The Undiagnosed Diseases Network International: Five years and more!

VarPPUD: Variant post prioritization developed for undiagnosed genetic disorders

Rare and ultra-rare genetic conditions are estimated to impact nearly 1 in 17 people worldwide, yet accurately pinpointing the diagnostic variants underlying each of these conditions remains a formidable challenge. Because comprehensive, in vivo functional assessment of all possible genetic variants is infeasible, clinicians instead consider in silico variant pathogenicity predictions to distinguish plausibly disease-causing from benign variants across the genome. However, in the most difficult undiagnosed cases, such as those accepted to the Undiagnosed Diseases Network (UDN), existing pathogenicity predictions cannot reliably discern true etiological variant(s) from other deleterious candidate variants that were prioritized through N-of-1 efforts. Pinpointing the disease-causing variant from a pool of plausible candidates remains a largely manual effort requiring extensive clinical workups, functional and experimental assays, and eventual identification of genotype- and phenotype-matched individuals. Here, we introduce VarPPUD, a tool trained on prioritized variants from UDN cases, that leverages gene-, amino acid-, and nucleotide-level features to discern pathogenic variants from other deleterious variants that are unlikely to be confirmed as disease relevant. VarPPUD achieves a cross-validated accuracy of 79.3% and precision of 77.5% on a held-out subset of uniquely challenging UDN cases, respectively representing an average 18.6% and 23.4% improvement over nine traditional pathogenicity prediction approaches on this task. We validate VarPPUD's ability to discriminate likely from unlikely pathogenic variants on synthetic, GAN-generated candidate variants as well. Finally, we show how VarPPUD can be probed to evaluate each input feature's importance and contribution toward prediction-an essential step toward understanding the distinct characteristics of newly-uncovered disease-causing variants.

Significance Statement

Patients with chronic, undiagnosed and underdiagnosed genetic conditions often endure expensive and excruciating years-long diagnostic odysseys without clear results. In many instances, clinical genome sequencing of patients and their family members fails to reveal known disease-causing variants, although compelling variants of uncertain significance are frequently encountered. Existing computational tools struggle to reliably differentiate truly disease-causing variants from other plausible candidate variants within these prioritized sets. Consequently, the confirmation of disease-causing variants often necessitates extensive experimental follow-up, including studies in model organisms and identification of other similarly presenting genotype-matched individuals, a process that can extend for several years. Here, we present VarPPUD, a tool trained specifically to distinguish likely from unlikely to be confirmed pathogenic variants that were prioritized across cases in the Undiagnosed Diseases Network. By evaluating the importance and impact of different input feature values on prediction, we gain deeper insights into the distinctive attributes of difficult-to-identify diagnostic variants. For patients who remain undiagnosed following comprehensive whole genome sequencing, our new method VarPPUD may reveal pathogenic variants amid a pool of candidate variants, thereby advancing diagnostic efforts where progress has otherwise stalled.

International Undiagnosed Diseases Programs (UDPs): components and outcomes

Over the last 15 years, Undiagnosed Diseases Programs have emerged to address the significant number of individuals with suspected but undiagnosed rare genetic diseases, integrating research and clinical care to optimize diagnostic outcomes. This narrative review summarizes the published literature surrounding Undiagnosed Diseases Programs worldwide, including thirteen studies that evaluate outcomes and two commentary papers. Commonalities in the diagnostic and research process of Undiagnosed Diseases Programs are explored through an appraisal of available literature. This exploration allowed for an assessment of the strengths and limitations of each of the six common steps, namely enrollment, comprehensive clinical phenotyping, research diagnostics, data sharing and matchmaking, results, and follow-up. Current literature highlights the potential utility of Undiagnosed Diseases Programs in research diagnostics. Since participants have often had extensive previous genetic studies, research pipelines allow for diagnostic approaches beyond exome or whole genome sequencing, through reanalysis using research-grade bioinformatics tools and multi-omics technologies. The overall diagnostic yield is presented by study, since different selection criteria at enrollment and reporting processes make comparisons challenging and not particularly informative. Nonetheless, diagnostic yield in an undiagnosed cohort reflects the potential of an Undiagnosed Diseases Program. Further comparisons and exploration of the outcomes of Undiagnosed Diseases Programs worldwide will allow for the development and improvement of the diagnostic and research process and in turn improve the value and utility of an Undiagnosed Diseases Program.

