| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
Technical Briefs |
1 Korean Hereditary Tumor Registry, Laboratory of Cell Biology, Cancer Research Institute and Cancer Research Center, Seoul National University, Seoul, Korea;2 Department of Surgery, Seoul National University College of Medicine, Seoul, Korea;3 Research Institute and Hospital, National Cancer Center, Goyang, Gyeonggi, Korea;
aaddress correspondence to this author at: National Cancer Center, 809 Madu-dong, Ilsan-gu, Goyang, Gyeonggi, 411-764, Korea; fax 82-31-920-1511, e-mail park{at}ncc.re.kr
In cancer research, gene expression and mutations are increasingly investigated by use of oligonucleotide microarrays, which use immobilized oligonucleotides and sequence-specific DNA probe hybridization to investigate differences between nondiseased and cancer tissues (1). In our previous works, we used oligonucleotide microarray-based mutation analysis to detect germline or somatic mutations (2)(3).
Activating mutations of the K-ras gene occur in 
20–50% of colorectal cancers, with 85% of the mutations restricted to codons 12 and 13 (4). K-ras gene mutations have been widely studied as markers for cancer prognosis, and population-based studies have suggested that mutated K-ras might be associated with some tumor phenotypes (4)(5)(6). Studies of associations between K-ras mutations and specific clinical features generally require the analysis of large numbers of samples (5). Thus, researchers need a high-throughput technique for assessing K-ras mutations. Oligonucleotide microarrays may provide a valid option because they allow scientists to accurately and rapidly process large numbers of samples.
Because the K-ras gene is known to have two mutational hot spots (codons 12 and 13), it has been used as a target gene for testing newly developed techniques for mutation detection, including various applications of the DNA chip (7)(8). Here we describe a new method for K-ras oligonucleotide microarray analysis called competitive DNA hybridization (CDH). CDH is a novel, efficient, high-capacity hybridization technique in which various fluorescently labeled samples are mixed to compete with each other in a hybridization reaction.
A total of 204 Korean patients with colorectal cancer from Seoul National University Hospital and the National Cancer Center of Korea were screened for somatic K-ras mutations in this study. Written informed consent was obtained from all patients; the cancers included 103 cancers originating from the proximal colon and 101 from the distal colorectum. Genomic DNA was extracted from frozen tumor tissues by use of the TRI reagent (Molecular Research Center) as described previously (3). PCR primers spanning codons 12 and 13, designed to amplify a 116-bp region, were as follows: forward, 5'-GGCCTGCTGAAAATGACTGAATAT-3'; reverse, 5'-TGTTGGATCATATTCGTCCACAAAATG-3'. PCR reactions (25 µL) containing 50 µM each of dATP, dTTP, and dGTP (MBI Fermentas) and 10 µM each of Cy5-dCTP (Amersham Biosciences) and dCTP were subjected to 35 cycles of amplification (94 °C for 30 s, 56 °C for 30 s, and 72 °C for 1 min, with a final elongation of 7 min at 72 °C) as reported previously (2)(3). To evaluate the CDH method, we initially tested addition of six different fluorescently labeled deoxynucleotide triphosphates (dNTPs); from these we selected two additional fluorescently labeled dNTPs: Cy3-dCTP (Amersham Biosciences) and AlexaTM 594-dUTP (Molecular Probes). In the CDH PCR reactions, DNA from each tumor tissue was amplified with one of the fluorescently labeled dNTPs, which were subsequently incorporated into the amplified DNA. Amplified PCR products were purified with the QIAquick PCR Purification Kit (Qiagen) and were subsequently digested with 0.05 U of DNase I (Takara) at 25 °C for 3 min to produce fragments. We then mixed these products together for microarray hybridization.
The K-ras oligonucleotide microarray, containing 21-bp-long oligonucleotides harboring all possible K-ras mismatch sequences (Table 1
), was manufactured as described previously (1). Briefly, the synthesized oligonucleotides were spotted with a microarrayer (Cartesian Microsys 5100; Cartesian Technologies); 20 oligonucleotides were arrayed in quadruplicate, including 2 wild-type sequences and 18 containing missense mutations in codons 12 and 13 (total of 80 spots per set). Three complete oligonucleotide sets were spotted separately on each slide, allowing us to hybridize three different samples on each microarray. The PCR-amplified patient samples (above) were dissolved in 5 µL of hybridization buffer (HybIt; TeleChem) and hybridized with the K-ras oligonucleotide microarray at 56 °C for 2.5 h in a general hybridization chamber (FINEPCR). The microarray was then rinsed with a buffer solution containing 2 g/L sodium dodecyl sulfate in 0.5x standard saline citrate, after which it was scanned with a microarray laser scanner (ScanArray5000; Packard Instruments) set to monitor the wavelengths 632.8, 543.8, and 594 nm, which correspond to Cy5, Cy3, and Alexa 594, respectively (9). The intensity of each spot, representing the amount of hybridized tumor DNA, was calculated by the Scanarray and Quantarray (Packard Instrument) image analysis software packages. This procedure allowed the amplified DNA fragments from each patient to compete with each other in a hybridization reaction taking place within the limited space of a spotted oligonucleotide.
|
The overall steps for the mutation detection criteria are as follows (1)(2)(3): (a) calculate the mean value for the wild-type spots to normalize the wild-type signals; (b) calculate each normalization factor from each wild-type signal; (c) multiply the normalization factors of all oligonucleotides at the same codons or of the same groups; (d) calculate the mean (A) and SD; (e) set a proper cutoff value after considering statistical significance (99% confidence interval); and (f) regard any signal above the cutoff as indicative of a mutation.
