k-ras mutations are correlated to lymph node metastasis and tumor stage, but not to the growth...

K-ras mutations are correlated to lymph node metastasis

and tumor stage, but not to the growth pattern of colon

carcinoma

ABDULMANNAN and VICTORIA HAHN-STROMBERG

Department of Laboratory Medicine, Section for Pathology, Orebro University Hospital, Orebro, Sweden

Mannan A, Hahn-Stromberg V. K-ras mutations are correlated to lymph node metastasis and tumorstage, but not to the growth pattern of colon carcinoma. APMIS 2012; 120: 459–68.

In colorectal carcinoma, pathological assessment of tumors is essential for determining therapy andprognosis of the disease. Molecular associations of tumor complexity index and genetic alternationscan be helpful to understand the tumor progression mechanism. Oncogenic K-ras is one of the majorcolorectal cancer associated genes, and is mutated in up to 50% of colorectal cancers. In this currentstudy, we correlated tumor complexity index with mutations in K-ras codon 12, 13, and 61 in associa-tion with different clinicopathological parameters such as TNM stage, localization, sex, and age. For-malin-fixed paraffin embedded tissue blocks from colon cancer samples was selected from 88 patientsdiagnosed with adenocarcinoma. Mutations in the K-ras gene were detected using pyrosequencingtechnique. Tumor complexity index was calculated using immunohistochemically stained images of thetumor outline of the specimens and then analyzing these pictures using Photoshop CS, Fovea Pro, andImage J computer programs. Statistical analysis was performed with SPSS. K-ras mutations weredetected in 17 (19.3%) colon cancer samples. Most of the samples were at a lower complexity index.No correlation was observed between K-ras mutations and complexity index. However, K-ras muta-tions were correlated with regional lymph node metastasis and tumor stages and complexity index withtumor wall penetration. In conclusion, complexity index and K-ras mutations are independent events;however, both correlate with tumor progression and are important in the biologic development ofcolon carcinoma.

Key words: Complexity index; K-ras mutations; metastasis; tumor progression.

Abdul Mannan, Department of Laboratory Medicine, Section for Pathology, Orebro UniversityHospital, Orebro 70185, Sweden. e-mail: [email protected]

Colorectal cancer (CRC) is the third mostoccurring multistage type of cancer (1). Eachyear, 1 200 000 new cases of CRC are beingdiagnosed in the world and 525 000 CRCpatients die due to this cancer (2). According toan estimated figure, 436 000 CRC in Europealone were diagnosed in 2008 (1).The grading of tumors in CRC specimens

involving, tumor stage, lymphnode involvement,

metastasis (TNM), tumor growth pattern, stageand differentiation are exceedingly vital to deter-mine a rational therapy and possible outcome ofthe disease (3). Determining tumor growth pat-tern by examining the invasive front of thetumor can also be helpful to determine the prog-nosis. For example, in a recent study by Junget al. (4) a specific growth pattern in papillarythyroid carcinoma was associated with lymphnode metastasis. Expansive and infiltrative typesof growth pattern have been described in CRC.In the expansive growth pattern, the invasive

Received 3 November 2011. Accepted 18 November2011

APMIS 120: 459–468 � 2011 The Authors

APMIS � 2011 APMIS

DOI 10.1111/j.1600-0463.2011.02852.x

459

front of the tumor is smooth and regular. How-ever, in the infiltrative growth pattern, the inva-sive front is highly coarse and irregular withseparated tumor cells and tumor cell islands (5).As the grading of growth pattern depends on theevaluation of the pathologist, variation existsamong the individual pthologists (5). Recently,a noble computer-based method was introducedby Franzen and Hahn-Stromberg to quantita-tively determine the growth pattern of CRCusing computer software. This method givesquantitative and reproducible results with anumeric value from 1 to 5 for the growth patternof the tumor named complexity index (6).Genetic, epigenetic, and environmental fac-

