Recommendations from the IRDiRC Working Group on methodologies to assess the impact of diagnoses and therapies on rare disease patients

NanoRanger enables rapid single-base-pair resolution of genomic disorders

BACKGROUND

Delineating base-resolution breakpoints of complex rearrangements is crucial for an accurate clinical understanding of pathogenic variants and for carrier screening within family networks or the broader population. However, despite advances in genetic testing using short-read sequencing (SRS), this task remains costly and challenging.

METHODS

This study addresses the challenges of resolving missing disease-causing breakpoints in complex genomic disorders with suspected homozygous rearrangements by employing multiple long-read sequencing (LRS) strategies, including a novel and efficient strategy named nanopore-based rapid acquisition of neighboring genomic regions (NanoRanger). NanoRanger does not require large amounts of ultrahigh-molecular-weight DNA and stands out for its ease of use and rapid acquisition of large genomic regions of interest with deep coverage.

FINDINGS

We describe a cohort of 16 familial cases, each harboring homozygous rearrangements that defied breakpoint determination by SRS and optical genome mapping (OGM). NanoRanger identified the breakpoints with single-base-pair resolution, enabling accurate determination of the carrier status of unaffected family members as well as the founder nature of these genomic lesions and their frequency in the local population. The resolved breakpoints revealed that repetitive DNA, gene regulatory elements, and transcription activity contribute to genome instability in these novel recessive rearrangements.

CONCLUSIONS

Our data suggest that NanoRanger greatly improves the success rate of resolving base-resolution breakpoints of complex genomic disorders and expands access to LRS for the benefit of patients with Mendelian disorders.

FUNDING

M.L. is supported by KAUST Baseline Award no. BAS/1/1080-01-01 and KAUST Research Translation Fund Award no. REI/1/4742-01.

Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies

BACKGROUND

There are approximately 8,000 different rare diseases that affect roughly 400 million people worldwide. Many of them suffer from delayed diagnosis. Ciliopathies are rare monogenic disorders characterized by a significant phenotypic and genetic heterogeneity that raises an important challenge for clinical diagnosis. Diagnosis support systems (DSS) applied to electronic health record (EHR) data may help identify undiagnosed patients, which is of paramount importance to improve patients' care. Our objective was to evaluate three online-accessible rare disease DSSs using phenotypes derived from EHRs for the diagnosis of ciliopathies.

METHODS

Two datasets of ciliopathy cases, either proven or suspected, and two datasets of controls were used to evaluate the DSSs. Patient phenotypes were automatically extracted from their EHRs and converted to Human Phenotype Ontology terms. We tested the ability of the DSSs to diagnose cases in contrast to controls based on Orphanet ontology.

RESULTS

A total of 79 cases and 38 controls were selected. Performances of the DSSs on ciliopathy real world data (best DSS with area under the ROC curve = 0.72) were not as good as published performances on the test set used in the DSS development phase. None of these systems obtained results which could be described as "expert-level". Patients with multisystemic symptoms were generally easier to diagnose than patients with isolated symptoms. Diseases easily confused with ciliopathy generally affected multiple organs and had overlapping phenotypes. Four challenges need to be considered to improve the performances: to make the DSSs interoperable with EHR systems, to validate the performances in real-life settings, to deal with data quality, and to leverage methods and resources for rare and complex diseases.

CONCLUSION

Our study provides insights into the complexities of diagnosing highly heterogenous rare diseases and offers lessons derived from evaluation existing DSSs in real-world settings. These insights are not only beneficial for ciliopathy diagnosis but also hold relevance for the enhancement of DSS for various complex rare disorders, by guiding the development of more clinically relevant rare disease DSSs, that could support early diagnosis and finally make more patients eligible for treatment.

An algorithm to identify patients aged 0-3 with rare genetic disorders

BACKGROUND

With over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured electronic medical record data is challenging given incompleteness of records, inaccurate medical diagnosis coding, as well as heterogeneity in clinical symptoms and procedures for specific disorders. We sought to develop a digital phenotyping algorithm (PheIndex) using electronic medical records to identify children aged 0-3 diagnosed with genetic disorders or who present with illness with an increased risk for genetic disorders.

