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Cancer Therapy: Preclinical |
Authors' Affiliations: 1 Laboratory of Biosystems and Cancer and 2 Radiation Biology Branch, Center for Cancer Research, and 3 Pediatric Oncology Branch, National Cancer Institute, Bethesda, Maryland; 4 Gynecology and Breast Research Laboratory, Department of Surgery, and 5 Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York; and 6 Department of Cell and Cancer Biology, Center for Cancer Research, National Cancer Institute, Rockville, Maryland
Requests for reprints: Jeff Boyd, Department of Surgery, Memorial Sloan-Kettering Cancer Center, Box 201, 1275 York Avenue, New York, NY 10021. Phone: 212-639-8608; Fax: 212-717-3538; E-mail: boydj{at}mskcc.org.
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Abstract |
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Experimental Design: Gene expression profiles were generated from 21 primary chemosensitive tumors and 24 primary chemoresistant tumors using cDNA-based microarrays. Gene expression profiles of both groups of primary tumors were then compared with those of 15 ovarian carcinomas obtained following platinum-based chemotherapy ("postchemotherapy" tumors). A theme discovery tool was used to identify functional categories of genes involved in drug resistance.
Results: Comparison of primary chemosensitive and chemoresistant tumors revealed differential expression of 85 genes (P < 0.001). Comparison of gene expression profiles of primary chemosensitive tumors and postchemotherapy tumors revealed more robust differences with 760 genes differentiating the two groups (P < 0.001). In contrast, only 230 genes were differentially expressed between primary chemoresistant and postchemotherapy groups (P < 0.001). Common to both gene lists were 178 genes representing transcripts differentially expressed between postchemotherapy tumors and all primary tumors irrespective of intrinsic chemosensitivity. The gene expression profile of postchemotherapy tumors compared with that of primary tumors revealed statistically significant overrepresentation of genes encoding extracellular matrix–related proteins.
Conclusions: These data show that gene expression profiling can discriminate primary chemoresistant from primary chemosensitive ovarian cancers. Gene expression profiles were also identified that correlate with states of intrinsic and acquired chemoresistance and that represent targets for future investigation and potential therapeutic interventions.
75% of patients diagnosed with advanced-stage disease, 20% to 30% progress on or rapidly become resistant to this treatment and subsequently show low response rates to other second-line agents (1–3). Early identification of this group of patients could lead to their enrollment in clinical trials or treatment with other experimental therapeutics because standard treatment affords them little benefit. Among initially chemosensitive patients, the vast majority will eventually relapse. Thus, chemoresistance may be present at the outset of treatment (intrinsic resistance) or may develop during treatment (acquired resistance). In practice, ovarian cancers are considered "platinum sensitive" if the clinical progression free interval is >6 months, and evidence suggests that the longer this interval, the higher the subsequent response rates to additional chemotherapy (4–6). Understanding the biological mechanisms underlying chemoresistance is of utmost importance for improving the treatment and outcome of ovarian cancer. This topic has been the subject of intense research, and previous studies on chemoresistance in ovarian cancer have investigated potential involvement of molecules involved in drug transport, apoptosis, DNA repair, and detoxification pathways (7–11). Much of this research has been done using cell culture models and far fewer data are available on the relevance of these studies to, and biomarkers and potential mechanisms of drug resistance for, clinical samples.
The availability of new high-throughput screening techniques has allowed for more global investigations of molecular profiles associated with chemoresistance. In the present study, cDNA microarrays were used to investigate gene expression patterns associated with both intrinsic and acquired chemoresistance in ovarian cancer. The first aim of this investigation was to determine if intrinsically chemoresistant and chemosensitive tumors could be distinguished based on their gene expression profiles.
For this part of the investigation, a case was classified as intrinsic chemoresistant based on persistent or recurrent disease within 6 months of initiating first-line platinum-based combination chemotherapy. Chemosensitive tumors were classified as such based on a complete response to chemotherapy and a platinum-free interval of 
13 months. These conservative clinical criteria for defining platinum sensitivity and resistance were employed to exclude tumors with intermediate levels of resistance. In the second part of the investigation, gene expression profiles of tumors obtained following chemotherapy ("postchemotherapy" samples) were compared with those of the chemosensitive and chemoresistant primary tumors. The postchemotherapy group consisted of tumors from nine patients treated with neoadjuvant chemotherapy who subsequently underwent an interval cytoreductive surgery and from six patients who had residual cancer present at the time of second-look surgery following chemotherapy.
