Prognostic significance and gene expression profiles of p53 mutations in microsatellite-stable stage III colorectal adenocarcinomas.
ABSTRACT Although the prognostic value of p53 abnormalities in Stage III microsatellite stable (MSS) colorectal cancers (CRCs) is known, the gene expression profiles specific to the p53 status in the MSS background are not known. Therefore, the current investigation has focused on identification and validation of the gene expression profiles associated with p53 mutant phenotypes in MSS Stage III CRCs. Genomic DNA extracted from 135 formalin-fixed paraffin-embedded tissues, was analyzed for microsatellite instability (MSI) and p53 mutations. Further, mRNA samples extracted from five p53-mutant and five p53-wild-type MSS-CRC snap-frozen tissues were profiled for differential gene expression by Affymetrix Human Genome U133 Plus 2.0 arrays. Differentially expressed genes were further validated by the high-throughput quantitative nuclease protection assay (qNPA), and confirmed by quantitative real-time polymerase chain reaction (qRT-PCR) and by immunohistochemistry (IHC). Survival rates were estimated by Kaplan-Meier and Cox regression analyses. A higher incidence of p53 mutations was found in MSS (58%) than in MSI (30%) phenotypes. Both univariate (log-rank, P = 0.025) and multivariate (hazard ratio, 2.52; 95% confidence interval, 1.25-5.08) analyses have demonstrated that patients with MSS-p53 mutant phenotypes had poor CRC-specific survival when compared to MSS-p53 wild-type phenotypes. Gene expression analyses identified 84 differentially expressed genes. Of 49 down-regulated genes, LPAR6, PDLIM3, and PLAT, and, of 35 up-regulated genes, TRIM29, FUT3, IQGAP3, and SLC6A8 were confirmed by qNPA, qRT-PCR, and IHC platforms. p53 mutations are associated with poor survival of patients with Stage III MSS CRCs and p53-mutant and wild-type phenotypes have distinct gene expression profiles that might be helpful in identifying aggressive subsets.
- SourceAvailable from: Elizabeth Hale Hammond[Show abstract] [Hide abstract]
ABSTRACT: Under the auspices of the College of American Pathologists, the current state of knowledge regarding pathologic prognostic factors (factors linked to outcome) and predictive factors (factors predicting response to therapy) in colorectal carcinoma was evaluated. A multidisciplinary group of clinical (including the disciplines of medical oncology, surgical oncology, and radiation oncology), pathologic, and statistical experts in colorectal cancer reviewed all relevant medical literature and stratified the reported prognostic factors into categories that reflected the strength of the published evidence demonstrating their prognostic value. Accordingly, the following categories of prognostic factors were defined. Category I includes factors definitively proven to be of prognostic import based on evidence from multiple statistically robust published trials and generally used in patient management. Category IIA includes factors extensively studied biologically and/or clinically and repeatedly shown to have prognostic value for outcome and/or predictive value for therapy that is of sufficient import to be included in the pathology report but that remains to be validated in statistically robust studies. Category IIB includes factors shown to be promising in multiple studies but lacking sufficient data for inclusion in category I or IIA. Category III includes factors not yet sufficiently studied to determine their prognostic value. Category IV includes factors well studied and shown to have no prognostic significance. The medical literature was critically reviewed, and the analysis revealed specific points of variability in approach that prevented direct comparisons among published studies and compromised the quality of the collective data. Categories of variability recognized included the following: (1) methods of analysis, (2) interpretation of findings, (3) reporting of data, and (4) statistical evaluation. Additional points of variability within these categories were defined from the collective experience of the group. Reasons for the assignment of an individual prognostic factor to category I, II, III, or IV (categories defined by the level of scientific validation) were outlined with reference to the specific types of variability associated with the supportive data. For each factor and category of variability related to that factor, detailed recommendations for improvement were made. The recommendations were based on the following aims: (1) to increase the uniformity and completeness of pathologic evaluation of tumor specimens, (2) to enhance the quality of the data needed for definitive evaluation of the prognostic value of individual prognostic factors, and (3) ultimately, to improve patient care. Factors that were determined to merit inclusion in category I were as follows: the local extent of tumor assessed pathologically (the pT category of the TNM staging system of the American Joint Committee on Cancer and the Union Internationale Contre le Cancer [AJCC/UICC]); regional lymph node metastasis (the pN category of the TNM staging system); blood or lymphatic vessel invasion; residual tumor following surgery with curative intent (the R classification of the AJCC/UICC staging system), especially as it relates to positive surgical margins; and preoperative elevation of carcinoembryonic antigen elevation (a factor established by laboratory medicine methods rather than anatomic pathology). Factors in category IIA included the following: tumor grade, radial margin status (for resection specimens with nonperitonealized surfaces), and residual tumor in the resection specimen following neoadjuvant therapy (the ypTNM category of the TNM staging system of the AJCC/UICC). (ABSTRACT TRUNCATED)Archives of pathology & laboratory medicine 08/2000; 124(7):979-94. · 2.88 Impact Factor
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ABSTRACT: A recent study published by Sjoblom and colleagues [T. Sjoblom, S. Jones, L.D. Wood, D.W. Parsons, J. Lin, T.D. Barber, D. Mandelker, R.J. Leary, J. Ptak, N. Silliman, S. Szabo, P. Buckhaults, C. Farrell, P. Meeh, S.D. Markowitz, J. Willis, D. Dawson, J.K. Willson, A.F. Gazdar, J. Hartigan, L. Wu, C. Liu, G. Parmigiani, B.H. Park, K.E. Bachman, N. Papadopoulos, B. Vogelstein, K.W. Kinzler, V.E. Velculescu, The consensus coding sequences of human breast and colorectal cancers. Science 314 (2006) 268-274.] performed comprehensive sequencing of 13,023 human genes and identified mutations in genes specific to breast and colorectal tumors, providing insight into organ-specific tumor biology. Here we present a systematic analysis of the functional classifications of Sjoblom's "CAN" genes, a subset of these validated mutant genes, that identifies novel organ-specific biological themes and molecular pathways associated with disease-specific etiology. This analysis links four somatically mutated genes associated with diverse oncological types to colorectal and breast cancers through established TGF-beta1-regulated interactions, revealing mechanistic differences in these cancers and providing potential diagnostic and therapeutic targets.Genomics 07/2008; 91(6):508-11. · 2.79 Impact Factor
