Originally published as JCO Early Release 10.1200/JCO.2004.00.2253 on September 6 2005

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Gene Expression Profile Predicts Patient Survival of Gastric Cancer After Surgical Resection

Chiung-Nien Chen, Jen-Jen Lin, Jeremy J. W. Chen, Po-Huang Lee, Ching-Yao Yang, Min-Liang Kuo, King-Jen Chang, Fon-Jou Hsieh

From the Department of Surgery, Traumatology, Graduate Institute of Clinical Research, National Taiwan University Hospital and College of Medicine; Laboratory of Molecular and Cellular Toxicology, Institute of Toxicology, College of Medicine; Angiogenesis Research Center, National Taiwan University; Department of Applied Statistics and Information Science, Ming Chuan University, Taipei; and the Institutes of Biomedical Sciences and Molecular Biology, National Chung Hsing University, Taichung, Taiwan

Address reprint requests to Fon-Jou Hsieh, MD, Graduate Institute of Clinical Research, or King-Jen Chang, MD, PhD, Department of Surgery, National Taiwan University Hospital and College of Medicine, No. 7, Chung-Shan S. Rd, Taipei, Taiwan; e-mail: fjhsieh{at}ha.mc.ntu.edu.tw or kingjen{at}ha.mc.ntu.edu.tw.

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION Authors' Disclosures of... REFERENCES

 
PURPOSE: This study was conducted to characterize gene expression profile of survival in patients with surgically curable gastric cancer by using an in-house membrane microarray and developing a survival prediction model.

MATERIALS AND METHODS: Data of cDNA microarrays were obtained from 18 pairs of cancerous and noncancerous gastric tissues. Nine patients who survived > 30 months were identified as good survival, and the other nine, who survived < 12 months, were identified as poor survival. Supervised analysis was performed to identify a gene expression profile by good and poor survival. Semiquantitative reverse transcriptase polymerase chain reaction (RT-PCR) was used to confirm the microarray data in 10 patients with sufficient RNA. Using these 10 patients and another 10 patients selected randomly from 40 newly enrolled patients as the training group, the RT-PCR status of the confirmed genes was used for predicting good versus poor survival. Finally, the prediction model was tested in the remaining 30 newly enrolled gastric cancer patients.

RESULTS: A survival prediction model consisting of three genes (CD36, SLAM, PIM-1) was developed. This model could correctly predict poor or good survival in 23 (76.7%) of 30 newly enrolled patients, and yielded a specificity of 80% and a sensitivity of 73.3%. The survival rate of the patients predicted to have good survival was significantly higher than that of those predicted to have poor survival in the test group as a whole (N = 30; P = .00531) and in stage III patients (n = 16; P = .04467).

CONCLUSION: The semiquantitative RT-PCR gene expression profiling of three genes extracted from microarray study can accurately predict surgery-related outcome in gastric cancer patients.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION Authors' Disclosures of... REFERENCES

 
Gastric cancer is one of the most frequent cancers in the world, and it is the fourth most common in Taiwan. Early-stage disease tends to be diagnosed in countries in which endoscopic screening is more common; however, there still exist patients whose tumor is advanced at the time of diagnosis.¹ Patients with stage I disease have a good prognosis, and those with stage IV disease show a poor prognosis. Bewilderingly, the prognosis varies widely in patients with stage II or III disease for as of yet undetermined biologic reasons.²

Traditional clinicopathologic factors and several interesting molecules, including cell cycle regulation factors such as p27 or cyclin E,^3,4 cell adhesion molecules such as E-cadherin,⁵ angiogenic factors such as vascular endothelial growth factor and placenta growth factor,^6,7 oncogenes such as c-erbB2 and c-myc,⁸ and tumor suppressor genes such as p53,⁸ have been reported to correlate to the prognosis of gastric cancer patients. However, there exists inconsistency among different studies, and the reported parameters provided limited information about prognosis of individual patients because of complex biology of the disease.⁹ The cellular and molecular heterogeneity of gastric cancers and the large number of genes potentially involved in the multi-step process of gastric cancer pathogenesis emphasize the importance of studying multiple genetic alterations in concert.

