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Chromatin, Epigenetics, and RNA Regulation
Nomogram Integrating Genomics withClinicopathologic Features
Improves PrognosisPrediction for Colorectal CancerYongfu
Xiong1,Wenxian You1, Min Hou2, Linglong Peng1, He Zhou1, and
Zhongxue Fu1
Abstract
The current tumor staging system is insufficient for
predictingthe outcomes for patients with colorectal cancer because
of itsphenotypic and genomic heterogeneity. Integrating gene
ex-pression signatures with clinicopathologic factors may yield
apredictive accuracy exceeding that of the currently available
sys-tem. Twenty-seven signatures that used gene expression datato
predict colorectal cancer prognosis were identified and re-analyzed
using bioinformatic methods. Next, clinically annotat-ed colorectal
cancer samples (n¼ 1710) with the correspondingexpression profiles,
that predicted a patient's probability of can-cer recurrence, were
pooled to evaluate their prognostic valuesand establish a
clinicopathologic–genomic nomogram. Only 2of the 27 signatures
evaluated showed a significant associationwith prognosis and
provided a reasonable prediction accuracyin the pooled cohort (HR,
2.46; 95%CI, 1.183–5.132,P< 0.001;AUC, 60.83; HR, 2.33; 95% CI,
1.218–4.453, P < 0.001; AUC,
71.34). By integrating the above signatures with
prognosticclinicopathologic features, a clinicopathologic–genomic
nomo-gram was cautiously constructed. The nomogram
successfullystratified colorectal cancer patients into three risk
groups withremarkably different DFS rates and further stratified
stage II andIII patients into distinct risk subgroups. Importantly,
amongpatients receiving chemotherapy, the nomogram determinedthat
those in the intermediate- (HR, 0.98; 95% CI, 0.255–0.679, P <
0.001) and high-risk (HR, 0.67; 95% CI, 0.469–0.957, P ¼ 0.028)
groups had favorable responses.
Implications: These findings offer evidence that genomicdata
provide independent and complementary progno-stic information, and
incorporation of this information re-fines the prognosis of
colorectal cancer. Mol Cancer Res; 16(9);1373–84. �2018 AACR.
IntroductionColorectal carcinoma is the third most commonly
diagnosed
malignant disease and the second leading cause of cancer
deathworldwide (1). Despite advances in colorectal cancer
screening,diagnosis, and curative resection, its prognosis is not
entirelysatisfactory because optimalmanagement and individual
therapystrategies still present great challenges, as colorectal
cancer is awell-recognizedheterogeneous disease. Currently,
treatment deci-sions and prognoses for patients with colorectal
cancer are pri-marily driven by assessment of the
tumor–node–metastasis(TNM) staging system, which is based merely on
anatomicalinformation (2). Previous studies revealed that patients
with stageI colorectal cancer have a 5-year survival rate of
approximately93%, which decreases to approximately 80% for patients
withstage II disease and to 60% for patients with stage III disease
(3).
However, discrepancies in the survival outcomes of patients at
thesame stage and receiving similar treatments are
commonlyobserved.
More importantly, according to the current TNM stage, adju-vant
therapy is generally recommended for all patients withstage III
disease (4). However, patients with T1–2N1M0 tumors(stage IIIA)
have significantly higher survival rates than thosewith stage IIB
tumors (3), suggesting that stratifying patients athigh risk of
recurrence, who are most likely to benefit fromadjuvant therapy, is
critical. Therefore, substantial effort has beenmade to discover
new clinicopathologic indicators to optimizethe current staging
system. To date, some clinicopathologic fea-tures, such as
emergency presentation, adjacent organ involve-ment (T4),
intestinal perforation or obstruction, high tumorgrade, inadequate
sampling of lymph nodes, and lymphatic/vascular invasion, with
prognostic value to classify colorectalcancer patients who are at a
high risk of recurrence have beenapplied in clinical
practice.However, these factors are all relativelyweak and
insufficient to identify colorectal cancer patients whomay benefit
from adjuvant therapy, which leads to potentialunder-treatment or
over-treatment (5).
Colorectal cancer biological behavior (e.g., recurrence,
metas-tasis, drug resistance) is a tightly regulated process that
requiresthe aberrant expression of related genes to empower
carcinomacells with their corresponding abilities. Accordingly,
before clin-icopathologically detectable changes occur, the
underlying altera-tions necessary for recurrence have already
occurred in the pri-mary colorectal cancer, providing the
possibility to developrobust prognostic tools by using multiple
genes in combination
1Department of Gastrointestinal Surgery, The First Affiliated
Hospital ofChongqing Medical University, Chongqing, China.
2Department of Oncology,Affiliated Hospital of North Sichuan
Medical College, Nanchong, China.
Note: Supplementary data for this article are available at
Molecular CancerResearch Online (http://mcr.aacrjournals.org/).
