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Tumor Biology |
Department of Molecular Genome Analysis [R. W., R. D. W., J. M., R. S., E. M., P. K., A. P.], Department of Molecular Genetics, [M. N., P. L.], and Skin Cancer Unit [H. H., B. G., D. S.], Deutsches Krebsforschungszentrum, and Resource Center for Genome Research [M. S., B. K.], D-69120 Heidelberg, Germany
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ABSTRACT |
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 Top ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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2.5 years, the fotemustine- and cisplatin-resistant sublines exhibited considerable variability. Initially representing distinct entities, these two sublines finally converged in their expression patterns. A total of 110 genes was transiently or permanently deregulated in at least two resistant sublines. Fourteen genes displayed differential expression in all three of the sublines. We hypothesize that the variations in fotemustine and cisplatin resistance are based on progressive optimization and/or polyclonality. This, in addition to genomic alterations investigated by comparative genomic hybridization and evaluation of short-term response genes, can be used as a criterion for the selection of promising candidates. Among these are CYR61, AHCYL1, and MPP1, as well as several apoptosis-related genes, in particular STK17A and CRYAB. As MPP1 and CRYAB are also among the 14 genes differentially expressed in all three of the drug-resistant sublines, they represent the strongest candidates for resistance against DNA-damaging drugs. |
INTRODUCTION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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In general, the response of malignant melanoma to chemotherapy is poor, and an improvement of therapeutic protocols is highly desirable. Several well-characterized drug resistance mechanisms have been studied in regard to their importance for malignant melanoma. Divergent results have been obtained by these analyses indicating that the molecular mechanisms underlying drug resistance in melanoma may be multifaceted and poorly understood (12 , 13) .
The malignant melanoma cell line MeWo has been treated with various cytotoxic compounds, among these the commonly used anticancer drugs cisplatin, fotemustine, and etoposide, to give rise to sublines with resistance to different concentrations of the respective drugs (14) . Whereas cisplatin and fotemustine are thought to form DNA adducts, etoposide interferes with topoisomerase II resulting in double strand breaks (5 , 15, 16, 17) . Thus, the three drugs have in common the ability to either directly or indirectly lead to DNA damage.
The resistant cell lines have been characterized in regard to their pharmacological properties and genomic alterations (14 , 18) . Differences in gene expression have initially been determined using differential display reverse transcription-PCR and two-dimensional protein gel electrophoresis (19 , 20) . These efforts have retrieved 15 primary candidate genes for resistance against the three DNA-damaging drugs, of which one has been confirmed to date by functional assays (21) . The sublines with resistance to DNA-damaging agents have been shown to display alterations of pathways involved in the maintenance of DNA integrity (22, 23, 24, 25) .
In specific regard to melanoma therapy cisplatin, but not fotemustine or etoposide, presently plays a central role (26) . However, the three MeWo sublines with the highest levels of resistance to cisplatin, (MeWoCis1), fotemustine (MeWoFote40), and etoposide (MeWoEto1), offer the opportunity to reveal communities in the resistance to DNA-damaging drugs under standardized conditions. Because this is of importance for a series of cancer types, we conducted a comprehensive and detailed analysis of differential gene expression specifically in these three MeWo sublines.
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MATERIALS AND METHODS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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80% confluence for 24 h with the concentrations depicted above. In parallel, MeWo control cells were cultivated for the same time without drugs. For the determination of the effects of etoposide treatment on gene expression in MeWoEto1, we grew cells with and without etoposide supplementation. For the array hybridizations, RNA was prepared from at least three different time points (t1-t3) for each of the cell lines. The cell lines were reconstituted from frozen stocks collected during continuous growth over a period of 2.5 years. For the sensitive MeWo reference, the time point t1 represents the parental cells from which the drug-resistant sublines were established. The time points t2 and t3 represent cells that were continuously cultured for an additional 23 and 42 months, respectively. For all of the drug-resistant sublines, t1 denotes the time point of the definition of drug resistance (14)
. The nomenclature for additional time points of drug-resistant sublines is as follows: MeWoCis1, t2: 12 months and t3: 31 months; MeWoFote40, t2: 11 months and t3: 30 months; and MeWoEto1, t2: 3 months, t3: 22 months, and t4: 46 months after definition of drug resistance. In CGH4
analyses, t1+ of MeWoCis1 and MeWoFote40 corresponds to t1 + 8–9 months.
cDNA Arrays.
