biobanks, and clinical bioinformatics) received funding from the Seventh Framework (FP7) Programme of the European Union under grant agreement No 305444. Data were analyzed using the RD-Connect GPAP, which received funding from EU ...