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Cancer mutation screening: Comparison of high-resolution melt analysis between two platforms.

ABSTRACT: High-resolution melt analysis (HRMA) is a cheap and reliable post-polymerase chain reaction (PCR) cancer mutation screening technique, which is fast gaining clinical relevance. The HRMA capabilities of the LightScanner (Idaho Technology) have been severally studied. However, the ABI 7500 HRM has not been tested against the purpose-built HRM instrument such as the LightScanner. DNA from formalin-fixed, paraffin-embedded gastric cancer, colorectal cancer, and normal tissue as well as from colorectal cancer cell lines were amplified at exons 2, 3, and 4 of KRAS, and at exons 11 and 15 of BRAF in the ABI 7500 fast real-time PCR machine and subjected to melting both on the ABI and on the LightScanner. HRMA data were analysed with the ABI HRM software v2.0.1 and the LightScanner Call-IT 2.5. We tested the ABI 7500 HRM for internal precision, accuracy, sensitivity, and specificity at mutation screening relative to the LightScanner, using crude percentage concordance, kappa statistics, and the area under the receiver operator characteristics (AUROC) curve on SPSS version 19. The results show that the ABI 7500 HRMA has a high internal precision, and excellent concordance, sensitivity, and specificity at mutation screening compared with the LightScanner. However, in contrast to the LightScanner HRM software analysis, the ABI HRM software v.2.0.1, cannot distinguish real from certain pseudovariations in PCR amplicons that are sometimes brought about by the artefacts of the melting process. In conclusion, the ABI HRM has a comparable performance level with the LightScanner, although in certain respects mentioned previously, the LightScanner has an edge over the ABI.

SUBMITTER: Ebili HO

PROVIDER: S-EPMC4406528 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Cancer mutation screening: Comparison of high-resolution melt analysis between two platforms.

Ebili Henry O HO Ilyas Mohammad M

Ecancermedicalscience 20150415

High-resolution melt analysis (HRMA) is a cheap and reliable post-polymerase chain reaction (PCR) cancer mutation screening technique, which is fast gaining clinical relevance. The HRMA capabilities of the LightScanner (Idaho Technology) have been severally studied. However, the ABI 7500 HRM has not been tested against the purpose-built HRM instrument such as the LightScanner. DNA from formalin-fixed, paraffin-embedded gastric cancer, colorectal cancer, and normal tissue as well as from colorect ...[more]

PMID: 25932046

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Project description:BackgroundHigh resolution melting curve analysis is a cost-effective rapid screening method for detection of somatic gene mutation. The performance characteristics of this technique has been explored previously, however, analytical parameters such as limit of detection of mutant allele fraction and total concentration of DNA, have not been addressed. The current study focuses on comparing the mutation detection efficiency of High-Resolution Melt Analysis (HRM) with Sanger Sequencing in somatic mutations of the EGFR gene in non-small cell lung cancer.MethodsThe minor allele fraction of somatic mutations was titrated against total DNA concentration using Sanger sequencing and HRM to determine the limit of detection. The mutant and wildtype allele fractions were validated by multiplex allele-specific real-time PCR. Somatic mutation detection efficiency, for exons 19 & 21 of the EGFR gene, was compared in 116 formalin fixed paraffin embedded tumor tissues, after screening 275 tumor tissues by Sanger sequencing.ResultsThe limit of detection of minor allele fraction of exon 19 mutation was 1% with sequencing, and 0.25% with HRM, whereas for exon 21 mutation, 0.25% MAF was detected using both methods. Multiplex allele-specific real-time PCR revealed that the wildtype DNA did not impede the amplification of mutant allele in mixed DNA assays. All mutation positive samples detected by Sanger sequencing, were also detected by HRM. About 28% cases in exon 19 and 40% in exon 21, detected as mutated in HRM, were not detected by sequencing. Overall, sensitivity and specificity of HRM were found to be 100 and 67% respectively, and the negative predictive value was 100%, while positive predictive value was 80%.ConclusionThe comparative series study suggests that HRM is a modest initial screening test for somatic mutation detection of EGFR, which must further be confirmed by Sanger sequencing. With the modification of annealing temperature of initial PCR, the limit of detection of Sanger sequencing can be improved.