Gene expression profiles of these postchemotherapy tumors were compared with those of our primary (i.e., chemonaive) chemosensitive and chemoresistant tumors. The rationale for this approach was 2-fold. First, after each cycle of cytotoxic chemotherapy, the "log kill" effect leads to a significant reduction in the number of tumor cells that are sensitive to the administered therapy (12, 13). Second, tumor cells that survive the treatment are likely to experience changes in gene expression that allow them to withstand the selective pressure of the drugs used. Hence, tumor samples obtained shortly following chemotherapy are enriched in resistant clones and are likely to display the molecular signature associated with chemoresistance.
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Materials and Methods |
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13 months. The postchemotherapy group is composed of patients who had either surgical debulking following chemotherapy (i.e., neoadjuvant chemotherapy) or residual tumor at the time of a second-look procedure. All of these tumor samples were obtained within 6 weeks of the last cycle of chemotherapy. Ovarian tissues from two postmenopausal women obtained at the time of salpingoophorectomy for benign indications were used for comparative purposes. RNA preparation and cDNA microarray analysis. Isolation of RNA was done using the RNeasy column (Qiagen, Valencia, CA) according to the manufacturer's instructions. The integrity of RNA was verified by denaturing gel electrophoresis. Total RNA was linearly amplified using a modification of the Eberwine procedure (14). Briefly, total RNA was reverse transcribed by using a 63-nucleotide synthetic primer containing the T7 RNA polymerase binding site [5'-GGCCAGTGAATTGTAATACGACTCACTATAGGGA-GGCGG(T)24-3']. Second-strand cDNA synthesis (producing double-stranded cDNA) was done with RNase H, Escherichia coli DNA polymerase I, and E. coli DNA ligase (Invitrogen, Carlsbad, CA). After cDNA was blunt ended with T4 DNA polymerase (Invitrogen), purification was accomplished by phenol/chloroform/isoamyl alcohol extraction and ammonium acetate/ethanol precipitation. The double-stranded cDNA was then transcribed using T7 polymerase (T7 Megascript kit, Ambion, Austin, TX), yielding amplified antisense RNA that was purified using RNeasy mini-columns. Commercially available pooled total RNA from 10 different human cell lines (Stratagene, La Jolla, CA) was amplified and used as the reference for cDNA microarray experiments.
Investigation of gene expression differences between primary chemosensitive and chemoresistant tumors was done using two separate cDNA microarrays to maximize the number of genes screened. The two cDNA microarrays contained 32,448 and 7,585 features each for a combined total of 40,033 transcripts. The comparison between the postchemotherapy samples and the primary tumors was done using the 7,585-feature cDNA microarray. All cDNA microarrays were manufactured at the National Cancer Institute. Amplified RNA (4 µg) was reverse transcribed and directly labeled using cyanine 5–conjugated dUTP (tumor RNA) or cyanine 3–conjugated dUTP (pooled reference RNA). Hybridization was done in the presence of 5x SSC and 25% formamide for 14 to 16 hours at 42°C. Slides were washed, dried, and scanned using an Axon Instruments 4000a laser scanner. A detailed protocol for RNA amplification as well as cDNA probe labeling and hybridization is available at http://nciarray.nci.nih.gov/reference/ (under "Alternative Methods and Protocols"). Genepix software (Molecular Devices, Sunnyvale, CA) was used to analyze the raw data that were then uploaded to a relational database maintained by the Center for Information Technology at the NIH.
Data analysis. The log expression ratios for the spots on each array were normalized by subtracting the median log ratio for the same array. Data were filtered to exclude spots with size <25 µm, intensity less than twice background or <500 units in both red and green channels, and any flagged or missing spots. In addition, any features found to be missing or flagged in >10% of the arrays were not included in the analysis. The Genepix software assigns intensity levels in arbitrary units with a range between 0 and 65,535. For reference, typical median background on arrays used in this investigation were 250, and the median probe signal was 4,500 (after background subtraction). Statistical comparison between tumors groups was done using the "BRB Array Tools" software (http://linus.nci.nih.gov/BRB-ArrayTools.html), consisting of a modified F test with P < 0.001 considered significant. This stringent P was selected in lieu of the Bonferroni correction for multiple comparisons, which was deemed excessively restrictive. Gene lists were interrogated using EASE software (15) to identify possible overrepresentation of genes belonging to the same biological or functional class.
