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From the Departments of Pathology,* Pediatrics, Surgery, and Biostatistics, University of Michigan Health System, Ann Arbor, Michigan
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10,500 unique genes. The expression data set was used for unsupervised hierarchical 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 adrenocortical tumors, such as insulin-like growth factor (IGF2), as well as novel differentially expressed genes such as osteopontin (SPP) and serine threonine kinase 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 tissue microarrays. Our results demonstrated significant and consistent gene expression changes in ACCs compared to benign adrenocortical lesions. Moreover, we identified several genes that represent potential diagnostic markers and may play a role in the pathogenesis of ACC.
Several genes have been reported to have diagnostic significance in adrenocortical neoplasms. Numerous studies have documented the utility of 
-inhibin immunohistochemistry as a marker of adrenocortical differentiation,4-8
with the ability to distinguish adrenocortical tumors from adrenomedullary tumors, hepatocellular carcinoma and renal tumors,9
resulting in the acceptance of -inhibin as a clinically useful diagnostic marker. Several studies have investigated the ability of proliferative immunohistochemical markers, such as Ki-67 and topoisomerase II (TOP2A), to distinguish benign and malignant tumors,10-15
resulting in a general consensus that proliferative activity as measured by these markers is significantly higher in ACCs than benign lesions. Interestingly, assessment of proliferation by proliferative cell nuclear antigen immunohistochemistry did not show a correlation with biological behavior.10
Finally, several studies have shown that immunoreactivity to p53 is essentially restricted to ACCs.14-16
Despite some recent advances, the molecular pathogenesis of ACC is poorly understood. Mutations of the p53 tumor suppressor gene have been implicated because of the association of ACC with Li-Fraumeni syndrome17 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 IGF2 in ACC, resulting in an autocrine stimulatory loop.24
Gene expression profiling provides the opportunity to assess the expression of thousands of genes simultaneously in a cohort of related tumors. Several practical applications, including but not limited to tumor classification,25-27 biomarker discovery,28 and prediction of therapeutic response,29 are being developed for a variety of tumors, including those of the endocrine system.30 Furthermore, expression profiles can provide insight into tumorigenesis and identify targets for therapeutic intervention.31,32 Here, we report the generation of extensive gene expression profiles of a cohort of normal, hyperplastic, and neoplastic adrenocortical tissues and show that these profiles can readily distinguish benign from malignant tumors and identify tumors with unusual histopathological features. In addition, we identify numerous differentially expressed transcripts and demonstrate increased IGF2 expression as one of the dominant transcriptional changes in ACC.
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Materials and Methods |
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The adrenocortical tissues analyzed in this study were procured from the University of Michigan Health System between 1994 and 2001 by the Tissue Procurement Service. The transcriptional profiling and validation studies were approved by the University of Michigan Institutional Review Board (IRB-Medicine). All tissues were processed in a similar manner. Frozen tumor samples were embedded in OCT freezing media (Miles Scientific, Naperville, IL), cryotome sectioned (5 µm), 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 sections from original paraffin blocks and surgical pathology reports were reviewed and evaluated for various pathological 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 limited clinical and laboratory information, are presented in Table 1
. Standard diagnostic criteria2
were used to diagnose the adrenocortical tumors.
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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 oligonucleotide microarrays (HG_U95Av2; Affymetrix, Santa Clara, CA). Preparation of cRNA, hybridization, scanning, and image analysis of the arrays were performed according to manufacturer’s protocols and as previously described.25,33 The U95A arrays consist of 12,625 probe sets, each representing a transcript. Each probe set typically 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 standard and probe pairs for which PM-MM <-100 on the standard were excluded from further analysis. The average 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 quantiles 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) samples, 12 adrenocortical hyperplasias (including both diffuse and macronodular types), 62 ACC samples, along with 3 pheochromocytomas and several various normal tissues, was constructed for immunohistochemical validation 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 Michigan Department of Pathology and the corresponding H&E slides were reviewed and representative viable areas were chosen for sampling. Given the relatively homogeneous nature of adrenocortical tumors in general, each case was arrayed in duplicate.
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, Carpinteria, 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-ethylenediaminetetraacetic acid, microwave, pH 9.0.
