Original Article
Whole-Genome MicroRNA Expression Profiling Identifies a 5-MicroRNA Signature as a Prognostic Biomarker in Chinese Patients With Primary Glioblastoma Multiforme Wei Zhang, MD1; Jing Zhang, PhD2; Wei Yan, MD1; Gan You, MD1; Zhaoshi Bao, MD1; Shouwei Li, MD1; Chunsheng Kang, MD4; Chuanlu Jiang, MD5; Yongping You, MD6; Yuxiang Zhang, MD7; Clark C. Chen, MD8; Sonya Wei Song, PhD3; and Tao Jiang, MD1
BACKGROUND: More reliable clinical outcome prediction is required to better guide more personalized treatment for patients with primary glioblastoma multiforme (GBM). The objective of this study was to identify a microRNA expression signature to improve outcome prediction for patients with primary GBM. METHODS: A cohort of Chinese patients with primary GBM (n ¼ 82) was analyzed using whole-genome microRNA expression profiling with patients divided into a training set and a testing set. Cox regression and risk-score analyses were used to develop a 5-microRNA signature using 41 training samples. The signature was validated in 41 other test samples, in an independent cohort of 35 patients with GBM, and in the Cancer Genome Atlas data set. RESULTS: Patients who had high risk scores according to the 5-microRNA signature had poor overall survival and progression-free survival compared with patients who had low risk scores. Multivariate Cox analysis indicated that the 5-microRNA signature was an independent prognostic biomarker after adjusting for other clinicopathologic and genetic factors, such as extent of resection, temozolomide chemotherapy, preoperative Karnofsky performance status score, isocitrate dehydrogenase 1 (IDH1) mutation, and O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. CONCLUSIONS: The 5-microRNA signature was identified as an independent risk predictor that identified patients who had a high risk of unfavorable outcome, demonstrating its potential for personalizing cancer management. The authors concluded that this signature should be evaluated in further prospective studies. Cancer 2013;119:814-24. C 2012 American Cancer Society. V KEYWORDS: glioblastoma, microRNA, risk score, biomarker, prognosis.
INTRODUCTION Glioblastoma multiforme (GBM) is the most malignant brain tumor in the central nervous system. Primary GBM, which accounts for >90% of glioblastomas, occurs de novo without any evidence of a less malignant precursor. The median survival of patients with primary GBM is approximately 1 year, but it varies remarkably from 3 years after diagnosis,1 suggesting the limitations of the current clinicopathologic determinants of prognosis and in the choice of better therapeutic strategies. The introduction of molecular biomarkers in the management of patients with cancer may improve their clinical outcomes. Many biomarker candidates have been generated by high-throughput technologies such, as microarray gene expression profiling.2 Recent reports suggest that microRNA (miRNA) expression profiles be more effective for tumor classification than protein-coding gene expression profiles.3-5 Several advantages have been demonstrated for miRNAs over messenger RNAs (mRNAs) as biomarkers. It is predicted that the human genome has nearly 1000 miRNAs and >40,000 protein-coding genes. Accordingly, genome-wide gene expression data are much more extensive than miRNA expression data. Therefore, it is more workable to identify reliable miRNA biomarkers from genome-wide miRNA expression data than from genome-wide gene expression data. Furthermore, miRNAs are subjected relatively less to degradation and, thus, are more applicable to formalin-fixed, paraffin-embedded tissues.