Class prediction was done using a compound covariate predictor tool available as part of the BRB Array Tools software. This tool creates a multivariate predictor for one of two classes to each sample. The genes included in the multivariate predictor were those that were univariately significant at the selected significance cutoff of P < 0.0001. The multivariate predictor is a weighed linear combination of log ratios for genes that are univariately significant. The weight consists of the univariate t statistics for comparing the classes. A leave-one-out approach was then employed to test the ability of the compound covariate predictor to assign chemosensitive or chemoresistant class to each sample. A permutation test was used to assess the significance of our cross-validated error rate. The random permutations test the null hypothesis that there are no systematic differences in gene expression profiles of the chemoresistant and chemosensitive tumors. This assumption can be tested by randomly permuting labels among the gene expression profiles and determining what proportion of the permuted data sets have a misclassification error rate less than or equal to the observed error. This rate serves as the achieved significance level in a test against the null hypothesis. Detailed information about the compound covariate predictor and the permutation test for significance is provided by the Biometric Research Branch, National Cancer Institute and is available at http://www.healthsystem.virginia.edu/internet/obgyn/supplemental-figure.pdf.
Immunohistochemical analyses. Immunohistochemical staining was done on 5 µm sections of formalin-fixed, paraffin-embedded ovarian tumor specimens. After deparaffinization, sections were pretreated with steam for 20 minutes in citrate buffer (pH 6.0). Slides were stained with primary antibodies against Ki-67 (mouse monoclonal clone MIB-1; 1:75 dilution; DAKOCytomation, Carpinteria, CA), proliferating cell nuclear antigen (PCNA; mouse monoclonal clone PC10; ready-to-use; DAKOCytomation), and cathepsin D (CTSD; rabbit polyclonal; ready-to-use; DAKOCytomation). Staining was done on a DAKO Autostainer using the LSAB2 kit (DAKOCytomation) consisting of biotinylated anti-mouse and anti-rabbit ready-to-use secondary antibodies, streptavidin-horseradish peroxidase, and the chromogen diaminobenzidine. The slides were counterstained with methyl green, dehydrated, and mounted. Quantitative scoring was done as the product of two staining characteristics, the percentage of immunopositive cells and the intensity of the staining. Slides were examined microscopically at 20x power, and five separate areas of each tumor specimen were examined, with 100 cells per area analyzed. Scoring for the number of immunopositive cells was accomplished by assigning 0% to 25% as 1, 26% to 50% as 2, 51% to 75% as 3, and 76% to 100% as 4. Intensity was scored as 1 to 3. The final score consisted of the product of the immunopositive score (averaged over the five areas) and the intensity score (averaged over the five areas). Statistical analysis was accomplished using Student's t test.
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Results |
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2-fold. The ability of the nine most significantly differentially expressed genes (P < 0.0001) to predict clinical response was tested using a leave-one-out prediction model. This analysis revealed an accuracy of 77.8% in correctly classifying refractory and responsive tumors. After 5,000 random permutations, the likelihood of these nine genes differentiating the two groups with equal or higher accuracy by chance (i.e., the null hypothesis) was calculated as P = 0.018. These data showed that, although statistically significant differences at the mRNA level existed between chemosensitive and chemoresistant primary tumors, the magnitude of these differences was modest in primary tumor samples obtained before chemotherapy.
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Semiquantitative immunohistochemical analyses were used to determine if the gene expression differences between primary chemoresistant and chemosensitive ovarian cancers were associated with significant protein expression differences. The protein product of the CTSD gene was selected for this purpose because of the availability of a commercial antibody and prior studies implicating this protease in the pathogenesis of several cancers, including ovarian and breast (16, 17). Using a semiquantitative immunohistochemical scoring system, chemosensitive samples displayed significantly higher CTSD protein expression than the chemoresistant samples (Fig. 1A). Because CTSD expression correlates with high proliferation states in several tumors (17–19), the expression of Ki-67 and PCNA was also analyzed in this set of tumors using semiquantitative immunohistochemistry (Fig. 1B and C). Both markers showed significantly higher expression in the chemosensitive primary tumors. These results suggest that the higher expression of CTSD in the chemosensitive tumors correlates with a higher proliferative state that may in turn render them more sensitive to cytotoxic chemotherapy. Notably, CTSD expression was also significantly higher in the primary chemosensitive group compared with the postchemotherapy group (two-tailed t test; P = 0.0008), consistent with the hypothesis that the postchemotherapy tumors are enriched for chemoresistant clones.
