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Int J Exp Pathol. Dec 2010; 91(6): 500–505.
PMCID: PMC3010548

Comparative analysis of pyrosequencing and QMC-PCR in conjunction with high resolution melting for KRAS/BRAF mutation detection

Abstract

Mutation detection is important in cancer management. Several methods are available of which high resolution melting (HRM) analysis and pyrosequencing are the most versatile. We undertook a comparative analysis of these techniques. The methods are:

  • To compare the limit of detection (LOD), mutations in KRAS (codon 12/13 hotspot) and BRAF (V600E hotspot) were tested. DNA mixtures containing mutant alleles at a frequency of around 25%/12.5%/6%/3%/ 1.5%/0.8% were analysed.
  • To compare frequency of mutation detection, 22 DNA samples (nine high quality samples from cell lines, 13 low quality samples from formalin-fixed paraffin-embedded tissue) were tested for three hotspots in KRAS (codons 12/13, 61 and 146) and two hotspots in BRAF (V600E and exon 11).

HRM analysis of KRAS (codon12/13) and BRAF (V600E) showed that 3% and 1.5% mutant alleles respectively could be reliably detected whilst pyrosequencing reliably detected 6% mutant alleles in each case. Of 110 tests performed on 22 DNA samples, in 109 cases HRM and pyrosequencing gave identical results. Two of the samples tested had previously been called as wild type for KRAS by direct Sanger sequencing but were found to be mutant by both HRM and pyrosequencing.

Both HRM and pyrosequencing can detect small numbers of mutant alleles although HRM has a lower limit of detection. Both are suitable for use in mutation detection and are both more sensitive than Sanger sequencing.

Keywords: formalin-fixed paraffin-embedded, high resolution melting, mutation detection, PCR, pyrosequencing

The landscape of cancer treatment has begun to change dramatically in recent years with the development of novel targeted therapies. In particular, biological agents – such as monoclonal antibodies – are able to target specific molecules (Weiner et al. 2009). In parallel with therapeutic developments has been an increased understanding of functional ‘pathways’ in cancer biology; clear delineation of these pathways has led to identification of points at which they may be disrupted (Parsons et al. 2005). It follows that each pathway needs to be targeted appropriately and that therapies targeting molecules which lie upstream of the point of disruption will be ineffective. This is best exemplified by recent data showing that, in colorectal cancer (CRC), biologics directed against the epidermal growth factor receptor (EGFR) can be very effective (Cunningham et al. 2004). However, response is not absolutely correlated with expression of EGFR because the EGFR signals through Kras. CRCs harbouring a KRAS mutation will not respond to anti-EGFR therapy irrespective of the levels of EGFR expression whilst those with wild type KRAS are more likely to respond (De Roock et al. 2008; Karapetis et al. 2008; Lievre et al. 2008; Van Cutsem et al. 2009). Thus mutation of KRAS in any one of the three hotspots (i.e. codons 12/13, 61 and 146) is ‘predictive’ of failure of anti-EGFR therapy (Loupakis et al. 2009). Furthermore, even tumours with wild type KRAS may not respond if they harbour a BRAF mutation as this lies downstream of KRAS (Di Nicolantonio et al. 2008).

The demand for predictive mutation detection is increasing. This has, however, raised new technical challenges – most importantly how to test multiple possible mutation sites in poor quality DNA derived from formalin-fixed paraffin embedded (FFPE) tissue. Sanger sequencing is usually regarded as the ‘gold standard’ for mutation detection but there are some doubts regarding its sensitivity. For any mutation detection method, the lower limit of detection of mutant alleles is an important consideration and a variety of more sensitive techniques are available (Gupta et al. 2005; Lee et al. 2005; Janne et al. 2006; van den Boom & Ehrich 2007; Koren-Michowitz et al. 2008). Many of these are expensive, complicated and of unproven use when dealing with FFPE tissue. High Resolution Melting (HRM) analysis (Wittwer et al. 2003) and pyrosequencing are probably the most versatile and easy to interpret techniques. HRM is a cheap gel-free method that depends on the formation of heteroduplexes between mutant and wild type alleles. It can be used to screen multiple hotspots for mutation but still requires confirmatory sequencing. Pyrosequencing is an alternative method to Sanger sequencing which is quick, gel-free and reportedly has a lower limit of detection that Sanger sequencing. In this study we sought to compare the utility of HRM (using the QMC-PCR protocol) and pyrosequencing for mutation detection.

