Identifying High-Risk Stage II Colon Cancer Patients: A Three-MicroRNA-Based Score as a Prognostic Biomarker

Original Study

Identifying High-Risk Stage II Colon Cancer Patients: A Three-MicroRNA-Based Score as a Prognostic Biomarker Oriol Caritg,1 Alfons Navarro,1 Isabel Moreno,2 Francisco Martínez-Rodenas,2 Anna Cordeiro,1 Carmen Muñoz,1 Marc Ruiz-Martinez,1 Sandra Santasusagna,1 Joan Josep Castellano,1 Mariano Monzó1 Abstract Adjuvant treatment for patients with stage II colon cancer remains controversial. We tested a panel of microRNAs to identify high-risk stage II colon cancer patients who would potentially benefit from postoperative chemotherapy. We constructed a simple 3-microRNA-based score that can predict the prognosis in this subset of patients. Background: The potential benefit of adjuvant chemotherapy in surgically resected patients with stage II colorectal cancer is controversial. The current guidelines, which are based solely on clinical factors, have limited usefulness, and a clear need exists for biomarkers to supplement the clinical information. MicroRNAs (miRNAs) have previously been shown to be useful cancer biomarkers. In the present study, we assessed the usefulness of a miRNA score to help identify the subset of high-risk patients likely to benefit from adjuvant chemotherapy. Patients and Methods: Six miRNAs previously identified as prognostic markers in Asian patients (miR-21-5p, miR-20a-5p, miR-103a-3p, miR106b-5p, miR-143-5p, and miR-215) were studied in tumor samples from 71 white patients with stage II colon cancer. Results: Three miRNAs (miR-103a-3p, miR-143-5p, and miR-215) emerged as independent prognostic markers on multivariate analysis and were used to construct a miRNA-based score that classified patients into highand low-risk groups. The patients in the high-risk group had significantly shorter disease-free survival compared with their low-risk counterparts (P ¼ .003). The time-dependent receiver operating characteristic curve analysis showed that our 3-miRNA score improved the prediction of outcome when added to the clinical features (P ¼ .023). Conclusion: Our 3-miRNA score added valuable prognostic information to the clinical features in stage II colon cancer. Further research in this field could provide useful tools to determine whether adjuvant chemotherapy would benefit patients with stage II colon cancer after surgery. Clinical Colorectal Cancer, Vol. 15, No. 4, e175-82 ª 2016 Elsevier Inc. All rights reserved. Keywords: Adjuvant chemotherapy, Disease-free survival, miR-103a, miR-143-5p, miR-215

Introduction Colorectal cancer is the third most common cancer and the third leading cause of death from cancer.1 The standard treatment is surgery alone for stage I disease and surgery plus adjuvant therapy for 1 Molecular Oncology and Embryology Laboratory, Human Anatomy Unit, School of Medicine, University of Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain 2 Department of Medical Oncology and Surgery, Hospital Municipal de Badalona, Badalona, Spain

Submitted: Dec 1, 2015; Revised: Apr 1, 2016; Accepted: Apr 27, 2016; Epub: May 7, 2016 Address for correspondence: Alfons Navarro, PhD, Molecular Oncology and Embryology Laboratory, Human Anatomy Unit, School of Medicine, C/Casanova 143, Barcelona 08036, Spain E-mail contact: [email protected]

1533-0028/$ - see frontmatter ª 2016 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.clcc.2016.04.008

stage III.2 However, approximately 1 in 4 patients will present with stage II disease—tumors that have spread to the muscularis propria (stage T3) or beyond (T4) but that show no evidence of regional lymph node involvement (N0).3 Approximately 25% of these stage II patients will develop fatal disease recurrence after surgery1; however, the potential benefit of adjuvant therapy for these patients remains controversial.4 The current guidelines recommend adding adjuvant chemotherapy for stage II disease according to the clinical risk factors, including the presence of T4 lesions, poor histologic differentiation, a clinical presentation at diagnosis of intestinal obstruction or perforation, lymphovascular invasion, and < 12 lymph nodes obtained for analysis.2,5 However, these factors do not always reliably predict whether a patient will benefit from chemotherapy. According to data from the QUick And Simple And Reliable (QUASAR) collaborative group, the improvement in survival with adjuvant chemotherapy has

