Genetic diagnosis of neurofibromatosis type 1: targeted next- generation sequencing with Multiple Ligation-Dependent Probe Amplification analysis

型神经纤维瘤病的基因诊断:利用多重连接依赖性探针扩增分析进行靶向下一代测序

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作者:Yah-Huei Wu-Chou, Tzu-Chao Hung, Yin-Ting Lin, Hsing-Wen Cheng, Ju-Li Lin, Chih-Hung Lin, Chung-Chih Yu, Kuo-Ting Chen, Tu-Hsueh Yeh, Yu-Ray Chen

Background

Neurofibromatosis type 1 (NF1) is a dominantly inherited tumor predisposition syndrome that targets the peripheral nervous system. It is caused by mutations of the NF1 gene which serve as a negative regulator of the cellular Ras/MAPK (mitogen-activated protein kinases) signaling pathway. Owing to the complexity in some parts of clinical diagnoses and the need for better understanding of its molecular relationships, a genetic characterization of this disorder will be helpful in the clinical setting.

Conclusions

Our results suggested that a genetic screening using a NGS panel with high coverage of Ras-signaling components combined with Multiple Ligation-Dependent Probe Amplification analysis will enable differential diagnosis of patients with overlapping clinical features.

Methods

In this study, we present a customized targeted gene panel of NF1/KRAS/BRAF/p53 and SPRED1 genes combined with Multiple Ligation-Dependent Probe Amplification analysis for the NF1 mutation screening in a cohort of patients clinically suspected as NF1.

Results

In this study, we identified 73 NF1 mutations and two BRAF novel variants from 100 NF1 patients who were suspected as having NF1. These genetic alterations are heterogeneous and distribute in a complicated way without clustering in either cysteine-serine-rich domain or within the GAP-related domain. We also detected fifteen multi-exon deletions within the NF1 gene by MLPA Analysis. Conclusions: Our results suggested that a genetic screening using a NGS panel with high coverage of Ras-signaling components combined with Multiple Ligation-Dependent Probe Amplification analysis will enable differential diagnosis of patients with overlapping clinical features.

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