Pituitary adenomas comprise 10% of intracranial tumors and occur in about 20% of the population. They cause significant morbidity by compression of regional structures or the inappropriate expression of pituitary hormones. Their molecular pathogenesis is unclear, and the current classification of clinically nonfunctional tumors does not reflect any molecular distinctions between the subtypes. To further elucidate the molecular changes that contribute to the development of these tumors and reclassify them according to the molecular basis, we investigated 11 nonfunctional pituitary adenomas and eight normal pituitary glands, using 33 oligonucleotide GeneChip microarrays. We validated microarray results with the reverse transcription real-time quantitative PCR, using a larger number of nonfunctional adenomas. We also used proteomic analysis to examine protein expression in these nonfunctional adenomas. Microarray analysis identified significant increases in the expression of 115 genes and decreases in 169 genes, whereas proteomic analysis identified 21 up-regulated and 29 down-regulated proteins. We observed changes in expression of SFRP1, TLE2, PITX2, NOTCH3, and DLK1, suggesting that the developmental Wnt and Notch pathways are activated and important for the progression of nonfunctional pituitary adenomas. We further analyzed gene expression profiles of all nonfunctional pituitary subtypes to each other and identified genes that were affected uniquely in each subtype. These results show distinct gene and protein expression patterns in adenomas, provide new insight into the pathogenesis and molecular classification of nonfunctional pituitary adenomas, and suggest that therapeutic targeting of the Notch pathway could be effective for these tumors.

Pituitary adenomas account for ∼10% of intracranial tumors and occur in about 20% of the population. They cause significant morbidity by compression of regional structures and the inappropriate expression of pituitary hormones (1, 2). Nonfunctional pituitary adenomas, so-called because they do not cause clinical hormone hypersecretion (25), account for ∼30% of pituitary tumors (3). The nonfunctional tumors are uniquely heterogeneous (Table 1), typically quite large, and cause hypopituitarism or blindness from regional compression (1).

Table 1.

Classification of nonfunctional pituitary adenomas by cell of origin

Cell typeHormone expression% Nonfunctional tumors
Null cell None 17 
Oncocytoma None 
Silent corticotroph ACTH 
Silent somatotroph GH 
Gonadotrophs Intact LH/FSH or subunits 40-79 
Cell typeHormone expression% Nonfunctional tumors
Null cell None 17 
Oncocytoma None 
Silent corticotroph ACTH 
Silent somatotroph GH 
Gonadotrophs Intact LH/FSH or subunits 40-79 

Despite the lack of clinical hormone hypersecretion, immunocytochemical staining for hormones reveals evidence for hormone expression in up to 79% of these tumors, which we will refer to as immunohistochemically positive nonfunctional (NF+) tumors. The remainder are negative for hormone expression (2, 5) and will be referred to as immunohistochemically negative nonfunctional (NF) tumors. However, current pathologic classification (Table 1) of these tumors has no molecular basis, and surprisingly, is based only on anterior pituitary hormone histochemistry for hormones, or on electron microscopy, which is of limited use.

Unlike the functional pituitary tumors, there is no available effective medical therapy for the nonfunctional tumors, and only a better understanding of the molecular biology of these tumors will provide needed medical treatment options.

Although pituitary tumors are mostly benign, 5% to 35% of them are locally invasive. A small number exhibit a more aggressive course, infiltrate dura, bone and sinuses and are highly aggressive. A significantly smaller number are truly malignant; that is, they metastasize outside the central nervous system. It is not known what molecular profiles result in local invasion or presage a more aggressive course.

Molecular genetic studies have shown that these tumors are monoclonal in origin (6, 7). A minority is part of an autosomal-dominant syndrome, multiple endocrine neoplasia type 1 (MEN1), which is associated with mutations in the MEN1 tumor suppressor gene. Others are associated with loss of heterozygosity on the 11q13 chromosome (2, 810). A dominant mutation occurs in the Gαs gene in ∼30% of somatotrophinomas, but this mutation is rare in other pituitary tumors (2, 11, 12). In nonfunctional tumors, reduced levels of expression of the retinoid X receptor, estrogen receptor, and thyroid hormone receptor have been found and may contribute to abnormal thyroid hormone regulation of α-subunit production in these tumors. However, the significance of these data to pituitary tumorigenesis is unknown (13). The epidermal growth factor receptor (EGFR) is overexpressed in 80% of nonfunctional adenomas and is virtually undetectable in functional adenomas. In vitro, nonfunctional tumors in culture proliferate in response to EGF administration and up-regulate EGFR mRNA (14). In addition, we recently found that the folate receptor is overexpressed in nonfunctional pituitary adenomas (15, 16).

To further elucidate the molecular changes that contribute to the development of these tumors and reclassify them according to the molecular basis, we used microarray analysis to elucidate the gene expression profile of 11 nonfunctional pituitary adenomas compared with eight normal pituitary glands.

We verified the gene expression changes of four genes that were detected by microarray analysis in 23 nonfunctional pituitary tumors and eight normal pituitary glands by reverse transcription real-time quantitative PCR (RT-qPCR). To complement and extend the expression profiling data, a comparative proteomics system based upon two-dimensional PAGE and mass spectrometry (MS) were used to characterize each differentially expressed protein in the same pituitary adenoma tissues.

Patients and tumor characterization. Sporadic pituitary adenomas were obtained from patients at the Emory University Hospital during transsphenoidal surgery (Supplementary Table 1). Informed consent for inclusion in this study was obtained. Pituitary adenomas are anatomically and pathologically distinct from the normal anterior lobe, making them easy to dissect under the surgical microscope. All tumors were microdissected and removed using the surgical microscope, rinsed in sterile saline, snap-frozen in liquid nitrogen, and stored (−80°C) for molecular analysis and proteomics. Each tumor fragment was then confirmed independently by a neuropathologist as being homogenous and unadulterated by histology and immunohistochemistry before molecular analysis.