Materials and methods

Sample preparation

High quality DNA was obtained from well described cell lines as previously described (Seth et al. 2009a,b;). DNA was also extracted from13 cases of FFPE CRC tumour tissue using the QIAamp® DNA FFPE tissue kit (Qiagen, Crawley, UK) as previously described (Fadhil et al. 2010). Ethical approval was obtained for the use of patient materials in this study (ref no. C02.310).

In order to test limit of detection, two diploid CRC cell lines were chosen for spiking experiments. HCT116 contains a heterozygous KRAS G13D mutation and is wild type for BRAF. Vaco5 contains a heterozygous BRAFV600E mutation and is wild type for KRAS. Varying quantities of DNA from the cell lines were admixed to produce mixtures containing mutant alleles for each gene at a frequency of 25%/12.5%/6.25%/3.125%/1.625%/0.8%. Each sample underwent analysis for KRAS and BRAF twice – once by HRM and once by pyrosequencing.

In order to test comparative performance in DNA derived from FFPE tissue, all 13 cases of colorectal cancer (CRC) were tested by both HRM and pyrosequencing. These cases had been previously tested by direct Sanger Sequencing and all five hotspots in both genes were examined.

QMC-PCR and high resolution melting analysis

For HRM analysis, the samples underwent initial PCR using the QMC-PCR protocol (Fadhil et al. 2010). This consists of a pre-diagnostic multiplex (PDM) reaction using five sets of outer primers covering the mutation hotspots in KRAS and BRAF. The PCR product is diluted 1:100 and used as template for a specific single diagnostic (SSD) reaction using an inner pair of primers for each hotspot. The PCR products of the SSD reaction are transferred to Roche Light Cycler® capillaries (Roche Diagnostics, Mannheim, Germany) and melted in the HR-1 high resolution melting instrument (Idaho Technology Inc, Salt Lake City, UT, USA) as previously described (Seth et al. 2009a,b;).

Pyrosequencing and Sanger sequencing

For pyrosequencing analysis, all samples underwent testing for mutation in KRAS codon 12/13 (in exon 2), KRAS codon 61 (in exon 3), BRAF exon11 and BRAF V600E (in exon 15) using the commercially pyrosequencing kits Pyromark™ Q24 Kras v2.0 (Biotage) and Pyromark™ Q24 BRAF (Biotage AB, Uppsala, Sweden) in accordance with the manufacturer's instruction. For KRAS codon 146 (exon 4) PCR and sequencing primers were designed de-novo (see supplementary Data S1). The pyrosequencing kits use a biotinylated reverse primer allowing isolation of single stranded templates (of the reverse strand) from the PCR products by adding Binding Buffer (Qiagen), streptavidin sepharose high-performance beads (GE Biosciences, Uppsala, Sweden), sterile water and eluting product using the PyroMark Vacuum Prep WorkStation (Qiagen). Pyrosequencing reactions were carried out with a nested sequencing primer in the PyroMark MD machine (Qiagen) using PyroGold Reagents (Qiagen) in accordance with the manufacturer's instructions. The results were analysed using pyro Q-CpG Software (Qiagen). Direct Sanger sequencing was performed previously as described (Seth et al. 2009a,b;). and interpreted using the Chromas Lite software version 2.01 (http://www.technelysium.com.au/chromas.html; Technelysium Pty Ltd, Australia).