RESULTS

Through expert opinion, we established 13 criteria for the algorithm and derived a score and a classification. The performance of each criterion and the classification were validated by chart review. PheIndex identified 1,088 children out of 93,154 live births who may be at an increased risk for genetic disorders. Chart review demonstrated that the algorithm achieved 90% sensitivity, 97% specificity, and 94% accuracy.

CONCLUSIONS

The PheIndex algorithm can help identify when a rare genetic disorder may be present, alerting providers to consider ordering a diagnostic genetic test and/or referring a patient to a medical geneticist.

Stigma associated with genetic testing for rare diseases-causes and recommendations

Rare disease (RD) is a term used to describe numerous, heterogeneous diseases that are geographically disparate. Approximately 400 million people worldwide live with an RD equating to roughly 1 in 10 people, with 71.9% of RDs having a genetic origin. RDs present a distinctive set of challenges to people living with rare diseases (PLWRDs), their families, healthcare professionals (HCPs), healthcare system, and societies at large. The possibility of inheriting a genetic disease has a substantial social and psychological impact on affected families. In addition to other concerns, PLWRDs and their families may feel stigmatized, experience guilt, feel blamed, and stress about passing the disease to future generations. Stigma can affect all stages of the journey of PLWRDs and their families, from pre-diagnosis to treatment access, care and support, and compliance. It adversely impacts the quality of life of RD patients. To better explore the impact of stigma associated with genetic testing for RDs, we conducted a literature search on PubMed and Embase databases to identify articles published on stigma and RDs from January 2013 to February 2023. There is a dearth of literature investigating the dynamics of stigma and RD genetic testing. The authors observed that the research into the implications of stigma for patient outcomes in low- and middle-income countries (LMICs) and potential interventions is limited. Herein, the authors present a review of published literature on stigma with a focus on RD genetic testing, the associated challenges, and possible ways to address these.

Neuroendocrine neoplasms: Consensus on a patient care pathway

People with neuroendocrine neoplasms (NENs) face a multitude of challenges, including delayed diagnosis, low awareness of the cancer among healthcare professionals and limited access to multidisciplinary care and expert centres. We have developed the first patient care pathway for people living with NENs in England to guide disease management and help overcome these barriers. The pathway was developed in two phases. First, a pragmatic review of the literature was conducted, which was used to develop a draft patient care pathway. Second, the draft pathway was then updated following semi-structured interviews with carefully selected expert stakeholders. After each phase, the pathway was discussed among a multidisciplinary, expert advisory group (which comprised the authors and the Deputy Chief Operating Officer, West Suffolk NHS Foundation Trust), who reached a consensus on the ideal care pathway. This article presents the outputs of this research. The pathway identified key barriers to care and highlighted how these may be addressed, with many of the findings relevant to the rest of the UK and international audiences. NENs are increasing in incidence and prevalence in England, compounding pre-existing inequities in diagnosis and disease management. Effective integration of this pathway within NHS England will help achieve optimal, equitable care provision for all people with NENs, and should be feasible within the existing expert multidisciplinary teams across the country.

Drug Repurposing in Crohn's Disease Using Danish Real-World Data

Aim

Drug repurposing, utilizing electronic healthcare records (EHRs), offers a promising alternative by repurposing existing drugs for new therapeutic indications, especially for patients lacking effective therapies. Intestinal fibrosis, a severe complication of Crohn's disease (CD), poses significant challenges, increasing morbidity and mortality without available pharmacological treatments. This article focuses on identifying medications associated with an elevated or reduced risk of fibrosis in CD patients through a population-wide real-world data and artificial intelligence (AI) approach.

Methods

Patients aged 65 or older with a diagnosis of CD from 1996 to 2019 in the Danish EHRs were followed for up to 24 years. The primary outcome was the need of specific surgical procedures, namely proctocolectomy with ileostomy and ileocecal resection as proxies of intestinal fibrosis. The study explored drugs linked to an increased or reduced risk of the study outcome through machine-learning driven survival analysis.

Results

Among the 9179 CD patients, 1029 (11.2%) underwent surgery, primarily men (58.5%), with a mean age of 76 years, 10 drugs were linked to an elevated risk of surgery for proctocolectomy with ileostomy and ileocecal resection. In contrast, 10 drugs were associated with a reduced risk of undergoing surgery for these conditions.