Eight normal pituitary glands obtained from the National Hormone and Pituitary Program, National Institute of Diabetes and Digestive and Kidney Diseases (Bethesda, MD; n = 3) and from the National Disease Research Interchange (Philadelphia, PA; n = 5) were used as controls for microarray and RT-qPCR analyses. Eight normal pituitary glands obtained from the Memphis Regional Medical Center (n = 7) and the National Disease Research Interchange (n = 1) were used as controls in proteomics.

Synthesis of biotin-labeled cRNA and microarray hybridization. Total RNA extraction was previously described (15, 16). Briefly, total RNA (100 μg) was purified, using the RNeasy Mini Kit (Qiagen, Inc., Valencia, CA) with minor modifications. Total RNA was eluted twice with 50 μL of 65°C DEPC-treated water. Double-strand cDNA was synthesized from 25 μg total RNA with the Superscript II (Invitrogen, Carlsbad, CA) and a T7-(dT) 24 oligomer then purified by phase-lock gel (Eppendof, Wesbury, NY) with phenol/chloroform extraction. Biotin-labeled cRNA was produced with Enzo BioArray High Yield RNA Transcription Labeling kit according to the manufacturer's instructions. The biotinylated cRNA was fragmented to 50 to 200 nucleotides by heating (94°C for 35 minutes) and chilled on ice.

For microarray analysis, three normal pituitary glands and 11 nonfunctional pituitary adenomas were analyzed using HG-U95Av2 GeneChips (Affymetrix, Santa Clara, CA) at the Emory/Veterans' Administration Medical Center (Atlanta, GA). All samples were analyzed in duplicate, starting from the extraction of total RNA, GeneChip hybridization, washing, scanning, and data analysis. Five additional normal pituitary glands were analyzed once using the same chips, HG-U95Av2 GeneChips at the Moffit Comprehensive Cancer Center, University of South Florida (Tampa, FL).

Data analysis. Gene expression data from 12,625 probe sets on the HG-U95Av2 GeneChips were normalized, using GCRMA normalization with GeneTraffic Software (Iobion, La Jolla, CA). After data normalization, genes with uniformly low expression were removed from consideration, leaving 7,241 probe sets for analysis using significance analysis of microarrays (SAM) software (17). The following are the relevant variables for the SAM analysis: imputation engine, 10-nearest neighbor; number of permutations, 500; RNG seed, 1234567; delta, 1.063; fold change, 2.0. Normalized expression data from the 297 significant probe sets were analyzed by a two-dimensional hierarchical clustering, using Spotfire DecisionSite 8.1 software. Data was clustered using unweighed averages and ordered using average Euclidian distance.

For K-nearest neighbor (KNN) prediction, the normalized RT-qPCR data was analyzed with GenePattern software (http://www.broad.mit.edu/cancer/software/genepattern/), and both the KNN cross-validation and class prediction modules were used (KNN = 3). For these analyses, the four genes (or features) that were included were NADP-dependent isocitrate dehydrogenase (IDH1), paired-like homeodomain transcription factor 2 (PITX2), Notch homologue 3 (NOTCH3), and delta-like 1 homologue (Drosophila, DLK1).

To identify genes uniquely altered in tumor subtypes, SAM analysis was done with both the normal samples and other tumor subtypes as the control group, and the subtype of interest as the experimental group. Analyses were done with 500 permutations, fold change of 2.0, and false discovery rate (FDR) < 1%.

Reverse transcriptase real-time quantitative PCR. RT-qPCR was done as described (15, 16) on four gene transcripts in 23 nonfunctional pituitary adenomas and eight normal pituitary glands in a blinded fashion. Primers were selected using Primer Express software, version 2.0 (PE Applied Biosytems, Foster City, CA), BLASTed against all Homo sapiens gene sequences in Genbank for selectivity. The following are the primers of these genes:

 Forward primer Reverse primer 
IDH1 CACTACCGCATGTACCAGAAAGG TCTGGTCCAGGCAAAAATGG 
PITX2 GCCGGGATCGTAGGACCTT GTGCCCACGACCTTCTAGCA 
NOTCH3 TCTCAGACTGGTCCGAATCCAC CCAAGATCTAAGAACTGACGAGCG 
DLK1 CACATGCTGCGGAAGAAGAAGAAC ACCGCGTATAGTAAGCTCTGAGGA 
 Forward primer Reverse primer 
IDH1 CACTACCGCATGTACCAGAAAGG TCTGGTCCAGGCAAAAATGG 
PITX2 GCCGGGATCGTAGGACCTT GTGCCCACGACCTTCTAGCA 
NOTCH3 TCTCAGACTGGTCCGAATCCAC CCAAGATCTAAGAACTGACGAGCG 
DLK1 CACATGCTGCGGAAGAAGAAGAAC ACCGCGTATAGTAAGCTCTGAGGA 

rRNA (18S rRNA from PE Applied Biosystems) was used as an internal control. All PCR reactions were done at least in duplicate and cycled in the GeneAmp 5700 Sequence Detection System: 50.0°C for 2 minutes, 95.0°C for 10 minutes, and 40 cycles of 95.0°C for 15 seconds and 60.0°C for 1 minute. The quantity of the specific genes obtained from standard curves was normalized to that of the 18S rRNA of the same sample. Fold change of each gene was calculated as the ratio of the mean of the normalized mRNA of nonfunctional compared with that of the normal pituitary controls.

