Diagnosis Of Rare Diseases Research Articles

RDKG-115: Assisting drug repurposing and discovery for rare diseases by trimodal knowledge graph embedding

Rare diseases (RDs) may affect individuals in small numbers, but they have a significant impact on a global scale. Accurate diagnosis of RDs is challenging, and there is a severe lack of drugs available for treatment. Pharmaceutical companies have shown a preference for drug repurposing from existing drugs developed for other diseases due to the high investment, high risk, and long cycle involved in RD drug development. Compared to traditional approaches, knowledge graph embedding (KGE) based methods are more efficient and convenient, as they treat drug repurposing as a link prediction task. KGE models allow for the enrichment of existing knowledge by incorporating multimodal information from various sources. In this study, we constructed RDKG-115, a rare disease knowledge graph involving 115 RDs, composed of 35,643 entities, 25 relations, and 5,539,839 refined triplets, based on 372,384 high-quality literature and 4 biomedical datasets: DRKG, Pathway Commons, PharmKG, and PMapp. Subsequently, we developed a trimodal KGE model containing structure, category, and description embeddings using reverse-hyperplane projection. We utilized this model to infer 4199 reliable new inferred triplets from RDKG-115. Finally, we calculated potential drugs and small molecules for each of the 115 RDs, taking multiple sclerosis as a case study. This study provides a paradigm for large-scale screening of drug repurposing and discovery for RDs, which will speed up the drug development process and ultimately benefit patients with RDs. The source code and data are available at https://github.com/ZhuChaoY/RDKG-115.

Expert recommendations on homogenization management of the diagnosis and treatment of rare diseases in children

Rare diseases in children are characterized by low prevalence, complex pathogenesis, variety, and difficulty in the diagnosis and treatment. With the development of medical services, progress has been made in the diagnosis and treatment of rare diseases. However, due to asymmetric allocation of medical resources at different levels, there are still many shortcomings in the establishment and promotion of the homogenized management system of rare disease diagnosis and treatment. In order to further standardize the homogenized management of rare diseases in children, achieve early and accurate diagnosis and treatment, and improve the quality of life of the children, the Rare Disease Diagnosis and Treatment Center of Tianjin Children's Hospital (Tianjin University Children's Hospital) invited relevant experts in the field to develop recommendations for the management model of homogenized diagnosis and treatment of rare diseases in children from the aspects of information construction, hierarchical diagnosis and treatment, personnel training, scientific popularization, and multi-participation. The recommendations provide reference for the regional homogenization of clinical diagnosis and treatment management system for children with rare diseases.

Identification of gastric signet ring cell carcinoma based on endoscopic images using few-shot learning

BackgroundDeep learning (DL) models perform poorly when there are limited gastric signet ring cell carcinoma (SRCC) samples. Few-shot learning (FSL) can address the small sample problem. MethodsEfficientNetV2-S was first pretrained on ImageNet and then pretrained on esophageal endoscopic images (i.e., base classes: normal vs. early cancer vs. advanced cancer) using transfer learning. Second, images of gastric benign ulcers, adenocarcinoma and SRCC, i.e., novel classes (n = 50 per class), were included. Image features were extracted as vectors using the dual pretrained EfficientNetV2-S. Finally, a k-nearest neighbor classifier was used to identify SRCC. The above proposed 3-way 3-shot FSL framework was conducted in three rounds. ResultsDual pretrained FSL performed better than single pretrained FSL, endoscopists and traditional EfficientNetV2-S models. Dual pretrained FSL obtained the highest accuracy (79.4%), sensitivity (68.8%), recall (68.8%), precision (69.3%) and F1-score (0.691), and the senior endoscopist achieved the highest specificity of 93.6% when identifying SRCC. The macro-AUC and F1-score for dual pretraining (0.763 and 0.703, respectively) were higher than those for single pretraining (0.656 and 0.537, respectively), along with endoscopists and traditional EfficientNetV2-S models. The 2-way 3-shot FSL also performed better. ConclusionThe proposed FSL framework showed practical performance in the differentiation of SRCC on endoscopic images, suggesting the potential of FSL in the computer-aided diagnosis for rare diseases.