Results

Limit of detection of HRM and pyrosequencing

All sample DNA (both cell line and FFPE-tissue derived) was of sufficient quality to allow successful PCR for all the mutation hotspots tested. HRM analysis of PCR products depends on the formation of heteroduplexes between the mutant and wild type alleles. These are more unstable than homoduplexes of either wild type or mutant sequence and thus will melt at a lower temperature. This is manifest as a ‘left shift’ of the melting curve due to the early loss fluorescence as alleles separate to release the dye. The greater the proportion of heteroduplexes, the more pronounced the difference of the melting curves compared to pure homoduplexes. For KRAS exon 2 mutation detection, there was sufficient difference in melting characteristics for confident detection of mutation when as few as 3% mutant alleles were present in the sample (Figure 1a). Pyrosequencing generates sequence data by sequential addition of bases to primed template and the chemistry is such that incorporation of bases into the DNA results in light emission. There is also background ‘noise’ even when the bases are not incorporated. Testing of the DNA mixture for KRAS exon 2 by pyrosequencing allowed confident detection of mutation when 6% mutant alleles were present in the sample (Figure 1a). For BRAF exon 15 mutation, HRM allowed confident detection of 1.5% mutant alleles. In contrast, pyrosequencing had a lower limit of detection of 6% (Figure 1b).

Figure 1
Limit of detection of HRM and pyrosequencing. Cell line DNA was admixed to produce samples containing differing proportions of mutant alleles (percentage numbers indicate calculated percentage mutant alleles present). With HRM the presence of mutant alleles ...

Comparative performance for mutation detection

Five different hotspots in KRAS and BRAF were tested in 22 DNA samples (nine obtained from CRC cell lines, 13 obtained from FFPE tumour tissue) by both pyrosequencing and HRM. Identical results were obtained in 109/110 tests performed (99% concordance, chi-squared test of association: P < 0.001, Table S1). Of 90 cases called as wild type by pyrosequencing, all were called wild type by HRM (100% sensitivity). Of 20 samples shown to have a mutation by pyrosequencing, 19 were also called as mutant by HRM (95% specificity) with the single discrepancy seen in BRAF exon 11. All of the cases have been previously tested by direct Sanger sequencing and two cases previously called as wild type, were found to have mutations by both HRM and pyrosequencing. In each case a mutation in KRAS exon 2 was clearly identified (G12V in one case and G12D in the other case, Figure 2) demonstrating that these cases were actually ‘false negatives’ by Sanger sequencing. In both cases the DNA was derived from non-microdissected FFPE tumour tissue.

Figure 2
Direct Sanger sequencing failed to detect mutation in two cases of CRC (central panel). However in both cases aberrant melting was seen by HRM for KRAS exon 2 (left panel shows the aberrant melting peaks seen in contradistinction from the wild types which ...

Discussion

The identification of ‘predictive’ mutations has been a particularly exciting development as it is one step closer to bespoke tailoring of therapy for cancer patients. For any predictive test, the lower limit of detection is an important consideration since tumours frequently contain abundant non-neoplastic stromal cells which may even outnumber the tumour cells.

In this study we initially compared the limit of mutation detection of HRM and pyrosequencing for KRAS exon 2 and BRAF exon15 using wild type DNA spiked with varying quantities of mutant DNA. We showed that, for KRAS exon 2, HRM and pyrosequencing had similar levels of sensitivity and were able to confidently detect 3% and 6% of mutant alleles respectively. For BRAF exon15, HRM could detect around 1.5% mutant alleles whilst pyrosequencing was able to detect 6% mutant alleles with confidence. The differences between HRM assays for different hotspots is not unexpected since the stability of the heteroduplex is dependent on both the specific changes that occur and context in which they occur [the ‘nearest neighbour’ effect (Peyret et al. 1999)]. The level of 1.5% mutant alleles for the BRAF V600E mutation is consistent with other studies of HRM analysis of this mutation (Pichler et al. 2009). The similar levels in sensitivity between pyrosequencing assays for different hotspots is as expected since single base variation in non-primer sequence should not affect PCR efficiency and the mutation does not create a mononucleotide run of repeat sequences (which may impede sequence analysis).