Two-dimensional gel electrophoresis of pituitary proteins. The detailed experimental protocols have been published (18, 19). Briefly, each whole control pituitary tissue (0.45-0.70 g; n = 8) and each adenoma tissue (15-75 mg; n = 11) was homogenized and lyophilized, and the protein content was measured. For an 18-cm IPG strip, pH 3 to 10 nonlinear (Amersham Pharmacia Biotech, Piscataway, NJ), a total of 70 μg of pituitary protein was used for two-dimensional gel electrophoresis. Isoelectric focusing was done on a Multiphor II instrument (Amersham Pharmacia Biotech) with precast IPG strips (pH 3-10, nonlinear, 180 × 3 × 0.5 mm). SDS-PAGE was done on a PROTEAN plus Dodeca Cell (Bio-Rad, Hercules, CA) that can analyze up to 12 gels at a time with a 12% PAGE resolving gel (190 × 205 × 1.0 mm) that was cast with a PROTEAN plus Multicasting Chamber (Bio-Rad). The two-dimensional gel electrophoresis–separated proteins were visualized with a modified silver-staining method. The differential spots were determined between control pituitaries (n = 8, number of gels = 30) versus NF (n = 2, number of gels = 6), LH+ (n = 3, number of gels = 9), FSH+ (n = 3, number of gels = 9), FSH+ and LH+ (n = 3, number of gels = 9).

Image analysis of digitized two-dimensional gels. The silver-stained two-dimensional gels were digitized, and the digitized gels were analyzed qualitatively and quantitatively with the PDQuest two-dimensional Gel Analysis software for a PC computer (version 7.1.0, Bio-Rad). The total density in a gel image was used to normalize each spot volume in the gel image to minimize the effect of any experimental factor on the quantitative analysis (1921). Gel images in the match set were grouped into the following: control, NF, LH+, FSH+, and FSH+ and LH+. The comparison analyses were done with the average normalized-volume among the five groups.

Mass spectrometry characterization of proteins. Each differential spot was labeled, excised from the two-dimensional gels, and subjected to in-gel trypsin digestion (18). That mixture of tryptic peptides was purified with a ZipTipC18 microcolumn (ZTC18S096, Millipore, Bedford, MA) according to the manufacturer's instructions. The purified tryptic-peptide mixture was analyzed with a Perseptive Biosystems matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) Voyager DE-RP mass spectrometer (Framingham, MA) and with an LCQDeca mass spectrometer (LC-ESI-Q-IT) equipped with a standard electrospray source (ThermoFinnigan, San Jose, CA). For MALDI-TOF MS analysis, the peptide-mass fingerprinting (PMF) data were generated. For liquid chromatography electrospray ionization ion-quadrupole-ion trap (LC-ESI-Q-IT) analysis, the amino acid sequence of each LC-separated tryptic peptide was obtained. The MALDI-TOF MS PMF data were used to identify the protein by searching the SWISS-PROT/TrEMBL database with PeptIdent software (http://us.expasy.org/tools/peptident.html). The LC-ESI-Q-IT tandem MS (MS/MS) data were used to identify the protein by searching the SWISS-PROT and NCBInr databases with the SEQUEST software that is a part of the LCQDeca software package.

The clinical and pathologic characteristics of all nonfunctional pituitary tumors in this study are summarized in Supplementary Table 1. The 11 pituitary tumors used in microarray analysis included tumors immunohistochemically negative for anterior pituitary hormones (NF, n = 2), positive for luteinizing hormone (LH+, n = 3), positive for follicle-stimulating hormone (FSH+, n = 3), and positive for both FSH and LH (FSH+ and LH+, n = 3). These same 11 tumors were used in proteomic and RT-qPCR analyses. A total of 23 nonfunctional tumors were used for RT-PCR analysis (Supplementary Table 1).

Cluster analysis of gene expression by microarray analysis. Data from two replicate hybridizations (57-2 and 208-2) were of poor quality and removed from the analysis. To identify genes that were differentially expressed between tumor and normal pituitary samples in a statistically significant manner, we used the SAM software (17). Using a highly conservative threshold (fold change > 2.0, FDR < 1%), we identified 297 probe sets corresponding to 284 unique genes that were significantly different between pituitary tumors and normal tissues. Normalized expression data from these 297 probe sets were analyzed by two-dimensional hierarchical clustering (Fig. 1A). A complete list of these 284 unique genes is given in Supplementary Table 2, and a subset of those genes is provided in Table 2. In general, tumor suppressors (e.g., NBL1) and apoptosis inducers (BNIP3) were down-regulated, whereas antiapoptotic genes (PIK3C2B and FAIM2) were up-regulated. In addition, the apoptosis inhibitor BCL2 was down-regulated by >3-fold compared with normal pituitary, suggesting that other antiapoptotic mechanisms are at work in nonfunctional pituitary adenomas. Positive regulators of cell cycle progression, such as PCTAIRE protein kinase 3, exhibited an increased expression, whereas negative cell cycle regulators, such as GADD45β and GAS1, were down-regulated.

Figure 1.

A, expression pattern of genes that are significantly different between nonfunctional pituitary adenomas and normal pituitary tissues analyzed by two-dimensional hierarchical clustering. Red, higher expression; green, lower expression; black, nonsignificant genes. B, RT-qPCR of relative expression of IDH1, PITX2, NOTCH3, and DLK1 mRNAs in nonfunctional adenomas (n = 23) compared with normal pituitaries (n = 8). Columns, mean mRNA expression of each gene in nonfunctional tumors or normal pituitary glands; bars, SE. Increased expression for IDH1 (41-fold), PITX2 (14-fold), and NOTCH3 (14-fold) were all confirmed by RT-qPCR. Decreased expression for DLK1 was also confirmed (−717-fold). C, prediction results of KNN analysis using RT-qPCR data for IDH1, PITX2, NOTCH3, and DLK1. Predictions generated with GenePattern software were 100% correct with both leave-one-out cross-validation and with independent training and test sets.

Figure 1.

A, expression pattern of genes that are significantly different between nonfunctional pituitary adenomas and normal pituitary tissues analyzed by two-dimensional hierarchical clustering. Red, higher expression; green, lower expression; black, nonsignificant genes. B, RT-qPCR of relative expression of IDH1, PITX2, NOTCH3, and DLK1 mRNAs in nonfunctional adenomas (n = 23) compared with normal pituitaries (n = 8). Columns, mean mRNA expression of each gene in nonfunctional tumors or normal pituitary glands; bars, SE. Increased expression for IDH1 (41-fold), PITX2 (14-fold), and NOTCH3 (14-fold) were all confirmed by RT-qPCR. Decreased expression for DLK1 was also confirmed (−717-fold). C, prediction results of KNN analysis using RT-qPCR data for IDH1, PITX2, NOTCH3, and DLK1. Predictions generated with GenePattern software were 100% correct with both leave-one-out cross-validation and with independent training and test sets.

