Optimus-Era Dose Finding for Rare Cancers.

SUMMARY: Advances in cancer biology and diagnostics have led to the recognition of a multitude of rare cancer subtypes, emphasizing the pressing need for strategies to accelerate drug development for patients with these cancers. This paper addresses the unique challenges of dose finding in trials that accrue small numbers of patients with rare cancers; strategies for dose optimization are proposed, in line with evolving approaches to dose determination in the age of the US Food and Drug Administration's Project Optimus.

Genome Sequencing for Diagnosing Rare Diseases.

BACKGROUND: Genetic variants that cause rare disorders may remain elusive even after expansive testing, such as exome sequencing. The diagnostic yield of genome sequencing, particularly after a negative evaluation, remains poorly defined. METHODS: We sequenced and analyzed the genomes of families with diverse phenotypes who were suspected to have a rare monogenic disease and for whom genetic testing had not revealed a diagnosis, as well as the genomes of a replication cohort at an independent clinical center. RESULTS: We sequenced the genomes of 822 families (744 in the initial cohort and 78 in the replication cohort) and made a molecular diagnosis in 218 of 744 families (29.3%). Of the 218 families, 61 (28.0%) - 8.2% of families in the initial cohort - had variants that required genome sequencing for identification, including coding variants, intronic variants, small structural variants, copy-neutral inversions, complex rearrangements, and tandem repeat expansions. Most families in which a molecular diagnosis was made after previous nondiagnostic exome sequencing (63.5%) had variants that could be detected by reanalysis of the exome-sequence data (53.4%) or by additional analytic methods, such as copy-number variant calling, to exome-sequence data (10.8%). We obtained similar results in the replication cohort: in 33% of the families in which a molecular diagnosis was made, or 8% of the cohort, genome sequencing was required, which showed the applicability of these findings to both research and clinical environments. CONCLUSIONS: The diagnostic yield of genome sequencing in a large, diverse research cohort and in a small clinical cohort of persons who had previously undergone genetic testing was approximately 8% and included several types of pathogenic variation that had not previously been detected by means of exome sequencing or other techniques. (Funded by the National Human Genome Research Institute and others.).

Comparison of Bayesian approaches for developing prediction models in rare disease: application to the identification of patients with Maturity-Onset Diabetes of the Young.

BACKGROUND: Clinical prediction models can help identify high-risk patients and facilitate timely interventions. However, developing such models for rare diseases presents challenges due to the scarcity of affected patients for developing and calibrating models. Methods that pool information from multiple sources can help with these challenges. METHODS: We compared three approaches for developing clinical prediction models for population screening based on an example of discriminating a rare form of diabetes (Maturity-Onset Diabetes of the Young - MODY) in insulin-treated patients from the more common Type 1 diabetes (T1D). Two datasets were used: a case-control dataset (278 T1D, 177 MODY) and a population-representative dataset (1418 patients, 96 MODY tested with biomarker testing, 7 MODY positive). To build a population-level prediction model, we compared three methods for recalibrating models developed in case-control data. These were prevalence adjustment ("offset"), shrinkage recalibration in the population-level dataset ("recalibration"), and a refitting of the model to the population-level dataset ("re-estimation"). We then developed a Bayesian hierarchical mixture model combining shrinkage recalibration with additional informative biomarker information only available in the population-representative dataset. We developed a method for dealing with missing biomarker and outcome information using prior information from the literature and other data sources to ensure the clinical validity of predictions for certain biomarker combinations. RESULTS: The offset, re-estimation, and recalibration methods showed good calibration in the population-representative dataset. The offset and recalibration methods displayed the lowest predictive uncertainty due to borrowing information from the fitted case-control model. We demonstrate the potential of a mixture model for incorporating informative biomarkers, which significantly enhanced the model's predictive accuracy, reduced uncertainty, and showed higher stability in all ranges of predictive outcome probabilities. CONCLUSION: We have compared several approaches that could be used to develop prediction models for rare diseases. Our findings highlight the recalibration mixture model as the optimal strategy if a population-level dataset is available. This approach offers the flexibility to incorporate additional predictors and informed prior probabilities, contributing to enhanced prediction accuracy for rare diseases. It also allows predictions without these additional tests, providing additional information on whether a patient should undergo further biomarker testing before genetic testing.