This is the first direct comparison of limit-of-detection (LOD) between HRM and pyrosequencing. Since we used diploid cell lines for the spiking experiments, our limit of detection tests are probably more accurate that studies using aneuploid cell lines or tumour samples (Dufort et al. 2009). Neither technique is as sensitive as the DxS test (quoted as being around 1% mutant alleles) but both are sufficiently sensitive for use in clinical diagnosis.

Next we undertook a comparison of the performance of both types of mutation detection assay for five hotspots in two different genes. Of 110 tests performed, there was concordance in 109 showing that both tests were performing equally well. The samples had all been previously examined by Sanger sequencing and both HRM and pyrosequencing identified two cases which had been previously been incorrectly called negative by the Sanger method. Although both assays are more sensitive than Sanger sequencing and are suitable for clinical applications, they have complementary strengths. The HRM assay can only be used for screening and positive cases still need to need to be confirmed by a sequencing method in order to exclude the possibility of aberrant melting due to a single nucleotide polymorphism. Pyrosequencing is more expensive and less flexible than HRM when searching for unknown mutations. A combined strategy of screening for mutation by HRM followed by confirmation of mutation by pyrosequencing is probably the best method for mutation detection when it is necessary to test multiple hotspots in multiple genes.

The choice of mutation detection strategy is an important one since capital equipment and staff training costs will probably mean that institutes will not be able to invest in multiple different platforms. Our findings using HRM and pyrosequencing are consistent with much of published literature which has tended to test just one method (Do et al. 2008; Packham et al. 2009). A large study by Whitehall et al. evaluated seven different methodologies (including HRM and pyrosequencing) for mutation detection in both frozen and FFPE tissue (Whitehall et al. 2009). The authors found that there was around 96% concordance amongst the top five performing assays in FFPE tissue. As expected, HRM had some false positives whilst pyrosequencing had some false negatives but both assays were deemed acceptable.

Whitehall's study also found that Sanger sequencing had a sensitivity of 90% thereby re-iterating the facts that due to a low limit-of-detection, Sanger sequencing will miss some cases. At first glance it would seem that Sanger sequencing should no longer be regarded as the gold standard for mutation detection in predictive testing. However, careful selection of tumour blocks prior to testing will obviate the problem of low tumour cell population within the sample but in itself raises another question about the utilisation of such exquisitely sensitive techniques. The biological significance of a minority KRAS mutant population, within a tumour which is otherwise wild type, is uncertain. It is possible that treatment of such a tumour with anti-EGFR biologics could select for the growth of therapy resistant KRAS mutant sub-clones (Ilyas et al. 1999). Alternatively, it is equally possible that such a small population will be dealt with by standard therapies given alongside the biologics and thus an oversensitive technique will inappropriately deny this therapy to patients.

In summary, we have undertaken a comparative analysis of HRM and pyrosequencing. We conclude that HRM has a lower limit of detection than pyrosequencing and both are more sensitive than direct Sanger sequencing. Since HRM can screen for multiple hotspots, it should be viewed as a complementary technique to pyrosequencing.

Acknowledgments

This work was funded by the University of Nottingham and a grant from Nottingham University Hospital Charities.

Supporting information

Additional Supporting Information may be found in the online version of this article:

Data S1. Kras 146 Forward primer 5′ AGGCTCAG-GACTTAGCAAGAAGTT 3′, Reverse (Biotinylated at 5′ end)–TAACAGTTATGATTTTGCAGAAAACAGA 3′ sequencing primer is -5′-AATTCCTTTTATTGAAACAT 3′.

Table S1. Mutation analysis of 22 DNA samples by QMC-PCR and pyrosequencing.

Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

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