Distinct Transcriptional Profiles of Adrenocortical Tumors Uncovered by DNA Microarray Analysis
Thomas J. Giordano,* Dafydd G. Thomas,* Rork Kuick,+ Michelle Lizyness,* David E. Misek,+ Angela L. Smith,* Donita Sanders,* Rima T. Aljundi,* Paul G. Gauger,+ Norman W. Thompson,+ Jeremy M. G. Taylor,§ and Samir M. Hanash+
From the Departments of Pathology,* Pediatrics,1 Surgery,t and Biostatistics,5 University of Michigan Health System, Ann Arbor, Michigan
Comprehensive expression profiling of tumors using DNA microarrays has been used recently for molecu- lar classification and biomarker discovery, as well as a tool to identify and investigate genes involved in tumorigenesis. Application of this approach to a co- hort of benign and malignant adrenocortical tissues would be potentially informative in all of these as- pects. In this study, we generated transcriptional profiles of 11 adrenocortical carcinomas (ACCs), 4 adrenocortical adenomas (ACAs), 3 normal adrenal cortices (NCs), and 1 macronodular hyperplasia (MNH) using Affymetrix HG_U95Av2 oligonucleotide arrays representing ~10,500 unique genes. The ex- pression data set was used for unsupervised hierar- chical cluster analysis as well as principal component analysis to visually represent the expression data. An analysis of variance on the three classes (NC, ACA plus MNH, and ACC) revealed 91 genes that displayed at least threefold differential expression between the ACC cohort and both the NC and ACA cohorts at a significance level of P < 0.01. Included in these 91 genes were those known to be up-regulated in adre- nocortical tumors, such as insulin-like growth factor (IGF2), as well as novel differentially expressed genes such as osteopontin (SPP) and serine threonine ki- nase 15 (STK15). Increased expression of IGF2 was identified in 10 of 11 ACCs (90.9%) and was verified by quantitative reverse transcriptase-polymerase chain reaction. Select proliferation-related genes (TOP2A and Ki-67) were validated at the protein level using immunohistochemistry and adrenocortical tis- sue microarrays. Our results demonstrated significant and consistent gene expression changes in ACCs com- pared to benign adrenocortical lesions. Moreover, we identified several genes that represent potential diag- nostic markers and may play a role in the pathogen- esis of ACC. (Am J Pathol 2003, 162:521-531)
Adrenocortical carcinoma (ACC) is a rare but highly le- thal cancer with an annual incidence of 0.5 to 2 patients per million population.1 The pathological diagnosis of ACC is straightforward in most cases, based on well- recognized features of malignancy, including large tumor size and weight, solid growth pattern, extensive tumor necrosis, fibrous bands, lipid-poor cells, abundant mito- ses, atypical mitoses, nuclear pleomorphism, capsular invasion, and vascular invasion.2 However, there are oc- casional adrenocortical tumors whose malignant poten- tial is uncertain. Additionally, based on mitotic activity, it is possible to divide ACCs into prognostically significant low- and high-grade subgroups.3 There are also undif- ferentiated tumors of the retroperitoneum that are not readily identified as adrenocortical in origin using routine pathological methods. Thus, additional insight into the pathology of these tumors is clearly needed.
Several genes have been reported to have diagnostic significance in adrenocortical neoplasms. Numerous studies have documented the utility of @-inhibin immuno- histochemistry as a marker of adrenocortical differentia- tion,4-8 with the ability to distinguish adrenocortical tu- mors from adrenomedullary tumors, hepatocellular carcinoma and renal tumors,9 resulting in the acceptance of æ-inhibin as a clinically useful diagnostic marker. Sev- eral studies have investigated the ability of proliferative immunohistochemical markers, such as Ki-67 and topo- isomerase Il « (TOP2A), to distinguish benign and malig- nant tumors, 10-15 resulting in a general consensus that proliferative activity as measured by these markers is significantly higher in ACCs than benign lesions. Interest- ingly, assessment of proliferation by proliferative cell nu- clear antigen immunohistochemistry did not show a cor- relation with biological behavior.1º Finally, several studies
Supported by funds from the Millie Schembechler Adrenal Cancer Program of the University of Michigan Comprehensive Cancer Center, the Michigan National Institute of Diabetes and Digest and Kidney Diseases Biotechnology Center (National Institutes of Health no. DK58771), the University of Michigan Comprehensive Cancer Center Tissue Core (National Institutes of Health no. 46952), and indirectly by infrastructure provided by the National Cancer Institute Director’s Challenge project at the University of Michigan (National Institutes of Health no. 84952).
Accepted for publication October 29, 2002.
Address reprint requests to Thomas J. Giordano, M.D., Ph.D., Depart- ment of Pathology, University Hospital, 2G332/0054, 1500 E. Medical Center Dr., Ann Arbor, MI 48109-0054 E-mail: giordano@umich.edu.
have shown that immunoreactivity to p53 is essentially restricted to ACCs. 14-16
Despite some recent advances, the molecular patho- genesis of ACC is poorly understood. Mutations of the p53 tumor suppressor gene have been implicated be- cause of the association of ACC with Li-Fraumeni syn- drome 17 and confirmed by mutational analyses. 18-20 The insulin-like growth factor II gene (IGF2) is involved in the pathogenesis of both familial ACCs, as is the case in Beckwith-Wiedemann syndrome,21 as well as in sporadic ACCs.22,23 Dysregulation or rearrangement at 11p15.5 results in significant up-regulation of /GF2 in ACC, result- ing in an autocrine stimulatory loop.24
Gene expression profiling provides the opportunity to assess the expression of thousands of genes simulta- neously in a cohort of related tumors. Several practical applications, including but not limited to tumor classifica- tion,25-27 biomarker discovery,28 and prediction of ther- apeutic response,29 are being developed for a variety of tumors, including those of the endocrine system.3º Fur- thermore, expression profiles can provide insight into tumorigenesis and identify targets for therapeutic inter- vention.31,32 Here, we report the generation of extensive gene expression profiles of a cohort of normal, hyper- plastic, and neoplastic adrenocortical tissues and show that these profiles can readily distinguish benign from malignant tumors and identify tumors with unusual his- topathological features. In addition, we identify numerous differentially expressed transcripts and demonstrate in- creased /GF2 expression as one of the dominant tran- scriptional changes in ACC.