The mib-1 and TOP2A immunostains were evaluated by counting the number of mib-1 or TOP2A immunoreactive 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 positive 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 IGF2 transcripts was assessed using the 5' fluorogenic nuclease assay to perform real-time Q-PCR.36 IGF2 primers (forward 5''-CCGTGCTTCCGGACAACT-3, reverse 5'-GGACTGCTTCCAGGTGTCATATT-3') and a fluorogenic probe (5'-CCCCAGATACCCCGTGGGCAA-3') were designed using the Primer Express software package (Applied Biosystems, Foster City, CA) and obtained from Applied Biosystems. The sequence of the IGF2 amplicon 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 temperature of the hybridization step. Multiplex Q-PCR using a SmartCycler (Cepheid, Sunnyvale, CA) was performed in 30-µl reactions consisting of 1x Q-PCR Supermix-UDG reaction mix (Life Technologies, Inc., Gaithersburg, MD) supplemented with the appropriate MgCl2 concentration. Relative expression of mRNA for IGF2 was calculated using the comparative CT method as described37 using the CT of GAPDH as the reference.
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Results |
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Transcriptional profiles of 11 ACCs (nine primary and two metastatic), 4 ACAs, 3 NCs, and 1 macronodular hyperplasia (MNH) were generated using oligonucleotide arrays 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 cohorts (Figure 1A)
, a not entirely unexpected finding based on their similar morphologies.
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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-differentiated nature. However, the dendrogram clearly showed a difference between ACC13 and the NC/ACA cohort. With respect to this cohort, the hierarchical clustering 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 interest, 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 number 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 thousands 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 ACC branch, showed the least amount of relatedness within the ACC cohort, again reflecting its well-differentiated 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
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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 IGF2 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 (Figure 3)
. Q-RT-PCR for IGF2 and GAPDH transcripts confirmed the pattern of expression for the arrayed tissues (Figure 4B)
. Significant IGF2 transcripts were present in the same 10 of 11 ACCs, whereas ACC19 contained few IGF2 transcripts relative to GAPDH transcripts.
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Given the recent development of therapies targeted to growth factor receptors (eg, trastuzumab and ERB-B2), 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 expression 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 tumors already existed. Accordingly, two proliferation-related markers, Ki-67 and TOP2A, were chosen for validation. 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 corresponding 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 immunoreactive 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)
. Transcript levels and the percentage of immunoreactive tumor nuclei showed a high level of correlation for both proliferation markers (Table 2)
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Discussion |
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TopAbstractMaterials and MethodsResultsDiscussionReferences |
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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 additional and vital level of validation. For example, ample evidence exists that increased expression of IGF2 occurs in ACC23 and this was robustly confirmed by this study. Furthermore, TOP2A has been reported to be up-regulated 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 usually 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 immunohistochemical studies of IGF2 protein as a marker are promising48 and immunohistochemical validation studies using antibodies to osteopontin are currently underway.
Our results clearly identify increased IGF2 as a characteristic transcriptional event in the pathogenesis of ACC. This finding is consistent with a large body of evidence demonstrating that increased IGF2 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 IGF2 expression and the lack of other signal transduction-related changes identified in this albeit partial genome transcriptional 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 tyrosine 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 kinase.55 Although the IGF1 receptor shares the nucleotide-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 additional therapeutic targets.
Our study failed to identify significant differences in gene expression between the normal adrenal cortex and 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 represent differentially expressed genes. Validation of some of these genes awaits additional protein-based studies.
The relative absence of growth factors and their receptors other than IGF2 is significant given the recent availability of targeted therapeutic agents, such as imatinib mesylate (Gleevec) and trastuzumab (Herceptin), and the expected availability of orally bioavailable EGFR inhibitors 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 provides 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 centrosome abnormalities and thus aneuploidy and transformation 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 commonly 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 expected, and that anti-angiogenic therapies currently being 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 mitosis 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, suggests 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 mesenchymal tumors63 and is amplified and overexpressed in prolactinomas, 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, comprehensive 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 conventional and microarray-based comparative genomic hybridization (CGH) data. For instance, a recent CGH study65 of 35 adrenocortical tumors identified co-amplification 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 amplification may play a role in ACC pathogenesis, a similar role for MDM2 is not likely.
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Acknowledgements |
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Footnotes |
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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.