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ABSTRACT: Microsatellite-stable, near-diploid (MSI-CIN-) colorectal carcinomas have been reported, but it is not clear as to whether these tumours form a discrete group or represent one end of the distribution of MSI-CIN+ cancers. In order to address this question, we screened 23 MSI-CIN- colorectal cancers for gains and losses using array-based comparative genomic hybridization (aCGH) based on large-insert clones at about 1 Mb density. We compared our findings with those from a small set of MSI+CIN+ cancers, and with our reported data from MSI-CIN+ and MSI+CIN- cancers. We found no evidence of any form of genomic instability in MSI-CIN- cancers. At the level of the chromosome arm, the MSI-CIN- cancers had significantly fewer gains and losses than MSI-CIN+ tumours, but more than the MSI+CIN- and MSI+CIN+ lesions. The chromosomal-scale changes found in MSI-CIN- cancers generally involved the same sites as those in MSI-CIN+ tumours, and in both cancer groups, the best predictor of a specific change was the total number of such changes in that tumour. A few chromosomal-scale changes did, however, differ between the MSI-CIN- and MSI-CIN+ pathways. MSI-CIN- cancers showed: low frequencies of gain of 9p and 19p; infrequent loss of 5q and a high frequency of 20p gain. Overall, our data suggested that the MSI-CIN- group is heterogeneous, one type of MSI-CIN- cancer having few (< or =6) chromosomal-scale changes and the other with more (> or =10) changes resembling MSI-CIN+ cancers. At the level of individual clones, frequent and/or discrete gains or losses were generally located within regions of chromosomal-scale changes in both MSI-CIN- and MSI-CIN+ cancers, and fewer losses and gains were present in MSI-CIN- than MSI-CIN+ tumours. No changes by clone, which were specific to the MSI-CIN- cancers, were found. In addition to indicating differences among the cancer groups, our results also detected over 50 sites (amplifications, potential homozygous deletion and gains or losses which extended over only a few megabases) which might harbour uncharacterized oncogenes or tumour suppressor loci. In conclusion, our data support the suggestion that some MSI-CIN- carcinomas form a qualitatively different group from the other cancer types, and also suggest that the MSI-CIN- group is itself heterogeneous.Oncogene 01/2005; 24(1):118-29. · 8.56 Impact Factor
Prognostic Significance and Gene Expression Profiles of
p53 Mutations in Microsatellite-Stable Stage III
Venkat R. Katkoori1, Chandrakumar Shanmugam1, Xu Jia1, Swaroop P. Vitta1, Meenakshi Sthanam1,
Tom Callens2, Ludwine Messiaen2, Dongquan Chen3, Bin Zhang4, Harvey L. Bumpers5, Temesgen
Samuel6, Upender Manne1,7*
1Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America, 2Department of Genetics, University of Alabama at
Birmingham, Birmingham, Alabama, United States of America, 3Division of Preventive Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United
States of America, 4Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America, 5Department of Surgery,
Morehouse School of Medicine, Atlanta, Georgia, United States of America, 6Department of Pathology, Tuskegee University, Tuskegee, Alabama, United States of
America, 7UAB Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
Although the prognostic value of p53 abnormalities in Stage III microsatellite stable (MSS) colorectal cancers (CRCs) is
known, the gene expression profiles specific to the p53 status in the MSS background are not known. Therefore, the current
investigation has focused on identification and validation of the gene expression profiles associated with p53 mutant
phenotypes in MSS Stage III CRCs. Genomic DNA extracted from 135 formalin-fixed paraffin-embedded tissues, was
analyzed for microsatellite instability (MSI) and p53 mutations. Further, mRNA samples extracted from five p53-mutant and
five p53-wild-type MSS-CRC snap-frozen tissues were profiled for differential gene expression by Affymetrix Human Genome
U133 Plus 2.0 arrays. Differentially expressed genes were further validated by the high-throughput quantitative nuclease
protection assay (qNPA), and confirmed by quantitative real-time polymerase chain reaction (qRT-PCR) and by
immunohistochemistry (IHC). Survival rates were estimated by Kaplan-Meier and Cox regression analyses. A higher
incidence of p53 mutations was found in MSS (58%) than in MSI (30%) phenotypes. Both univariate (log-rank, P=0.025) and
multivariate (hazard ratio, 2.52; 95% confidence interval, 1.25–5.08) analyses have demonstrated that patients with MSS-p53
mutant phenotypes had poor CRC-specific survival when compared to MSS-p53 wild-type phenotypes. Gene expression
analyses identified 84 differentially expressed genes. Of 49 down-regulated genes, LPAR6, PDLIM3, and PLAT, and, of 35 up-
regulated genes, TRIM29, FUT3, IQGAP3, and SLC6A8 were confirmed by qNPA, qRT-PCR, and IHC platforms. p53 mutations
are associated with poor survival of patients with Stage III MSS CRCs and p53-mutant and wild-type phenotypes have
distinct gene expression profiles that might be helpful in identifying aggressive subsets.
Citation: Katkoori VR, Shanmugam C, Jia X, Vitta SP, Sthanam M, et al. (2012) Prognostic Significance and Gene Expression Profiles of p53 Mutations in
Microsatellite-Stable Stage III Colorectal Adenocarcinomas. PLoS ONE 7(1): e30020. doi:10.1371/journal.pone.0030020
Editor: Ganesh Chandra Jagetia, Mizoram University, India
Received March 25, 2011; Accepted December 12, 2011; Published January 19, 2012
Copyright: ? 2012 Katkoori et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by funds from the National Institutes of Health/National Cancer Institute (2U54-CA118948, R01-CA98932, and R03-CA139629)
to Dr. Manne. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
For patients with colorectal cancers (CRCs), the presence of
lymph node metastases is a factor in determining treatment
modalities and for predicting clinical outcomes . Since the
outcomes for all CRC patients with metastases are not the same
, there is a need for molecular markers that identify candidates
for therapy and predict patient survival. To be clinically useful,
such markers should provide information independent of clinico-
CRC is a heterogeneous disease that develops through various
genetic pathways. The chromosomal instability pathway accounts
for 85% of sporadic CRCs . These aggressive tumors are
characterized by allelic losses at 17p and 18q and by the presence
of mutations in common oncogenes and tumor suppressor genes
[3,4]. Chromosomal abnormalities, such as aneuploidy, amplifi-
cations, and translocations, are common in familial CRCs .
Microsatellite instability (MSI) is associated with the remaining
15% of sporadic CRCs; these tumors are less aggressive and
characterized by inefficient DNA mismatch repair . MSI is a
characteristic of nearly all cases of hereditary non-polyposis
colorectal cancer . Relative to microsatellite-stable (MSS)
CRCs, the APC, KRAS, and p53 genes are less frequently mutated
in sporadic MSI CRCs [8,9]. Furthermore, for patients with Stage
III tumors, p53 mutations and MSS are associated with poorer
prognoses and therapy responses [10,11,12]. Thus, these molec-
ular phenotypes are related to the aggressiveness of CRCs. The
current investigation is primarily focused on validation of the
prognostic value of the p53 status in Stage III MSS CRCs and on
identifying and validating the gene expression profiles that are
specific to p53 mutant phenotypes in Stage III MSS CRCs. These
profiles may lead to identification of therapeutic targets for this
subgroup of patients.
PLoS ONE | www.plosone.org1 January 2012 | Volume 7 | Issue 1 | e30020
Characteristics of the study cohort
At the time of surgeries, the mean age of the patients was 65
years (range, 31–94 years). The distribution of Stage III-CRCs in
the colorectum was 51, 29, and 20% in the proximal colon, distal
colon, and rectum, respectively. There was a preponderance of
non-Hispanic Caucasian patients (87 of 135, 64%) and pT3 type
of depth of invasion (85 of 135, 63%). At the last follow-up, 28%
(34 of 122) of the patients were alive. Those dead because of
colorectal neoplasia were 56% (68), and those dead because of
other causes was 16% (20).
Associations between clinicopathological and molecular
There was a higher frequency of CRCs with the MSS
phenotype (112 of 135, 83%) than the MSI-H phenotype (23 of
135, 17%) (Table S1). The incidence of the MSI-H phenotype was
higher in the proximal colon (15 of 23, 65%) and in high-grade
CRCs (12 of 23, 52%, x2P=0.03). We also analyzed the
distribution of MSI among the three sites (proximal colon, distal
colon, and rectum). Consistent with previous studies, there was a
higher incidence of the MSI phenotype in proximal tumors (15 of
69, 22%) as compared to distal colon tumors (3 of 38, 8%) and
rectal tumors (5 of 27, 19%) (data not shown). The incidence of
p53 mutations in Stage III CRCs was 53% (72 of 135). The key
observation was that the incidences of p53 mutations were higher
in CRCs with the MSS phenotype (65 of 112, 58%) compared to
the MSI-H phenotype (7 of 23, 30%, x2P=0.02). Relative to wt-
p53, p53 mutations were more common in high-grade CRCs (29 of
72, 40%, x2P=0.04). Among Stage III CRCs, 68% of patients
with p53 mutations died due to CRCs compared to 42% with wt-
p53 (x2P=0.02). Further analysis of MSS CRCs showed a
correlation between p53 mutations and high-grade disease (23 of
65, 35%, x2P=0.04) and death due to CRC (x2P=0.03).
