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Enregistrement W4417004439 · doi:10.1182/blood-2025-630

Germline genetic testing uncovers a high frequency of inborn error of immunity diagnoses in children with single and multi-lineage immune cytopenias in a large US cohort

2025· article· en· W4417004439 sur OpenAlexaff
Emily Harris, Thomas Pincez, Kirsty Hillier, Yi-Lee Ting, Jennifer MacWhirter - DiRaimo, Hannah L. Helber, Candelaria O’Farrell, Estelle Lecluze, Taylor Kim, Rachael F. Grace, Michael E. Scheurer, Rebecca Hale, Kristin A. Shimano, Diane J. Nugent, Ellis J. Neufeld, Craig D. Platt, Michele P. Lambert, Amanda B. Grimes, Shipra Kaicker

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueBlood disorders and treatments
Établissements canadiensUniversité de Montréal
Organismes subventionnairesnon disponible
Mots-clésAutoimmune hemolytic anemiaGenetic testingNewborn screeningPrimary immunodeficiencyImmune systemNeutropeniaAutoimmunityCommon variable immunodeficiency

Résumé

récupéré en direct d'OpenAlex

Abstract Background Immune cytopenias including Immune Thrombocytopenia (ITP), Autoimmune Hemolytic Anemia (AIHA), or multilineage immune cytopenias/Evans syndrome (ES) can have variable clinical courses ranging from self-resolving to chronic or relapsing. They may occur in isolation or as the initial presentation of an underlying inborn error of immunity (IEI). Recent studies have found a high frequency of disease-causing variants in immune-related genes in children with ES. However, little is known about variants associated with single-lineage cytopenias. Aim To identify the prevalence of pathogenic (P) and likely pathogenic (LP) variants identified via clinical genetic testing in children with immune cytopenias. Methods This ITP Consortium of North America (ICON)-led retrospective study included children evaluated for immune cytopenias at centers across the United States who underwent clinical genetic testing through a commercial clinical laboratory (Invitae, now part of Labcorp). Included patients were age 0-21 years at time of genetic testing and had a diagnosis of ITP, AIHA, or ES as determined by clinician-provided ICD-10 code and/or recorded indication for genetic testing. Patients with isolated neutropenia were excluded from this study. Patients were included if their clinical genetic testing included any of the following targeted next generation sequencing (NGS) panels performed: Inborn Errors of Immunity and Cytopenias panel, Primary Immunodeficiency Panel, Autoimmune Lymphoproliferative Disorders (ALPS) Panel, Phagocytic Disorders Including Neutropenia Panel, Autoinflammatory and Autoimmunity Syndromes Panel, and/or Common Variable Immunodeficiency Panel. Variants identified as P or LP (P/LP), as determined by a semiquantitative, validated variant classification framework were evaluated in this study. Variants of uncertain significance were not included. Molecular IEI diagnosis was defined as presence of a single P/LP variant in monoallelic/autosomal dominant gene (or X-linked for individuals who are XY) or presence of two P/LP variants in a biallelic/autosomal recessive gene, depending on the gene and International Union of Immunological Societies (IUIS) classification. Results In total, there were 715 patients with immune cytopenias identified who had targeted NGS panels: 430 (60.1%) with ITP, 119 (16.6%) with AIHA, and 166 (23.2%) with ES. Median age at time of testing was 12 years (range 0-21) and 47.4% (n=339) were female. The frequency of patients with P/LP variants was 30.1% (215/715) in the total cohort, including 30.9% (133/430) in patients with ITP, 30.3% (36/119) in patients with AIHA, and 27.7% (46/166) in patients with ES. The most common variants identified were in genes related to diseases of immune dysregulation (37 patients), combined immunodeficiencies with associated syndromic features (33 patients), and immunodeficiencies affecting cellular and humoral immunity (29 patients). However, variants were spread across many IUIS categories at lower frequencies. Molecular IEI diagnoses were found in a subset of patients with P/LP variants. The frequency of molecular IEI diagnoses in the entire cohort was 7.7% (55/715), including 6.5% (28/430) in patients with ITP, 9.2% (11/119) in patients with AIHA, and 9.6% (16/166) in patients with ES. Median age of patients with IEI diagnoses was 13 years (range 2-20) and 30.9% (n=17) were female. The most common genetically identified conditions were related to dominantly inherited variants in TBX1 (n=8), CTLA4 (n=7), and STAT3 (n=4), associated with DiGeorge syndrome, CTLA4 haploinsufficiency, and STAT3 gain-of-function, respectively. Additionally, heterozygous TNFRSF13B variants were identified in 16 patients. Monoallelic variants in TNFRSF13B have been associated with increased risk of CVID with autoimmunity. Conclusions Genetically defined IEI were identified in approximately 7.7% of children with immune cytopenias who underwent genetic testing, and with similar frequency in children with single- and multi-lineage cytopenias. The high rate of genetic diagnoses in patients with single and multi-lineage immune cytopenias who were tested supports the clinical utility of genetic testing in these patients. Establishing these diagnoses informs selection of targeted therapies and potential need for stem cell transplant. Molecular diagnosis offers the opportunity for rational selection of targeted therapies rather than empiric broad immunosuppression.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,018

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,007
Tête enseignante GPT0,229
Écart entre enseignants0,222 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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