Here, we report the 2024 update of the phenotypic classification by the International Union of Immunological Societies (IUIS) expert committee (EC) on inborn errors of immunity (IEI), which accompanies and complements the 2024 genotypic classification. The aim of this classification is to help diagnosis for clinicians at the bedside and focuses on clinical features and basic laboratory phenotypes of specific IEI. In this update, 559 IEI are described, including 67 novel monogenic defects and 2 new phenocopies. This phenotypic classification is presented in the form of decision trees when possible, with essential clinical or immunological phenotype entries.
Introduction
Human inborn errors of immunity (IEI) include a large group of disorders resulting from genetic defects that compromise innate and adaptive immunity, non-hematopoietic cell-mediated immunity, as well as immune regulation. They can be dominantly or recessively inherited, autosomal or X-linked, and with complete or incomplete penetrance of the clinical phenotype. Patients can present with increased susceptibility to a broad or narrow spectrum of infectious diseases, as well as autoimmune, autoinflammatory, allergic, and/or malignant diseases. The number of disorders being discovered is growing at an unprecedented rate since the development of next-generation sequencing, including not only rare but also common genetic defects (1). Progress in the molecular genetics and cellular immunology of IEI has resulted in the development of innovative, preventive, and therapeutic approaches (2).
In 2024, the International Union of Immunological Societies (IUIS) expert committee on IEI added 67 novel monogenic defects and 2 phenocopies in the classification (3). While most IEI are individually rare, as a group they represent a major cause of morbidity and mortality—particularly so in the case of childhood disease (4).
Since 2013, the IUIS IEI expert committee has periodically published an updated phenotypic classification of all these disorders, which facilitates the diagnosis of these conditions worldwide. Organized as diagnostic algorithms, this phenotypic classification was also adapted for smartphone applications (5).
Here, we report the 2024 update of the phenotypic classification of IEI reported and evaluated until June 2024. This decision tree–based process is aimed at physicians, regardless of their expertise in and knowledge of IEI. Its purpose is to guide the physician toward the most probable diagnosis based on the clinical and laboratory features of their patient.
Methodology
All disorders indexed in the 2024 update of the IUIS IEI classification (3) are included in phenotypic algorithms assigned to each of the 10 main groups/tables of the classification, except for phenocopies that were integrated in their respective phenotypic group. The same color was used for each group of similar conditions. Given the exponential number of diseases, several categories have been divided into two or three sub-figures to be more informative. New disorders or new genes causing a known disorder are highlighted with a red frame.
A new decision tree has been added in the first step to guide physician through the best fitted category based on the main clinical features (Fig. 1).
Disease names are presented in red (darker red for phenocopies) and genes in bold italic. The OMIM number for phenotype has been added and is preceded by a #. When no OMIM phenotype is available, an asterisk precedes the OMIM code for the gene.
An asterisk is added to highlight extremely rare disorders (<10 reported cases or kindreds to the best of our knowledge). However, the reader should keep in mind that some genes have only been very recently described and that the true prevalence of individual IEIs is unknown. A double asterisk indicates that only a single case or single kindred affected by the indicated genotype has been reported to date. In these cases, it is difficult to confirm that the observed phenotype would be reproducible in other patients carrying the same defect or if it is an atypical presentation.
Results
Discussion
These algorithms present the typical phenotype described for each disorder. However, clinicians should keep in mind the limitations of such an approach. First, the phenotypes of a given IEI are continuously expanding with the identification and clinical description of more patients. Moreover, hypomorphic or even neomorphic variants in a given IEI gene can present atypically. Second, there is the well-known incomplete penetrance and incomplete expressivity of the phenotype, due to autosomal random monoallelic expression (6). Moreover, from a practical point of view, the growing number of disorders to include in these tables makes them less and less readable. Here, we tried to reduce phenotypic complexity to the most relevant features and provided OMIM numbers to complete the clinical synopsis.
The clinical phenotype of patients with the same and different IEI may be quite variable and overlapping, respectively. This is related to pleiotropic effects and the genotype-phenotype relationship, which may not be fully appreciated with the first description of these novel genetic disease entities. Special caution is warranted when first publications report only one or a few cases.
We aimed to simplify as much as possible the classification, and this is probably our biggest limitation. Many disorders could have been included in several categories, and some secondary features (based on typical presentation) could have been present before the predominant features reported here. So users should be aware that the correct diagnosis is not always reached at the first try and consider the complete clinical and laboratory presentation when navigating through the decision tree–based process.
Based on these facts, our algorithms suggest the possible genotype and the lab tests useful for a more precise diagnosis to help in genetic diagnosis. However, with many overlapping phenotypes, the recommendation for genetic diagnostic testing would be the use of broad panels/exome, rather than targeted panels, except for a few specific diseases, such as X-linked agammaglobulinemia for which a logical rationale can be applied (7).
Conclusion
This phenotypic classification of IEI should be used as a diagnostic resource, aimed to complement the 2024 IUIS genotypic classification. This user-friendly diagnostic orientation tool provides a basic approach for physicians and biologists who are not necessarily experts in the field of IEI. This can help them to reach a probable diagnosis for patients with clinical or biological features evocative of IEI and guide them in exploration of such patients.
Acknowledgments
The members of the IEI committee would like to thank the International Union of Immunological Societies for funding, as well as CSL Behring, Baxalta, and Shire/Takeda for providing educational grants to enable us to compile this update to novel causes of immune diseases. This work was also supported in part by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases, National Institutes of Health.
I. Meyts is a senior clinical investigator at the Fonds Wetenschappelijk Onderzoek—Flanders and is supported by the CSL Behring Chair of Primary Immunodeficiencies and by the Jeffrey Modell Foundation. This project has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 948959). This work is supported by The European Reference Network on immunodeficiency, autoinflammatory, autoimmune diseases and paediatric rheumatology (ERN-RITA). S.G. Tangye is supported by an Investigator Grant (Leadership 3; 1176665) awarded by the National Health and Medical Research Council of Australia.
Author contributions: A.A. Bousfiha: conceptualization, methodology, supervision, visualization, and writing—original draft, review, and editing. L. Jeddane: methodology, visualization, and writing—original draft. A. Moundir: data curation, visualization, and writing—original draft, review, and editing. M.C. Poli: data curation and writing—review and editing. I. Aksentijevich: conceptualization, data curation, and writing—review and editing. C. Cunningham-Rundles: conceptualization, data curation, resources, and writing—review and editing. S. Hambleton: investigation and writing—review and editing. C. Klein: conceptualization, investigation, validation, and writing—review and editing. T. Morio: conceptualization, data curation, and validation. C. Picard: resources, validation, and writing—review and editing. A. Puel: writing—review and editing. N. Rezaei: conceptualization, formal analysis, investigation, methodology, project administration, supervision, validation, and writing—original draft, review, and editing. M.R.J. Seppänen: data curation, formal analysis, resources, and writing—review and editing. R. Somech: conceptualization, formal analysis, investigation, methodology, and validation. H.C. Su: writingreview and editing. K.E. Sullivan: conceptualization, data curation, and writing—review and editing. T.R. Torgerson: formal analysis, investigation, and writing—review and editing. S.G. Tangye: conceptualization, project administration, supervision, and writing—original draft, review, and editing. I. Meyts: conceptualization, data curation, supervision, validation, and writing—review and editing.
Ethics approval: No human research studies were performed to produce this classification. Thus, no approvals by appropriate institutional review boards or human research ethics committees were required to undertake the preparation of this report.
References