Conclusion

This study focuses on repurposing existing drugs to prevent surgery related to intestinal fibrosis in CD patients, using Danish EHRs and advanced statistical methods. The findings offer valuable insights into potential treatments for this condition, addressing a critical unmet medical need. Further research and clinical trials are warranted to validate the effectiveness of these repurposed drugs in preventing surgery related to intestinal fibrosis in CD patients.

Performance and clinical utility of a new supervised machine-learning pipeline in detecting rare ciliopathy patients based on deep phenotyping from electronic health records and semantic similarity

BACKGROUND

Rare diseases affect approximately 400 million people worldwide. Many of them suffer from delayed diagnosis. Among them, NPHP1-related renal ciliopathies need to be diagnosed as early as possible as potential treatments have been recently investigated with promising results. Our objective was to develop a supervised machine learning pipeline for the detection of NPHP1 ciliopathy patients from a large number of nephrology patients using electronic health records (EHRs).

METHODS AND RESULTS

We designed a pipeline combining a phenotyping module re-using unstructured EHR data, a semantic similarity module to address the phenotype dependence, a feature selection step to deal with high dimensionality, an undersampling step to address the class imbalance, and a classification step with multiple train-test split for the small number of rare cases. The pipeline was applied to thirty NPHP1 patients and 7231 controls and achieved good performances (sensitivity 86% with specificity 90%). A qualitative review of the EHRs of 40 misclassified controls showed that 25% had phenotypes belonging to the ciliopathy spectrum, which demonstrates the ability of our system to detect patients with similar conditions.

CONCLUSIONS

Our pipeline reached very encouraging performance scores for pre-diagnosing ciliopathy patients. The identified patients could then undergo genetic testing. The same data-driven approach can be adapted to other rare diseases facing underdiagnosis challenges.

Diagnostic journey for individuals with fibrous dysplasia / McCune albright syndrome (FD/MAS)

BACKGROUND

Reducing delayed diagnosis is a significant healthcare priority for individuals with rare diseases. Fibrous Dysplasia/ McCune Albright Syndrome (FD/MAS) is a rare bone disease caused by somatic activation mutations of NASA. FD/MAS has a broad clinical phenotype reflecting variable involvement of bone, endocrine and other tissues, distribution and severity. The variable phenotype is likely to prolong the diagnostic journey for patients further.

AIM

To describe the time from symptom onset to final diagnosis in individuals living with FDMAS.

METHODS

We used the UK-based RUDY research database ( www.rudystudy.org ), where patients self-report their diagnosis of FD/MAS. Participants are invited to complete the diagnostic journey based on the EPIRARE criteria.

RESULTS

51 individuals diagnosed with FD/MAS were included in this analysis. Among them, 70% were female, and the median age was 51.0 years (IQR 34.5-57.5]. 12 (35%) individuals reported McCune Albright Syndrome, 11 (21.6%) craniofacial and 11(21.6%) for each of poly- and mono-ostotic FD and 6 (11.8%) did not know their type of FD/MAS. Pain was the commonest first symptom (58.8%), and 47.1% received another diagnosis before the diagnosis of FD/MAS. The median time to final diagnosis from the first symptom was two years with a wide IQR (1,18) and range (0-59 years). Only 12 (23.5%) of individuals were diagnosed within 12 months of their first symptoms. The type of FD/MAS was not associated with the reported time to diagnosis. Significant independent predictors of longer time to final diagnosis included older current age, younger age at first symptom and diagnosis after 2010.

CONCLUSION

Individuals with FDMAS have a variable time to diagnosis that can span decades. This study highlights the need for further research on how to improve diagnostic pathways within Orthopaedic and Ear, Nose and Throat (ENT)/Maxillofacial services. Our data provides a baseline to assess the impact of novel NHS diagnostic networks on reducing the diagnostic odyssey.

