Background
Gastric cancer is the third most common malignancy affecting the general population worldwide [
1]. Specific genetic changes have been reported in gastric cancer, including the amplifications of
KSAM,
MET and
ERBB2, and mutations in
p53,
APC, and
CDH1 [
2]. While gain-of-function mutations of
KRAS are some of the most commonly observed genetic alterations in a variety of tumors, including pancreatic (60%), biliary tract (33%) and colon (32%) [
3], these mutations are infrequent in gastric cancer (2–7%) [
4‐
7]. In general,
RAS mutations associated with tumorigenesis "lock" RAS in an active GTP-bound state. GTP-RAS binds to a number of effector proteins to stimulate downstream signaling pathways, among which the RAF-MAP kinase cascade and the phosphatidylinositol 3-kinase (PI3K)-AKT pathways of cell growth and oncogenesis are the best characterized [
3]. Prolonged activation of RAS can also occur through mechanisms that do not involve mutations in RAS. For example, reduced expression of let-7 microRNAs, which suppresses RAS by targeting the 3'untranslated region of
RAS mRNAs, is often associated with a higher RAS protein level in tumors [
8]. To date, the molecular mechanisms of oncogenic activation of RAS in gastric cancer have not been fully elucidated.
Amplification of genomic sequences containing genes that are critical for cell growth is one of the primary mechanisms of activation of oncogenes in cancer, and is often associated with tumor progression, poor prognosis and/or drug resistance [
9]. Of the numerous methods currently available for detecting copy number alterations genome-wide, the current gold standard is the array CGH method (aCGH). Over the past few years, the resolution of aCGH has improved rapidly through the use of oligonucleotide probes, and has surpassed that of aCGH using standard BAC probes [
10]. However, aCGH is also susceptible to the inherent noise of hybridization-based intensity measurements, as the signal quality is affected by repetitive sequences and is dependent on probe quality [
11]. In fact, optimization of probe design has been a major challenge in the development of tiling arrays [
12,
13].
Digital karyotyping (DK) was developed by Wang et al. [
14], and is not limited by the inherent problems of array techniques. DK involves the digital enumeration of short fragments of genomic DNA (termed tags), providing a quantitative measurement of DNA copy number through tag density analysis along each chromosome. DK has been applied successfully to a variety of tumor types to detect copy-number alterations, including the amplification of
TYMS,
RSF1 and
OTX2, and deletion of
MKK4 and
dystrophin [
15‐
19]. Despite the efficiency of DK, it is technically challenging for broad applications, because it involves PCR amplification and the generation of tags of 21-base pairs (bp) in length to precisely represent the chromosome location of interest.
We report here the development of a novel method, termed DGS, for the quantitative analysis of copy number variation, which is based on the tag-counting concept of DK, but uses a simplified process of tag preparation. DGS of gastric cancer cell lines detected the amplification of the KRAS locus on chromosome 12p12.1. Our results provide a molecular basis for the overactivation of KRAS, and suggest that the activation of KRAS downstream signaling events may promote gastric cancer cell proliferation.
Methods
Cell lines and tissues
The cell lines analyzed in the current study are listed in Additional file
1. The HSC and SH101P4 cell lines were established by Kazuyoshi Yanagihara [
20]; all others were obtained from American Type Culture Collection or the Japanese Collection of Research Bioresources (Tokyo, Japan). All cell lines were cultured in the recommended media. For serum stimulation, cells were incubated in media that lacked serum for 24 hours (h), and then either unstimulated, or stimulated for 1 h with media containing 10% fetal calf serum (FCS). Primary gastric cancer specimens were obtained from the Department of Surgery, Keiyukai Sapporo Hospital, with informed consent from each patient. Genomic DNA was extracted using the phenol-chloroform method, followed by RNase treatment. Total RNA was extracted using Trizol (Invitrogen, Carlsbad, CA, USA), according to the manufacturer's instructions. Genomic DNA of normal peripheral blood leukocytes (Biochain, Hayward, CA, USA) and total RNA from normal gastric mucosa from healthy individuals (Biochain and Invitrogen) were purchased. Primary gastric cancers were classified using clinicopathological features, as shown in Additional file
2, according to the pTNM classification scheme (5th edition, 1997) [
21] and the Lauren's classification system [
22].