Corresponding author: Tao Jiang, MD, Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China; Fax: (011) 861067021832;
[email protected]; Sonya Wei Song, PhD, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing 100142, China; Fax: (011) 861088196764;
[email protected] 1 Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; 2Laboratory of Disease Genomics and Individualized Medicine, Center of Computational Biology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China; 3Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China; 4Laboratory of Neuro-Oncology, Tianjin Neurological Institute, Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China; 5Department of Neurosurgery, Harbin Medical University Second Affiliated Hospital, Harbin, China; 6Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China; 7Department of Biochemistry and Molecular Biology, College of Basic Medical Science, Capital Medical University, Beijing, China; 8Division of Neurosurgery, University of California at San Diego, San Diego, California
The first 2 authors contributed equally to this work. DOI: 10.1002/cncr.27826, Received: April 25, 2012; Revised: August 6, 2012; Accepted: August 13, 2012, Published online September 18, 2012 in Wiley Online Library (wileyonlinelibrary.com)
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5-MicroRNA Signature and GBM Outcomes/Zhang et al
MicroRNAs are approximately 22-nucleotide long, single-stranded, noncoding RNAs that post-transcriptionally regulate the expression of hundreds of genes by translational repression or transcript degradation, thereby modulating a variety of biologic functions.6 Several miRNAs reportedly have been associated with clinical outcomes in some cancers, such as chronic lymphocytic leukemia,7 lung cancer,8 pancreatic cancer,9 and colon adenocarcinoma.10 However, whether an miRNA signature is able to predict clinical outcomes in patients with primary GBM has not been reported in the Chinese population. Therefore, we performed miRNA expression profiling in a cohort of 82 primary GBMs and identified a 5-miRNA prognostic signature, which was revalidated in an independent cohort of 35 primary GBMs and in the Cancer Genome Atlas (TCGA) data set. MATERIALS AND METHODS Patients and Samples
In total, 117 eligible patients who had primary GBM histologically diagnosed according to the 2007 World Health Organization classification of tumors of the central nervous system were included in this study (82 patients from Beijing Tiantan hospital [the Tiantan cohort] and 35 patients from Jiangsu Provincial People’s Hospital and the Second Affiliated Hospital of Harbin Medical University [the independent cohort]). The patients underwent surgical resection and then received radiation therapy and alkylating agent-based chemotherapy. Tumor tissues were obtained by surgical resection. Five control brain tissue samples from areas surrounding arteriovenous malformations were collected from Tiantan Hospital. The tissues were immediately snap-frozen in liquid nitrogen after resection. Hematoxylin and eosin staining of tissue sections was conducted to assess the percentage of tumor cells. Only samples that contained >80% tumor cells were selected. This study was approved by the institutional review boards of the participating hospitals, and written informed consent was obtained from all patients. The clinicopathologic characteristics of the patients are listed in Table 1. The TCGA data set also was used to validate the miRNA signature. MicroRNA Expression Profiling
Total RNA (tRNA) was extracted from frozen tissues by using the mirVana miRNA Isolation Kit (Ambion, Inc., Austin, Tex), and its concentration and quality were determined with the NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, Del). Cancer
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TABLE 1. Clinicopathologic Characteristics of Patients With Primary Glioblastoma Multiforme in the Tiantan Cohort (n ¼ 82) and the Independent Cohort (n ¼ 35) No. of Patients (%) Tiantan Cohort
Characteristic Sex Men Women Age, y MeanSD Range Extent of resection Total Subtotal TMZ chemotherapy Yes No Preoperative KPS score 70 0.2, allowing us to have most of the variations in miRNA expression across the samples in the training set. In this step, 311 probes were filtered out, and 834 probes remained. Then, permutation tests were performed to identify miRNAs that were associated significantly with overall survival (OS). The permuted P value for each miRNA was corrected by multiple comparison correction using the Benjamini-Hochberg false discovery rate (FDR). The miRNAs with corrected permutation P values < .01 were selected as the candidate miRNAs. In this step, 825 probes were filtered out, and 9 probes (miR816
NAs) remained. Finally, 5 of the 9 remaining probes (miRNAs) with fold changes >1.5 between GBM and normal brain were identified as miRNAs that were associated significantly with survival. Protective or risky miRNAs were defined as those with hazard ratios for death 1. To assess the miRNAs that were identified for survival prediction, a risk-score formula for predicting survival was developed based on a linear combination of the miRNA expression level weighted by the regression coefficient derived from the univariate Cox regression analysis.12,13 The risk score for each patient was calculated as follows: risk score ¼ (0.00281 expression level of miR181d) þ (0.00045 expression level of miR-518b) þ (0.05976 expression level of miR-524-5p) þ (0.00079 expression level of miR-566) þ (0.00472 expression level of miR-1227). Patients with high risk scores are expected to have poor survival. According to the risk score (cutoff value, 2.29), patients in the training set were stratified into a high-risk group and a low-risk group. The risk-score threshold was determined by receiver operating characteristics (ROC) analysis with an area under the curve of 0.773. The same threshold was applied to the testing set and the independent cohort. The differences in OS and progression-free survival (PFS) between high-risk patients and low-risk patients were estimated by using the Kaplan-Meier method and 2-sided log-rank tests. Cox proportional hazards regression analyses were performed to assess the independent contribution of the miRNA signature and clinicopathologic variables to survival prediction. By using the same method that was used for the profiling data, we developed the risk score formula using the RT-PCR data normalized against U6 rRNA. The risk score was calculated as follows: risk score ¼ (14.692 expression level of miR-181d) þ (3.434 expression level of miR-524-5p) þ (16.1 expression level of miR-1227) þ (12.797 expression level of miR-518b) þ (12.109 expression level of miR-566). All statistical analyses were performed with MATLAB software (The MathWorks, Inc., Natick, Mass), and SPSS 13.0 for Windows (SPSS, Inc., Chicago, Ill) was used to conduct survival analyses. All tests were 2-tailed, and the significance level was set at P < .05. RESULTS Detection of the 5-MicroRNA Signature and its Association With Survival From the Training Set
The 82 patients with primary GBM were assigned randomly to either a training set (n ¼ 41) or a testing set (n ¼ 41). Cancer
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5-MicroRNA Signature and GBM Outcomes/Zhang et al
Figure 1. These Kaplan-Meier estimates of overall and progression-free survival in patients with glioblastoma multiforme were constructed the 5 microRNA signature. P values are indicated for the high-risk and low-risk groups stratified according to the 5 microRNA risk score in (A,B) the training set (41 patients), (C,D) the testing set (41 patients), and (E,F) the combined set (82 patients). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
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Figure 2. Analysis of the 5-microRNA (miRNA) signature risk score is illustrated for (A) patients in the training set and (B) patients in the testing set, including (Top) miRNA signature risk score distribution and (Middle) patient survival status and duration. Circled symbols indicate patients who had isocitrate dehydrogenase 1 (IDH1) mutations. (Bottom) In these color grams of miRNA expression profiles in patients with glioblastoma multiforme, the rows represent risky and protective miRNAs, and the columns represent patients. The black dotted lines on the top in A and B represent the miRNA signature cutoff.