Gene expression profiles
Gene expression analyses identified 84 differentially expressed
genes (35 up-regulated and 49 down-regulated) (Figure 1). All of
these passed the stringent background filter (intensity P=#0.001
Figure 1. Gene expression profiles of CRCs based on p53 status. Genes and samples were clustered independently by hierarchical clustering.
Rows represent genes, and columns represent samples, which are color-coded by p53 status (blue and red correspond to wt-p53 and p53 mutant,
respectively. The color scale is shown at bottom right. Values are expressed as log2-ratios of expression in CRCs with p53 mutant phenotypes to that
in wt-p53 phenotypes. (A & B) Clusters of 35 and 49 genes showing consistent up-regulation and down-regulation in p53 mutant phenotypes.
Value of p53 Mutations in MSS Stage III CRC
PLoS ONE | www.plosone.org2 January 2012 | Volume 7 | Issue 1 | e30020
and intensity $3 standard deviation above mean background) and
differential expression ratio (x2P=#0.05 and fold change $2.0).
These analyses suggested a transcriptional repression regulated by
p53 mutant phenotypes. In these phenotypes, GBP1, PSMB9,
BST2, FUT3, IQGAP3, SLC6A8, TRIM29, and TFF1 genes were
up-regulated (Figure 1A) and genes that regulate cell-cycle arrest
(CDKN1B and p21waf-1) and tumor growth suppression (ACVR1B,
SESN1, WFDC1, LPAR6, PDLIM3, PLAT, and VAV3) were down-
regulated (Figure 1B).
Validation of differentially expressed genes
To examine the reliability of microarray data and to validate the
expression of differentially expressed genes, all 84 genes were
subjected to qNPA. This was performed on 14 wt-p53 and 14
mutated-p53 Stage III MSS CRCs matched for patient age,
gender, race, tumor location, and tumor grade. For the expression
of seven genes, the findings were consistent with those derived with
the Affymetrix platform (Table 1). Three genes (LPAR6, PDLIM3
and PLAT) were down-regulated, and four (FUT3, IQGAP3,
SLC6A8 and TRIM29) were up-regulated. qRT-PCR, to measure
RNA levels, and IHC analyses, for protein levels of the genes, were
then performed for confirmation. The results were similar to those
obtained with the qNPA and Affymetrix assay platforms (Table 1).
Immunophenotypic expression analysis
There were differences in expression of these markers between
CRCs with and without p53 mutations as well as tumors versus
normal colonic epithelium (NCE). There was moderate-to-strong
staining for FUT3, IQGAP3, SLC6A8 and TRIM29 in samples
with p53 mutations compared to those with wt-p53 (Figure 2). In
contrast, staining for LPAR6, PDLIM3, and PLAT was moderate
in CRCs with wt-p53 as compared to those with p53 mutations
(Figure 3). NCE demonstrated moderate cytoplasmic and weak
membrane FUT3 staining, whereas CRCs exhibited strong to
weak cytoplasmic and moderate to weak membrane FUT3
staining. TRIM29 exhibited weak cytoplasmic staining in the
NCE and moderate to strong cytoplasmic staining with a punctate
pattern on the luminal surface. In the NCE, IQGAP3 demon-
strated weak to absence of staining in the cytoplasm; lymphocytes
showed moderate cytoplasmic staining. CRCs exhibited strong
cytoplasmic or lack of staining for IQGAP3. SLC6A8 staining was
moderate in the cytoplasm and, in CRCs, there was strong
cytoplasmic staining with luminal accentuation. Moderate nuclear
and weak cytoplasmic PDLIM3 staining was seen in NCE.
Lymphocytes in the stroma showed moderate nuclear staining and
served as an internal control. For PDLIM3, CRCs exhibited either
lack of staining or strong nuclear and weak cytoplasmic staining.
There was weak staining for LPAR6 in the cytoplasm of NCE.
CRCs demonstrated weak to moderate cytoplasmic and focal
nuclear staining. NCE and CRCs demonstrated moderate to
strong cytoplasmic staining for PLAT.
Univariate Kaplan-Meier survival analyses for the MSS
phenotype group demonstrated that CRCs with p53 mutations
(n=59) were significantly associated with shorter disease-specific
survival relative to those with wt-p53 (n=42) (log rank, P=0.025)
(Figure 4A). For patients with the MSI-H phenotype, CRCs with
p53 mutations did not demonstrate such a difference (log-rank,
P=0.695) (Figure 4B).
Evaluation of the prognostic significance of p53 mutations on
CRC-specific survival of MSS Stage III CRCs confirmed the
independent effect of p53 mutations on CRC-specific survival
(Table 2). Overall, patients with p53 mutations were 2.52 times
(hazard ratio [HR=2.52]; 95% confidence interval [95% CI],
1.25–5.08) more likely to die of CRC compared to those with wt-
p53 (log-rank, P=0.01). Age ($65), distal colon site, and pN2
(greater number of nodes with metastases) were independent
prognostic indicators of MSS Stage III CRC patients (Table 2).
The median p-value for each variable obtained from the
bootstrapping of the Cox regression models was selected as an
estimate of the statistical significance. The median p-value for the
p53 mutations in MSS CRCs was 0.005 (relative to wt). Since the
median p-values (0.01) were consistent with the final model
(Table 2), the results were robust.
The prognostic value of p53 mutations in MSS phenotypes of
Stage III CRCs was assessed, and other molecular markers
associated with p53 mutant phenotypes were identified. The
prevalence of p53 mutations was higher in CRCs with the MSS
phenotype (59%) as compared to the MSI-H phenotype (30%).
Both univariate and multivariate survival analyses revealed that
Table 1. Validation of deferentially expressed genes by qNPA and qRT-PCR based on p53 status in Stage III-MSS CRCs.
Probe ID Gene SymbolChGene name Fold Change P Fold Change P Fold Change P
218589_atLPAR6 13Lysophosphatidic acid receptor 6 2.20.03 1.40.04 1.60.02
209621_s_atPDLIM34PDZ and LIM domain 32.20.02 1.60.04 1.50.01
201860_s_atPLAT8Plasminogen activator tissue 2.10.04 1.30.04 4.30.03
214088_s_atFUT319Fucosyltransferase 3 (Lewis blood group) 2.30.005 1.40.033.50.04
229538_s_atIQGAP31IQ motif containing GTPase activating protein 3 2.10.011.6 0.00912.0 0.001
202219_at SLC6A816Solute carrier family 6 member 87.10.0011.60.031.60.02
202504_atTRIM2911 Tripartite motif containing 297.30.0012.60.0043.10.002
Abbreviations: Ch, chromosome number; AHG, Affymetrix human genome plus 2.0 array.
1Fold change in differential expression of genes by AHG;
2Fold change by qNPA;
3Fold change qRT-PCR.
Value of p53 Mutations in MSS Stage III CRC
PLoS ONE | www.plosone.org3January 2012 | Volume 7 | Issue 1 | e30020
p53 mutations in Stage III-MSS-CRCs were associated with
shorter cancer-specific survival relative to those with wt-p53. After
rigorous validation on four platforms, a molecular signature
associated with p53 mutant phenotypes in this subset of CRCs was
The differentially expressed genes are involved in remodeling of
the extracellular matrix, adhesion, cytoskeleton plasticity, and
signal transduction. The p53 mutant signature was characterized
by transcriptional repression, which is distinct from the expression
associated with wt-p53. Thus, metastasis mediated by p53
mutations is a selective process with a specific molecular signature
of Stage III-MSS CRCs.
