Accurate Classification of NF1 Gene Variants in 84 Italian Patients with Neurofibromatosis Type 1.
Alessandro StellaPatrizia LastellaDaria Carmela LoconteNenad BukvicDora VarvaraMargherita PatrunoRosanna BagnuloRosaura LovaglioBartolomeo NicolaGabriella SerioNicoletta RestaPublished in: Genes (2018)
Neurofibromatosis type 1 (NF1) is one of the most common autosomal dominant genetic diseases. It is caused by mutations in the NF1 gene encoding for the large protein, neurofibromin. Genetic testing of NF1 is cumbersome because 50% of cases are sporadic, and there are no mutation hot spots. In addition, the most recognizable NF1 clinical features—café-au-lait (CALs) spots and axillary and/or inguinal freckling—appear early in childhood but are rather non-specific. Thus, the identification of causative variants is extremely important for early diagnosis, especially in paediatric patients. Here, we aimed to identify the underlying genetic defects in 72 index patients referred to our centre for NF1. Causative mutations were identified in 58 subjects, with 29 being novel changes. We evaluated missense and non-canonical splicing mutations with both protein and splicing prediction algorithms. The ratio of splicing mutations detected was higher than that reported in recent patients’ series and in the Human Gene Mutation Database (HGMD). After applying in silico predictive tools to 41 previously reported missense variants, we demonstrated that 46.3% of these putatively missense mutations were forecasted to alter splicing instead. Our data suggest that mutations affecting splicing can be frequently underscored if not analysed in depth. We confirm that hamartomas can be useful for diagnosing NF1 in children. Lisch nodules and cutaneous neurofibromas were more frequent in patients with frameshifting mutations. In conclusion, we demonstrated that comprehensive in silico analysis can be a highly specific method for predicting the nature of NF1 mutations and may help in assuring proper patient care.
Keyphrases
- signaling pathway
- lps induced
- end stage renal disease
- copy number
- oxidative stress
- ejection fraction
- pi k akt
- nuclear factor
- newly diagnosed
- prognostic factors
- peritoneal dialysis
- intellectual disability
- intensive care unit
- emergency department
- deep learning
- patient reported outcomes
- machine learning
- high resolution
- inflammatory response
- radiation therapy
- squamous cell carcinoma
- mass spectrometry
- molecular docking
- toll like receptor
- artificial intelligence
- amino acid
- patient reported
- binding protein
- data analysis
- rectal cancer
- drug induced
- pluripotent stem cells