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Molecular Biology, Pathobiology, and Genetics |
1 Department of Medical Oncology, Dana-Farber Cancer Institute, and Departments of 2 Medicine and 3 Pathology, Harvard Medical School; Departments of 4 Pathology and 5 Medicine, Brigham and Women's Hospital, Boston, Massachusetts; and 6 Center for Cancer Research, Massachusetts General Hospital, Charlestown, Massachusetts
Requests for reprints: David A. Frank, Department of Medical Oncology, Dana-Farber Cancer Institute, 44 Binney Street, Mayer 522B, Boston, MA 02115. Phone: 617-632-4714; Fax: 617-632-6356; E-mail: david_frank{at}dfci.harvard.edu.
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Abstract |
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Introduction |
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Several targets of STAT3 have been identified using human tumor cell lines and rodent fibroblasts. Proteins that regulate cell survival, including bcl-2, bcl-xL, mcl-1, and Fas, are direct targets of STAT3 (5–8). Cell cycle regulators cyclin D1, cyclin E1, and p21 can be activated by STAT3 in certain contexts (9). Finally, other transcription factors, including myc, jun, and fos, are themselves STAT3 targets (10–13). However, little is known about which of these targets are critical mediators of STAT3 in transformation. Furthermore, none of these targets is overexpressed in tumors in which STAT3 activation is observed. Thus, although many of these genes have independently been implicated in oncogenesis, their role in and contribution to human cancers in which STAT3 is activated is unclear.
In the present study, we undertake a genome-wide analysis of STAT3 target genes and analyze the expression of these genes in human tumor expression data sets. We find evidence for an expression signature for STAT3 and show that the expression of the genes composing this signature correlates with STAT3 activation. We thus identify several genes that may mediate the role of STAT3 in cancer. Further, this study suggests the benefit of combining cancer genomics with traditional cell biological methods to better understand the role of individual genes in the biology of human tumors.
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Materials and Methods |
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Cell culture, transfection, and cytokines. To create an inducible STAT3-C cell line, STAT3-C/pGene-stop and pSwitch or pGene-stop and pSwitch were cotransfected into NIH3T3 cells using LipofectAMINE 2000 (Invitrogen). Cells were selected in 400 µg hygromycin (Roche Applied Science, Indianapolis, IN) and 700 µg zeocin (Invitrogen). Individual clones were picked using cloning rings (Fisher Scientific, Pittsburgh, PA), or transfected cells were pooled. Clones were screened for those that expressed no STAT3-C before induction and near-physiologic levels following induction. Mifepristone (Invitrogen) was used at 0.1 nmol/L for all experiments unless otherwise stated.
NIH3T3 cells, mouse embryonic fibroblasts, and MDA-MB-231 cells were grown in DMEM supplemented with 10% fetal bovine serum. Interleukin-6 (IL-6; R&D Systems, Minneapolis, MN) was used at 30 ng/mL, soluble IL-6 receptor (R&D Systems) was used at 50 ng/mL, and murine OSM (R&D Systems) was used at 25 ng/mL. Cells were pretreated with soluble IL-6 receptor for 1 hour before addition of IL-6.
Western blot analysis. Cells were lysed in radioimmunoprecipitation assay buffer [50 mmol/L Tris (pH 7.4), 150 mmol/L NaCl, 1% NP40, 0.5% sodium deoxycholate, 0.1% SDS] containing 1 mmol/L phenylmethylsulfonyl fluoride, 1 µg/mL pepstatin, and 1 mmol/L sodium vanadate on ice for 15 minutes. Protein (50 µg) was resolved on 8% SDS-polyacrylamide gels and transferred to nitrocellulose. Blots were probed with antibodies against the FLAG epitope (M2, Sigma, St. Louis, MO), STAT3 (Santa Cruz, Santa Cruz, CA), phospho-STAT3 (pSTAT3; Cell Signaling, Inc., Beverly, MA), or tubulin (Sigma).
Luciferase assays. 3T3/STAT3-C.CE, 3T3/STAT3-C.Pool, or 3T3/pGene cells were plated on 24-well plates and transfected with 1.8 µg m67-luc and 0.2 µg phRL TK-luc with LipofectAMINE 2000 for 16 hours. After transfection, cells were treated with the indicated concentration of mifepristone for 24 hours. Cells were lysed and luminescence was measured using the dual-luciferase reagents from Promega, according to the manufacturer's instructions, using a Luminoskan Ascent luminometer (ThermoLab Systems, Helsinki, Finland). STAT3-dependent luciferase production was normalized to control Renilla values.