KRAS-amplification status according to age was compared using the Student t test; according to grade, pT status, pN status, and disease stage using the Mann-Whitney U test; and according to gender, histology and pM status using the Fisher exact test. All tests were 2-tailed, and a
P value of < 0.05 was considered statistically significant.
Digital genome scanning
Briefly, 40 μg of genomic DNA were subjected to restriction enzyme digestion using
MboI (Takara, Tokyo, Japan) and then separated by electrophoresis on a 3% Nusieve GTG agarose gel. Short fragments (30–60 bp, termed real tags) were electroeluted, concatenated and subcloned into
BamHI-digested pBluescript II KS+ (Stratagene, La Jolla, CA) using Mighty Mix DNA ligation solution (Takara).
Escherichia coli DH10B were transformed with the recombinant plasmids, the transformants were pooled and the plasmid DNA was purified to generate the 1st library. Concatemers of real tags were excised by
SpeI/
PstI digestion from the 1st library, and fragments in the range of 140 to 800 bp were electroeluted, concatenated and subcloned into pBluescript II KS+ to generate the 2nd library. Second library plasmids containing concatemers of
SpeI/
PstI fragments were sequenced using an ABI3130 Genetic Analyzer (Applied Biosystems, Foster City, CA, USA), according to manufacturer's instructions. Unique real tags were mapped to human chromosome sequences, and tag density, defined as the ratio of real tags to virtual tags over moving windows, was calculated to detect abnormalities in DNA content using threshold values defined by DGS simulations. Tag positions and tag density ratios were visualized using Custom Tracks and Genome Graphs from the University of California, Santa Cruz (UCSC) genome browser (Mar. 2006 freeze, hg18) [
23‐
25]. The detailed protocols for DGS, virtual tag characterization and
in silico simulations are available in Additional file
3.
Quantitative real-time PCR
Relative DNA copy number was determined by quantitative real-time PCR using a SYBR Green PCR Master Mix (Applied Biosystems) and the ABI PRISM 7000 (Applied Biosystems). DNA content per haploid genome was normalized to that of a repetitive element, Line-1, and calculated by the comparative CT (ΔΔCT) relative quantification method using the formula 2
(Nt - Nline)-(Xt - Xline), where
N
t
is the threshold cycle number observed for an experimental primer in normal leukocyte DNA,
N
line
is the threshold cycle number observed for the Line-1 primer in normal leukocyte DNA,
Xt is the average threshold cycle number observed for the experimental primer in cancer cell DNA, and
X
line
is the average threshold cycle number observed for the Line-1 primer in cancer cell DNA [
14]. Genomic amplification was defined as a greater than 4-fold increase in DNA content. The primer sequences for each locus are available in Additional file
4. The allelic proportion of mutant
KRAS (G12V, ggt→gTt) was determined by employing a modified real-time PCR procedure according to Itabashi
et al [
26]. The detailed protocol is available in Additional file
3. cDNA was prepared using SuperScript III reverse transcriptase (RT, Invitrogen), and the mRNA level of each gene was determined by real-time RT-PCR using the TaqMan Gene Expression Assay (Applied Biosystems). Relative mRNA levels were calculated by the comparative CT method using
GAPDH as an endogenous control. The primer/probe sets used are shown in Additional file
5.