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2-fold difference in their geometric mean expression. The comparison of the primary chemoresistant group with the postchemotherapy samples revealed 13 genes that showed at least a 1.5-fold difference in mean expression levels (Fig. 3D). Furthermore, only one gene, SPP1 (also known as osteopontin), had a >2-fold change in expression and was higher expressed in the primary chemoresistant group. This gene has been implicated previously in ovarian cancer (25) and has been proposed to represent a diagnostic biomarker for ovarian cancer (26). Interestingly, the postchemotherapy samples expressed higher levels of CDKN1C (p57KIP2) and ADAMTS1, both of which have been shown to function as negative regulators of proliferation (27, 28). However, these levels were still far lower than those observed in normal postmenopausal ovarian samples (Fig. 3D). In the comparison of the postchemotherapy tumors with the primary chemosensitive samples, 41 genes showed at least 2-fold higher expression and 10 genes showed at least a 2-fold lower expression in the postchemotherapy tumors (Fig. 3B and 3C). When the expression levels of these 51 differentially expressed genes in normal postmenopausal ovary were graphed along with the tumor expression levels, an interesting and unexpected pattern emerged. The expression profile of the postchemotherapy samples resembled that of normal ovarian tissue to a much greater degree than that of the primary chemosensitive tumors. These data are consistent with a model in which the postchemotherapy samples show a partial "return to normal" or "low proliferative state" molecular expression profile with respect to this set of genes. One notable exception to the overall similarities between postchemotherapy and normal ovarian gene expression pattern was in the expression of CYR61. This gene has been implicated in angiogenesis (29) and chemoresistance (30) and was expressed at a significantly higher level in postchemotherapy samples compared with both primary chemosensitive and normal ovarian samples (Fig. 3A). In addition, several of the genes higher expressed in the postchemotherapy group were noted to be components of the extracellular matrix (ECM) or involved in its remodeling. This impression was more formally investigated by using EASE software (15) to analyze the biological categories within this gene list. This analysis confirmed the statistically significant (P < 0.05 after Bonferroni correction) overrepresentation of genes involved in ECM among the genes differentiating the postchemotherapy and primary chemosensitive tumors. One hypothesis that may be derived from this observation is that stromal-epithelial interactions or the ECM per se may be involved in acquired chemoresistance in ovarian cancer.
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Discussion |
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Although highly statistically significant, the magnitude of the mean expression differences between chemosensitive and chemoresistant groups were modest for the discriminating gene set. The observed magnitude of expression differences is likely to be an underestimation for three reasons that are not mutually exclusive. First, according to the Goldie-Coldman hypothesis, chemoresistance is believed to result from a clonal selection process driven by the acquisition of drug resistance mutations (31). Thus, in primary tumors, only a small percentage of the cells are likely to possess a chemoresistant phenotype and the associated molecular changes. This results in a "dilution" of the observed gene expression differences when such tumors are compared with chemosensitive cancers. Second, the comparison of gene expression profiles in primary tumors evaluates only intrinsic chemoresistance. It is likely that a substantial component of clinical chemoresistance is biologically acquired and is therefore only manifested following exposure to chemotherapeutic agents. In support of this hypothesis, the greatest differences in gene expression were observed between postchemotherapy samples and primary chemosensitive tumors. Finally, as is the case with most investigations involving cDNA microarrays, the primary expression data are in the form of logarithmic intensity ratios. Secondary data, such as average expression levels for genes within a group, are derived by calculating the geometric rather than the arithmetic means of logarithmic intensity ratios, resulting in smaller values and smaller apparent differences.