Epimutation detection in the clinical context: guidelines and a use case from a new Bioconductor package

ABSTRACT Epimutations are rare alterations of the normal DNA methylation pattern at specific loci, which can lead to rare diseases. Methylation microarrays enable genome-wide epimutation detection, but technical limitations prevent their use in clinical settings: methods applied to rare diseases’ data cannot be easily incorporated to standard analyses pipelines, while epimutation methods implemented in R packages (ramr) have not been validated for rare diseases. We have developed epimutacions, a Bioconductor package (https://bioconductor.org/packages/release/bioc/html/epimutacions.html). epimutacions implements two previously reported methods and four new statistical approaches to detect epimutations, along with functions to annotate and visualize epimutations. Additionally, we have developed an user-friendly Shiny app to facilitate epimutations detection (https://github.com/isglobal-brge/epimutacionsShiny) to non-bioinformatician users. We first compared the performance of epimutacions and ramr packages using three public datasets with experimentally validated epimutations. Methods in epimutacions had a high performance at low sample sizes and outperformed methods in ramr. Second, we used two general population children cohorts (INMA and HELIX) to determine the technical and biological factors that affect epimutations detection, providing guidelines on how designing the experiments or preprocessing the data. In these cohorts, most epimutations did not correlate with detectable regional gene expression changes. Finally, we exemplified how epimutacions can be used in a clinical context. We run epimutacions in a cohort of children with autism disorder and identified novel recurrent epimutations in candidate genes for autism. Overall, we present epimutacions a new Bioconductor package for incorporating epimutations detection to rare disease diagnosis and provide guidelines for the design and data analyses.

RDBridge: a knowledge graph of rare diseases based on large-scale text mining.

Despite low prevalence, rare diseases affect 300 million people worldwide. Research on pathogenesis and drug development lags due to limited commercial potential, insufficient epidemiological data, and a dearth of publications. The unique characteristics of rare diseases, including limited annotated data, intricate processes for extracting pertinent entity relationships, and difficulties in standardizing data, represent challenges for text mining. We developed a rare disease data acquisition framework using text mining and knowledge graphs and constructed the most comprehensive rare disease knowledge graph to date, Rare Disease Bridge (RDBridge). RDBridge offers search functions for genes, potential drugs, pathways, literature, and medical imaging data that will support mechanistic research, drug development, diagnosis, and treatment for rare diseases. RDBridge is freely available at http://rdb.lifesynther.com/. Supplementary data are available at Bioinformatics online.

Multi-omics for better and faster rare disease diagnosis.

Multi-omics for better and faster rare disease diagnosis.

Increasing differential diagnosis between lipoma and liposarcoma through radiomics: a narrative review.

Soft tissue sarcomas (STSs) are rare, heterogeneous, and very often asymptomatic diseases. Their diagnosis is fundamental, as is the identification of the degree of malignancy, which may be high, medium, or low. The Italian Medical Oncology Association and European Society of Medical Oncology (ESMO) guidelines recommend magnetic resonance imaging (MRI) because the clinical examination is typically ineffective. The diagnosis of these rare diseases with artificial intelligence (AI) techniques presents reduced datasets and therefore less robust methods. However, the combination of AI techniques with radiomics may be a new angle in diagnosing rare diseases such as STSs. Results obtained are promising within the literature, not only for the performance but also for the explicability of the data. In fact, one can make tumor classification, site localization, and prediction of the risk of developing metastasis. Thanks to the synergy between computer scientists and radiologists, linking numerical features to radiological evidence with excellent performance could be a new step forward for the diagnosis of rare diseases.