Precision medicine in rare diseases: What is next?

Molecular diagnostics is a cornerstone of modern precision medicine, broadly understood as tailoring an individual's treatment, follow-up, and care based on molecular data. In rare diseases (RDs), molecular diagnoses reveal valuable information about the cause of symptoms, disease progression, familial risk, and in certain cases, unlock access to targeted therapies. Due to decreasing DNA sequencing costs, genome sequencing (GS) is emerging as the primary method for precision diagnostics in RDs. Several ongoing European initiatives for precision medicine have chosen GS as their method of choice. Recent research supports the role for GS as first-line genetic investigation in individuals with suspected RD, due to its improved diagnostic yield compared to other methods. Moreover, GS can detect a broad range of genetic aberrations including those in noncoding regions, producing comprehensive data that can be periodically reanalyzed for years to come when further evidence emerges. Indeed, targeted drug development and repurposing of medicines can be accelerated as more individuals with RDs receive a molecular diagnosis. Multidisciplinary teams in which clinical specialists collaborate with geneticists, genomics education of professionals and the public, and dialogue with patient advocacy groups are essential elements for the integration of precision medicine into clinical practice worldwide. It is also paramount that large research projects share genetic data and leverage novel technologies to fully diagnose individuals with RDs. In conclusion, GS increases diagnostic yields and is a crucial step toward precision medicine for RDs. Its clinical implementation will enable better patient management, unlock targeted therapies, and guide the development of innovative treatments.

Living with a rare disease - experiences and needs in pediatric patients and their parents

BACKGROUND

A rare disease (RD) diagnosis and therapy can affect the family's quality of life and mental health. A lack of information and missing care options lead to helplessness and psychological stress within families. This work aims to identify patients' and parents' experiences in daily life and with the health care system as well as their needs and current pathways to psychosocial care to develop implementation strategies adapted to the families' needs.

METHODS

The present analysis is part of the national multicenter study "Children Affected by Rare Disease and Their Families-Network (CARE-FAM-NET)." We conducted semi-structured telephone interviews with children, adolescents, and young adults with RD (aged 12 to 21 years) and parents of children with RD (aged 0 to 17 years). We analyzed the transcribed and anonymized interviews using the method of focused interview analyses to identify previous experiences with medical and psychosocial care and possible needs for improvement and support.

RESULTS

Seventy-four parents of children with RD and 15 children, adolescents, and young adults with RD participated. Five main themes emerged. Daily life with an RD: RD affects the everyday and social life of the respondents, negatively impacting mental well-being. Experiences with the health care system: The long diagnostic path is stressful for families. Professionals' lack of information/education leads to inadequate care for those affected. Psychosocial support: Families do not know about psychosocial care services. In some cases, the families take advantage of psychosocial support services (such as support groups or advocacy groups), which are predominantly very helpful. Difficulties and barriers: Time, socio-legal and organizational problems burden families and lead to advantages in using psychosocial services. Improvements for patient-oriented support: Those affected wished for timely, preventive support (especially in administrative and socio-legal matters) and education regarding psychosocial care services.

CONCLUSION

RD represent a great challenge for all family members - patients, parents, and siblings. The patients' and parents' previous experiences in daily life, medical and psychosocial care show a need for target-group specific support, including training of health care professionals and low-threshold access care services and practical help for all family members.