Recent advances in the cDNA microarray technique that can investigate gene expression systematically enable us to visualize gene expression profiles in human tumors, and those gene expression profiles can help to identify gene activity patterns that can distinguish subclasses of gastric tumors. Gene profiling studies were also used to better stratify and select patients for adjuvant therapies who may be at higher risk for recurrence.^10-13 Recently, Gordon et al¹⁴ have shown that simple patterns of gene expression levels, using as few as four to six genes selected from microarray, are highly accurate in the outcome prediction of methothelioma.

Unfortunately, practical methods for identification of individuals with gastric cancer who are at risk for recurrence after surgical resection are not currently available. To explore the possibility of using a limited gene analysis as a predictor of outcome in resected gastric cancer and to find out genomic information related to the survival of gastric cancer patients, an in-house nylon membrane cDNA microarray with colorimetric detection system, which includes 328 genes known as cell cycle regulation factors, matrix proteinases, cell adhesion molecules, angiogenic factors, immunity related factors, oncogenes, and tumor suppressor genes, was constructed to characterize discriminative gene expression for survival, and to develop a quantitative model consisting of selected gene profile which is capable of predicting outcome of gastric cancer patients after curative resection.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION Authors' Disclosures of... REFERENCES

 
Summary of Experimental Design
Eighteen tumor and healthy pairs of gastric tissue samples were obtained from 18 patients with gastric cancer who underwent D2 gastrectomy without gross residual tumor at the National Taiwan University Hospital (Taipei, Taiwan) between 1995 and 2002. The tumor stage ranged from stage I to stage IV. Nine patients who died as a result of tumor recurrence within 12 months after surgery were identified as poor survival, and the other nine patients, who survived beyond 30 months after surgery were identified as good survival. The poor-survival group included two patients with stage II disease, four with stage III, and three with stage IV. The good-survival group included three patients with stage I disease, two with stage II, and four with stage III. There was a balance of stage II and III between survival groups. There was no stage I patient in poor-survival group, and no stage IV patient in the good-survival group. All patients did not receive postoperative chemotherapy and radiotherapy. Pair samples of tumor and healthy tissues of these 18 patients were dissected and frozen within 30 minutes of removal in a liquid nitrogen tank. Healthy mucosa samples were taken from areas of grossly normal mucosa located at least 3 cm away from tumor border. RNAs of these 18 pairs of the specimens were extracted and were used to perform microarray hybridizations; microarray data of these 18 pairs of samples were used to select gene profiles supervised by poor and good survival through a statistical algorithm. After genes characterizing survival were selected, semiquantitative reverse transcriptase polymerase chain reaction (RT-PCR) analysis of the selected genes was used to verify microarray data in 10 patients with sufficient RNA; thereafter, the genes with RT-PCR results consistent with microarray data were used for further data training and prediction model selection. In addition to these 10 patients, RT–PCR analyses of the selected genes characterizing survival were also performed in 40 newly enrolled patients. We assigned the RT-PCR status of the selected genes into categoric variables that were used in a logistic regression model to predict survival based upon 20 patients (10 patients with good survival; 10 patients with poor survival). To avoid the overfitting problem, these 20 patients (training samples) included 10 patients who had sufficient RNA to confirm microarray data and another 10 patients who were selected randomly from 40 newly enrolled patients. To assess the robustness, 1,000 possible sets of 20 training samples were generated. A stepwise approach, based on Akaike's information criterion (AIC), was used for model selection. A candidate prediction model was selected by using the smallest AIC of the stepwise process in each set of generated training samples. Then, the final effective prediction model, selected from 1,000 candidate prediction models, was the one with the largest AIC among 1,000 smallest AIC. RT-PCR analyses of the selected genes characterizing survival were then performed in the 30 remaining newly enrolled patients (ie, the test group), which included seven patients with stage I, five with stage II, 16 with stage III, and two with stage IV. The RT-PCR status of 30 patients of the test group was translated into the aforementioned categoric variables to test the predictive value of the model extracted from training group. There was no postoperative chemotherapy and/or radiotherapy for patients of either training or test group in this study. This is our standard practice for managing gastric cancer patients who underwent D2 gastrectomy without gross residual tumor in this hospital. Studies using human tissues were approved by and conducted in accordance with the policies of the institutional review board at National Taiwan University Hospital.