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miR-Based Score as Prognostic Biomarker for Stage II Colon Cancer been only 3% to 6%.4 Moreover, recent data from a retrospective study based on the National Cancer Database showed that patients diagnosed with stage II colon cancer who were < 50 years received adjuvant chemotherapy more frequently than did their older counterparts but with no significant improvement in survival.6 This finding highlights the limited usefulness of the current protocols using solely the clinical factors and the need for biomarkers to supplement the clinical information. Many genetic assays have been developed to assess the risk of recurrence in colorectal cancer using genomic factors and gene expression signatures.7-11 Although some of these studies have identified good prognostic markers, none of these markers have been included in either the American or the European guidelines.2,5 Various studies12,13 have confirmed that 10% to 20% of stage II colorectal tumors exhibit a defective mismatch repair (MMR-D) or microsatellite-unstable (MSI-H) phenotype that confers a good prognosis but also a potential resistance to 5fluorouracil (5-FU). Thus, adjuvant chemotherapy has not been recommended for patients with this phenotype, because they would be unlikely to benefit from such treatment. However, this is one of the few molecular markers currently used to determine the administration of adjuvant treatment to patients with stage II disease.14 Recently, noncoding RNAs, including microRNAs (miRNAs), have emerged as promising biomarkers. miRNAs are small noncoding RNAs that play an important regulatory role in most physiologic processes and in tumorigenesis, mainly by inhibiting target mRNAs.15 miRNAs can often classify cancers better than can gene expression, suggesting that they might well be superior biomarkers.16 miRNA expression has been extensively studied in colorectal cancer,17,18 and several miRNAs have been identified as having prognostic value.19,20 In stage II colon cancer, miRNAs have been related to metastasis and worse prognosis21,22; however, few studies have found an association between miRNAs and both prognosis and treatment response. One such study, by Zhang et al,23 was of an Asian population and identified a prognostic 6miRNA score (hereafter, the Zhang score) that included miR-215p, miR-20a-5p, miR-103a-3p, miR-106b-5p, miR-143-5p, and miR-215. The score was analyzed in an original cohort of 138 Asian patients and then validated in an internal cohort of 137 patients and in an independent cohort of 460 patients. The Zhang score was able to predict disease recurrence in Asian patients with stage II colon cancer and to identify which patients could benefit from adjuvant chemotherapy. To evaluate the value of the Zhang score in non-Asian patients, we examined the prognostic significance of the 6 miRNAs in white patients with stage II colon cancer. We identified both similarities and differences with the findings from Zhang et al.23 We assessed the capacity of the Zhang score to identify high-risk patients and evaluated the usefulness of adding this molecular information to the traditional clinical risk factors.

Patients and Methods Eligibility and Patient Evaluation From December 2002 to July 2011, tumor tissue samples were obtained from 71 consecutively selected white patients with stage II colon cancer who had undergone surgical resection

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at the Municipal Hospital of Badalona (Badalona, Spain). The institutional review board of the Municipal Hospital of Badalona approved the study, and all patients provided written informed consent in accordance with the Declaration of Helsinki. All patients underwent a complete history and physical examination, including routine hematologic and biochemical analyses, chest radiographs, and computed tomography of the thorax and abdomen. Target lesions detected by abdominal ultrasonography were also assessed by computed tomography or magnetic resonance imaging. The decision of whether to administer 5-FUebased adjuvant chemotherapy was determined using the current clinical standards.2,5 For patients receiving adjuvant chemotherapy, hematologic and biochemical analyses were repeated after each cycle of therapy. In accordance with routine practice at our center during the study period, mismatch repair gene status was initially assessed by immunohistochemistry for MLH1, MSH2, and MSH6; only samples with negative expression of 1 of these 3 genes or with a nonspecific result were also analyzed for PMS2 expression. Tumors showing nuclear expression of all MMR genes were classified as MMR intact, and those with the loss of expression of any gene were classified as MMR deficient. KRAS mutational status in exons 12 and 13 was assessed in patients with sufficient tumor samples.

