Secreted ERBB3 Isoforms Are Serum Markers for Early Hepatoma in

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Secreted ERBB3 Isoforms Are Serum Markers for Early Hepatoma in Patients with Chronic Hepatitis and Cirrhosis Sen-Yung Hsieh,*,†,‡,§,|| Jung-Ru He,†,§ Ming-Chin Yu,^ Wei-Chen Lee,^ Tse-Chin Chen,# Shao-Jung Lo,† Rabindranath Bera,† Chang-Mung Sung,‡ and Cheng-Tang Chiu‡ †

Liver Research Unit, ‡Department of Gastroenterology and Hepatology, ^Department of General Surgery, Department of Anatomic Pathology, and §Clinical Proteomics Center, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan Chang Gung University School of Medicine, Taoyuan 333, Taiwan

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bS Supporting Information ABSTRACT: Most hepatocellular carcinoma (HCC) is generated from chronic hepatitis and cirrhosis. To discover new markers for early HCC in patients with chronic hepatitis and cirrhosis, we initiated our search in the interstitial fluid of tumor (TIF) via differential gel electrophoresis and antibody arrays and identified secreted ERBB3 isoforms (sERBB3). The performance of serum sERBB3 in diagnosis of HCC was analyzed using receiver operating characteristic curves (ROC). The serum sERBB3 level was significantly higher in HCC than in cirrhosis (p < 0.001) and chronic hepatitis (p < 0.001). The accuracy of serum sERBB3 in detection of HCC was further validated in two independent sets of patients. In discrimination of early HCC from chronic hepatitis or cirrhosis, serum sERBB3 had a better performance than alpha-fetoprotein (AFP) (areas under ROC [AUC]: sERBB3 vs AFP = 93.1 vs 81.0% from chronic hepatitis and 70.9 vs 62.7% from cirrhosis). Combination of sERBB3 and AFP further improved the accuracy in detection of early HCC from chronic hepatitis (AUC = 97.1%) or cirrhosis (AUC = 77.5%). Higher serum sERBB3 levels were associated with portal-vein invasion and extrahepatic metastasis of HCC (p = 0.017). Therefore, sERBB3 are serum markers for early HCC in patients with chronic hepatitis and cirrhosis. KEYWORDS: hepatoma, HCC, tumor interstitial fluid, serum biomarker, proteomics, ERBB3, EGFR

’ INTRODUCTION Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death worldwide.1 Despite great advances in diagnosis and treatment in the past decade, the long-term outcome of patients with HCC remains very poor.2 4 The 5-year survival rate is lower than 50%. The primary cause of this undesirable outcome is late diagnosis.4,5 Most HCC is generated from chronic hepatitis, particularly with cirrhosis. Alpha-fetoprotein (AFP) has been used as a serum marker for HCC for decades. However, the serum AFP level is frequently elevated in chronic hepatitis and cirrhosis without HCC, while more than 30% of HCCs, particularly at an early stage, are not associated with elevation of serum AFP.6 These result in high false positive and high false negative rates by using AFP to diagnose HCC in patients with chronic hepatitis and cirrhosis.6,7 Therefore, the most challenging task in diagnosis of HCC is to discriminate early HCC from chronic hepatitis and cirrhosis. On the other hand, serum Lens culinaris agglutinin-reactive AFP (AFP-L3) and des-gamma carboxyprothrombin (DCP) are potential markers for diagnosis of early HCC.7 12 However, their performance in discriminate early HCC from cirrhosis remains to be addressed. To improve the long-term survival of patients with HCC, there is an urgent r 2011 American Chemical Society

need to identify new serum markers for early diagnosis of HCC in patients with chronic hepatitis and cirrhosis. Proteomics with the aids of recent accomplishments in genome projects, techniques of mass spectrometry, and introduction of bioinformatics has sufficiently advanced to allow indepth analysis on proteome alterations in disease, so-called “disease proteomics”,13,14 which fosters a better understanding of pathogenic processes, development of new biomarkers for early diagnosis of disease, and acceleration of drug development.15 However, considering the complexity of protein components in serum/plasma, tremendous dilution, and rapid clearance of all candidate markers shed from cancer tissues, search for new serum markers for human disease remains a great challenge.16,17 Therefore, disclosure of new sources for novel serum biomarkers is mandatory. Potential cancer markers might be derived not only from tumor cells but also from interactions between tumor cells and their microenvironment. As a medium of communication between circulation and tumor cells, interstitial fluid of tumor tissues (TIF) should contain all of candidate serum markers with Received: May 31, 2011 Published: August 31, 2011 4715

dx.doi.org/10.1021/pr200519q | J. Proteome Res. 2011, 10, 4715–4724

Journal of Proteome Research much higher concentrations than those in serum/plasma. It is, therefore, tempting to speculate that TIF is an ideal source for tumor marker discovery.18 20 Herein we report our studies by using the interstitial fluid from tumors (TIF) and their matched nontumor liver tissues (NIF) as the source of serum marker discovery for HCC. To have an accurate quantitative comparison of proteins between paired TIF and NIF and across samples from different patients, we used 2-dimensional difference in gel electrophoresis (2D-DIGE).21 To identify potential candidate markers at low abundance, we used antibody arrays. The secreted isoforms of ERBB3 (sERBB3), a member of the epidermal growth factor receptor (EGFR)/ERBB family, was found to be significantly up-regulated in TIF. Further studies disclosed that sERBB3 was shed into bloodstream and could be better serum markers than AFP in discrimination of early HCC from chronic hepatitis and cirrhosis.