Undiagnosed diseases: Needs and opportunities in 20 countries participating in the Undiagnosed Diseases Network International

Introduction

Rare diseases (RD) are a health priority worldwide, overall affecting hundreds of millions of people globally. Early and accurate diagnosis is essential to support clinical care but remains challenging in many countries, especially the low- and medium-income ones. Hence, undiagnosed RD (URD) account for a significant portion of the overall RD burden.

Methods

In October 2020, the Developing Nations Working Group of the Undiagnosed Diseases Network International (DNWG-UDNI) launched a survey among its members, belonging to 20 countries across all continents, to map unmet needs and opportunities for patients with URD. The survey was based on questions with open answers and included eight different domains. Conflicting interpretations were resolved in contact with the partners involved.

Results

All members responded to the survey. The results indicated that the scientific and medical centers make substantial efforts to respond to the unmet needs of patients. In most countries, there is a high awareness of RD issues. Scarcity of resources was highlighted as a major problem, leading to reduced availability of diagnostic expertise and research. Serious equity in accessibility to services were highlighted both within and between participating countries. Regulatory problems, including securing informed consent, difficulties in sending DNA to foreign laboratories, protection of intellectual property, and conflicts of interest on the part of service providers, remain issues of concern. Finally, most respondents stressed the need to strengthen international cooperation in terms of data sharing, clinical research, and diagnostic expertise for URD patients in low and medium income countries.

Discussion

The survey highlighted that many countries experienced a discrepancy between the growing expertise and scientific value, the level of awareness and commitment on the part of relevant parties, and funding bodies. Country-tailored public health actions, including general syllabus of medical schools and of the education of other health professionals, are needed to reduce such gaps.

Diagnosis and discovery: Insights from the NIH Undiagnosed Diseases Program

Living with an undiagnosed medical condition places a tremendous burden on patients, their families, and their healthcare providers. The Undiagnosed Diseases Program (UDP) was established at the National Institutes of Health (NIH) in 2008 with the primary goals of providing a diagnosis for patients with mysterious conditions and advancing medical knowledge about rare and common diseases. The program reviews applications from referring clinicians for cases that are considered undiagnosed despite a thorough evaluation. Those that are accepted receive clinical evaluations involving deep phenotyping and genetic testing that includes exome and genomic sequencing. Selected candidate gene variants are evaluated by collaborators using functional assays. Since its inception, the UDP has received more than 4500 applications and has completed evaluations on nearly 1300 individuals. Here we present six cases that exemplify the discovery of novel disease mechanisms, the importance of deep phenotyping for rare diseases, and how genetic diagnoses have led to appropriate treatment. The creation of the Undiagnosed Diseases Network (UDN) in 2014 has substantially increased the number of patients evaluated and allowed for greater opportunities for data sharing. Expansion to the Undiagnosed Diseases Network International (UDNI) has the possibility to extend this reach even farther. Together, networks of undiagnosed diseases programs are powerful tools to advance our knowledge of pathophysiology, accelerate accurate diagnoses, and improve patient care for patients with rare conditions.

Supporting undiagnosed participants when clinical genomics studies end

Many large research initiatives have cumulatively enrolled thousands of patients with a range of complex medical issues but no clear genetic etiology. However, it is unclear how researchers, institutions, and funders should manage the data and relationships with those participants who remain undiagnosed when these studies end. In this comment, we outline the current literature relevant to post-study obligations in clinical genomics research and discuss the application of current guidelines to research with undiagnosed participants.

ModelMatcher: A scientist-centric online platform to facilitate collaborations between stakeholders of rare and undiagnosed disease research