In about 50% of human cancers, including CRCs, the p53 gene
is mutated. The gain of oncogenicity or loss of tumor suppressor
function of p53 due to its inactivation through missense mutations
contributes to tumor aggressiveness and results in poor patient
survival [13,14,15,16,17,18,19]. p53 mutations have been consid-
ered as metastatic signatures in CRCs . The mutations that
affect structural or functional domains and those in evolutionary
conserved regions are associated with aggressive tumors  and
chemo-resistance . p53 mutations within certain domains [L2
and L3 loops, and the Loop Sheet Helix (LSH) motif] and
mutations in evolutionary non-conserved region of the p53 gene
 are associated with aggressive phenotypes . These
observations, in conjunction with our previous findings ,
suggest a function for the p53 protein in metastasis due to
inactivation and indicate that patients with CRCs exhibiting p53
mutations are at risk for aggressive progression and early death.
The present study demonstrates an association between p53
mutations and poor survival in Stage III-MSS-CRCs. Genomic
instability, common in CRCs , correlates with p53 mutations
. Thus, for some Stage III-MSS-CRCs, genomic instability
associated with p53 mutations is the cause of developing a risk
phenotype and for poor survival.
Previous studies have demonstrated that sporadic colon cancers
with the MSI phenotype are less aggressive, because these tumors
commonly show a lower frequency of p53 mutations , as
observed in the current study. This relationship is consistent with
the concept that most CRCs develop either along the chromo-
somal instability pathway associated with TP53 mutations and
MSS tumors or the aberrant mismatch repair pathway associated
with wild-type p53 and MSI tumors [8,29,30]. This association
may also explain why tumors with MSI seem to have less
aggressiveness as compared to those with a proficient mismatch
repair system. In the current investigation, there was no significant
association between p53 status and poor patient survival for Stage
Figure 2. IHC expression patterns of up-regulated genes in p53 mutant phenotypes of MS CRCs. a, normal colonic epithelium (NCE)
demonstrating moderate cytoplasmic (thick arrow) and weak membrane (thin arrow) FUT3 staining (400 mm). b, CRCs exhibiting strong cytoplasmic
(thick arrows) and moderate to weak membrane FUT3 staining (thin arrows) (400 mm). c, CRCs with weak cytoplasmic staining (thin arrows) (400 mm).
d, NCE demonstrating weak cytoplasmic TRIM29 staining with a focal punctuate pattern (thin arrows) (400 mm). e, CRCs exhibiting moderate to
strong cytoplasmic TRIM29 staining with a punctate pattern on the luminal aspect (thin arrows) (600 mm). f, CRCs with weak cytoplasmic TRIM29
staining (thin arrows) (400 mm). g, NCE demonstrating weak cytoplasmic to complete lack of IQGAP3 staining. [Note: Lymphocytes in the stroma
show moderate cytoplasmic staining (thin arrows); the adjacent tumor demonstrates moderate cytoplasmic and nuclear immunostaining (thick
arrows) (400 mm)]. h, CRCs exhibiting strong cytoplasmic IQGAP3 staining (thick arrows) (400 mm). i, CRCs with lack of staining for IQGAP3 (thick
arrows) (400 mm). j, NCE demonstrating moderate cytoplasmic staining of SLC6A8 staining (thin arrows) (600 mm). k, CRCs exhibiting strong
cytoplasmic SLC6A8 staining with luminal accentuation (thick arrows) (600 mm). l, CRCs negative for SLC6A8 staining (thick arrows) (600 mm).
Value of p53 Mutations in MSS Stage III CRC
PLoS ONE | www.plosone.org4 January 2012 | Volume 7 | Issue 1 | e30020
III CRCs with the MSI phenotype, suggesting different effects of
p53 mutations in Stage III CRCs with MSS and MSI phenotypes.
Microarray analyses, based on the p53 status in Stage III-MSS
CRCs, identified 84 genes differentially expressed in tumors with
p53 mutant and wt-p53 phenotypes. In tumors of the p53 mutant
phenotype, 35 genes were up-regulated and 49 down-regulated,
suggesting that transcriptional repression of genes is important for
developing risk of aggressive cancer and for poor survival
manifested by p53 mutations. The repression affects known tumor
or metastasis suppressor genes, such as TRIM 29, ACVR1B, and
LPAR6 , and the sestrins, SESN1 and SESN3 ; however, in
our analyses, ACVR1B, LPAR6, and sestrins were not validated
by the qNPA method. Thus, the results support the concept that
silencing of genes is essential for tumor progression . One
explanation for this is that the inactivation of p53 by mutation
results in the overexpression of transcriptional repressors, which
suppress other genes, including those suppressing metastasis.
Gene microarray is an excellent approach to evaluate the
expression profiles of thousands of genes; however, it has several
limitations related to accuracy and reproducibility [34,35]. For a
differentially expressed gene to serve as a biomarker with high
accuracy, verification of analysis of gene expression by multiple
methods is required . Inaccuracies in identifying the ‘true’
expression of potentially useful molecular markers may be the
basis for the lack of translation of the findings obtained from
studies involving gene microarrays. Thus, to validate results
obtained from these microarrays, the genes selected in the present
investigation were subjected to the quantitative nuclease protec-
tion assay (qNPA) and qRT-PCR. The qNPA assay validated only
seven of the differentially expressed genes identified by Affymetrix
microarrays. These seven genes were further validated by qRT-
PCR and IHC. There are associations between dysregulation of all
seven genes and cancer development [37,38,39,40,41].
Although little is known about the properties of TRIM29, a
member of the tripartite motif (TRIM) family, three proteins of
this family (TRIM19, TRIM24 and TRIM27) are involved in
cellular growth or development  and become oncogenic as a
result of chromosomal translocations , suggesting their
involvement in tumor progression. Increased expression of
TRIM29 is associated with tumor differentiation, tumor growth,
tumor invasion, and lymph node metastasis . In the present
study, TRIM29 was over-expressed in p53 mutant phenotypes,
supporting its function in p53 dependent-pathways that lead to
aggressive behavior of Stage III-MSS CRCs. Further mechanistic
studies are needed to clarify the function of TRIM29 in CRC
IQGAP family proteins modulate cytoskeletal architecture and
cell adhesion [45,46], and they may be involved in metastasis of
CRCs, lung cancers, and cholangiocarcinomas . IQGAP3,
which encodes a putative 180-kDa protein with RasGAP, IQG1,
CH, and COG5022 domains, regulates cell proliferation through
the Ras/ERK signaling cascade . Our findings show up-
regulation of IQGAP3 in MSS and p53 mutant phenotypes and
indicate that, for Stage III-MSS CRCs, the underlying molecular
mechanism for IQGAP3 is different in p53 mutant and wt-p53
phenotypes. Also, the results indicate that IQGAP3 is involved in
metastasis. Although it might be useful for predicting CRC
progression, its function and prognostic capacity remain to be
It is important to compare the gene expression profiles of MSS
Stage III CRCs with and without p53 mutations to understand the
molecular mechanisms involved in their progression and to
Figure 3. IHC expression patterns of down-regulated genes in p53 mutant phenotypes of MS CRCs. a, NCE demonstrating moderate
nuclear and weak cytoplasmic PDLIM3 staining [Note: Lymphocytes in the stroma and the normal epithelial cells show moderate nuclear staining
(thin arrows); the adjacent tumor demonstrates strong nuclear and moderate cytoplasmic immunostaining (thick arrows) (400 mm)]. b, CRCs
exhibiting lack of PDLIM3 immunostaining (thin arrows) (600 mm). c, CRCs with strong nuclear and weak cytoplasmic PDLIM3 staining (thick arrows)
(600 mm). d, NCE demonstrating weak cytoplasmic LPAR6 staining (thin arrows) (600 mm). e, CRCs exhibiting focal weak cytoplasmic LPAR6 staining
(thin arrows) (600 mm). f, CRCs with weak to moderate cytoplasmic (thick arrows) and focal nuclear LPAR6 staining (thin arrows) (600 mm). g, NCE
demonstrating moderate to strong cytoplasmic PLAT staining (thin arrows) (600 mm). h, CRCs exhibiting focal moderate cytoplasmic PLAT staining
(thick arrows) (600 mm). i, CRCs with moderate to strong staining for PLAT (thick arrows) (600 mm).