Materials and Methods
Tumors and Histopathology
The adrenocortical tissues analyzed in this study were procured from the University of Michigan Health System between 1994 and 2001 by the Tissue Procurement Ser- vice. The transcriptional profiling and validation studies were approved by the University of Michigan Institutional Review Board (IRB-Medicine). All tissues were pro- cessed in a similar manner. Frozen tumor samples were embedded in OCT freezing media (Miles Scientific, Na- perville, IL), cryotome sectioned (5 um), and evaluated by routine hematoxylin and eosin (H&E) stains. Areas of relatively pure tumor (at least 90% tumor cells) without necrosis when present in the carcinoma samples were selected for RNA isolation. The corresponding H&E sec- tions from original paraffin blocks and surgical pathology reports were reviewed and evaluated for various patho- logical features, such as tumor size, tumor weight, tumor grade, and the presence of necrosis, vascular invasion, capsular invasion, and cell type. The characteristics of the tissues used for expression profiling, along with lim- ited clinical and laboratory information, are presented in Table 1. Standard diagnostic criteria2 were used to diag- nose the adrenocortical tumors.
RNA Isolation
Single isolates of tissue samples were homogenized in the presence of Trizol reagent (Life Technologies, Inc., Gaithersburg, MD) and total cellular RNA was purified according to manufacturer’s procedures. RNA samples were further purified using acid phenol extraction and RNeasy spin columns (Qiagen, Valencia, CA) and used to prepare cRNA probes. RNA quality was assessed by 1% agarose gel electrophoresis in the presence of ethidium bromide. Samples that did not reveal intact 18S and 28S ribosomal bands were excluded from further study.
CRNA Synthesis, Gene Expression Profiling, and Statistical Analysis
This study used commercially available high-density oli- gonucleotide microarrays (HG_U95Av2; Affymetrix, Santa Clara, CA). Preparation of cRNA, hybridization, scanning, and image analysis of the arrays were performed accord- ing to manufacturer’s protocols and as previously de- scribed.25,33 The U95A arrays consist of 12,625 probe sets, each representing a transcript. Each probe set typ- ically consists of 16 perfectly complementary 25 base long probes (PMs) as well as 16 mismatch probes (MMs) that are identical except for an altered central base. A normal adrenal cortex sample was selected as the stan- dard and probe pairs for which PM-MM ← 100 on the standard were excluded from further analysis. The aver- age of the middle 50% of the PM-MM differences was used as the expression measure for each probe set.
A quantile normalization procedure was used to adjust for differences in the probe intensity distribution across different chips. We applied a monotone linear spline to each chip that mapped quantiles 0.01 up to 0.99 (in increments of 0.01) exactly to the corresponding quan- tiles of the standard. Then, the transform log[100 + max(X + 100; 0)] was applied to the data from each chip. Code to perform these computations is freely available at http://dot.ped.umich.edu:2000/ourimage/pub/index.html.
Tissue Microarrays
A tissue microarray34 containing 4 normal adrenal cortex (NC) samples, 24 adrenocortical adenoma (ACA) sam- ples, 12 adrenocortical hyperplasias (including both dif- fuse and macronodular types), 62 ACC samples, along with 3 pheochromocytomas and several various normal tissues, was constructed for immunohistochemical vali- dation studies using the Beecher manual tissue arrayer and 0.6-mm-diameter cylindrical cores. Donor blocks were retrieved from the archives of the University of Mich- igan Department of Pathology and the corresponding H&E slides were reviewed and representative viable ar- eas were chosen for sampling. Given the relatively ho- mogeneous nature of adrenocortical tumors in general, each case was arrayed in duplicate.