Fluorescence in situhybridization (FISH)
BACs that contained the KRAS locus (RP11-636P12) and chromosome 12q24.2 (RP11-91M21) were labeled with Cy3 and Cy5, respectively, and then incubated with slides prepared with interphase and metaphase chromosomes. Nuclei were counter-stained with 4',6-diamino-2-phenylindole (DAPI), and slides were analyzed using a fluorescence microscope (Leica CW-4000).
Mutational analysis of KRAS and PIK3CA
Amplified genomic fragments were either sequenced directly, or subcloned using the TOPO TA-cloning kit (Invitrogen) and then sequenced. At least ten clones from two independent PCR assays per locus were sequenced using M13 Forward and Reverse primers (Invitrogen). The sequences of the primers used for amplification of
KRAS (exons 1 and 2) and
PIK3CA (exons 9 and 20) are shown in Additional file
6.
Immunoblot analysis
Cells were lysed in Lysis buffer containing 20 mM Tris-HCl (pH7.5) buffer, 150 mM NaCl, 1 mM EDTA, 1% Triton X, 10% glycerol, 10 mM NaF, 1 mM sodium vanadate, 50 mM β-glycerophosphate, 1 mM phenylmethansulfonyl fluoride, 1 mM dithiothreitol, and a protease inhibitor cocktail (Roche, Mannheim, Germany). Proteins were separated by SDS-PAGE and electroblotted onto an Immobilon-P membrane (Millipore, Billerica, MA, USA). The membranes were analyzed by immunoblot using the following antibodies, as indicated: mouse monoclonal anti-KRAS, -NRAS, and -HRAS antibodies (sc-30, sc-31, and sc-29, respectively, Santa Cruz Biotechnology, Santa Cruz, CA, USA); anti-actin antibody (Millipore); rabbit polyclonal anti-p44/42 MAP kinase, -phosho-p44/42 MAP kinase (Thr202/Tyr204), -Akt and -phospho-Akt (Ser473) antisera (Cell Signaling Technology, Danvers, MA, USA).
GTP-RAS pull-down assay
The activation of RAS was detected using an EZ-Detect Ras Activation Kit (Pierce, Rockford, IL, USA). Briefly, cell lysate (500 μg) was incubated with immobilized Raf1 Ras-binding domain fused to glutathione S-transferase (GST-Raf1-RBD). Precipitates were washed 3 times, and bound proteins were eluted by boiling for 5 minutes (min). Proteins were resolved on a 12% polyacrylamide gel, transferred to an Immobilon-P membrane, and subjected to immunoblot analysis using anti-KRAS, -NRAS, or -HRAS antibodies.
RNA interference
A custom-designed KRAS siRNA (5'-AGAGUGCCUUGACGAUACAdTdT-3'), targeting a region of KRAS that is not associated with known oncogenic mutations, was synthesized by Dharmacon (Lafayette, Co, USA). siRNAs targeting LRMP, LYRM5 and CASC1 were purchased from Ambion (No.144181, 284911 and 147715). A universal non-targeting siRNA (non-specific control VII, Dharmacon) was used as a negative control. In each experiment, 5 × 106 cells were transfected with 7.5 μl of 20 μM siRNA by electroporation (Amaxa, Cologne, Germany) using Nucleofector kit V or T, according to the manufacturer's instructions.
Cell proliferation assay
Following transfection with siRNAs, the gastric cancer cell lines HSC45, MKN1, AGS and NUGC4 were seeded in 96-well plates at a density of 8000 cells/100 μl in standard medium containing 10% FCS. Cell number at 48, 72 and 96 h post-transfection was determined indirectly by colorimetric assay using Cell Counting Kit-8 solution (Dojindo, Kumamoto, Japan). The assay is based on the reduction of a tetrazolium salt ([2-(2-methoxy-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfophenyl)-2-tetrazolium, monosodium salt], WST-8) and is used as a measure of live cells. The absorbance of each well at 450 nm was measured using a microplate reader (Model 680, Bio-Rad, Hercules, CA, USA).