Value of p53 Mutations in MSS Stage III CRC
PLoS ONE | www.plosone.org5 January 2012 | Volume 7 | Issue 1 | e30020
identify aggressive subsets. The present results, demonstrating that
p53 mutations are associated with a poor prognosis for Stage III
microsatellite-stable CRCs, may lead to development of individ-
ualized therapies for Stage III-MSS CRCs. Furthermore, robust
validations of the expression profiles, including those for TRIM29
and IQGAP3, may allow identification of novel therapeutic targets.
Materials and Methods
The Institutional Review Board of the University of Alabama at
Birmingham (UAB) approved these studies and we obtained
informed written consent from all study participants. The UAB
Bioethics Committee reviewed the proposed effort.
From 1986 through 2004, there were 303 patients with Stage III
CRCs at the UAB Hospital. Use of these patients maximized post-
surgery follow-up. The following were excluded from the study:
those who died within a week of surgery (n=20); those with
surgical margin-involvement (n=16), unspecified tumor location
(n=15), multiple primaries within the colorectum (n=14),
multiple malignancies (n=32), unknown tumor grade (n=5),
those with a family history of hereditary non-polyposis colorectal
cancer (n=6), familial adenomatous polyposis coli (n=4), a
personal history of CRC (n=11), or inflammatory bowel disease
(n=9). Also excluded, were patients who received pre-surgical
chemo- or radiation therapy (n=19).
Formalin-fixed, paraffin-embedded (FFPE) tissue blocks or
snap-frozen CRC tissues were obtained from the Anatomic
Pathology Division. These CRCs and corresponding normal
tissues (colonic tissues collected 8 cm from CRCs) were analyzed.
Suitable tissue specimens were unavailable for 17 patients. Thus,
there were 135 patients for analysis.
Of these 135 patients, 51 had received adjuvant chemotherapy.
Fifteen patients received 5-fluorouracil (5-FU) alone, 9 received 5-
FU plus levamisole (LV), 8 received 5-FU plus leucovorin (LC), 14
received 5-FU/leucovorin/oxaliplatin, 3 received 5-FU plus
doxorubicin, 1 received 5-FU/1-(2-chloroethyl)-3-cyclohexyl-1-
nitrosourea), and 1 received 5-FU/LV/LC. The remaining 84
patients received only surgery. None of these patients received
neoadjuvant therapy. The number treated with adjuvant therapy
was low because the Food and Drug Administration approved the
usage of 5-FU-based adjuvant chemotherapy for advanced stages
of CRCs only in the early 1990’s.
Figure 4. The prognostic significance of p53 mutations in survival of Stage III CRC patients with MSS or MSI-H phenotypes (Kaplan-
Meier survival curves). p53 mutations were associated with worse patient survival (log-rank, P=0.025) (A) than wt-p53 in the subset of MSS
phenotype, but not in the subset of MSI-H phenotype (log-rank, P=0.695) (B).
Value of p53 Mutations in MSS Stage III CRC
PLoS ONE | www.plosone.org6 January 2012 | Volume 7 | Issue 1 | e30020
Two pathologists (CKS & WEG) individually reviewed slides
stained with hematoxylin and eosin (H and E) for the degree of
histologic differentiation and re-graded lesions as well-, moderate-,
poor-, or undifferentiated. Cases with disagreement were resolved
by reevaluating the slides to reach a consensus. Well and
moderately differentiated tumors were pooled into a low-grade
group and poor and undifferentiated tumors into a high-grade
Pathologic staging conformed to the criteria of the American
Joint Commission on Cancer . The International Classifica-
tion of Diseases for Oncology codes were used to specify the
anatomic locations of tumors . The anatomic sites were the
proximal colon, distal colon, and rectum. Of the tumor
dimensions, the largest was used for statistical purposes. Before
the tissues were analyzed, a section was cut from each block and
stained with H and E to permit macro-dissection to separate tumor
from uninvolved tissue.
Patient demographics and follow-up information
Patient demographics and clinical and follow-up information
were retrieved retrospectively from medical records, physician
charts, pathology reports, and from the UAB Tumor Registry.
Patients were followed by their physicians or by the Registry until
their death or the date of the last documented contact within the
study timeframe. This information was validated against the state
death registry. The mean follow-up period, which ended in
December 2010, was 10.9 years (,1 to ,22 years). The laboratory
investigators (VRK, XJ, & CS) were blinded to the outcome
information until completion of the assays.
Microsatellite instability analysis
DNA extraction from FFPE archival tissues was performed
following a modified deparaffinization protocol . Five
polymorphic microsatellite markers (BAT25, BAT26, D2S123,
D5S346, and D17S250) from the Bethesda consensus panel .
were amplified by PCR using marker-specific, fluorescence-labeled
microsatellite primers (Applied Biosystems Inc, Foster City, CA)
. The reaction mixture (25 ml) consisted of 10 ng of genomic
DNA, 106PCR buffer, 1.5 mM MgCl2, 50 mM dNTPs, 10 pmol
of each primer, and 0.3 units of platinum Taq DNA polymerase
(Invitrogen, Carlsbad, CA). The thermal profile was as follows:
after initial denaturation at 94uC for 5 min, amplification was
accomplished for 36 cycles, 30 sec at 94uC, 30 sec at 55uC, and
1 min at 70uC. The final extension step was at 70uC for 7 min.
The 6FAM-, HEX, and NED-labeled PCR products (1 ml each)
was added to the mixture of 13 ml deionized formamide and 1 ml
of gene scan 500 TAMRA. This preparation was denatured at
88uC for 4 min, followed by chilling on ice for 2 min and
centrifugation for 15 sec. The denatured samples were analyzed
with an ABI 3100 genetic analyzer (Applied Biosystems) using the
Performance Optimized Polymer4 gel in a 47-cm650-mm
capillary. The data, collected automatically, were analyzed by
Genotyper 2.1 software (Applied Biosystem). MSI was determined
by the presence of one or more additional peaks in tumor samples
as compared to corresponding normal samples.
p53 mutational analysis
The status of the p53 gene was assessed by PCR and direct
sequencing of exons 4 through 9, by use of exon-specific primers
. The purified PCR product was sequenced on an ABI 3100
Table 2. Cox regression analysis to determine prognostic significance of p53 mutations.
Prognostic variables Indicator of poor prognosis
(95% confidence intervals)P
Mutated vs. wt Mutated2.52 (1.25–5.08)0.01
High vs. lowHigh grade 1.11 (0.59–2.09)0.74
Proximal vs. distal colonProximal colon0.38 (0.18–0.81)0.01
Proximal colon vs. rectum Proximal colon 0.71 (0.35–1.46)0.35
Distal colon vs. rectumDistal colon 0.25 (0.11–0.58)0.001
Distal vs. proximal colonProximal colon 0.38 (0.18–0.81)0.01
.5 cm vs. #5 cm
.5 cm0.77 (0.42–1.41) 0.4
$65 vs. ,65
Male vs. femaleMale0.84 (0.45–1.58) 0.59
African Americans vs. CaucasiansAfrican Americans0.98 (0.49–1.96)0.95
pT1 vs. pT2, pT3, pT4pT2,pT3, pT41.23 (0.74–2.03)0.43
pN1vs. pN2 pN21.84 (1.09–3.09) 0.02
1Adjusted for the p53 mutations, age, gender, tumor location, tumor grade, tumor size, tumor depth of invasion, nodal involvement, and adjuvant therapy.