The genome-wide cDNA array (RZPD p950 UniGene1) was manufactured as described previously (27)
. The drug resistance subarray was composed of the 126 candidate genes retrieved from the initial screening of the UniGene array, 52 control genes, and 1143 genes amplified from the RZPD Onco-library. The entire RZPD Onco-library comprises 2800 cancer candidate genes. For the subarray, cluster-specific oligonucleotides (based on the NCBI UniGene built 90, September 1999) were selected for the amplification of 300-bp cluster segments that are free of low complexity regions and corresponding to 3' termini of mRNA molecules. For the PCR amplification we used pools of plasmid DNA (1 ng/reaction, 96–384 different template species per pool) as templates. PCR was performed in 96-well plates for 30 cycles (20 s 94°C, 15 s 46°C, and 30 s 72°C). These elements were spotted in a 3 x 3 pattern on a 7 x 10 cm nylon membrane. All of the PCR products were spotted in duplicate as described before (27)
to provide internal controls. For image processing, each block was provided with a guide spot that contains heterologous DNA from the bacterial kanamycin resistance gene.
Complex cDNA Hybridization.
mRNA was isolated using the RNeasy kit and the Oligotex mRNA kit (Qiagen) according to the manufacturer’s recommendations. The mRNA integrity was confirmed by electrophoresis in denaturing 1% agarose gels with formaldehyde, blotting to Hybond N+ Nylon membranes (Amersham-Pharmacia Biotech), and hybridization with radiolabeled dT18V oligonucleotide. The generation of complex cDNA probes was performed as described previously (27)
. After purification, the specific activity of the probes was determined, and volumes of the probes were adjusted to ascertain equivalent conditions for the array hybridizations. The arrays were hybridized under the conditions described before (27)
. UniGene arrays were hybridized at 65°C, whereas the drug resistance subarray was hybridized at 63°C to adjust for the smaller average fragment length.
Image Acquisition and Analysis of Array Hybridizations.
For RZPD UniGene 1 arrays, image acquisition, grid alignment, and spot quantification was performed as described previously (27)
. Spot by spot comparison, normalization, and determination of differential expression was carried out using fdiffs, an algorithm developed by Tim Beissbarth (Theoretical Bioinformatics, Deutsches Krebsforschungszentrum) based on the matlab package (MathWorks, Natick, MA). For the analysis of the drug resistance subarrays we used ArrayVision Software (Imaging Research). Here, after spot finding using an automated algorithm, signal intensities were calculated as mean pixel values minus a regional background calculated for each 3 x 3 spot group, respectively. For array comparisons, signal intensities were then normalized to the mean of all human cDNA containing spots on one filter, and expression ratios for single spots were calculated. Ratios of 2.5 and 1/2.5 were set as thresholds to identify differentially expressed genes from the UniGene arrays. For the drug resistance subarray, we used ratios of 1.5 and 1/1.5 as the threshold values to adapt to the decrease in the average size of the array elements.
Northern Blot Analyses.
Fifteen µg of total RNA was separated by electrophoresis in 1% agarose gels under denaturing conditions in the presence of 2.2 M formaldehyde, and stained with SYBR Green II (FMC, Rockland, ME) for visualization under UV light. RNA was transferred to Hybond N+ Nylon membranes (Amersham-Pharmacia Biotech) by capillary transfer overnight and subsequently immobilized by UV cross-linking. Hybridizations were performed with 32P-labeled cDNA probes in 500 mM Na2HPO4 (pH 7.2), 7% SDS, and 10 mM EDTA at 65°C overnight. After washing (2 x 10 min in 0.5 x SSC, 0.1% SDS at 65°C) membranes were exposed to X-ray films and imaging plates. In the latter case image acquisition was done using a Fuji FLA3000 phosphorimager and AIDA software.
CGH.
