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1 Gastrointestinal Unit, 2 Department of Medicine, 3 Cancer Center, 4 Center for Computational and Integrative Biology, 5 Department of Pathology and 6 Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts 02114, USA7 Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts 02114, USA8 Department of Adult Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02114, USA
(Correspondence should be addressed to D C Chung, GI Unit, GRJ 825, Massachusetts General Hospital, 50 Blossom Street, Boston, Massachusetts 02114, USA; Email: dchung{at}partners.org)
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 Top Abstract IntroductionMaterials and methodsResultsDiscussionReferences |
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Introduction |
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TopAbstractIntroductionMaterials and methodsResultsDiscussionReferences |
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Materials and methods |
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TopAbstractIntroductionMaterials and methodsResultsDiscussionReferences |
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Fresh frozen tissue samples of 24 PNETs (5 benign WDETs, 11 low-grade malignant WDETs, and 8 WDECs) and 6 malignant GI-NETs were obtained as surgical discards from Massachusetts General Hospital and Brigham and Women's Hospital/Dana-Farber Cancer Institute respectively. Tumors were classified according to the WHO 2004 criteria. All of the PNET samples were primary tumors. Clinical characteristics are summarized in Tables 2 and 3. This protocol was approved by the institutional review board of each institution.
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RNA was extracted from frozen tumors following dissection from normal surrounding tissue using Trizol following the manufacturer's recommendations (Invitrogen, Carlsbad, CA, USA) and purified using the RNeasy MinElute Cleanup kit (Qiagen, Valencia, CA, USA).
DNA microarrays
RNA analyses were performed at the DNA Microarray Core Facility at Massachusetts General Hospital Cancer Center. Amounts, purity, and integrity of RNA were evaluated by u.v. spectrophotometry and an RNA-nano Bioanalyzer (Agilent, Palo Alto, CA, USA). Probe synthesis and hybridization of human U-133A GeneChip DNA microarrays (Affymetrix, Santa Clara, CA, USA) were performed following the manufacturer's instructions.
Microarray data analysis
Data analysis was performed using DChip software (www.dchip.org). CEL files (primary Affymetrix array data files) were loaded and normalized at the probe cell level by the Invariate Set Normalization method (Li & Hung Wong 2001). The model-based method (Li & Hung Wong 2001) was used for probe selection and computing expression values. These expression values were attached with standard errors as measurement accuracy. The lower confidence intervals of fold changes were conservative estimates of real fold changes. The ANOVA test was carried out using a P value <0.05 in order to define a set of significantly up- or down-regulated genes. The resulting genes were filtered for gene presence calls of >20 in >50% of samples. Two-group comparison was employed selecting for increased or decreased gene expression by more than 1.5-fold. Hierarchical clustering analysis (Eisen et al. 1998) was performed on the genes that met the above criteria.
Gene ontology
Enrichments of gene ontology (GO) categories were computed using the hypergeometric probability distribution, which identifies GO molecular function categories overrepresented in the set of differentially induced genes relative to their representation on the Affymetrix U133A array. The analysis was performed using Onto-Tools (Draghici et al. 2003) and GO molecular function categories with P value <0.05 are considered significantly overrepresented.
Protein interaction network
The network was constructed by iteratively connecting interacting proteins, with protein interaction data obtained from the Human Protein Reference Database (Peri et al. 2004). The network uses graph theory, which represents components (gene products) as nodes and interactions between components as edges. Graph layout descriptions were written in the Dot language (Gansner & North 2000) that implements a multidimensional scaling heuristic, which creates a virtual physical model (Spring model; Kamada & Kawai 1989) and is coupled to an iterative solver (Newton–Raphson algorithm) that searches for low-energy configurations to optimize the graph layout.
Quantitative real-time PCR (qRT-PCR)
qRT-PCR of RNA from the 24 tumor samples used in the microarray analysis and 3 normal pancreas samples was performed utilizing the SuperScript III platinum Two-Step qRT-PCR Kit (Invitrogen). The 18S rRNA served as an endogenous control. Primer sequences and PCR conditions for FEV, adenylate cyclase 2 (ADCY2), nuclear receptor subfamily 4, group A, member 2 (NR4A2), growth arrest and DNA-damage-inducible, beta (GADD45β), extracellular matrix protein 1 (ECM1), vesicular monoamine member 1 (VMAT1), LGALS4, RET, and 18S are available upon request. A fluorogenic SYBR Green and MJ research detection system were used for real-time quantification. Relative mRNA expression was calculated using the parameter threshold cycle (CT) values. 