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Table 2.

Expression changes of selected genes in nonfunctional adenomas

Probe set IDSymbolGene nameGene Ontology categoryFold change
39930_at EPHB6 EphB6 Signal transduction 15.09 
203_at GATA2 GATA binding protein 2 Transcription 14.55 
35243_at PCTK3 PCTAIRE protein kinase 3 Cell cycle 14.07 
39023_at IDH1 Isocitrate dehydrogenase 1 (NADP+), soluble Metabolism 13.10 
40511_at GATA3 GATA binding protein 3 Transcription 12.76 
41014_s_at PITX2 Paired-like homeodomain transcription factor 2 Transcription 12.44 
821_s_at FOLR1 Folate receptor 1 (adult) Transport 5.66 
424_s_at FGFR1 Fibroblast growth factor receptor 1 Signal transduction 4.27 
38750_at NOTCH3 Notch homologue 3 Transcription 4.26 
2054_g_at CDH2 N-cadherin Cell adhesion 3.23 
32565_at SMARCD3 SWI/SNF related d3 Transcription 3.00 
37920_at PITX1 Paired-like homeodomain transcription factor 1 Transcription 2.92 
469_at EFNB3 Ephrin-B3 Cell-cell signaling 2.42 
871_s_at HLF Hepatic leukemia factor Transcription 2.34 
41715_at PIK3C2B Phosphoinositide-3-kinase, class 2, beta Signal transduction 2.26 
823_at CX3CL1 Chemokine (C-X3-C motif) ligand 1 Cell motility 2.24 
40837_at TLE2 Transducin-like enhancer of split 2 Development 2.03 
33596_at SPOCK3 Testican 3 Integral membrane 2.01 
41536_at ID4 Inhibitor of DNA binding 4 Transcription −2.18 
33904_at CLDN3 Claudin 3 Cell adhesion −2.47 
1586_at IGFBP3 Insulin-like growth factor binding protein 3 Apoptosis −2.59 
1909_at BCL2 B-cell CLL/lymphoma 2 Apoptosis −2.71 
41839_at GAS1 Growth arrest–specific 1 Cell cycle −3.62 
38010_at BNIP3 BCL2/adenovirus E1B 19-kDa interacting protein 3 Apoptosis −3.85 
37043_at ID3 Inhibitor of DNA binding 3 Transcription −4.10 
36199_at DAP Death-associated protein Apoptosis −4.56 
40570_at FOXO1A Forkhead box O1A (rhabdomyosarcoma) Transcription −5.79 
37005_at NBL1 Neuroblastoma, suppression of tumorigenicity 1 Oncogenesis −6.08 
32786_at JUNB jun B proto-oncogene Transcription −6.12 
39822_s_at GADD45B Growth arrest and DNA damage–inducible, β Signal transduction −6.24 
41215_s_at ID2 Inhibitor of DNA binding 2 Transcription −6.30 
39352_at CGA Glycoprotein hormones, α polypeptide Cell-cell signaling −7.53 
31874_at GAS2L1 Growth arrest–specific 2 like 1 Cell cycle −9.04 
36617_at ID1 Inhibitor of DNA binding 1 Transcription −9.73 
35378_at LHB LH β polypeptide Hormone −12.96 
1916_s_at FOS v-fos osteosarcoma viral oncogene homologue Transcription −15.90 
649_s_at CXCR4 Chemokine (C-X-C motif) receptor 4 Cell motility −16.36 
36784_at CSHL1 Chorionic somatomammotropin hormone-like 1 Hormone −85.03 
878_s_at PRL Prolactin Hormone −100.81 
40544_g_at ASCL1 Achaete-scute complex-like 1 (DrosophilaNeurogenesis −116.82 
40316_at GH2 Growth hormone 2 Hormone −307.69 
309_f_at GH2 Growth hormone 2 Hormone −578.03 
32648_at DLK1 Delta-like 1 homologue (DrosophilaDevelopment −917.43 
1332_f_at GH1 Growth hormone 1 Hormone −5,000.00 
Probe set IDSymbolGene nameGene Ontology categoryFold change
39930_at EPHB6 EphB6 Signal transduction 15.09 
203_at GATA2 GATA binding protein 2 Transcription 14.55 
35243_at PCTK3 PCTAIRE protein kinase 3 Cell cycle 14.07 
39023_at IDH1 Isocitrate dehydrogenase 1 (NADP+), soluble Metabolism 13.10 
40511_at GATA3 GATA binding protein 3 Transcription 12.76 
41014_s_at PITX2 Paired-like homeodomain transcription factor 2 Transcription 12.44 
821_s_at FOLR1 Folate receptor 1 (adult) Transport 5.66 
424_s_at FGFR1 Fibroblast growth factor receptor 1 Signal transduction 4.27 
38750_at NOTCH3 Notch homologue 3 Transcription 4.26 
2054_g_at CDH2 N-cadherin Cell adhesion 3.23 
32565_at SMARCD3 SWI/SNF related d3 Transcription 3.00 
37920_at PITX1 Paired-like homeodomain transcription factor 1 Transcription 2.92 
469_at EFNB3 Ephrin-B3 Cell-cell signaling 2.42 
871_s_at HLF Hepatic leukemia factor Transcription 2.34 
41715_at PIK3C2B Phosphoinositide-3-kinase, class 2, beta Signal transduction 2.26 
823_at CX3CL1 Chemokine (C-X3-C motif) ligand 1 Cell motility 2.24 
40837_at TLE2 Transducin-like enhancer of split 2 Development 2.03 
33596_at SPOCK3 Testican 3 Integral membrane 2.01 
41536_at ID4 Inhibitor of DNA binding 4 Transcription −2.18 
33904_at CLDN3 Claudin 3 Cell adhesion −2.47 
1586_at IGFBP3 Insulin-like growth factor binding protein 3 Apoptosis −2.59 
1909_at BCL2 B-cell CLL/lymphoma 2 Apoptosis −2.71 
41839_at GAS1 Growth arrest–specific 1 Cell cycle −3.62 
38010_at BNIP3 BCL2/adenovirus E1B 19-kDa interacting protein 3 Apoptosis −3.85 
37043_at ID3 Inhibitor of DNA binding 3 Transcription −4.10 
36199_at DAP Death-associated protein Apoptosis −4.56 
40570_at FOXO1A Forkhead box O1A (rhabdomyosarcoma) Transcription −5.79 
37005_at NBL1 Neuroblastoma, suppression of tumorigenicity 1 Oncogenesis −6.08 
32786_at JUNB jun B proto-oncogene Transcription −6.12 
39822_s_at GADD45B Growth arrest and DNA damage–inducible, β Signal transduction −6.24 
41215_s_at ID2 Inhibitor of DNA binding 2 Transcription −6.30 
39352_at CGA Glycoprotein hormones, α polypeptide Cell-cell signaling −7.53 
31874_at GAS2L1 Growth arrest–specific 2 like 1 Cell cycle −9.04 
36617_at ID1 Inhibitor of DNA binding 1 Transcription −9.73 
35378_at LHB LH β polypeptide Hormone −12.96 
1916_s_at FOS v-fos osteosarcoma viral oncogene homologue Transcription −15.90 
649_s_at CXCR4 Chemokine (C-X-C motif) receptor 4 Cell motility −16.36 
36784_at CSHL1 Chorionic somatomammotropin hormone-like 1 Hormone −85.03 
878_s_at PRL Prolactin Hormone −100.81 
40544_g_at ASCL1 Achaete-scute complex-like 1 (DrosophilaNeurogenesis −116.82 
40316_at GH2 Growth hormone 2 Hormone −307.69 
309_f_at GH2 Growth hormone 2 Hormone −578.03 
32648_at DLK1 Delta-like 1 homologue (DrosophilaDevelopment −917.43 
1332_f_at GH1 Growth hormone 1 Hormone −5,000.00 

