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1 Molecular Diagnostic Laboratory, Department of Clinical Biochemistry, Aarhus University Hospital, Skejby, Aarhus N; 2 University Institute of Pathology, Aarhus University Hospital, Aarhus C; 3 Department of Theoretical Statistics, Department of Mathematical Sciences, Ny Munkegade, Aarhus C; and 4 Department of Urology, Aarhus University Hospital, Skejby, Aarhus N, Denmark
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ABSTRACT |
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 Top ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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INTRODUCTION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Complete genome screenings for diagnostic and prognostic markers using DNA microarray technology have demonstrated improvements recently in the ability to diagnose and predict disease outcome in cancer patients (10, 11, 12, 13, 14, 15) . In a recent study, we found a large difference in gene expression patterns between superficial transitional cell carcinoma (sTCC) with and without surrounding CIS using microarrays (13) . Superficial tumors with surrounding CIS lesions (4 cases) showed, in most cases, gene regulations similar to those observed in muscle invasive tumors. This indicated that transitional cell carcinoma (TCC) adjacent to CIS may share the more aggressive genotype associated with the CIS lesions, probably because of the mono- or oligoclonal nature of many bladder tumors (16) . In the present project, we used a much larger material for microarray expression profiling of the expression patterns associated with sTCC with surrounding CIS compared with sTCC with no surrounding CIS and to muscle invasive carcinomas (mTCC). We compared our results with the gene expression patterns found in normal bladder biopsies and in biopsies from cystectomy specimens with CIS lesions. Our results showed a large difference in expression patterns between TCC with and without surrounding CIS. We found expression similarities among TCC with surrounding CIS, biopsies harboring CIS, and invasive carcinomas. Furthermore, the expression patterns identified in the CIS lesions were also present in histologically normal biopsies adjacent to the CIS lesions. The present data support the value of microarray-based gene expression signatures because these identify clinically important cellular properties.
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MATERIALS AND METHODS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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shows an overview of the different groups of tissue used for expression profiling in this study.
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The cRNA Preparation, Array Hybridization, and Scanning.
Purification of total RNA, preparation of cRNA from cDNA, and hybridization and scanning were performed as described previously (13)
. The labeled samples were hybridized to Affymetrix U133A GeneChips.
Expression Data Analysis.
After scanning, all of the data were normalized using the Robust Multi-array Analysis (RMA) normalization approach in the Bioconductor Affy package to the R project for statistical computing (17)
. Variation filters were applied to the data to eliminate nonvarying and, presumably, nonexpressed genes. For gene set 1, this was done by including only genes with a minimum expression >200 in at least 5 samples and genes with maximum/minimum expression intensities 
3. The filtering for gene set 2 included genes with only a minimum expression of 200 in at least 3 samples and genes with maximum/minimum expression intensities 3. Average linkage hierarchical cluster analysis was carried out using the Cluster software with a modified Pearson correlation as similarity metric (18)
. We used the TreeView software for visualization of the cluster analysis results (18)
. Genes were log-transformed, median-centered, and normalized to have equal variance before clustering. We used GeneCluster 2.05
for the supervised selection of markers and permutation testing. The algorithms used in the software are based on methods described previously (14
, 19)
. Classifiers for CIS detection were built using the same methods as described previously (13)
. We used EASE software in the search for overrepresented functional categories within the gene clusters (20)
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RESULTS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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22,000 genes in biopsies from 28 superficial bladder tumors (13 tumors with surrounding CIS and 15 without surrounding CIS) and 13 invasive carcinomas. (See Table 1
for patient disease course descriptions.) Furthermore, expression profiles were obtained from 9 normal biopsies and from 10 biopsies from 5 cystectomy specimens (5 histologically normal biopsies and 5 biopsies with CIS). Fig. 2
shows representative histological compositions from each group of samples investigated. The scanning images obtained were quality checked for errors, and the gene expression data were generated and normalized using RMA (17)
. All of the samples were labeled and hybridized simultaneously in one session, and we did not observe any group-specific differences in overall array intensity (Supplementary Data, Fig. 1).6
Histological examination of the superficial tumors used for microarray analysis did not show any systematic variation in content of inflammatory cells between tumors with or without surrounding CIS.