| Designation | Tissue type | Side | Age* | Sex | Clinical/laboratory aspects | Size of 1° (cm) | Weight of 1° (gm) | Gradet | Cell type | Necrosis | Caps inv | Vasc inv | Metastatic sites | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ACC 4 | 1° Carcinoma | R | A | F | UN | >8.0 | UN | High | Mixed | Y | N | N | None | |
| ACC 7 | 1° Carcinoma | L | A | F | UN | 17 | 2300 | High | Lipid-poor | Y | N | Y | Liver | |
| ACC 8 | 1° Carcinoma | L | A | F | UN | 9 | 180 | High | Mixed | Y | Y | Y | Skull | |
| ACC 11 | Metastatic | L | A | F | Liver metastasis | UN | UN | High | Mixed | Y | NA | NA | Liver | |
| carcinoma | ||||||||||||||
| ACC 13 | 1º | Carcinoma | L | A | F | UN | 16 | 460 | Low | Mixed | Y | N | N | Lung, liver, |
| bone | ||||||||||||||
| ACC 14 | 1° Carcinoma | R | A | M | R leg edema | 22 | 2000 | High | Lipid-poor | Y | Y | Y | Liver | |
| ACC 15 | Metastatic | R | A | M | Lung metastasis | UN | UN | High | Mixed | Y | NA | NA | Lung and | |
| carcinoma | chest wall | |||||||||||||
| ACC 17 | 1° Carcinoma | R | A | F | Right abdominal | 26 | 2300 | High | Lipid-poor | Y | Y | Y | Lung and | |
| pain | liver | |||||||||||||
| ACC 18 | 1º Carcinoma | L | A | F | Cushing's | 18 | 900 | High | Mixed | Y | Y | Y | None | |
| syndrome | ||||||||||||||
| ACC 19 | 1° Carcinoma | R | A | F | R Flank pain | 9 | UN | High | Lipid-poor | Y | Y | N | Liver | |
| ACC 33 | 1º Carcinoma | L | P | M | Increased | 12 | 450 | High | Lipid-poor | Y | Y | N | Liver, lymph | |
| testosterone | nodes | |||||||||||||
| ACA 21 | Adenoma | R | A | F | Cushing's | 4.5 | 65 | NA | Lipid-rich | N | NA | NA | NA | |
| syndrome | ||||||||||||||
| ACA 22 | Adenoma | L | A | F | Cushing's | 2.5 | 12 | NA | Mixed | N | NA | NA | NA | |
| syndrome | ||||||||||||||
| ACA 28 | Adenoma | L | A | F | Cushing's | 3.5 | 25.2 | NA | Mixed | N | NA | NA | NA | |
| syndrome | ||||||||||||||
| ACA 30 | Adenoma | R | A | F | Cushing's | 2.5 | 16 | NA | Mixed | N | NA | NA | NA | |
| syndrome | ||||||||||||||
| MNH 29 | Macronodular | R | A | F | Cushing's | 5.5 | 50 | NA | Lipid-rich | N | NA | NA | NA | |
| hyperplasia | syndrome | |||||||||||||
| NC 6 | Normal adrenal | L | A | F | Adrenalectomy | NA | NA | NA | NA | NA | NA | NA | NA | |
| cortex | for metastatic | |||||||||||||
| lung carcinoma | ||||||||||||||
| NC 9 | Normal adrenal | UN | A | UN | Adrenalectomy | NA | NA | NA | NA | NA | NA | NA | NA | |
| cortex | for metastatic | |||||||||||||
| renal cell carcinoma | ||||||||||||||
| NC 10 | Normal adrenal | UN | A | UN | Adrenalectomy | NA | NA | NA | NA | NA | NA | NA | NA | |
| cortex | for metastatic | |||||||||||||
| gastrinoma | ||||||||||||||
*A, Adult; P, child.
*Tumors graded according to Weiss et al3.
NA, not applicable; UN, unknown.
Immunohistochemistry
Immunohistochemistry was performed using formalin- fixed, paraffin-embedded sections from routine paraffin blocks (mib-1) or the adrenal tissue microarray (TOP2A) using the avidin-biotin complex method.35 The following antibodies, dilutions, and pretreatment conditions were used: anti-human Ki-67, mib-1 antibody (DAKO, Carpin- teria, CA), 1:100 dilution, Tris-ethylenediaminetetraacetic acid, microwave, pH 9.0; anti-human TOP2A, clone 3F6 (Novocastra Laboratories, Newcastle, UK), 1:40 dilution, citric acid, microwave, pH 6.0; and anti-human «-inhibin (Serotec, Raleigh, NC), prediluted antibody, Tris-ethyl- enediaminetetraacetic acid, microwave, pH 9.0.
The mib-1 and TOP2A immunostains were evaluated by counting the number of mib-1 or TOP2A immunoreac- tive tumor nuclei and the total number of tumor nuclei. The results for both immunostains were recorded as the percentage of immunoreactive tumor nuclei/total tumor nuclei. Tonsil with germinal centers was used as a posi- tive control and negative controls were performed with no primary antibody.
Quantitative Reverse Transcriptase-Polymerase Chain Reaction (Q-RT-PCR)
cDNA was synthesized from 1 µg of total RNA using a first strand synthesis kit for RT-PCR (Retroscript; Ambion, Austin, TX) and poly(dT) primers. The relative abundance of /GF2 transcripts was assessed using the 5’ fluorogenic nuclease assay to perform real-time Q-PCR.36 IGF2 prim- ers (forward 5”-CCGTGCTTCCGGACAACT-3, reverse 5’-GGACTGCTTCCAGGTGTCATATT-3’) and a fluoro- genic probe (5’-CCCCAGATACCCCGTGGGCAA-3’) were designed using the Primer Express software pack- age (Applied Biosystems, Foster City, CA) and obtained from Applied Biosystems. The sequence of the /GF2 am- plicon was compared to reported genomic sequences using the BLAST program to assure the amplicon was unique. The primers and probe set were optimized with respect to MgCl2 concentration and time and tempera- ture of the hybridization step. Multiplex Q-PCR using a SmartCycler (Cepheid, Sunnyvale, CA) was performed in 30-ul reactions consisting of 1x Q-PCR Supermix-UDG
A
70
First two principal components using 2913 probe-sets (mean>200, CV>0.5)
B
+ Normals
C7
C19
Second Principal Component
C14
Adenoma
C14
50
Carcinoma
C11
C18
C33
C15
30
C8
C4
C17
10
C13
A22
A21
H29
-10
C19
A28
C13
A30
N6
N10
-30
N9
-70
-50
First Principal Component
-30
-10
10
30
C
D
6
First two principal components using 91 probe-sets (p <. 01, FC>3)
Normal
C13
Second Principal Component
4
C13
Adenoma
C19
C14
A Carcinoma
C17
2
C4
C18
C14
C15
0
C8
C33
C11
-2
C7
A22
A21
-4
A28
H29
A30
-6
C19
N10
N9
N6
-8
-10
-8
-6
-4
First Principal Component
-2
0
2
4
6
8
10
reaction mix (Life Technologies, Inc., Gaithersburg, MD) supplemented with the appropriate MgCl2 concentration. Relative expression of mRNA for /GF2 was calculated using the comparative CT method as described37 using the CT of GAPDH as the reference.
