Somatic GNA11/GNAQ variants in a cohort of Chinese children with phakomatosis pigmentovascularis

Importance:Postzygotic mutations in the GNAQ/GNA11 genes,which encode the G-protein nucleotide binding protein alpha subunits,have been identified in patients with phakomatosis pigmentovascularis(PPV).However,little is known about the Chinese population.Objective:To identify pathogenic mutations in pediatric patients with PPV within the Chinese population.Methods:We performed whole-exome sequencing(WES)using skin lesion tissues from pediatric patients diagnosed with PPV.Additionally,ultradeep-targeted sequencing was conducted to validate the somatic mutations.A genotype-phenotype correlation was analyzed by integrating data from previous reports with the findings of the present study.Results:Thirteen patients were enrolled,all diagnosed with the cesioflammea type of PPV,except for one patient with an unclassifiable type.We identified somatic GNA11 c.547C>T(p.R183C)variant in seven patients and GNAQ c.548G>A(p.R183Q)in four patients,with low allelic fractions ranging from 2.1%to 8.6%through ultradeep sequencing.Besides,a GNAQ c.548G>A(p.R183Q)variant was detected through targeted sequencing in one of two patients who did not exhibit detectable variants via WES.The genotype-phenotype correlation analysis,involving 15 patients with a GNA11 variant and 10 with a GNAQ variant,revealed that facial capillary malformation(87%vs.50%,P=0.075)and ocular melanocytosis(80%vs.40%,P=0.087)appeared to be more frequent in patients with GNA11 mutation compared to those with GNAQ mutations.All four patients diagnosed with cesiomarmorata type or overlapping cesioflammea and cesiomarmorata type PPV carried the GNA11 variant.Interpretation:Our study demonstrated that the majority of PPV patients in the Chinese population carried a postzygotic variant of GNAQ/GNA11,thus further confirming the pathogenic role of GNAQ/GNA11 mosaicism in the development of PPV cesioflammea type.

DeepFilter: A Deep Learning Based Variant Filter for VarDict

With the development of sequencing technologies,somatic mutation analysis has become an important component in cancer research and treatment.VarDict is a commonly used somatic variant caller for this task.Although the heuristic-based VarDict algorithm exhibits high sensitivity and versatility,it may detect higher amounts of false positive variants than callers,limiting its clinical practicality.To address this problem,we propose DeepFilter,a deep-learning based filter for VarDict,which can filter out the false positive variants detected by VarDict effectively.Our approach trains two models for insertion-deletion mutations(InDels)and single nucleotide variants(SNVs),respectively.Experiments show that DeepFilter can filter at least 98.5%of false positive variants and retain 93.5%of true positive variants for InDels and SNVs in the commonly used tumor-normal paired mode.Source code and pre-trained models are available at https://github.com/LeiHaoa/DeepFilter.