Y. Xiong, W. You, and M. Hou contributed equally to this
article.
CorrespondingAuthor: Zhongxue Fu, The First Affiliated Hospital
of ChongqingMedical University, Chongqing 400016, China. Phone:
28-8754-3627; E-mail:[email protected]
doi: 10.1158/1541-7786.MCR-18-0063
�2018 American Association for Cancer Research.
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(6). During the last decade, gene signatures have shown
greatpromise in predicting the long-term outcomes and
treatmentresponses of individual patients (7). Of note, because of
theoutstanding ability to predict the prognoses of patients
withbreast cancer, multigene assays, such as Oncotype DX
andMammaPrint, have been successfully approved by the US Foodand
Drug Administration and are available in routine clinicalpractice
(8). Actually, genomic signatures to predict the progno-sis of
colorectal cancer have also been continually developed inthe past
10 years, but none are commercially used in the clinic (9).For
example, Agesen and colleagues established a 13-gene expres-sion
classifier, ColoGuideEx, for prognosis predictions specificto
patients with stage II colorectal cancer (10). Based on
theessential role of lipid metabolism in carcinogenesis, Teodoro
andcolleagues identified a group of 4 genes that predict survival
inintermediate-stage colon cancer patients, allowing the
delinea-tion of a high-risk group that may benefit from adjuvant
therapy.In addition, Smith (11), Chen (12), Oh (13), and
Popoviciand colleagues (14) also published their own signatures
basedon different mechanisms. Despite a large number of studies,no
signatures have remained credible in either meta-analyses
orprospective trials (15). However, these published
signaturesclearly show low prediction accuracies but moderate
clinicalusefulness (15). Moreover, when confined to a specific
colorectalcancer stage, promising results regarding risk
stratification havealso been reported. Therefore, investigating the
predictive abilityof these published signatures on comprehensive
large-scale data-sets and identifying whether any can be used to
clinically guidetreatment decisions is necessary.
Currently, while doubts about the predictive value of
clinico-pathologic features are increasing, they still provide the
mostreliable guidelines for the prognostication and treatment
ofcolorectal cancer (16). Thus, we hypothesized that
integratinggenomic signatures with clinicopathologic features in a
modelwould yield a predictive accuracy exceeding that of the
currentlyavailable prognostic system. Nomogram is a statistical
predictionmodel that combines multiple prognostic factors to make
intu-itive graphical and individualized predictions (17). Here,
weaimed to apply a systematic approach to evaluate the
clinicalusefulness of colorectal cancer–related signatures and then
con-struct a composite clinicopathologic–genomic nomogram
byintegrating factors with potential prognostic value in a
trainingset. Moreover, using another independent set, the capacity
of thenomogram to stratify colorectal cancer patients most likely
tobenefit from chemotherapy was further validated.
Materials and MethodsPatients and prognostic signatures
To identify gene expression data arrayed using the
Affymetrixplatform with clinically annotated data, we
systematically search-ed Gene Expression Omnibus (GEO,
http://www.ncbi.nlm.nih.gov/geo/), ArrayExpress
(http://www.ebi.ac.uk/arrayexpress/)and related literature with the
keywords "colorectal cancer,""colorectal cancer," "colon cancer,"
"survival," "relapse,""recurrence," "prognostic," "prognosis," and
"outcomes" pub-lished through August 1, 2017. For some datasets
whose clinicaldata did not accompany their gene expression
profiles, we eithersearched the supplements or contacted one or
more of theinvestigators to obtain the missing information.
Moreover, data-sets with small sample sizes and duplications were
excluded. Raw
microarray data and the corresponding clinical data of
thesedatasets were retrieved and manually organized when
available.Only patients diagnosed with colorectal cancer having
clinico-pathologic and survival information available were
included.Patients with follow-up or survival times of less than 1
monthas well as patients with missing or insufficient data on age,
localinvasion, lymph node metastasis, distant metastasis, and
TNMstage were excluded from subsequent processing. Eventually,
allpatients satisfying the inclusion criteria were combined
andsummarized in Supplementary Tables S1 and S2.
Expression data processingFor raw CEL files available from
Affymetrix microarrays, the
datawere normalized and annotated using aMAS5 algorithm andthe
corresponding annotationfiles fromRBioconductor to obtainsummarized
values for each probe set; otherwise, preprocesseddata as provided
by the contributorswere used. For each sample inevery data set,
measurements without a gene annotation wereexcluded, and multiple
probe sets corresponding to a single genewere summarized into a
gene symbol by taking the most variableprobe set measured by the
interquartile range (IQR).
