A Systematic Assessment of Accuracy in Detecting Somatic Mosaic Variants by Deep Amplicon Sequencing: Application to <i>NF2</i> Gene

<div><p>The accurate detection of low-allelic variants is still challenging, particularly for the identification of somatic mosaicism, where matched control sample is not available. High throughput sequencing, by the simultaneous and independent analysis of thousands of different DNA fragments, might overcome many of the limits of traditional methods, greatly increasing the sensitivity. However, it is necessary to take into account the high number of false positives that may arise due to the lack of matched control samples. Here, we applied deep amplicon sequencing to the analysis of samples with known genotype and variant allele fraction (VAF) followed by a tailored statistical analysis. This method allowed to define a minimum value of VAF for detecting mosaic variants with high accuracy. Then, we exploited the estimated VAF to select candidate alterations in <i>NF2</i> gene in 34 samples with unknown genotype (30 blood and 4 tumor DNAs), demonstrating the suitability of our method. The strategy we propose optimizes the use of deep amplicon sequencing for the identification of low abundance variants. Moreover, our method can be applied to different high throughput sequencing approaches to estimate the background noise and define the accuracy of the experimental design.</p></div

Results of ROC curve analysis on calibration samples.

<p>^a TPR and FPR are calculated from ROC curves at the best VAF cut-off. This data do not contain recurrent events.</p><p>^b The number of FPs at each cut-off value was estimated by the product of FPR and the number of detected variants per sample</p><p>Results of ROC curve analysis on calibration samples.</p

Validation of <i>NF2</i> variant in an unknown NF2 mosaic patient.

<p>The c.459C>T variant in exon 5 of the <i>NF2</i> gene was identified by deep sequencing. A) 410 DNA sample differed appreciably from wild-type melting curves at HRMA analysis; but the alteration was not detectable by Sanger sequencing after standard PCR (B). C) COLD-PCR allowed to enrich the variant allele as much as necessary to make the alteration clearly visible by Sanger sequencing.</p

Features of false positives events detected in calibration samples.

<p>(a) Boxplot of VAFs of events detected by MuTect (SNVs) and IndelGenotyperV2 (InDels). n is the total number of detected events, n* is the mean number of events (per sample) with the corresponding standard deviation. (b) Histogram of events recurrence among calibration samples (n = 30) for SNVs (black) and InDels (grey). Corresponding cumulative percentages are reported in dashed black (SNVs) and grey (InDels) lines.</p

ROC curve analysis of variants found in calibration samples.

<p>ROC Curve analysis for SNVs (a) and InDels (b) events. Data are obtained by averaging results of calibration samples with the same dilution. Cross, triangle and circle points are relative to 1%, 5% and 10% dilution degree. Data do not contain recurrent events.</p

Known variants present in calibration samples.

<p>^<i>a</i>: <i>NF2</i>: NM_181832.2; <i>SMARCB1</i>:NM_003073.3</p><p>^<i>b</i>: The DNA variant numbering is based on cDNA sequences for both genes, with the A of the ATG translation-initiation codon numbered as +1.</p><p>Known variants present in calibration samples.</p

Sample results obtained by filtering on the basis of VAF cut-off and functional criteria.

<p>^a: B: blood; T: tumor.</p><p>^b:The DNA variant numbering is based on the <i>NF2</i> cDNA sequences (GenBank accession number NM_181832.2) with the A of the ATG translation-initiation codon numbered as +1.</p><p>^c: Variants already characterized.</p><p>^d: COLD-PCR protocol.</p><p>Sample results obtained by filtering on the basis of VAF cut-off and functional criteria.</p