RNA isolation, reverse transcription-PCR, and microarray analysis. RNA was isolated from cells using either Trizol reagent (Invitrogen) for microarray analysis or RNeasy kits (Qiagen, Valencia, CA) for reverse transcription-PCR (RT-PCR) according to each manufacturer's protocol. For semiquantitative RT-PCR, RNA (10 ng) was reverse transcribed and amplified using SuperScript One-Step RT-PCR kit (Invitrogen) for 30 cycles. For real-time RT-PCR, RNA (2 µg) was reverse transcribed with a polydeoxythymidylic acid primer using the SuperScript First-Strand Synthesis kit (Invitrogen). Real-time PCR was then done with a SYBR Green Master Mix (Stratagene, La Jolla, CA) on an ABI Prism 7000 instrument.
Primer pairs are as follows: mouse primers: SOCS3, forward GTTCCTGGATCAGTATGATGC and reverse CGCTTGTCAAAGGTATTGTCC; c-met, forward TCTCTCGAACAGCACACCTC and reverse TTGAGTCCATGTACCGCTGG; mcl-1, forward GACCGGCTCCAAGGACTC and reverse TGTCCAGTTTCCGGAGCAT; bcl-6, forward GAGCCCATAAGACAGTGCTCA and reverse GGTTGCATTTCAACTGGTCA; junB, forward GGTCAGGGATCAGACACA and reverse AAAGTACTGTCCCGGAGG; and actvr, forward GGGGACTGGTGTAACAGGAA and reverse TACTGCAAACACCACCGAGA; and human primers: bcl-6: forward TACCTGCAGATGGAGCAT and reverse ACTCTTCACGAGGAGGCT; mcl-1: forward GAGACCTTACGACGGGTT and reverse TTTGATGTCCAGTTTCCG; junB: forward AAATGGAACAGCCCTTCT and reverse TGTAGAGAGAGGCCACCA; egr1: forward AGCCCTACGAGCACCTGAC and reverse AGCGGCCAGTATAGGTGATG; calpain: forward GCAGGGATCTTTCACTTCCA and reverse GCTGAATGCACAAAGAGCAG; KLF4: forward TCCCATCTTTCTCCACGTTC and reverse AGTCGCTTCATGTGGGAGAG; and ß-actin: forward TCCCTGGAGAAGAGCTACGA and reverse AGCACTGTGTTGGCGTACAG.
Microarray analysis, including the preparation of cRNA, oligonucleotide array hybridization to MG-U74Av2 GeneChip arrays (Affymetrix, Santa Clara, CA), and scanning of the arrays, was done by the Dana-Farber Microarray Core Facility. Briefly, RNA was harvested using Trizol reagent from untreated cells or cells treated with mifepristone for 4.5 hours. RNA was converted to cDNA and in vitro transcribed in the presence of biotinylated CTP and UTP. Labeled RNA was hybridized to an Affymetrix MG-U74Av2 chip containing 6,000 annotated genes and 6,000 expressed sequence tags, washed, and detected using phycoerythrin-conjugated streptavidin. The mean fluorescence intensity of each chip was normalized and the expression levels of all genes were compared. Data analysis was done using the DNA-Chip Analyzer software (14). Gene array data were normalized, and perfect match–only, model-based expression intensities were obtained in DNA-Chip Analyzer.
Hierarchical clustering and statistical analysis. Human tumor data sets (global cancer map, prostate, and leukemia) that we used have been described elsewhere and are available on the Broad Institute Web site.7 The breast tumor data set is unpublished data (A. Richardson). Hierarchical clustering was done using DNA-Chip Analyzer software. Kolmogorov-Smirnov analysis, used to measure coexpression of STAT3 targets and their association with the pSTAT3 phenotype, was done essentially as described (15, 16) using software designed for this purpose.8 Briefly, for Kolmogorov-Smirnov analysis, each gene present on the Affymetrix chip was queried with the set of STAT3 targets to generate a Kolmogorov-Smirnov score for that gene. All genes were ordered based on their Kolmogorov-Smirnov score, and the location of STAT3 targets on this ordered list was considered. The position of each STAT3 target on the list represents a nominal P for that gene; those targets in the top 5% of the list are coexpressed with the remaining STAT3 target genes to a statistically significant extent.