Cells were lysed by incubation in 50 mM Tris, 100 mM EDTA, and 200 mM NaCl (pH 9) with 1% SDS and 0.5 mg/ml proteinase K at 37°C overnight. Genomic DNA was purified by a standard phenol-chloroform extraction procedure and subsequent ethanol precipitation. Genomic DNA was reconstituted in 10 mM Tris and 1 mM EDTA (pH 7.5). Probe preparation, hybridization, image acquisition, and analysis of CGH were performed as described previously (28)
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RESULTS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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31,500 elements (RZPD p950 UniGene 1; Ref. 27
) for a genome-wide analysis of differentially expressed genes in drug-resistant MeWo sublines. For the initial selection of drug resistance candidate genes, mRNA obtained from three different time points of cultivation under permanent selective pressure (t1 to t3, see "Materials and Methods") was separately compared with an mRNA pool of three different time points (t1 to t3, see "Materials and Methods") of the long-term cultivated, drug-sensitive parental MeWo cell line. This was intended to select for stable alterations in the expression pattern of the drug-resistant sublines and against genes that may be transiently deregulated because of differences in the cell culture conditions. Genes showing distorted ratios of least 2.5 or 1/2.5, respectively, in at least two of three comparisons were selected for additional evaluation. This resulted in an initial number of 139 candidate genes, of which 87 were retrieved from MeWoEto1. From MeWoCis1 and MeWoFote40, respectively, only 23 and 29 candidate genes were recovered. Seven genes were deregulated in two resistances, whereas 3 genes displayed an overlap in all three of the sublines, so that, after elimination of these redundancies, a final set of 126 genes was obtained.
Candidate Gene Evaluation.
To additionally confirm and evaluate candidate genes, a drug resistance subarray with a final number of 1321 elements was composed. These elements included the 126 candidate genes retrieved from the initial screen, 52 control elements for monitoring the hybridization quality, and 1143 additional elements from the RZPD Onco-library. The latter ones were included to specifically and more sensitively address known and putative cancer candidate genes in regard to their differential expression. For the complex hybridizations, we used the same mRNA populations as in the initial analyses, i.e., t1 to t3 for each resistant subline.
The subarray analyses indicated consistent changes in the expression patterns of all three of the time points investigated for MeWoEto1. Stable gene deregulation in MeWoEto1 was additionally confirmed by analysis of mRNA of a fourth time point (t4). In contrast, the different time points of sublines MeWoCis1 and MeWoFote40 showed substantial variations. This was confirmed by two to three independent hybridization experiments as well as by Northern blot analyses of selected candidates (Fig. 1)
. Thus, we concluded that the differences in the stability of the transcriptomes had led to a differential efficacy in recovering candidates from the three sublines. To adjust to this phenomenon, we applied subline-specific criteria to identify primary candidates. For MeWoEto1, only genes showing consistent quantitative changes in all of the data points for three of four analyzed time points were scored as primarily confirmed candidates. For MeWoCis1 and MeWoFote40, genes displaying consistent changes in at least five of six data points for t1 and t2 and in at least three of four data points for t3, respectively, were scored as primarily confirmed candidates. Using these subline-specific criteria, 57 of the initial 126 genes (45%) were confirmed as being deregulated in at least one of the drug-resistant sublines. Remarkably, 209 of the 1143 (18%) included Onco-library elements were suggested to be differentially expressed compared with the sensitive parental MeWo cell line. Thus, a total of 266 candidate genes resulted from these efforts (for the primary data, see supplementary data2
). We selected a subset of 15 genes for additional verification by Northern blot analyses. The expression of these candidates was analyzed for the three drug-resistant sublines at time points t1 to t3 so that a total of 135 data points was investigated. For 110 of the 135 data points (81.4%) we obtained qualitatively concordant results (Fig. 1
; Fig 2F
), confirming the applicability of the subline-specific criteria that were set previously.
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. The clone ID133864 mapped to two UniGene clusters, Hs.24305 and Hs.191045, and therefore was represented by two elements on the drug resistance subarray (Fig. 1A)
. By reverse transcription-PCR analyses, we identified the sequence of ID133864 as part of the 3'-untranslated region of the PDE3A gene. In Northern blot analysis using a PDE3A open reading frame sequence as a probe, a 7-kb transcript was detected (data not shown) pointing to the 7-kb PDE3A transcript as the gene identified as differentially regulated in the etoposide- and fotemustine-resistant cell lines. In the case of MPP1 (Fig. 1C)
, array data matched to the expression of the 2.2-kb variant, which is likely to represent the annotated mRNA (NM_002436). Both recently reported variants of the PDCD6IP gene (29)
displayed a down-regulation in MeWoEto1, which was congruent with the array results (Fig. 1O
; Fig. 2F
). For EGR1 (Fig. 1H)
, the expression pattern of the 3.2-kb variant correlated with the array data, and the size of this variant corresponded to the annotated mRNA sequence (X52541). The STMN3 gene (Fig. 1J)
had been reported to give rise to a 2.3-kb transcript (NM_015894), which could be detected in Northern blot analysis. The quantitative changes in the levels of this transcript corresponded to the array data. For these 5 genes, other hybridizing bands may represent alternative splice forms, polyadenylation variants, or closely related genes. In the remaining 10 cases, the detected transcript sizes corresponded to the annotated mRNA sequences in GenBank.
