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Carcinogenesis Advance Access originally published online on October 11, 2005
Carcinogenesis 2006 27(3):392-404; doi:10.1093/carcin/bgi237
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Carcinogenesis vol.27 no.3 © Oxford University Press 2005; all rights reserved.

Oligonucleotide microarray analysis of distinct gene expression patterns in colorectal cancer tissues harboring BRAF and K-ras mutations

Il-Jin Kim 1, {dagger}, Hio Chung Kang 1, 2, {dagger}, Sang-Geun Jang 1, Kun Kim 1, Sun-A Ahn 1, Hyun-Ju Yoon 1, Sang Nam Yoon 3 and Jae-Gahb Park 1, 2, 3, *

1 Korean Hereditary Tumor Registry, Cancer Research Institute and Cancer Research Center, Seoul National University, Seoul, Korea, 2 Research Institute and Hospital, National Cancer Center, 809 Madu-dong, Ilsan-gu, Goyang, Gyeonggi 411-764, Korea and 3 Department of Surgery, Seoul National University College of Medicine, Seoul, Korea

* To whom correspondence should be addressed. E-mail: park{at}ncc.re.kr


   Abstract
Top
 Abstract
Introduction
 Materials and methods
 Results
 Discussion
 References
 
Various types of human cancers harbor BRAF somatic mutations, leading researchers to seek molecular targets for BRAF inhibitors. A mutually exclusive relationship has been observed between the BRAF-V600E mutation and K-ras mutations, suggesting that the BRAF-V600E mutation may differ from the other BRAF mutant types. Here, we used microarray analysis to examine differences between the BRAF and K-ras mutant colorectal samples and within the BRAF group (V600E versus non-V600E), in the hope that the identified gene sets could form the basis for new target development. Eleven colorectal cancers (CRCs) with BRAF mutations and nine with K-ras mutations were examined by high-density microarray analysis. We also tested whether other significant genetic or clinical status involved in CRC development, such as APC and TP53 mutations, MSI and TNM-Duke's staging, were related with the observed BRAF- or K-ras associated expression profiles. Unsupervised two-way hierarchical clustering and multidimensional scaling revealed that the differentially expressed genes clustered according to the mutation status of BRAF and K-ras, and that samples with the BRAF-V600E and non-V600E mutants could be distinguished from each other by gene profiling. Examination of TNM–Duke's staging, MSI and mutations in APC and TP53 revealed that these significant mutations could not account for the hierarchical clustering results observed in our study. We herein identified distinct gene expression patterns and gene sets that may form the basis for identification of BRAF-targeting molecules or provide researchers with a better understanding of the molecular pathogenesis underlying RAS–RAF signaling.

Abbreviations: CRC, colorectal cancer; GIST, gastrointestinal stromal tumors; HNPCC, hereditary non-polyposis colorectal cancer; LOOCV, leave-one-out cross validation; MDS, multidimensional scaling; MMP1, matrix metalloproteinase 1; MMR, mismatch repair; MSI, microsatellite instability; PAM, prediction analysis of microarrays; ROC, receiver operating characteristic; SPRED2, sprouty-related, EVH1 domain containing 2


   Introduction
Top
 Abstract
 Introduction
Materials and methods
 Results
 Discussion
 References
 
Colorectal cancer (CRC) is an important human cancer, and incidence rates of this cancer are increasing in Asian countries such as Korea (1). CRC development is a multi-step process (2) involving microsatellite instability (MSI), mutations in mismatch repair (MMR) of genes such as MLH1 and MSH2, and mutations in APC, SMAD4, K-ras, TP53 and ß-catenin (2–9). Somatic BRAF mutations have been reported in 5.1–18% of CRCs (10–12). BRAF is one of three serine/threonine kinases (ARAF, BRAF, CRAF/RAF1) that act within the RAS–RAF–MEK–ERK–MAPK signaling pathway (13). BRAF mutations have also been reported in 80% of primary melanomas (10), 68% of metastatic melanomas (14) and 14–33% of ovarian carcinomas (10,15). Interestingly, a mutually exclusive relationship between the BRAF-V600E mutation and K-ras mutations has been found in most human cancers (10,12,16). In CRCs, BRAF-V600E mutations appeared to be associated with MMR deficiency and were not found in samples with K-ras mutations (12). This suggests that the BRAF-V600E and K-ras mutations may have equivalent effects on tumorigenesis (12). However, other studies have shown that BRAF mutations were associated with MLH1 promoter methylation but not MMR deficiency (17–19). Moreover, BRAF mutations were not found in MMR-deficient hereditary non-polyposis colorectal cancer (HNPCC) samples (18,19) but were found in sporadic CRCs, suggesting that BRAF may be involved in the carcinogenesis of non-inheritable CRCs (18,19). This is unexpected because it was believed that MMR-deficient phenotypes were associated with both sporadic and hereditary CRCs (18). Thus, researchers postulated that HNPCC patients could be screened for BRAF mutations prior to MMR gene screening, as a weed-out technique (18). Finally, BRAF mutations have been closely correlated with methylator phenotypes in several genes including MLH1 (20), and BRAF mutations have been associated with longer disease-free survival and a shorter duration of response to treatment was reported (21). Collectively, these observations seem to indicate that BRAF mutations could be used as therapeutic markers (18,20,21) and/or could be molecularly targeted for development of new anticancer strategies.

