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A lack of robust knowledge of the number of rare diseases and the number of people affected by them limits the development of approaches to ameliorate the substantial cumulative burden of rare diseases. Here, we call for coordinated efforts to more precisely define rare diseases.A lack of robust knowledge of the number of rare diseases and the number of people affected by them limits the development of approaches to ameliorate the substantial cumulative burden of rare diseases. Here, we call for coordinated efforts to more precisely define rare diseases.

Base editors can correct disease-causing genetic variants. After a neonate had received a diagnosis of severe carbamoyl-phosphate synthetase 1 deficiency, a disease with an estimated 50% mortality in early infancy, we immediately began to develop a customized lipid nanoparticle–delivered base-editing therapy. After regulatory approval had been obtained for the therapy, the patient received two infusions at approximately 7 and 8 months of age. In the 7 weeks after the initial infusion, the patient was able to receive an increased amount of dietary protein and a reduced dose of a nitrogen-scavenger medication to half the starting dose, without unacceptable adverse events and despite viral illnesses. No serious adverse events occurred. Longer follow-up is warranted to assess safety and efficacy. (Funded by the National Institutes of Health and others.) A lipid nanoparticle–delivered base-editing therapy was custom designed for an infant with a urea-cycle disorder. The affected infant was treated at approximately 7 and 8 months of age.

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. 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. 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. 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.).

For many diseases there are delays in diagnosis due to a lack of objective biomarkers for disease onset. Here, in 41,931 individuals from the United Kingdom Biobank Pharma Proteomics Project, we integrated measurements of ~3,000 plasma proteins with clinical information to derive sparse prediction models for the 10-year incidence of 218 common and rare diseases (81–6,038 cases). We then compared prediction models developed using proteomic data with models developed using either basic clinical information alone or clinical information combined with data from 37 clinical assays. The predictive performance of sparse models including as few as 5 to 20 proteins was superior to the performance of models developed using basic clinical information for 67 pathologically diverse diseases (median delta C-index = 0.07; range = 0.02–0.31). Sparse protein models further outperformed models developed using basic information combined with clinical assay data for 52 diseases, including multiple myeloma, non-Hodgkin lymphoma, motor neuron disease, pulmonary fibrosis and dilated cardiomyopathy. For multiple myeloma, single-cell RNA sequencing from bone marrow in newly diagnosed patients showed that four of the five predictor proteins were expressed specifically in plasma cells, consistent with the strong predictive power of these proteins. External replication of sparse protein models in the EPIC-Norfolk study showed good generalizability for prediction of the six diseases tested. These findings show that sparse plasma protein signatures, including both disease-specific proteins and protein predictors shared across several diseases, offer clinically useful prediction of common and rare diseases. Proteomic prediction models developed using a large-scale dataset from the UK Biobank Pharma Proteomics Project were superior to clinical models for assessing the 10-year risk of 67 diseases across different types of pathology, including multiple myeloma, motor neuron disease, pulmonary fibrosis, celiac disease and dilated cardiomyopathy.

Background Graves’ orbitopathy (GO) is an autoimmune condition, which is associated with poor clinical outcomes including impaired quality of life and socio-economic status. Current evidence suggests that the incidence of GO in Europe may be declining, however data on the prevalence of this disease are sparse. Several clinical variants of GO exist, including euthyroid GO, recently listed as a rare disease in Europe (ORPHA466682). The objective was to estimate the prevalence of GO and its clinical variants in Europe, based on available literature, and to consider whether they may potentially qualify as rare. Recent published data on the incidence of GO and Graves’ hyperthyroidism in Europe were used to estimate the prevalence of GO. The position statement was developed by a series of reviews of drafts and electronic discussions by members of the European Group on Graves’ Orbitopathy. The prevalence of GO in Europe is about 10/10,000 persons. The prevalence of other clinical variants is also low: hypothyroid GO 0.02–1.10/10,000; GO associated with dermopathy 0.15/10,000; GO associated with acropachy 0.03/10,000; asymmetrical GO 1.00–5.00/10,000; unilateral GO 0.50–1.50/10,000. Conclusion GO has a prevalence that is clearly above the threshold for rarity in Europe. However, each of its clinical variants have a low prevalence and could potentially qualify for being considered as a rare condition, providing that future research establishes that they have a distinct pathophysiology. EUGOGO considers this area of academic activity a priority.