Genotype-Transcriptome Correlations.
A comparison between the original CGH data (18)
and CGH data obtained from the resistant sublines at t3 indicated consistent genomic alterations on eight chromosomes (Fig. 3)
. The specific genomic alterations allowed verifying the integrity of the respective sublines. We determined the cytogenetic localizations of the candidate genes by screening of NCBI5
(LocusLink; UniGene), RZPD,6
and Genome7
databases. The differential expression of 28 genes could be correlated with gains and losses of the respective chromosomal regions (Table 1)
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, columns 17–27). MeWoCis1t1 (Fig. 2
, columns 14–16) was related to MeWoEto1 according to the clustering, whereas MeWoFote40t1 and t2 (Fig. 2
, columns 01–06) represented a distinct entity. However, MeWoCis1t2/t3 (Fig. 2
, columns 09–13) and MeWoFote40t3 (Fig. 2
, columns 07–08) deviated from their original patterns and appeared to finally converge in regard to their expression patterns. We verified these results by Northern blot analysis of selected candidates. Twelve of 15 genes showed consistent quantitative changes over all MeWoEto1 time points analyzed (for example ID133864, APOD, CRYAB, and AHCYL1 in Fig. 1
) Six of the 15 genes displayed convergence in their expression levels at the later time points when comparing MeWoCis1 and MeWoFote40 (see ID133864, APOD, CRYAB, PEPP2, PLAB, IFITM3 in Fig. 1
). CRYAB, one of the genes represented and up-regulated in both groups, has been related to the repression of apoptosis (32)
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Evaluation of Short-Term Response and Cross-Resistance Candidate Genes.
To distinguish between stably deregulated genes and genes deregulated because of the presence of the drugs, we conducted a series of additional experiments. At first, we included RNA samples of the sensitive parental MeWo cell line treated for 24 h with cisplatin and fotemustine in the Northern blot analyses. Secondly, we additionally carried out Northern blot and subarray analyses with 24-h etoposide-induced MeWo cells as well as with MeWoEto1 omitting the etoposide from the culture medium. The latter subarray analyses indicated no major influence of etoposide on the overall expression pattern in MeWoEto1, as suggested by clustering (Fig. 2
, columns 17–24). In contrast, the patterns observed for short-term treated MeWo substantially differed from both untreated MeWo and drug-resistant MeWoEto1 (Fig. 2
, columns 28–30). These data suggested that genes deregulated because of the short-term response to the presence of the drug are represented only to a minor extent in the candidate panel. Basically, these data were confirmed by the Northern blot analyses. Twelve of the 15 genes showing quantitative differences in the resistant sublines were not responsive to short-term drug treatment. Remarkably, however, 3 of the 15 genes (AHCYL1, CYR61, and the 4.5-kb variant of STMN3) displayed a down-regulation in the course of the short-term response to all three of the DNA-damaging drugs. The down-regulation of all 3 of the genes became manifested in the long-term resistant cell line MeWoEto1, and 2 of these (CYR61 and the 4.5-kb transcript of STMN3) were also transiently down-regulated at one of the time points in MeWoCis1. The transcriptional repression of the 3 genes in MeWoEto1 was maintained even in the absence of etoposide (Fig. 1)
. Moreover, 2 of these short-term response genes mapped close to a region at chromosome 1 identified as genomic loss in MeWoEto1 by CGH (Table 1
Fig. 3
). CYR61 was located at 1p31-p22 (1p22.3 according to GeneCards), whereas AHCYL1 had been mapped to 1p12.
To identify potential cross-resistance candidates, we compared the data obtained for the three different drug-resistant sublines from the subarray analyses. This revealed substantial overlaps in the differential expression of genes during acquired drug resistance. In total, 110 differentially expressed genes showed overlaps in their deregulation (Fig. 2, A–D)
. Fourteen of these were found to be deregulated in all three of the drug-resistant cell lines (Fig. 2D)
.
Arrangement According to Common Pathways Points to Frequent Deregulation of Apoptosis-related Genes.