CT was the difference in the CT values derived from the specific gene being assayed and the 18S rRNA. CT represented the difference between the paired samples, as calculated by the formula CT of a sample –CT of a reference (the average CT of three normal pancreas samples). The amount of target, normalized to 18S and the reference, was calculated as 
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Protein lysates and western blot analysis
Protein lysates were prepared from 18 snap-frozen PNET samples and 3 normal tissues (two from pancreas and one from duodenum). Thirteen samples were from the same PNETs used for the microarray studies (Table 2). Total cell lysate (150 µg) was separated by SDS-PAGE (NuPAGE, Invitrogen) and transferred to PVDF membranes (Millipore, Billerica, MA, USA). Immunoblotting was performed with anti-platelet-derived growth factor receptor-β (PDGFR-β), anti-PDGFR-
, anti-phospho PDGFR-β Tyr716 (Upstate, Billerica, MA, USA), anti-phospho PDGFR-β Tyr751 (Sugen), and anti-β-Actin (Sigma, St Louis, MO, USA).
RET sequencing
The cDNA of seven WDECs and six GI-NETs was PCR amplified using three different primer sets spanning codons 573–666 (exons 10 and 11), 729–826 (exons 13 and 14), and 858–940 (exons 15 and 16). PCR products were purified (QIAquick gel extraction kit, Qiagen) and sequenced on an ABI 3730XL DNA analyzer (Applied Biosystems, Foster City, CA, USA). Primer sequences are available upon request.
Ret immunohistochemistry
Formalin-fixed paraffin-embedded samples of 21 cases of small intestinal NETs from Brigham and Women's Hospital and 65 cases of PNETs from Massachusetts General Hospital were assembled as part of a tissue microarray. Multiple independent cores from each sample were placed onto the microarray (range 2–6). Ret expression was assessed by immunohistochemistry with a Ret antibody (Santa Cruz, Santa Cruz, CA, USA) at 1:50 dilution after treatment with formic acid, as previously described (Lee et al. 2005). Ret staining was scored from 0 to 3+, and each core sample was scored separately. A papillary thyroid cancer sample was included as a positive control.
Statistical analysis
The P values were calculated utilizing the Wilcoxon rank-sum test with a P value <0.05 considered statistically significant.
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Results |
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Based upon the WHO criteria, 19 PNET samples were initially classified into 3 histologic groups: WDETs of benign behavior (n=3), WDETs of low-grade malignant behavior (n=9), and WDECs (n=7; Table 2). When comparing benign and low-grade malignant WDETs with WDECs, 112 genes were differentially expressed by at least 1.5-fold with a P value <0.05. Hierarchical clustering revealed two distinct clusters (Fig. 1): the 3 benign WDETs clustered together with 8/9 low-grade malignant WDETs and 1 WDEC (‘benign cluster’), and 6/7 WDECs clustered together with the 1 remaining low-grade malignant WDET (‘malignant cluster’).
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Of note, a correlation was observed between mRNA expression and the hormonal profile of these tumors. Insulin mRNA levels were 18.6-fold higher in insulinomas compared with non-insulinomas, gastrin mRNA levels were 31.6-fold higher in gastrinomas compared with non-gastrinomas, and glucagon mRNA levels were 26-fold higher in glucagonomas compared with non-glucagonomas.
Validation of selected genes with quantitative real-time-PCR
The four most highly up-regulated genes in the ‘malignant cluster’ of PNETs (FEV, ADCY2, NR4A2, GADD45β) were selected for further validation by qRT-PCR. In the microarray studies, FEV was up-regulated 11.61-fold, ADCY2 was up-regulated 4.47-fold, NR4A2 was up-regulated 4.45-fold, and GADD45β was up-regulated 3.28-fold in the ‘malignant’ cluster. Statistically significant overexpression of all four genes was confirmed by qRT-PCR (FEV: 37-fold, P=0.007; ADCY2: 55-fold, P=0.026; NR4A2: 15.2-fold, P=0.0006; GADD45β: 5-fold, P=0.002; Fig. 3). In all cases, the microarray studies underestimated the extent of up-regulation.