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

Efforts to identify the genetic underpinnings of rare undiagnosed diseases increasingly involve the use of next-generation sequencing and comparative genomic hybridization methods. These efforts are limited by a lack of knowledge regarding gene function, and an inability to predict the impact of genetic variation on the encoded protein function. Diagnostic challenges posed by undiagnosed diseases have solutions in model organism research, which provides a wealth of detailed biological information. Model organism geneticists are by necessity experts in particular genes, gene families, specific organs, and biological functions. Here, we review the current state of research into undiagnosed diseases, highlighting large efforts in North America and internationally, including the Undiagnosed Diseases Network (UDN) (Supplemental Material, File S1) and UDN International (UDNI), the Centers for Mendelian Genomics (CMG), and the Canadian Rare Diseases Models and Mechanisms Network (RDMM). We discuss how merging human genetics with model organism research guides experimental studies to solve these medical mysteries, gain new insights into disease pathogenesis, and uncover new therapeutic strategies.

Timely diagnosis is one of the most serious challenges faced by people living with a rare disease (PLWRD), and this study estimates that in Europe, the average total diagnosis time (TDT) is close to 5 years. We investigated the duration of the TDT for PLWRD in Europe, the difficulties associated with their diagnosis odyssey and the main determinants of diagnosis delays for all rare diseases (RD). We conducted a survey of PLWRD and their families using Rare Barometer, the survey initiative of EURORDIS-Rare Diseases Europe. In geographical Europe, we surveyed 6507 people living with 1675 RD in 41 countries. We then performed a descriptive analysis and ordinal logistic regressions to identify the main determinants of diagnosis delays. Average TDT is 4.7 years. 56% of respondents were diagnosed more than 6 months after a first medical contact. The main determinants of diagnosis delays are symptom onset before 30 years of age, especially during childhood (OR = 3.11; 95% CI: 2.4–4.0) and adolescence (OR = 4.79; 95% CI: 3.7–6.2), being a woman (OR = 1.22; 95% CI:1.1–1.4), living in Northern Europe (OR = 2.15; 95% CI:1.8–2.6) or Western Europe (OR = 1.96; 95% CI:1.6–2.3), the number of healthcare professionals consulted (OR = 5.15; 95% CI:4.1–6.4), misdiagnosis (OR = 2.48; 95% CI:2.1–2.9), referral to a centre of expertise (OR = 1.17; 95% CI:1.0–1.3), unmet needs for psychological support (OR = 1.34; 95% CI:1.2–1.5) and financial support (OR = 1.16; 95% CI:1.0–1.3), having a genetic disease (OR = 1.33; 95% CI:1.1–1.5) and a family history of an RD (OR = 1.36; 95% CI:1.1–1.6). These determinants can inform policies and actions to improve access to diagnosis for all PLWRD.

The analysis of histopathology images with artificial intelligence aims to enable clinical decision support systems and precision medicine. The success of such applications depends on the ability to model the diverse patterns observed in pathology images. To this end, we present Virchow, the largest foundation model for computational pathology to date. In addition to the evaluation of biomarker prediction and cell identification, we demonstrate that a large foundation model enables pan-cancer detection, achieving 0.95 specimen-level area under the (receiver operating characteristic) curve across nine common and seven rare cancers. Furthermore, we show that with less training data, the pan-cancer detector built on Virchow can achieve similar performance to tissue-specific clinical-grade models in production and outperform them on some rare variants of cancer. Virchow’s performance gains highlight the value of a foundation model and open possibilities for many high-impact applications with limited amounts of labeled training data. Trained on 1.5 million whole-slide images from 100,000 patients, a pathology foundation model is shown to improve performance of specialized models in detection of rare cancers.

Rare genetic diseases are rare by themselves with prevalence of 1 in 25,000, but collectively they are a significant cause of morbidity and mortality. Till date, collectively there are more than 9,000 rare diseases documented, which impose a devastating impact on patients, their families, and the healthcare system, including enormous societal burden. Obtaining a conclusive diagnosis for a patient with a rare genetic disease can be long and gruelling. For some patients it takes months or years to receive a definite diagnosis, and around 50% of the patients remain undiagnosed even with expert clinical and advanced high-end laboratory investigations. Owing to the large population and practice of consanguinity the Indian population is a pool of indigenous variants and unreported phenotypes or diseases. A mission program on pediatric rare diseases is an unparalleled initiative to study unique clinical conditions via the use of latest state-of-art technologies and with the combination of a mulit-omics approach. Our initiative will not only provide diagnosis to patients with rare disease but also build a platform for translational research for rare disease screening, management, and treatment.