HRM analysis using different amplicon lengths.

<p>Multiple amplicon lengths (90, 129, and 212 bp) were tested for mutation analyses in order to optimize the HRM analysis. First row: -<i>d</i>¹ curve; second row: -<i>d</i>² curve. The 90-bp amplicon showed the most interpretable heteroduplex-derived peaks.</p

Analysis of IDH1 mutation in an inappropriate tissue sample.

<p>Analysis of a tumor tissue showing a sampling discrepancy. Whereas wild-type calls were obtained by SS and conventional HRM for the first sampling, the mutation was detected using our present method based on a heteroduplex-derived curve that presented a positive HRM-MI value (first column). The second sampling confirmed the mutation, with consistent results among the three approaches (second column).</p

Differential calculus analysis of HRM data.

<p>A) Representative results of differential calculus analyses of HRM for <i>IDH1</i>^<i>R132</i>. First row: Sanger sequencing; second row: fluorescence intensity curve; third row: -<i>d</i>¹ curve; fourth row: -<i>d</i>² curve. First column: A result of sequence-wild type DNA containing no heteroduplex-derived peaks in either the -<i>d</i>¹ or -<i>d</i>² curves. Second column: A result of sequence-mutant DNA. Whereas the heteroduplex-derived peak is recognized in the -<i>d</i>¹ curve as a slight change of shape, the -<i>d</i>² curve demonstrates a more distinct peak formation. B) Distributions of run-to-run variability in the low-temperature melting transition (LTMT) (blue) and high-temperature melting transition (HTMT) (red) positions from <i>Tm</i>. Bars indicate the number of observations within the bins of width 0.01°C. The curved black line shows the approximate normal distributions of LTMT and HTMT, in which the standard deviations were 0.076°C and 0.048°C, respectively.</p

Precise Detection of <i>IDH1/2</i> and <i>BRAF</i> Hotspot Mutations in Clinical Glioma Tissues by a Differential Calculus Analysis of High-Resolution Melting Data

<div><p>High resolution melting (HRM) is a simple and rapid method for screening mutations. It offers various advantages for clinical diagnostic applications. Conventional HRM analysis often yields equivocal results, especially for surgically obtained tissues. We attempted to improve HRM analyses for more effective applications to clinical diagnostics. HRM analyses were performed for <i>IDH1</i>^<i>R132</i> and <i>IDH2</i>^<i>R172</i> mutations in 192 clinical glioma samples in duplicate and these results were compared with sequencing results. <i>BRAF</i>^<i>V600E</i> mutations were analyzed in 52 additional brain tumor samples. The melting profiles were used for differential calculus analyses. Negative second derivative plots revealed additional peaks derived from heteroduplexes in PCR products that contained mutations; this enabled unequivocal visual discrimination of the mutations. We further developed a numerical expression, the HRM-mutation index (MI), to quantify the heteroduplex-derived peak of the mutational curves. Using this expression, all <i>IDH1</i> mutation statuses matched those ascertained by sequencing, with the exception of three samples. These discordant results were all derived from the misinterpretation of sequencing data. The effectiveness of our approach was further validated by analyses of <i>IDH2</i>^<i>R172</i> and <i>BRAF</i>^<i>V600E</i> mutations. The present analytical method enabled an unequivocal and objective HRM analysis and is suitable for reliable mutation scanning in surgically obtained glioma tissues. This approach could facilitate molecular diagnostics in clinical environments.</p></div

HRM analysis using mixed samples.

<p>A) Distribution plots of HRM-MI results obtained using an HRM analysis with mixed samples. The <i>x</i>-axis shows the fraction of tumor DNA in the mixture. The <i>y</i>-axis shows the HRM-MI value. Each dot represents the HRM-MI value for each assay, performed with multiple replicate across the 6 different mixing ratios. Green and blue lines indicate mean and SD HRM-MI values, respectively. B) Mutation detection ratios for HRM-MI (red line) and conventional HRM analyses (blue line). At a mixing ratio of 20% (i.e., 20% tumor DNA and 80% nontumor DNA), the sensitivities of the conventional HRM method and HRM-MI were 30% and 100%, respectively. C) The representative -<i>d</i>² curves of samples with tumor-to-nontumor DNA ratios. Even the 10% tumor DNA sample presented an interpretable heteroduplex-derived peak.</p

Concordance rates between different analytical methods.

<p>Concordance rates between different analytical methods.</p

HRM analysis for rare IDH1 mutation genotypes.

<p>First row: -<i>d</i>¹ curve; second row: -<i>d</i>² curve. First column: <i>IDH1</i>^<i>R132H</i> genotype results; second column: <i>IDH1</i>^<i>R132G</i> genotype results; third column: <i>IDH1</i>^<i>R132S</i> genotype results. The tolerance to rare genotype variants is revealed.</p

HRM analysis for hotspot mutations of IDH2 and BRAF.

<p>First column: Sequence-wild-type DNA results, including no heteroduplex-derived peak in either derivative curve. Second column: Clear heteroduplex-derived peaks are seen in both derivative curves of the sequence-mutant DNA. C) Distribution plots of HRM-MI for 52 DNA samples analyzed for <i>BRAF</i>^<i>V600E</i>. HRM-MI values completely match the sequence results.</p

Primer sequences for high resolution melting analysis.

<p>Primer sequences for high resolution melting analysis.</p

Discrepant results between a duplicate HRM analyses.

<p>Difference plots of discriminated wild-type calls and variant (i.e., mutant) calls from the first (left upper) and second (right upper) 96 runs, which are displayed as light blue and light red curves, respectively. A representative discrepancy for a duplicate HRM analysis, i.e., one with a wild-type call in the first run and a variant call in the second run, is shown as a black curve in both difference plots. Negative derivative curves of this discrepancy were similar to those of less interpretable plots (left lower, first run; right lower, second run).</p