Microarray
We used an in-house nylon membrane cDNA microarray made of nylon membrane containing 384 spots and aligned by 16 spots in each row and 24 spots in each column with a spot diameter of 250 µM. It contains sequence-verified cDNA clones of 328 selected known human genes that are considered to be cancer related. These include oncogenes, tumor suppressor genes, apoptosis-related genes, matrix proteinase genes, angiogenesis-related genes, immune-related genes, and so on. The internal control genes included 16 plant genes and GAPDH. This cDNA microarray system and membranes (Boehringer, Mannheim, Germany) were prepared as previously described.¹⁵ These 328 sequence verified cDNA clones served as the hybridization targets.

RNA Extraction, Biotinylated Probe Preparation and Microarray Hybridization
Total RNAs were extracted with Trizol reagent (Invitrogen Life Technologies Inc, Carlsbad, CA). Thirty micrograms of the total RNAs derived from each gastric cancer tumor tissue and corresponding nontumor part were reversed transcribed and labeled with biotin. The microarray carrying the double-stranded cDNAs was prehybridized in 1 mL of hybridization buffer (5x RNA extraction standard saline citrate [lsqb]SSC[rsqb], 0.1% N-lauroylsarcosine, 0.1% sodium dodecyl sulfate [lsqb]SDS[rsqb], 1% blocking reagent mixture manufactured by Roche Molecular Biochemicals (Mannheim, Germany), and salmon-sperm DNA [50 µg/mL]) at 63°C for 1.5 hour. The biotin-labeled cDNA probes and hybridization solution (13 µL) containing human COT-1 DNA instead of salmon-sperm DNA were sealed with a microarray in a hybridization bag, and the bag was incubated at 63°C for 10 hours. The membrane was then washed with 2x SSC containing 0.1% SDS for 5 minutes at room temperature followed by three washes with 0.1x SSC containing 0.1% SDS at 63°C for 15 minutes each. After hybridization, the color reaction was initiated by incubating the membrane for 1 hour in 1 mL containing alkaline phosphatase-conjugated streptavidin (diluted 1:1,000; Roche Molecular Biochemicals), 4% polyethylene glycol (molecular weight, 8,000; Sigma Chemical Co, St Louis, MO), and 0.3% bovine serum albumin (BSA) in 1x phosphate-buffered saline (PBS) buffer. The substrate used for color development is BCIP/NBT (5-bromo-4-chloro-3-indolyl-phosphate/ nitro blue tetrazolium) substrate (Promega, Madison, WI). Color reaction was stopped with 1x PBS buffer that contains 20 µM EDTA.

Image Processing
After color development, the membrane was scanned and images taken by using a flat-bed scanner (UMAX Fremont, CA MagicScan at 3,000 dpi). This scanner provided 3,000-dpi resolution and was suitable for our arrays with 384 elements and the resulting database was stored in a tagged image file format (Tiff). To quantify the expression levels of the genes, the digitized images were measured by GenePix Pro software program (Axon Instruments, Foster City, CA).