Phenotypic similarity-based approach for variant prioritization for unsolved rare disease: a preliminary methodological report

Rare diseases (RD) have a prevalence of not more than 1/2000 persons in the European population, and are characterised by the difficulty experienced in obtaining a correct and timely diagnosis. According to Orphanet, 72.5% of RD have a genetic origin although 35% of them do not yet have an identified causative gene. A significant proportion of patients suspected to have a genetic RD receive an inconclusive exome/genome sequencing. Working towards the International Rare Diseases Research Consortium (IRDiRC)'s goal for 2027 to ensure that all people living with a RD receive a diagnosis within one year of coming to medical attention, the Solve-RD project aims to identify the molecular causes underlying undiagnosed RD. As part of this strategy, we developed a phenotypic similarity-based variant prioritization methodology comparing submitted cases with other submitted cases and with known RD in Orphanet. Three complementary approaches based on phenotypic similarity calculations using the Human Phenotype Ontology (HPO), the Orphanet Rare Diseases Ontology (ORDO) and the HPO-ORDO Ontological Module (HOOM) were developed; genomic data reanalysis was performed by the RD-Connect Genome-Phenome Analysis Platform (GPAP). The methodology was tested in 4 exemplary cases discussed with experts from European Reference Networks. Variants of interest (pathogenic or likely pathogenic) were detected in 8.8% of the 725 cases clustered by similarity calculations. Diagnostic hypotheses were validated in 42.1% of them and needed further exploration in another 10.9%. Based on the promising results, we are devising an automated standardized phenotypic-based re-analysis pipeline to be applied to the entire unsolved cases cohort.

Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT

To enhance phenotype recognition in clinical notes of genetic diseases, we developed two models-PhenoBCBERT and PhenoGPT-for expanding the vocabularies of Human Phenotype Ontology (HPO) terms. While HPO offers a standardized vocabulary for phenotypes, existing tools often fail to capture the full scope of phenotypes due to limitations from traditional heuristic or rule-based approaches. Our models leverage large language models to automate the detection of phenotype terms, including those not in the current HPO. We compare these models with PhenoTagger, another HPO recognition tool, and found that our models identify a wider range of phenotype concepts, including previously uncharacterized ones. Our models also show strong performance in case studies on biomedical literature. We evaluate the strengths and weaknesses of BERT- and GPT-based models in aspects such as architecture and accuracy. Overall, our models enhance automated phenotype detection from clinical texts, improving downstream analyses on human diseases.

Phenotype-Driven Molecular Genetic Test Recommendation for Diagnosing Pediatric Rare Disorders

Rare disease patients often endure prolonged diagnostic odysseys and may still remain undiagnosed for years. Selecting the appropriate genetic tests is crucial to lead to timely diagnosis. Phenotypic features offer great potential for aiding genomic diagnosis in rare disease cases. We see great promise in effective integration of phenotypic information into genetic test selection workflow. In this study, we present a phenotype-driven molecular genetic test recommendation (Phen2Test) for pediatric rare disease diagnosis. Phen2Test was constructed using frequency matrix of phecodes and demographic data from the EHR before ordering genetic tests, with the objective to streamline the selection of molecular genetic tests (whole-exome / whole-genome sequencing, or gene panels) for clinicians with minimum genetic training expertise. We developed and evaluated binary classifiers based on 1,005 individuals referred to genetic counselors for potential genetic evaluation. In the evaluation using the gold standard cohort, the model achieved strong performance with an AUROC of 0.82 and an AUPRC of 0.92. Furthermore, we tested the model on another silver standard cohort (n=6,458), achieving an overall AUROC of 0.72 and an AUPRC of 0.671. Phen2Test was adjusted to align with current clinical guidelines, showing superior performance with more recent data, demonstrating its potential for use within a learning healthcare system as a genomic medicine intervention that adapts to guideline updates. This study showcases the practical utility of phenotypic features in recommending molecular genetic tests with performance comparable to clinical geneticists. Phen2Test could assist clinicians with limited genetic training and knowledge to order appropriate genetic tests.