A good example for molecularly targeted anti-cancer drug is imatinib (formerly STI-571), which inhibits the BCR–ABL kinase in chronic myeloid leukemia and KIT in gastrointestinal stromal tumors (GIST) (22). In fact, several candidate molecular BRAF inhibitors have entered clinical trials (23,24). However, the V600E BRAF mutation clearly differs from the non-V600E BRAF mutations (10) in that V600E is independent of Ras signaling and elevates basal kinase activity without K-ras mutations (10), while the non-V600E BRAF mutations are Ras-dependent and are not mutually exclusive with K-ras mutations (10). As the V600E mutation accounts for 80% of all BRAF mutations (10), it would seem logical to search for V600E-specific molecular inhibitors.

Microarray analysis is commonly used to screen genome-wide gene expression profiles in human diseases, including cancers (25–27). This technique can also be used to identify new cancer subgroups (class discovery), to classify samples into known cancer subgroups (class prediction) (28) and to predict the prognosis or therapeutic responsiveness of cancer patients (29,30). A previous microarray study revealed distinct gene expression patterns in BRCA1/2 mutant tissues sampled from hereditary ovarian/breast cancer patients versus the non-mutant group (BRCAx) (31,32). In CRCs, microarray analysis was used to divide samples into two groups based on the existence of MSI, which is an important marker in CRC (33). Recently, BRAF mutant melanoma samples were distinguished from BRAF wild-type samples by supervised microarray analysis (34), suggesting that gene expression profiling according to BRAF status might be useful for the identification of molecular markers involved in RAS–RAS–MEK–ERK–MAPK signaling. However, no previous work has used microarray analysis to investigate the molecular differences among samples with BRAF-V600E and non-V600E mutations, and K-ras mutations.

Here, we examined whether microarray analysis was capable of distinguishing colorectal samples according to BRAF and K-ras mutation status. Once these divisions were established, we tested whether other significant genetic changes involved in CRC development, such as APC and TP53 mutations, MSI and TNM-Duke's staging, were related with the observed BRAF- or K-ras-associated expression profiles.


   Materials and methods
Top
 Abstract
 Introduction
 Materials and methods
Results
 Discussion
 References
 
CRC tissue samples
A total of 20 sporadic CRC cases were selected, including 11 samples with BRAF mutations and 9 samples with K-ras mutations, as previously described (9). Clinical characteristics, including age, sex, preoperative CEA level, tumor location, differentiation, lymphatic invasion, venous invasion, neural invasion, A–C stage, Duke's stage and TNM stage were noted. CRC tissues were collected from the Seoul National University Hospital and the National Cancer Center, Korea. The fresh cancer tissues were stored at –70°C in a liquid nitrogen tank. DNAs and total RNAs from tumor samples (portions with >60% cancer cells) were extracted using the Trizol reagent (Invitrogen, Carlsbad, CA) according to the manufacturer's instructions. The extracted genomic DNA was used for mutational analysis of BRAF, K-ras, APC, TP53 and MSI. The extracted total RNAs were used for gene expression analysis on an Affymetrix U133A 2.0 GeneChip (Affymetrix, Santa Clara, CA) containing 22 277 probes. The Universal Human Reference RNA (Stratagene, CA) was used as a control for comparison with the gene expressions in the 20 CRCs.

