Identification of rare and novel PHEX variants in X-linked hypophosphatemia.

CONTEXT: X-linked hypophosphatemia (XLH) is a rare metabolic bone disease caused by inactivation mutations in the PHEX gene. Despite the extensive number of reported PHEX variants, only a few cases of chromosomal abnormalities have been documented. OBJECTIVE: We aimed to identify the pathogenic variants in six unrelated families with a clinical diagnosis of XLH and to propose a genetic workflow for hypophosphatemia patients suspected of XLH. METHODS: Multiple genetic testing assays were used to analyze the six families' genetic profiles, including whole exome sequencing, multiplex ligation-dependent probe amplification, whole genome sequencing, reverse transcript polymerase chain reaction, Sanger sequencing, and karyotyping. RESULTS: The study identified six novel pathogenic variants, including one mosaic variant (exon 16-22 deletion), three chromosomal abnormalities (46, XN, inv[X][pter→p22.11::q21.31→p22.11::q21.31 →qter], 46, XN, inv[X][p22.11p22.11], and XXY), a nonclassical intron variant (NM_000444.6, c.1701_31A > G), and a deletion variant (NM_000444.6, c.64_5464-186 del5215) of PHEX. Additionally, a genetic testing workflow was proposed to aid in diagnosing patients suspected of XLH. CONCLUSION: Our research expands the mutation spectrum of PHEX and highlights the significance of utilizing multiple genetic testing methods to diagnose XLH.

Development and internal validation of a novel predictive model for SDHB mutations in pheochromocytomas and retroperitoneal paragangliomas.

Aim: To develop and internally validate a novel predictive model for SDHB mutations in pheochromocytomas and retroperitoneal paragangliomas (PPGLs). Methods: Clinical data of patients with PPGLs who presented to Peking Union Medical College Hospital from 2013 to 2022 and underwent genetic testing were retrospectively collected. Variables were screened by backward stepwise and clinical significance and were used to construct multivariable logistic models in 50 newly generated datasets after the multiple imputation. Bootstrapping was used for internal validation. A corresponding nomogram was generated based on the model. Sensitivity analyses were also performed. Results: A total of 556 patients with PPGLs were included, of which 99 had a germline SDHB mutation. The prediction model revealed that younger age of onset [Odds ratio (OR): 0.93, 95% CI: 0.91-0.95], synchronous metastasis (OR: 6.43, 95% CI: 2.62-15.80), multiple lesion (OR: 0.22, 95% CI: 0.09-0.54), retroperitoneal origin (OR: 5.72, 95% CI: 3.13-10.47), negative 131I-meta-iodobenzylguanidine (MIBG) (OR: 0.34, 95% CI: 0.15-0.73), positive octreotide scintigraphy (OR: 3.24, 95% CI: 1.25-8.43), elevated 24h urinary dopamine (DA) (OR: 1.72, 95% CI: 0.93-3.17), NE secretory type (OR: 2.83, 95% CI: 1.22- 6.59), normal secretory function (OR: 3.04, 95% CI: 1.04-8.85) and larger tumor size (OR: 1.09, 95% CI: 0.99-1.20) were predictors of SDHB mutations in PPGLs, and showed good and stable predictive performance with a mean area under the ROC curve (AUC) of 0.865 and coefficient of variation of 2.2%. Conclusions: This study provided a novel and useful tool for predicting SDHB mutations by integrating easily obtained clinical data. It may help clinicians select suitable genetic testing methods and make appropriate clinical decisions for these high-risk patients.