Diagnosis Of Rare Diseases Research Articles

Ontology-based expansion of virtual gene panels to improve diagnostic efficiency for rare genetic diseases

BackgroundVirtual Gene Panels (VGP) comprising disease-associated causal genes are utilized in the diagnosis of rare genetic diseases to evaluate candidate genes identified by whole-genome and whole-exome sequencing. VGPs generated by the PanelApp software were utilized in a UK 100,000 Genome Project pilot study to filter candidate genes, thus enhancing diagnostic efficiency for rare diseases. However, PanelApp also filtered out disease-causing genes in nearly 50% of the cases.MethodsHere, we propose various methods for optimized approach to design VGPs that significantly improve the diagnostic efficiency by leveraging the hierarchical structure of the Mondo disease ontology, without excluding disease-causing genes. We also performed computational experiments on an evaluation dataset comprising 74 patients to determine the optimal VGP design method.ResultsOur results demonstrate that the proposed method can significantly enhance rare disease diagnosis efficiency by automatically identifying candidate genes. The proposed method successfully designed VGPs that improve diagnosis efficiency without excluding disease-causing genes.ConclusionWe have developed novel methods for VGP design that leverage the hierarchical structure of the Mondo disease ontology to improve rare genetic disease diagnosis efficiency. This approach identifies candidate genes without excluding disease-causing genes, and thereby improves diagnostic efficiency.

The implementation of genome sequencing in rare genetic diseases diagnosis: a pilot study from the Hong Kong genome project

The implementation of genome sequencing in rare genetic diseases diagnosis: a pilot study from the Hong Kong genome project

UniVar: A variant interpretation platform enhancing rare disease diagnosis through robust filtering and unified analysis of SNV, INDEL, CNV and SV.

Interpreting the pathogenicity of genetic variants associated with rare diseases is a laborious and time-consuming endeavour. To streamline the diagnostic process and lighten the burden of variant interpretation, it is crucial to automate variant annotation and prioritization. Unfortunately, currently available variant interpretation tools lack a unified and comprehensive workflow that can collectively assess the clinical significance of these types of variants together: small nucleotide variants (SNVs), small insertions/deletions (INDELs), copy number variants (CNVs) and structural variants (SVs). The Unified Variant Interpretation Platform (UniVar) is a free web server tool that offers an automated and comprehensive workflow on annotation, filtering and prioritization for SNV, INDEL, CNV and SV collectively to identify disease-causing variants for rare diseases in one interface, ensuring accessibility for users even without programming expertise. To filter common CNVs/SVs, a diverse SV catalogue has been generated, that enables robust filtering of common SVs based on population allele frequency. Through benchmarking our SV catalogue, we showed that it is more complete and accurate than the state-of-the-art SV catalogues. Furthermore, to cope with those patients without detailed clinical information, we have developed a novel computational method that enables variant prioritization from gene panels. Our analysis shows that our approach could prioritize pathogenic variants as effective as using HPO terms assigned by clinicians, which adds value for cases without specific clinically assigned HPO terms. Lastly, through a practical case study of disease-causing compound heterozygous variants across SNV and SV, we demonstrated the uniqueness and effectiveness in variant interpretation of UniVar, edging over any existing interpretation tools. UniVar is a unified and versatile platform that empowers researchers and clinicians to identify and interpret disease-causing variants in rare diseases efficiently through a single holistic interface and without a prerequisite for HPO terms. It is freely available without login and installation at https://univar.live/.

RNA4RD: revolutionising rare disease diagnosis with RNA testing

RNA4RD: revolutionising rare disease diagnosis with RNA testing

Study of the impact of ClinGen Revisions on ACMG/AMP variant semi-automatic classification for Rare Diseases diagnosis