Mutational analysis of BRAF, K-ras, TP53, APC and MSI
Exons 11 and 15 of the BRAF gene were screened by oligonucleotide microarray and direct sequencing with the previously described primer sets (16). PCR reactions were carried out in a volume of 25 µl containing 100 ng genomic DNA, 10 pmol of each primer, 250 µM each dNTP, 0.5 U of Taq polymerase and the reaction buffer provided by the supplier (Qiagen, Hilden, Germany). Samples were denatured for 5 min at 94°C in a GeneAmp PCR system 9700 (Applied Biosystems, Foster City, CA), and then amplified by 35 cycles of 94°C for 30 s, 55°C for 30 s and 72°C for 1 min, with a final elongation of 10 min at 72°C. Codons 12 and 13 of the K-ras gene were screened by oligonucleotide microarray as described previously (9) and bidirectionally sequenced using the Taq dideoxy terminator cycle sequencing kit and an ABI 3730 DNA sequencer (Applied Biosystems).

All the coding regions (all 15 exons) (35) of APC and exons 2–11 of TP53 (36,37) were entirely screened by an automatic bidirectional sequencing method. Five microsatellite markers (BAT-25, BAT-26, D2S123, D5S346 and D17S250) were used for determining MSI status using DHPLC (denaturing high performance liquid chromatography) and the capillary-based method (38). MSI-H cancers are defined as having MSI in ≥2 of the 5 Bethesda panel markers and MSI-L cancers show MSI in only 1 of the 5 markers. Labeled samples were run on an ABI 3100 sequencer (Applied Biosystems), and the Genescan software (Genotyper 2.1, ABI, Foster City, CA) was used to calculate the size of each fluorescent PCR product for MSI determination (38).

Sample hybridization using oligonucleotide microarrays
The microarray experiments were performed according to our previous experimental protocol (39) with the addition of 10% DMSO (dimethyl sulfoxide) to the hybridization mixture. Briefly, the extracted total RNA was quantified by spectrophotometer (Beckman Coulter, Fullerton, CA), checked by 1% agarose gel electrophoresis and then purified with an RNeasy kit (Qiagen, Valencia, CA). An aliquot of 40 µl of purified RNA was ethanol-precipitated with 3 M sodium acetate (pH 5.2), 1 µl of glycogen (5 µg/ml) and 100 µl of 100% ice-cold ethanol, and 20 µg of purified RNA was used to synthesize double-strand cDNAs using SuperScript II reverse transcriptase (Life Technologies, Rockville, MD) and an HPLC-purified T7-(dT)24 primer (Metabion, Germany). The synthesized double-strand cDNA was purified with a Qiagen DNA purification kit (Qiagen) and ethanol-precipitated with 1 µl of glycogen, 20 µl of 7.5 M of ammonium acetate, and 100 µl of 100% ethanol. Biotinylated cRNA was synthesized from the double-stranded cDNA using the GeneChip Expression 3'-Amplification Reagents (Affymetrix), and then purified and fragmented. The fragmented cRNA was quantified, and 10 µg of cRNA was hybridized to the oligonucleotide microarray, which was subsequently washed and stained with streptavidin-phycoerythrin. Scanning was performed with an Affymetrix Scanner (Affymetrix).

Microarray data analysis
The scanned GeneChip data were analyzed with the GCOS 1.1 (GeneChip Operating Software, Affymetrix) and DMT 3.0 (Data Mining Tool, Affymetrix) programs. All genes present on the GeneChip were globally normalized and adjusted to a user-specific target signal value (500). The Universal Human Reference RNA (Stratagene) sample was also hybridized to the microarray, and its values served as the base line (control) for calculating the signal log ratios in the 20 CRC samples. After normalization, the present calls (P-calls) generated by the DMT software were calculated for each gene in the 20 samples. Probes without a P-call from any of 20 samples were excluded from the analysis and 16 332 probes were left.

For the unsupervised hierarchical clustering, two-way hierarchical clustering was applied to both genes and arrays, using the Cluster and Treeview programs (40). The two-way median center was selected for adjusting data, and the uncentered correlation was used for average linkage clustering. A total of 11 310 probes showing >50% expression (>10 P-calls) in the 20 samples selected and the signal–log ratio values of 11 310 probes were used for hierarchical clustering. To clearly identify differentially expressed genes, the Student's t-test was performed using DMT. This allowed identification of 2526 probes (P < 0.05) differentially expressed between the BRAF and K-ras groups, and 1703 probes (P < 0.05) differentially expressed between the BRAF-V600E and non-V600E groups. These two gene sets were further analyzed by hierarchical clustering to identify distinct gene expression patterns.