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. The sTCC with surrounding CIS clustered in the one main branch of the dendrogram, whereas the sTCC without CIS and the mTCC clustered in two separate sub-branches in the other main branch of the dendrogram. The only exceptions were that the mTCC samples 1044-1 and 1124-1 clustered in the CIS group and 2 TCC with CIS clustered in the sTCC without CIS group (samples 1330-1 and 956-2). The only sTCC without CIS that clustered in the CIS group was sample 1482-1. The distinct clustering of the tumor groups indicated a large difference in gene expression patterns.
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identified large clusters of genes characteristic for each tumor phenotype. Cluster 1 showed a cluster of genes down-regulated in cystectomy biopsies, sTCC with adjacent CIS, and some invasive carcinomas (Fig. 3C)
. No obvious functional relationship between the genes in this cluster was found (see "Materials and Methods"). Cluster 2 showed a tight cluster of genes related to immunology, and cluster 3 contained genes expressed mostly in muscle and connective tissue. Expression of genes in this cluster was observed in the normal and cystectomy samples, in a fraction of the sTCC with CIS, and in the invasive tumors. Cluster 4 contained genes up-regulated in the cystectomy biopsies, sTCC with adjacent CIS, and invasive carcinomas (Fig. 3C)
. This cluster included genes involved in cell cycle regulation, cell proliferation, and apoptosis. However, for most of the genes in this cluster, there is no apparent functional relationship either. Comparisons of chromosomal location of the genes in the clusters revealed no correlation between the observed gene clusters and chromosomal position of the identified genes. A positive correlation could have indicated chromosomal loss or gain or chromosomal inactivation by, for example, methylation of promoter regions.
To analyze additionally the impact of surrounding CIS lesions, we used the 28 superficial tumors only and created a new gene set consisting of 5252 varying genes (gene set 2). This new gene set included 4882 (93%) of the genes from gene set 1. Hierarchical cluster analysis of the tumor samples (Fig. 3B)
based on the new gene set separated the samples according to the presence of CIS in the surrounding urothelium with only one exception (P < 0.000001; 
2-test). Sample 1482-1 clustered in the sTCC with the CIS group; however, no CIS was detected in selected site biopsies during routine examinations of this patient. Tumor samples 1182-1 and 1093-1 did not have CIS in selected site biopsies in the same visit as the profiled tumor but showed this in later visits. However, the profile of these 2 superficial tumor samples already showed the adjacent CIS profile.
To validate the strength of the observed sample clustering, we applied different filtering criteria to gene set 1 and gene set 2 (SD 200). Minor changes were observed; however, the filtering did not introduce any overall changes to the observed clusters in Fig. 3, A and B
(data not shown).
Marker Selection.
To delineate the tumors with surrounding CIS from the tumors without surrounding CIS, we used t test statistics to select the 50 most up-regulated genes in each group. The relative expressions of these 100 genes together with the expressions of the genes in mTCC biopsies are shown in Fig. 4A
. The CIS profile was identified in almost all of the mTCC samples. Permutation of the sample labels 500 times revealed that the 50 genes up-regulated in the CIS group are highly significantly differentially expressed and unlikely to be found by chance, because all of the markers were significant at a 5% confidence level. This means that the t test value for each of these genes was so high that similar high values were found in <5% of 500 random data sets. The 50 genes up-regulated in the no-CIS group showed a poorer performance in the permutation tests, because these were not significant at a 5% confidence level (see Supplementary Data for details).6
Fig. 4B
shows the relative expression of the marker genes in 9 biopsies from normal bladder and 10 biopsies (5 histologically normal and 5 with CIS) from cystectomies with CIS. The no-CIS profile was found in all of the normal samples. However, all of the histologically normal samples adjacent to the CIS lesions as well as the CIS biopsies showed the CIS profile.
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Construction of a Molecular CIS Classifier.
A classifier able to diagnose CIS from gene expressions in TCC or in bladder biopsies might increase the detection rate of CIS. Our first approach was to try to classify sTCC with or without CIS in the surrounding mucosa, based on tissue from the sTCC.
We build a CIS classifier as described previously (13)
using cross-validation for determining the optimal number of genes for classifying CIS with the fewest errors. The best classifier performance (one error) was obtained in cross-validation loops using 25 genes (see Supplementary Data, Fig. 2);6
16 of these were included in 70% of the cross-validation loops, and these were selected to represent our final classifier for CIS diagnosis (Fig. 5A
; Supplementary Data, Table 2).6
Permutation analysis showed that 13 of these were significant at a 1% confidence level; the remaining 3 genes were above a 10% confidence level.