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.

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.

Diagnostic yield of exome and genome sequencing after non-diagnostic multi-gene panels in patients with single-system diseases.

BACKGROUND: Though next-generation sequencing (NGS) tests like exome sequencing (ES), genome sequencing (GS), and panels derived from exome and genome data (EGBP) are effective for rare diseases, the ideal diagnostic approach is debated. Limited research has explored reanalyzing raw ES and GS data post-negative EGBP results for diagnostics. RESULTS: We analyzed complete ES/GS raw sequencing data from Mayo Clinic's Program for Rare and Undiagnosed Diseases (PRaUD) patients to assess whether supplementary findings could augment diagnostic yield. ES data from 80 patients (59 adults) and GS data from 20 patients (10 adults), averaging 43 years in age, were analyzed. Most patients had renal (n=44) and auto-inflammatory (n=29) phenotypes. Ninety-six cases had negative findings and in four cases additional genetic variants were found, including a variant related to a recently described disease (RRAGD-related hypomagnesemia), a variant missed due to discordant inheritance pattern (COL4A3), a variant with high allelic frequency (NPHS2) in the general population, and a variant associated with an initially untargeted phenotype (HNF1A). CONCLUSION: ES and GS show diagnostic yields comparable to EGBP for single-system diseases. However, EGBP's limitations in detecting new disease-associated genes underscore the necessity for periodic updates.

Rare genetic disorders that impair parathyroid hormone synthesis, secretion, or bioactivity provide insights into the diagnostic utility of different parathyroid hormone assays.

PURPOSE OF REVIEW: Parathyroid hormone (PTH) is the major peptide hormone regulator of blood calcium homeostasis. Abnormal PTH levels can be observed in patients with various congenital and acquired disorders, including chronic kidney disease (CKD). This review will focus on rare human diseases caused by PTH mutations that have provided insights into the regulation of PTH synthesis and secretion as well as the diagnostic utility of different PTH assays. RECENT FINDINGS: Over the past years, numerous diseases affecting calcium and phosphate homeostasis have been defined at the molecular level that are responsible for reduced or increased serum PTH levels. The underlying genetic mutations impair parathyroid gland development, involve the PTH gene itself, or alter function of the calcium-sensing receptor (CaSR) or its downstream signaling partners that contribute to regulation of PTH synthesis or secretion. Mutations in the pre sequence of the mature PTH peptide can, for instance, impair hormone synthesis or intracellular processing, while amino acid substitutions affecting the secreted PTH(1-84) impair PTH receptor (PTH1R) activation, or cause defective cleavage of the pro-sequence and thus secretion of a pro- PTH with much reduced biological activity. Mutations affecting the secreted hormone can alter detection by different PTH assays, thus requiring detailed knowledge of the utilized diagnostic test. SUMMARY: Rare diseases affecting PTH synthesis and secretion have offered helpful insights into parathyroid biology and the diagnostic utility of commonly used PTH assays, which may have implications for the interpretation of PTH measurements in more common disorders such as CKD.

A quantitative and qualitative analysis of patient group narratives suggests common biopsychosocial red flags of undiagnosed rare disease.