Identification and analysis of gene signatures
potentiallyrelated to colorectal cancer prognosis
Gene signatures potentially related to colorectal cancer
prog-nosis were systematically retrieved from PubMed. The search
wasrestricted to recent papers to increase validity (from January
2004to August 2017). The selection criteria are detailed in
Supple-mentary Fig. S1. Articles that provided a list of
differentiallyexpressed genes in primary tumor samples associated
with colo-rectal cancer prognosis were included in our study.
Studies basedon tissue microarray and those that were exclusively
focused ondifferences between stages or between primary tumors
andmetas-taseswere excluded. The signaturesfinally included inour
analysisare described in Table 1 (detailed descriptions provided in
Sup-plementary Table S3). In addition, the probe sets or genes of
thosesignatures were re-annotated using the SOURCE web tool
toaddress the retired gene symbols and their differences in
thetested platforms. The re-annotated genes were then subjected
tobiological function enrichment analysis, and the online
analyticaltool DAVID (Database for Annotation, Visualization and
Inte-grated Discovery; ref. 18) was used to enrich gene ontology
(GO)functions and Kyoto Encyclopedia of Genes and Genomes(KEGG)
pathways. GO terms and KEGG pathways with signifi-cant enrichment
false discovery rate (FDR) values less than 0.05were selected for
further analysis. In addition, genes from theabovementioned
signatures were mapped and imported into theRetrieval of
Interacting Genes/Proteins (STRING) 9.1 database,which queried the
human protein–protein interaction networkfor interactions between
effective linkers and seeds to construct afunctional subnetwork.
Cluster analyses were performed usingcorrelation distance metrics
and the average linkage agglomera-tion algorithm (R package nclust
version 1.9.0). Nonnegativematrix factorization (NMF) was done with
the NMF package(version 0.20.5) and standard strategies.
Subclass prediction of colorectal cancer patients in thetraining
set
The re-preprocessed microarray dataset, which represents
thegenomic features of the individual, was classified with the
prog-nostic signatures identified above using the nearest
template
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prediction (NTP) method (19) as implemented in Gene
Patternsoftware (Broad Institute of Harvard and MIT, Boston,
MA;ref. 20). NTP requires only a list of prespecified template
signaturegenes and a dataset to be tested and not a corresponding
trainingdataset to capture good and poor gene expression patterns
in eachsample. Briefly, a template containing representative
expressionpatterns of the signature genes was defined based on
publishedgene signatures from their respective studies. The
proximity of thesignature gene expression patterns of the sample to
the templatewas evaluated by calculating the cosine distance. The
FDR wasused to correct the P values for multiple hypothesis
testing, andprediction analysis was performed separately for each
dataset. Aprediction of good or poor for the related signature was
deter-mined based on FDR predictions
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published as valid prognostic tools in colorectal cancer
wereobtained from 507 potentially related articles. Among
thesesignatures, those with genes not clearly described in
theirrespective studies or investigating the prognostic value
basedon tissue array, immune cells, circulation blood, or
RT-PCRresults were further excluded (see flowchart in
SupplementaryFig. S1 for details). Finally, a total of 27
signatures from26 studies met all of the inclusion criteria and
were retainedfor subsequent analysis (Supplementary Table S3).
Amongthese signatures, almost all were obtained from
experimentsbased on colorectal cancer tissues except for one, which
wasderived from an animal experiment (23). The 27
signaturescontained 1274 total genes, among which 1041 were
unique,and the signature sizes ranged from 4 to 264 (Fig. 1A).
Thetop overlapping genes in the above signatures were FN1
(10times), CYP1B1 and POSTN (6 times), 7 genes (5 times), 7
genes (4 times), 29 genes (3 times), and 107 genes (twice). As
isclearly shown in Fig. 1A, none of these genes appeared inall
signatures. In addition, some repeated genes in differentsignatures
even presented opposite prognostic values. Thislower-than-expected
overlap, at least in part, accountedfor the low reproducibility of
the signatures and the failure inclinical practice.
Accumulating evidence strongly suggests that the prognosisof
colorectal cancer patients, such as recurrence and metastasis,is
tightly regulated by aberrantly expressed genes via
specificbiological processes (16). To gain insight into the
underlyingbiological meanings of the gene set obtained from the
abovesignatures, enrichment (GO and KEGG) and
protein–proteininteraction (PPI) analyses were conducted
(Supplemen-tary Table S4). Figure 1B–D shows that specific GO
cate-gories closely related to colorectal cancer prognosis, such
as
Figure 1.
Bioinformatic analysis of gene lists included in this study. A,
Landscape distribution characteristics of the gene lists as
reported in 27 prognostic signatures.Genes with good and poor
prognostic values in each signature are shown as short green and
red vertical bars, respectively. The number of gene occurrences
isgraphically depicted on the top. Each row represents one
signature annotated by the first author of the corresponding
article. All the unique genes weresorted alphabetically. B–E, GO
and KEGG pathway analyses of genes in the 27 signatures. The
vertical axis represents Go or KEGG pathway annotations.