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A. Horvath, L. Mathyakina, Q. Vong, V. Baxendale, A. L. Y. Pang, W.-Y. Chan, and C. A. Stratakis Serial Analysis of Gene Expression in Adrenocortical Hyperplasia Caused by a Germline PRKAR1A Mutation J. Clin. Endocrinol. Metab., February 1, 2006; 91(2): 584 - 596. [Abstract] [Full Text] [PDF]
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L. S. Kirschner Emerging Treatment Strategies for Adrenocortical Carcinoma: A New Hope J. Clin. Endocrinol. Metab., January 1, 2006; 91(1): 14 - 21. [Abstract] [Full Text] [PDF]
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R. Libe and J. Bertherat Molecular genetics of adrenocortical tumours, from familial to sporadic diseases Eur. J. Endocrinol., October 1, 2005; 153(4): 477 - 487. [Abstract] [Full Text] [PDF]
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F. Tissier, C. Cavard, L. Groussin, K. Perlemoine, G. Fumey, A.-M. Hagnere, F. Rene-Corail, E. Jullian, C. Gicquel, X. Bertagna, et al. Mutations of {beta}-Catenin in Adrenocortical Tumors: Activation of the Wnt Signaling Pathway Is a Frequent Event in both Benign and Malignant Adrenocortical Tumors Cancer Res., September 1, 2005; 65(17): 7622 - 7627. [Abstract] [Full Text] [PDF]
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M. H. Bassett, B. Mayhew, K. Rehman, P. C. White, F. Mantero, G. Arnaldi, P. M. Stewart, I. Bujalska, and W. E. Rainey Expression Profiles for Steroidogenic Enzymes in Adrenocortical Disease J. Clin. Endocrinol. Metab., September 1, 2005; 90(9): 5446 - 5455. [Abstract] [Full Text] [PDF]
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M. Fassnacht, D. Weismann, S. Ebert, P. Adam, M. Zink, F. Beuschlein, S. Hahner, and B. Allolio AKT Is Highly Phosphorylated in Pheochromocytomas But Not in Benign Adrenocortical Tumors J. Clin. Endocrinol. Metab., July 1, 2005; 90(7): 4366 - 4370. [Abstract] [Full Text] [PDF]
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M. J. Betz, I. Shapiro, M. Fassnacht, S. Hahner, M. Reincke, F. Beuschlein, and for the German Austrian Adrenal Network Peroxisome Proliferator-Activated Receptor-{gamma} Agonists Suppress Adrenocortical Tumor Cell Proliferation and Induce Differentiation J. Clin. Endocrinol. Metab., July 1, 2005; 90(7): 3886 - 3896. [Abstract] [Full Text] [PDF]
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S. R. Bornstein and P. J. Hornsby What Can We Learn from Gene Expression Profiling for Adrenal Tumor Management? J. Clin. Endocrinol. Metab., March 1, 2005; 90(3): 1900 - 1902. [Full Text] [PDF]
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F. de Fraipont, M. El Atifi, N. Cherradi, G. Le Moigne, G. Defaye, R. Houlgatte, J. Bertherat, X. Bertagna, P.-F. Plouin, E. Baudin, et al. Gene Expression Profiling of Human Adrenocortical Tumors Using Complementary Deoxyribonucleic Acid Microarrays Identifies Several Candidate Genes as Markers of Malignancy J. Clin. Endocrinol. Metab., March 1, 2005; 90(3): 1819 - 1829. [Abstract] [Full Text] [PDF]
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 C. Morrison, W. Farrar, J. Kneile, N. Williams, Y. Liu-Stratton, A. Bakaletz, M. A. Aldred, and C. Eng Molecular Classification of Parathyroid Neoplasia by Gene Expression Profiling Am. J. Pathol., August 1, 2004; 165(2): 565 - 576. [Abstract] [Full Text] [PDF]
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D. R. Rhodes, J. Yu, K. Shanker, N. Deshpande, R. Varambally, D. Ghosh, T. Barrette, A. Pandey, and A. M. Chinnaiyan Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression PNAS, June 22, 2004; 101(25): 9309 - 9314. [Abstract] [Full Text] [PDF]
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M. A. Rubin, S. Varambally, R. Beroukhim, S. A. Tomlins, D. R. Rhodes, P. L. Paris, M. D. Hofer, M. Storz-Schweizer, R. Kuefer, J. A. Fletcher, et al. Overexpression, Amplification, and Androgen Regulation of TPD52 in Prostate Cancer Cancer Res., June 1, 2004; 64(11): 3814 - 3822. [Abstract] [Full Text] [PDF]
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K. Struckmann, P. Schraml, R. Simon, K. Elmenhorst, M. Mirlacher, J. Kononen, and H. Moch Impaired Expression of the Cell Cycle Regulator BTG2 Is Common in Clear Cell Renal Cell Carcinoma Cancer Res., March 1, 2004; 64(5): 1632 - 1638. [Abstract] [Full Text] [PDF]
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 P.-L. Chang, M. Cao, and P. Hicks Osteopontin induction is required for tumor promoter-induced transformation of preneoplastic mouse cells Carcinogenesis, November 1, 2003; 24(11): 1749 - 1758. [Abstract] [Full Text] [PDF]
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