RNA Extraction and miRNA Quantification Total RNA was extracted from frozen tumor tissue samples using Trizol (Life Technologies, Foster City, CA) according to the manufacturer’s protocol. RNA from the samples was quantified using a NanoDrop 1000 Spectrophotometer. miRNA detection was performed using commercial assays (TaqMan microRNA assays; Life Technologies) for miR-21, miR-20a, miR-103a, miR-106b, miR-143-5p, and miR-215 in the 7500 Sequence Detection System (Life Technologies). The appropriate negative controls (non-template control) were also run in each reaction. All reactions were performed in duplicate. Normalization was performed with RNU6B as an endogenous control. Data are shown as DCt ¼ miRNA Ct  RNU6B Ct.

miRNA-Based Risk Scores The formula used for the Zhang score was as follows: (0.108  status of miR-21) þ (0.086  status of miR-20a) þ (0.240  status of miR-103a) þ (0.095  status of miR-106b)  (0.238  status of miR143-5p) þ (0.237  status of miR-215), where the miRNA status was coded as 1 when expression was high and 0 when expression was low.23 We used this same formula in our cohort of patients and classified the patients as high risk when the formula result was  0.3. A multivariate analysis of the 6 miRNAs in the Zhang score identified 3 of these miRNAs as significant independent prognostic markers: miR-103a, miR-215, and miR-143-5p. High levels of miR-103a or miR-215 and low levels of miR-143-5p were identified as risk factors for a worse prognosis. We then calculated a new simplified risk score using these 3 miRNAs. The patients were classified into 2 groups according to the number of risk factors: low-risk, 0 to 1 factor; and high-risk, 2 to 3 factors.

Oriol Caritg et al Statistical Analysis Disease-free survival (DFS) was defined as the time (months) from the date of surgical resection to either relapse or death. DFS was calculated using the Kaplan-Meier method and compared using the log-rank test. The 6 miRNAs of the Zhang score and all clinical prognostic variables included in the guidelines2,5 were included in a multivariate analysis using the stepwise Cox regression model. The optimal cutoffs of miRNA expression data for DFS were identified using the X tile plot software, version 3.6.1 (Yale University School of Medicine, New Haven, CT)24 and the maxstat package of R (R Statistical Package; Institute for Statistics and Mathematics, Vienna, Austria).25 To estimate the performance of clinical variables and the improvements provided by the miRNA risk score, we performed a receiver operating characteristic (ROC) curve analysis. Because dichotomous variables cannot be used to calculate ROC curves, we calculated the sum of the clinical risk factors and the sum of the clinical factors plus the sum of high-risk miRNAs for each patient, obtaining 2 stratified variables.26 The sensitivity, specificity, positive predictive value, and negative predictive value were calculated as dichotomous variables. Time-dependent ROC curves were constructed and compared using the timeROC package of R (Institute for Statistics and Mathematics).27 Calculation and comparison of the sensitivity, specificity, positive predictive value, and negative predictive value were performed using the DTComPair package of R (Institute for Statistics and Mathematics). All statistical analyses were performed with R, version 3.1.3 (Institute for Statistics and Mathematics). Statistical significance was set at P  .05.

Results Patient Characteristics At the start of the study, tissue samples were available from 71 patients. However, 2 patients were excluded from further analysis. One patient with hereditary colon cancer (Lynch syndrome) was excluded because Lynch syndrome has been reported to harbor a differential miRNA expression compared with sporadic colon cancer.28 Another patient, who died of a second malignancy not related to colon cancer, was also excluded. The median age was 67 years, and 43 patients (62.3%) were men. Of the 69 tumors, 30 (43.5%) were located proximally to the splenic flexure (16 in the ascending colon and 14 in the transverse colon) and 39 were located distally (8 in the descending colon and 31 in the sigmoid colon). Thirty-one patients (44.9%) presented with  1 of the risk factors included in the current clinical guidelines.2,5 All 69 patients underwent surgical resection as primary treatment, and 36 (52.2%) received adjuvant treatment with 5-FU alone (n ¼ 29) or combined with oxaliplatin (n ¼ 7; Table 1).

miRNA Expression and DFS The 5-year DFS rate was 87%, with a median DFS of 134.3 months. Univariate analysis identified older age (P ¼ .003) and higher Eastern Cooperative Oncology Group performance status (P ¼ .003) as markers of shorter DFS (Table 1). Other variables that showed a trend toward significance were the presence of intestinal obstruction or perforation (P ¼ .061) and polyp (P ¼ .055) at diagnosis. The presence of  1 classic risk factors also showed a trend toward a worse prognosis (P ¼ .081).

