Open-Source Artificial Intelligence System Supports Diagnosis of Mendelian Diseases in Acutely Ill Infants.

Mendelian disorders are prevalent in neonatal and pediatric intensive care units and are a leading cause of morbidity and mortality in these settings. Current diagnostic pipelines that integrate phenotypic and genotypic data are expert-dependent and time-intensive. Artificial intelligence (AI) tools may help address these challenges. Dx29 is an open-source AI tool designed for use by clinicians. It analyzes the patient's phenotype and genotype to generate a ranked differential diagnosis. We used Dx29 to retrospectively analyze 25 acutely ill infants who had been diagnosed with a Mendelian disorder, using a targeted panel of ~5000 genes. For each case, a trio (proband and both parents) file containing gene variant information was analyzed, alongside patient phenotype, which was provided to Dx29 by three approaches: (1) AI extraction from medical records, (2) AI extraction with manual review/editing, and (3) manual entry. We then identified the rank of the correct diagnosis in Dx29's differential diagnosis. With these three approaches, Dx29 ranked the correct diagnosis in the top 10 in 92-96% of cases. These results suggest that non-expert use of Dx29's automated phenotyping and subsequent data analysis may compare favorably to standard workflows utilized by bioinformatics experts to analyze genomic data and diagnose Mendelian diseases.

Gene therapies and COVID-19 vaccines: a necessary discussion in relation with viral vector-based approaches.

The COVID-19 pandemic is adding an unanticipated concern for those affected by genetic diseases. Most of the new treatment achievements for these patients are made possible as a result of advances in viral-based products. Among them, adenoviruses (AdV) and especially adeno-associated viruses (AAV) are important players. The concerns and the conversation around this issue have increased as COVID-19 vaccines approach the market. What if the viral vectors become the mainstream strategy for vaccine development? Will the immune response elicited against the vector compromise the efficacy of future gene therapies? Patients with genetic diseases and patient advocacy groups are requesting information to the medical community about the potential impact of these vaccines in future gene therapy treatments, and physicians and scientists are not able to provide satisfactory answer yet. Importantly, the frequency of cross-reactivity among different AAV serotypes can be as high as 50%. This would have potential implications for patients with genetic disorders who could benefit from gene therapies, often coming in the form of AAV-based gene therapies. As in many other aspects, this pandemic is challenging our capacity to coordinate, plan ahead and align different medical objectives. In this case, having such conversation early on might allow us to make the right choices while we are still on time.

Phen2Gene: rapid phenotype-driven gene prioritization for rare diseases.

Human Phenotype Ontology (HPO) terms are increasingly used in diagnostic settings to aid in the characterization of patient phenotypes. The HPO annotation database is updated frequently and can provide detailed phenotype knowledge on various human diseases, and many HPO terms are now mapped to candidate causal genes with binary relationships. To further improve the genetic diagnosis of rare diseases, we incorporated these HPO annotations, gene-disease databases and gene-gene databases in a probabilistic model to build a novel HPO-driven gene prioritization tool, Phen2Gene. Phen2Gene accesses a database built upon this information called the HPO2Gene Knowledgebase (H2GKB), which provides weighted and ranked gene lists for every HPO term. Phen2Gene is then able to access the H2GKB for patient-specific lists of HPO terms or PhenoPacket descriptions supported by GA4GH (http://phenopackets.org/), calculate a prioritized gene list based on a probabilistic model and output gene-disease relationships with great accuracy. Phen2Gene outperforms existing gene prioritization tools in speed and acts as a real-time phenotype-driven gene prioritization tool to aid the clinical diagnosis of rare undiagnosed diseases. In addition to a command line tool released under the MIT license (https://github.com/WGLab/Phen2Gene), we also developed a web server and web service (https://phen2gene.wglab.org/) for running the tool via web interface or RESTful API queries. Finally, we have curated a large amount of benchmarking data for phenotype-to-gene tools involving 197 patients across 76 scientific articles and 85 patients' de-identified HPO term data from the Children's Hospital of Philadelphia.

The European patient with Dravet syndrome: results from a parent-reported survey on antiepileptic drug use in the European population with Dravet syndrome.

Dravet syndrome is a rare form of epilepsy largely refractory to current antiepileptic medications. The only precedents of randomized placebo-controlled trials in Dravet syndrome are the two small trials that led to the approval of stiripentol. With the arrival of new clinical trials for Dravet syndrome, we sought to determine the characteristics of the patient population with Dravet syndrome in Europe today, which has possibly evolved subsequent to the approval of stiripentol and the ability to diagnose milder clinical cases via genetic testing. From May to June 2014, we conducted an online parent-reported survey to collect information about the demographics, disease-specific clinical characteristics, as well as current and past use of antiepileptic medications by European patients with Dravet syndrome. We present data from 274 patients with Dravet syndrome from 15 European countries. Most patients were between 4 and 8years of age, and 90% had known mutations in SCN1A. Their epilepsy was characterized by multiple seizure types, although only 45% had more than 4 tonic-clonic seizures per month on average. The most common drug combination was valproate, clobazam, and stiripentol, with 42% of the total population currently taking stiripentol. Over a third of patients with Dravet syndrome had taken sodium channel blockers in the past, and most had motor and behavioral comorbidities. Our study helps define the current typical European patient with Dravet syndrome. The results from this survey may have important implications for the design of future clinical trials that investigate new treatments for Dravet syndrome.