Multidimensional scaling (MDS) was performed (SPSS, Chicago, IL) and the SigmaPlot (SPSS) program utilized. The signal–log ratio values of all 20 samples were entered in the SPSS, the multidimensional scaling factors (X, Y and Z-values) were calculated for each sample, and these values were used for MDS analysis with the signal plot software. The initial MDS was performed using 11 310 probes in all 20 samples, with further analyses performed using the 2526 and 1703 probes identified above. Prediction analysis of microarrays (PAM) analysis was performed as described previously (41), and was used to identify a gene set for classification by ranking genes with a penalized t-statistic and user-applied threshold (41). Leave-one-out cross validation (LOOCV) analysis was performed using GeneCluster2.0 software (http://www.broad.mit.edu/cancer/software/software.html). ‘S2N (Signal-to-Noise)’ value was used for ‘distance function’ and 100 top ranked genes (number of neighbors) were selected from each BRAF and K-ras group. A permutation analysis was performed 1000 times using ‘median’ class estimate (P = 0.05) in the top ranked 100 genes. The LOOCV results were compared using different gene numbers from 10 to 100 genes (10, 20, 30, 40, 50, 60, 70, 80, 90 and 100) and the best LOOCV results and ‘predictor’ gene set were obtained. The best LOOCV results were judged by the lowest receiver operating characteristic (ROC) error value. The selected ‘predictor’ was statistically confirmed one more time using the Fisher test. Gene ontology and function analyses were performed with the Affymetrix NetAffx software package (Affymetrix).

Statistical analysis
Statistical analyses were performed using the {chi}2 or Fisher's exact tests to determine the strength of the correlations between the mutations in BRAF, K-ras, APC, TP53, MSI, TNM–Duke's stage and the two clusters (left and right clusters). P = 0.05 was set as the significance level using the SPSS software (SPSS). Tests for associations between BRAF and K-ras quantitative RT–PCR (qRT–PCR) expression were performed with the one-way ANOVA and Wilcoxon–Mann–Whitney tests (SPSS).

Quantitative RT–PCR
We selected four genes (IL8, MMP1, TUBA1 and PTS) for real-time qRT–PCR for validation of the microarray data. Using the SuperScript Preamplification System for first strand cDNA synthesis, 5 µg of total RNA was used for creation of single-stranded cDNA (Life Technologies). The cDNA was diluted and quantitatively equalized for PCR amplification. For real-time qRT–PCR, the ABI Prism 7900 sequence detection system (Applied Biosystems) was used. SYBR® Premix Ex TaqTM (Takara, Kyoto Japan) were used for each PCR reaction and GAPDH gene was simultaneously run as a control and used for normalization. Non-template-control wells without cDNA were included as negative controls. Each test sample was run in triplicate. The primer sets for PCR amplification were designed as follows: IL8-F: 5'-CATACTCCAAACCTTTCCAC-3', IL8-R: 5'-AGCCCTCTTCAAAAACTTCT-3', MMP1-F: 5'-AAATCTTGCTCATGCTTTTC-3', MMP1-R: 5'-CACTGAAGGTGTAGCTAGGG-3', PTS-F: 5'-TACGGGAATGGTTATGAATC-3', PTS-R: 5'-CTCACCACATCTGCAAAGTA-3', TUBA1-F: 5'-CAACCTACACCAACCTCAAT-3', TUBA1-R: 5'-GAACTCTGTCAGGTCCACAT-3'. Following the standard curve method, the expression quantities of the examined genes were determined using the standard curves and the CT values and normalized using GAPDH expression quantities.


   Results
Top
 Abstract
 Introduction
 Materials and methods
 Results
Discussion
 References
 
BRAF, K-ras, APC and TP53 mutation screening and MSI analysis in CRCs
We previously screened CRC tissues for BRAF and K-ras mutations using oligonucleotide microarrays and direct sequencing (9). Here, we selected 11 CRC tissues with BRAF mutations; 9 harbored the V600E (codon 600, Val -> Glu, GTG -> GAG, exon 15) mutation, 1 harbored the G464V (codon 464, Glu -> Val, GGA -> GTA, exon 11) and 1 harbored the D594G (codon 594, Asp -> Gly, GAT -> GGT, exon 15) mutation. We also selected nine CRC samples with K-ras mutations for comparison purposes; five harbored mutations at codon 12 and the other four had mutations at codon 13. After we analyzed the gene expression profiling of these 20 CRC samples, we further screened the specimens for APC and TP53 mutations and for MSI (via five Bethesda panel). We found that eight samples (four from the BRAF mutation group and four from the K-ras mutation group) harbored truncating APC mutations, six samples (four from the BRAF mutation group and two from the K-ras mutation group) had TP53 somatic mutations, and five samples (four from the BRAF mutation group and one from the K-ras mutation group) exhibited MSI-H and one showed MSI-L (from the BRAF group). Detailed mutational data for the 20 samples are presented in Table I.