Hypertrophic cardiomyopathy in the structure of infiltrative diseases in children

Introduction. In 2006, the American Heart Association identified two main groups of cardiomyopathies (CM) as primary and secondary, referring to the primary CM heart diseases of genetic, acquired or mixed etiology, and to the secondary — pathological involvement of the myocardium as a part of a systemic pathology.
 Aim: to determine the most common phenocopies of hypertrophic CM (HCM) in children, due to the accumulation of pathological substances in the myocardium and present their differences.
 Materials and methods. Instrumental diagnostic methods (echocardiography, electrocardiography, 24-hour Holter ECG monitoring), laboratory tests (N-terminal propeptide of natriuretic hormone, creatine phosphokinase, creatine phosphokinase-MB, lactate dehydrogenase, aspartate aminotransferase, alanine aminotransferase, lactate, ammonia), and molecular genetic tests were used. 
 Results. Nucleotide variants in non-sarcomeric genes causing myocardial hypertrophy were identified in one hundred four (39%) patients: infiltrative diseases with heart damage were diagnosed in 46 cases, syndromes from the RAS-pathy group were diagnosed in 58 cases. Patients with storage diseases included 12 children with Pompe disease, 2 cases with PRKAG2 syndrome, 11 cases had Danon disease, 15 — Corey–Forbes disease, and 6 — Friedreich ataxia. Adverse events were reported in group of patients with Pompe disease (9 deaths), and with Danon’s disease (2 deaths). 
 Conclusion. The phenocopy varieties of HCM in children are represented by a wide variety of genetic variants and often by diseases from the group of glycogen metabolism disorders, fatty acid oxidation disorders, and mitochondrial diseases. Identification of the genetic causes of ventricular myocardial hypertrophy in children is the key to early diagnosis of rare diseases, timely and adequate treatment, as well as predicting the course and outcome of the disease.

FindZebra online search delving into rare disease case reports using natural language processing.

Early diagnosis is crucial for well-being and life quality of the rare disease patient. Access to the most complete knowledge about diseases through intelligent user interfaces can play an important role in supporting the physician reaching the correct diagnosis. Case reports may offer information about heterogeneous phenotypes which often further complicate rare disease diagnosis. The rare disease search engine FindZebra.com is extended to also access case report abstracts extracted from PubMed for several diseases. A search index for each disease is built in Apache Solr adding age, sex and clinical features extracted using text segmentation to enhance the specificity of search. Clinical experts performed retrospective validation of the search engine, utilising real-world Outcomes Survey data on Gaucher and Fabry patients. Medical experts evaluated the search results as being clinically relevant for the Fabry patients and less clinically relevant for the Gaucher patients. The shortcomings for Gaucher patients mainly reflect a mismatch between the current understanding and treatment of the disease and how it is reported in PubMed, notably in the older case reports. In response to this observation, a filter for the publication date was added in the final version of the tool available from deep.findzebra.com/<disease> with <disease> = gaucher, fabry, hae (Hereditary angioedema).

Classification of missense variants in the N-methyl-d-aspartate receptor GRIN gene family as gain- or loss-of-function.

Advances in sequencing technology have generated a large amount of genetic data from patients with neurological conditions. These data have provided diagnosis of many rare diseases, including a number of pathogenic de novo missense variants in GRIN genes encoding N-methyl-d-aspartate receptors (NMDARs). To understand the ramifications for neurons and brain circuits affected by rare patient variants, functional analysis of the variant receptor is necessary in model systems. For NMDARs, this functional analysis needs to assess multiple properties in order to understand how variants could impact receptor function in neurons. One can then use these data to determine whether the overall actions will increase or decrease NMDAR-mediated charge transfer. Here, we describe an analytical and comprehensive framework by which to categorize GRIN variants as either gain-of-function (GoF) or loss-of-function (LoF) and apply this approach to GRIN2B variants identified in patients and the general population. This framework draws on results from six different assays that assess the impact of the variant on NMDAR sensitivity to agonists and endogenous modulators, trafficking to the plasma membrane, response time course and channel open probability. We propose to integrate data from multiple in vitro assays to arrive at a variant classification, and suggest threshold levels that guide confidence. The data supporting GoF and LoF determination are essential to assessing pathogenicity and patient stratification for clinical trials as personalized pharmacological and genetic agents that can enhance or reduce receptor function are advanced. This approach to functional variant classification can generalize to other disorders associated with missense variants.