Subsets of the deregulated genes could be identified to belong to common pathways. For example, 6 genes, G1P3, ISG15, IFI27, IFITM1, IFITM3, and PRKR, represent a group of IFN-inducible genes. These genes were highly overexpressed in MeWoEto1 and in MeWoCis1t1, which is in agreement with the data obtained for a paclitaxel-resistant subline of the breast cancer cell line MCF-7 (33)
. However, whereas these changes were persistent in MeWoEto1, these genes again displayed the lower levels of the parental MeWo cells in MeWoCis1t2/t3 (Fig. 2B)
. Secondly, several stress-inducible genes, e.g., PLAB, HSPA5, HSPA9B, DDIT3, and GADD45A, were often down-regulated in the drug-resistant sublines, to the major part in MeWoEto1 (Figs. 1
and 2)
. Remarkably, 12 genes belonging to apoptosis-related pathways were commonly found to be deregulated, as for example CRYAB, DFFA, PDCD6IP, or SH3BP5 (Fig. 1
; Fig. 2E
).
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DISCUSSION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Starting from a genome-wide scan for differentially expressed genes, we identified 57 genes confirmed by subarray analyses. Inclusion of genes of the Onco-library to more specifically address known and putative cancer genes retrieved an additional set of 209 candidates. The recovery of the latter genes presumably bases on the redefinition of the parameters for the subarray analyses, which was necessary to adapt for the decrease of the mean element length, and for the fluctuations in MeWoCis1 and MeWoFote40. Because Northern blot analyses suggested an 81.4% concordance with the subarray analyses for the genes tested, we hypothesize that a major subset of the 266 genes is truly differentially expressed in the drug-resistant sublines. Twelve of these genes are involved in the modulation of apoptosis pathways. Three of these originated from the 57 confirmed UniGene clones, whereas 9 derived from the 209 genes recovered from the Onco-library. This suggests that by using genes from the Onco-library for the subarray no bias toward the isolation of apoptosis-related genes was introduced.
The genes recovered may basically subdivide into two categories with differential importance for drug resistance itself. The first category comprises genes that could directly be related to the phenotypes. The second category may represent genes of which the deregulation is rather caused by genomic rearrangements or coactivation, or corepression because of deregulation of transcription factors. In fact, at least 35 of 266 deregulated genes have known or putative functions in the regulation of transcription. To date, a clear distinction between genes belonging to these two groups is not possible, because this would require assays for high throughput screening. However, based on the design of our experiments and the definition of subgroups, we can encircle the most promising candidates.
Primarily, the comparison of the expression patterns over 2.5 years may point to certain limitations, even within this well-defined in vitro system of acquired drug resistance. The constant deregulation of some genes as well as the consistency of the genomic alterations confirmed the integrity of MeWoCis1 and MeWoFote40. Although being resistant to exposure to the respective drugs, the two sublines showed considerable variations in their expression patterns over the time course. However, we speculate that one can take several advantages out of this phenomenon.
At first, both cluster analyses and Northern blot studies suggest distinct changes in the gene expression profiles of MeWoCis1 and MeWoFote40 at early time points but convergence at later time points. We hypothesize that this convergence reflects progressive optimization of the drug resistance response. Moreover, convergence because of optimization would be in line with the similar mode of action of cisplatin and fotemustine (15, 16, 17) . Indeed, earlier studies support the view that MeWoCis1 and MeWoFote40 exert cross-resistance against cisplatin and fotemustine (14) . Thus, converging genes identify as strong candidates for drug resistance. PEPP2 and CRYAB, for example, are noteworthy. PEPP2, which has been suggested to take part in phosphoinositide-mediated signaling (37) , is successively silenced over the time course. In contrast, CRYAB, an antagonist of caspase 3-mediated apoptosis (32) , is consecutively up-regulated.
Secondly, however, progressive optimization cannot completely explain the variations in the expression pattern of MeWoFote40. When overlaying the expression levels of the genes PLAB, SH3BP5, PDE3A, and CRYAB in t2 and t3 (Fig. 1)
, the combinatorial pattern agrees with the one obtained for t1. Thus, most probably, a split of the cell population has taken place. Consequently, MeWoFote40 was polyclonal at t1. Possibly, differential reconstitution from the frozen stocks has led to shifts in the otherwise stable polyclonal population. Under this premise, genes with differential behavior in t2 and t3 would participate in alternative pathways of fotemustine resistance. A very suggestive example is that t3 shows an activation of the apoptosis antagonist CRYAB, whereas it is absent from t2. Strikingly, however, t2 shows a repression of the proapoptotic genes CRADD (38)
, RIPK1 (39)
, DFFA (40)
, and DAPK3 (41)
, which, in fact, could be able to compensate for the lack of the antagonistic action of CRYAB (Fig. 2E)
.