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In addition to the identification of FEV, ADCY2, NR4A2, and GADD45β as novel genes that may play a role in tumor progression, we were curious whether specific candidate oncogenes and tumor suppressor genes such as MEN1, retinoic acid receptor-β, hMLH1, RASSF1, Her2/neu, cyclin D1, p16INK4a/p14ARF, p18INK4c, and p27Kip1 were differentially regulated. However, none of these genes was differentially regulated in our microarray study. In addition, angiogenic factors including aFGF, bFGF, or VEGF were not differentially regulated.
Another group of candidate genes are the receptor tyrosine kinases, which are frequently activated in human cancers. These are particularly attractive candidates, as tyrosine kinase inhibitors are promising as molecularly targeted agents. There was no statistically significant difference in expression of PDGFR- or PDGFR-β, although there was a trend towards higher expression levels (2.3-fold increase) of PDGFR- in WDECs compared with WDETs. However, given their potential clinical importance and potential biological relevance in neuroendocrine tumorigenesis, we performed immunoblot analysis to evaluate protein expression levels (Fig. 4). PDGFR- was expressed in 94% of PNETs. It was present in 4/5 (80%) benign WDETs, 8/8 (100%) low-grade malignant WDETs, and 5/5 (100%) WDECs. It was also detected in 1/3 (33%) normal samples, although at much lower levels. PDFGR-β was expressed in 17/18 (94%) PNETs with no difference among tumor stages, and it was also expressed in 2/3 (66%) normal pancreatic samples.
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Differentially expressed genes in GI-NETs versus PNETs identified by cDNA microarray analysis
The 25 malignant tumor samples were grouped into 6 GI-NETs and 19 PNETs (for sample details see Tables 2 and 3). Between the two groups 385 genes were differentially expressed by at least 1.5-fold with a P value <0.05. Hierarchical clustering revealed that GI-NETs clustered together in one group and PNETs in another (Fig. 5), indicating that gene expression patterns can indeed distinguish these NET subtypes. When compared with PNETs, 157 genes were up-regulated and 228 genes were down-regulated in the GI-NETs (Fig. 5). Supplementary Tables 3 and 4, which can be viewed online at http://erc.endocrinology-journals.org/supplemental/, illustrate the genes over- and under-expressed in GI-NETs. We also performed an analysis excluding samples in which only a liver metastasis was available and confirmed that GI-NETs did differ in their genetic signature from PNETs. The four remaining primary GI endocrine tumors clustered together with one WDEC and the other 18 PNETs represented another cluster (data not shown).
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Validation of selected genes with quantitative real-time-PCR
The three most highly up-regulated genes in GI-NETs identified by the microarray studies (ECM1, VMAT1, and LGALS4) were verified by qRT-PCR. In addition, we analyzed RET because it is critical in the pathogenesis of medullary thyroid cancer, another NET type. In the microarray studies, ECM1 protein was up-regulated 28-fold, VMAT1 by 25-fold, galectin 4 (LGALS4) by 24-fold, and RET by 3.62-fold in GI-NETs compared with PNETs. qRT-PCR confirmed the up-regulation of all these genes in GI-NETs (ECM1: 39-fold, P=0.0011; VMAT1: 523-fold, P=0.0029; LGALS4: 43-fold, P=0.012; RET: 28-fold, P=0.012; Fig. 7). VMAT1 was not detectable in normal pancreatic tissue and most WDECs. Immunohistochemistry was performed for Ret on a larger series of small intestinal NETs and PNETs. There were 21 cases of small intestinal NETs (8 WDETs and 13 WDECs) and 65 cases of PNETs (14 benign WDETs, 43 low-grade malignant WDETs, and 8 WDECs) on the tissue microarrays. Among the intestinal NETs, 11% of the samples displayed weak or no Ret staining (0–1+), whereas 89% exhibited strong staining (2+–3+). By contrast, 65% of PNETs exhibited weak staining (0–1+) and only 35% exhibited strong staining (2+–3+; Fig. 8).
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RET is mutated in the MEN2 syndrome and familial medullary thyroid cancer. Because of this critical role in another NET type and the high levels of expression in GI-NETs, we sought to determine whether mutations in RET may also underlie GI-NET pathogenesis. Mutations occur primarily at three ‘hotspot’ regions within the cysteine-rich domain and the tyrosine kinase domains 1 and 2. DNA sequencing of these ‘hotspot’ regions in six GI-NETs and seven WDECs did not reveal any mutations. Incidentally, we identified two single base pair polymorphisms (CTT to CTA at codon 769 and TCG to TCC at codon 904), neither of which resulted in an amino acid change.