’ MATERIALS AND METHODS Sample Collection

Tumor and adjacent nontumor liver tissues were collected from 8 patients who underwent hepatectomy for HCC at our hospital. The Internal Review Board for Medical Ethics of Chang Gung Memorial Hospital approved the specimen collection procedures, and informed consent was obtained from each subject. Tumor and nontumor liver tissues were confirmed by histopathological examination.22 Serum samples were collected before treatment for HCC from our liver clinics (set 1for primary screening, Table S1, Supporting Information) and from the tissue bank of Chang Gung Memorial Hospital (set 2 and set 3 for validation). Chronic viral hepatitis was defined by detectable serum hepatitis B surface antigen (HBsAg), antibodies against hepatitis C virus, or both for longer than 6 months. Diagnosis of cirrhosis was based on histopathological findings or the medical record of symptoms and signs of portal hypertension. To focus on differentiation between nontumor and early stage tumor, and between tumor without and with portal vein invasion and extrahepatic metastasis, we stratified HCC into four stages: T1, solitary e2 cm, without vascular or biliary invasion; T2, solitary >2 cm or multiple, without vascular or biliary invasion; T3, tumors with portal vein invasion; and T4, tumor with direct invasion to adjacent organs and/or distal metastasis. The histopathological grade of HCC was defined as that by Edmondson and Steiner: well-differentiated and moderately differentiated tumors were defined as grades 1 and 2; poorly differentiated and undifferentiated tumors were defined as grades 3 and 4.23 Preparation of Interstitial Fluid of Tumor (TIF) and the Interstitial Fluid of Nontumor Liver Tissues (NIF)

The protocol of tissue interstitial fluid extraction was modified from Celis’ method.18 About 0.2 0.3 g of fresh tissue was cut to small pieces (about 1 3 mm3) in PBS, washed twice with PBS, and then incubated with PBS for 1 h at 37 C in a humidified CO2 incubator. The samples were then centrifuged at 1000 g for 2 min to remove cell debris followed by further centrifugation of the supernatant at 20 000 g for 20 min at 4 C to collect the final extract. After determining the protein concentration, the samples were aliquot and snap-frozen in liquid nitrogen and stored at 80 C until use. 2D-DIGE and Image Analysis

Interstitial fluid was precipitated using the 2D Clean-up kit (GE Healthcare, Waukesha, WI) and resuspended in 2D-DIGE

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lysis buffer containing 7 M urea, 2 M thiourea, 25 mM Tris-HCl (pH 8.5), and 4% CHAPS. Fifty microgram aliquots of each interstitial fluid sample were labeled with 400 pmol CyDyes on ice for 30 min in the dark according to the manufacturer’s protocol (GE Healthcare). Paired TIF and NIF were labeled with Cy5 and Cy3 respectively. To avoid labeling bias, reverse labeling TIF and NIF with Cy3 and Cy5, respectively, was performed. In addition, an equal amount of all paired TIF and NIF was pooled and labeled with Cy2 and was included in all gel runs as the intergel controls, so that the abundance of each protein spot on a gel can be measured relative to its corresponding spot in the internal standard present on the same gel. Protein 2D-gel electrophoresis (2DE) was performed as described previously.24 Briefly, each pooled sample was applied to 17-cm IPG strips for isoelectric focusing by Protean IEF Cell (BioRad) according to the manufacturer’s instructions. After treatment with 2.5% iodoacetamide, the strips were placed on a 12.5% polyacrylamide gel slab for separation in the second dimension at a current setting of 5 mA/gel for the initial 1 h and 10 mA/gel thereafter. Simultaneously, a preparative gel containing 450 μg of each pooled, unlabeled TIF and NIF sample was subjected to 2D-gel electrophoresis and stained with Sypro Ruby (Invitrogen) for protein visualization and spot picking for protein identification by mass spectrometry (MS). Unsupervised hierarchical clustering analysis was used to justify the unique protein profiles between TIF and NIF and an accurately discrimination of TIF from NIF was obtained.25 The number of proteins for classification was further reduced to 35 via one-way analysis of variation (ANOVA, p < 0.05). Proteins were visualized with the fluorescence scanner (ProXPRESS 2D scanner, Perkin-Elmer). Images were processed using an analyzing software (Progenesis Workstation, Version 2005; Nonlinear Dynamics, Durham, NC). Spot warping, matching, background subtraction, normalization, and filtering across gels were performed automatically using the Progenesis software package and verified manually. Matching between gels was performed using the internal pool included in each gel. Spots with volume less than 0.01% were excluded in the subsequent analysis. Relative protein expression levels of matched protein spots were systematically compared for differences across gels using paired Student’s t-test p-values for each protein across the different gels. Protein spots that showed at least 2-fold changes and p < 0.01 (Student’s t-test) in abundance between TIF and NIF were selected for further protein identification by MS. The data about the volumes of each corresponding protein spots with their IDs across gels derived from 8 pairs of TIF and NIF are provided as Supporting Information (Table S2 and Table S3). Protein Identification by Mass Spectrometry (MS)