Next-generation sequencing is a prevalent diagnostic tool for undiagnosed diseases and has played a significant role in rare disease gene discovery. Although this technology resolves some cases, others are given a list of possibly damaging genetic variants necessitating functional studies. Productive collaborations between scientists, clinicians, and patients (affected individuals) can help resolve such medical mysteries and provide insights into in vivo function of human genes. Furthermore, facilitating interactions between scientists and research funders, including nonprofit organizations or commercial entities, can dramatically reduce the time to translate discoveries from bench to bedside. Several systems designed to connect clinicians and researchers with a shared gene of interest have been successful. However, these platforms exclude some stakeholders based on their role or geography. Here we describe ModelMatcher, a global online matchmaking tool designed to facilitate cross-disciplinary collaborations, especially between scientists and other stakeholders of rare and undiagnosed disease research. ModelMatcher is integrated into the Rare Diseases Models and Mechanisms Network and Matchmaker Exchange, allowing users to identify potential collaborators in other registries. This living database decreases the time from when a scientist or clinician is making discoveries regarding their genes of interest, to when they identify collaborators and sponsors to facilitate translational and therapeutic research.

FAIR Genomes metadata schema promoting Next Generation Sequencing data reuse in Dutch healthcare and research

The genomes of thousands of individuals are profiled within Dutch healthcare and research each year. However, this valuable genomic data, associated clinical data and consent are captured in different ways and stored across many systems and organizations. This makes it difficult to discover rare disease patients, reuse data for personalized medicine and establish research cohorts based on specific parameters. FAIR Genomes aims to enable NGS data reuse by developing metadata standards for the data descriptions needed to FAIRify genomic data while also addressing ELSI issues. We developed a semantic schema of essential data elements harmonized with international FAIR initiatives. The FAIR Genomes schema v1.1 contains 110 elements in 9 modules. It reuses common ontologies such as NCIT, DUO and EDAM, only introducing new terms when necessary. The schema is represented by a YAML file that can be transformed into templates for data entry software (EDC) and programmatic interfaces (JSON, RDF) to ease genomic data sharing in research and healthcare. The schema, documentation and MOLGENIS reference implementation are available at https://fairgenomes.org .

Current Status, Issues and Future Prospects of Personalized Medicine for Each Disease

In recent years, with the advancement of next-generation sequencing (NGS) technology, gene panel tests have been approved in the field of cancer diseases, and approaches to prescribe optimal molecular target drugs to patients are being developed. In the field of rare diseases, whole-genome and whole-exome analysis has been used to identify the causative genes of undiagnosed diseases and to diagnose patients’ diseases, and further progress in personalized medicine is expected. In order to promote personalized medicine in the future, we investigated the current status and progress of personalized medicine in disease areas other than cancer and rare diseases, where personalized medicine is most advanced. We selected rheumatoid arthritis and psoriasis as the inflammatory disease, in addition to Alzheimer’s disease. These diseases have high unmet needs for personalized medicine from the viewpoints of disease mechanisms, diagnostic biomarkers, therapeutic drugs with diagnostic markers and treatment satisfaction. In rheumatoid arthritis and psoriasis, there are many therapeutic options; however, diagnostic methods have not been developed to select the best treatment for each patient. In addition, there are few effective therapeutic agents in Alzheimer’s disease, although clinical trials of many candidate drugs have been conducted. In rheumatoid arthritis and psoriasis, further elucidation of the disease mechanism is desired to enable the selection of appropriate therapeutic agents according to the patient profile. In the case of Alzheimer’s disease, progress in preventive medicine is desired through the establishment of an early diagnosis method as well as the research and development of innovative therapeutic agents. To this end, we hope for further research and development of diagnostic markers and new drugs through progress in comprehensive data analysis such as comprehensive genomic and transcriptomic information. Furthermore, new types of markers such as miRNAs and the gut microbiome are desired to be utilized in clinical diagnostics.