tors are three major groups of CRC relatedfactors (7). Among genetics, oncogene K-ras isone of the most important genes in the develop-ment of CRC (8). The K-ras gene family consistof Kirsten -(K), Harvey-(H), and N-ras, andthese genes share 90% homology and encode21 kDa monomeric membrane bound proteinsthat play a central part in the control of cellulargrowth, production, and differentiation by thetransduction of extracellular growth signals (9,10). Mutations in the K-ras gene, principally incodons 12, 13, and 61 produce constitutivelyactive forms of K-ras protein, which continu-ously transmit growth signals even in theabsence of extracellular growth stimuli. Onco-genic mutations in K-ras are observed in20–50% of CRCs, and about 90% of all K-rasmutations are found in ‘hotspots’ regions,codon 12, 13, and 61. However, the frequencyof K-ras gene mutations in codon 61 is very low(8, 11, 12).Cancers with these mutations are observed

refractory to anti-epidermal growth factorreceptor treatment (anti-EGFR). Due to theineffectiveness of anti-EGFR therapy, muta-tions are considered to determine the prognosisof the disease (13–19). In addition to anti-EGFR therapy, K-ras mutations also partici-pate in increasing cellular invasion in varioustumor models by activation of mitogen-acti-vated protein kinases (20, 21). K-ras mutationsin codon 12 are also associated with liver metas-tases in CRC (22).Molecular association of CRC growth pattern

with genetic alterations will be helpful to under-stand the mechanism of tumor progressionwhich, in the future, can be used to improve

cancer patient management and cancer therapy.The aim of this study was to determine if thereis an association between K-ras mutations incodon 12, 13, and 61, with tumor complexityindex along with other clinicopathologicalparameters.

MATERIALS AND METHODS

Specimen selection

A total of 88 formalin-fixed paraffin embedded(FFPE) colon cancer tissue samples were selectedfrom patients diagnosed with adenocarcinoma in thecolon from 2003 to 2009. Samples were confirmedby routine histology. Of the 88 selected colon cancersamples, 50 samples were from men and 38 fromwomen with an average age of 71 years (37–96).Samples were selected randomly from the pathologybiobank, Department of Laboratory medicine, Ore-bro University hospital, Orebro, Sweden and werecoded by assigning numbers from 1 to 88. Mucinouscarcinomas were not used because complexity indexcannot be calculated as there is no clear tumor bor-der. Rectal carcinomas were also excluded as mostof the patients receive radiation therapy before sur-gery resulting in a false tumor border and inaccurategenetic results. This study was approved by the EPNEthical Committee, Uppsala, Sweden.

DNA extraction

Tumor areas in the FFPE tissue blocks were circledby a pathologist. Punches of 2mm diameters weretaken from the tumor area for genomic DNA extrac-tions. Viogene blood and tissue genomic DNA extrac-tion Miniprep kit (Viogene-Biotek corporation, SijhihTaipei, Taiwan) was used for the extractions of geno-mic DNA in accordance with manufacturer instruc-tions. DNA concentrations were measured usingNanoDrop� ND-1000 spectrophotometer (Thermo-Fisher Scientific, Waltham, MA, USA) and extractedgenomic DNA samples were stored at )20.

Polymerase Chain Reaction (PCR)

PyroMark Assay Design 2.0 software (Qiagen sampleand Assay technology, Uppsala, Sweden) was used todesign primers. Primer sequences, their amplifiedproduct size, and annealing temperatures are given inTable 1.A total of 50 ll master mixture was prepared for

each PCR reaction, and amplifications were carriedout in a solution containing 80–100 ng of genomicDNA, 0.3 lM each of reverse and forwardPCR primers (Biomers.net, GmbH, Germany), 1X

MANNAN &HAHN-STROMBERG

460 � 2011 The Authors APMIS � 2011 APMIS

KAPA2G buffer M, 1.5 mM, MgCl2, 200 lM of eachdeoxyribonucleotide triphosphate (dNTP), and 1UKAPA2G Fast HotStart polymerase (KAPA Biosys-tem, Boston, Massachusetts, USA). Thermal cycler2700 GeneAmp� (Applied Biosystems, Foster city,CA, USA) was used for product amplification withfollowing PCR conditions; initial denaturation tem-perature 96 �C for 10 min, proceeded by 49 PCRcycles with denaturation at 94 �C for 45 s, annealingat 58 �C for 30 s, and extension at 72 �C for 30 s. Afinal extension was carried out at 72 �C for 7 min.Amplified PCR products were run in a 2% solutionof high-resolution agarose gel electrophoresis(Sigma-Aldrich Co., St. Louis, MO, USA) with aproduct size MassRuler� DNA ladder Mix (Thermo-Fisher Scientific).