The higher expression of CTSD in chemosensitive tumors, as determined by immunohistochemistry, shows that small differences in geometric mean expression as determined by cDNA microarrays may be associated with substantially greater differences in protein expression. Although one previous report failed to show prognostic value of CTSD expression in ovarian cancer (32), most other studies show low CTSD expression to be an adverse prognostic indicator in ovarian cancer (16, 33, 34). In view of our findings, the prognostic value of CTSD may be related to its higher expression in intrinsically chemosensitive tumors. Consistent with this hypothesis, CTSD has been implicated in p53-depenedent apoptosis following DNA damage induced by drugs and 
-irradiation (35). Further investigations are needed to evaluate a possible causal relationship between these observations and to better define the relationship between CTSD and chemosensitivity. The chemosensitive tumors also showed higher expression of the proliferative markers PCNA and Ki-67. In agreement with this observation, a previous morphologic study found a highly significant correlation between proliferation and mitotic indices and the presence of apoptotic bodies in primary ovarian cancers (36). Thus, higher rate of proliferation may contribute to the chemosensitive nature of these tumors by predisposing them to undergo apoptosis following chemotherapy.
This investigation also revealed substantially different gene expression between primary ovarian cancers and tumor samples obtained following chemotherapy. The postchemotherapy tumors are difficult to classify, based on customary clinical criteria, as either chemoresistant (cancer progression on chemotherapy or recurrence within 6 months of completing chemotherapy) or chemosensitive (disease-free interval of at least 12 months) as they fit neither clinical criterion. They all eventually became resistant to chemotherapy. Our assertion is that these tumor samples represent a state of enrichment in chemoresistant clones, as these are tumors that have survived three to six cycles of chemotherapy. A reasonable hypothesis is that the gene expression profile of these postchemotherapy samples is likely to include molecular changes associated with acquired chemoresistance, as these samples were obtained within a few weeks of completing three to six cycles of chemotherapy. Consistent with this hypothesis, fewer and smaller magnitude gene expression differences were observed between postchemotherapy and primary chemoresistant samples. However, the data also suggest that intrinsic and acquired chemoresistance are likely to manifest through nonoverlapping molecular pathways (Fig. 4). This was evident by the lack of significant overlap (some genes are part of both lists) between the gene list differentiating primary chemosensitive and chemoresistant groups and the list differentiating each group from the postchemotherapy samples. In comparing the primary tumors with postchemotherapy samples, three separate lists of differentially expressed genes were generated. Two lists identified genes that uniquely differentiated primary chemoresistant and chemosensitive tumors from the postchemotherapy samples and one that included genes that discriminated the latter from both former groups. All three lists contain genes that have been implicated previously in tumorigenesis and provide targets for prospective investigations of acquired chemoresistance in ovarian cancers.
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Another member of this group found to be overexpressed in postchemotherapy tumors was SPARC (also known as osteonectin), a matricellular glycoprotein involved in angiogenesis, cell adhesion, and ECM turnover (38). This gene is also up-regulated following chemotherapy in breast cancer (39) and has antiproliferative and tumor suppressor function in ovarian (40, 41) and breast (42) cancer cells. Furthermore, SPARC stimulates matrix metalloproteinase-2 expression in other tissues (43, 44) and may account for the observed higher expression of matrix metalloproteinase-2 in the postchemotherapy tumors. Another related ECM gene, SPARCL1 (also known as hevin, SC1, and MAST-9), was significantly higher expressed in the postchemotherapy samples compared with both groups of primary tumors, is down-regulated in several cancers, and has a negative effect on cell proliferation (45).
The higher expression of these and other antiproliferative genes (e.g., KLF4 and CAV1; refs. 21, 46) in the postchemotherapy samples, as well as similarities in the gene expression profiles of the postchemotherapy tumors and normal postmenopausal ovaries, support the concept that a decreased proliferative state may be involved in the development of acquired chemoresistance. Furthermore, given that the primary chemoresistant tumors exhibited significantly lower Ki-67, PCNA, and CTSD protein expression compared with the chemosensitive samples, decreased proliferation may also be a contributing feature to intrinsic chemoresistance. Senescent or slow-growing cells may be more tolerant of cytotoxic chemotherapy, thus allowing more time for selection of advantageous mutations and development of resistant clones. Consistent with such a hypothesis, the majority of postchemotherapy samples exhibit a decreased mitotic index compared with the prechemotherapy sample of the same tumor (47).
This investigation represents an initial effort to discover potentially important molecular mediators of intrinsic and acquired chemoresistance in ovarian cancer in vivo. It is critical to further investigate the molecular basis for chemoresistance prospectively in a large cohort with prechemotherapy and postchemotherapy sampling from the same patients. This investigation provides preliminary data that may inform such future studies and perhaps clinical protocols.
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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.
Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).
Received 12/27/04; revised 5/31/05; accepted 6/16/05.
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