Results
Transcriptional Profiles Distinguish Benign and Malignant Adrenocortical Tumors, Reflect Morphology, and Identify Differentially Expressed Transcripts
Transcriptional profiles of 11 ACCs (nine primary and two metastatic), 4 ACAs, 3 NCs, and 1 macronodular hyper- plasia (MNH) were generated using oligonucleotide ar- rays with 12,625 probe sets interrogating ~10,500 genes. To provide visual representations of the tissues and tumors based on gene expression, we used principal component analysis (PCA) to locate the two-dimensional views that capture the greatest amount of variability in the data, using several thousand of the most variable probe sets. The resulting PCA view showed a clear separation between the ACC and benign cohorts (Figure 1A). The greatest variability was seen within the ACC cohort, with two tumors, ACC19 and ACC14, located far from the remaining ACCs. One of these tumors (ACC19) was a high-grade ACC with little morphological (Figure 2) or transcriptional evidence of adrenocortical differentiation,
although the tumor was focally and weakly positive for @-inhibin (not shown). The other outlying tumor, ACC14, was a myxoid variant of ACC, a rare but recognized variant38 (Figure 2). Interestingly, the ACC closest to the NC/ACA cohort, ACC13, was a low-grade ACC2 that shared some histological features with the benign cohort (Figure 2). A typical high-grade ACC (ACC17) is also shown for comparison in Figure 2.The PCA view showed little differences between the NC and the ACA/MNH co- horts (Figure 1A), a not entirely unexpected finding based on their similar morphologies.
In addition to PCA, we used hierarchical clustering to generate another visual relationship between the adreno- cortical tissues and tumors. The resulting dendrogram (Figure 1B) placed 10 of 11 ACCs on the same branch, with the low-grade tumor ACC13 on the branch with the NCs, ACAs, and MNH, which likely reflects its well-differ- entiated nature. However, the dendrogram clearly showed a difference between ACC13 and the NC/ACA cohort. With respect to this cohort, the hierarchical clus- tering was in agreement with the PCA analysis (Figure 1A), as the dendrogram (Figure 1B) showed minimal differences within the NC/ACA cohort.
One of the main interests in this study was to identify transcripts of greater abundance in ACC samples than in ACA/MNH and NC samples. A simple one-way analysis of variance using the entire data set of 12,625 probe sets on these three groups of samples obtained 677 probe sets that gave P < 0.01 when comparing ACCs with ACAs, and 473 when ACCs were compared with NCs. These numbers of probe sets are several times larger than the number expected by chance alone. To highlight those differences of potentially greatest biological inter- est, as well as to reduce the fraction of false-positive findings, we further required that expression values in the ACCs be at least twofold different from the average value in the other group, and that the probe sets meet these requirements in both comparisons (ACC versus NC, ACC versus ACA). One hundred fifty-eight probe sets met these criteria, and for 91 probe sets the fold changes were greater than three for both comparisons. Randomly permuting the sample labels 10,000 times, on average less than one probe set met the final criterion of P < 0.01 and fold change larger than three for both comparisons, and no permutations gave more than the observed num- ber of 91 probe sets. This permutation result shows that very few of the selected 91 genes have been identified because of chance variation alone.
A PCA view of the data for these 91 probe sets (Figure 1C), similar to the previous PCA view based on thou- sands of variable probe sets (Figure 1A), showed a greater relative distance between the ACC and benign cohorts, as expected because of the method of probe set selection. Interestingly, there was persistent intragroup variability within the ACC group, with a trend toward displaying the tumors along a differentiation spectrum.
As above, hierarchical clustering was applied to the data set with the 91 differentially expressed genes. As expected, the resulting dendrogram (Figure 1D) showed a significant difference between the ACC and NC/ACA cohorts. Tumor ACC13, while now included within the
ACC13
ACC14
ACC19
ACC17
ACC branch, showed the least amount of relatedness within the ACC cohort, again reflecting its well-differenti- ated nature.
The probe set designation, identity of the individual genes, as well as the fold change of the mean expression level relative to mean expression level of NC for the 91 differentially expressed probe sets described above are shown in Figure 3.
Few transcriptional changes were found between the NC and ACA cohorts because just 58 probe sets with a fold change greater than 1.5 in either direction were identified at a significance level of P < 0.01. Because the chip contains 12,625 probe sets, 126 probe sets would be expected by chance alone at P < 0.01, suggesting that many of these 58 are likely to be false-positives. Examination of the expression data for the three NC samples strongly suggests that some normal adrenal medulla contaminated two of the specimens, a not unex- pected finding given the adjacent and often interdigitat- ing relationship between adrenal cortex and medulla. These two samples, NC9 and NC10, showed increased expression levels for several probable medulla-related genes, such as SCG2 (secretogranin II), TH (tyrosine hydroxylase), PENK (proenkephalin), DBH (dopamine B- hydroxylase), DDC (dopa-decarboxylase), SST (soma- tostatin), and CART (cocaine- and amphetamine-regu- lated transcript). However, most of the remaining candidate differentially expressed genes were not the result of contamination based on consistent expression across all three NC samples (Figure 3) and some, such as SGK (serum/glucocorticoid regulated kinase), likely represent true positives.