Progress, challenges and global approaches to rare diseases

Rare diseases occur globally at every stage of life. Patients, families and caregivers have many unmet medical and social needs leading to extraordinary psychosocial and economic burdens. Efforts to improve diagnostic capabilities and to develop therapies for an estimated 7000 rare diseases have met with considerable success. In the United States, a rare disease or condition is one affecting fewer than 200,000 people. In the European Union (EU), a rare disease is any disease affecting fewer than 5 people in 10,000 (less than 1 in 2000 people). However, there are no effective treatments for 90 per cent of rare diseases. There is a need to expand awareness, advocacy and outreach to everyone including those with low incomes, poor literacy, minority ethnic status and living in underserved and marginalised populations in urban and rural areas as well as in developing nations throughout the world. The acceptance of patients as research partners complements the increased research emphasis and major regulatory initiatives leading to expedited review and approval programmes for products for serious or life-threatening conditions. The pipeline of new therapies provides hope to untreated patients. Advances in medical bioinformatics, artificial intelligence and machine learning with access to big data continue to identify novel therapeutics for screening and evaluation. Advanced analytics can identify the patterns of disease occurrence, predict disease progression, identify patient response to treatments, establish optimal care guidelines and generate research hypotheses with the narrowly identified research patient populations.

Patient engagement in preclinical laboratory research: A scoping review

BACKGROUND

'Patient engagement' involves meaningful collaboration between researchers and 'patient partners' to co-create research. It helps ensure that research being conducted is relevant to its ultimate end-users. Although patient engagement within clinical research has been well documented, the prevalence and effects of patient engagement in translational preclinical laboratory research remain unclear. The aim of this scoping review is to present current patient engagement activities reported in preclinical laboratory research.

METHODS

MEDLINE, Embase, and grey literature were systematically searched from inception to April 2021. Studies that described or investigated patient engagement in preclinical laboratory research were included. Patient engagement activities where patients (i.e. patients, family members, caregivers or community members) provided input, or consultation on at least one element of the research process were eligible for inclusion. Study characteristics and outcomes were extracted and organized thematically.

FINDINGS

32 reports were included (30 primary studies, 1 narrative review, and 1 researcher guide). Most studies engaged patients at the education or priority setting stages (n=26). The most frequently reported benefit of patient engagement was 'providing a mutual learning opportunity'. Reported barriers to patient engagement reflected concerns around 'differences in knowledge and research experience' and how this may challenge communication and limit meaningful collaboration.

INTERPRETATION

Patient engagement is feasible and beneficial for preclinical laboratory research. Future work should focus on assessing the impacts of patient engagement in this area of research.

Multifactorial Rare Diseases: Can Uncertainty Analysis Bring Added Value to the Search for Risk Factors and Etiopathogenesis?

Uncertainty analysis is the process of identifying limitations in knowledge and evaluating their implications for scientific conclusions. Uncertainty analysis is a stable component of risk assessment and is increasingly used in decision making on complex health issues. Uncertainties should be identified in a structured way and prioritized according to their likely impact on the outcome of scientific conclusions. Uncertainty is inherent to the rare diseases (RD) area, where research and healthcare have to cope with knowledge gaps due to the rarity of the conditions; yet a systematic approach toward uncertainties is not usually undertaken. The uncertainty issue is particularly relevant to multifactorial RD, whose etiopathogenesis involves environmental factors and genetic predisposition. Three case studies are presented: the newly recognized acute multisystem inflammatory syndrome in children and adolescents associated with SARS-CoV-2 infection; the assessment of risk factors for neural tube defects; and the genotype-phenotype correlation in familial Mediterranean fever. Each case study proposes the initial identification of the main epistemic and sampling uncertainties and their impacts. Uncertainty analysis in RD may present aspects similar to those encountered when conducting risk assessment in data-poor scenarios; therefore, approaches such as expert knowledge elicitation may be considered. The RD community has a main strength in managing uncertainty, as it proactively develops stakeholder involvement, data sharing and open science. The open science approaches can be profitably integrated by structured uncertainty analysis, especially when dealing with multifactorial RD involving environmental and genetic risk factors.