Two components of the delta-Notch pathway, NOTCH3 and DLK1, were strongly altered in opposite directions. NOTCH3 was up-regulated nearly 5-fold, whereas DLK1 was repressed over 900-fold. In addition, human achaete-scute homologue (ASCL1/HASH1), which is essential for DLK1 expression, was down-regulated over 100-fold. The fibroblast growth factor receptor 1 (FGFR1) was up-regulated over 3-fold in nonfunctional pituitary adenomas. In addition, expression of the insulin-like growth factor binding protein 3 (IGFBP3), which decreases IGF availability and signaling, was reduced 3-fold compared with normal tissues.

Over 50 transcription factors were altered in their expression in nonfunctional pituitary adenomas, making it one of the largest functional categories (Supplementary Table 2). Several developmental transcription factors, including NOTCH3, PITX1, PITX2, and PBX3, were up-regulated, whereas others, such as C/EBPδ, were down-regulated. Surprisingly, the inhibitors of the DNA-binding family (ID1, ID2, ID3, and ID4), which are inhibitors of neural differentiation and are frequently overexpressed in neuroectodermal tumors (22), were all down-regulated in pituitary adenomas. Some transcription factors associated with oncogenesis, such as FOS, JUNB, and FOXO1A/FKHR, were also down-regulated. Nevertheless, decreased levels of FOXO1A/FKHR are consistent with an elevated signaling through the phosphatidylinositol 3-kinase/PTEN/AKT pathway, because AKT activation results in the exclusion of FKHR from the nucleus (23).

Several genes that enhance cell motility and invasion were increased in nonfunctional pituitary adenomas, including ephrin receptor B6, ephrin B3, testican 3, N-cadherin 2, and the chemokine ligand CX3CL1. The down-regulation of the tight-junction molecule claudin 3 was consistent with a cellular phenotype of increased motility. Moreover, our gene expression profiling results were consistent with the expectations for the nonfunctional tumors. For example, the up-regulation of the folate receptor (FOLR1) was consistent with our previous observations (15, 16). In addition, LH, growth hormone 1, growth hormone 2, chorionic somatomammotropin hormone-like 1, and prolactin were all strongly down-regulated as expected.

Validation of expression array result by reverse transcription real-time quantitative PCR analysis. To validate the microarray analysis, we measured with RT-qPCR, using SYBR Green I dye detection the mRNA expression levels of IDH1, PITX2, NOTCH3, and DLK1 using blinded samples which consisted of 23 nonfunctional tumors and eight normal pituitary glands. The 23 nonfunctional tumors composed of six FSH+, six LH+, six FSH+ and LH+, and five NF (Supplementary Table 1). Using blinded samples to do RT-qPCR, the normalized value of each gene of each sample was used to classify the sample in two groups (nonfunctional and control groups) correctly. RT-qPCR analysis showed that IDH1, PITX2, and NOTCH3 mRNAs were significantly up-regulated respectively 41-fold, 14-fold, and 14-fold in nonfunctional pituitary adenomas, and that DLK1 mRNA expression was down-regulated 717-fold (Fig. 1B).

We next used the RT-qPCR data to attempt to classify the nonfunctional tumor and normal samples using the KNN method. With the GenePattern software (http://www.broad.mit.edu/cancer/software/genepattern/), the tumor and normal samples were correctly predicted in 100% of the cases using the normalized RT-qPCR data for these four genes (Fig. 1C). This level of accuracy was achieved both with leave-one-out cross-validation and when the data was separated into independent training sets (n = 21) and test sets (n = 10).