Project description:Super-resolution (SR) techniques are used in PET imaging to generate a high-resolution image by combining multiple low-resolution images that have been acquired from different points of view (POV). In this article, the authors propose a novel implementation of the SR technique whereby the required multiple low-resolution images are generated by shifting the reconstruction pixel grid during the image reconstruction process rather than being acquired from different POVs. The objective of this article is to compare the performances of the two SR implementations using theoretical and experimental studies. A mathematical framework is first provided to support the hypothesis that the two SR implementations have similar performance in current PET/CT scanners that use block detectors. Based on this framework, a simulation study, a point source study, and a NEMA/IEC phantom study were conducted to compare the performance of these two SR implementations with respect to contrast, resolution, noise, and SNR. For reference purposes, a comparison with a native reconstruction (NR) image using a high-resolution pixel grid was also performed. The mathematical framework showed that the two SR implementations are expected to achieve similar contrast and resolution but different noise contents. These results were confirmed by the simulation and experimental studies. The simulation study showed that the two SR implementations have an average contrast difference of 2.3%, while the point source study showed that their average differences in contrast and resolution were 0.5% and 1.2%, respectively. Comparisons between the SR and NR images for the point source study showed that the NR image exhibited averages of 30% and 8% lower contrast and resolution, respectively. The NEMA/IEC phantom study showed that the three images (two SR and NR) exhibited different noise structures. The SNR of the new SR implementation was, on average, 21.5% lower than the original implementation largely due to an increase in background noise, while the NR image had averages of 18.5% and 8% lower SNR and contrast, respectively, versus the two SR images. The new SR implementation can potentially replace the original SR approach in current PET scanners that use block detectors while maintaining similar contrast and resolution, but at a relatively lower SNR. A major advantage of the new SR implementation is its shorter overall scan duration which results in an increase in scanner throughput and a reduction in patient motion.

Project description:AimThis study has three broad aims: to (a) develop genus-specific primers for High Resolution Melt analysis (HRM) of members of Cyclopia Vent., (b) test the haplotype discrimination of HRM compared to Sanger sequencing, and (c) provide an example of using HRM to detect novel haplotype variation in wild C. subternata Vogel. populations.LocationThe Cape Floristic Region (CFR), located along the southern Cape of South Africa.MethodsPolymorphic loci were detected through a screening process of sequencing 12 non-coding chloroplast DNA segments across 14 Cyclopia species. Twelve genus-specific primer combinations were designed around variable cpDNA loci, four of which failed to amplify under PCR; the eight remaining were applied to test the specificity, sensitivity and accuracy of HRM. The three top performing HRM Primer combinations were then applied to detect novel haplotypes in wild C. subternata populations, and phylogeographic patterns of C. subternata were explored.ResultsWe present a framework for applying HRM to non-model systems. HRM accuracy varied across the PCR products screened using the genus-specific primers developed, ranging between 56 and 100%. The nucleotide variation failing to produce distinct melt curves is discussed. The top three performing regions, having 100% specificity (i.e. different haplotypes were never grouped into the same cluster, no false negatives), were able to detect novel haplotypes in wild C. subternata populations with high accuracy (96%). Sensitivity below 100% (i.e. a single haplotype being clustered into multiple unique groups during HRM curve analysis, false positives) was resolved through sequence confirmation of each cluster resulting in a final accuracy of 100%. Phylogeographic analyses revealed that wild C. subternata populations tend to exhibit phylogeographic structuring across mountain ranges (accounting for 73.8% of genetic variation base on an AMOVA), and genetic differentiation between populations increases with distance (p < 0.05 for IBD analyses).ConclusionsAfter screening for regions with high HRM clustering specificity-akin to the screening process associated with most PCR based markers-the technology was found to be a high throughput tool for detecting genetic variation in non-model plants.

Project description:BackgroundHigh-resolution microarray technology is routinely used in basic research and clinical practice to efficiently detect copy number variants (CNVs) across the entire human genome. A new generation of arrays combining high probe densities with optimized designs will comprise essential tools for genome analysis in the coming years. We systematically compared the genome-wide CNV detection power of all 17 available array designs from the Affymetrix, Agilent, and Illumina platforms by hybridizing the well-characterized genome of 1000 Genomes Project subject NA12878 to all arrays, and performing data analysis using both manufacturer-recommended and platform-independent software. We benchmarked the resulting CNV call sets from each array using a gold standard set of CNVs for this genome derived from 1000 Genomes Project whole genome sequencing data.ResultsThe arrays tested comprise both SNP and aCGH platforms with varying designs and contain between ~0.5 to ~4.6 million probes. Across the arrays CNV detection varied widely in number of CNV calls (4-489), CNV size range (~40 bp to ~8 Mbp), and percentage of non-validated CNVs (0-86%). We discovered strikingly strong effects of specific array design principles on performance. For example, some SNP array designs with the largest numbers of probes and extensive exonic coverage produced a considerable number of CNV calls that could not be validated, compared to designs with probe numbers that are sometimes an order of magnitude smaller. This effect was only partially ameliorated using different analysis software and optimizing data analysis parameters.ConclusionsHigh-resolution microarrays will continue to be used as reliable, cost- and time-efficient tools for CNV analysis. However, different applications tolerate different limitations in CNV detection. Our study quantified how these arrays differ in total number and size range of detected CNVs as well as sensitivity, and determined how each array balances these attributes. This analysis will inform appropriate array selection for future CNV studies, and allow better assessment of the CNV-analytical power of both published and ongoing array-based genomics studies. Furthermore, our findings emphasize the importance of concurrent use of multiple analysis algorithms and independent experimental validation in array-based CNV detection studies.

Dataset Information

Cancer mutation screening: Comparison of high-resolution melt analysis between two platforms.

Publications

Cancer mutation screening: Comparison of high-resolution melt analysis between two platforms.

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