With the rapid development of massive sequencing technologies, the analysis of genetic variants for clinical diagnosis has exponentially escalated, particularly in the context of Rare Diseases (RDs). Diagnosing them involves identifying the genetic variants responsible for the underlying pathology development.In 2015, the American College of Medical Genetics (ACMG) established a set of recommendations to assess the evidence associated with each variant, aiming to achieve a standardized five tier classification. Over the past 5 years, ClinGen, the NIH-funded Clinical Genome Resource, has reviewed these criteria in order to make variant classification a more reproducible and rigorous process.This paper examines the impact of ClinGen-Rev modifications on variant classification, comparing them with the ACMG-2015 original recommendations. After analyzing sets of genetic variants, extracted from VCFs samples, using both criteria, we observed a change in 8.0 % of the clinical verdicts for these variants. ClinGen-Rev modifications correctly categorized 89.2 % of the curated variants, representing a significant improvement compared to the 65.6 % achieved by ACMG-2015. We also analyzed the modifications impact in a real like clinical setting, showing a significant overall reduction of VUS variants and thus potential reduction in analysis time. Finally, we discuss the underlying reasons for the most relevant changes in terms of specific labels and present their implications on the prioritization and selection process of variants, identifying some recommendations of key significant importance.

Machine learning in genomic diagnostics for precision medicine

The integration of machine learning (ML) in genomic diagnostics has revolutionized precision medicine, fundamentally transforming how genetic variations are identified, interpreted, and utilized in clinical settings. The article examines the current state, implementation challenges, and future directions of ML applications in genomic medicine. With the global market for AI in genomics projected to reach billions by future years, growing at a significant CAGR, the field is experiencing rapid advancement. Modern ML algorithms demonstrate unprecedented accuracy, achieving high accuracy in pathogenic variant identification, while processing capabilities have expanded to handle large volumes of genomic data annually. The implementation of distributed computing frameworks has enabled substantial processing rates of genomic data per hour, while maintaining excellent accuracy in variant identification. The article discusses the evolution of data processing pipelines, challenges in data quality and standardization, ethical considerations including privacy protection, and emerging technologies in multi-modal learning systems. The article reveals that ML-based approaches have reduced diagnostic times from weeks to hours, improved rare disease diagnosis rates significantly, and achieved impressive accuracy in identifying driver mutations across multiple cancer types. These advancements suggest a promising future for ML-driven precision medicine, despite existing challenges in data diversity and standardization.

A genomic strategy for precision medicine in rare diseases: integrating customized algorithms into clinical practice

BackgroundDespite the use of Next-Generation Sequencing (NGS) as the gold standard for the diagnosis of rare diseases, its clinical implementation has been challenging, limiting the cost-effectiveness of NGS and the understanding, control and safety essential for decision-making in clinical applications. Here, we describe a personalized NGS-based strategy integrating precision medicine into a public healthcare system and its implementation in the routine diagnosis process during a five-year pilot program.MethodsOur approach involved customized probe designs, the generation of virtual panels and the development of a personalized medicine module (PMM) for variant prioritization. This strategy was applied to 6500 individuals including 6267 index patients and 233 NGS-based carrier screenings.ResultsCausative variants were identified in 2061 index patients (average 32.9%, ranging from 12 to 62% by condition). Also, 131 autosomal-recessive cases could be partially genetically diagnosed. These results led to over 5000 additional studies including carrier, prenatal and preimplantational tests or pharmacological and gene therapy treatments.ConclusionThis strategy has shown promising improvements in the diagnostic rate, facilitating timely diagnosis and gradually expanding our services portfolio for rare diseases. The steps taken towards the integration of clinical and genomic data are opening new possibilities for conducting both retrospective and prospective healthcare studies. Overall, this study represents a major milestone in the ongoing efforts to improve our understanding and clinical management of rare diseases, a crucial area of medical research and care.

Genomic reanalysis of a pan-European rare-disease resource yields new diagnoses.

Genetic diagnosis of rare diseases requires accurate identification and interpretation of genomic variants. Clinical and molecular scientists from 37 expert centers across Europe created the Solve-Rare Diseases Consortium (Solve-RD) resource, encompassing clinical, pedigree and genomic rare-disease data (94.5% exomes, 5.5% genomes), and performed systematic reanalysis for 6,447 individuals (3,592 male, 2,855 female) with previously undiagnosed rare diseases from 6,004 families. We established a collaborative, two-level expert review infrastructure that allowed a genetic diagnosis in 506 (8.4%) families. Of 552 disease-causing variants identified, 464 (84.1%) were single-nucleotide variants or short insertions/deletions. These variants were either located in recently published novel disease genes (n = 67), recently reclassified in ClinVar (n = 187) or reclassified by consensus expert decision within Solve-RD (n = 210). Bespoke bioinformatics analyses identified the remaining 15.9% of causative variants (n = 88). Ad hoc expert review, parallel to the systematic reanalysis, diagnosed 249 (4.1%) additional families for an overall diagnostic yield of 12.6%. The infrastructure and collaborative networks set up by Solve-RD can serve as a blueprint for future further scalable international efforts. The resource is open to the global rare-disease community, allowing phenotype, variant and gene queries, as well as genome-wide discoveries.