Exploration of clinical pathway-oriented optimal management diagnosis and treatment model for rare diseases

This study takes Cushing's syndrome, a rare disease, as a model, and adopts the path of "Plan, Do, Check, Action" (PDCA) to explore new methods to optimize the clinical path, can improve the quality and efficiency of diagnosis and treatment of rare diseases. After sorting out the problems existing in the previous diagnosis and treatment mode, our team optimizes the path in various ways and establishes a standard operation procedure (SOP) for the new path. In the evaluation of the optimized mode, 55 patients with Cushing's syndrome were admitted to the Department of Endocrinology, Peking Union Medical College Hospital, including 19 males and 36 females, aged (41.8±14.4) years (6-68 years). The pathway group (28 cases) and the control group (27 cases) were divided according to whether they were included in the new path management at the time of admission, and the effect of path optimization was assessed in terms of time, efficacy, safety and cost. The results showed that compared with the control group, the pathway group had a shorter time of hospitalization in the Department of Endocrinology and critical tests, such as blood cortisol rhythm, low-dose dexamethasone inhibition test, and bilateral inferior petrosal sinus sampling (all P<0.05). There was no significant differences in the decrease of total cortisol after operation, the incidence of postoperative complications, and hospitalization expenses (all P>0.05). The optimized path improves the medical efficiency while ensuring medical quality, safety and no increase in cost. This study proposes PDCA path optimization for complex diseases and establishes SOP process, which provides experience in management optimization for the patient-centered and clinical path-oriented diagnosis and treatment mode of rare diseases.

#4037 DEVELOPMENT OF AN EXPERT MODEL FOR THE DIAGNOSIS OF INHERITED KIDNEY DISEASES

Abstract Background and Aims Inherited kidney diseases (IKDs) account for 10-20% of cases of Chronic Kidney Disease (CKD). Mutations in over 400 genes are known as drivers of more than 150 monogenic kidney disorders. Clinical diagnosis is often challenging as these disorders are characterized by numerous clinical features often shared by many diseases as well as very variable expression. Over the years, rare-disease databases such as OMIM and Orphanet have complied information to facilitate diagnosis of rare diseases. Based on these, The Human Phenotype Ontology (HPO) developed a standardized directory for phenotypes with phenotype-disease associations. New phenotype-driven gene-priorization tools such as Phenomizer have been developed in the recent years using these resources, however structural inaccuracies prevent a proper uptake of these tools. To date, no specific renal tool has been developed. The aim of the study was to develop an expert model for the diagnosis of IKDs based on phenotype semantic similarity distances such over Human Phenotype Annotations. Method Using MONDO ontology which encompasses HPO phenotypes, OMIM and Orphanet, HPO annotations corresponding to 196 IKDs were exported an analyzed. Based on literature, original HPO annotations for each IKD were manually curated (inaccurate terms were eliminated, new known HPOs were added and new annotations were generated in case they were missing). Frequencies were added to each annotation. Specific kidney data such as CKD stage, age at need of kidney replacement therapy and degree of proteinuria were included. Age of onset, prevalence, and specific gender sub analysis in case of diseases with X-linked inheritance was also integrated. All curated phenotype-disease associations were reviewed by European experts. Using MONDO annotations for each IKD as synthetic patients a statistical model with a friendly user web-based interface was developed in Java. Results Analysis of current ontologies by experts in the filed revealed that HPO terms associated to IKDs were inaccurate and non-specific. A new phenotype-driven tool fed with new curated terms and specific kidney data was found to be superior to current tools. Future contributions to ontologies such as HPO, MONDO and open data-sources with curated terms and updated classifications concerning IKDs will help to improving the diagnosis and characterization of IKDs. Conclusion Current phenotype-based gene prioritization tools and rare disease databases have contributed significantly to improving the clinical diagnosis of rare diseases. However, these tools are nonspecific and imprecise when focusing on a specific field such as IKDs. Curation of current annotations by experts in the field will greatly improve the accuracy of these tools and facilitate clinical diagnosis of IKDs.

The future role of facial image analysis in ACMG classification guidelines.