To test the enrichment of STAT3 targets in tumors with pSTAT3, all the genes on the chip are ranked based on their correlation with a given phenotype. We ranked the genes based on differential expression between tumors with and without pSTAT3 using the signal-to-noise score. For prostate tumors, we compared tumors with no pSTAT3 (score = 0) and those with high pSTAT3 (score = 3), and for breast tumors, we compared tumors with no pSTAT3 (score = 0) and those with intermediate and high pSTAT3 (score = 2 and 3). We then determined the location of STAT3 targets on this ordered list using the Kolmogorov-Smirnov score as described above; this Kolmogorov-Smirnov score measures the degree of enrichment and so is termed an enrichment score in Fig. 5. The statistical significance of this score was measured by randomly permuting the class labels (pSTAT3 status), recalculating signal-to-noise scores, and generating a Kolmogorov-Smirnov score for these random class distinctions. The nominal P represents how many times a Kolmogorov-Smirnov score from random class labels exceeds the test Kolmogorov-Smirnov score.
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RNA interference. pRetroSuper (pRS) was generously provided by Anton Berns (Netherlands Cancer Institute, Amsterdam, the Netherlands). Oligonucleotides were designed, using guidelines described previously (17), to target the following sequence present in human STAT3: AACTTCAGACCCGTCAACAAA.
The oligonucleotides were designed with overhangs on each end to facilitate cloning into pRS. The sequence of the oligonucleotides were forward GATCCCCCTTCAGACCCGTCAACAAATTCAAGAGATTTGTTGACGGGTCTGAAGTTTTTGGAA and reverse AGCTTTTCCAAAAACTTCAGACCCGTCAACAAATCTCTTGAATTTGTTGACGGGTCTGAAGGGG. The targeting sequence and its complement are shown in bold.
pRS was transfected into 293 cells for packaging of virus (18). Viral supernatant (4 mL), supplemented with 4 mL growth medium, was used to infect MDA-MB-231 cells on a 10 cm plate for 16 hours in the presence of 4 µg/mL polybrene. Culture medium was then changed and puromycin was added to cells 24 hours later at 1 µg/mL. Puromycin-resistant cells were pooled and screened for STAT3 expression by Western blot.
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Results |
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1.5-fold following mifepristone treatment in the clone, whereas 67 were increased by this magnitude in the pool (Fig. 2A; Supplementary Table S1). We chose this cutoff because a known STAT3 target, mcl-1, was increased by approximately this magnitude and because a previous study found known STAT3 targets to be induced by this magnitude (20). The clone and pool yielded nearly identical results (Fig. 2A): using a threshold of 1.5-fold, the correlation of probes sets between the clone and the pool was high (Pearson correlation = 0.944), so only data from the clone are discussed in detail here. Only one of these probe sets (TIEG, Gene ID 21847) was increased in the vector cells treated with mifepristone (data not shown), indicating that the genes identified are specific STAT3 targets and are not responding to mifepristone alone. Fourteen probes in total were changed by 1.5-fold in the pool, although none of these changed by >2-fold. Three probe sets, representing two genes (Ndr1, Gene ID 17990; Slc1a4, Gene ID 55963), were decreased 1.5-fold in response to mifepristone (data not shown). Several genes described previously as STAT3 targets were identified in the microarray screen, thus validating this approach for finding STAT3 targets. These include SOCS3, junB, CCAAT/enhancer binding protein ß, mcl-1, and vascular endothelial growth factor (VEGF). A large number of the targets were themselves transcription factors, suggesting that STAT3 initiates a cascade of changes in the transcriptional profile of these cells. Notably, several of the genes suggested previously to mediate the role of STAT3 in oncogenesis, like c-myc, cyclin D1, and bcl-xL, were not activated by STAT3 at this time point. Although STAT3 has clearly been shown to be necessary for the expression of these genes in several contexts (9, 10), these findings reveal that STAT3 is not sufficient for their expression at this early time point. This raises the possibility that the protein products of other STAT3 target genes are required to collaborate with STAT3 to activate expression of these genes.
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Signal transducer and activator of transcription 3 targets are coordinately expressed in human tumors. It is likely that only a few of these STAT3 targets are the key mediators of STAT3 in human cancers. We sought an alternative to in vitro systems to identify the important cancer-related STAT3 targets. Several data sets exist containing expression data for thousands of genes across hundreds of human tumors. We considered whether these data sets could reveal which of these STAT3 targets were likely to play a role in cancer.