Management of rare and undiagnosed diseases: insights from researchers and healthcare professionals in Türkiye.

Diagnosis, treatment and management of rare diseases (RD) pose unique challenges due to their complex nature, significantly impacting the daily experiences of researchers and healthcare professionals working in this field. Despite increasing awareness and progress in the field of RD worldwide in recent years, a significant gap remains in our understanding of the specific barriers that these professionals face in their work with RD. This study provides a detailed survey analysis that sheds light on the challenges that researchers and healthcare professionals face in diagnosing, treating, managing and conducting research on RD. We developed a national online survey with three RD stakeholder groups (Researchers, Healthcare professionals and researcher-healthcare professionals) to identify the main challenges and needs in Türkiye for the diagnosis, treatment and follow-up processes of rare and undiagnosed diseases. The survey was completed by 363 participants, revealing that participants face key challenges such as the need to refer patients to specialized centers, financial burdens, limited access to necessary tests, inadequate support for rare disease research and a lack of interdisciplinary collaboration. Participants also noted that RD are inherently difficult to conduct research on with small cohorts. Survey results also suggest a number of policy improvements to accelerate research on RD: increased funding, establishment of robust surveillance systems, and development of comprehensive national action plans and guidelines on RD. To the best of our knowledge, this is the first study to be conducted in Türkiye. This study contributes to the understanding of the needs of professionals in rare disease research and highlights the urgent need for system improvements to support them.

A DNA language model based on multispecies alignment predicts the effects of genome-wide variants.

Protein language models have demonstrated remarkable performance in predicting the effects of missense variants but DNA language models have not yet shown a competitive edge for complex genomes such as that of humans. This limitation is particularly evident when dealing with the vast complexity of noncoding regions that comprise approximately 98% of the human genome. To tackle this challenge, we introduce GPN-MSA (genomic pretrained network with multiple-sequence alignment), a framework that leverages whole-genome alignments across multiple species while taking only a few hours to train. Across several benchmarks on clinical databases (ClinVar, COSMIC and OMIM), experimental functional assays (deep mutational scanning and DepMap) and population genomic data (gnomAD), our model for the human genome achieves outstanding performance on deleteriousness prediction for both coding and noncoding variants. We provide precomputed scores for all ~9 billion possible single-nucleotide variants in the human genome. We anticipate that our advances in genome-wide variant effect prediction will enable more accurate rare disease diagnosis and improve rare variant burden testing.

Expanding Upon Genomics in Rare Diseases: Epigenomic Insights.

DNA methylation is an essential epigenetic modification that plays a crucial role in regulating gene expression and maintaining genomic stability. With the advancement in sequencing technology, methylation studies have provided valuable insights into the diagnosis of rare diseases through the various identification of episignatures, epivariation, epioutliers, and allele-specific methylation. However, current methylation studies are not without limitations. This mini-review explores the current understanding of DNA methylation in rare diseases, highlighting the key mechanisms and diagnostic potential, and emphasizing the need for advanced methodologies and integrative approaches to enhance the understanding of disease progression and design more personable treatment for patients, given the nature of rare diseases.

Use of Estonian Biobank data and participant recall to improve Wilson's disease management.