Using the cutoffs identified by X tile24 and maxstat25 (Table 2), the patients were classified as having high or low expression of each of the 6 miRNAs in the Zhang score. The impact on DFS of the expression of each miRNA was then analyzed. Only 3 of the miRNAs were significantly associated with DFS: miR-103a, miR215, and miR-143-5p (Figure 1). The DFS for patients with high levels of miR-103a was 87 months (95% confidence interval [CI], 63-111) and was 120 months (95% CI, 109-131) for those with low levels (P ¼ .026). The DFS for patients with high levels of miR215 was 89 months (95% CI, 72-107) and was 124 months (95% CI, 114-135) for those with low levels (P ¼ .002). The DFS for patients with low levels of miR-143-5p was 109 months (95% CI, 97-121) and was 133 months (95% CI, 125-142) for those with high levels (P ¼ .031). miR-103a (P ¼ .200) and miR-21 (P ¼ .179) were not associated with DFS, and miR-106b showed a nonsignificant trend toward an association with DFS (P ¼ .081; Figure 1).

miRNA Score and DFS Using the model of the Zhang score, 23 patients (33.3%) were classified as high risk and 46 (66%) as low risk. The DFS was 86 months (95% CI, 69-103) for high-risk patients and 126 months (95% CI, 115-136) for low-risk patients (P ¼ .0002). Because only 3 of the 6 miRNAs of the Zhang score had an individual effect on DFS in our cohort (Figure 1), we performed multivariate Cox analysis to identify the miRNAs with an independent effect on DFS (Table 2). Only miR-103a (odds ratio [OR], 3.48; 95% CI, 1.0911.18; P ¼ .036), miR-143-5p (OR, 0.104; 95% CI, 0.01-0.82; P ¼ .031), and miR-215 (OR, 3.68; 95% CI, 1.37-9.89; P ¼ .010) emerged as independent prognostic markers. Using these 3 miRNAs, we constructed a simplified version of the Zhang score in which patients are classified into 2 groups according to the number of their high-risk factors: low-risk miRNA score, 0 to 1 factor; and high-risk miRNA score, 2 to 3 factors (Figure 2). Our results were the same as those using the Zhang score. Our 3-miRNA score classified the same 46 patients as low risk and the same 23 as high risk (P ¼ .0002). These findings were maintained when patients were stratified by whether they had received adjuvant chemotherapy (Figure 3). For further analyses, we therefore used the simplified 3miRNA score.

Multivariate Analysis The following variables were included in the Cox multivariate analysis for DFS: gender, age, tumor site, intestinal perforation or occlusion at diagnosis, lymphovascular invasion, T stage, Eastern Cooperative Oncology Group performance status, presence of a preexistent polyp, number of lymph nodes resected, histologic differentiation, and the 3-miRNA score. Age > 65 years (OR, 6.44; 95% CI, 1.39-29.96; P ¼ .018), occlusion or perforation (OR, 8.12; 95% CI, 2.11-31.28; P ¼ .002), and the 3-miRNA score (OR, 5.69; 95% CI, 1.81-17.84; P ¼ .003) emerged as independent prognostic factors for DFS (Table 3).

Prognostic Accuracy of 3-miRNA Score The accuracy of the 3-miRNA score as a prognostic factor for DFS was evaluated and compared with that of recognized clinical factors2,5 using time-dependent ROC curves (Figure 4). At 86

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miR-Based Score as Prognostic Biomarker for Stage II Colon Cancer Table 1 Main Clinical and Molecular Characteristics of Included Patients (n [ 69) Characteristic

n (%)

Sex

.115

Male

43 (62.3)

Female

26 (37.3)

Median age (year)

n (%)

NA

.249 64 (92.7) 5 (7.3)

Adjuvant treatment

.155

Fluoropyrimidines

29 (42.0)

65

30 (43.5)

>65

XELOX

7 (10.1)

39 (56.5)

None

33 (47.8)

.003

0

47 (68.1)

1

20 (28.9)

2

2 (3.0)

CEA level

.444

5

46 (66.6)

>5

14 (20.3)

NA

9 (13.1)