Protein identification was analyzed by using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS, and further validated by MS/MS24 (Supporting Information). Antibody Arrays

To detect cytokines, growth factors and their receptors, and extracellular proteases in the interstitial fluid, we used the RayBio Human Cytokine Array G Series 2000 (RayBiotech, Inc.). Each array was incubated with 200 μg of protein in each interstitial fluid at 4 C overnight, and bound antigens were specifically recognized by the biotin-conjugated antibodies followed by detection with Cy3-conjugated streptavidin according to the manufacturer’s instructions. The signals were imaged and analyzed by using a confocal scanner chip reader and the packed software (Axon GenePix 4000B). The sensitivity of the antibodies 4716

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present in the arrays ranges from 1 2000 pg/mL (for further details, see www.raybiotech.com/human_array_sensitivity.pdf). Immuno-blotting Analysis

Immuno-blotting assays were performed using the protocols as previously described.26 In brief, cells or tissue samples were homogenized and lysed in a buffer containing 50 mM Tris-HCl [pH 8.0], 120 mM NaCl, 0.5% NP-40, 0.25% Na deoxycholate, 1 mM DTT, 2 mg/mL aprotinin, and 2 mg/mL leupeptin. Thirty micrograms of the protein lysate were then subjected to electrophoresis followed by immuno-blotting analysis using polyclonal antibodies raised against different domains of ERBB3 (Abcam ab5470 for extracellular domain and Abcam ab34641 for intracellular domain, respectively). After being stripped, the same blots were used to detect β-actin, which was used as an input control. Enzyme-linked Immunosorbent Assays (ELISA)

The DuoSet ELISA Development kit (R&D Systems, Inc. Minneapolis, MN) was used to develop the ELISA for detection of serum sERBB3. Experiments were conducted according to the manufacturer’s instructions. In brief, a monoclonal antibody against the extracellular domain of human ERBB3 (aa 20 643) (MA3481, R&D Systems) was used as the capture antibody, and the other biotin-conjugated monoclonal antibody against the extracellular domain of human ERBB3 (aa 20 643)(BMA348, R&D Systems) was used as the detection antibody. A recombinant human ERBB3 purchased from R&D Systems was used as the standard for quantification of ERBB3 concentration in each ELISA with a 7-point standard curve in 2-fold serial dilutions. Quantification was finally determined by adding the substrate to the biotin-bound horseradish-peroxidase conjugated streptavidin. On the other hand, serum AFP concentration was determined using a commercially available kit (Abbott Laboratories). Cell Migration and Invasion Assays

Cell migration and invasion assays were conducted as previously described27 (Supporting Information). Split Point Analysis

To evaluate the accuracy of combined serum ERBB3 and AFP in detection of HCC, we used the split-point analysis to generate a score-based classification method.28 We first used the highest Youden index as the cutoff value (split point) to define 2 intervals: one for non-HCC and the other for HCC. A score of 0 is assigned to an individual if the related observation falls in the non-HCC interval; otherwise, a score of 1 is assigned. Overall, an individual’s assigned score is the sum of the 2 assigned scores for serum ERBB3 and serum AFP. Therefore, the range of such scores is between 0 and 2. The ROC curve was then used to evaluate the performance of split-point analysis in discriminating HCC from non-HCC. Receiver Operating Characteristic (ROC) Analysis

To select candidate HCC serum markers and to evaluate the accuracy of serum markers in distinguishing HCC from non-HCC, we used ROC curve analysis. ROC curve is a graphical plot of true positives (sensitivity) vs false positives (1 specificity), for serum candidate markers as its discrimination threshold is varied. The area under the ROC curve (AUC) is equal to the probability that this classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one. The ROC curve and AUC were used to screen candidate biomarkers of HCC, and only an AUC of >0.86 in the primary screening was selected for further evaluation as HCC serum markers.

Figure 1. Flowchart of the study to identify novel serum markers for human HCC using interstitial fluid of hepatoma tissues as the source for candidate marker discovery. Step1: Interstitial fluid was prepared from 8 pairs of surgically removed hepatoma and their matched nonhepatoma tissues. Step 2: Comparative proteomics via 2D-DIGE and antibody arrays was conducted to identify proteins that were up-regulated in TIF. Step 3: Primary screening by using ELISA in set 1 patients for the relative concentrations of the candidate markers in the sera of patients with HCC, chronic hepatitis and cirrhosis. Step 4: For those candidate markers with their serum concentrations significantly higher in HCC and p < 0.001, secondary validation was performed using two independent groups of patients. Step 5: The accuracy of candidate markers along with AFP in discrimination of HCC from chronic hepatitis and cirrhosis, individually or in combination, was compared.