We identified the human orthologues of the known mouse genes and analyzed their expression levels in various tumor data sets. The genes represented by probe sets varied slightly among the data sets (Supplementary Table S2). One such set, the global cancer map, contains expression data for 190 human tumors representing 14 distinct tumor types (21). We first did hierarchical clustering of the STAT3 target genes across these tumors (Supplementary Fig. S2). A subset of these genes was overexpressed in central nervous system tumors, leukemias, and prostate tumors; notably, STAT3 activation has been shown in each of these tumor types (22–24).
It is difficult to determine whether an apparent clustering of genes, as revealed by hierarchical clustering, is statistically significant. We reasoned that those STAT3 targets that subserve the critical oncogenic functions of STAT3 should be consistently highly expressed in tumors in which STAT3 is activated and expressed at low levels in tumors that lack STAT3 activation. These genes should thus be coordinately expressed in human tumor data sets, and their expression will serve as a genetic signature of STAT3 activation.
A strategy based on Kolmogorov-Smirnov analysis was used to determine which STAT3 targets were coexpressed in human tumors (Fig. 3A). Kolmogorov-Smirnov scanning has been used to discover genes whose expression correlates with the aggregate expression of a set of genes (15). We used Kolmogorov-Smirnov scanning to determine which of the STAT3 target genes were significantly coexpressed with the remaining targets using four independent data sets: the global cancer map; one data set comprising leukemias (25); a data set comprising 96 breast tumors;9 and a data set comprising prostate tumors (26). In this manner, we identified 12 genes that are coexpressed to a statistically significant extent in at least two of the four tumor data sets (Fig. 3B). These genes constitute an expression signature for STAT3 activation and, as suggested by their highly significant coexpression in human tumors, may represent the critical effectors of STAT3 activation in malignancy.
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We tested whether the STAT3 signature genes, as well as the entire group of STAT3 targets we identified, were enriched in tumors with pSTAT3. The entire set of STAT3 targets we identified in our in vitro system showed a significant enrichment in prostate tumors harboring pSTAT3 (P = 0.024; Fig. 5A), and the genes composing the STAT3 signature also showed notable enrichment in tumors with pSTAT3 (P = 0.052; Fig. 5B). The entire set of STAT3 targets showed enrichment in breast tumors with pSTAT3, although this did not reach statistical significance (P = 0.079; Fig. 5C). In contrast, the refined list of STAT3 targets, which constitute the STAT3 signature (see Fig. 3B), did show statistically significant association with pSTAT3 in breast tumors (P = 0.043; Fig. 5D). As there is some tissue specificity to the STAT3 signature, we also tested whether the subset of STAT3 signature genes showing significant Kolmogorov-Smirnov scores in prostate were enriched in prostate tumors with pSTAT3 and whether those genes showing significant Kolmogorov-Smirnov scores in breast were enriched in breast tumors with pSTAT3. We found no greater enrichment with breast cancer–specific STAT3 signature genes but found greater enrichment with prostate cancer–specific STAT3 signature genes (P = 0.037).
These results indicate that the STAT3 targets we identified in NIH3T3 cells, and specifically the STAT3 signature we culled from this set using human tumor data sets, are expressed more highly in prostate and breast tumors with STAT3 activation. This provides further evidence that these genes may be critically involved in the contribution of STAT3 to human cancer.
Signal transducer and activator of transcription 3 is required for target gene expression in human cell lines with signal transducer and activator of transcription 3 activation. Although these results strongly suggest that STAT3 activation is responsible for the expression of these genes in tumors, we wished to directly address whether there is a causal relationship between STAT3 activation and expression of the STAT3 signature genes in human tumor cells. To do this, we used MDA-MB-231 cells, a human breast cancer cell line that has been shown previously to have phosphorylated STAT3. If activated STAT3 was driving expression of these target genes, then interrupting this signaling would lead to decreased expression of these genes.