Pyrosequencing

K-ras mutations were detected using PyroMark Q 96ID pyrosequencer (Qiagen, Uppsala, Sweden) accord-ing to manufacturers’ instructions. In short, 25 ll ofbiotinylated amplified PCR products were immobi-lized and converted to single stranded DNA usingstreptavidin sepharose� high performance (GEHealthcare Bio-Science AB, Uppsala, Sweden) andsequencing was performed using 0.5 lM internalprimers. PyroMark � Gold Q96 Reagent Kit (Qia-gen, Hilden, Germany) was used to perform pyrose-quencing according to the manufacturer instructions.

Immunohistochemistry

Immunohistochemistry was performed using theEnvision technique. Four-micron sections fromFFPE tissue blocks containing colon cancer were col-lected on glass slides coated with adhesive polylysin.Sections were deparaffinized in xylene, hydrated bypassing through decreasing concentrations of 99%,95%, and 70% ethanol, and were washed in distilledwater. For antigen retrieval, samples were boiled intris EDTA buffer, pH 9.0 for 30 min in microwaveoven followed by washing in distilled water.

Monoclonal anti-cytokeratin 8, CK8, (Bioscience,San Jose, CA, USA) was used at a dilution rate of1:25. Mountings were done after passing the sectionsfrom ascending concentrations of ethanol and xylenesolutions.

Computer image analysis

Images were analyzed according to the methoddescribed by Franzen et al. in 2008 (6). Images fromthe invasive front i.e. the part of the tumor that growsinto the mucosa of immunohistochemically CK8stained tumor slides were captured using LeicaDC200 digital camera that was mounted on LeicaDMRXE microscope (Leica Microsystems GmbH,Germany) with objective lens of 10X (Fig. 1A,B). Thenumbers of images captured form each specimendepended on the length of tumor invasive front. Onan average, eight images were captured with a rangeof 4–17. Captured images were stored in TIF formatwithout change in original size. Images were pro-cessed and thresholded to convert immunohistochem-ically stained tumor area into black and thebackground white (Fig. 1C–F). The black color wasthen removed, so that only the tumor outline was left.The thresholded image of the tumor cells were used tocount the numbers of tumor cell clusters and separatetumor cells. The tumor outline was used to calculatefractal dimensions. The average values of fractaldimensions and tumor cell clusters taken from all thecaptured images of each tumor were compared with atree diagram to get the complexity index value (6).The complexity index values ranged from 1 to 5,where 1 represents the tumors with smooth invasivefront and no tumor cell clusters and five highly irregu-lar tumors with lot of separate tumor cells and tumorcell clusters.

Statistical analysis

Statistical analysis was made using software SPSSversion 16 (SPSS Inc., Chicago, IL, USA). Quantita-tive parameters were analyzed by mean and standard

Table 1. PCR and internal primers for K-ras gene codon 12, 13, and 61

Primers Sequence 5¢–3¢ Annealingtemperature (C)

Productsize (Bp)

Codon 12 and 13Forward 1TATAAGGCCTGCTGAAAATGACTG 58 87Reverse TTAGCTGTATCGTCAAGGCACTCTInternal ⁄Sequencing GTCAAGGCACTCTTGCCTA 59 NA

Codon 61Forward 1CAGACTGTGTTTCTCCCTTCTCA 58 133Reverse TCCTCATGTACTGGTCCCTCATTInternal ⁄Sequencing CGACACAGCAGGTCA 53 NA

1biotinylated primer; NA, not applicable.