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  		Table I. Mutations of BRAF, K-ras, APC, TP53 and MSI in 20 CRCs

 
Unsupervised hierarchical clustering of CRC tissues with BRAF and K-ras mutations
We analyzed 20 CRC tissues with either BRAF or K-ras mutations by two-way hierarchical clustering to see whether the microarray analysis-derived gene expression profiles would cluster into two groups according to the mutation status of BRAF and K-ras. We selected 11 310 probes showing >50% expression (>10 P-calls) in the 20 samples and performed unsupervised hierarchical clustering, which revealed that CRC samples harboring BRAF and K-ras mutations differed in terms of their gene expression profiles (P = 0.002) (Figure 1A). Samples with K-ras mutations tended toward the left of the hierarchical cluster, with the exception of samples 456 and 218, while samples with BRAF mutations clustered to the right, with the exception of sample 640. The latter sample (640), which clustered with the K-ras group, harbored the G464V mutation in exon 11. Similarly, K-ras sample 456 (mutation at codon 12) clustered with sample with BRAF sample 481, which was the other non-V600E mutation included in the study (D594G).


Figure 1
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  		Fig. 1. Unsupervised hierarchical clustering of 20 CRCs with BRAF and K-ras mutations. (A) Unsupervised two-way hierarchical clustering was performed using 11 310 probes showing >50% expression (>10 P-calls) in the 20 CRC samples. For each gene, red indicates higher gene expression relative to the control and green indicates lower gene expression. Red circle, K-ras mutant group; blue circle, BRAF mutant group; triangle, non-V600E BRAF mutant samples. The numbers indicate sample names. (B) Unsupervised clustering result of the 11 BRAF mutant samples. Clustering was performed using the 11 310 probes used in (A). Arrows indicates non-V600E BRAF mutant samples, and the remaining are BRAF-V600E samples. (C) Mutation status of APC, TP53, MSI and TNM–Duke's stage was analyzed and correlations were examined by unsupervised clustering. Blue circles indicate mutation or MSI in each gene, while empty boxes indicate no mutation.

 
Next, we analyzed the 11 samples with BRAF mutations by two-way hierarchical clustering using 11 310 probes, and found that the non-V600E mutant samples (481 and 640) clustered very near each other (Figure 1B). We also developed a clustering dendrogram for APC, TP53 and MSI status, to examine whether these genetic alterations affected the unsupervised gene clustering results (Figure 1C). There was no observable relationship between mutations in APC (P = 0.264) or TP53 (P = 0.690) and the observed gene expression patterns. In terms of MSI, although five of six samples tended to the right panel, there was no statistically significant association between clustering and MSI (P = 0.163). No significant relationship between TNM–Duke's staging and gene expression patterns was also observed.

MDS of the 20 CRC samples with the full complement of 11 310 expressed probes and those identified by t-test
MDS was used to investigate differences in the gene expression patterns between BRAF and K-ras mutated CRC tissues. The 11 310 probes selected above were used for MDS, which revealed distinct patterns between the BRAF and K-ras groups (Figure 2A), consistent with the results of our hierarchical clustering. Unlike the hierarchical clustering results, non-V600E samples 481 and 640 were generally located between the K-ras and BRAF-V600E groups in MDS. However, consistent with the clustering results, K-ras sample 218 was placed outside the K-ras mutation group. We then sought to examine a detailed MDS by analyzing the BRAF and K-ras groups by t-test using the DMT 3.0 (Data Mining Tool, Affymetrix) program. We identified 2526 probes showing statistically significant differences in expression (P < 0.05) between the two groups, and used these probes for a second round of MDS. A more clear distinction was observed between BRAF and K-ras groups (Figure 2B). We then compared BRAF-V600E samples and non-V600E by t-test and identified 1703 probes. When we used these probes for MDS, the V600E and non-V600E groups were more clearly separated (Figure 2C).


Figure 2
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  		Fig. 2. MDS (multidimensional scaling) of 20 CRC samples with BRAF or K-ras mutations. (A) MDS analysis of the 20 samples was performed using 11 310 probes to confirm and compare the unsupervised hierarchical clustering results. Red circle, K-ras mutant group; blue square, BRAF-V600E group; dark triangle, BRAF non-V600E group; arrow (diamond), sample 218. The letters X, Y and Z indicate the multidimensional scaling values calculated by the SPSS software. (B) MDS analysis of all 20 samples using 2526 probes identified by t-test (P < 0.05) as being differentially expressed in the BRAF group versus the K-ras group. Red circle, K-ras mutant group; blue square, BRAF-V600E group; dark triangle, BRAF non-V600E group; arrow (diamond), sample 218. (C) Eleven BRAF mutant samples were analyzed by MDS using 1703 probes which significantly (P < 0.05) distinguished the BRAF-V600E and non-V600E groups. Blue circle, BRAF mutant group; dark triangle, BRAF non-V600E mutant samples.