Statistical Analyses

To compare serum sERBB3 levels between HCC, cirrhosis, or hepatitis B patients, we used 2-sample t-test for any 2 groups. To correlate serum ERBB3 levels with tumor stages or histological grades, we used Kruskal Wallis test; on the other hand, to compare serum sERBB3 levels between any 2 tumor stages or histological grades, we used the Mann Whitney test or Wilcoxon rank-sum test.

’ RESULTS Search for Novel Biomarkers in the Interstitial Fluid of Human Hepatoma Tissues

The flowchart of the process for the discovery of new serum HCC markers is shown in Figure 1. Because TIF contains all candidate markers in much higher concentrations, we started our search for novel HCC markers in the interstitial fluid of hepatoma tissues (TIF) instead of serum or plasma. Tissue interstitial fluid was prepared from 8 pairs of hepatoma and their corresponding nontumor liver tissues (NIF) along with a normal liver tissue as the control (step 1). To minimize gel to gel variation and to increase accuracy and reproducibility, we used 2D-DIGE to analyze paired TIF and NIF. To detect low abundance proteins, antibody arrays were used (Figure 1, step 2 and Figure 2A E). Proteins with more significantly increased in TIF than in NIF were

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Figure 2. Detection of ERBB3 proteins in the interstitial fluid of tumor tissues. (A) and (B) are representative 2D-DIGE images of proteins extracted from a pair of tumor (TIF, Cy3-labeled, green) and nontumor liver tissues (NIF, Cy5-labeled, red). (A) Representative 2D-DIGE image. Local regions with clusters of differentially expressed proteins are indicated. (B) Corresponding image of TIF. Proteins that were upregulated in TIF are labeled in red colon. (C) Unsupervised hierarchical clustering analysis accurately discriminates TIF from NIF derived from 9 pairs of tumor and nontumor liver tissues via the proteins identified by 2D-DIGE. The heatmap shows the results of hierarchical clustering analysis of TIF and NIF versus a panel of 39 proteins selected by p < 0.05, one-way ANOVA. (D) and (E) Representative images of antibody arrays. The duplicated spots labeted #1 in red color are ERBB3. (F) Immunoblotting for ERRB3 proteins in HCC tissues (lanes 1 8) and in a normal liver tissue (lane 9). Full-length ERBB3 with MW of approximately 180 kDa was detected in all HCC tissues but not in the normal liver tissue (lane 9). (G) Immunoblotting for the secreted isoforms of ERBB3 in the interstitial fluid of HCC tissues (lanes 1 8) and of a normal liver tissue (lane 9). Specific proteins with MW of approximately 40 55 kDa were detected in the interstitial fluid of HCC by a polyclonal antibody raised against the extracellular domain of ERBB3 but not by a polyclonal antibody raised against the intracellular domain of ERBB3 (data not shown). These secreted isoforms of ERBB3 were not detected in the interstitial fluid extracted from a normal liver (lane 9).

selected for evaluation as candidate markers in serum via ELISA to examine their relative concentrations in set 1 patients containing

113 cases with HCC, 47 cases with cirrhosis, and 64 cases with chronic hepatitis (Figure 1, step 3: primary screening). Those 4718



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Figure 3. Serum sERBB3 concentrations in patients with HCC, cirrhosis, and chronic hepatitis. (A) Distribution of serum concentrations of sERBB3 in set 1 patients including 113 cases with HCC, 47 cases with cirrhosis without HCC (LC), and 64 cases with chronic hepatitis without cirrhosis or HCC (CH). (B) Mean concentrations and the standard error of the means of (A) are shown. (C), (D) and (E) ROC curves of serum sERBB3 and AFP in discriminating HCC from nonHCC in two additional sets of patients. (C) Set 2 comprised 57 patients with HCC and 35 patients with chronic hepatitis without HCC. (D) Set 3 comprised 56 patients with HCC and 36 patients with chronic hepatitis without HCC. (E) ROC curves derived from patients pooled from set 2 and set 3. Of note, set 2 and set 3 patients used as independent validation sets were collected from the departments and hospitals other than our department where set 1 patients were collected.