K-RAS MUTATIONS AND GROWTH PATTERN IN COLON CARCINOMA

� 2011 The Authors APMIS � 2011 APMIS 461

deviation, whereas qualitative parameters were ana-lyzed with percentage and frequencies. Chi-squareand Spearman correlations were applied between thegrouped variables. All P values were calculated fromtwo-sided test, and a p value of less than 0.05 with95% confidence interval was considered statisticallysignificant.

RESULTS

Patient clinicopathological data

In this study, a total of 88 colon cancer patientsamples were used. A total of 50 (57%) sampleswere from men and 38 (43%) were from women.The age of the patients varied from 37 to96 years with an average of 71 years. Thirty-two (36.4%) tumors were located in the rightcolon flexure, 26 (29.5%) in the sigmoid, 9(10.2%) in the left colon flexure, 9(10.2%) in the

transverse, 6 (6.8%) in the cecum, 5 (5.7%) inthe ascending colon, and 1 (1.1%) in thedescending colon.According to the TNM classification; for the

value of T, 1 (1.1%) tumor was at T1, 17(19.3%) at T2, 52 (59.1%) at T3 and 18 (20.5%)at T4; while for N, 48 (54.5%) samples were atN0, 22 (25%) at N1, 17 (19.3%) at N2, and only1 (1.1%) at N3. For the value of M, only 3(3.4%) were M1, whereas all others were MX.For the differentiation of the tumors, 11 (12.5%)tumors were low differentiated, 58 (65.9%) med-ium differentiated, and 19 (21.6%) highly differ-entiated. According to the staging system of theinternational union against cancer (UICC), 15(17%) tumors were at stage I, 33 (37.5%) atstage II, 37 (42%) at stage III, and 3 (3.4%) atstage IV. Complexity index showed that, 36(40.9%) tumors had a complexity index of 1, 32(36.4%) a complexity index of 2, 13 (14.8%) a

A B

C D

E F

Fig. 1. Images captured from tumors with expansive and infiltrative growth patterns. (A) represent the expansivegrowth pattern with a smooth invasive front and (B) the infiltrative growth pattern with an irregular invasivefront and separate tumor cells and tumor cell islands. (C, D) show the thresholded image of the same pictures,and (E, F) represent the tumor outline borders used to calculate the fractal dimension value.



complexity index of 3, 5 (5.7%) a complexityindex of 4, and 2 (2.3%) a complexity index of 5.

Genetic data analysis

Representative pyrograms of wild type (WT)and different mutant forms of K-ras in codon 12

and 13 are shown in Fig. 2. K-ras mutationswere observed in 18 (20%) of the colon cancersamples. Of the observed 17 mutations, 10 werein codon 12 and 7 in codon 13. Coexistence ofmutations in codon 12 and 13 were notobserved. No mutation was observed in codon61 of K-ras. Summaries of the K-ras mutations

Codon 13 Codon 12

CCCCWT codon 13 Mut codon12

WT codon 13 Mut codon12


Mut codon13 WT codon12

CC C

CC

CC

CC

T

A

G

T

C

C

C


A

B

C

D

E

Fig. 2. Representative pyrograms of K-ras codon 12 and 13. Mutations (Mut) are shown by arrows. (A) Showswild type (WT) K-ras in codon 12 (GGT) and 13 (GGC), whereas (B–D) represent mutated codon 12(GGT.GAT, GCT and GTT, respectively) and WT codon 13. (E) Shows WT codon 12 and mutant codon 13(GGC.GAT).



and corresponding change in amino acids aregiven in Table 2. K-ras gene transition of baseG ⁄A (12 ⁄17, 70.5%), which results into the sub-stitution of glycine with aspartate being themost frequently observed mutation followed bytransversions of G ⁄T (3 ⁄17, 17.6%) and twocases (2 ⁄16, 12.5%) of G ⁄C transversion.