 
Supervised hierarchical clustering of CRC tissues with genes showing significant differences between the BRAF and K-ras groups
Having observed distinct gene expression patterns between the BRAF and K-ras groups by unsupervised clustering and MDS, we performed a supervised hierarchical clustering analysis of the 20 samples using the 2526 differentially expressed genes identified by t-test. This analysis yielded distinct clustering dendrograms, with the 20 samples clearly separated into the BRAF and K-ras groups (data not shown). Furthermore, the V600E and non-V600E BRAF mutation groups yielded dendrograms in which the 11 BRAF samples were divided correctly into two groups (data not shown).

PAM-based identification of 98 genes capable of distinguishing the BRAF and K-ras groups
PAM analysis was used to identify genes capable of distinguishing between the BRAF and K-ras groups. Cross-validated probabilities were used to set the threshold level at 2.19. We identified 98 genes that were differentially expressed between two groups, indicating that they could be used as classifiers (Table II).


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  		Table II. List of 98 genes identified by PAM as distinguishing the BRAF and K-ras mutant groups

 
LOOCV analysis
LOOCV analysis was used to confirm the unsupervised hierarchical clustering analysis and 80 genes were selected as a classifier (Table III). The identified 80 classifiers correctly predicted 18 out of 20 samples (90%). Samples 218 and 640 which were misclassified in the unsupervised hierarchical clustering were also wrongly predicted by the LOOCV. The ROC error obtained by LOOCV was 0.101. By the 1000 permutation test, all 80 genes but 1 (TUBA1) showed statistically significant LOOCV score than that of 1% of permutation. The identified 80 genes were confirmed by Fisher test (P = 0.0009) (Figure 3).


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  		Table III. List of 80 genes identified by LOOCV as distinguishing the BRAF and K-ras mutant groups

 

Figure 3
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  		Fig. 3. LOOCV analysis. (A) LOOCV results in all 20 BRAF and K-ras group samples. Eighty genes were selected as a classifier with 0.101 ROC error value. All but two samples (218 and 640) were correctly predicted. (B) All 20 samples were analyzed by a hierarchical clustering using 80 genes identified by the LOOCV.

 
Quantitative real-time RT–PCR for validating microarray results
To validate the oligonucleotide microarray results, we performed a SYBR green-based real-time quantitative PCR assay of four genes, IL8, MMP1, PTS and TUBA1 that showed significantly different expressions between the BRAF and K-ras groups. The quantitative PCR results were consistent with microarray data (Figure 4). Statistical differences between BRAF and K-ras group were found in all four genes, IL8 (P = 0.0004), MMP1 (P = 0.0006), PTS (P = 0.0003) and TUBA1 (P = 0.0018).


Figure 4
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  		Fig. 4. Real-time qRT–PCR for validating microarray results. Four genes (IL8, MMP1, PTS and TUBA1) that showed significantly different expressions between the BRAF and K-ras groups were used for SYBR green-based real-time quantitative PCR assay. Significantly different expressions between the BRAF and K-ras groups were found in IL8 (A), MMP1 (B), PTS (C) and TUBA1 (D). Statistical P-value was calculated with one-way ANOVA method.

 
Clinical characterization of 20 CRCs
No clinicopathological differences were found between the BRAF and K-ras mutant samples, with one exception. Lymphatic invasion of the tumor was found in 6 of 11 BRAF mutant samples, but in 0 of 9 K-ras mutant samples (P = 0.014).


   Discussion
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
References
 
BRAF somatic mutations have been found in most human cancers, with the exception of gastric cancers (10,16,42,43). The V600E mutation accounts for >80% of the identified BRAF mutations regardless of cancer type, and the remainder of the identified BRAF mutations are restricted to exons 11 and 15. The V600E BRAF mutation appears to have a mutually exclusive relationship with K-ras mutations, which are an important component of the multi-step development of CRC (2). As K-ras somatic mutations are found in many human cancers (9,10), the mutually exclusive relationship with BRAF-V600E may have large value in molecular and clinical research. BRAF is a serine/threonine kinase; since enzymes are considered good candidate molecular targets for drug development (23,24), it is possible that BRAF or BRAF-related genes could be good targets for anti-cancer drugs (23,24).