There was no significant difference in clinicopathologic features between the 2 sets (Table 1). We used Cox regression to analyze each of 1145 miRNAs in the training set and identified 5 miRNAs (miR-181d, miR-518b, miR-524-5p, miR-566, and miR-1227) that were associated significantly with OS (P < .01) and had expression levels with a 1.5fold difference between GBM and brain control. We then applied the 5 miRNAs to develop a signature using the risk-score method. The 5-miRNA signature risk score was calculated for each of the 41 patients in the training set and then was used to divide them into a highrisk group and a low-risk group based on the cutoff value. We observed that patients with a high-risk miRNA signature had shorter median OS and PFS than patients with a low-risk signature: 381 days versus not reached (P ¼ .002) (Fig. 1A) and 218 days versus 516 days (P ¼ .006), respectively (Fig. 1B). Validation of the 5-MicroRNA Signature for Survival Prediction in the Testing and Combined Sets
We used the same risk-score formula and cutoff value obtained from the training set for 41 patients in the testing set and 82 patients of in the combined set. Similarly, patients with a high-risk miRNA signature had shorter median OS than patients with a low-risk miRNA signature: 382 days versus 563 days (P ¼ .029) for the testing set (Fig. 1C) and 382 days versus 591 days (P < .001) for the combined set (Tiantan cohort) (Fig. 1E). Also, patients with a high-risk signature had a shorter median PFS than patients with a low-risk signature: 257 days ver818
sus 325 days P ¼ .068; marginally significant) for the testing set (Fig. 1D) and 226 days versus 459 days (P ¼ .001) for the combined set (Fig. 1F). The distribution of patient risk scores, OS, and miRNA expression in GBM is illustrated in Figure 2 for the training set and the testing set (Fig. 2). Patients with high risk scores appeared to express higher levels of risky miRNAs (miR-518b and miR-566), and patients with low risk scores tended to express higher levels of protective miRNAs (miR-181d, miR-524-5p, and miR-1227). Patients with low risk scores survived longer than those with high risk scores. Additional Validation of the 5-MicroRNA Signature in an Independent Cohort and in the Cancer Genome Atlas Data Set for Survival Prediction
First, to confirm whether changes in expression levels of the 5 miRNAs measured both by profiling and by quantitative RT-PCR (qRT-PCR) analysis were consistent and convertible, we randomly selected 3 patients from each of the highrisk group and the low-risk group in the Tiantan cohort and measured the expression of the 5 miRNAs in those 6 patients by using qRT-PCR. We observed consistent changes in expression levels of the 5 miRNAs using both methods. Next, we applied qRT-PCR to validate the 5miRNA signature in the independent cohort. Because, in general, no ‘‘housekeeping’’ miRNAs were available to make the profiling data and qRT-PCR data convertible, to use the same cutoff value from the profiling data, we normalized the qRT-PCR data against the average Cancer
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5-MicroRNA Signature and GBM Outcomes/Zhang et al
Figure 3. These Kaplan-Meier estimates of overall and progression-free survival in patients with glioblastoma multiforme illustrate a risk score analysis using the 5 microRNA signature in the independent cohort based on (A,B,E) profiling data and (C,D,F) reverse transcriptase-polymerase chain reaction data. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
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expression of the 5 miRNAs in the 6 patient samples by qRT-PCR and then normalized against the average expression of the 5 miRNAs in the same 6 samples by profiling. After normalizing, expression levels of the 5 miRNAs were used to calculate patient risk scores. The patients were stratified into high-risk and low-risk groups based on the same cutoff point that was determined in the training set. The patients with high-risk signatures had significantly shorter median OS and PFS than the patients with low-risk signatures (OS: 263 days vs not reached; P ¼ .005 [Fig. 3A]; PFS: 151 days vs 366 days; P ¼ .002 [Fig. 3B]), consistent with findings in the Tiantan cohort. To determine whether the method of miRNA measurement could affect patient stratification according to the