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2-test; P < 0.002). Only 2 of the genes were included also in the 16-gene classifier, which is understandable considering the many tests performed and the limitations in sample size. This classification performance is notable considering the few samples used for training the classifier.
The gene expression profiles of the 16-classifier genes in the 9 normal biopsies from patients without a bladder cancer history and in the 10 biopsies (CIS and histologically normal samples) from cystectomy specimens are shown in Fig. 5B
. The samples were separated based on the normalized gene expression values using hierarchical cluster analysis. The clustering separated the samples from cystectomies (both histologically normal biopsies and CIS biopsies) from the normal samples from patients with no bladder cancer history with only a few exceptions. Eight of the 10 biopsies from cystectomies were found in the one main branch of the dendrogram, and 8 of the 9 normal biopsies were found on the other main branch (2-test; P < 0.002). The 16-gene CIS classifier was not able to recognize the CIS signature in these biopsies, probably because of the large difference in sample composition between the sTCC samples (papillomas) used for training the classifier and the cystectomy and normal biopsies (flat lesions), which contain large amounts of connective tissue and muscle cells.
We conclude that it is possible to detect a CIS expression signature in sTCCs and histologically normal mucosa from bladders having CIS lesions. Thus, analyzing the sTCC papilloma alone may provide information that makes the sampling of random biopsies unnecessary.
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DISCUSSION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The observation that histologically normal areas in the bladder adjacent to CIS lesions harbored the same gene expression patterns as observed in the CIS lesions is remarkable. These findings are supported by observations made by Hartmann et al. (22) , who found genetic alterations on chromosome 9 in normal samples adjacent to superficial papillary tumors. In addition, Steidl et al. (23) found chromosome copy number changes in normal biopsies adjacent to superficial tumors. Consequently, our and other studies suggest that apparently normal mucosa adjacent to neoplastic lesions may harbor some of the genetic changes associated with neoplasias. The mechanism for such alteration is not understood. A hypothesis could be that alterations in the underlying connective tissue are driving the expression changes in the cells surrounding the CIS lesion, due to tumor host communication during tumor development and progression (24) . These changes may not be restricted to appear solely below the carcinoma but are likely to span a larger area. Such a phenomenon may also help explain the frequently observed multifocality and recurrence patterns of mono- or oligoclonal bladder tumors. However, the tumors are separated both in time and location in the bladder, and, thus, an unknown effect seems to modify the tumor formation.
Delineation of the 100 best markers to separate TCC with CIS from TCC without CIS identified several interesting genes. Among the genes with higher expression in TCC without adjacent CIS were the LAMB3 and ITGB4 genes, which encode proteins involved in cell adhesion (25) . This group also contained the UPK2 gene, which encodes the bladder-specific uroplakin 2 protein. Uroplakin 2 is a marker of advanced-stage urothelial differentiation (26) . The FABP4 gene also showed elevated expression in the non-CIS group compared with the group with adjacent CIS. Fatty acid binding protein has been shown to be down-regulated in invasive bladder tumors (27) . Lastly, the group contains the FGFR3 gene, which has been shown to be mutated in sTCC with low recurrence rates (28) . In the group of genes showing increased expression in the TCC with adjacent CIS, we found several genes encoding proteins being part of connective tissue and the immune response.