Barriers and Considerations for Diagnosing Rare Diseases in Indigenous Populations

Advances in omics and specifically genomic technologies are increasingly transforming rare disease diagnosis. However, the benefits of these advances are disproportionately experienced within and between populations, with Indigenous populations frequently experiencing diagnostic and therapeutic inequities. The International Rare Disease Research Consortium (IRDiRC) multi-stakeholder partnership has been advancing toward the vision of all people living with a rare disease receiving an accurate diagnosis, care, and available therapy within 1 year of coming to medical attention. In order to further progress toward this vision, IRDiRC has created a taskforce to explore the access barriers to diagnosis of rare genetic diseases faced by Indigenous peoples, with a view of developing recommendations to overcome them. Herein, we provide an overview of the state of play of current barriers and considerations identified by the taskforce, to further stimulate awareness of these issues and the passage toward solutions. We focus on analyzing barriers to accessing genetic services, participating in genomic research, and other aspects such as concerns about data sharing, the handling of biospecimens, and the importance of capacity building.

Improving diagnosis for rare diseases: the experience of the Italian undiagnosed Rare diseases network

Background

For a number of persons with rare diseases (RDs) a definite diagnosis remains undiscovered with relevant physical, psychological and social consequences. Undiagnosed RDs (URDs) require other than specialised clinical centres, outstanding molecular investigations, common protocols and dedicated actions at national and international levels; thus, many “Undiagnosed RDs programs” have been gradually developed on the grounds of a well-structured multidisciplinary approach.

Methods

The Italian Undiagnosed Rare Diseases Network (IURDN) was established in 2016 to improve the level of diagnosis of persons with URD living in Italy. Six Italian Centres of Expertise represented the network. The National Centre for Rare Diseases at the Istituto Superiore di Sanità coordinates the whole project. The software PhenoTips was used to collect the information of the clinical cases.

Results

One hundred and ten cases were analysed between March 2016 and June 2019. The age of onset of the diseases ranged from prenatal age to 51 years. Conditions were predominantly sporadic; almost all patients had multiple organs involvements. A total of 13/71 family cases were characterized by WES; in some families more than one individual was affected, so leading to 20/71 individuals investigated. Disease causing variants were identified in two cases and were associated to previously undescribed phenotypes. In 5 cases, new candidate genes were identified, although confirmatory tests are pending. In three families, investigations were not completed due to the scarce compliance of members and molecular investigations were temporary suspended. Finally, three cases (one familial) remain still unsolved. Twelve undiagnosed clinical cases were then selected to be shared at International level through PhenomeCentral in accordance to the UDNI statement.

Conclusions

Our results showed a molecular diagnostic yield of 53,8%; this value is comparable to the diagnostic rates reported in other international studies. Cases collected were also pooled with those collected by UDNI International Network. This represents a unique example of global initiative aimed at sharing and validating knowledge and experience in this field. IURDN is a multidisciplinary and useful initiative linking National and International efforts aimed at making timely and appropriate diagnoses in RD patients who still do not have a confirmed diagnosis even after a long time.

Rosy Beginnings: Studying Peroxisomes in Drosophila

Research using the fruit fly Drosophila melanogaster has traditionally focused on understanding how mutations affecting gene regulation or function affect processes linked to animal development. Accordingly, flies have become an essential foundation of modern medical research through repeated contributions to our fundamental understanding of how their homologs of human genes function. Peroxisomes are organelles that metabolize lipids and reactive oxygen species like peroxides. However, despite clear linkage of mutations in human genes affecting peroxisomes to developmental defects, for many years fly models were conspicuously absent from the study of peroxisomes. Now, the few early studies linking the Rosy eye color phenotype to peroxisomes in flies have been joined by a growing body of research establishing novel roles for peroxisomes during the development or function of specific tissues or cell types. Similarly, unique properties of cultured fly Schneider 2 cells have advanced our understanding of how peroxisomes move on the cytoskeleton. Here, we profile how those past and more recent Drosophila studies started to link specific effects of peroxisome dysfunction to organ development and highlight the utility of flies as a model for human peroxisomal diseases. We also identify key differences in the function and proliferation of fly peroxisomes compared to yeast or mammals. Finally, we discuss the future of the fly model system for peroxisome research including new techniques that should support identification of additional tissue specific regulation of and roles for peroxisomes.