CA19.9 level

.628

37

60 (86.9)

>37

8 (11.6)

NA

1 (1.5)

Tumor location

.175

Proximal to splenic flexure

30 (43.5)

Distal to splenic flexure

39 (56.5)

Intestinal obstruction or perforation

.061

No

62 (89.9)

Yes

7 (10.1)

Histologic subtype Well differentiated Poorly differentiated

DFS P Value

10 (14.5)

Mismatch repair status Deficient

.003

ECOG PS

.789 61 (88.4) .986

No

52 (73.4)

Yes

17 (24.6)

Lymphovascular invasion

.517

No

66 (95.6)

Yes

3 (4.4)

T stage

Abbreviations: CA ¼ cancer antigen; CEA ¼ carcinoembryonic antigen; DFS ¼ disease-free survival; ECOG ¼ Eastern Cooperative Oncology Group; NA ¼ not available; PS ¼ performance status; XELOX ¼ capecitabine plus oxaliplatin. a At least 1 of the poor prognostic factors included in the clinical guidelines2,5: intestinal occlusion or perforation, lymphovascular invasion, T4, poorly differentiated tumor, <12 lymph nodes examined.

months (median DFS), the 3-miRNA score had a sensitivity of 0.91 (95% CI, 0.66-0.99), a specificity of 0.2 (95% CI, 0.14-0.27), and an area under the curve (AUC) of 0.907. The clinical factors had a sensitivity of 0.59 (95% CI, 0.36-0.79), a specificity of 0.60 (95% CI, 0.47-0.73), and an AUC of 0.584. The addition of the miRNA information to the clinical factors led to an increase of the AUC from 0.584 to 0.796 (P ¼ .023). This was also associated with increased sensitivity from 0.59 to 0.91 (95% CI, 0.66-0.99) and a slight increase in specificity from 0.60 to 0.63 (95% CI, 0.49-0.75; Figure 4A). When we compared the AUC obtained from the timedependent ROC curves at different time points, we observed that the improvement provided by the 3-miRNA score was significant from 62 months onward (Figure 4B).

Discussion

8 (11.6)

Mucin secretion

Clinical decisions regarding adjuvant therapy for stage II colon cancer have usually been based on traditional risk factors: T4 lesions, poor histologic differentiation, first clinical presentation as intestinal obstruction or perforation, < 12 lymph nodes obtained for analysis, and lymphovascular invasion.2,5 These high-risk features have mostly been adopted from indirect evidence from stage III studies.4 Their clinical performance has been limited, and the

.977

T3

59 (85.5)

T4

10 (14.5)

Pre-existent polyp

.055

No

57 (82.6)

Yes

12 (17.4)

Lymph nodes examined <12

9 (13.1)

12

60 (86.9)

miRNA

.087

No

38 (55.1)

Yes

31 (44.9)

KRAS mutation

.466

No

41 (59.4)

Yes

18 (26.1)

Clinical Colorectal Cancer December 2016

Table 2 Cutoffs Used to Dichotomize Each miRNA and Cox Analysis for DFS Including Only the 6 miRNAs Cox Analysis of DFS

.570

Any poor prognostic factora

-

Characteristic

Intact

67

Age group (year)

e178

DFS P Value

Table 1 Continued

miR-21 miR-20a miR-103a miR-106b miR-143-5p miR-215

a

Cutoff Used 2.04 1.21 1.25 3.53 2.7 2.79

OR (95% CI)

3.48 (1.09-11.18) 0.104 (0.01-0.82) 3.68 (1.37-9.89)

P Value .515 .919 .036b .585 .031b .010b

Abbreviations: CI ¼ confidence interval; DFS ¼ disease-free survival; miRNA ¼ microRNA; OR ¼ odds ratio. a Formula used to determine cutoff point: DCt ¼ miRNA Ct  RNU6B Ct. b Statistically significant.