In a first step towards identifying some of these molecular targets, we herein examined whether a gene set could be identified as distinguishing BRAF mutated CRC samples (BRAF group) from those harboring K-ras mutations (K-ras group), and furthermore whether we could distinguish the BRAF-V600E group from the non-V600E group. We obtained 20 CRC samples, 11 with BRAF mutations and 9 with K-ras mutations. Clinicopathological examination revealed that lymphatic invasion of the tumor was significantly associated with the BRAF group (P = 0.014), but further study with a larger sample size will be required to confirm this result. We then used microarray analysis followed by unsupervised hierarchical clustering and MDS (multidimensional scaling) methods. Two-way hierarchical clustering, revealed that the K-ras group clustered to the left and the BRAF group clustered to the right (P = 0.002) (Figure 1A). There were a few exceptions to this trend. K-ras sample 218 (G12D) clustered closer to the BRAF group. This sample showed no clinicopathological differences with the other K-ras samples; the reason for its outlier status is not clear, but may be related to the heterogeneity of CRCs. More notably, BRAF sample 640 clustered with the K-ras samples; sample 640 harbored the BRAF G464V mutation in exon 11 and is one of two non-V600E samples examined in this study. The second of these, sample 481 (D594G), was found to cluster with another K-ras outlier, sample 456. These results seem to suggest that the non-V600E group was more similar to the K-ras group than the V600E group. Furthermore, when we used unsupervised hierarchical clustering of the 11 BRAF samples to confirm that the non-V600E samples had similar gene expression patterns (Figure 1B), samples 481 and 640 clustered together. These results strongly suggested that the V600E samples could be distinguished from the non-V600E samples based on their gene expression profiles. Thus, it is possible that drugs or inhibitors targeting BRAF should potentially be divided into BRAF-V600E- and non-V600E-targeting molecules. The arms in cluster dendrogram between BRAF and K-ras groups are relatively short and these might have resulted from low sample numbers. Additional studies with larger sample sets will be required to confirm this possibility.

To exclude the possibility that our clustering results were affected by genetic alterations in other cancer-related genes, we examined the 20 CRC samples for MSI and APC and TP53 mutations, and then tested these results for associations with our unsupervised clustering results (Figure 1C). Although high frequencies of APC and TP53 mutations have been reported in CRCs (2,4,5), we did not observe any relationship between the clustering data and mutations in APC or TP53. While we did observe a tendency for MSI-positive samples to cluster to the right along with the BRAF mutant group, this association was not statistically significant (P = 0.163). Thus, it appears as though the observed associations were related to BRAF or K-ras status rather than to genetic alterations in APC, TP53 or MSI.

We then used MDS to confirm the apparent differences in the gene expression patterns between the BRAF and K-ras groups and between the V600E and non-V600E BRAF groups (Figure 2). In contrast to clustering, MDS can uncover multiple layers of meaning within microarray data, permit sample classification in multiple independent dimensions (components) and provide a quantitative measure of the sample variance generated by each component versus that across the entire dataset (44). Our MDS revealed that while the BRAF and K-ras groups were clearly distinct from each other, K-ras sample 218 (previously mentioned as an outlier in the cluster analysis) was far from both groups, though more closely aligned with the BRAF group (Figure 2A). The distinct differences between the BRAF and K-ras groups were even more pronounced when MDS was performed using 2526 probes identified by t-test (P < 0.05) as being significantly different between the two groups. Similarly, the BRAF-V600E group was also clearly distinguished from the non-V600E when MDS was performed using 1703 probes identified by t-test (P < 0.05) as being significantly different between the two groups (Figure 2C). We further used hierarchical clustering (supervised) to analyze the 2526 statistically significant probes in the BRAF versus K-ras samples and the 1703 significant probes differentiating the V600E and non-V559E groups. Throughout these analyses, the BRAF and K-ras groups could be distinguished from each other, as could the V600E and non-V600E groups.