The use of next-generation sequencing (NGS) has dramatically improved the diagnosis of rare diseases. However, the analysis of genomic data has become complex with the increasing detection of variants by exome and genome sequencing. The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) developed a 5-tier classification scheme in 2015 for variant interpretation, that has since been widely adopted. Despite efforts to minimise discrepancies in the application of these criteria, inconsistencies still occur. Further specifications for individual genes were developed by Variant Curation Expert Panels (VCEPs) of the Clinical Genome Resource (ClinGen) consortium, that also take into consideration gene or disease specific features. For instance, in disorders with a highly characerstic facial gestalt a "phenotypic match" (PP4) has higher pathogenic evidence than e.g. in a non-syndromic form of intellectual disability. With computational approaches for quantifying the similarity of dysmorphic features results of such analysis can now be used in a refined Bayesian framework for the ACMG/AMP criteria.

Integrated multi-omics for rapid rare disease diagnosis on a national scale

Critically ill infants and children with rare diseases need equitable access to rapid and accurate diagnosis to direct clinical management. Over 2 years, the Acute Care Genomics program provided whole-genome sequencing to 290 families whose critically ill infants and children were admitted to hospitals throughout Australia with suspected genetic conditions. The average time to result was 2.9 d and diagnostic yield was 47%. We performed additional bioinformatic analyses and transcriptome sequencing in all patients who remained undiagnosed. Long-read sequencing and functional assays, ranging from clinically accredited enzyme analysis to bespoke quantitative proteomics, were deployed in selected cases. This resulted in an additional 19 diagnoses and an overall diagnostic yield of 54%. Diagnostic variants ranged from structural chromosomal abnormalities through to an intronic retrotransposon, disrupting splicing. Critical care management changed in 120 diagnosed patients (77%). This included major impacts, such as informing precision treatments, surgical and transplant decisions and palliation, in 94 patients (60%). Our results provide preliminary evidence of the clinical utility of integrating multi-omic approaches into mainstream diagnostic practice to fully realize the potential of rare disease genomic testing in a timely manner.

DECIPHER: Improving Genetic Diagnosis Through Dynamic Integration of Genomic and Clinical Data.

DECIPHER (Database of Genomic Variation and Phenotype in Humans Using Ensembl Resources) shares candidate diagnostic variants and phenotypic data from patients with genetic disorders to facilitate research and improve the diagnosis, management, and therapy of rare diseases. The platform sits at the boundary between genomic research and the clinical community. DECIPHER aims to ensure that the most up-to-date data are made rapidly available within its interpretation interfaces to improve clinical care. Newly integrated cardiac case-control data that provide evidence of gene-disease associations and inform variant interpretation exemplify this mission. New research resources are presented in a format optimized for use by a broad range of professionals supporting the delivery of genomic medicine. The interfaces within DECIPHER integrate and contextualize variant and phenotypic data, helping to determine a robust clinico-molecular diagnosis for rare-disease patients, which combines both variant classification and clinical fit. DECIPHER supports discovery research, connecting individuals within the rare-disease community to pursue hypothesis-driven research.

SynthEye: Investigating the Impact of Synthetic Data on Artificial Intelligence-assisted Gene Diagnosis of Inherited Retinal Disease.