Proteomic analysis of human nonfunctional pituitary adenoma and control samples. The proteomes from control pituitary versus NF, LH+, FSH+, and FSH+ and LH+ tumors were analyzed by two-dimensional gel electrophoresis. Each sample was analyzed three to five times, and ca. 1,000 protein spots were detected in each gel (Fig. 2 contains a digital master gel map). For each sample, the correlation coefficient (r) of the normalized volumes between-gel matched spots was >0.73 (range, 0.76-0.92), and the mean between-gel, matched percentage was 85% to 99% for the controls and 81% to 90 % for the adenomas. A total of 93 differential protein spots were found among the different cell types of nonfunctional adenomas. Each differential spot was labeled in the digital master gel map (Fig. 2). Supplementary Table 3 contains those MS-characterized protein spots for which the spot volume in the control group was significantly different from each adenoma group (P < 0.001). Differential spots were excised, in-gel trypsin digestion was done, and MS (MALDI-TOF PMF and/or LC-ESI-Q-IT MS/MS) was used to characterize the protein in each differential spot (18). Among those 93 differential spots, 72 spots contained 50 differentially regulated proteins (21 up-regulated and 29 down-regulated) that were characterized. MS/MS data were used to search the database with SEQUEST software.

Figure 2.

A digitized two-dimensional gel electrophoresis reference map (Gaussian image) from a human control pituitary proteome labeled with the 93 differential spots and the four protein standard markers. Isoelectric focusing (IEF) was done with an 18-cm IPG strip (pH 3-10, nonlinear), and vertical SDS-PAGE was done with a 12% polyacrylamide gel.

Figure 2.

A digitized two-dimensional gel electrophoresis reference map (Gaussian image) from a human control pituitary proteome labeled with the 93 differential spots and the four protein standard markers. Isoelectric focusing (IEF) was done with an 18-cm IPG strip (pH 3-10, nonlinear), and vertical SDS-PAGE was done with a 12% polyacrylamide gel.

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The 50 MS-characterized differentially expressed proteins were categorized into several different functional groups (Supplementary Table 3). Identified proteins that were affected in nonfunctional adenomas included the following: (a) Neuroendocrine-related proteins, including somatotropin, secretagogin, and mu-crystallin homologue, were down-regulated in the nonfunctional pituitary adenomas. (b) At least 17 isoforms of somatotropin were down-regulated. (c) Immunologic regulation proteins and tumor-related antigen (immunoglobulin, tumor rejection antigen 1) were down-regulated. (d) Cell proliferation, differentiation, and apoptosis-related proteins were down-regulated. (e) Cell defense and stress resistance proteins (phospholipid hydroperoxide glutathione peroxidase, CD59 glycoprotein, and heat shock 27-kDa protein) were down-regulated. (f) Some metabolic enzyme-related proteins (IDH [NADP] cytoplasmic, tryptophan 5-hydroxylase 2, matrix metalloproteinase-9, aldose reductase, lactoylglutathione lyase, acyl-CoA-binding protein) were up-regulated in the nonfunctional pituitary adenomas.

Proteomic validation of gene expression profiling data. We compared the proteomic data with gene expression profiling data for consistency at the protein and the mRNA levels. Whereas expression profiling identified 284 altered genes, proteomics identified 50 altered proteins (10.7%). Of these 50 proteins, only 40 had corresponding probe sets present on the U95A GeneChip. Of those 40 detectable genes, 31 genes (77.5%) were detected as present at the mRNA level. Four of these genes (IDH1, GH1, GH2, and PRL) met our mRNA significance criteria and were identified by both experimental approaches. Thirteen additional genes (32.5%) did not meet our significance criteria but nevertheless had mRNA changes of >1.3-fold in the same direction as observed changes at the protein level. Only one gene (Thioredoxin domain containing 9, O14530) had opposite changes and was decreased at the protein level but increased (1.32-fold) at the mRNA level. Finally, 32.5% of the altered proteins showed essentially no change at the mRNA level. In general, there was quite good agreement between the proteomics and expression data (43%), although not surprisingly, there were proteins with altered abundance that showed little if any change at the level of transcription. These genes are likely regulated by altered translational efficiencies and posttranslational effects on protein stabilities.

Molecular classification of nonfunctional pituitary tumors by subtype. We further analyzed the gene expression profiles of all four nonfunctional pituitary subtypes to each other and identified genes that were affected uniquely in each subtype (Table 3). To qualify for this description, the genes had to be significantly different in only a single subtype relative to normal tissue and also be significantly different between that tumor subtype and the other tumor subtypes. No genes were uniquely altered in the nonfunctional tumors that expressed both the LH+ and FSH+ subtype. In the nonfunctional tumors that expressed FSH+ subtype, CXCL13 and hematopoietic PBX-interacting protein were up-regulated. In the nonfunctional tumors that expressed LH+ subtype, uniquely up-regulated genes included MLP, GUCY1A3, TMPRSS6, and BASP1. In the NF subtype tumors, 14 genes were uniquely up-regulated, including four Histone 2b variants, two Histone 2a variants, and synaptotagmin I.

Table 3.