An LOOCV analysis was performed to validate clustering results. We performed an LOOCV in which one sample is held, a predictor is trained on the remaining samples, the left out sample is classified by this predictor and the process is repeated iteratively (http://www.broad.mit.edu/cancer/software/software.html). All but two samples were correctly predicted by LOOCV (90% accuracy, 18/20). Samples 218 and 640 were wrongly predicted and these two samples were also misclassified in the unsupervised hierarchical clustering (Figure 1A). The LOOCV data confirmed the unsupervised hierarchical clustering data and suggested that gene expression profiles of the BRAF and K-ras groups could be distinguished from each other. We then examined the differentially expressed probes to identify possible molecular targets for BRAF or BRAF-related pathways. PAM analysis identified 123 probes capable of classifying the BRAF and K-ras groups. Exclusion of the redundant and hypothetical genes revealed a total of 98 possible classifier genes, including several reported to play important roles in the RAS–RAF–MEK–ERK–MAPK pathway, including IL-8, TGFBR2, SPRED2, MMP1 and IQGAP1. IL-8, which was generally upregulated in the BRAF group (P < 0.001), was reportedly upregulated by activation of RAF, MEK and ERK (45), and has been identified as a transcriptional target of RAS-RAF signaling, suggesting that it may be involved with BRAF (46). IL-8 secretion was required for the initiation of tumor-associated inflammation and neovascularization (47), and a previous study suggested the possibility of treating melanoma patients with a combination of inhibitors against IL-8 and MEK (45).

TGFBR2 (transforming growth factor, beta receptor II) was significantly downregulated in the BRAF group (P = 0.003). TGFBR2 frameshift mutations are commonly found in MSI CRC samples. Notably, TGFBR2 was significantly downregulated in MSI-H CRCs, while IL-8, IL-1ß, ICAM1 and CD68 were all reportedly upregulated (48). These findings were completely concordant with our findings in the BRAF group. Another interesting gene is SPRED2 (sprouty-related, EVH1 domain containing 2, P < 0.001), which was significantly downregulated in the BRAF group and upregulated in the K-ras group. SPREDs have been shown to inhibit ERK activation, and researchers have suggested that SPRED induction could be a novel strategy for preventing cancer cell metastasis (49). Furthermore, SPREDs modulate the RAS–RAF interaction and MAPK signaling; the latter effect may be due to suppression of RAF phosphorylation (50).

Another gene of interest is MMP1 (matrix metalloproteinase 1), which was upregulated in the BRAF group. MMP1 expression is known to increase during melanoma progression, and has been associated with shorter disease-free survival (51). Furthermore, high MMP1 levels in melanoma have been associated with the constitutive activation of the RAS–RAF–MEK–ERK pathway due to activating BRAF mutations (51). Consistent with these findings, we observed that MMP1 was more highly expressed in the BRAF mutant group than in the K-ras group (P = 0.006). As BRAF mutations are found in ~80% of melanoma cases, and blockade of MEK–ERK activity has been shown to inhibit melanoma cell proliferation and metastatic potential (51), MMP1 is expected to be an important target for BRAF inhibitors. IQGAP1 (IQ motif containing GTPase activating protein 1) was also significantly downregulated in the BRAF group (P < 0.001). IQGAP1 was reported to bind with ERK2, suggesting that it may modulate the RAS–MAPK signaling cascade (52).

Having identified the obvious molecular targets for BRAF revealed by our expression profiling, we next compared our differentially expressed genes (P < 0.05) with those identified by Pavey et al. (34) as being differentially expressed in BRAF mutant and wild-type melanoma cell lines. A number of genes appeared on both lists, including SENP7, SKD3, CDH1, HSF1, MBD2, UBE3B, DACH, TIA1, ANXA7 and CLTA. Of these, HSF1 (heat shock transcription factor 1) was reportedly phosphorylated by other members of the MAPK family in a ras-dependent manner (53). In addition, MBD2 (methyl-CpG binding domain protein 2) is involved in the methylator phenotypes of many genes (54). As BRAF is associated with MLH1 methylation and methylator phenotypes (17–20), MBD2 appears to be a likely candidate for a BRAF-related epigenetic target. Out of 98 genes from PAM and 80 genes from LOOCV, only 24 genes were overlapped. The low numbers of overlapped genes between PAM and LOOCV may be due to the differences of data analysis algorithm in these two methods.

In sum, we herein showed that in sporadic CRCs, the gene expression profiles of the BRAF and K-ras groups could be distinguished from each other, as could those of the BRAF-V600E and non-V600E groups. Although the sample size is limited in this work, the identified expression patterns and gene sets will hopefully form the basis for future development of molecular targets for BRAF.


   Notes
 
{dagger} These authors contributed equally to this work. Back


   Acknowledgments
 
This work was supported by a research grant from the National Cancer Center, Korea and the BK21 project for Medicine, Dentistry and Pharmacy.

Conflict of Interest Statement: None declared.


   References
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 

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Received June 3, 2005; revised August 15, 2005; accepted September 28, 2005.


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