Thehorizontal axis represents the number of genes assigned to the
corresponding annotation. B, Biological process; C, cellular
components; D, molecular function;and E, KEGG pathway. F,
Protein–protein interaction (PPI) networks. Nodes represent the
proteins, and edges represent the physical interaction. The
blackand gray nodes represent geneswith good and poor prognostic
values, respectively. Colorectal cancer recurrence-related pathways
that were significantly enrichedby KEGG analysis form some closely
connected regions and are highlighted in the colored box.
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cell cycle (FDR < 0.0001), regulation of cell
proliferation(FDR < 0.0001), extracellular matrix (FDR <
0.0001), andchemokine activity (FDR < 0.0001), were
significantly enriched.Additionally, some KEGGpathways well known
to be involved incolorectal cancer metastasis and recurrence were
enrich-ed, including colorectal cancer metastasis signaling (FDR
<0.0001), wnt-b catenin signaling (FDR ¼ 0.0002), VEGFsignaling
(FDR ¼ 0.0008), and chemokine signaling (FDR ¼0.0021; Fig. 1E).
More importantly, a visualized network of thesegene interactions
revealed that the abovementioned pathways notonly form highly
integrated modules but also play essential rolesin the entire
network (Fig. 1F). These findings suggested thatnotwithstanding the
low reproducibility in the system test andclinical validation,
genes obtained from colorectal cancer signa-tures actually have
potential prognostic value.
Furthermore, to assess the global performance of thesesignature
and construct a composite nomogram, publicly avail-able datasets
with whole-genome profiles and correspondingclinical information in
colorectal cancer patients were screenedand downloaded (see
flowchart in Supplementary Fig. S1 fordetails). Ultimately, 1710
colorectal cancer patients pertainingto 9 cohorts (Supplementary
Table S5) met the set criteria andwere retained. Although the
profiles were obtained using dif-ferent technologies and based on
fresh or formalin-fixed andparaffin-embedded (FFPE) specimens as
source material, theyshowed no evidence of cohort-bias clustering
by principalcomponent analysis (PCA; Supplementary Fig. S2A–S2C)
orhierarchical clustering (Supplementary Fig. S2D), suggestingthat
patients from different cohorts in the present study couldbe
blended together. Moreover, to balance the baseline
char-acteristics, all eligible patients were randomly divided into
atraining set (n ¼ 855) and a validation set (n ¼ 855).
Thedemographic and major clinicopathologic characteristics of
thepatients are summarized in Supplementary Table S2. As
weexpected, no significant differences were observed between
thestatistical properties of the training and validation sets.
Global prognostic performance of the published signaturesTo
determine the correlations of signatures and colorectal
cancer patient outcomes, the prognostic performances of the27
signatures were evaluated in the training set using a modifiedNTP
method as previously described (19). Among the 27 signa-tures
evaluated, all except one (Teodoro's (24) signature had theonly
poor prognostic gene) were able to confidently stratifypatients
(FDR < 0.05) into good and poor subgroups. Table 1and Fig. 2A
summarize the prediction results obtained for each ofthe 855
patients. Popovici signature (34) was the most prevalentprediction
in the training set (83.6%; 615of 855), whereas Agesensignature
(42) was securely identified in only 19.9% (167 of 855)of
colorectal cancer patients. Of note, conflicting prognostic
out-comeswere commonly observed among the training set, and only34%
patients had consistent results (good or poor prognosis) inmore
than 5 signatures. However, as demonstrated in Fig. 2A, theoverall
tendency of the NTP analysis was consistent and roughlyconsistent
with previous studies (25). In addition, global CramerV
coefficients were calculated for each signature to gain insightinto
their concordances. Unsupervised clustering based on
thesecoefficients indicated a substantial association among these
sig-natures (Fig. 2B). Obviously, 5 signatures obtained from
thestudies of Oh (13), Minh (11), Chang (9), Jiang (26),
andFritzmann and colleagues (27)were at leastmoderately
correlated
with each other. Moreover, another 4 signatures
(Toshiaki's,Aziz's, Chen's, Xu's) were also clustered with similar
coefficients.These findings are largely consistent with the initial
purpose ofeach signature, which further confirms the potential
prognosticvalue of these signatures.