Value of p53 Mutations in MSS Stage III CRC
PLoS ONE | www.plosone.org7 January 2012 | Volume 7 | Issue 1 | e30020
sequencer (Applied Biosystems). Compilation and sequence
analysis was performed with LASERGENE (DNA STAR Inc,
Madison, WI) software.
Microarray for expression profiling
Gene expression analyses of CRC samples were conducted
using Affymetrix Human GeneChip U133 plus 2.0 arrays to
determine the profiles based on the p53 status in Stage III MSS
CRCs. The gene expression studies were performed on surgically
resected specimens of five wt-p53 and five mutant p53 Stage III
MSS CRCs. These mutations, localized at codons 126, 135, 183,
248, and 276, are inactivating mutations . For gene expression
analyses, 5 mg of total RNA, extracted from snap-frozen
specimens, was used. The quantity and quality of the RNA was
determined by the RNA nanochip on an Agilent BioAnalyzer. The
transcriptional activity of genes was determined by hybridizing
fluorescently labeled, first-strand cDNAs corresponding to p53
mutant versus wt-p53 categories of MSS Stage III CRCs, to a
microarray as described earlier .
Statistical and bioinformatics analyses of the GeneChip data
were conducted using GeneSpring (Agilent) and Partek (St Louis,
MO) software. The raw GeneChip files were uploaded, back-
ground was subtracted, variance was stabilized, and normalized by
GC-RMA . The expression levels in normal (benign epithelial)
tissues were used to calculate the intensity ratio or fold change of
CRCs with MSS p53 mutant and MSS wt-p53 separately, and to
compare these two groups for identification of differentially
expressed genes. Multiple hypotheses testing, such as Benjamini-
Hochberg false discovery rate P-value correction, was performed
for all comparisons . If there were no significant differences in
gene expression levels, however, we performed only the t-test. P-
values and fold changes were used to identify candidate genes.
To generate a heat-map, raw data were quantile-normalized,
log2-transformed, and intensity-filtered before being subjected to
an unpaired t-test. The selected gene lists were clustered by
hierarchical methods and visualized as normalized, log2-trans-
formed intensities using GeneSpring (Agilent, CA, US).
The gene expression data is ‘minimum information about a
microarray experiment’ (MIAME) compliant and the raw data has
been deposited with the gene expression omnibus (GEO) of NCBI,
and the accession number is GSE27157. The following link has
been created to allow review of record GSE27157: http://www.
Quantitative nuclease protection assay (qNPA)
To confirm the expression levels of key genes identified by the
Affymetrix GeneArrays (Figure 1), qNPA was performed on RNA
 extracted from FFPE CRC sections , and the processed
samples were transferred to programmed (linker-modified) Array-
Plates (High Throughput Genomics, Inc, Tucson, AZ) [56,57].
The ArrayPlates that received the chemiluminescent peroxidase
substrate were viewed from the bottom with an OMIX HD
imager. The digital images of ArrayPlates were analyzed by
ArrayPlate Fit (v.3.31a) software. The resulting data were analyzed
by ArrayPlate Crunch software to normalize signals with b-actin
and to calculate gene expression levels.
RNA (1 mg) from the tissues was reverse transcribed by PCR as
performed in SYBR green reagent supermix (Bio-Rad laborato-
ries, Hercules, CA) consisting of a gene-specific, real-time (RT)
primer set . PCR reactions were performed using an i-Cycler
RT-PCR system (Bio-Rad). PCR products were subjected to
melting curve analysis to exclude non-specific amplification. All
PCR reactions were performed in sets of four. The means of the
specific gene mRNA and b-actin mRNA copy numbers were
calculated for each patient separately, and ratios were generated.
Tissue sections (5-mm) were cut from paraffin blocks represen-
tative of normal and tumor tissues of each case and were mounted
on Superfrost/Plus (Fisher Scientific, Pittsburg, PA). IHC was
performed as described earlier [59,60]. In brief, heat-induced
epitope (antigen) retrieval was performed on the sections for
10 min with citrate buffer (0.01 M, pH 6) for LPAR6 and
PDLIM3; and EDTA buffer (0.01 M, pH 9) for PLAT, FUT3,
IQGAP3, SLC6A8, and TRIM29. Tissue sections were then
incubated with polyclonal antibodies for LPAR6, PDLIM3,
PLAT, IQGAP3, and SLC6A8 (Santa Cruz Biotech Inc, CA)
for TRIM29 (Abnova, Walnut, CA), or with monoclonal antibody
(121 SLE mouse monoclonal) for FUT3 (Santa Cruz Biotech Inc).
Sections to which the antibody was not applied served as negative
controls. Secondary detection was accomplished using a multi-
species detection system (Signet Lab Inc., Dedham, MA). A
diaminobenzidine tetrachloride super-sensitive substrate kit (Bio-
Genex, San Ramon, CA) was used to visualize antibody-antigen
complexes. Then each section was counterstained with hematox-
ylin, dehydrated with graded alcohols, and soaked in xylene before
Sample size and power calculations.
power analysis were estimated based on a prior Stage III CRC
study . In that study (n=70), p53 mutations increased the risk
of death by 2.39 times in patients with Stage III CRC. Based on
these results , there was enough power to detect a hazard ratio
$2.39. Therefore, the sample size (n=135) was sufficient to
identify a significant prognostic value for p53.
Deaths due to CRC were the outcomes
(events) of interest. Patients who died within one month after
surgery were excluded. Since 13 of 135 patients (11 with the MSS
and 2 with the MSI phenotype) were lost to follow-up, survival
analyses were performed on the remaining 122 (101 with MSS and
21 with MSI) based on their p53 mutational status. Chi-square
tests were used to compare baseline characteristics in each group
. These analyses were also used to examine univariate
associations with covariates and potential confounders. The
baseline characteristics included demographic (age, gender and
race/ethnicity) and pathologic variables (tumor location, depth of
invasion, nodal involvement, tumor grade, and tumor size). All
analyses were performed with SAS statistical software, version 9.2
. Survival analysis was used to model time from date of surgery
until death due to CRC. Those patients who died of any cause
other than CRC and those who were alive at the end of the study
were censored. A log-rank test and Kaplan-Meier survival curves
 were used to compare the survival in each group. The type I
error rate of each test was controlled at ,0.05.
In addition to the primary analysis determining the effect of the
p53 mutations, secondary analyses were performed to consider
covariates known to be confounders or independent risk factors for
death. These included age; gender; tumor location, depth of
invasion, and nodal involvement; tumor grade and size; and
adjuvant chemotherapy. For these analyses, Cox regression
models  were used within each molecular group, with a final
model including those covariates for which P,0.05.
The bootstrap method was used to demonstrate the robustness
of the results. By re-sampling the rows of the data matrix with
The sample size and
Value of p53 Mutations in MSS Stage III CRC
PLoS ONE | www.plosone.org8 January 2012 | Volume 7 | Issue 1 | e30020
replacement, separate datasets (200) were constructed . The
final models were fit to each of the datasets. The median P-values
for each variable were selected as estimates of the statistical
logical and molecular characteristics based on p53 or MSI status of
Stage III CRCs.