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Discussion |
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Hierarchical clustering revealed that PNETs could be segregated on a molecular level into two groups. The ‘benign cluster’ comprised all benign WDETs, 8/9 low-grade malignant WDETs, and 1/7 WDECs. The ‘malignant cluster’ comprised 1/9 low-grade malignant WDETs and 6/7 WDECs. This is the first demonstration that the histologic subgroup of low-grade malignant WDETs shares more molecular similarities with benign WDETs than with WDECs and provides a molecular correlation of the WHO classification scheme. The clinical behavior of low-grade malignant WDETs is generally good and consistent with this clustering result. We cannot completely exclude an influence of the heterogeneity in the tissue samples on our results. However, due to the rarity of PNETs, it was unfeasible to perform gene expression analyses for each individual hormonal subtype, and it was hypothesized that there may be fundamentally similar mechanisms that underlie all tumor subtypes. Of note, within the group of low-grade malignant WDETs, only 55% were insulinomas, indicating that there was a diversity of tumor types at each stage analyzed.
GO analysis revealed that the molecular functions of ‘binding’ and ‘transcriptional regulation’ were significantly overrepresented in the malignant cluster, possibly reflecting novel pathways that are critical for tumor progression. In addition, genes on chromosomes 11 and 17 were overrepresented in PNETs. This is consistent with published comparative genomic hybridization (CGH) literature, which has shown that genomic gains are common on chromosome 17 (Terris et al. 1998, Speel et al. 1999, Stumpf et al. 2000) and frequently associated with malignant behavior (Speel et al. 2001).
The four most highly up-regulated genes in WDECs (FEV, ADCY2, GADD45β, and NR4A2) have not previously been implicated in the pathogenesis of PNETs. Two of these, GADD45β and NR4A2, regulate apoptosis. GADD45β can block apoptosis induced by IL-1β (interleukin-1β) in cultured islet cells (Larsen et al. 2006). FEV is a member of the ETS family of oncogenic transcription factors (Peter et al. 1997). Further functional studies will be necessary to determine the specific roles these genes may play in PNET pathogenesis and whether they may ultimately serve as novel therapeutic targets that have an impact upon patient management.
We then investigated whether certain target genes that have been previously implicated in PNET pathogenesis were differentially expressed. Immunoblot analysis revealed that PDGFR- and -β were expressed in PNETs regardless of stage. More importantly, PDGFR-β was activated by phosphorylation in the majority of PNETs. Others have reported high levels of expression of PDGFR-, PDGFR-β, and c-Kit in PNETs, but no assessment of receptor activation has been previously performed (Fjallskog et al. 2003). The possibility that PDGFR is expressed in mesenchymal components such as fibroblasts or pericytes (Pietras et al. 2003) cannot be excluded. Nevertheless, a specific PDGFR tyrosine kinase inhibitor may be a promising option for the treatment of PNETs. Observations of antitumor activity associated with receptor tyrosine kinase inhibitors further support a potential role for PDGFR in PNETs. In a multi-institutional study, treatment with sorafenib, a small molecule inhibitor with a spectrum of activity that includes VEGFR-2 and PDGFR-β, was associated with objective radiologic partial responses in 11% of PNET patients (Hobday et al. 2007). In a second study, treatment with sunitinib, which targets a similar spectrum of receptor tyrosine kinases, was associated with a 13% partial response rate in PNETs (Kulke et al. 2005). These observations, combined with our findings of PDGFR-β activation in PNETs, support further investigation of specific PDGFR-β inhibition as a clinical strategy in this tumor type. Interestingly, construction of a protein–protein interaction map revealed a novel connection between PDGFR-β and two highly expressed genes in PNETs, GADD45β, and NR4A2 (Supplementary Figure 1, which can be viewed online at http://erc.endocrinology-journals.org/supplemental/). Activation of PDGFR-β may therefore be involved in the regulation of apoptosis in PNETs.
We provide the first description that GI-NETs cluster separately from PNETs by microarray analysis. Although the GI-NETs in this study comprised both primary tumors (n=4) and metastases (n=2), we can exclude an influence of the heterogeneity of samples on our clustering result, as GI-NETs still clustered independently from PNETs when metastases were excluded from the analysis (data not shown). GO analysis revealed that in contrast to PNETs, genes involved in ion transport, channel transport, and neurotransmitter transport were significantly overrepresented in GI-NETs. This may provide new insights into the pathogenesis of GI-NETs. Hierarchical clustering also revealed that genes on chromosomes 9 and 18 were underexpressed in GI-NETs, possibly reflecting chromosomal deletions that have been reported in CGH and LOH (loss of heterozygosity) studies of these tumors (Kytola et al. 2001, Tonnies et al. 2001, Wang et al. 2005).