Flow cytometry
Flow cytometry was carried out as described previously [
27]. Briefly, adherent and detached cells were harvested, fixed in 90% cold ethanol, treated with RNase A (500 units/ml), and then stained with propidium iodide (50 μg/ml). For each sample, 30000 events were analyzed using the cell cycle analysis platform of FlowJo program (Tree Star, Ashland, OR, USA).
Immunohistochemistry
Formalin-fixed, paraffin-embedded sections of gastric tumors were deparaffinized, hydrated, and then treated with peroxidase blocking solution (3% H2O2 in Methanol). Sections were autoclaved at 105°C for 10 min in target retrieval solution (Dako, Glostrup, Denmark). Sections were incubated with a mouse anti-KRAS antibody (1:100 dilution; Santa Cruz Biotechnology) for 1 h at room temperature, and immunoreactivity was detected using ENVISION-Plus reagents (Dako).
Discussion
In this report, we described a novel method, termed DGS, of detecting copy number alterations in the human genome, which is based on the analysis of short fragments of genomic DNA generated by restriction enzyme digestion. Although DGS is modeled on the basic concept of DK, we developed a modified tag preparation technique that involves single restriction enzyme digestion without PCR to minimize complex handling regimes and potential biases generated by PCR. Our relatively small-scale sequencing of approximately 5000 tags successfully detected discrete 500-kb amplifications of
KRAS and
CACNA1C in HSC45 cells, which were not previously reported in an independent experiment using BAC-based aCGH analysis [
31].
To date, however, this DGS method has some limitations as compared to DK and other methods. First, the resolution of DGS using short
MboI tags is lower than DK due to the difference of the theoretical number of virtual tags produced by restriction digestion. The number of virtual tags in our analysis (approximately 394,000 virtual tags in the range of 30 to 60 bp) was less than that of DK (approximately 731,000 tags) [
14]. Thus, while the current pilot study demonstrates the feasibility of using DGS to estimate copy number using a simplified tag preparation method, additional studies are needed, using different or combinations of restriction enzymes to produce more short tags, to improve the resolution of DGS. Second, DGS method has several limitations involved in labor, cost, and amount of material: (a) this method needs the generation of two rounds of plasmid libraries and the propagation of plasmid libraries, (b) this method costs higher than microarray platform and DK, (c) a large amount of starting material DNA is required.
Recently, the use of single nucleotide polymorphism (SNP) arrays for the detection of allele-specific copy-number alterations at high resolution using 906,600 SNP probes has been reported [
32]. Because DGS and DK do not rely on pre-designed probes, they are "open" platform techniques. For example, DK could be used to explore exogenous pathogenic DNA in infectious or neoplastic states [
14]. However, tag-counting methods, including DGS and DK, have similar limitations. First, they generally do not estimate allele-specific copy number, which SNP array analysis does. Second, the number of sequence reads, which is to say, the depth of sequencing, affects the sensitivity and the resolution of tag density profiles. The results of simulated DGS indicated that DGS using deep sequencing will have a higher level of sensitivity in detecting subtle copy-number alterations. However, even in reports of successful DK [
14‐
17], the depth of sequencing was less than 0.3 (when the theoretical number of unique virtual tags was defined as 1.0), partly due to practical limitations, such as the low through-put rate and labor intensive methods required when using standard sequencers [
33]. In the next step of improving DGS, DGS should be combined with the next-generation sequencing technologies [
34]. The recent introduction of instruments capable of sequence millions of nucleotides in a single run is changing the landscape of human genetics. By applying next-generation sequencing technologies to DGS, it should be possible to simplify the protocol and improve efficiency and resolution by bypassing the multi-step process of tag concatemerization, as well as conserve starting genomic DNA. With some next-generation sequencers, tag preparation by restriction digestion might generate more reproducible DNA fragmentation than current random-shearing approaches [
35,
36].