with higher serum concentrations in HCC patients with p values 2 cm or multiple tumors and no vascular or biliary invasion or metastasis); T3, 17 cases of HCC with portal vein invasion; T4, 11 cases of HCC with extrahepatic invasion and/or metastasis. (B) Serum sERBB3 levels in different histological grades of HCC. Hisotopathological grades are based on Edmondson-Steiner system: from 1 to 4 represents from well differentiated, moderately differentiated, poorly differentiated to undifferentiated HCC.23

carcinogenesis and its relatively higher abundance in all of the TIF samples than in the corresponding NIF (Figure 2D and E). We next used immunoblotting assays to examine ERBB3 in HCC tissues and their TIF (Figure 2F G). Full-length ERBB3 (∼180 kDa) was detected in all HCC tissues (Figure 2F, lane 1 8), but not in a normal liver (Figure 2F, lane 9). In contrast, the major isoforms of ERBB3 detected in TIF were about 40 55 kDa (Figure 2G, lanes 1 8). These secreted ERBB3 isoforms (sERBB3) in TIF were only detected by antibodies against the extracellular domain but not by antibodies against the intracellular domain of ERBB3 (data not shown), suggesting that sERBB3 contains only the extracellular domain of ERBB3. sERBB3 was barely detectable in the interstitial fluid of a normal liver tissue (Figure 2G, lane 9).

respectively). Serum sERBB3 apparently had a better performance than AFP in identifying HCC (Figure 3C E). In addition, the performance of serum sERBB3 as the classifier in detection of HCC was equally good in patients with chronic hepatitis B (AUC = 96.4%) and patients with chronic hepatitis C (AUC = 98.1%) (Supporting Information, Figure S1). We also used the data from set 1 patients to optimize the cutoff value of serum sERBB3 concentration for the detection of HCC. Based on Youden’s index (the maximum of sensitivity + specificity 1), the optimized cutoff value was 792 pg/mL, which was then used to identify HCC. Accordingly, a sensitivity of 78.4% and a specificity of 96.9% for the detection of HCC in set 2 + 3 patients were obtained. For detection of early HCC, a sensitivity of 71% and a specificity of 96.9% were obtained.

Serum sERBB3 as a Candidate Marker for Detection of HCC

Serum sERBB3 in Detection of Early HCC

To examine whether sERBB3 was shed into the circulation as a candidate serum marker for HCC, we used ELISA to assay its concentration in the sera of 224 cases (Set 1; Supporting Information, Table S1) including 113 patients with HCC, 47 patients with cirrhosis, and 64 patients with chronic hepatitis. The serum level of sERBB3 was statistically higher in patients with HCC (mean ( SE = 1341 ( 109 pg/mL) than in patients with cirrhosis (741 ( 84 pg/mL, p < 0.001) and patients with chronic hepatitis (296 ( 27 pg/mL, p < 0.001) (Figure 3A and B). We next used 2 independent sets of patients (collected from the departments or hospitals different from those of the set 1 patients) to further validate the above findings. Set 2 contained 57 patients with HCC and 35 patients with chronic hepatitis, and set 3 consisted of 56 patients with HCC and 36 patients with chronic hepatitis. Since sensitivity and specificity vary with changes in the cutoff values, we used ROC curve to plot the fraction of true positives (sensitivity) vs the fraction of false positives (1 specificity). The AUC was used to evaluate the performance of the classifiers. ROC analysis for discrimination of HCC from non-HCC by serum sERBB3 on set 2 and set 3 patients revealed AUCs of 97.9% and 96.1%, respectively (Figure 3C and D). In addition, an AUC of 96.8% was obtained for the combined set 2 + 3 patients (Figure 3E). In comparison, using serum AFP as a classifier, AUCs for set 2, set 3, and combined set 2 + 3 were 81.8%, 88.6%, and 84.3%, respectively, which were much lower than those obtained by using serum sERBB3 as the classifier (97.9%, 96.1%, and 96.8%,

The most crucial need in improving the therapeutic efficacy of HCC is to detect HCC at an early stage, while the most challenging task in diagnosis of early HCC is to identify early HCC from cirrhosis, from which 80% of HCC are generated. We examined whether serum sERBB3 could identify early HCC (e2 cm, solitary and without invasion) in patients with chronic hepatitis and cirrhosis. Serum sERBB3 level was significantly higher in 31 cases with early HCC patients (1130 ( 177 pg/mL) (mean ( S.E.) than in 64 cases with chronic hepatitis (296 ( 27 pg/mL ; p < 0.001) and in 47 cases with cirrhosis (740 ( 83 pg/mL; p = 0.003) (Figure 4A). Evidently, serum sERBB3 could be a marker for detection of early HCC. We further used ROC analysis to compare the performance between serum sERBB3 and AFP in detection of early HCC (Figure 5). Discriminating early HCC from chronic hepatitis or from cirrhosis, serum sERBB3 had AUCs of 93.1% and 70.9%, respectively, while serum AFP had AUCs of 81.0 and 62.7%, respectively (Figure 5A and B). Therefore, with regard to detection of early HCC among the highest risk groups of patients with chronic hepatitis or cirrhosis, serum sERBB3 apparently was better than serum AFP. Combination of Serum sERBB3 with AFP in Diagnosis of Early HCC

We evaluated whether the combination of serum AFP and sERBB3 would further increase the accuracy in detection of early HCC. To develop a rapid assessment method for future testing, we used the split-point analysis28 for the combined use of serum 4720