BACKGROUND: The 'diagnostic odyssey' is a common challenge faced by patients living with rare diseases and poses a significant burden for patients, their families and carers, and the healthcare system. The diagnosis of rare diseases in clinical settings is challenging, with patients typically experiencing a multitude of unnecessary tests and procedures. To improve diagnosis of rare disease, clinicians require evidence-based guidance on when their patient may be presenting with a rare disease. This study aims to identify common experiences amongst patients with rare diseases, to inform a series of 'red flags' that can aid diagnosis of rare diseases in non-specialist settings. A questionnaire was developed by Medics for Rare Diseases, informed by the experiences of clinicians, rare disease patients and patient advocates, and was shared with UK-based rare disease patient groups. Study participants were engaged via social media platforms, blogs and email newsletters of three umbrella rare disease organisations. The questionnaire, comprising 22 questions, was designed to identify typical experiences relating to physical and psychosocial manifestations and presentation of disease, patient interactions with healthcare providers, and family history. RESULTS: Questionnaire responses were received from 79 different rare disease patient groups and the common experiences identified were used to inform seven red flags of rare disease: multi-system involvement (3 or more); genetic inheritance pattern; continued presentation throughout childhood and adulthood; difficulties at school, especially relating to absences, difficulty participating in physical education and experiences of bullying or social isolation; multiple specialist referrals; extended period with unexplained symptoms; and misdiagnosis. In light of the red flags identified, recommendations for primary care and education settings have been proposed, focusing on the need for holistic assessment and awareness of both physical and psychosocial factors. CONCLUSIONS: This study identified key commonalities experienced by patients with rare disease across physical and psychosocial domains, in addition to understanding patients' history and experiences with healthcare providers. These findings could be used to develop a clinical decision­making tool to support non-specialist practitioners to consider when their patient may have an undiagnosed rare condition, which may minimise the challenges of the 'diagnostic odyssey' and improve the patient experience.

The value of knowing: preferences for genetic testing to diagnose rare muscle diseases.

BACKGROUND: Genetic testing can offer early diagnosis and subsequent treatment of rare neuromuscular diseases. Options for these tests could be improved by understanding the preferences of patients for the features of different genetic tests, especially features that increase information available to patients. METHODS: We developed an online discrete-choice experiment using key attributes of currently available tests for Pompe disease with six test attributes: number of rare muscle diseases tested for with corresponding probability of diagnosis, treatment availability, time from testing to results, inclusion of secondary findings, necessity of a muscle biopsy, and average time until final diagnosis if the first test is negative. Respondents were presented a choice between two tests with different costs, with respondents randomly assigned to one of two costs. Data were analyzed using random-parameters logit. RESULTS: A total of 600 online respondents, aged 18 to 50 years, were recruited from the U.S. general population and included in the final analysis. Tests that targeted more diseases, required less time from testing to results, included information about unrelated health risks, and were linked to shorter time to the final diagnosis were preferred and associated with diseases with available treatment. Men placed relatively more importance than women on tests for diseases with available treatments. Most of the respondents would be more willing to get a genetic test that might return unrelated health information, with women exhibiting a statistically significant preference. While respondents were sensitive to cost, 30% of the sample assigned to the highest cost was willing to pay $500 for a test that could offer a diagnosis almost 2 years earlier. CONCLUSION: The results highlight the value people place on the information genetic tests can provide about their health, including faster diagnosis of rare, unexplained muscle weakness, but also the value of tests for multiple diseases, diseases without treatments, and incidental findings. An earlier time to diagnosis can provide faster access to treatment and an end to the diagnostic journey, which patients highly prefer.

Landscape analysis of available European data sources amenable for machine learning and recommendations on usability for rare diseases screening.