It is believed that muscle invasive TCCs arise from different pathways: (a) a pathway from a stepwise progression of transitional cell papillomas; or (b) a pathway from CIS precursor lesions. Genetic analysis (loss of heterozygosity studies and mutation analysis studies of TP53) has identified the CIS lesions to be the most common precursor of invasive carcinomas, because the CIS lesions harbor many of the chromosomal abnormalities and mutations frequently observed in invasive carcinomas (4 , 5) . There is no direct evidence that invasive tumors arise solely from CIS lesions, although it has been demonstrated, using a transgenic mouse model expressing the SV40 oncogene, that only CIS and invasive tumors develop in the mouse bladder depending on the gene dose (29) . In our present study, we did not see a large resemblance in gene expression patterns between TCC with adjacent CIS and mTCC, which might otherwise be expected from the similar genetic alterations observed for the two tumor types. We observed that the TCC with adjacent CIS harbored their own characteristic expression profiles distinct from invasive carcinomas and TCC without adjacent CIS. In our previous expression profiling study of bladder carcinoma, we did find a close relationship between invasive carcinomas and some (four) papillary tumors with synchronous CIS lesions (13) . These tumors showed up-regulation of genes involved in matrix remodeling, angiogenesis, and the immune response. However, this was not a general finding for all of the tumors with adjacent CIS studied in this article. We did identify clusters of genes with expression similarities between the TCC with CIS and the invasive tumors (cluster 1 and 4); however, a systematic functional relationship for most of the genes was not found. Among the genes with elevated expression in cystectomy biopsies (with or without CIS) and in the TCC with CIS and the invasive tumors (cluster 4) were a few genes that highlight the aggressive nature of CIS. We found the GG2-1 gene, which may encode an antiapoptotic protein (inferred from structural domains), and the VEGF gene, which encodes a protein that is a prognostic marker for stage progression and recurrence in bladder cancer (30 , 31) and is involved in angiogenesis (32) . Furthermore, the cluster included the TGFBR2 gene, which has been shown to be mutated in colon cancer cell lines with high rates of microsatellite instability. It is believed that the mutations make cancer cells able to escape the transforming growth factor ß-mediated growth control (33) . Interestingly, the TIEG gene (transforming growth factor ß-inducible early gene) encoding a transcription factor that plays an important role in the transforming growth factor ß signaling pathway (34) is also found in this cluster. Most other genes identified in these clusters remain interesting candidates for additional studies.
Because inflammation is a common finding in CIS mucosa, and an inflammation cluster was detected in some of the sTCC+CIS samples, it could be argued that inflammation was driving the clustering of sTCC with or without surrounding CIS. However, the inflammatory genes were only expressed in a subset of the sTCC+CIS samples, and we did not find an overrepresentation of immunology-related genes among the 100 marker genes or the 16-classifier genes. One could argue, as seen from a classification point of view, that it would not matter whether the genes that work well for classification were involved in immunology or other functions. A classifier should classify correctly, and the function of the genes working well in a classifier may be of less importance.
We found that it is possible to classify tumor samples according to the presence or absence of concomitant CIS using relatively few genes. This may lead to a better diagnosis of CIS and, as shown in this work, the expression profiles of these genes were found in the actual CIS lesions as well as in histologically normal biopsies next to the CIS lesions. The fact that CIS may be diagnosed from histologically normal areas in the bladder by gene expression profiling may increase the CIS detection rate and, as a result, lead to better optimized treatment regiments. Because TCC with surrounding CIS is relatively rare, we were not able to include more samples for validation of the CIS classifier. Consequently, the classifier for classification of CIS was validated by the "leave one out" cross-validation methodology; this procedure indicates the robustness of the classifier, but validation using independent samples is a better test of the classifier performance, because the samples in the training set may be biased because of, for example, the selection process. On the basis of this, we used an approach in which we randomly divided the samples into two groups, one for training and one for testing. The classification performance using this approach was good despite the few samples involved in the training procedure, and we would expect a successful validation of the 16-gene classifier on independent samples.
Some of the biopsies used for expression profiling in this study were taken from cystectomy specimens, and the histopathological diagnosis of the profiled sample was determined from two adjacent biopsies. This procedure involved some uncertainty, which may explain the finding that a few of the biopsies with CIS showed the no-CIS profile. Laser-assisted microdissection of the CIS lesions and adjacent normal mucosa samples may partly eliminate this problem in the future if the RNA quality can be kept at a high level during the procedure. Such technology may be useful also for analyzing the origin of the gene expressions identified in this work and, in this way, delineate the signaling between the carcinoma cells and the underlying connective tissue.
In conclusion, we have detected a CIS signature that is reflected not only in CIS biopsies but also in TCCs and histologically normal mucosa from bladders containing CIS. We have constructed a 16-gene molecular classifier for identification of the CIS gene expression signature. This signature could be useful in the follow-up of bladder cancer patients.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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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.
Requests for reprints: Torben F. Ørntoft, Molecular Diagnostic Laboratory, Department of Clinical Biochemistry, Aarhus University Hospital, Skejby, DK-8200 Aarhus N, Denmark. Phone: 45-89495100; Fax: 45-89496018; E-mail: orntoft{at}kba.sks.au.dk
5 Internet address: http://www-genome.wi.mit.edu/cancer/software/genecluster2/gc2.html. 
6 Supplementary data is available online at http://www.mdl.dk.
Received 11/18/03. Revised 3/17/04. Accepted 3/29/04.
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