Correlation between K-ras mutations and Clinicopatho-

logical parameters

The main objective of the study was to deter-mine the correlation between K-ras mutationsand complexity index; however, no correlationwas observed between K-ras mutations andcomplexity index (p > 0.05), which suggeststhat K-ras mutations and complexity indexare independent events. There was no statisti-cally significant correlation between K-rasmutations, and gender, age, differentiation,distant metastasis, and tumor penetration(p > 0.05). However, K-ras mutations were sig-nificantly correlated with regional lymph nodemetastasis (p = 0.02) and UICC tumor stages(p = 0.024). When K-ras mutations in codon12 and 13 were correlated separately with differ-ent clinicopathological parameters (data notshown), mutations in codon 12 were signifi-cantly correlated with regional lymph nodemetastasis (p = 0.02) and UICC tumor stages(p = 0.048). None of the clinicopathologicalparameters were significantly correlated withK-ras mutations in codon 13. No correlationwas observed between specific type of mutationand clinicopathologic parameters.Tumor complexity was also compared with

different clinicopathologic parameters (Table 4).No correlation was observed between complex-ity index, and age, gender, differentiation, regio-nal lymph nodes metastasis, and distant

metastasis (p > 0.05). Although there was anassociation between stage III and complexityindex 3 and 4, results were statistically insignifi-cant (p = 0.069). However, complexity indexwas strongly correlated with tumor wall inva-sion (p = 0.019).

DISCUSSION

Assessment of different pathological dataobtained after surgical resection of tumor speci-mens is essential for the evaluation of tumorcomplexity, rational therapy, post-operativecare, tumor aggression, and possible outcome of

Table 2. K-ras gene mutations and correspondingsubstituted amino acids

K-rascodon

WTcodon

Mutatedcodon

N (%) WTaa

Mutatedaa

12 GGT fi GCT 2 (2.3) Gly fi AlaGAT 5 (5.7) AspGTT 3 (3.4) Val

13 GGC fi GAC 7 (7.9) Gly fi Asp61 CAA fi CAC 0 (0) Gln fi His

aa, amino acid; N, number of patients with muta-tions; WT, wild type.

Table 3. Association of K-ras gene mutations withdifferent clinicopathologic parameters

Clinicopathologiccharacters

WTKrasN (%)

MutKrasN (%)

p Value

GenderMale 40 (56.3) 10 (58.8) 0.85Female 31 (43.7) 7 (41.2)

Age60< 10 (14.1) 1 (5.9) 0.36‡60 61 (85.9) 16 (94.1)

DifferentiationLow 8 (11.3) 3 (17.6) 0.82Medium 48 (67.6) 10 (58.8)High 15 (21.1) 4 (23.5)

Complexity index1 + 2 54 (76.1) 14 (82.4) 0.553 + 4 15 (21.1) 3 (17.6)5 2 (2.8) 0 (0.0)

Tumor penetration (T)T1 1 (1.4) 0 (0.0) 0.09T2 16 (22.5) 1 (5.9)T3 41 (57.7) 11 (64.7)T4 13 (18.3) 5 (29.4)

Lymph node (N)Absent 43 (60.6) 5 (29.4) 10.02Present 28 (39.4) 12 (70.6)

M1 2 (2.8) 1 (5.9) 0.57X 69 (97.2) 16 (94.1)

UICC stagingI 15 (21.1) 0 (0.0) 20.024II 27 (38) 6 (35.3)III 27 (38) 10 (58.8)IV 2 (2.9) 1 (5.9)

N, number of patients with mutations.1Significant correlation between K-ras mutationslymph node metastasis.2Significant correlation between K-ras mutations withtumor stages.



the therapy (3). In this study, we evaluatedK-ras mutations in codon 12, 13, and 61, andcalculated the complexity index in colon carci-nomas by estimating fractal dimensions andcounting tumor cell clusters.After the introduction of anti-epidermal

growth factor receptor (EGFR) therapy, prog-nosis of the patients having metastatic CRC hasimproved (23). The improvement in prognosis isdue to a disruption in the EGFR pathway.However, many recent studies have shown thatcertain mutations in the K-ras gene results inthe absence of response to anti-EGFR treat-ment in patients (24, 25). In addition to non-responsiveness, some studies have shown thatactivation of mitogen-activated protein kinaseby oncogenic ras mutations are likely to impli-cate in an increased cellular invasion in varioustumor models (20, 21).The main objective of our study was to deter-

mine if there is a possible correlation betweenK-ras mutations at codon 12, 13, and 61with tumor growth pattern of colon cancer.