Increased IGF2 Expression Is One of the Dominant Transcriptional Events in ACC
Three distinct probe sets for the IGF2 gene were present on the U95A chip, thereby providing triplicate measures for each of the tissues. All three probe sets showed substantially increased expression of IGF2 in 10 of 11 (90.9%) ACCs compared to the mean of the NC/ACA cohort (Figure 4A). One carcinoma (ACC19) did not show increased /GF2 expression for the three probe sets and was one of the two ACCs identified by PCA as an outlier (see above). The fold changes of the mean expression of the ACC cohort compared to the mean of the NC/ACA cohort were 105.5, 40.9, and 14.9 for probe sets 36782_s_at, 1591_s_at, and 2079_s_at, respectively (Fig- ure 3). Q-RT-PCR for /GF2 and GAPDH transcripts con- firmed the pattern of expression for the arrayed tissues (Figure 4B). Significant /GF2 transcripts were present in the same 10 of 11 ACCs, whereas ACC19 contained few IGF2 transcripts relative to GAPDH transcripts.
Relative Absence of Growth Factor Receptor Expression
Given the recent development of therapies targeted to growth factor receptors (eg, trastuzumab and ERB-B2),
| N6 N9 N10 A21 | A22 H29 A28 A30 | C4 C13 C11 C14 C15 C8 C7 C17 C19 C33 C18 | Probe-Set | Gene Symbol | Unigene Title | Fold change |
|---|---|---|---|---|---|---|
| 36782_s_at | IGF2 | insulin-like growth factor 2 (somatomedin A) | 105.5 | |||
| 1591_s_at 1651_at | IGF2 UBCH10 | insulin-like growth factor 2 (somatomedin A) | 40.9 | |||
| ubiquitin carrier protein E2-C | 32.4 | |||||
| 38116_at | KIAA0101 | KIAA0101 gene product | 28.2 | |||
| 34342_S_at | SPP1 | secreted phosphoprotein 1 (osteopontin, bone sialoprotein I, early T-lymphocyte activation 1) | 20.9 | |||
| 2092_s_at | SPP1 | secreted phosphoprotein 1 (osteopontin, bone sialoprotein I, early T-lymphocyte activation 1) | 16.6 | |||
| 39109_at | C20ORF1 | chromosome 20 open reading frame 1 | 15.6 | |||
| 2079_s_at 39542_at | IGF2 ENC1 | insulin-like growth factor 2 (somatomedin A) ectodermal-neural cortex (with BTB-like domain) | 14.9 11.4 | |||
| 39230_at | DJ742C19.2 2 | phorbolin (similar to apolipoprotein B mRNA editing protein) | 10.8 | |||
| 41060_at | CCNE1 | cyclin E1 | 10.4 | |||
| 32263_at | CCNB2 | cyclin B2 | 8.88 | |||
| 40145_at | TOP2A | topoisomerase (DNA) Il alpha (170kD) | 8.75 | |||
| 37050_r_at | TOM34 | translocase of outer mitochondrial membrane 34 | 8.70 | |||
| 40328_at | TWIST | twist (Drosophila) homolog (acrocephalosyndactyly 3; Saethre-Chotzen syndrome) | 8.44 | |||
| 1803_at | CDC2 | cell division cycle 2, G1 to S and G2 to M | 7.70 | |||
| 37263_at | GGH | gamma-glutamyl hydrolase (conjugase, folyipolygammaglutamyl hydrolase) | 7.22 | |||
| 39677_at | KIAA0186 | KIAA0186 gene product | 7.09 | |||
| 904_s_at | TOP2A | topoisomerase (DNA) Il alpha (170kD) | 6.86 | |||
| 1599_at | CDKN3 | cyclin-dependent kinase inhibitor 3 (CDK2-associated dual specificity phosphatase) | 6.84 | |||
| 40726_at | KNSL1 | kinesin-like 1 | 6.68 | |||
| 34715_at | FOXM1 | forkhead box M1 | 6.40 | |||
| 39945_at | FAP | fibroblast activation protein, alpha | 6.17 | |||
| 36909_at | WEE1 | wee1+ (S. pombe) homolog | 5.49 | |||
| 37883_i_at | AF038169 | hypothetical protein | 5.30 | |||
| 34878_at | SMC4L1 | SMC4 (structural maintenance of chromosomes 4, yeast)-like 1 | 5.27 | |||
| 1801_at | BARD1 | BRCA1 associated RING domain 1 | 5.23 | |||
| 31845_at | ELF4 | E74-like factor 4 (ets domain transcription factor) | 5.20 | |||
| 35699_at | BUB1B | budding uninhibited by benzimidazoles 1 (yeast homolog), beta | 4.89 | |||
| 34852_g_at | STK15 | serine/threonine kinase 15 | 4.68 | |||
| 1505_at | TYMS | thymidylate synthetase | 4.61 | |||
| 2057_g_at | FGFR1 | fibroblast growth factor receptor 1 (fms-related tyrosine kinase 2, Pfeiffer syndrome) | 4.44 | |||
| 37461_at | ANGPT2 | angiopoietin 2 | 4.37 | |||
| 35314_at | KIAA0159 | chromosome condensation-related SMC-associated protein 1 | 4.09 | |||
| 1721_g_at | MAD2L1 | MAD2 (mitotic arrest deficient, yeast, homolog)-like 1 | 3.96 | |||
| 38065_at | HMG2 | high-mobility group (nonhistone chromosomal) protein 2 | 3.54 | |||
| 41401_at | CSRP2 | cysteine and glycine-rich protein 2 | 3.44 | |||
| 34390_at | P4HA2 | procollagen-proline, 2-oxoglutarate 4-dioxygenase (proline 4-hydroxylase), alpha polypeptide Il | 3.44 | |||
| 1951_at | ANGPT2 | angiopoietin 2 | 3.38 | |||
| 38656_s_at | MGC5576 | hypothetical protein MGC5576 | 3.37 | |||
| 39337_at | H2AFZ | H2A histone family, member Z | 3.27 | |||