BACKGROUND: Patient registries and databases are essential tools for advancing clinical research in the area of rare diseases, as well as for enhancing patient care and healthcare planning. The primary aim of this study is a landscape analysis of available European data sources amenable to machine learning (ML) and their usability for Rare Diseases screening, in terms of findable, accessible, interoperable, reusable(FAIR), legal, and business considerations. Second, recommendations will be proposed to provide a better understanding of the health data ecosystem. METHODS: In the period of March 2022 to December 2022, a cross-sectional study using a semi-structured questionnaire was conducted among potential respondents, identified as main contact person of a health-related databases. The design of the self-completed questionnaire survey instrument was based on information drawn from relevant scientific publications, quantitative and qualitative research, and scoping review on challenges in mapping European rare disease (RD) databases. To determine database characteristics associated with the adherence to the FAIR principles, legal and business aspects of database management Bayesian models were fitted. RESULTS: In total, 330 unique replies were processed and analyzed, reflecting the same number of distinct databases (no duplicates included). In terms of geographical scope, we observed 24.2% (n = 80) national, 10.0% (n = 33) regional, 8.8% (n = 29) European, and 5.5% (n = 18) international registries coordinated in Europe. Over 80.0% (n = 269) of the databases were still active, with approximately 60.0% (n = 191) established after the year 2000 and 71.0% last collected new data in 2022. Regarding their geographical scope, European registries were associated with the highest overall FAIR adherence, while registries with regional and "other" geographical scope were ranked at the bottom of the list with the lowest proportion. Responders' willingness to share data as a contribution to the goals of the Screen4Care project was evaluated at the end of the survey. This question was completed by 108 respondents; however, only 18 of them (16.7%) expressed a direct willingness to contribute to the project by sharing their databases. Among them, an equal split between pro-bono and paid services was observed. CONCLUSIONS: The most important results of our study demonstrate not enough sufficient FAIR principles adherence and low willingness of the EU health databases to share patient information, combined with some legislation incapacities, resulting in barriers to the secondary use of data.

Patient preferences in genetic newborn screening for rare diseases: study protocol.

INTRODUCTION: Rare diseases (RDs) collectively impact over 30 million people in Europe. Most individual conditions have a low prevalence which has resulted in a lack of research and expertise in this field, especially regarding genetic newborn screening (gNBS). There is increasing recognition of the importance of incorporating patients' needs and general public perspectives into the shared decision-making process regarding gNBS. This study is part of the Innovative Medicine Initiative project Screen4Care which aims at shortening the diagnostic journey for RDs by accelerating diagnosis for patients living with RDs through gNBS and the use of digital technologies, such as artificial intelligence and machine learning. Our objective will be to assess expecting parent's perspectives, attitudes and preferences regarding gNBS for RDs in Italy and Germany. METHODS AND ANALYSIS: A mixed method approach will assess perspectives, attitudes and preferences of (1) expecting parents seeking genetic consultation and (2) 'healthy' expecting parents from the general population in two countries (Germany and Italy). Focus groups and interviews using the nominal group technique and ranking exercises will be performed (qualitative phase). The results will inform the treatment of attributes to be assessed via a survey and a discrete choice experiment (DCE). The total recruitment sample will be 2084 participants (approximatively 1000 participants in each country for the online survey). A combination of thematic qualitative and logit-based quantitative approaches will be used to analyse the results of the study. ETHICS AND DISSEMINATION: This study has been approved by the Erlangen University Ethics Committee (22-246_1-B), the Freiburg University Ethics Committee (23-1005 S1-AV) and clinical centres in Italy (University of FerraraCE: 357/2023/Oss/AOUFe and Hospedale Bambino Gesu: No.2997 of 2 November 2023, Prot. No. _902) and approved for data storage and handling at the Uppsala University (2022-05806-01). The dissemination of the results will be ensured via scientific journal publication (open access).

Exome copy number variant detection, analysis, and classification in a large cohort of families with undiagnosed rare genetic disease.

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and, with new innovative methods, can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the Genomics Research to Elucidate the Genetics of Rare Diseases consortium and analyzed using the seqr platform. The addition of CNV detection to exome analysis identified causal CNVs for 171 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb. The causal CNVs consisted of 140 deletions, 15 duplications, 3 suspected complex structural variants (SVs), 3 insertions, and 10 complex SVs, the latter two groups being identified by orthogonal confirmation methods. To classify CNV variant pathogenicity, we used the 2020 American College of Medical Genetics and Genomics/ClinGen CNV interpretation standards and developed additional criteria to evaluate allelic and functional data as well as variants on the X chromosome to further advance the framework. We interpreted 151 CNVs as likely pathogenic/pathogenic and 20 CNVs as high-interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher-resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

BACKGROUND: A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. METHODS: We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. RESULTS: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. CONCLUSIONS: Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

Genomic Answers for Kids: Toward more equitable access to genomic testing for rare diseases in rural populations.