Enhancing Phenotype Recognition in Clinical Notes Using Large Language Models: PhenoBCBERT and PhenoGPT

To enhance phenotype recognition in clinical notes of genetic diseases, we developed two models - PhenoBCBERT and PhenoGPT - for expanding the vocabularies of Human Phenotype Ontology (HPO) terms. While HPO offers a standardized vocabulary for phenotypes, existing tools often fail to capture the full scope of phenotypes, due to limitations from traditional heuristic or rule-based approaches. Our models leverage large language models (LLMs) to automate the detection of phenotype terms, including those not in the current HPO. We compared these models to PhenoTagger, another HPO recognition tool, and found that our models identify a wider range of phenotype concepts, including previously uncharacterized ones. Our models also showed strong performance in case studies on biomedical literature. We evaluated the strengths and weaknesses of BERT-based and GPT-based models in aspects such as architecture and accuracy. Overall, our models enhance automated phenotype detection from clinical texts, improving downstream analyses on human diseases.

Adult Moyamoya Disease and Syndrome: Current Perspectives and Future Directions: A Scientific Statement From the American Heart Association/American Stroke Association

Adult moyamoya disease and syndrome are rare disorders with significant morbidity and mortality. A writing group of experts was selected to conduct a literature search, summarize the current knowledge on the topic, and provide a road map for future investigation. The document presents an update in the definitions of moyamoya disease and syndrome, modern methods for diagnosis, and updated information on pathophysiology, epidemiology, and both medical and surgical treatment. Despite recent advancements, there are still many unresolved questions about moyamoya disease and syndrome, including lack of unified diagnostic criteria, reliable biomarkers, better understanding of the underlying pathophysiology, and stronger evidence for treatment guidelines. To advance progress in this area, it is crucial to acknowledge the limitations and weaknesses of current studies and explore new approaches, which are outlined in this scientific statement for future research strategies.

Psychosocial impact at the time of a rare disease diagnosis

Over half of all persons with rare diseases (RDs) in Spain experience diagnostic delay (DD) but little is known about its consequences. This study therefore aimed to analyze the psychological impact of obtaining a diagnosis of an RD, and to ascertain what social determinants are influenced and what the personal consequences are, according to whether or not patients experienced DD. Data were obtained from a purpose-designed form completed by persons registered at the Spanish Rare Diseases Patient Registry. The following were performed: a descriptive analysis; a principal component analysis (PCA); and logistic regressions. Results revealed that while searching for a diagnosis, people who experienced DD were more in need of psychological care than those diagnosed in less than one year (36.2% vs 23.2%; p = 0.002; n = 524). The PCA identified three principal components, i.e., psychological effects, social implications, and functional impact. Reducing DD would improve psychological effects, such as irritability (OR 3.6; 95%CI 1.5-8.5), frustration (OR 3.4; 95%CI 1.7-7.1) and concentration on everyday life (OR 3.3; 95%CI 1.4-7.7). The influence of the social implications and functional repercussions of the disease was greater in persons with DD (scores of 22.4 vs 20 and 10.6 vs 9.4, respectively) in terms of the difficulty in explaining symptoms to close friends and family (3.3 vs 2.9), and loss of independence (3.3 vs 2.9). In conclusion, this is the first study to analyze the psychosocial impact of diagnosis of RDs in Spain and one of few to assess it in the patients themselves, based on data drawn from a purpose-designed form from a national registry open to any RD. People affected by RDs who underwent DD experienced greater psychosocial impact than did those who were diagnosed within the space of one year.

Phenotypic screening models for rapid diagnosis of genetic variants and discovery of personalized therapeutics

Precision medicine strives for highly individualized treatments for disease under the notion that each individual's unique genetic makeup and environmental exposures imprints upon them not only a disposition to illness, but also an optimal therapeutic approach. In the realm of rare disorders, genetic predisposition is often the predominant mechanism driving disease presentation. For such, mostly, monogenic disorders, a causal gene to phenotype association is likely. As a result, it becomes important to query the patient's genome for the presence of pathogenic variations that are likely to cause the disease. Determining whether a variant is pathogenic or not is critical to these analyses and can be challenging, as many disease-causing variants are novel and, ergo, have no available functional data to help categorize them. This problem is exacerbated by the need for rapid evaluation of pathogenicity, since many genetic diseases present in young children who will experience increased morbidity and mortality without rapid diagnosis and therapeutics. Here, we discuss the utility of animal models, with a focus mainly on C. elegans, as a contrast to tissue culture and in silico approaches, with emphasis on how these systems are used in determining pathogenicity of variants with uncertain significance and then used to screen for novel therapeutics.