Microarray Data and Statistical Analysis
Eighteen pairs of patient samples with clinical information were submitted for microarray studies. After the experimental processes, the color of a spot yielded by the enzymatic reaction was converted to gray levels, of which the brightness value was assigned to each pixel of that spot ranging from 0 to 256 (from black, through shades of gray, to white). The spots' expression values of the microarray generated by the GenePix Pro 2.0 software by scanning the microarray images with UMAX 6,000 were collected as an Excel file (Microsoft, Redmond, WA). Expression level of each gene on the microarray was defined as the log ratio (base 2) of the spot expression value of each gene of tumor tissue to that of nontumor tissue. Eighteen patients consisting of poor- and good-survival groups were used specifically to characterize gene expression of survival. To identify genes whose expression levels could be used to discriminate patients with good and poor survival, a three-step supervised classification method was used to extract the most significantly differentially expressed genes between these two survival groups (Fig 1). In the first step, to avoid the systematic error at each microarray sample, the nonlinear LOWESS method,^16,17 which fits a curve to the log ratios using robust locally weighted regression, was used to normalize the log ratios of the 328 genes to a LOWESS curve fitting through the MA plot. Second, to extract the significantly regulated genes among all 18 microarray samples, the fold-change method¹⁸ which defined the threshold of the significance for expression microarrays, was used to define the regulated genes whose fold-change (normalized log ratios) are greater in magnitude than one at each microarray sample. Among all 18 microarray samples, the significantly regulated genes whose fold-changes are greater in magnitude than one for at least two samples among 18 samples were extracted. Third, to extract the most significant differentially expressed genes between the two survival groups, the multiple permutation test^17,19 was used to test simultaneously all the significantly regulated genes filtered at step two, and the adjusted P value for each gene was obtained. The permutation test, reflecting the actual random assignment, is to obtain the original P value of each gene by resampling and recomputing the test statistic several times and comparing the test statistics to the initial test statistics. The multiple test is a method to control the probability of producing incorrect test conclusions (false positives and false negatives) for the whole gene set from original P value to the adjusted P value by the modified statistical algorithm. To assess the internal consistency of the 18 samples, the leave-one-out cross validation (CV) method¹³ was used to generate the 18 CV samples, and the differentially expressed genes were extracted whose adjusted P value was < .05 family-wise error rate for all 18 CV samples.

View larger version (53K): [in this window] [in a new window]   Fig 1. Three steps for selection of differential expression genes using 18 microarray samples: LOWESS normalization, extraction of 141 regulated genes, and extraction of the six most significant differential expression genes.

Translation of Semiquantitative RT-PCR Data Into Survival Prediction Model
To establish a survival prediction model for gastric cancer patients, the differentially expressed genes whose consistent rates between microarray and RT-PCR results were greater than 60% or Spearman rank correlation coefficient showed significant and P < .05 were selected for prediction model training. Then, we assigned one of four categories to RT-PCR expression data for each sample. The frequencies of the four categoric RT-PCR results at each predictor in the training group were used to establish the prediction model. In the testing samples, each predictor was assigned the same frequencies of the four categoric PCR results as those obtained in the training group. Next, we applied the logistic regression together with stepwise model selection to find the effective prediction model by the AIC.²¹

Finally, we applied this prediction model to a test group of 30 pair samples and assessed the accuracy of the model by evaluating the sensitivity, specificity, positive prediction value, and negative prediction value on the test group as a whole and the stage III patients. The Kaplan-Meier survival model was also used to estimate the survival of patients stratified by this prediction model. The log-rank test was used to statistically assess significance between survival curves. All differences were considered statistically significant if the P value was < .05.

	RESULTS

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Gene Selection, Confirmation, and Prediction Model Selection
The three-step supervised classification method was applied to the 18 patients consisting of poor- and good-survival groups to search for gene expression profile of survival (Fig 2). At the normalization step, we used 328 genes to normalize the log ratios at each sample. One hundred forty-one genes are extracted with fold-change method after normalization of log ratios. Finally, the multiple permutation test together with CV was used to select the six most significant differentially expressed genes. The selected genes included CD36 antigen, SLAM, TFAP, insulin-like growth factor 1 (IGF-1), PIM-1 oncogene, and tissue inhibitor of metalloproteinase-4 (TIMP-4).

View larger version (47K): [in this window] [in a new window]   Fig 2. Eighteen paired microarrays were used to select gene profile supervised by poor and good survival through a statistical algorithm, and six genes were selected. After confirmation by using semiquantitative reverse transcriptase polymerase chain reaction (RT-PCR), four genes (SLAM, CD36, PIM-1, and TFAP) were included in prediction model training. Through stepwise model selection, final logistic regression model defined by SLAM, CD36, and PIM-1 was selected and was used to predict survival of another 30 gastric cancer patients.