Rare disease diagnosis is challenging in medical image-based artificial intelligence due to a natural class imbalance in datasets, leading to biased prediction models. Inherited retinal diseases (IRDs) are a research domain that particularly faces this issue. This study investigates the applicability of synthetic data in improving artificial intelligence-enabled diagnosis of IRDs using generative adversarial networks (GANs). Diagnostic study of gene-labeled fundus autofluorescence (FAF) IRD images using deep learning. Moorfields Eye Hospital (MEH) dataset of 15692 FAF images obtained from 1800 patients with confirmed genetic diagnosis of 1 of 36 IRD genes. A StyleGAN2 model is trained on the IRD dataset to generate 512× 512 resolution images. Convolutional neural networks are trained for classification using different synthetically augmented datasets, including real IRD images plus 1800 and 3600 synthetic images, and a fully rebalanced dataset. We also perform an experiment with only synthetic data. All models are compared against a baseline convolutional neural network trained only on real data. We evaluated synthetic data quality using a Visual Turing Test conducted with 4 ophthalmologists from MEH. Synthetic and real images were compared using feature space visualization, similarity analysis to detect memorized images, and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score for no-reference-based quality evaluation. Convolutional neural network diagnostic performance was determined on a held-out test set using the area under the receiver operating characteristic curve (AUROC) and Cohen's Kappa (κ). An average true recognition rate of 63% and fake recognition rate of 47% was obtained from the Visual Turing Test. Thus, a considerable proportion of the synthetic images were classified as real by clinical experts. Similarity analysis showed that the synthetic images were not copies of the real images, indicating thatcopied real images, meaning the GAN was able to generalize. However, BRISQUE score analysis indicated that synthetic images were of significantly lower quality overall than real images (P < 0.05). Comparing the rebalanced model (RB) with the baseline (R), no significant change in the average AUROC and κ was found (R-AUROC=0.86[0.85-88], RB-AUROC=0.88[0.86-0.89], R-k=0.51[0.49-0.53], and RB-k=0.52[0.50-0.54]). The synthetic data trained model (S) achieved similar performance as the baseline (S-AUROC=0.86[0.85-87], S-k=0.48[0.46-0.50]). Synthetic generation of realistic IRD FAF images is feasible. Synthetic data augmentation does not deliver improvements in classification performance. However, synthetic data alone deliver a similar performance as real data, and hence may be useful as a proxy to real data. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references.

P52 Barriers to Reducing Time to Diagnosis and Treatment in Rare Disease

P52 Barriers to Reducing Time to Diagnosis and Treatment in Rare Disease

Transitioning single-cell genomics into the clinic.

The use of genomics is firmly established in clinical practice, resulting in innovations across a wide range of disciplines such as genetic screening, rare disease diagnosis and molecularly guided therapy choice. This new field of genomic medicine has led to improvements in patient outcomes. However, most clinical applications of genomics rely on information generated from bulk approaches, which do not directly capture the genomic variation that underlies cellular heterogeneity. With the advent of single-cell technologies, research is rapidly uncovering how genomic data at cellular resolution can be used to understand disease pathology and mechanisms. Both DNA-based and RNA-based single-cell technologies have the potential to improve existing clinical applications and open new application spaces for genomics in clinical practice, with oncology, immunology and haematology poised for initial adoption. However, challenges in translating cellular genomics from research to a clinical setting must first be overcome.

A Comprehensive Review on the Prospects and Impediments for the Innovation of Orphan Drug Development in India, Australia and the USA

This article highlights the comprehensive review of the prospects and impediments to the innovation of orphan drug development. Orphan medications are used to treat rare diseases. Orphan medication products have been one of the fastest-increasing sectors of drug research. Orphan diseases represent an issue for the world's health sector and a chance to concentrate on innovation. Many nations around the world continue to struggle with rare diseases. Several parties must come up with fresh and innovative solutions to assist the millions of people worldwide who suffer from uncommon diseases. To strategically develop their treatments, pharmaceutical industries concentrate on rare diseases and hinge on novel alliances. To accelerate the research and approval of new drugs, regulatory agencies like US FDA and TGA have developed a wide variety of strategies like financial aid. The process of developing an orphan medicine is the same as for common diseases but requires substantial resources and infrastructure. Several nations including the USA and Australia have implemented orphan drug legislation, which successfully advocated research and development for the creation of novel medication used in the treatment of rare disorders. The purpose of orphan drug regulations was to encourage research and development by providing incentives for the creation of drugs that have the potential to cure millions of individuals suffering from rare illnesses. Prospects related to innovation of orphan drug developments like fee waivers, reimbursements, tax credits and market exclusivity and the majority of the impediments are related to orphan drug pricing patterns, the lack of treatment and diagnosis for rare diseases, regulatory constraints, and the approval process. The individual patient is the major focus of the orphan medication market. For example, the majority of the impediments are related to orphan drug pricing patterns, the lack of treatment and diagnosis for rare diseases, regulatory constraints, and the approval process.

Resources and tools for rare disease variant interpretation.

Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.