We then applied Cox regression analysis to assess whether
anysignatures were statistically significant for colorectal
cancer–related recurrence/progression and independent of clinical
fea-tures in the training set. The univariate and clinical
factor-adjustedmultivariate analysis results are displayed in
SupplementaryTable S6 and Fig. 2C. Univariate analyses revealed
that age(HR,1.12; 95% CI, 1.021–1.269, P ¼ 0.048), local invasion(T
stage; HR, 2.21; 95% CI, 1.139–7.029, P ¼ 0.027), lymphnode
metastasis (N stage; HR, 2.28; 95% CI, 1.189–3.784, P ¼0.021),
distant metastasis (M stage; HR, 2.46; 95% CI, 1.363–4.453, P ¼
0.003), and TNM stage (HR, 2.15; 95% CI, 1.165–3.971, P ¼ 0.014)
were confidently associated with poor prog-nosis. However, with
respect to genomic factors, only 8 signatureswere validated to have
significant effects on DFS (Fig. 2C). Next,we used multivariate Cox
analysis with the covariates, includingage, local invasion, lymph
node metastasis, distant metastasis,and TNM stage, to evaluate the
predictive values of the 8 signa-tures. As shown in Fig. 4C, after
each covariate was adjusted,Popovici signature (HR, 1.92; 95% CI,
1.298–2.841, P ¼ 0.001)and Fritzmann signature (HR, 2.16; 95% CI,
1.144–4.069,P < 0.0001) remained powerful enough to indicate
prognosis inthe training set for DFS, which revealed that these two
signaturesare independent prognostic factors.
Construction and comparison analysis of the
compositenomogram
Accurately predicting DFS in patients with colorectal cancer
isimportant for counseling, follow-up planning, and selection
ofappropriate adjuvant therapy. Thus, toprovide the clinicianwith
aquantitative and user-friendly method to generate
individualizedpredictions, we constructed a nomogram that
integrated thegenomic signatures identified above with
clinicopathologic fea-tures (Fig. 3A). Calibration plots revealed
excellent agreementbetween the nomogram-predicted probabilities and
the actualobservations of 1-, 3-, and 5-year DFS (Fig. 3B). The
nomogramdemonstrated that distant metastasis made the largest
contribu-tion to prognosis, followed by the TNM stage and lymph
nodemetastasis. Age and Popovici signature had moderate effects
onsurvival rate. Each category within these variables was assigned
apoint on the top scale based on the coefficients from Cox
regres-sion. By summing all of the assigned points for the seven
variablesand drawing a vertical line between total points and the
survivalprobability axis, we easily obtained the estimated
probabilities of1-, 3-, and 5-year DFS. The risk score cutoff
values (�10.6, 10.6–21.2, and �21.2) were selected in terms of
total points to stratifypatients into roughly equal tertiles in the
training set, whichaccurately divided patients into low-
(reference), intermediate-(HR, 2.227; 95% CI, 1.507–3.291, P <
0.001), and high-risk(HR, 6.787; 95% CI, 4.786–9.624, P < 0.001)
subgroups withsignificantly different DFS rates (Fig. 3C and
D).
Although the TNM stage in combination with other clinicalfactors
is a well-recognized prediction system for colorectal
cancerprognosis (28), its effectiveness urgently needs to be
increased. Todetermine whether the genomic signatures added
additionalprognostic value to the current system, time-dependent
receiveroperating characteristic (ROC) analysis was applied to
compare
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the performances between the nomogram, clinicopathologic
fac-tors, and genomic signatures (Fig. 3E). Expectedly, the
nomogramachieved the greatest area under the ROC curve (AUCs at
5-yearDFS, 78.07, 71.40, 71.34, and 60.83, P < 0.05), suggesting
that theintegrated clinicopathologic genomic nomogram had a
prognos-tic performance superior to those of clinicopathologic and
geno-mic information by themselves.
Validating the nomogram for stratifying colorectal cancerpatient
risks
To validate the correlation between the nomogram score
(totalpoints) and DFS statuses of patients with colorectal
cancer,
Kaplan–Meier analysis and log-rank tests based on the samecutoff
value were conducted on the validation set. As shownin Fig. 4A and
B, applying the clinicopathologic–genomic nomo-gram stratified
patients into three distinct risk subgroups withsignificantly
different DFS rates. In addition, the prognostic accu-racy of the
nomogram was remarkably better than those of bothclinicopathologic
and genomic information by themselves (Fig.4C), which was
consistent with the results obtained from thetraining set.
Disagreements on potentially appropriate candidatesfor adjuvant
therapy prevail, especially for patients belonging toAJCC stages II
and III. In the present study, the survival ratespredicted by the
nomogram were significantly distinct between
Figure 2.
Global prognostic performance of the published colorectal cancer
signatures. A, Concordance of signature-based prediction results in
the training set. Eachcolumn represents the prediction of each
individual sample. The orange, blue, and white bars indicate good,
poor, and uncertainty prognoses of thecorresponding signature,
respectively. B, Heatmap of Cramer V coefficients showing
correlation between these published signatures. C, Analysis of
prognosticperformance using Cox regression; left: univariate
analysis; right: clinical factors adjusted for multivariate
analysis.