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Figure 5. Serum sERBB3 in discrimination of early HCC from chronic hepatitis and cirrhosis. ROC curves were used to analyze the performance of the classifiers in discrimination of early HCC from (A) chronic hepatitis and from (B) cirrhosis by serum sERBB3, AFP, or the combination of sERBB3 and AFP. A total of 31 cases of early HCC, 64 cases of chronic hepatitis, and 47 cases of cirrhosis were included. The combination of serum sERBB3 and AFP as the classifier to identify early HCC from chronic hepatitis or from cirrhosis was analyzed by using the split-point analysis.28.

sERBB3 and AFP in identifying early HCC from chronic hepatitis or from cirrhosis. Based on the split score, AUC of AFP + sERBB3 in discrimination of early HCC from chronic hepatitis was 97.1%, whereas AUC of serum AFP or sERBB3 alone was 81.0 or 93.1%, respectively (Figure 5A). Again, AUC of AFP + sERBB3 in discrimination of early HCC from cirrhosis was 77.5%, whereas AUC of serum AFP or sERBB3 alone was 62.7 or 70.9%, respectively (Figure 5B). Apparently, combined use of serum ERBB3 and AFP further improved the performance in detection of early HCC in the patients with chronic hepatitis and the patients with cirrhosis. Serum sERBB3 in Detection of Invasive Hepatoma

An ideal marker should correlate its level with the severity and progression of the disease. Serum sERBB3 concentration was associated with progression of HCC (Figure 4A and Table 1). Serum sERBB3 levels in HCC with portal vein invasion (stage 3) and/or extrahepatic metastasis (stage 4) were significantly higher than those in HCC without portal vein invasion or extrahepatic metastasis [stage (3 + 4) vs stage (1 + 2): mean ( SE = 1688 ( 371 vs 1141 ( 102 pg/mL; p = 0.017] (Figure 5A). On the other hand, serum sERBB3 level was significantly higher in HCC with histopathologic grade 4 than grade 1 3 (Edmondson-Steiner grade 4) (H4 vs H1, H2, and H3, p = 0.004, < 0.001, and 2000 pg/mL) was mostly seen in male patients.

ERBB3 Regulates Motility and Invasion Activity of HCC Cells

Recently, we reported the frequent overexpression of ERBB3 in human HCC and its association with microscopic tumor invasion and early recurrence.29 Herein we found that high serum level of sERBB3 was associated with portal-vein invasion and extrahepatic metastasis, indicating that overexpression of ERBB3 is involved in enhancing tumor invasiveness. Indeed, induction of ERBB3-dependent signaling by treatment of HCC cells with recombinant neuregulin 1b, an ERBB3-specific ligand, promoted migration and invasion of HepG2, SK-Hep1, and Huh7 cells (Supporting Information, Figure S2), whereas silencing the expression of ERBB3 by using RNA interference suppressed migration and invasion activity of Mahlavu and Huh7 cells (Supporting Information, Figure S3).

’ DISCUSSION The primary hurdle on the way to improve the long-term outcome of patients with HCC is to diagnose HCC at an early stage, while the greatest challenging task is to detect early HCC in patients with cirrhosis, from which 80% of HCC are generated. This study reported for the first time the identification of the secreted isoforms of ERBB3 (sERBB3) as novel serum markers for diagnosis of HCC, particularly in discrimination of early HCC from chronic hepatitis and from cirrhosis. Moreover, as a diagnostic marker for early HCC, serum sERBB3 was a better marker than serum AFP. The combination of serum sERBB3 and AFP further improved the accuracy in the detection of early HCC from patients with chronic hepatitis and cirrhosis. ERBB3 belongs to the human epidermal growth factor receptor (EGFR/ERBB) family, which is responsible for transferring extracellular signaling and relaying downstream intracellular signaling cascades to regulate cell proliferation, movement, and differentiation. Deregulation of EGFR/ERBB signaling is seen in almost all human epithelial cancers30,31 and a wealth of clinical and experimental data indicate direct implication of EGFR/ ERBB-dependent signals in cancer development. EGFR and HER2 have been validated targets for anticancer therapy in many human cancers.32 Recent studies disclosed the pivotal roles of ERBB3 in the activation of PI3K/AKT pathways elicited from EGFR/ERBB signaling.29,33,34 Both the primary and acquired 4721



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Table 1. Correlation of Serum sERBB3 Levels to Clinical Presentations of 113 Patients with HCC serum sERBB3 conc. (ng/mL)a

Age (yr)

Gender

Cause

AFP (ng/mL)