Population-based biobanks enable genomic screening to support initiatives that prevent disease onset or slow its progression and to estimate the prevalence of genetic diseases in the population. Wilson's disease (WD) is a rare genetic copper-accumulation disorder for which timely intervention is crucial, as treatment is readily available. We studied WD in the Estonian Biobank population to advance patient screening, swift diagnosis, and subsequent treatment. Combined analysis of genotype and phenotype data from electronic health records (EHRs) consolidated at the Estonian biobank led to the identification of 17 individuals at high risk of developing WD, who were recalled for further examination and deep phenotyping. All recall study participants, regardless of phenotype, age, and prior WD diagnosis, had low serum ceruloplasmin and copper levels, and 87% also exhibited signs of early to late neurodegeneration. The p.His1069Gln variant in ATP7B, a prevalent pathogenic mutation, showed a striking four- to five-fold enrichment in Estonians compared with other populations. Based on our analysis of genetic and nationwide health registry data, we estimate that WD remains underdiagnosed and undertreated in Estonia. Our study demonstrates that personalized medicine, implemented with the collaboration of medical professionals, has the potential to reduce the healthcare burden by facilitating the accurate diagnosis of rare genetic diseases. To our knowledge, this report is the first to describe a large-scale national biobank-based study of WD.

Privacy-by-Design with Federated Learning will drive future Rare Disease Research

Up to 6% of the global population is estimated to be affected by one of about 10,000 distinct rare diseases (RDs). RDs are, to this day, often not understood, and thus, patients are heavily underserved. Most RD studies are chronically underfunded, and research faces inherent difficulties in analyzing scarce data. Furthermore, the creation and analysis of representative datasets are often constrained by stringent data protection regulations, such as the EU General Data Protection Regulation. This review examines the potential of federated learning (FL) as a privacy-by-design approach to training machine learning on distributed datasets while ensuring data privacy by maintaining the local patient data and only sharing model parameters, which is particularly beneficial in the context of sensitive data that cannot be collected in a centralized manner. FL enhances model accuracy by leveraging diverse datasets without compromising data privacy. This is particularly relevant in rare diseases, where heterogeneity and small sample sizes impede the development of robust models. FL further has the potential to enable the discovery of novel biomarkers, enhance patient stratification, and facilitate the development of personalized treatment plans. This review illustrates how FL can facilitate large-scale, cross-institutional collaboration, thereby enabling the development of more accurate and generalizable models for improved diagnosis and treatment of rare diseases. However, challenges such as non-independently distributed data and significant computational and bandwidth requirements still need to be addressed. Future research must focus on applying FL technology for rare disease datasets while exploring standardized protocols for cross-border collaborations that can ultimately pave the way for a new era of privacy-preserving and distributed data-driven rare disease research.

Inter-relational dynamics of factors affecting the emergence of orphan drugs.

Orphan drugs are medications that are produced for the treatment of rare diseases. As there is less number of patients, the drug manufacturing companies are not keen in producing these drugs. Due to high costs of research and development and low profitability, companies do not want to invest in manufacturing of orphan drugs. Several laws have been passed by Governments of different nations to encourage the development of orphan drugs and make it available to patients. This study explores the interrelation dynamics of factors that has resulted in the greater availability of orphan drugs in recent times. Ten factors: internet technology, legislation, online patient support groups, government subsidiary, biotechnological advancements, corporate social responsibility, awareness and diagnosis of rare diseases and exclusive budgeting by pharmaceutical industries for orphan drugs related research and development and production were taken for the study. With a sample size of 38 experts, the technique of decision-making trial and evaluation laboratory (DEMATEL) was used for the study. It was found that information technology, legislation, support groups, and budget were the causes and the factors awareness, diagnosis, medicine availability, subsidiary, CSR and biotechnology emerged to be the effect.

HSD22 Perceptions of Physicians on the Diagnosis of Rare Diseases in Europe: Results of a Cross-Sectional Survey

HSD22 Perceptions of Physicians on the Diagnosis of Rare Diseases in Europe: Results of a Cross-Sectional Survey

HSD136 Assessing Gaps in the Timely Referral of Patients for a Rare Disease Diagnosis: A Cross-Sectional Survey of Physicians in Europe

HSD136 Assessing Gaps in the Timely Referral of Patients for a Rare Disease Diagnosis: A Cross-Sectional Survey of Physicians in Europe

Zero-Shot Learning Algorithms for Object Recognition in Medical and Navigation Applications