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the Kaplan–Meier curves (Fig. 4D and E) in patients
categorizedby the abovementioned major clinicopathologic features
(AJCCstages II and III).
Moreover, to ensure the effectiveness of statistical
power,patients with available chemotherapy information were
integrat-ed together regardless of the set to which they belonged
(trainingset or validation set). Intriguingly, we noted that
adjuvant che-motherapy did not enhanceOS in all 869 patients (HR,
0.98; 95%CI, 0.769–1.258, P ¼ 0.873; Fig. 5A) or in patients who
weredefined as low risk by the nomogram (HR, 0.93; 95% CI,
0.515–1.695, P ¼ 0.824; Fig. 5B). However, patients in the
nomogramdefined as intermediate risk (HR, 0.98; 95% CI,
0.255–0.679,P < 0.001; Fig. 5C) or high risk (HR, 0.67; 95% CI,
0.469–0.957,P ¼ 0.028; Fig. 5D) had favorable responses to adjuvant
chemo-therapy. Taken together, these results suggested that the
clinico-pathologic–genomic nomogram has the discriminatory powerto
identify colorectal cancer patients who are suitable candidatesfor
adjuvant chemotherapy.
DiscussionAlthough surgery remains the mainstay of curative
treatment,
the prognosis of patients with colorectal cancer, especially
forlong-term outcome, depends more on pre- or
postoperativeindividualized therapies (29), including various
strategies ofchemotherapy and chemoradiation. However, as a
well-recog-nized heterogeneous cancer, its prognosis varies
significantly, andoptimal management presents challenges.
Currently, adjuvantchemotherapy is regarded as standard treatment
for patients withstage III colon cancer (4). However, patients with
T1–2N1M0tumors (stage IIIA) have significantly higher survival
rates thanthose with stage IIB tumors (3). Moreover, for some
patients,long-term survival continues to be jeopardized by the high
risk ofrecurrence even after chemotherapy, and personalized
therapyneeds to further optimized. With respect to stage II
patients,disagreement regarding which patients are potentially
appropri-ate candidates for adjuvant therapy prevails. The MOSAIC
trial
Figure 3.
Development of the composite clinicopathologic–genomic nomogram.
A, Composite nomogram to predict disease-free survival (DFS) for
patients with colorectalcancer. Each category within these
variables was assigned to a point on the top scale based on the
coefficients from Cox regression. By summing all of theassigned
points for the seven variables and drawing a vertical line between
the total points and survival probability axis, we easily obtained
the estimatedprobabilities of 1-, 3-, and 5-year DFS. B,
Calibration curves for predicting patient survival at 1, 3, and 5
years. The actual DFS is plotted on the y-axis, andthe
nomogram-predicted DFS is plotted on the x-axis; a plot along the
45-degree line would indicate a perfect calibration model in which
the predictedprobabilities are identical to the actual outcomes. C,
The total point distribution of each colorectal cancer patient and
their disease-free survival time and status.The blue dotted line
represents the cutoff value dividing patients into low-,
intermediate-, and high-risk groups. D, Kaplan–Meier survival
curves of DFS fordifferent risk groups using the nomogram in the
training set. E, Time-dependent ROC curves comparing the prognostic
accuracies of 5-year disease-freesurvival among the prognostic
signatures combined with clinicopathologic features and the
nomogram in the training set. The AUC 95% CIs were calculatedfrom
1,000 bootstraps of the survival data. ROC, receiver operator
characteristic. AUC, area under the curve.
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(30) has shown only a 20% benefit from adjuvant therapy at
3years for patients with stage II disease and a 28% benefit
forpatients with recognized clinical risk factors. Thus, for
approxi-mately 75% of stage II patients who receive curative
surgery, theadministration of adjuvant chemotherapy is unnecessary
andharmful. Unfortunately, low-risk stage II patients have not
beenaccurately defined. In addition, patients with microsatellite
insta-bility (MSI colorectal cancer) have a better prognosis (31)
and donot benefit from 5-fluorouracil (5-FU)–based adjuvant
chemo-therapy (32), similar to patients with CpG island
methylation(33). All the abovementioned studies highlight that the
rationalapplication of therapeutic strategies requires accurate
prognosisprediction and risk stratification for colorectal cancer
patients.
Considering the inherent deficiency of TNM stages,
substantialeffort has been placed on their optimization (2), and
they havebeen continuously developed over the past decade.
Substantialchanges in colorectal cancer classifications, based
mainly on theanalysis of Surveillance, Epidemiology, and End
Results (SEER)data, were made in the seventh edition (34). However,
a com-
parative studydemonstrated that the seventh
TNMeditiondidnotprovide a greater accuracy for predicting
colorectal cancer patientprognoses but rather resulted in amore
complex classification fordaily clinical use (35). The eighth
edition of the colorectal cancerstaging system, containing major
advances, including the intro-duction of molecular markers, such as
MSI, KRAS, and BRAFmutations, to strengthen its discriminatory
power, were imple-mented worldwide on January 1, 2018 (2).