Cirrhosis

AJCC/TNM

Histology grade

Tumor stagec

mean ( SD

mean ( SE

p valueb 0.616

e50

1.667 ( 1.843

1.667 ( 0.286

>50

1.222 ( 0.651

1.222 ( 0.077

male

1.500 ( 1.347

1.500 ( 0.269

femail

1.295 ( 1.104

1.295 ( 0.118

HBV

1.257 ( 0.840

1.257 ( 0.098

HCV

1.174 ( 0.473

1.174 ( 0.095

0.469

0.888

e40

1.335 ( 1.174

1.335 ( 0.143

0.963

>40 e200

1.237 ( 0.771 1.282 ( 1.098

1.237 ( 0.123 1.282 ( 0.124

0.358

>200

1.349 ( 0.869

1.349 ( 0.167

yes

1.228 ( 0.831

1.228 ( 0.131

no

1.291 ( 1.091

1.291 ( 0.143

1+2

1.147 ( 0.688

1.147 ( 0.077

3+4

1.809 ( 1.791

1.809 ( 0.312

1+2

1.128 ( 0.724

1.128 ( 0.093

3+4

1.483 ( 1.133

1.483 ( 0.165

1+2

1.151 ( 0.688

1.151 ( 0.079

3+4

1.688 ( 1.382

1.688 ( 0.261

0.980

0.025

0.044

0.017

Mean ( SD = mean ( standard deviation; Mean ( SE = Mean ( standard error of mean. b P values were determined via Mann Whitney U test. c Tumor stage: stage 1, solitary and e2 cm; stage 2, solitary >2 cm or multiple; stage 3, portal vein invasion; stage 4, extrahepatic invasion or metastasis. a

resistance to EGFR- and HER2-targeted therapies in lung and breast cancers is attributable to persistent activation of ERBB3dependent signaling.33 35 Moreover, overexpression and aberrant activation of ERBB3 in many human cancers, including the lung, breast, prostate, and liver highlights the crucial role of ERBB3 in oncogenic signaling in human cancers.29,30,36 38 Interestingly, the levels of sERBB3 in TIF were correlated with that in sera, while they were not associated with the expression levels of ERBB3 in tumor tissues. Probably, in addition to the expression level, post-translation modification and/or interaction between tumor and its microenvironment also determine the shedding of ERBB3 out of cells. Further studies to address the mechanisms related to shedding of sERBB3 into circulation and their biological significance are warranted. Recently, Pitteri et al. reported the detection of elevated plasma level of EGFR before clinical diagnosis of breast cancer, suggesting that proteins that are elevated preclinincally in patients who go on to develop breast cancer can be discovered and used as a biomarker for early detection of breast cancer.39 We found that serum sERBB3 level was significantly higher in cirrhosis than in chronic hepatitis. Since 80% of HCC occurs in cirrhosis and at least some of cirrhosis is regarded as precancerous, it is intriguing to speculate that serum sERBB3 might be

useful for prediction of preclinical HCC. To examine this hypothesis, prospective studies with long-term follow-up of the patients with cirrhosis are warranted. Interestingly, high serum level of sERBB3 was also strongly associated with portal vein invasion and extrahepatic metastasis of HCC. These findings are important, since high invasiveness of HCC leads to unusually high recurrence and poor therapeutic efficacy for HCC. However, reliable markers for prediction and detection of invasion of HCC have not been available yet. Previously we found overexpression of ERBB3 in human HCC.36 More recently, we found the association of overexpression of ERBB3 with microscopic invasion and early recurrence of HCC.29 Interestingly, overexpression of ERBB3 is also associated with constitutive activation of ERBB3-dependent signaling via a neuregulin/ERBB3 autocrine loop.29 Induction of ERBB3-dependent signaling by treatment of HCC cells with recombinant neuregulin 1b promoted migration of HCC cells, whereas silencing of the expression of ERBB3 by RNA interference suppressed migration and invasion activity of HCC cells. We thus conclude that ERBB3 plays a crucial role in regulating tumor invasion and metastasis of HCC. Overexpression of ERBB3 might be associated with shedding of sERBB3 into the circulation, which are then serving as markers for detection of invasion and metastasis of HCC. It is, therefore, intriguing to speculate that high serum level of sERBB3 might predict the occult invasion and late recurrence of HCC and can be used as a marker to select subgroups of HCC patients, who have a high risk of recurrence and are required adjuvant therapies to prevent recurrence of HCC. Further studies to validate this speculation are warranted.

’ CONCLUSION We demonstrated the successful use of TIF as the initial source to discover novel serum markers for diagnosis and prognosis of human HCC. Similar approaches can also be applied to identify novel serum markers for other human cancers. Serum sERBB3 was a serum marker for detection of early HCC in patients with chronic hepatitis and cirrhosis with a better performance than AFP. High serum sERBB3 levels suggested invasion and metastasis of HCC. Further studies to examine whether serum sERBB3 can be used to predict HCC from patients with cirrhosis before clinical presentation and to identify subgroups of HCC with occult invasion for adjuvant therapies to prevent recurrence are warranted. ’ ASSOCIATED CONTENT

bS

Supporting Information Clinical information for the patients with HCC in set 1 was provided in Table S1. Lists of proteins identified by 2-DE, the PMF and MS/MS and by antibody arrays are provided in Table S2, Table S3, and Table S4. The ROC curves for discrimination of HCC in patients with chronic hepatitis B or chronic hepatitis C by using serum sERBB3 are presented in Figure S1. The results of activation or suppression of the ERBB3-dependent signaling on cell migration and invasion are provided in Figure S2 and Figure S3, respectively. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

* Professor Sen-Yung Hsieh, Liver Research Unit, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan. E-mail: siming@adm. 4722


Journal of Proteome Research cgmh.org.tw; [email protected]. Phone: 886-3-3281200 ext 8128. Fax: 886-3-3272236.