Of the three most highly up-regulated genes in GI-NETs (VMAT1, ECM1, and LGALS4), VMAT1 and LGALS4 have been previously described in this context (Nilsson et al. 2004, Vikman et al. 2005, Rumilla et al. 2006). Galectin 4 is expressed in the alimentary tract, where it is a component of adherens junctions or lipid rafts in the microvillus membrane (Huflejt & Leffler 2004) and is strongly expressed in ileal carcinoids (Rumilla et al. 2006). ECM1 is expressed in highly vascularized organs (Mongiat et al. 2003) and overexpressed in a number of malignant epithelial tumors (Kebebew et al. 2005). Finally, we demonstrated that RET, an oncogene encoding a transmembrane receptor tyrosine kinase, is up-regulated in GI-NETs. This observation was confirmed by immunohistochemistry of a large panel of intestinal and PNETs. Ret binds glial cell line-derived neurotrophic factor family members and activates MAPK/ERK, PI3K, JNK, p38MAPK, and phospholipase C 
(Arighi et al. 2005). Although no somatic mutations were identified, the high expression of RET in GI-NETs suggests that it may be an attractive therapeutic target. SU11248 is an inhibitor of multiple tyrosine kinases including RET (Kim et al. 2006), and a phase II study of SU11248 as a single agent in 39 patients with advanced GI-NETs revealed a 5% partial response rate (Kulke et al. 2005). RET may therefore play a pathogenic role in GI-NETs, but further investigation of targeted agents is required.
In comparison to published reports of gene expression profiles in NETs, our study has provided several new insights. Previous studies compared WDETs with normal islet controls (Maitra et al. 2003), MEN-1 associated NETs with normal islets (Dilley et al. 2005), PNETs with normal pancreas, pancreatitis, and pancreatic adenocarcinoma (Bloomston et al. 2004), non-functioning PNETs and their metastases with normal islets (Capurso et al. 2006), and metastatic with non-metastatic PNETs, primarily non-functioning (Hansel et al. 2004, Couvelard et al. 2006). Interestingly, there was no significant overlap between the identified genes in these studies and our current analysis. We hypothesize that this poor concordance is most likely a reflection of the different study designs, software platforms, data analysis parameters, and sample subtypes. In contrast to three reports that are most similar to ours (Hansel et al. 2004, Capurso et al. 2006, Couvelard et al. 2006), we studied a broader mix of PNET subtypes, not exclusively non-functioning PNETs, and this may potentially explain the disparity. With respect to technical differences, we utilized an Affymetrix platform, whereas Couvelard et al. obtained microarray chips from the Sanger center. In addition, we utilized the DChip program for data analysis, whereas Couvelard et al. performed their analysis with GeneSpring. Capurso et al. also utilized Affymetrix chips. However, their study differed significantly in that they compared PNETs with normal islets, whereas our comparison was between PNETs of different stages. Although Affymetrix chips were also used by Hansel et al. their analysis comprised only 12 tumors, whereas our analysis included 24 tumors. Nevertheless, it should be noted that there were some similarities, as one study also identified an up-regulation of PDGFR-β in WDECs (Couvelard et al. 2006). In addition, GO analysis in one study also revealed the molecular function classifier ‘binding’ as the most frequent in their up-regulated genes (Capurso et al. 2006). In aggregate, our results enhance the spectrum of genes implicated in NET pathogenesis.
In summary, we have identified a novel set of genes that may play a role in the pathogenesis and progression of PNETs and GI-NETs. Our results reveal a correlation with the WHO histologic classification on a molecular level. Furthermore, there are molecular signatures that distinguish PNETs from GI-NETs, reinforcing the principle that these two groups must be studied separately. By improving the molecular classification of these tumor subtypes, we may ultimately enhance our ability to predict tumor behavior, provide important new insights into the molecular biology and tumor pathogenesis, and design the next generation of targeted therapies. In this context, a potentially important role for PDGFR in the pathogenesis and treatment of PNETs has been revealed.
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Acknowledgements |
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Footnotes |
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References |
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