Molecular classification of nonfunctional tumors by subtype by gene expression profile analysis

SubtypeFold changeGene symbolProbe set IDAccession no.Title
FSH+ 4.7 CXCL13 41104_at NM_006419 Chemokine (C-X-C motif) ligand 13 
FSH+ 2.7 HPIP 38063_at NM_020524 Hematopoietic PBX-interacting protein 
FSH+ 2.2 KRT19 40899_at NM_002276 Keratin 19 
LH+ 4.5 MLP 36174_at NM_023009 MARCKS-like protein 
LH+ 2.7 GUCY1A3 36918_at NM_000856 Guanylate cyclase 1, soluble, α 3 
LH+ 2.4 TMPRSS6 34067_at NM_153019 Type II transmembrane serine protease 6 
LH+ 2.4 BASP1 32607_at NM_006317 Brain abundant, membrane attached signal protein 1 
NF 15.9 C7 37394_at NM_000587 Complement component 7 
NF 5.9 PDLIM1 36937_s_at NM_020992 PDZ and LIM domain 1 (elfin) 
NF 4.6 HIST1H2AC 34308_at NM_003512 Histone 1, H2ac 
NF 4.1 GPR49 41073_at NM_003667 G protein–coupled receptor 49 
NF 3.9 SYT1 40075_at NM_005639 Synaptotagmin I 
NF 3.7 ALS2CR3 40064_at NM_015049 Amyotrophic lateral sclerosis 2, candidate 3 
NF 3.6 HIST1H2BL 35576_f_at NM_003519 Histone 1, H2bl 
NF 3.3 HIST2H2AA 286_at NM_003516 Histone 2, H2aa 
NF 3.1 HIST1H2BN 36347_f_at NM_003520 Histone 1, H2bn 
NF 3.0 PTPN3 31885_at NM_002829 Protein tyrosine phosphatase, nonreceptor type 3 
NF 2.3 — 34114_at AL109678 cDNA clone EUROIMAGE 28993. 
NF 2.2 HIST1H2BM 31528_f_at NM_003521 Histone 1, H2bm 
NF 2.2 HIST1H2BK 32819_at NM_080593 Histone 1, H2bk 
NF 2.2 AGPAT1 32836_at NM_006411 1-Acylglycerol-3-phosphate O-acyltransferase 1 
SubtypeFold changeGene symbolProbe set IDAccession no.Title
FSH+ 4.7 CXCL13 41104_at NM_006419 Chemokine (C-X-C motif) ligand 13 
FSH+ 2.7 HPIP 38063_at NM_020524 Hematopoietic PBX-interacting protein 
FSH+ 2.2 KRT19 40899_at NM_002276 Keratin 19 
LH+ 4.5 MLP 36174_at NM_023009 MARCKS-like protein 
LH+ 2.7 GUCY1A3 36918_at NM_000856 Guanylate cyclase 1, soluble, α 3 
LH+ 2.4 TMPRSS6 34067_at NM_153019 Type II transmembrane serine protease 6 
LH+ 2.4 BASP1 32607_at NM_006317 Brain abundant, membrane attached signal protein 1 
NF 15.9 C7 37394_at NM_000587 Complement component 7 
NF 5.9 PDLIM1 36937_s_at NM_020992 PDZ and LIM domain 1 (elfin) 
NF 4.6 HIST1H2AC 34308_at NM_003512 Histone 1, H2ac 
NF 4.1 GPR49 41073_at NM_003667 G protein–coupled receptor 49 
NF 3.9 SYT1 40075_at NM_005639 Synaptotagmin I 
NF 3.7 ALS2CR3 40064_at NM_015049 Amyotrophic lateral sclerosis 2, candidate 3 
NF 3.6 HIST1H2BL 35576_f_at NM_003519 Histone 1, H2bl 
NF 3.3 HIST2H2AA 286_at NM_003516 Histone 2, H2aa 
NF 3.1 HIST1H2BN 36347_f_at NM_003520 Histone 1, H2bn 
NF 3.0 PTPN3 31885_at NM_002829 Protein tyrosine phosphatase, nonreceptor type 3 
NF 2.3 — 34114_at AL109678 cDNA clone EUROIMAGE 28993. 
NF 2.2 HIST1H2BM 31528_f_at NM_003521 Histone 1, H2bm 
NF 2.2 HIST1H2BK 32819_at NM_080593 Histone 1, H2bk 
NF 2.2 AGPAT1 32836_at NM_006411 1-Acylglycerol-3-phosphate O-acyltransferase 1 

NOTE: Genes that were altered specifically in each subtype of nonfunctional adenomas are shown. NF, negative immunohistochemical stains for ACTH, LH, FSH, PRL, GH, and TSH.

Adenomas were graded blindly by a neuropathologist from 0 to 4 for intensity of staining for each peptide hormone.

Our gene expression profiling of nonfunctional pituitary adenomas identified 115 up-regulated genes and 169 down-regulated genes. Some gene profiles showed good agreement with the proteomic data. In addition, the gene expression data were consistent with expected results for a number of genes, such as FOLR1, growth hormone, and prolactin Pit-1. The RT-qPCR results of all four genes tested (IDH1, PITX2, NOTCH3, and DLK1) confirmed our gene expression profiling data.

We observed increased levels of IDH1 mRNA by gene expression profiling, RT-qPCR, and proteomic analysis. IDHs (24) catalyze oxidative decarboxylation of isocitrate into α-ketoglutarate. In addition, mitochondrial and cytosolic NADP(+)-dependent IDHs play an important role in cellular defense against oxidative damage as a source of NADPH (25, 26). Moreover, NADP-dependent IDH is up-regulated in human colon tumors (27) and may prevent apoptosis in tumors cells via detoxification of tumor therapeutic drugs (28).

Our observation that PITX2 is overexpressed in nonfunctional pituitary adenomas is consistent with previous analyses of human pituitary adenomas (29). PITX1 (also known as P-OTX) and PITX2 specify closely related bicoid transcription factors that appear early, are made in multiple tissues, and continue to be expressed in adult life (30, 31). PITX2a and PITX1 both interact with Pit-1, a master regulator of pituitary cell differentiation, thyroid-stimulating hormone, growth hormone, and prolactin genes (31, 32).