| 34637_f_at | ADH1 | alcohol dehydrogenase 1 (class I), alpha polypeptide | 0.04 | |||
| 35730_at | ADH2 | alcohol dehydrogenase 2 (class I), beta polypeptide | 0.06 | |||
| 40578_s_at | TMOD | tropomodulin | 0.07 | |||
| 32666_at | SDF1 | stromal cell-derived factor 1 | 0.07 | |||
| 36271_at | KIAA1024 | KIAA1024 protein | 0.08 | |||
| 533_g_at | PTHR1 | parathyroid hormone receptor 1 | 0.10 | |||
| 38786_at | NULL | Homo sapiens mRNA full length insert cDNA clone EUROIMAGE 248114 | 0.10 | |||
| 33118_at | SEMA3B | sema domain, immunoglobulin domain (lg), short basic domain, secreted, (semaphorin) 3B | 0.10 | |||
| 38026_at | FBLN1 | fibulin 1 | 0.11 | |||
| 1736_at | IGFBP6 | insulin-like growth factor binding protein 6 | 0.11 | |||
| 32056_at | PNUTL2 | peanut (Drosophila)-like 2 | 0.13 | |||
| 34042_at | CHAD | chondroadherin | 0.14 | |||
| 36512_at | AADAC | arylacetamide deacetylase (esterase) | 0.15 | |||
| 37972_at | DNASE1L3 | deoxyribonuclease I-like 3 | 0.15 | |||
| 37599_at | AOX1 | aldehyde oxidase 1 | 0.15 | |||
| 37674_at | ALAS1 | aminolevulinate, delta-, synthase 1 | 0.15 | |||
| 770_at | GPX3 | glutathione peroxidase 3 (plasma) | 0.16 | |||
| 1412_g_at | CYP11B1 | cytochrome P450, subfamily XIB (steroid 11-beta-hydroxylase), polypeptide 1 | 0.16 | |||
| 40527_at | KCNQ1 | potassium voltage-gated channel, KQT-like subfamily, member 1 | 0.16 | |||
| 34548_at | CYP11B1 | cytochrome P450, subfamily XIB (steroid 11-beta-hydroxylase), polypeptide 1 | 0.16 | |||
| 1731_at | PDGFRA | platelet-derived growth factor receptor, alpha polypeptide | 0.17 | |||
| 216_at | PTGDS | prostaglandin D2 synthase (21kD, brain) | 0.18 | |||
| 36661_s_at | CD14 | CD14 antigen | 0.18 | |||
| 39295_s_at | ARGBP2 | Arg/Abl-interacting protein ArgBP2 | 0.18 | |||
| 37765_at | LMOD1 | leiomodin 1 (smooth muscle) | 0.18 | |||
| 37191_at | KIAA0273 | KIAA0273 gene product | 0.19 | |||
| 38053_s_at | BRE | brain and reproductive organ-expressed (TNFRSF1A modulator) | 0.19 | |||
| 39545_at | CDKN1C | cyclin-dependent kinase Inhibitor 1C (p57, Kip2) | 0.20 | |||
| 34002_at | HSD3B2 | hydroxy-delta-5-steroid dehydrogenase, 3 beta- and steroid delta-isomerase 2 | 0.21 | |||
| 33235_at | KIAA0938 | KIAA0938 protein | 0.21 | |||
| 32077_s_at | KCNQ1 | potassium voltage-gated channel, KQT-like subfamily, member 1 | 0.23 | |||
| 36495_at | FBP1 | fructose-1,6-bisphosphatase 1 | 0.23 | |||
| 40685_at | ALDH7 | aldehyde dehydrogenase 7 | 0.23 | |||
| 37405_at | SELENBP1 | selenium binding protein 1 | 0.23 | |||
| 32174_at | SLC9A3R1 | solute carrier family 9 (sodium/hydrogen exchanger), isoform 3 regulatory factor 1 | 0.23 | |||
| 38042_at | G6PD | glucose-6-phosphate dehydrogenase | 0.24 | |||
| 38844_at | PDK2 | pyruvate dehydrogenase kinase, isoenzyme 2 | 0.25 | |||
| 1930_at | ABCC3 | ATP-binding cassette, sub-family C (CFTR/MRP), member 3 | 0.26 | |||
| 41755_at | KIAA0977 | KIAA0977 protein | 0.26 | |||
| 41473_at | EPHX2 | epoxide hydrolase 2, cytoplasmic | 0.27 | |||
| 38487_at | KIAA0246 | KIAA0246 protein | 0.27 | |||
| 36275_at | Hs.29596 | Homo sapiens mRNA from chromosome 5q21-22, clone:FBR89 | 0.27 | |||
| 1501_at | IGF1 | insulin-like growth factor 1 (somatomedin C) | 0.28 | |||
| 39438_at | CREBL2 | CAMP responsive element binding protein-like 2 | 0.28 | |||
| 40409_at | ALDH10 | aldehyde dehydrogenase 10 (fatty aldehyde dehydrogenase) | 0.28 | |||
| 33197_at | MYO7A | myosin VIIA (Usher syndrome 1B (autosomal recessive, severe)) | 0.29 | |||
| 38406_f_at | PTGDS | brain) | 0.30 | |||
| 1327_s_at | MAP3K5 | prostaglandin D2 synthase (21kD, mitogen-activated protein kinase kinase kinase 5 | 0.32 | |||
| 38119_at 39842_at | GYPC CRLF1 | glycophorin C (Gerbich blood group) cytokine receptor-like factor 1 | 0.33 0.33 |
Fold change from the mean
1/4
1/2
1
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4
Figure 3. Ninety-one probe sets that gave P < 0.01 for F-tests of both carcinoma versus normal and carcinoma versus adenoma that also had both fold changes larger than three, or smaller than 0.333 (see text). A central value for each probe set was determined by averaging log-transformed data, and taking the anti- logarithm. Colors represent the fold change for each sample from this value. The heat map was made using Treeview.65 The probe set designation, the gene sym- bol, and the unigene title, as well as the fold change in transcript level, are shown. Samples are labeled as follows: N = NC, A = ACA, H = MNH, and C = ACC.