Nonetheless, theforthcoming edition may not provide substantial
improvementbecause the prognostic value of clinicopathologic
factors haspeaked. Moreover, the current models still lack genomic
infor-mation, which may directly reflect the heterogeneity of
colorectalcancer and present substantial potential prognostic
value.
Genomic signatures predicting the prognosis of colorectalcancer
have been continually developed in the past 10 years. Inthe present
study, we retrieved 81 articles related to the develop-ment of
signatures for predicting colorectal cancer prognosis,including OS,
DFS, disease-specific survival (DSS), and varioustypes of
recurrence. For instance, based on the association
Figure 4.
Verifying the prognostic value of the nomogram in the validation
set. A, The total point distribution of each colorectal cancer
patient and their disease-freesurvival time and status. The blue
dotted line represents the cutoff value dividing patients into
low-, intermediate-, and high-risk groups. B, Kaplan–Meiersurvival
curves of DFS for different risk groups using the nomogram in the
validation set. C, Time-dependent ROC curves comparing the
prognostic accuraciesof 5-year disease-free survival among the
prognostic signatures in combination with clinicopathologic
features and the nomogram in the validation set.D and E,
Kaplan–Meier survival curves for patients in different risk
subgroups, which were stratified by TNM stages II (D) and III
(E).
Xiong et al.
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between the aberrantly expressed gene and the duration
ofindividual DFS, Zhang and colleagues (36) constructed a
sixRNA-based classifier, which was further validated to have
reliablepredictive power for detecting recurrence in patients with
stage IIcolon cancer. Similarly, Anna and colleagues (23) developed
agenomic profile specific for adult intestinal stem cells
(ISC),whichwas highly enriched in genes that positively predicted
the risk ofrecurrence. Patients bearing primary colorectal cancers
with highaverage expression levels of ISC genes had a relative risk
of relapsethat was 10-fold higher than that for patients with low
levels (23).Moreover, recent evidence (37) suggests that gene
signatures thatclosely reflect specific biological processes or
oncogenic pathwaystatuses also have potential prognostic values for
stratifyingcolorectal cancer patients. Even though almost all of
these sig-natures were proven to have prognostic value in their
respectivepublications, none are routinely used in clinical
practice.
Potential explanations for the unsatisfactory results must
beconsidered. Thus, in the current study, we exhaustively
reviewedand analyzed the published multigene signatures in
colorectalcancer. To eliminate the potential confounding effects,
only dataderived from the Affymetrix human genome platform
havingclearly described genes were included. Of the 81
publishedsignatures, 27 signatures from 26 publications met all of
theinclusion criteria and were retained (Table 1;
SupplementaryTable S3). The 27 signatures contained 1,274 total
genes, among
which 1,041were unique, and the signature sizes ranged from4
to264 (Fig. 1A). The top overlapping genes in the above
signatureswere FN1 (10 times), CYP1B1 and POSTN (6 times), 7
genes(5 times), 7 genes (4 times), 29 genes (3 times), and 107
genes(twice). As is clearly shown in Fig. 2A, none of these
genesappeared in all signatures. These results were expected
becausethey were previously reported (15) and in agreement with
thesystematic analyses of lung cancer and breast cancer
prognosticsignatures (38). Obviously, the slight overlap of these
signaturesin gene identity was perplexing but sufficiently
explained the lowreproducibility. Moreover, genetic heterogeneity
was recentlyshown to exist not only between but also within
tumors,meaningthat biological statuses, including oncogenic and
carcinostaticstates, might regularly vary in the same patient (39).
Our NTPanalysis revealed that some patients concurrently harbored
morethan one signature, which further confirmed the
abovementionedresults and indicated that their tumors were genomic
instability atthe individual level. Taken together, the different
signaturesmightreflect diverse biological processes and be active
in different oridentical individual tumors, which partially
accounts for thewide nonoverlapping among signatures constructed in
differentcolorectal cancer studies.
In terms of global performance, in the training set, 8 of
27signatures showed a significant association with prognosisand
could reasonably predict the outcome. However, after
Figure 5.
Effect of chemotherapy on overallsurvival in patients stratified
by thenomogram. A, Kaplan–Meier survivalcurves for all patients who
receivedchemotherapy. B–D, Effects ofadjuvant chemotherapy on
overallsurvival in the different subgroups.Low-risk group (B),
intermediate-riskgroup (C), and high-risk group (D).
