Mutations in STAT3 and diagnostic guidelines for hyper-IgE syndrome

Authors	WOELLNER Cristina GERTZ E. Michael SCHAFFER Alejandro A. LAGOS Macarena PERRO Mario GLOCKER Erik-Oliver PIETROGRANDE Maria C. COSSU Fausto FRANCO José L. MATAMOROS Nuria PIETRUCHA Barbara HEROPOLITANSKA-PLISZKA Edyta YEGANEH Mehdi MOIN Mostafa ESPAŇOL Teresa EHL Stephan GENNERY Andrew ABINUM Mario BREBOROWICZ Anna NIEHUES Tim KILIC Sebnem Sara JUNKER Anna TURVEY Stuart E. PLEBANI Alesandro SÁNCHEZ Berta GARTY Ben-Zion PIGNATA Claudio CANCRINI Caterina LITZMAN Jiří SANAL Ozden BAUMANN Ulrich BACCHETA Rosa HSU Amy P. DAVIS Joie N. HAMMARSTROM Lennart DAVIES Graham E. EREN Efrem ARKWRIGHT Peter D. MOILANEN Jukka VIEMANN Dorothe KHAN Sujoy MARODI Lászlo CANT Andrew J. FREEMAN Alexandra F. PUCK Jennifer M. HOLLAND Steven M. GRIMBACHER Bodo
Year of publication	2010
Type	Article in Periodical
Magazine / Source	J Allergy Clin Immunol
MU Faculty or unit	Faculty of Medicine
Citation
Field	Immunology
Keywords	hyper-IgE syndrome; STAT3; diagnostic giudelines
Description	The hyper-IgE syndrome (HIES) is a primary immunodeficiency characterized by infections of the lung and skin, elevated serum IgE, and involvement of the soft and bony tissues. Recently, HIES has been associated with heterozygous dominant-negative mutations in the signal transducer and activator of transcription 3 (STAT3) and severe reductions of T(H)17 cells. OBJECTIVE: To determine whether there is a correlation between the genotype and the phenotype of patients with HIES and to establish diagnostic criteria to distinguish between STAT3 mutated and STAT3 wild-type patients. METHODS: We collected clinical data, determined T(H)17 cell numbers, and sequenced STAT3 in 100 patients with a strong clinical suspicion of HIES and serum IgE >1000 IU/mL. We explored diagnostic criteria by using a machine-learning approach to identify which features best predict a STAT3 mutation. RESULTS: In 64 patients, we identified 31 different STAT3 mutations, 18 of which were novel. These included mutations at splice sites and outside the previously implicated DNA-binding and Src homology 2 domains. A combination of 5 clinical features predicted STAT3 mutations with 85% accuracy. T(H)17 cells were profoundly reduced in patients harboring STAT3 mutations, whereas 10 of 13 patients without mutations had low (<1%) T(H)17 cells but were distinct by markedly reduced IFN-gamma-producing CD4(+)T cells. CONCLUSION: We propose the following diagnostic guidelines for STAT3-deficient HIES. Possible: IgE >1000IU/mL plus a weighted score of clinical features >30 based on recurrent pneumonia, newborn rash, pathologic bone fractures, characteristic face, and high palate. Probable: These characteristics plus lack of T(H)17 cells or a family history for definitive HIES. Definitive: These characteristics plus a dominant-negative heterozygous mutation in STAT3