A
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the transcript levels of several receptor tyrosine kinases were assessed. Such genes represented on the U95A chip included EGFR, ERB-B2, HER3, PDGFRA, PDGFRB, KIT, FGFR1, FGFR4, IGF1R, IGFR2, INSR, ESR1, ESR2, and PGR, among others. The only receptor tyrosine kinase that displayed differential transcript levels was FGFR1, which was represented four times on the array and showed 5.6-, 2.1-, and 2.8-fold increased expres- sion in the ACCs compared to the ACAs in three of the probe sets with statistical significance (2057_g_at, P = 0.00009; 2056_at, P = 0.0003; and 424_s_at, P = 0.003, respectively). Importantly, most of these other genes showed absolute transcript levels indicative of either no expression (eg, KIT) or low expression (eg, PDGFRA and PDGFRB).
Immunohistochemical Protein Validation Studies
To validate differential expression at the protein level, immunohistochemistry was performed for select genes for which specific literature regarding adrenocortical tu- mors already existed. Accordingly, two proliferation-re- lated markers, Ki-67 and TOP2A, were chosen for valida- tion. Although KI-67 was not on the most differentially
expressed gene list, it was up-regulated in the ACC cohort compared to the ACA (3.7-fold change, P < 0.006) and NC cohorts (3.7-fold change, P < 0.036). Comparison of the gene expression data with the corre- sponding immunostains of the same tumors showed strong correlation between transcript levels and tumoral immunoreactivity (Figure 5 and Table 2). Specifically, TOP2A, represented by three distinct probe sets, showed significantly increased expression in the ACC cohort compared to the ACA/MNH cohort, as expected (Figure 5A). Tumors with relatively high transcript levels of TOP2A (eg, ACC33) showed the highest percentage of immuno- reactive cells, whereas the tumors with low transcript levels (eg, ACC13) showed only rare immunoreactive cells (Figure 5A). Likewise, tumors with relatively high transcript levels of MKI67 showed a high percentage of Ki-67 (mib-1) immunoreactive cells (Figure 5B). Tran- script levels and the percentage of immunoreactive tu- mor nuclei showed a high level of correlation for both proliferation markers (Table 2).
Discussion
DNA microarray technology allows the opportunity to comprehensively examine the transcriptional profile of tumors. It is rapidly being deployed to address a variety of issues in pathology and oncology, such as tumor clas- sification,25,39-43 and as a useful gene discovery tool44,45 to complement other similar technologies such as SAGE.46,47 In this study, we used DNA microarrays to generate transcriptional profiles of benign and malignant adrenocortical tumors, as well as normal adrenal cortex and MNH. These profiles were used to discriminate nor- mal and benign tissues from malignant tumors and to identify differentially expressed genes that may have di- agnostic, pathogenetic, and therapeutic implications.
Select RNA and protein-based studies were performed to validate the array data; moreover, comparison of the array data to the published literature provides an addi- tional and vital level of validation. For example, ample evidence exists that increased expression of /GF2 occurs in ACC23 and this was robustly confirmed by this study. Furthermore, TOP2A has been reported to be up-regu- lated in ACC and used as a diagnostic marker10,14 and was independently identified as one of the significantly up-regulated genes in this cohort of ACCs. Thus, it is reasonable to conclude our approach is informative and has identified numerous differentially expressed genes.
Although the histopathological diagnosis of ACC is usu- ally not problematic, additional diagnostic markers would be useful in some challenging cases. Examination of the differentially expressed gene list yields several genes that may represent candidate diagnostic markers, including IGF2 and osteopontin, among others. Recent immunohisto- chemical studies of IGF2 protein as a marker are promis- ing48 and immunohistochemical validation studies using antibodies to osteopontin are currently underway.
Our results clearly identify increased /GF2 as a char- acteristic transcriptional event in the pathogenesis of ACC. This finding is consistent with a large body of
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evidence demonstrating that increased /GF2 expression is the result of dysregulation or rearrangement at 11p15.5.21,49-52 The significance of the current findings is twofold: the magnitude of the increased /GF2 expres- sion and the lack of other signal transduction-related changes identified in this albeit partial genome transcrip- tional survey. This suggests that interruption of IGF2- induced signal transduction may lead to a significant therapeutic advance, analogous to those related to STI- 571 in certain leukemias53 and gastrointestinal stromal tumors.54 One of the ways to block IGF2 signaling might be to design a small molecule inhibitor against the ty-
rosine kinase domain of the IGF1 receptor. Interestingly, a recent study described the 2.1 A-resolution crystal structure of the activated form of the IGF1 receptor ki- nase.55 Although the IGF1 receptor shares the nucleoti- de-binding cleft with the insulin receptor, the authors identified sequence differences in the nearby interlobe linker that might represent a target for anti-cancer drug design. Alternatively, proteins downstream in the IGF1 receptor signal transduction pathway may represent ad- ditional therapeutic targets.