We determined correlations between chromosomal aberrations and up- or down-regulation for 28 genes. This is particularly helpful for candidate evaluation. STK17A, which has a stimulatory effect on apoptosis (42) , is down-regulated in MeWoEto1 and locates to chromosome 7p that has undergone a loss in this subline. The perfect agreement between genomic, expression, and functional data points to this gene as a considerably strong candidate. MPP1 at Xq28 is to date poorly characterized at the functional level. Its up-regulation in MeWoFote40 attributes to the amplification of the respective genomic locus, which was additionally confirmed by Southern blot analyses (data not shown). In MeWoEto1 and MeWoCis1 a distinct mechanism must be responsible for the MPP1 up-regulation, because neither CGH nor additional Southern blot studies (data not shown) suggested the presence of an amplification at the gene locus. Thus, this phenomenon argues for a role in multidrug resistance and against being a bystander. Correlation with genomic data also allows resolving apparent contradictions between functional data and the direction of deregulation. For example, RAF1, an antagonist of apoptosis (43) , is down-regulated in MeWoEto1. Obviously, this is based on the loss of the respective chromosomal region, i.e., 3p. Thus, its down-regulation is likely a gene dosage effect attributable to the genomic alterations.
In association with data obtained from the experiments on the short-term responses, the genomic alterations provide additional valuable information. For example, the genes AHCYL1 and CYR61 reveal down-regulation in MeWo already during the short-term response to drug exposure. Apparently, this down-regulation became manifested in MeWoEto1, independently of the presence of etoposide in growth medium. This is possibly caused by a loss of the respective chromosomal region at 1p.
The utilization of a homogenous source offered the opportunity to identify genes commonly deregulated in more than one drug resistance phenotype basically independent of external variations such as different genetic backgrounds or tumor architecture. According to subarray analyses, a total number of 110 genes matches this criterion. The most promising candidates are 14 genes either up- or down-regulated in each of the three drug resistance phenotypes (Fig. 2D)
, among these the 4 IFN-inducible genes G1P3, ISG15, IFITM1, and IFITM3, as well as MPP1 and UP, which has also been shown to be down-regulated in gastric cancer cells with cross-resistance to cisplatin and doxorubicin (44)
, and again the apoptosis antagonist CRYAB. In specific regard to chemotherapy in melanoma, the consistent up-regulation of the four IFN-inducible genes may be noteworthy. Similar observations were made in the acquired resistance of the breast cancer cell line MCF-7 against the antimicrotubule agent paclitaxel. However, the resistant MCF-7 cells exerted no cross-resistance against the DNA-damaging drug doxorubicin (33)
. Nevertheless, it seems worthwhile to conduct additional studies on these 4 IFN-inducible genes, because IFN therapy is presently evaluated for its usefulness in melanoma treatment (26)
.
In conclusion, we present a set of 266 genes deregulated in melanoma cell lines resistant to etoposide, fotemustine, and cisplatin. Of these 110 are deregulated in more than one and 14 are differentially expressed in all three of the drug resistant phenotypes. Among the latter are MPP1 and CRYAB, which identify as excellent candidates.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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1 Supported by the Deutsche Forschungsgemeinschaft Grants Scha 422/7-2 and Scha 422/7-3 (to D. S.), and the Forschungsfond Mannheim (to D. S.). 
2 Supplementary data in this article are available at Cancer Research Online (http://cancerres.aacrjournals.org).
3 To whom requests for reprints should be addressed, at Department of Molecular Genome Analysis, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. Phone: 49-6221-424742; Fax: 49-6221-423454; E-mail: a.poustka{at}dkfz-heidelberg.de
4 The abbreviations used are: CGH, comparative genomic hybridization; RZPD, Resource Center for Genome Research; NCBI, National Center for Biotechnology Information; HGNC, Human Genome Organisation Gene Nomenclature Committee.
5 Internet address: http://www.ncbi.nlm.nih.gov/.
6 Internet address: http://www.rzpd.de/.
7 Internet address: http://www.gdb.org/.
8 Internet address: http://rana.lbl.gov/EisenSoftware.htm.
Received 5/ 6/02. Accepted 9/13/02.
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