One category-invariant methodology for object identification is zero-shot learning (ZSL), which uses semantic embeddings to categorize unseen categories. The ZSL method is indispensable in fields where data is scarce, including medical diagnosis and navigation. This paper proposes an Improved ZSL (I-ZSL) framework to increase the object recognition accuracy and generalization for the medical and navigation applications. The proposed framework is a hybrid architecture that uses Variational Autoencoders (VAEs) for robust feature generation and Transformer-based embeddings for semantic alignment. A domain-adaptive classifier, trained through contrastive learning, bridges the gap between the seen and unseen classes. The classifier has identified the framework with minimal training data in medical diagnostics for rare disease diagnosis. The proposed I-ZSL framework achieved a 20% improvement in F1-score over state-of-the-art models. In navigation, it demonstrated 25% better performance in novel landmark recognition under dynamic environmental conditions. These results show the framework's efficiency in addressing domain-specific challenges. This work presents ZSL with great potential to further object recognition in applications with significant impacts.

Unravelling the Diagnostic Dilemma of Juvenile Scleroderma: A Case Report

Juvenile scleroderma (JS) is a rare and often overlooked connective tissue disease in Paediatric populations. Its diagnosis is particularly challenging due to its multi-systemic involvement, and in developing countries like ours, the lack of diagnostic tools further complicates timely identification. We present the case of an 11-year-old male who presented to our children’s emergency room with a 2-year history of progressive skin thickening, flexion contracture and immobility of the right thumb. There was generalized body pain and swelling of the right upper limb. Additionally, the patient experienced recurrent cough, night sweats, easy fatigability, and occasional palpitations, though without significant weight loss. After an initial misdiagnosis and several ineffective treatments, the suspected diagnosis of JS led to a trial treatment with methylprednisolone and methotrexate, to which the patient responded favourably. This case underscores the importance of a high index of suspicion for the prompt diagnosis and management of rare childhood rheumatic diseases, as delay in diagnosis can exacerbate morbidity and increase mortality.

Phenotype driven molecular genetic test recommendation for diagnosing pediatric rare disorders

Patients with rare diseases often experience prolonged diagnostic delays. Ordering appropriate genetic tests is crucial yet challenging, especially for general pediatricians without genetic expertise. Recent American College of Medical Genetics (ACMG) guidelines embrace early use of exome sequencing (ES) or genome sequencing (GS) for conditions like congenital anomalies or developmental delays while still recommend gene panels for patients exhibiting strong manifestations of a specific disease. Recognizing the difficulty in navigating these options, we developed a machine learning model trained on 1005 patient records from Columbia University Irving Medical Center to recommend appropriate genetic tests based on the phenotype information. The model achieved a remarkable performance with an AUROC of 0.823 and AUPRC of 0.918, aligning closely with decisions made by genetic specialists, and demonstrated strong generalizability (AUROC:0.77, AUPRC: 0.816) in an external cohort, indicating its potential value for general pediatricians to expedite rare disease diagnosis by enhancing genetic test ordering.

Therapeutic implications of narrow or positive surgical margins in head and neck tumors

IntroductionThe treatment of squamous cell carcinomas of the head and neck region involves a multidisciplinary approach that combines surgery, radiotherapy and chemotherapy.AimThe aim of this article is to discuss the therapeutic implications of narrow or positive surgical margins in head and neck cancers.Material and methodsThe article was written based on the analysis of the literature on the subject.Results and discussionSurgical resection is an important part of a approach to treatment and the adequacy of resection during surgery is determined by the margin status. A margin greater than 5 mm is considered free, less than 5 mm - narrow, and less than one mm is considered positive. For proper planning of radiotherapy, i.e. adequate selection of areas and doses, the following are necessary: imaging, endoscopy, pathology report. It should also be remembered that the lack of important information from the treatment and the fear of making a geographical error and/or not matching the dose to the actual stage of the disease and the status of the margins; affects the decision of the radiotherapist who will escalate the dose, which may lead to long-term tissue damage with loss of their function and significantly affect the quality of life of these patients.ConclusionsBefore initiating treatment, each patient should undergo analysis in multidisciplinary consultations to tailor the optimal therapeutic decision to the stage of the disease and any coexisting conditions.Patients with advanced disease and/or challenging localization and/or rare disease diagnoses should be treated in highly specialized centers where close collaboration between facilities is essential.