View larger version (33K): [in this window] [in a new window]   Fig 3. (A) Representative reverse transcriptase polymerase chain reaction (RT-PCR) results of six genes and internal control of ß-actin. (B) Validation of microarray data for six selected genes by semiquantitative RT-PCR. The PCR products for six genes were semi-quantitatively analyzed with the use of National Institutes of Health Image 1.62 and calculated as levels relative to ß-actin. N, healthy; T, tumor; G, good survival; P, poor survival.

Translation of Expression of CD36, SLAM, TFAP, and PIM-1 Into Prediction Model of Survival
Four types of paired RT-PCR status for each selected genes could be identified in all blotting results. We assigned one of the following four categories to RT-PCR expression data for each sample: Tumor Healthy, Healthy Tumor, Tumor and Healthy equally positive, and Tumor and Healthy equally negative (Fig 4). The frequencies of the four categoric RT-PCR results at each predictor in the training group were used to establish the prediction model. In the testing samples, each predictor will be assigned the same frequencies of the four categoric PCR results as those obtained in the training group. The logistic regression together with stepwise model selection was used to select the prediction model by the AIC criteria, and the most effective logistic model consisting of three genes (CD36, SLAM, and PIM-1) was obtained as follows:

where is the probability of "poor survival status." Good survival (defined as survival time > 30 months) was predicted when 0.5. Poor survival (defined as survival time < 12 months) was predicted when >0.5. The SEs of the logistic regression coefficients are 0.411 for CD36, 0.436 for SLAM, and 0.173 for PIM-1.

View larger version (41K): [in this window] [in a new window]   Fig 4. Four representative examples of paired reverse transcriptase polymerase chain reaction (RT-PCR) status of each selected gene. (A) A total of four types of paired RT-PCR status could be identified in all blotting results. They were "expression level of tumor was higher than that of healthy," "expression level of tumor was lower than that of healthy," equally positive expression, and both negative expression. (B) The paired RT-PCR status of each gene could be identified easily as one of four types and was assigned to a categoric variable.

View larger version (16K): [in this window] [in a new window]   Fig 5. The survival curves of patients predicted to have good and poor survival in whole testing group and stage III subgroup. (A) The survival rate of the whole testing group of patients predicted to have good survival was significantly higher than that of the patients predicted to have poor survival (P = .00531). (B) The survival rate of the stage III patients predicted to have good survival was significantly higher than that of patients predicted to have poor survival (P = .04467).

	DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION Authors' Disclosures of... REFERENCES

Initially, six genes were selected from microarrays in this study. They were SLAM, CD36, PIM-1 oncogene, TFAP, IGF-1, and TIMP-4. After confirmation by using semiquantitative RT-PCR, SLAM, CD36, PIM-1 and TFAP were included in prediction model training because of their high consistency rate with microarray data. Finally, the most effective prediction logistic regression model defined by SLAM, CD36, and PIM-1, which can predict the survival of gastric cancer patients with reasonable efficacy, was achieved. Among those three genes, SLAM is a CD2-related surface receptor expressed by activated T cells, B cells, and dendritic cells. Th0/Th1 immunity, which is usually impaired in gastric cancer patients, could also be induced by SLAM to enhance proliferation and cytotoxic ability of CD8⁺ tumor-specific lymphocytes.^27-29 Although the real role of SLAM in tumor-associated immunity of gastric cancer patients remains unclear, it seems to have potential effects on anti-tumor immunity and deserves further investigation. CD36 is a trans-membrane receptor that regulates apoptosis and angiogenesis in response to its ligand thrombospondin-1 (TSP-1). TSP-1 is localized to tumor-associated extracellular matrix, and CD36 expressed on surface of tumor cells.³⁰ The regulation of CD36 expression in tumor cells may play an important role in tumor growth, metastasis and angiogenesis.³¹ CD36 overexpression, which decreased stromal vascularization by induction of apoptosis of vascular endothelial cells, is correlated with better prognosis of colon cancer.³² However, there existed conflicting reports in the association of CD36 expression with angiogenesis and patient survival in head and neck cancer.³³ PIM-1, a product of serine/threonine kinase which can be induced in gastric epithelial cells by Heliobacter pylori infection, may be involved in gastric carcinogenesis.³⁴ PIM-1 also plays an important roles in proliferation, differentiation and maturation of T cells, which may be associated with tumor immunity.³⁵ PIM-1 induced by hypoxia is involved in drug resistance and tumorigenesis of solid tumor cells and leads to genomic instability.³⁶ It is also required in endothelial cells for vascular endothelial growth factor-A–dependent proliferation and migration.³⁷ Recently, the expression of PIM-1 has been shown to correlate significantly with measures of clinical outcome in prostate cancer.³⁸ The three genes selected in our prediction model may be, in some way, involved in tumor angiogenesis and tumor immunity, which is closely related to patient survival. However, a wide range of other biologic processes and complex interactions among different cell types in cancer tissue should also modulate cancer biology and result in heterogeneity of disease. None of these genes has been reported to correlate with clinical outcome in gastric cancer. Further functional and clinical studies of these genes are worthwhile, and it is more likely that these genes in isolation have only limited predictive power, which highlights the need for a genomic approach based on the expression of multiple genes in our attempts of disease subtyping.