K-ras mutations were determined using pyrose-quencing technique (26). The frequency ofK-ras gene mutations was 20% where the domi-nant mutation was the substitution of glycinewith aspartate. The observed mutations werewithin the range of 12–50% in concordancewith earlier studies (18, 19, 26, 27). This widerange of results could be due to different tumorstorage conditions, different techniques used formutation detections, variable features of thepatients cohorts selected for the study and anal-ysis of different codons, and bases of codons indifferent studies (27–31). As we studied muta-tions in the second base of K-ras codon 12 and13, mutations observed in our study were at alower limit in the range determined in earlierstudies. No significant correlation was observedbetween K-ras mutations and tumor complexityindex in colon carcinomas indicating that K-rasis not the primary gene responsible for thedeciding the tumor growth pattern of colon car-cinoma. K-ras mutations were also correlatedwith different clinicopathological parameters

Table 4. Association of complexity index with different clinicopathologic parameters

Clinicopathologicalcharacters

Complexity index p Value

1N (%)

2N (%)

3N (%)

4N (%)

5N (%)

GenderMale 20 (55.6) 20 (62.5) 6 (46.2) 3 (60) 1 (50) 0.89Female 16 (44.4) 12 (37.5) 7 (53.8) 2 (40) 1 (50)

Age60< 2 (5.6) 5 (15.6) 3 (23.1) 0 (0.0) 1 (50) 0.1‡60 34 (94.4) 27 (84.4) 10 (76.9) 5 (100) 1 (50)

DifferentiationLow 1 (2.8) 6 (18.8) 3 (23.1) 1 (20) 0 (0.0) 0.97Medium 29 (80.6) 20 (62.5) 6 (46.2) 1 (20) 2 (100)High 6 (16.7) 6 (18.8) 4 (30.8) 3 (60) 0 (0.0)

Tumor stage (T)T1 0 (0.0) 0 (0.0) 1 (7.7) 0 (0.0) 0 (0.0) 10.019T2 11 (30.6) 4 (12.5) 2 (15.4) 0 (0.0) 0 (0.0)T3 20 (55.6) 22 (68.8) 8 (61.5) 2 (40) 0 (0.0)T4 5 (13.8) 6 (18.8) 2 (15.4) 3 (60) 2 (100)

Regional lymph nodes (N)Absent 22 (61.1) 17 (53.1) 7 (53.8) 2 (40) 0 (0.0) 0.2Present 14 (38.9) 15 (46.9) 6 (46.2) 3 (60) 2 (100)

Distant metastasis (M)1 1 (2.8) 1 (3.1) 0 (0.0) 1 (20) 0 (0.0) 0.60X 35 (97.2) 31 (96.9) 13 (100) 4 (80) 2 (100)

TNM stagingI 10 (27.8) 3 (9.4) 2 (15.4) 0 (0.0) 0 (0.0) 0.069II 12 (33.3) 14 (43.8) 5 (38.5) 2 (40) 0 (0.0)III 13 (36.1) 14 (43.8) 6 (46.2) 2 (40) 2 (100)IV 1 (2.8) 1 (3.1) 0 (0.0) 1 (20) 0 (0.0)

1Significant association between complexity index and tumor stage.