Oriol Caritg et al Figure 1 Kaplan-Meier Curves Showing Disease-Free Survival for 69 Patients According to the 6 Individual MicroRNAs: (A) miR-20a, (B) miR-21, (C) miR-103a, (D) miR106b, (E) miR-143-5p, and (F) miR-215

Figure 2 Kaplan-Meier Curves Showing Disease-Free Survival for Patients Classified as Low or High Risk According to the 3-MicroRNA Score

information obtained from these factors should be interpreted carefully. Some biomarkers have had promising results, including microsatellite instability, 18q loss of heterozygosity, KRAS, TP53, ERCC1, BRAF, and PIK3CA. However, none of these biomarkers has yet been accepted as a routine clinical marker.29 miRNAs have been extensively studied in colorectal cancer, and several have been identified as potential prognostic markers of risk of recurrence, including miR-21,30 miR-145, miR-320,31 miR-15b, miR-135b,21 and the miR-200 family.19 However, the results of many studies have not been independently validated, and few have focused on the risk of recurrence in stage II colon cancer. One of the largest studies to date was by Zhang et al.23 They analyzed 1849 miRNAs in 735 Asian patients and established a 6-miRNA score, composed of miR20a, miR-21, miR-103a, miR-106b, miR-143-5p, and miR-215, that accurately predicted the risk of disease recurrence.23 In the present study, we evaluated the capacity of these 6 miRNAs to predict recurrence in a white cohort of 71 patients with stage II colon cancer. We first studied the independent prognostic value of each miRNA and observed that only 3 (miR-103a, miR-143-5p, miR215) were significantly associated with DFS in our cohort. Just as in the study by Zhang et al,23 high levels of miR-103a and low levels of miR-143-5p were associated with a worse prognosis. However, high levels of miR-215 were significantly associated with shorter DFS in our cohort, and Zhang et al23 found that high levels of miR-215

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miR-Based Score as Prognostic Biomarker for Stage II Colon Cancer Figure 3 Kaplan-Meier Curves Showing Disease-Free Survival for Patients Classified as Low or High Risk According to the 3MicroRNA Score in (A) Patients Not Receiving Adjuvant Chemotherapy, and (B) Patients Receiving Adjuvant Chemotherapy

predicted a better outcome. This differential effect of miR-215 expression might well have resulted from ethnic differences in the patient populations. The patients in the study by Zhang et al23 were all Asian, and our patients were all white. Ethnic differences in gene and miRNA expression patterns have been found in several studies.32,33 Another study of Asian patients also reported that tumors with low miR-215 expression had a worse prognosis than those with higher expression.34 In contrast, a previous study of white patients found that although miR-215 was lower in tumor tissues than in controls, patients with tumors expressing high levels of miR-215 had shorter survival,35 in line with our results. Karaayvaz et al35 hypothesized that tumors with low levels of miR-215 could harbor a fast-proliferating phenotype that could eventually increase chemosensitivity. An alternative explanation for this effect

Table 3 Multivariate Analysis for DFS Characteristic Gender Age (>65 vs. 65 year) Tumor site (proximal vs. distal) Intestinal obstruction or perforation Lymphovascular invasion T stage (T3 vs. T4) ECOG PS Pre-existent polyp Lymph nodes examined (<12 vs. 12) Histologic differentiation 3-miRNA score

OR (95% CI) 6.44 (1.39-29.96) 8.12 (2.11-31.28)

5.69 (1.81-17.84)

P Value .968 .018 .188 .002 .229 .978 .721 .344 .114 .325 .003

Abbreviations: CI ¼ confidence interval; DFS ¼ disease-free survival; ECOG ¼ Eastern Cooperative Oncology Group; miRNA ¼ microRNA; OR ¼ odds ratio; PS ¼ performance status.

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could be that miR-215 targets thymidylate synthase, the main target of 5-FU.36 The role of miR-215 in colon cancer is thus far from clear. These contrasting findings highlight the potential importance of ethnicity in the effect of molecular biomarkers such as miRNAs and lead us to recommend that biomarkers need to be tested and validated in different populations. In addition to the ethnic differences, the study by Zhang et al23 included a high proportion of T4 tumors and almost 80% of patients had poor clinical prognostic features. In contrast, in our cohort, only 14.5% of the patients had T4 tumors and 44.9% had poor clinical features. Therefore, we included some minor modifications when we applied the 6-miRNA Zhang score to our cohort. Nonetheless, the Zhang score was able to predict a poor outcome in our cohort despite the differences observed in the individual miRNAs, thus partially validating their results. However, we found that the cutoff point of 0 proposed by Zhang et al23 did not effectively classify our patients as high or low risk. We, therefore, set a new optimal cutoff of 0.3. The problems involved in using cutoff points have been widely discussed in published studies, including the loss of statistical power, overestimation of any differences, and lack of validation in independent groups owing to heterogeneous cutoff point estimation.37 New technologies that allow more precise absolute quantification of miRNA, such as digital polymerase chain reaction, will surely lead to future improvements in this field.38 Moreover, because only 3 of the 6 miRNAs of the Zhang score were significantly associated with recurrence in our cohort, we established a new simplified version of the score that included only these 3 miRNAs. This simplified score attained the same level of accuracy as the Zhang score, suggesting that, at least in some populations, the score can be simplified with little or no loss of prognostic value. Such a simplification would make the use of the score more feasible in routine clinical practice.