Table S1 describes association between clinicopatho-
We thank Dr. Donald L. Hill, Division of Preventive Medicine, University
of Alabama at Birmingham, AL, for his critical review of this manuscript.
Conceived and designed the experiments: VRK HLB UM. Performed the
experiments: VRK CS XJ SPV TS MS HLB TC UM. Analyzed the data:
VRK CS XJ SPV TS DC BZ HLB LM UM. Contributed reagents/
materials/analysis tools: VRK CS XJ SPV MS TS DC BZ HLB TC LM
UM. Wrote the paper: VRK CS HLB UM.
1. Welter S, Jacobs J, Krbek T, Poettgen C, Stamatis G (2007) Prognostic impact of
lymph node involvement in pulmonary metastases from colorectal cancer.
Eur J Cardiothorac Surg 31: 167–172.
2. Yamashita H, Noguchi S, Murakami N, Kawamoto H, Watanabe S (1997)
Extracapsular invasion of lymph node metastasis is an indicator of distant
metastasis and poor prognosis in patients with thyroid papillary carcinoma.
Cancer 80: 2268–2272.
3. Vogelstein B, Fearon ER, Hamilton SR, Kern SE, Preisinger AC, et al. (1988)
Genetic alterations during colorectal-tumor development. N Engl J Med 319:
4. Popat S, Houlston RS (2005) A systematic review and meta-analysis of the
relationship between chromosome 18q genotype, DCC status and colorectal
cancer prognosis. Eur J Cancer 41: 2060–2070.
5. Jass JR, Young J, Leggett BA (2002) Evolution of colorectal cancer: change of
pace and change of direction. J Gastroenterol Hepatol 17: 17–26.
6. Salahshor S, Koelble K, Rubio C, Lindblom A (2001) Microsatellite Instability
and hMLH1 and hMSH2 expression analysis in familial and sporadic colorectal
cancer. Lab Invest 81: 535–541.
7. Jass JR, Cottier DS, Jeevaratnam P, Pokos V, Holdaway KM, et al. (1995)
Diagnostic use of microsatellite instability in hereditary non-polyposis colorectal
cancer. Lancet 346: 1200–1201.
8. Ionov Y, Peinado MA, Malkhosyan S, Shibata D, Perucho M (1993) Ubiquitous
somatic mutations in simple repeated sequences reveal a new mechanism for
colonic carcinogenesis. Nature 363: 558–561.
9. Samowitz WS, Holden JA, Curtin K, Edwards SL, Walker AR, et al. (2001)
Inverse relationship between microsatellite instability and K-ras and p53 gene
alterations in colon cancer. Am J Pathol 158: 1517–1524.
10. Tang R, Wang JY, Fan CW, Tsao KC, Chen HH, et al. (2004) p53 is an
independent pre-treatment markers for long-term survival in stage II and III
colorectal cancers: an analysis of interaction between genetic markers and
fluorouracil-based adjuvant therapy. Cancer Lett 210: 101–109.
11. Elsaleh H, Powell B, McCaul K, Grieu F, Grant R, et al. (2001) P53 alteration
and microsatellite instability have predictive value for survival benefit from
chemotherapy in stage III colorectal carcinoma. Clin Cancer Res 7: 1343–1349.
12. Zaanan A, Cuilliere-Dartigues P, Guilloux A, Parc Y, Louvet C, et al. (2010)
Impact of p53 expression and microsatellite instability on stage III colon cancer
disease-free survival in patients treated by 5-fluorouracil and leucovorin with or
without oxaliplatin. Ann Oncol 21: 772–780.
13. Borresen-Dale AL, Lothe RA, Meling GI, Hainaut P, Rognum TO, et al. (1998)
TP53 and long-term prognosis in colorectal cancer: mutations in the L3 zinc-
binding domain predict poor survival. Clin Cancer Res 4: 203–210.
14. Di Agostino S, Strano S, Emiliozzi V, Zerbini V, Mottolese M, et al. (2006) Gain
of function of mutant p53: the mutant p53/NF-Y protein complex reveals an
aberrant transcriptional mechanism of cell cycle regulation. Cancer Cell 10:
15. Greenblatt MS, Bennett WP, Hollstein M, Harris CC (1994) Mutations in the
p53 tumor suppressor gene: clues to cancer etiology and molecular pathogenesis.
Cancer Res 54: 4855–4878.
16. Joerger AC, Fersht AR (2007) Structure-function-rescue: the diverse nature of
common p53 cancer mutants. Oncogene 26: 2226–2242.
17. Levine AJ, Finlay CA, Hinds PW (2004) P53 is a tumor suppressor gene. Cell
116: S67–69, 61p following S69.
18. Liu DP, Song H, Xu Y (2009) A common gain of function of p53 cancer mutants
in inducing genetic instability. Oncogene 29: 949–956.
19. Powell B, Soong R, Iacopetta B, Seshadri R, Smith DR (2000) Prognostic
significance of mutations to different structural and functional regions of the p53
gene in breast cancer. Clin Cancer Res 6: 443–451.
20. Mollevi DG, Serrano T, Ginesta MM, Valls J, Torras J, et al. (2007) Mutations
in TP53 are a prognostic factor in colorectal hepatic metastases undergoing
surgical resection. Carcinogenesis 28: 1241–1246.
21. Goh HS, Chan CS, Khine K, Smith DR (1994) p53 and behaviour of colorectal
cancer. Lancet 344: 233–234.
22. Aas T, Borresen AL, Geisler S, Smith-Sorensen B, Johnsen H, et al. (1996)
Specific P53 mutations are associated with de novo resistance to doxorubicin in
breast cancer patients. Nat Med 2: 811–814.
23. Kressner U, Inganas M, Byding S, Blikstad I, Pahlman L, et al. (1999) Prognostic
value of p53 genetic changes in colorectal cancer. J Clin Oncol 17: 593–599.
24. Skaug V, Ryberg D, Kure EH, Arab MO, Stangeland L, et al. (2000) p53
mutations in defined structural and functional domains are related to poor
clinical outcome in non-small cell lung cancer patients. Clin Cancer Res 6:
25. Katkoori VR, Jia X, Shanmugam C, Wan W, Meleth S, et al. (2009) Prognostic
Significance of p53 Codon 72 Polymorphism Differs with Race in Colorectal
Adenocarcinoma. Clin Cancer Res 15: 2406–2416.
26. Chittenden TW, Howe EA, Culhane AC, Sultana R, Taylor JM, et al. (2008)
Functional classification analysis of somatically mutated genes in human breast
and colorectal cancers. Genomics 91: 508–511.
27. Sood AK, Skilling JS, Buller RE (1997) Ovarian cancer genomic instability
correlates with p53 frameshift mutations. Cancer Res 57: 1047–1049.
28. Popat S, Wort R, Houlston RS (2006) Inter-relationship between microsatellite
instability, thymidylate synthase expression, and p53 status in colorectal cancer:
implications for chemoresistance. BMC Cancer 6: 150.
29. Perucho M (1996) Cancer of the microsatellite mutator phenotype. Biol Chem
30. Jones AM, Douglas EJ, Halford SE, Fiegler H, Gorman PA, et al. (2005) Array-
CGH analysis of microsatellite-stable, near-diploid bowel cancers and
comparison with other types of colorectal carcinoma. Oncogene 24: 118–129.
31. Su GH, Bansal R, Murphy KM, Montgomery E, Yeo CJ, et al. (2001) ACVR1B
(ALK4, activin receptor type 1B) gene mutations in pancreatic carcinoma. Proc
Natl Acad Sci U S A 98: 3254–3257.
32. Kopnin PB, Agapova LS, Kopnin BP, Chumakov PM (2007) Repression of
sestrin family genes contributes to oncogenic Ras-induced reactive oxygen
species up-regulation and genetic instability. Cancer Res 67: 4671–4678.