Next-generation sequencing has revolutionized the speed of rare disease (RD) diagnoses. While clinical exome and genome sequencing represent an effective tool for many RD diagnoses, there is room to further improve the diagnostic odyssey of many RD patients. One recognizable intervention lies in increasing equitable access to genomic testing. Rural communities represent a significant portion of underserved and underrepresented individuals facing additional barriers to diagnosis and treatment. Primary care providers (PCPs) at local clinics, though sometimes suspicious of a potential benefit of genetic testing for their patients, have significant constraints in pursuing it themselves and rely on referrals to specialists. Yet, these referrals are typically followed by long waitlists and significant delays in clinical assessment, insurance clearance, testing, and initiation of diagnosis-informed care management. Not only is this process time intensive, but it also often requires multiple visits to urban medical centers for which distance may be a significant barrier to rural families. Therefore, providing early, "direct-to-provider" (DTP) local access to unrestrictive genomic testing is likely to help speed up diagnostic times and access to care for RD patients in rural communities. In a pilot study with a PCP clinic in rural Kansas, we observed a minimum 5.5 months shortening of time to diagnosis through the DTP exome sequencing program as compared to rural patients receiving genetic testing through the "traditional" PCP-referral-to-specialist scheme. We share our experience to encourage future partnerships beyond our center. Our efforts represent just one step in fostering greater diversity and equity in genomic studies.

Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders.

BACKGROUND: In the process of finding the causative variant of rare diseases, accurate assessment and prioritization of genetic variants is essential. Previous variant prioritization tools mainly depend on the in-silico prediction of the pathogenicity of variants, which results in low sensitivity and difficulty in interpreting the prioritization result. In this study, we propose an explainable algorithm for variant prioritization, named 3ASC, with higher sensitivity and ability to annotate evidence used for prioritization. 3ASC annotates each variant with the 28 criteria defined by the ACMG/AMP genome interpretation guidelines and features related to the clinical interpretation of the variants. The system can explain the result based on annotated evidence and feature contributions. RESULTS: We trained various machine learning algorithms using in-house patient data. The performance of variant ranking was assessed using the recall rate of identifying causative variants in the top-ranked variants. The best practice model was a random forest classifier that showed top 1 recall of 85.6% and top 3 recall of 94.4%. The 3ASC annotates the ACMG/AMP criteria for each genetic variant of a patient so that clinical geneticists can interpret the result as in the CAGI6 SickKids challenge. In the challenge, 3ASC identified causal genes for 10 out of 14 patient cases, with evidence of decreased gene expression for 6 cases. Among them, two genes (HDAC8 and CASK) had decreased gene expression profiles confirmed by transcriptome data. CONCLUSIONS: 3ASC can prioritize genetic variants with higher sensitivity compared to previous methods by integrating various features related to clinical interpretation, including features related to false positive risk such as quality control and disease inheritance pattern. The system allows interpretation of each variant based on the ACMG/AMP criteria and feature contribution assessed using explainable AI techniques.

Collaborative research in myositis-related disorders: MIHRA, a global shared community model.

Myositis International Health and Research Collaborative Alliance (MIHRA) is a newly formed purpose-built non-profit charitable research organization dedicated to accelerating international clinical trial readiness, global professional and lay education, career development and rare disease advocacy in IIM-related disorders. In its long form, the name expresses the community's scope of engagement and intent. In its abbreviation, MIHRA, conveys linguistic roots across many languages, that reflects the IIM community's spirit with meanings such as kindness, community, goodness, and peace. MIHRA unites the global multi-disciplinary community of adult and pediatric healthcare professionals, researchers, patient advisors and networks focused on conducting research in and providing care for pediatric and adult IIM-related disorders to ultimately find a cure. MIHRA serves as a resourced platform for collaborative efforts in investigator-initiated projects, consensus guidelines for IIM assessment and treatment, and IIM-specific career development through connecting research networks.MIHRA's infrastructure, mission, programming and operations are designed to address challenges unique to rare disease communities and aspires to contribute toward transformative models of rare disease research such as global expansion and inclusivity, utilization of community resources, streamlining ethics and data-sharing policies to facilitate collaborative research. Herein, summarises MIHRA operational cores, missions, vision, programming and provision of community resources to sustain, accelerate and grow global collaborative research in myositis-related disorders.