For confirmation of the microarray results, we amplified all 204 samples with a conventional dNTP mixture and sequenced them bidirectionally as reported previously (10)(11).
K-ras mutations were identified in 50 of the 204 colorectal cancer samples (24.5%) screened by oligonucleotide microarray. Of these, 28 were from proximal colon cancers (28 of 103; 27.2%), and 22 were from distal colorectal cancers (22 of 101; 21.8%). We detected a total of four missense mutation types causing amino acid changes in codons 12 or 13. The most common mutation was GGC (Gly)
GAC (Asp) in codon 13 (21 of 50 samples). We also identified mutations changing GGT (Gly) to GAT (Asp; 16 of 50), GTT (Val; 8 of 50), and TGT (Cys; 5 of 50). The mutations identified by CDH were 100% concordant with our direct sequencing data, with no false positives or false negatives.
To investigate possible associations between the mutation profile and phenotype, we performed statistical analyses using the 
2 or Fisher exact tests with SPSS software (
= 0.05 was set as the significance level). In agreement with previous reports (4)(6), we found that the GGTGAT mutation was more prevalent in proximal colon cancer (13 of 28 samples) than in distal colon cancer (3 of 22 samples; P = 0.014). However, we detected no significant relationship between the K-ras mutation and sex, age, tumor size, differentiation, or TNM stage (data not shown). We estimated the detection limit of the K-ras oligonucleotide microarray by serial dilution of a positive control mixed with wild-type DNA; the positive control was cancer cell line (SNU-601), which harbors the G12D (GGTGAT) K-ras mutation. We could detect the mutated DNA up to a ratio of 1:15, which means that 3% of mutated DNA can be detected by the K-ras oligonucleotide microarray. These results indicated that a K-ras oligonucleotide microarray could be used for samples containing a small fraction of mutated DNA.
Although multiple fluorophores have previously been used for genotyping and pooled DNA samples have been used for parallel genotyping (9)(12)(13), this is the first report of the use of competitive mixtures of fragments bearing multiple fluorescent dyes. In addition, we sought to reduce the "cross-talk" problem, in which the signal from one fluorophore is detected at more than one wavelength (12) because of overlapping excitation and emission spectra. Thus, we used dNTPs labeled with Cy5, Cy3, and Alexa 594, which have distinct spectra. Accordingly, our CDH results showed improved microarray imaging because there was less nonspecific hybridization (Fig. 1
). We also noted that the wild-type (codon 12 and 13) signals were slightly reduced. This can be explained by the fact that the digested wild-type DNA from each sample had to compete with each other for hybridization. In contrast, the mutant DNA, which rarely overlapped in the three mixed samples, did not compete and thus produced a strong signal. As a result, when the sample had a mutation, the signal ratios between mutant and wild-type DNA increased from 0.91 to 1.66 (Fig. 1
, A and B) and from 0.28 to 0.56 (Fig. 1
, C and D). Thus, our new CDH technique confers several advantages: (a) It reduces the nonspecific signals caused by binding of small DNA fragments that might have homology with the spotted oligonucleotide, because these small DNA fragments compete with each other. (b) Mutational analysis can rely on calculating the signal ratio of mutant to wild type (1)(8). Samples can be scored as containing a mutation when the ratio is above the threshold; therefore, the larger ratio can make mutational analysis more correct. (c) Mixing of samples reduces experimental cost and time. In addition, each K-ras microarray contained three separate oligonucleotide sets. This allows researchers to investigate nine samples per microarray, facilitating large-scale analysis. We hybridized three different DNA samples into which the fluorescently labeled dNTPs had been incorporated in one experiment to detect K-ras mutations by the K-ras oligonucleotide microarray. The CDH technology increased the discrimination of mutation signal from nonspecific signals (Fig. 1
).
|
In summary, we developed a K-ras oligonucleotide microarray and applied our new CDH method to identify 50 K-ras mutations in samples from 204 Korean colorectal cancer patients. The results of the K-ras microarray analysis agreed perfectly with conventional sequencing data. The new method increases efficiency through pooling of multiple samples, providing a sensitive, rapid, high-throughput system that may be suitable for large-scale studies that require simple, quick, and effective screening of large numbers of samples.
Acknowledgments
This work was supported by a research grant from the National Cancer Center, Korea, and the BK21 Project for Medicine, Dentistry and Pharmacy.
Footnotes
1 these authors contributed equally to this work; 
References
The following articles in journals at HighWire Press have cited this article:
|
|
 |
|
  I.-J. Kim, H. C. Kang, S. G. Jang, S.-A Ahn, H.-J. Yoon, and J.-G. Park Development and Applications of a BRAF Oligonucleotide Microarray J. Mol. Diagn., February 1, 2007; 9(1): 55 - 63. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 I.-J. Kim, H. C. Kang, S.-G. Jang, K. Kim, S.-A Ahn, H.-J. Yoon, S. N. Yoon, and J.-G. Park Oligonucleotide microarray analysis of distinct gene expression patterns in colorectal cancer tissues harboring BRAF and K-ras mutations Carcinogenesis, March 1, 2006; 27(3): 392 - 404. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