A Nomogram to Predict Colorectal Cancer Prognosis
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multivariate adjustment, only 2 signatures remained
powerfulenough to indicate prognosis for DFS, which directly
demon-strated the low reproducibility of colorectal cancer
genomicsignatures. More importantly, Popovici signature,
Fritzmannsignature and TNM stage had remarkable prognostic
abilitiesand were independent of each other, indicating that
thesesignatures may be used to refine the current prognostic
modeland facilitate further stratification of patients with
colorectalcancer at the same TNM stage.
Our primary goal was to construct a nomogram that couldintegrate
genomic signatures with clinicopathologic variables in alarge
cohort of colorectal cancer patients to add additional prog-nostic
value to the current staging system. Our multivariateanalysis in
the training set revealed that age, local invasion, lymphnode
metastasis, distant metastasis, TNM stage, Popovici signa-ture
andFritzmann signaturewere independent prognostic
factors(Supplementary Table S6), which was highly consistent
withprevious studies on colorectal cancer risk factors (40, 41).
Accord-ingly, we cautiously built a clinicopathologic–genomic
nomo-gram using the abovementioned significant factors. The AUC
ofthe nomogram showed a superior prediction accuracy comparedwith
that of the combined clinical factors in the training andvalidation
sets (Figs. 3E and 4C). Additionally, the calibrationplots showed
optimal agreement between the expected andobserved survival
probabilities (Fig. 3B), ensuring the reliabilityof our nomogram.
Moreover, the nomogram successfully strati-fied colorectal cancer
patients into three risk groups with remark-ably different DFS
values based on total points, which was furtherconfirmed using the
same cutoff value in the validation set.
Intriguingly, the TNMstaging system is themost important toolfor
guiding treatment in clinical practice. However,
disagreementprevails on potentially appropriate candidates for
adjuvant ther-apy, especially for stage II and III colorectal
cancer patients.Therefore, the identification of a high-risk
subgroup among thesepatients is of urgent clinical needed. Our
findings suggest that theclinicopathologic–genomic nomogramcould be
a promising toolto stratify stage II and III patients into distinct
risk subgroup andguide individualized therapy. Moreover, in the
current study, weclearly revealed that chemotherapy provides a
survival benefit topatients classified as intermediate- and high
risk by the nomo-gram (Fig. 5).
Despite the promising results, there were several shortcomingsin
this work. First, the NTP method uses only a list of signaturegenes
to make class predictions in each patient's expression
data,allowing the method to be less sensitive to differences in
exper-imental and analytical conditions and applicable to every
patientwithout optimization of the analysis parameters. In the
currentstudy, we applied the NTP method for all prediction
models,
which generally consisted of two major components: a
genesignature and a prediction algorithm. Obviously, the
intrinsiclimitation of the NTP method is that it inevitably causes
thealgorithm lose its prognostic value. Second, the forthcoming
8thedition of the colorectal cancer TNM staging system may
betterpredict prognosis, but data used in the present studywere
from thecurrent 7th edition or the 6th edition. Third, the
prognosticsignatures of colorectal cancer included in our study
were notexhaustive, and some promising signatures were
excludedbecause of insufficient information. In addition, some
clinico-pathologic factors, even those well recognized to have
prognosticvalue, were not included in the composite nomogram
because ofthe low availability of information.
In summary, based on analyzing a large-scale colorectal
cancercohort and published multigene prognostic signatures, we
con-firmed herein for the first time that genomic information
incombination with clinical data can improve patient
prognosticevaluations and should be considered as an independent
andcomplementary approach to the current clinicopathologic
prog-nostic model. Furthermore, considering the wide promise of
ournomogram, which integrates genomic signatures with
clinico-pathologic features to improve the prognosis prediction of
colo-rectal cancer, further systematic research is needed.
Disclosure of Potential Conflicts of InterestNo potential
conflicts of interest were disclosed.
Authors' ContributionsConception and design: Y. Xiong, Z.
FuDevelopment of methodology: W. YouAcquisition of data (provided
animals, acquired and managed patients,provided facilities, etc.):
Y. Xiong, M. HouAnalysis and interpretation of data (e.g.,
statistical analysis, biostatistics,computational analysis): Y.
Xiong, M. Hou, L. Peng, H. ZhouWriting, review, and/or revision of
the manuscript: Y. Xiong, W. You, L. PengAdministrative, technical,
or material support (i.e., reporting or organizingdata,
constructing databases): Z. Fu
AcknowledgmentsThis work was financially supported by a grant
from the National Natural
Science Foundation of China (grant no. 81572319; project
recipient: Z.. Fu).
The costs of publication of this article were defrayed in part
by thepayment of page charges. This article must therefore be
hereby markedadvertisement in accordance with 18 U.S.C. Section
1734 solely to indicatethis fact.
Received January 19, 2018; revised April 5, 2018; accepted May
2, 2018;published first May 21, 2018.
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Prediction for Colorectal CancerNomogram Integrating Genomics with
Clinicopathologic Features
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