’ ACKNOWLEDGMENT We thank Miss Chia-Hua Chen and Dr. Chia-Jung Yu for their technical assistance in performing 2D-DIGE. This study was supported in part by a grant from Chang Gung Memorial Hospital (CMRPG), a grant from (96HC006) from the National Research Program for Genomic Medicine, National Science Council, and a grant from the National Research Program for Biopharmaceuticals (100CA026). ’ ABBREVIATIONS AFP, alpha-fetoprotein; AUC, area under ROC curve; 2DE, 2dimentional gel electrophoresis; DIGE, differential gel electrophoresis; EGFR, epithelial growth factor receptor; ERBB3, erythroblastic leukemia viral oncogene homologue 3; HCC, hepatocellular carcinoma; NIF, interstitial fluid of nontumor tissues; ROC, receiver operating characteristic; sERBB3, secreted isoforms of ERBB3; TIF, tumor interstitial fluid ’ REFERENCES (1) Shariff, M. I.; Cox, I. J.; Gomaa, A. I.; Khan, S. A.; Gedroyc, W.; Taylor-Robinson, S. D. Hepatocellular carcinoma: current trends in worldwide epidemiology, risk factors, diagnosis and therapeutics. Expert Rev. Gastroenterol. Hepatol. 2009, 3 (4), 353–67. (2) Schutte, K.; Bornschein, J.; Malfertheiner, P. Hepatocellular carcinoma--epidemiological trends and risk factors. Dig. Dis. 2009, 27 (2), 80–92. (3) Verslype, C.; Van Cutsem, E.; Dicato, M.; Arber, N.; Berlin, J. D.; Cunningham, D.; De Gramont, A.; Diaz-Rubio, E.; Ducreux, M.; Gruenberger, T.; Haller, D.; Haustermans, K.; Hoff, P.; Kerr, D.; Labianca, R.; Moore, M.; Nordlinger, B.; Ohtsu, A.; Rougier, P.; Scheithauer, W.; Schmoll, H. J.; Sobrero, A.; Tabernero, J.; van de Velde, C. The management of hepatocellular carcinoma. Current expert opinion and recommendations derived from the 10th World Congress on Gastrointestinal Cancer, Barcelona, 2008. Ann. Oncol. 2009, 20 (Suppl 7), vii1–vii6. (4) Blum, H. E.; Spangenberg, H. C. Hepatocellular carcinoma: an update. Arch. Iran Med. 2007, 10 (3), 361–71. (5) Masuda, T.; Beppu, T.; Ishiko, T.; Horino, K.; Baba, Y.; Mizumoto, T.; Hayashi, H.; Okabe, H.; Horlad, H.; Doi, K.; Okabe, K.; Takamori, H.; Hirota, M.; Iyama, K.; Baba, H. Intrahepatic dissemination of hepatocellular carcinoma after local ablation therapy. J. Hepatobiliary Pancreat. Surg. 2008, 15 (6), 589–95. (6) Colli, A.; Fraquelli, M.; Casazza, G.; Massironi, S.; Colucci, A.; Conte, D.; Duca, P. Accuracy of ultrasonography, spiral CT, magnetic resonance, and alpha-fetoprotein in diagnosing hepatocellular carcinoma: a systematic review. Am. J. Gastroenterol. 2006, 101 (3), 513–23. (7) Gomaa, A. I.; Khan, S. A.; Leen, E. L.; Waked, I.; TaylorRobinson, S. D. Diagnosis of hepatocellular carcinoma. World J. Gastroenterol. 2009, 15 (11), 1301–14. (8) Marrero, J. A.; Feng, Z.; Wang, Y.; Nguyen, M. H.; Befeler, A. S.; Roberts, L. R.; Reddy, K. R.; Harnois, D.; Llovet, J. M.; Normolle, D.; Dalhgren, J.; Chia, D.; Lok, A. S.; Wagner, P. D.; Srivastava, S.; Schwartz, M. Alpha-fetoprotein, des-gamma carboxyprothrombin, and lectinbound alpha-fetoprotein in early hepatocellular carcinoma. Gastroenterology 2009, 137 (1), 110–8. (9) Kumada, T.; Toyoda, H.; Kiriyama, S.; Tanikawa, M.; Hisanaga, Y.; Kanamori, A.; Tada, T.; Tanaka, J.; Yoshizawa, H. Predictive value of tumor markers for hepatocarcinogenesis in patients with hepatitis C virus. J. Gastroenterol. 2011, 46 (4), 536–44. (10) Bertino, G.; Ardiri, A. M.; Calvagno, G. S.; Bertino, N.; Boemi, P. M. Prognostic and diagnostic value of des-gamma-carboxy prothrombin in liver cancer. Drug News Perspect. 2010, 23 (8), 498–508.

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