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.
BackgroundFamilial intestinal gastric cancer (FIGC) remains genetically unexplained and without testing/clinical criteria. Herein, we characterised the age of onset and disease spectrum of 50 FIGC families and searched for genetic causes potentially underlying a monogenic or an oligogenic/polygenic inheritance pattern.MethodsNormal and tumour DNA from 50 FIGC probands were sequenced using Illumina custom panels on MiSeq, and their respective germline and somatic landscapes were compared with corresponding landscapes from sporadic intestinal gastric cancer (SIGC) and hereditary diffuse gastric cancer cohorts.ResultsThe most prevalent phenotype in FIGC families was gastric cancer, detected in 138 of 208 patients (50 intestinal gastric cancer probands and 88 unknown gastric cancer histology relatives), followed by colorectal and breast cancers. After excluding benign and intronic variants lacking impact in splicing, 12 rare high-quality variants were found exclusively in 11 FIGC probands. Only two probands carried potentially deleterious variants, but lacked somatic second-hits, weakly supporting the monogenic hypothesis for FIGC. However, FIGC probands developed gastric cancer at least 10 years earlier and carried more TP53 germline common variants than SIGC (p=4.5E-03); FIGC and SIGC could be distinguished by specific germline and somatic variant profiles; there was an excess of FIGC tumours presenting microsatellite instability (38%); and FIGC tumours displayed significantly more somatic common variants than SIGC tumours (p=4.2E-06).ConclusionThis study proposed the first data-driven testing criteria for FIGC families, and supported FIGC as a genetically determined, likely polygenic, gastric cancer-predisposing disease, with earlier onset and distinct from patients with SIGC at the germline and somatic levels.
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.
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