We used virally delivered small interfering RNA (pRS) to suppress the levels of STAT3 protein. We screened pools stably expressing small interfering RNA targeting STAT3 and identified several pools with near complete suppression of STAT3 (Fig. 6A). As expected, these cells also had a reduction in the level of pSTAT3, suggesting that STAT3-dependent transcription would be abrogated. We isolated RNA from MDA-MB-231 cells stably expressing empty vector (pRS) or cells in which STAT3 was knocked down (pRS/STAT3) and analyzed the expression of STAT3 target genes by quantitative RT-PCR. The expression of all of the targets examined was decreased in cells lacking STAT3 (Fig. 6B). This suggests that STAT3 activation is required for the expression of these genes in human tumor cells that have acquired STAT3 activation during the course of transformation. Furthermore, cells in which STAT3 expression was knocked down grew more slowly than control cells and eventually lost knockdown of STAT3 (data not shown). This suggests that STAT3, and the expression of these STAT3 targets, is critical for the growth and survival of this breast cancer cell line.
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Discussion |
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We used two methods to identify a STAT3 expression signature, defined as a group of STAT3 targets that are highly coexpressed with each other in human tumors. We first did unsupervised hierarchical clustering across a range of diverse tumors. This led to the identification of a cluster of genes with high expression in central nervous system tumors, leukemias, and prostate tumors—all tumor types in which STAT3 activation has been observed. Next, to assess the statistical significance of this coexpression, we used a Kolmogorov-Smirnov test to measure the degree to which each STAT3 target was coexpressed with the remaining targets in several independent data sets. In this manner, we identified a signature for STAT3 activation. Most of the genes present in the signature are clustered together by hierarchical clustering, indicating that these two approaches yield similar results (see Supplementary Fig. S2). The STAT3 targets were also found to be more highly expressed in breast and prostate tumors with activated STAT3. This strongly suggests that the genes identified in vitro are regulated by STAT3 in vivo as well. The approach of combining a tractable in vitro system to identify direct transcriptional relationships, with human tumor samples and expression data sets that are most relevant to disease, is an especially informative method of studying the mechanism of oncogenic transcription factors.
Many of the STAT3 targets we identified, and specifically those present in the STAT3 signature, are themselves transcription factors. These include junB, egr1, KLF4, bcl-6, and NFIL3. Many of these have independently been implicated in oncogenesis likely through their regulation of proliferation (e.g., junB and egr1), survival (e.g., NFIL3), or differentiation (e.g., bcl-6; refs. 28–30). The large number of transcription factors also suggests that activated STAT3 initiates a widespread change in gene expression extending far beyond its direct, immediate targets, implying that the steady-state transcriptional profile of a cell in which STAT3 is chronically active might be profoundly different from a cell that lacks STAT3 activation. Another STAT3 target we identified, VEGF, has been well characterized as contributing to tumor progression through promoting angiogenesis (31). Further, STAT3 has been shown to activate VEGF expression in tumor cells (32). Our findings confirm that VEGF is a STAT3 target and, importantly, show association between STAT3 activation and VEGF expression in human tumors, providing further evidence for the role of STAT3 in promoting angiogenesis through VEGF. The protease calpain has been shown recently to be involved in cellular transformation and migration (33, 34), especially mediated by v-src. We show that calpain is a STAT3 target that is associated with STAT3 activation in human tumors, thus providing another potential mechanism by which STAT3 contributes to oncogenesis.
In conclusion, we have identified several transcriptional targets of STAT3, a subset of which are significantly coexpressed in human tumors and correlated with STAT3 activation in breast and prostate cancer. These target genes may mediate the role of STAT3 in these tumors and may provide novel targets for therapeutic intervention in tumors with activated STAT3.
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Acknowledgments |
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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.
We thank Aravind Subramanian for statistical and computational help with performing Kolmogorov-Smirnov analyses and Nandita Bhattacharjee for staining the tissue microarrays.
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Footnotes |
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Current address for P.G. Febbo: Departments of Medicine and Molecular Genetics and Microbiology, Duke Institute for Genome Sciences and Policy, Duke University, Durham, NC 27710.
7 http://www.broad.mit.edu/cgi-bin/cancer/datasets.cgi. 
8 A. Subramanian et al., manuscript in preparation.
9 A. Richardson, unpublished data.
Received 11/30/04. Revised 3/ 8/05. Accepted 4/ 1/05.
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-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 2003;34:267–73.[CrossRef][Medline]
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12,000 genes was determined using Affymetrix MG-U74Av2 microarrays. Relative expression is shown by a color scale, with red representing higher expression and blue representing lower expression. The expression of all genes whose expression changed by 