risk-score method, we used the independent cohort to compare profiling data and qRT-PCT data from the 5 miRNAs for patient stratification. By using the risk-score formula based on qRT-PCR data, we calculated the risk score for each patient. At a cutoff score of 0.79 (AUC, 0.738), the patients with a high-risk signature had significantly shorter median OS and PFS than the patients with a low-risk signature (OS: 263 days vs not reached; P ¼ .005 [Fig. 3C]; PFS: 211 days vs 366 days; P ¼ .009 [Fig. 3D]). Thirtythree of 35 patients (94.3%) were classified concordantly classified into a high-risk group and a low-risk group based on either the profiling data (Fig. 3A,B) or the qRT-PCR data (Fig. 3C,D), with disagreement for 2 patients between the 2 types of expression data (1 patients who had shorter survival was misclassified into the low-risk group by profiling, and another patient who had longer survival was misclassified into the high-risk group by the qRT-PCR data). Patient distribution is illustrated in Figure 3 according to the risk-score analysis based on profiling values (Fig. 3E) and qRT-PCR values (Fig. 3F). In the TCGA data set, we observed 3 of 5 miRNAs that were present in the TCGA set (miR-181d, miR518b, and miR-566) and used that 3-miRNA subset to validate 345 GBMs from the TCGA data set. First, we performed Z-score transformation on expression levels across the GBMs for each of the 3 miRNAs; then, we summed the Z-score–transformed expression levels of the 3 miRNAs into 1 score for each sample. Univariate Cox regression analysis indicated that the 3-miRNA subset was associated significantly with OS (P ¼ .003). By using the median value of the scores as the threshold, we divided GBMs into a high-risk group and a low-risk group. Kaplan-Meier analysis indicated that the 3miRNA subset significantly classified the patients with GBM into a high-risk group and a low-risk group (P ¼ .003) (Fig. 4). 820
Figure 4. Kaplan-Meier estimates illustrate overall survival for 345 patients with glioblastoma multiforme according to the 3-microRNA (miRNA) subset of the 5-miRNA signature from the Cancer Genome Atlas (TCGA) data set. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
The 5-MicroRNA Signature and Patient Survival Independent From Other Clinicopathologic Factors
We conducted univariate Cox regression analysis using clinical and genetic variables for the Tiantan cohort (Table 1) and observed that the variables 5-miRNA signature (risk score), extent of tumor resection, receipt temozolomide (TMZ) therapy, preoperative KPS score, and IDH1 mutation status were associated statistically with OS and PFS; however, the variables sex, age, and MGMT promoter methylation status were not associated with OS or PFS (Table 2). A multivariate Cox regression analyses with stepwise variable selection in the Tiantan and independent cohorts indicated that the 5-miRNA signature was an independent prognostic factor in the Tiantan cohort (OS: HR, 1.41; 95% CI, 1.19-1.66; P < .001; PFS: HR, 1.31; 95% CI, 1.12-1.53; P ¼ .001) (Table 2) and in the independent cohort (OS: HR, 2.57; 95% CI, 1.35-4.92; P ¼ .004; PFS: HR, 1.41; 95% CI, 1.05-1.89; P ¼ .025) (Table 3). TMZ therapy also was verified as an independent prognostic factor, because better survival was associated with the receipt of TMZ treatment (Tables 2 and 3). The 5-MicroRNA Signature and Patient Survival in the Temozolomide Treated and Untreated Subgroups
To assess the potential association of the 5-miRNA signature with the therapeutic outcome of TMZ treatment, we grouped the 117 patients with GBM into a TMZ-treated Cancer
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5-MicroRNA Signature and GBM Outcomes/Zhang et al
TABLE 2. Cox Hazard Regression Analysis of Clinicopathologic Factors and the Five-MicroRNA Signature (Risk Score) for Survival in the Tiantan Cohort (n ¼ 82) Univariate Cox Regression Variable Overall survival Sex Age Risk score Extent of resection TMZ chemotherapy Preoperative KPS score IDH1 mutation MGMT promoter methylation Progression-free survival Sex Age Risk score Extent of resection TMZ chemotherapy Preoperative KPS score IDH1 mutation MGMT promoter methylation
Multivariate Cox Regression
HR
95% CI
P
1.31 1.01 1.38 0.43 0.39 0.38 0.37 1.14
0.75-2.30 0.99-1.03 1.20-1.59 0.25-0.73 0.21-0.70 0.22-0.64 0.15-0.92 0.66-1.97
.340 .590 < .001 .002 .002