Normally, PITX2 mRNA displays a rapid turnover rate, but activation of the Wnt/β-catenin pathway stabilizes PITX2 mRNA (33). In fact, PITX2 is rapidly induced by the Wnt/Dvl/β-catenin pathway and is required for effective cell type–specific proliferation during pituitary development by directly activating cyclin D2 expression (34). Regulated exchange of HDAC1/β-catenin converts PITX2 from repressor to activator, analogous to the control of TCF/LEF1. PITX2 serves as a competence factor that is required for the temporally ordered and growth factor–dependent recruitment of a series of specific coactivator complexes that prove necessary for cyclin D2 and cyclin D1 (35) gene induction. Although we observed increased cyclin D1 levels, cyclin D2 expression was not increased. In addition, SFRP1 levels were up-regulated 9-fold, and SFRP1 has been shown to be capable of increasing Wnt signaling rather than antagonizing it in some conditions (36, 37). Taken together, the changes we observed in SFRP1, PITX2, and cyclin D1 are all consistent with a model in which elevated Wnt/β-catenin signaling is important for nonfunctional pituitary adenomas (Fig. 3). Moreover, increased nuclear accumulation of β-catenin has been detected by immunohistochemistry in 57% of pituitary adenomas (38), consistent with our conclusion that the Wnt/β-catenin pathway is important in the progression of this malignancy.

Figure 3.

A model of the effects of Wnt and Notch signaling on nonfunctional pituitary adenomas. Genes with an increased mRNA level (up arrow) and with a decreased expression (down arrow). In this model, increased SFRP1 activates the Wnt pathway, resulting in nuclear β-catenin, increased PITX2, elevated cyclin D1, and cellular proliferation. In parallel, increased NOTCH3 represses HASH1/ASCL1, resulting in reduced DLK1 and inhibiting cellular differentiation, and PIT1 and GH expression. How increased TLE2 and decreased HES-1 levels affect tumor progression requires further investigation but may enhance proliferation and inhibit differentiation.

Figure 3.

A model of the effects of Wnt and Notch signaling on nonfunctional pituitary adenomas. Genes with an increased mRNA level (up arrow) and with a decreased expression (down arrow). In this model, increased SFRP1 activates the Wnt pathway, resulting in nuclear β-catenin, increased PITX2, elevated cyclin D1, and cellular proliferation. In parallel, increased NOTCH3 represses HASH1/ASCL1, resulting in reduced DLK1 and inhibiting cellular differentiation, and PIT1 and GH expression. How increased TLE2 and decreased HES-1 levels affect tumor progression requires further investigation but may enhance proliferation and inhibit differentiation.

Close modal

NOTCH3 is strongly overexpressed, whereas DLK1, a potential ligand of NOTCH3, was one of the most strongly down-regulated gene in nonfunctional tumors. Members of the delta family serve as ligands in cell-to-cell interactions with Notch receptors that control cell fate during differentiation. Interaction of Notch receptors with their cell-bound ligands results in the proteolytic cleavage of Notch, translocation of the intracellular domain (Notch-IC) to the nucleus, and altered gene expression. Emerging data also support the role of the Notch signaling pathway in tumorigenesis and neural development. Constitutive expression of NOTCH3-IC in the peripheral epithelium in the developing lung of transgenic mice resulted in altered lung morphology and delayed development, suggesting that NOTCH3 signaling could contribute to lung cancer progression through the inhibition of terminal differentiation (39). Moreover, transgenic mice expressing NOTCH3-IC in thymocytes and T cells developed early and aggressive T-cell neoplasias (40). NOTCH3 and DLK1 expression is inversely correlated in neuroblastoma cell lines that can be divided into two groups with high DLK1/low NOTCH3 expression, or high NOTCH3/low DLK1 expression (41). Importantly, this study found a perfect correlation of mRNA and protein levels for both NOTCH3 and DLK1 (41). In neuroendocrine cell differentiation, changes in DLK1 levels seem to mark the decision to mature along different lineages. Thus, the high levels of NOTCH3 and low levels of DLK1 in nonfunctional pituitary adenomas suggest that they are derived from earlier-stage, undifferentiated cells, or that they have taken on a dedifferentiated state, consistent with the loss of Pit-1 expression.

The nonfunctional adenoma cells seem to have switched from a delta-expressing cell type to a Notch-expressing cell type and might receive stimulatory signals from neighboring normal pituitary cells that still express DLK1. This hypothesis suggests that inhibitors of Notch processing, such as γ-secretase inhibitors that were developed for Alzheimer's therapies and are now in clinical trials to treat Notch-activated T-cell acute lymphoblastic leukemia (42), could also prove of benefit to patients with nonfunctional pituitary adenomas.

Another gene involved in the delta-Notch pathway that was strongly down-regulated in nonfunctional adenomas was ASCL1. In Drosophila, Achaete-Scute genes are upstream of and directly activate the expression of delta and Notch (43), and in developing mouse neuroendocrine cells, DLK1 expression depends on the mouse Achaete-Scute homologue (44). Furthermore, high levels of Notch signaling can induce the transcriptional silencing of ASCL1 (45). Thus, the high levels of NOTCH3 could repress ASCL1 leading to the loss of DLK1 expression in nonfunctional adenomas. Transducin-like enhancer of split 2 (TLE2) is a mammalian homologue of the Drosophila transcriptional repressor groucho, which represses targets of β-catenin. TLE2 interacts with HES-1 and is expressed during neuronal development (46). Thus, TLE2, which was up-regulated 2-fold in nonfunctional adenomas, is a link between Wnt-signaling and Notch signaling. These data support a model (Fig. 3) in which NOTCH3 represses HASH1 and in cooperation with Wnt signaling; NOTCH3 maintains in these tumors in an undifferentiated state.

Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/).

The laboratory of D.M. Desiderio contributed the proteomic data, and the laboratory of N.M. Oyesiku contributed the microarray and reverse transcriptase real-time quantitative PCR data to this study.

Grant support: Department of Neurosurgery (N.M. Oyesiku), NIH grants K22-CA96560 (C.S. Moreno) and R01-CA106826 (C.S. Moreno), NS 42843 (D.M. Desiderio), RR-10522 (D.M. Desiderio), RR-14593 (D.M. Desiderio), and NSF DBI 9604633 (D.M. Desiderio).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

We thank the Department of Neuropathology, Emory University Hospital for the histology and immunohistochemistry analyses.

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Supplementary data