33. Varambally S, Dhanasekaran SM, Zhou M, Barrette TR, Kumar-Sinha C, et al.
(2002) The polycomb group protein EZH2 is involved in progression of prostate
cancer. Nature 419: 624–629.
34. Draghici S, Khatri P, Eklund AC, Szallasi Z (2006) Reliability and
reproducibility issues in DNA microarray measurements. Trends Genet 22:
35. Skrzypczak M, Goryca K, Rubel T, Paziewska A, Mikula M, et al. (2010)
Modeling oncogenic signaling in colon tumors by multidirectional analyses of
microarray data directed for maximization of analytical reliability. PLoS One 5.
36. Pepe MS, Etzioni R, Feng Z, Potter JD, Thompson ML, et al. (2001) Phases of
biomarker development for early detection of cancer. J Natl Cancer Inst 93:
37. Lee S, Jeong J, Majewski T, Scherer SE, Kim MS, et al. (2007) Forerunner genes
contiguous to RB1 contribute to the development of in situ neoplasia. Proc Natl
Acad Sci U S A 104: 13732–13737.
38. Sanchez-Palencia A, Gomez-Morales M, Gomez-Capilla JA, Pedraza V,
Boyero L, et al. (2010) Gene expression profiling reveals novel biomarkers in
nonsmall cell lung cancer. International Journal of Cancer: Early on-line
39. Stein L, Rothschild J, Luce J, Cowell JK, Thomas G, et al. (2010) Copy number
and gene expression alterations in radiation-induced papillary thyroid carcinoma
from chernobyl pediatric patients. Thyroid 20: 475–487.
40. Thakkar AD, Raj H, Chakrabarti D, Ravi S, Saravanan N, et al. (2010)
Identification of Gene Expression Signature in Estrogen Receptor Positive
Breast Carcinoma. Biomarkers in Cancer 2: 1–15.
41. Yin X, Rana K, Ponmudi V, King MR (2010) Knockdown of fucosyltransferase
III disrupts the adhesion of circulating cancer cells to E-selectin without affecting
hematopoietic cell adhesion. Carbohydr Res 345: 2334–2342.
42. Reymond A, Meroni G, Fantozzi A, Merla G, Cairo S, et al. (2001) The
tripartite motif family identifies cell compartments. EMBO J 20: 2140–2151.
43. Dho SH, Kwon KS (2003) The Ret finger protein induces apoptosis via its
RING finger-B box-coiled-coil motif. J Biol Chem 278: 31902–31908.
44. Kosaka Y, Inoue H, Ohmachi T, Yokoe T, Matsumoto T, et al. (2007)
Tripartite motif-containing 29 (TRIM29) is a novel marker for lymph node
metastasis in gastric cancer. Ann Surg Oncol 14: 2543–2549.
45. Bashour AM, Fullerton AT, Hart MJ, Bloom GS (1997) IQGAP1, a Rac- and
Cdc42-binding protein, directly binds and cross-links microfilaments. J Cell Biol
46. Noritake J, Watanabe T, Sato K, Wang S, Kaibuchi K (2005) IQGAP1: a key
regulator of adhesion and migration. J Cell Sci 118: 2085–2092.
Value of p53 Mutations in MSS Stage III CRC
PLoS ONE | www.plosone.org9 January 2012 | Volume 7 | Issue 1 | e30020
47. White CD, Brown MD, Sacks DB (2009) IQGAPs in cancer: a family of scaffold
proteins underlying tumorigenesis. FEBS Lett 583: 1817–1824.
48. Nojima H, Adachi M, Matsui T, Okawa K, Tsukita S (2008) IQGAP3 regulates
cell proliferation through the Ras/ERK signalling cascade. Nat Cell Biol 10:
49. Compton CC, Fielding LP, Burgart LJ, Conley B, Cooper HS, et al. (2000)
Prognostic factors in colorectal cancer. College of American Pathologists
Consensus Statement 1999. Arch Pathol Lab Med 124: 979–994.
50. Edge SB, Byrd DR, Compton CC, Fritz AG, Greene FL, et al. (2010) American
Joint Committee on Cancer, American Cancer Society: AJCC Cancer Staging
Manual. 7th ed. New YorkNY: Springer-Verlag.
51. WHO () International classification of diseases for oncology.; 1990; Geneva.
World Health Organization.
52. Boland CR, Thibodeau SN, Hamilton SR, Sidransky D, Eshleman JR, et al.
(1998) A National Cancer Institute Workshop on Microsatellite Instability for
cancer detection and familial predisposition: development of international
criteria for the determination of microsatellite instability in colorectal cancer.
Cancer Res 58: 5248–5257.
53. Loukola A, Eklin K, Laiho P, Salovaara R, Kristo P, et al. (2001) Microsatellite
marker analysis in screening for hereditary nonpolyposis colorectal cancer
(HNPCC). Cancer Res 61: 4545–4549.
54. Wagner F, Radelof U (2007) Performance of different small sample RNA
amplification techniques for hybridization on Affymetrix GeneChips.
J Biotechnol 129: 628–634.
55. Wu Z, Irizarry RA, Gentleman R, Martinez-Murillo F, Spencer F (2004) A
Model-Based Background Adjustment for Oligonucleotide Expression Arrays.
Journal of the American Statistical Association 99: 909–917.
56. Martel RR, Botros IW, Rounseville MP, Hinton JP, Staples RR, et al. (2002)
Multiplexed screening assay for mRNA combining nuclease protection with
luminescent array detection. Assay Drug Dev Technol 1: 61–71.
57. Roberts RA, Sabalos CM, LeBlanc ML, Martel RR, Frutiger YM, et al. (2007)
Quantitative nuclease protection assay in paraffin-embedded tissue replicates
prognostic microarray gene expression in diffuse large-B-cell lymphoma. Lab
Invest 87: 979–997.
58. Katkoori VR, Jia X, Chatla C, Kumar S, Ponnazhagan S, et al. (2008) Clinical
significance of a novel single nucleotide polymorphism in the 59 untranslated
region of the Rabphillin-3A-Like gene in colorectal adenocarcinoma. Front
Biosci 13: 1050–1061.
59. Manne U, Myers RB, Moron C, Poczatek RB, Dillard S, et al. (1997) Prognostic
significance of Bcl-2 expression and p53 nuclear accumulation in colorectal
adenocarcinoma. Int J Cancer 74: 346–358.
60. Shanmugam C, Jhala NC, Katkoori V, Wan W, Meleth S, et al. (2010)
Prognostic value of MUC4 expression in colorectal adenocarcinomas. Cancer:
Published Online: 29 Apr 2010.
61. Pricolo VE, Finkelstein SD, Hansen K, Cole BF, Bland KI (1997) Mutated p53
gene is an independent adverse predictor of survival in colon carcinoma. Arch
Surg 132: 371–374; discussion 374–375.
62. Fleiss J (1981) Statistical methods for rates and proportions. New YorkNY: John
Wiley and Sons.
63. Allison PD, SAS Institute. (1995) Survival analysis using the SAS
system: a practical guide. CaryNC: SAS Institute. pp viii, 292.
64. Kaplan E MP (1958) Non-parametric estimation from incomplete observations.
J AM Stat Assoc. pp 53.
65. Cox DR (1972) Regression models and life tables. J Roy Stat Soc 34: 187–220.
66. Mooney CZ, Duval RD (1993) Bootstrapping: A non-parametric approach to
statistical inference. McElroy S, ed. Newbury ParkCA: Sage.
Value of p53 Mutations in MSS Stage III CRC
PLoS ONE | www.plosone.org10 January 2012 | Volume 7 | Issue 1 | e30020