(Table 3). Significant correlations were observedbetween K-ras mutations and regional metasta-sis (p = 0.02) and UICC stages (p = 0.024).Our findings regarding K-ras mutations and itscorrelation with regional lymph node metastasiswere in agreement with an earlier study byOliveira et al. (2007) (19). Similarly, Ince et al.(32) also showed that K-ras mutations are asso-ciated with poor prognosis and increased diseaseseverity.Increase tendencies of K-ras mutations were

also observed with higher values of tumor pene-tration, but results were statistically non signifi-cant (p = 0.09), where 94% of the tumors withK-ras mutations were observed at T3 and T4stages. K-ras mutations are known to increasethe invasiveness of the tumor cells by disruptingcellular polarity, by increasing the tumor cellu-lar passage through epithelial membrane viaupregulation of the matrix metalloproteinasematrilysin pathway, and by the activation ofsmall GTPases, which increases cellular motility(33–38).When we correlated K-ras mutations in codon

12 and 13 separately with different clinicopatho-logic parameters (data not shown), mutations incodon 12 were associated with tumor staging(p = 0.02) and nodular metastasis (p = 0.048).The above results suggest that cancers withK-ras mutation in codon 12 are more aggressivethan in codon 13. This observation is supportedby earlier studies, for example, in an in vitrostudy by Guerrero (39) who observed that K-rasmutations in codon 12 were associated with can-cerous features of cultured cells by increasingtheir resistance to cellular apoptosis, by anchor-age independent growth, and loss of contactinhibitions. Similarly, in another in vitro studyby Vizan (40) showed that transfected NIH3T3fibroblasts showed higher levels of glycolysis inK-ras codon 12 mutants compared with K-rascodon 13 mutants. Besides these in vitro studies,clinical observations have also showed differentclinical outcomes with two mutant codons. Aclinical study report by Kraus et al. (41) showedthat K-ras mutation frequency in codon 12 and13 occurs at early stages of tumor development.However, with the advancement in tumorstages, prevalence of K-ras 12 mutationsincreases compared with codon 13, providingevidence that K-ras mutation in codon 12increases the aggressive potential of the K-ras

protein. Similarly, Schimanski (42) findings alsosupport this suggestion, showing that K-rasmutations in codon 12 were observed at a higherfrequency in liver metastasis. In contrast, Bazanet al. (43) described that K-ras codon 13 muta-tions were associated with tumor progressionand aggression in comparison to K-ras 12 muta-tions. The same study also showed that K-rasmutations in codon 12 are associated withmucinous carcinomas (43). Likewise, anotherreport by Conzelmann et al. (44) also deter-mined that codon 13 mutations are more associ-ated with lymph node metastasis. Theseconflicting results could be attributed to differ-ent numbers of the mucinous and rectal carcino-mas used in different studies that in some waymay affect the outcome of the disease.One limitation of our study was that, we

observed K-ras mutations only in the secondbase of codon 12 and 13. However, significantnumbers of mutations has been observed in thefirst base of codon 12 and 13. Therefore, itwould be interesting to study more bases of theK-ras codon 12 and 13 for more conclusiveresults.Tumor complexity was also correlated with

other different clinicopathologi parameters(Table 4). Complexity index was significantlycorrelated to tumor wall penetration (p =0.019). Most of the tumors with higher complex-ity index were at higher stages of UICC (p =0.069). These results suggest that tumors with anirregular invasive front can penetrate more eas-ily into the adjacent tissue compared withtumors with smooth invasive fronts, resulting ina more aggressive tumor development. Associa-tion of tumor complexity index with tumorprogression suggests that the growth pattern ofCRC is an important factor in determining theaggressiveness and prognosis of the disease.In conclusion, our results did not show statis-

tical correlation between tumor complexityindex and K-ras mutations, which suggests thatK-ras mutations and tumor growth pattern areindependent factors. However, both complexityindex and K-ras mutations are associated withadvanced tumor development, and are bothimportant independent factors for the prognosisand treatment of CRC.

We thank Biomedical Scientist Shlear Askari forhelping us with pyrosequencing as well as Orebro



University hospital Research committee, Lions Can-cer research foundation and Nyckelfonden, OrebroUniversity Hospital, Orebro, Sweden.

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k-ras mutations are correlated to lymph node metastasis and tumor stage, but not to the growth...

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