Oriol Caritg et al Figure 4 (A) Time-Dependent Receiver Operating Characteristic Curve at Median Disease-Free Survival (86.7 Months) Showing Improvement in Diagnostic Performance Provided by the Addition of the 3-MicroRNA Score (miR) to the Clinical Features (CFs). P Value Obtained From Comparing Area Under the Curve (AUC) of the Curves With and Without the Addition of the 3MicroRNA score. (B) Plot Showing the P Values at Different Time Points for Comparison of AUC of the Curves With and Without the Addition of the 3-MicroRNA Score

The time-dependent ROC curve analysis showed a significant improvement in the AUC from 62 months onward when the miRNA information was added to the clinical factors. The differences were not significant < 62 months, perhaps owing to the low number of events. In our cohort, just as for most cases of colorectal cancer, recurrence or death occurs after 70 months. This finding has led us to speculate that our 3-miRNA score might enhance risk stratification, except in rare cases of early recurrence. The decision of whether to administer chemotherapy is based on a number of factors, including its potential harmful effects. In earlystage colon cancer, chemotherapy is often ruled out for low-risk patients and for patients with advanced age, poor performance status, or comorbidities, who could potentially be harmed by the therapy without attaining a benefit.39 The clinical applicability of miRNAs in deciding the best treatment of stage II colon cancer warrants further investigation. Our results suggest that miRNA information added to the clinical features of the patients could improve the accuracy of risk stratification, mainly by identifying a subset of high-risk patients who would potentially benefit from adjuvant chemotherapy.

Conclusion miRNAs can act as reliable biomarkers in colorectal cancer because they can help to stratify patients into risk groups. In the present study, we have demonstrated the usefulness of miR-103a3p, miR-143-5p, and miR-215—both individually and in combination—to predict the risk of recurrence after surgery in a cohort of white patients with stage II colon cancer. Our 3-miRNA score adds valuable prognostic information to the clinical features and can improve risk stratification of these patients, thus providing a useful tool for determining which patients will most likely benefit from adjuvant chemotherapy.

Clinical Practice Points  It remains unclear whether patients with stage II colon cancer

should receive adjuvant therapy. The clinical features are used to

classify patients into low- and high-risk groups; however, the usefulness of these classic risk factors has been limited.  Several biomarkers have been tested to identify the group of patients at high risk of relapse, who would potentially benefit from adjuvant chemotherapy. However, none of them has been clinically validated.  We analyzed the prognostic value of a miRNA signature in a series of 71 white patients and found that a simple score using the expression of 3 miRNAs can accurately predict the prognosis in patients with stage II colon cancer. Furthermore, the addition of this miRNA score to the clinical variables significantly improved the diagnostic accuracy compared with the clinical variables alone.  The use of such a molecular signature would be helpful for clinicians to improve the prognosis prediction and decide whether to administer chemotherapy to patients with stage II colon cancer.

Acknowledgments This work was supported by grants from AECC-Catalunya 2013 (sponsored by Mat Holding) and was partially funded by the Servei de Donació del Cos a la Ciència from the Universitat de Barcelona. O. Caritg is a recipient of a grant from the Ministerio de Educación, Cultura y Deporte. A. Cordeiro, M. Ruiz-Martínez, and S. Santasusagna are APIF fellows of the Universitat de Barcelona. The authors thank Marta Ulldemolins and Marko Nikolic for their comments on the manuscript and Eva Cirera for her statistical comments.

Disclosure The authors declare that they have no competing interests.

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