A propósito de un caso de síndrome de Fraser. Autopsia de un feto de 37 semanas de gestación con múltiples malformaciones / About a case of Fraser syndrome. Autopsy of a 37 weeks gestation fetus with multiple malformations

El síndrome de Fraser o síndrome criptoftalmos/sindactilia es una enfermedad genética rara, cuyo diagnóstico se basa en una serie de criterios clínicos mayores y menores, y que puede apoyarse en pruebas genéticas. En este artículo se presenta el caso de una autopsia fetal de 37 semanas de gestación con sospecha de síndrome de CHAOS (síndrome obstructivo congénito de las vías aéreas altas). (AU)

Fraser syndrome or cryptophthalmos-syndactyly syndrome is a rare genetic disease, the diagnosis of which is based on a series of major and minor clinical criteria and that can be supported by genetic tests. This article presents the case of a fetal autopsy at 37 weeks of gestation with suspicion of CHAOS syndrome (congenital obstructive syndrome of the upper airways). (AU)

A propósito de un caso de síndrome de Fraser. Autopsia de un feto de 37 semanas de gestación con múltiples malformaciones / About a case of Fraser syndrome. Autopsy of a 37 weeks gestation fetus with multiple malformations

El síndrome de Fraser o síndrome criptoftalmos/sindactilia es una enfermedad genética rara, cuyo diagnóstico se basa en una serie de criterios clínicos mayores y menores, y que puede apoyarse en pruebas genéticas. En este artículo se presenta el caso de una autopsia fetal de 37 semanas de gestación con sospecha de síndrome de CHAOS (síndrome obstructivo congénito de las vías aéreas altas). (AU)

Fraser syndrome or cryptophthalmos-syndactyly syndrome is a rare genetic disease, the diagnosis of which is based on a series of major and minor clinical criteria and that can be supported by genetic tests. This article presents the case of a fetal autopsy at 37 weeks of gestation with suspicion of CHAOS syndrome (congenital obstructive syndrome of the upper airways). (AU)

Diagnosis of alpha-Mannosidosis: Practical approaches to reducing diagnostic delays in this ultra-rare disease.

Alpha-mannosidosis is an ultra-rare lysosomal disease that is caused by variants of the MAN2B1 gene on chromosome 19p13. These variants result in faulty or absent alpha-mannosidase in lysosomes, which leads to intracellular accumulation of mannose-containing oligosaccharides. Diagnosis of alpha-mannosidosis is often delayed, in part because of the rarity of the disease, its gradual onset and heterogeneity of presentation, but also because of the similarity of many signs and symptoms of the disease to those of other lysosomal diseases. Treatment of alpha-mannosidosis was previously limited to hematopoietic stem cell transplantation, but outcomes are variable and not all patients are eligible or have a suitable donor. Recently, an enzyme replacement therapy, recombinant human alpha-mannosidase (velmanase alfa), was approved for the treatment of non-neurological manifestations in adult and pediatric patients with alpha-mannosidosis. Treatment with velmanase alfa reduces serum levels of oligosaccharides, increases levels of immunoglobulin G, and improves patients' functional capacity and quality of life, although it is not effective for the neurologic phenotype because it does not cross the blood-brain barrier. Since the effects of velmanase alfa are more marked in children than adults, early diagnosis to allow early initiation of treatment has become more important. To support this, patient, parent/caregiver, and clinician awareness and education is imperative. A number of approaches can be taken to meet this goal, such as the development of disease registries, validated diagnostic algorithms, and screening tools, improved under-/post-graduate clinician education, easily accessible and reliable information for patients/families (such as that made available on the internet), and the formation of patient advocacy groups. Such approaches may raise awareness of alpha-mannosidosis, reduce the diagnostic delay and thus improve the lives of those affected.