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Solving the unsolved Rare Diseases

Periodic Reporting for period 1 - Solve-RD (Solving the unsolved Rare Diseases)

Reporting period: 2018-01-01 to 2019-06-30

“Solve-RD – solving the unsolved rare diseases” is a research project funded by the European Commission for five years (2018-2022). It echoes the ambitious goals set out by the International Rare Diseases Research Consortium (IRDiRC) to deliver diagnostic tests for most rare diseases by 2020. The current diagnostic and subsequent therapeutic management of rare diseases is still highly unsatisfactory for a large proportion of rare disease patients – the unsolved rare disease cases. For these unsolved rare diseases, we are unable to explain the etiology responsible for the disease phenotype, predict the individual disease risk and/or rate of disease progression, and/or quantitate the risk of relatives to develop the same disorder.
Our main ambitions are thus
• to solve large numbers of rare diseases, for which a molecular cause is not known yet by sophisticated combined omics approaches, and
• to improve diagnostics of rare disease patients through contribution to, participation in and implementation of a “genetic knowledge web” based on shared knowledge about genes, genomic variants and phenotypes.
Solve-RD fully integrates with the newly formed European Reference Networks (ERNs) for rare diseases. Four ERNs (ERN-RND, -EURO-NMD, -ITHACA, and -GENTURIS) build the core of Solve-RD but we will reach out to patient cohorts across all 24 ERNs as well as the undiagnosed disease programs in order to achieve our aims.
Solve-RD identified 3 main challenges and will deliver 7 implementation steps to address these challenges in work packages 1-3:
Challenge 1: Accessibility of unsolved rare disease cohorts with comprehensive genetic and phenotypic data
Challenge 2: New and improved approaches for the discovery of novel molecular causes
Challenge 3: Translate discoveries to patients’ live and clinical practice
In the first 18 months, Solve-RD paved the ground to successfully implement the activities addressing the three challenges.
We have collected >6,600 datasets (phenotypic and exome/genome sequencing data) from unsolved rare disease patients and their family members. Standardised phenotypic information (HPO, ORDO and OMIM encoded) has been collected via PhenoTips. Sequencing data has been processed through the RD-Connect GPAP standard analysis pipeline.
We also designed the means to model the standardised phenotypic description of unsolved cases in the form of an ontology that integrates a notion of phenotypic similarity to the known rare diseases or between unsolved cases, with the ultimate aim of enabling definition of new diseases or a better definition of known diseases.
A Data Analysis Task Force (DATF) has been formed to establish the latest tools to be used in data re-analysis. Four Data Interpretation Task Forces (DITFs) – one per core ERN – will help interpreting the results. They bring together the clinical and disease expertise with the technical expertise of the DATF. They have furthermore worked-out use cases, based on which working groups and tools are developed to find the most appropriate approaches to the respective research questions.
A market consultation for novel omics has been conducted to find the best price for each technology. The four DITFs are coordinating the selection and collection of samples per study cohort from their ERN.
We have implemented the RDMM-Europe registry based on the Canadian model. Model organism investigators are encouraged to register in the database and enter their genes of interest. The Solve-RD brokerage service that connects clinicians discovering new disease genes with basic scientists for validation of those genes in a model organism has been established.
In order to communicate (gen)omics test results to patients in an evidence-based manner an Experience-Based Co-Design (EBCD) method to co-design interventions or services to support the sharing of genomic sequencing information has been developed.
A cost-effectiveness analysis based on existing literature survey was initiated. 120 keywords and associated thesaurus have been defined and searched on Google Scholar, PubMed and ScienceDirect. About 7000 abstracts have been read and 1000 articles selected.
To alert the clinician about patients with treatable conditions at the time of reviewing NGS results, we initiated the development of a computer-readable and interoperable knowledgebase – the Treatabolome – that links treatable variants with the evidence for the treatment. A pilot study on congenital myasthenic syndromes has been published.
EURORDIS organised two Winter Schools with the aim to deepen patient representatives’ understanding of how pre-clinical research translates into real benefits for rare disease patients.
WP4 has developed a cloud-based methods ‘Sandbox’ to enable partners in different locations to work together on developing, testing and validating analytical tools and methods. MOLGENIS has been employed to set up a database (called RD3) for internal dataset and sample tracking. In order to make Solve-RD data FAIR, we have also focused on enabling discovery of patients/data/samples/researchers of interest in federated networks. RD-NEXUS builds on the Cafe Variome platform, which has been further enhanced for the project.
Solve-RD meets the biggest challenge of diagnosing patients with rare diseases since the implementation of NGS technology. Despite extensive studies of Whole Exome Sequencing (WES) in numerous rare disease patient cohorts, ~50% of all patients remain unsolved. By applying comprehensive advanced bioinformatics algorithms in four major patient cohorts we anticipate to increase diagnostic yield by about 3-5%. The extension of DNA analysis from WES to Whole Genome Sequencing (WGS) in >2,500 well characterized patients will raise this sensitivity to about 60-70%. WGS bioinformatics developed together with RD-Connect will lead to a world-wide not yet available, standardized analytical tool on how to approach genome data for structural variants. Also, no standardised multi-omics approach exists so far; neither at the experimental nor at the bioinformatics level. Solve-RD will develop different specific strategies for the different patient cohorts to cope with these complex analyses and addressing simultaneously cost-effective issues. The connection of sophisticated diagnostic approaches will only be successful with deep phenotyping of patient cohorts. The participating and the associated European Reference Networks will select cohorts of >800 unsolved patients with highly peculiar (ultra-rare) phenotypes, increasing the chance to find novel disease genes and novel disease mechanisms. We anticipate to solve >2,000 cases which will translate in a number of new genes and disease mechanisms to be discovered in the course of Solve-RD. Finding further patients with mutations in the newly discovered disease genes will be secured by newly developed matchmaking approaches and by screening using MIPs technology in the >20,000 unclassified patients of the ERNs. For the first time in Europe we will also implement a novel brokerage structure connecting clinicians, gene discoverer and basic researchers in a highly flexible and efficient way to quickly verify novel genes and disease mechanisms. To communicate all these data with patients and doctors, an evidence based approach called Experience-Based Co-Design (EBCD) will be applied.