Our study failed to identify significant differences in gene expression between the normal adrenal cortex and
| Probe set name | Gene symbol | ACA 21 | ACA 22 | ACA 28 | ACA 30 | MNH 29 | ACC 4 | ACC 7 | ACC 8 | ACC 11 | ACC 13 | ACC 14 | ACC 15 | ACC 17 | ACC 18 | ACC 19 | ACC 33 | Transcript versus IHC correlation (r value) | P value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40145_at | TOP2A | -10 | 63 | 21 | 0 | 63 | 955 | 0 | 776 | 483 | 52 | 1161.9 | 314 | 661 | 1448 | 1912.3 | 1857 | 0.7238989 | 0.002 |
| 904_s_at | TOP2A | 73 | 52 | 42 | 126 | 105 | 986.5 | 126 | 720.6 | 608.3 | 94 | 921.7 | 431 | 577.7 | 1225.2 | 1668 | 1847 | 0.7536438 | 0.001 |
| 1592_at | TOP2A | 105 | 126 | 283 | 126 | 178 | 895.9 | 147 | 1060 | 483 | 204.2 | 651 | 441 | 347 | 1018 | 2819 | 2733 | 0.8540047 | <0.0001 |
| Top2A IHC | 1.4 | 0 | NP | 4.3 | 2.3 | 0 | 0.9 | 2.9 | 5.8 | 0 | 7.2 | 4.4 | 17.3 | 0 | 33 | 38.2 | |||
| 419_at | MKI67 | 126 | 94 | -21 | 136 | 63 | 283 | 126 | 514 | 210 | 94 | 588 | 147 | 220 | 336 | 1090.4 | 509 | 0.7450237 | 0.0009 |
| mib-1 IHC | 0 | 0 | 0 | 3.8 | 0.5 | 7.3 | 0 | 3.5 | 6.7 | 1.5 | 5.5 | 0 | 17.5 | 10.7 | 29 | 29.6 |
NP, not performed.
benign cortical processes such as ACA and MNH, a not entirely unexpected result given the histological similarity of these tissues. However, a few genes, such as serum glucocorticoid-regulated kinase (SGK) gene, may repre- sent differentially expressed genes. Validation of some of these genes awaits additional protein-based studies.
The relative absence of growth factors and their recep- tors other than IGF2 is significant given the recent avail- ability of targeted therapeutic agents, such as imatinib mesylate (Gleevec) and trastuzumab (Herceptin), and the expected availability of orally bioavailable EGFR in- hibitors such as ZD1830 (Iressa). This suggests that the treatment of ACC with the available receptor tyrosine kinase inhibitors will likely not be effective and will have to await the development of additional compounds.
Examination of the list of genes differentially expressed between ACC and benign adrenocortical processes pro- vides insight into the pathogenesis of ACC and possible therapeutic approaches. The serine-threonine kinase STK15 (also known as BTAK and aurora2) is associated with centrosomes, plays a role in the induction of centro- some abnormalities and thus aneuploidy and transforma- tion in mammalian cells, and is up-regulated in some types of human tumors.56 Our finding of almost fivefold increased STK15 transcripts in ACC suggests that this gene may play a role in the marked aneuploidy com- monly observed in ACC.57,58 Angiopoietin 2 (Ang-2), a member of the one of the two major classes of angiogenic factors, acts synergistically with vascular endothelial growth factor to induce angiogenesis.59 Our results, which show a threefold to fourfold increase in Ang-2 transcripts in ACC, suggests that angiogenesis is an important aspect of adrenocortical tumorigenesis, as ex- pected, and that anti-angiogenic therapies currently be- ing developed for other more common cancers might be effective in ACC. The UbcH10 gene encodes a cyclin- selective ubiquitin carrier protein, E2-C, which catalyzes the destruction of mitotic cyclins (A and B) via ubiquitin- dependent proteolysis, permitting the completion of mi- tosis and entry into interphase of the next cell cycle.60,61 Thus, it is possible to speculate that marked increased expression of UbcH10, as seen in our cohort of ACCs, may be a proproliferative factor and play a role in the pathogenesis of ACC. The ectodermal-neural cortex one (ENC-1) gene is up-regulated in colorectal carcinoma and was recently identified as a potential target of the ß-catenin/T-cell factor complex.62 Up-regulation of
ENC-1 transcripts in ACC, as observed in our data, sug- gests that abnormal wnt signaling may play a role in ACC pathogenesis. The high mobility group A2 (HMG2) gene plays a role in the pathogenesis of common mesenchy- mal tumors63 and is amplified and overexpressed in pro- lactinomas, likely related to chromosomal rearrangement of 12q14-15 resulting in genomic amplification.64 Thus, our finding of a threefold increase in HMG2 transcripts in ACC is provocative and warrants further investigation. These examples represent just some of the interesting differentially expressed genes. Obviously, comprehen- sive gene expression profiles provide many interesting avenues for further investigation and will likely provide the foundation for significant therapeutic advances.
Finally, one of the uses of global gene expression data is correlation with existing genomic data such as conven- tional and microarray-based comparative genomic hy- bridization (CGH) data. For instance, a recent CGH study65 of 35 adrenocortical tumors identified co-ampli- fication of SAS/CDK4 and MDM2 in two advanced ACCs, suggesting that co-amplification of these genes may play a role in tumor progression. Our expression data does indicate increased expression of both SAS and CDK4 in one of the ACCs, but MDM2 expression is minimal or absent in all of the adrenocortical tissues based on data from four distinct probe sets. Thus, although increased expression of SAS and/or CDK4 via genomic amplifica- tion may play a role in ACC pathogenesis, a similar role for MDM2 is not likely.
Acknowledgments
We thank Doug Selby, Doug Johnson, John Weeks, and Enola Cushenberry, technicians of the University of Mich- igan Comprehensive Cancer Center Tissue Core for tis- sue procurement; Tina Fields for excellent technical im- munohistochemical advice and assistance; and Dr. Kathleen Cho for useful discussions and critical reading of the manuscript.
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