To our knowledge, this is the first study to use gene expression profiling techniques to develop a survival prediction model for human gastric cancer. In our study, a data acquisition platform of semiquantitative RT-PCR was developed and used to validate this model in an independent cohort. The use of RT-PCR–based gene expression patterns to predict outcome in gastric cancer patients overcomes several major obstacles that hinder the clinical use of microarray. Unlike other widely accepted supervised learning techniques with similar predictive accuracy,^10,13,24-26 this prediction model utilizes data from RT-PCR status to create categories of expression among the three genes in paired samples. Because of its independence of microarray platform after gene selection, our methodology requires only small quantities of RNA (as little as 2 µg when using RT-PCR) and can be performed easily in a common laboratory. In this study, microarray data normalized with the LOWESS method to avoid the systematic error within each microarray sample that might be overly normalized. Therefore, verification of selected genes with RT-PCR is essential in microarray study. The overfitting problem is a crucial issue in the selection process of the prediction model. We used the randomly generated samples to overcome this potential pitfall and established the three-gene prediction model, which had better sensitivity and specificity than the models with one or two genes.

The prognostic tool described herein could dramatically influence the current clinical treatment of gastric cancer patients with stage III disease by allowing the identification of the patients who are unlikely to respond to conventional surgical treatment only. Adjuvant chemotherapy has been reported to have marginal effect for overall gastric cancer patients with D2 gastrectomy (gastrectomy with D2 lymphadenectomy). If survival outcome of gastric cancer patients can be reasonably predicted, adjuvant therapies may help those patients who have high probability of poor survival, whereas those who have high probability of good survival can be spared from the adverse effects of adjuvant therapies. Although whether the patients who are predicted to have high probability of poor outcomes really benefit from adjuvant therapies remains unknown, the results of this work, if confirmed prospectively in a larger patient population, could be helpful in the development of meaningful clinical trials of new compounds or regimens in advanced gastric cancer patients after surgical resection, especially in those with stage III disease, by helping to select suitable candidates.

In the coming era of personalized medicine, we believe that molecular profiling attempts like this study will be the basis of individualized therapy of gastric cancer. In conclusion, this report demonstrated that the survival of individual gastric cancer patients can be predicted with reasonable accuracy by using a simple RT-PCR–based gene expression profile and a logistic regression model. Our methodology is one of the applicable models and might be important to both patients and their doctors in terms of life planning and therapeutic strategies.

	Authors' Disclosures of Potential Conflicts of Interest

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION Authors' Disclosures of... REFERENCES

 
The authors indicated no potential conflicts of interest.

	Acknowledgment

 
We thank Professor Chien C. Chang for his helpful discussion and comprehensive suggestion in preparing this manuscript. We also thank research assistant Su-I Chen for her excellent technique.

	NOTES

 
Supported by grant from National Science Council and Department of Industrial Technology, Ministry of Economic Affairs, Taipei, Taiwan.

F.J.H. and K.J.C. are joint senior authors.

Terms in blue are defined in the glossary, found at the end of this issue and online at www.jco.org.

Authors' disclosures of potential conflicts of interest are found at the end of this article.

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Submitted October 13, 2004; accepted July 12, 2005.

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