Gene amplification of
KRAS with or without mutation has been described in a limited number of cases, including lung, gastric, pancreatic and rectal cancers [
37‐
40]. More recently, aCGH analysis of various primary tumors, including lung, colorectal, pancreatic and gastric cancers, gliomas and testicular germ cell tumors, also detected amplification of chromosome 12p [
41‐
46]. In this report, we provided evidence that, while rare in colon and pancreatic cancers, the incidence of
KRAS gene amplification (greater than 4-fold) is increased in gastric cancer, and is responsible for KRAS activation.
Using MKN1 cells as a model system, we investigated the mechanism by which
KRAS amplification contributes to the growth of primary gastric cancers that lack mutations in
KRAS. Immunoblot analysis and knock-down of
KRAS in cells provided evidence that
KRAS gene amplification results in KRAS activation in the absence of mutation. To our knowledge, this is the first report to demonstrate a potential relationship between gene amplification of endogenous wild-type
KRAS, activation of KRAS signaling pathways, and cell growth in gastric cancer. In general, less than 10% of wild-type and over 50% of mutant RAS is in the GTP-bound state in cells [
47,
48]. Therefore, it is likely that amplification of endogenous wild-type
KRAS coupled overexpression in the MKN1 cells induces a biological effect that is similar to the effect of single-mutant alleles of
KRAS. We also found that while serum stimulation induced the activation of overexpressed KRAS and p44/42 in MKN1 cells, in cells that harbored amplified mutant
KRAS, KRAS and p44/42 were constitutively activated. Thus, amplified wild-type
KRAS might provide a growth advantage to cancer cells, not only by upregulating the basal cell growth, but also by conferring adaptability to changes in the environment, such as availability of growth factors and nutrients. Further studies will be needed to investigate potential functional connections for these correlations.
The
KRAS gene status of tumors is currently of great interest, because
KRAS mutations are linked to the response to anti-epidermal growth factor receptor (EGFR) therapies. Panitumumab and cetuximab are antibody-based drugs that inhibit EGFR, and are currently used in the treatment of colorectal cancer [
49]. However, several groups have reported that
KRAS mutations are significantly associated with lack of response to cetuximab or panitumumab in patients with advanced, chemotherapy-refractive colorectal cancer [
50,
51]. In gastric cancer, EGFR is a promising target since it is frequently overexpressed [
52,
53], and clinical trials of cetuximab in the treatment of gastric cancer are ongoing [
54,
55]. Our results showing that overexpressed wild-type KRAS is involved in the activation of downstream signaling pathways that govern cell proliferation indicate that the amplification of
KRAS might be of clinical significance in predicting response to cetuximab or to panitumumab in gastric cancer. Prospective studies are needed to determine the efficacy of patient-specific EGFR-targeted therapy based on
KRAS amplification and mutation status.
Acknowledgements
We thank Drs. Sam Thiagalingam and Joseph F. Costello for critical reading of this manuscript. We also thank Tomoko Takahashi, Miho Higuchi, Reia Hosokawa, Tetsuya Fujii, Masami Ashida, Mutsumi Toyota and Kyoko Fujii for their excellent technical support. Grant support: Grants-in-Aid for Scientific Research on Priority Areas from the Ministry of Education, Culture, Sports, Science, and Technology (MT, YS, KI and TT) and Industrial Technology Research Grant Program from New Energy and Industrial Technology Development Organization of Japan (HM).
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
HM performed molecular biological experiments including DGS, and wrote Perl scripts and the paper. FA, HA, RM and HT performed in silico genome analyses and constructed the tag database. YS and MI performed real-time PCR. LK performed Southern blotting. MT and HS extracted genomic DNA and performed sequencing. KY provided gastric cancer cell lines. MF, MH and MK provided primary samples and clinico-pathological data. SVS designed in silico DGS simulation and performed statistical analyses. KI, YS and TT conceived, coordinated the study and revised the paper. All authors read and approved the final manuscript.